pmid stringlengths 8 8 | pmcid stringlengths 8 11 ⌀ | source stringclasses 2
values | rank int64 1 9.78k | sections unknown | tokens int64 3 46.7k |
|---|---|---|---|---|---|
22470966 | null | s2 | 6,468 | {
"abstract": "Extracellular appendages of the dissimilatory metal-reducing bacterium Shewanella oneidensis MR-1 were recently shown to sustain currents of 10(10) electrons per second over distances of 0.5 microns [El-Naggar et al., Proc. Natl. Acad. Sci. U. S. A., 2010, 107, 18127]. However, the identity of the charge localizing sites and their organization along the \"nanowire\" remain unknown. We use theory to predict redox cofactor separation distances that would permit charge flow at rates of 10(10) electrons per second over 0.5 microns for voltage biases of < or = IV, using a steady-state analysis governed by a non-adiabatic electron transport mechanism. We find the observed currents necessitate a multi-step hopping transport mechanism, with charge localizing sites separated by less than 1 nm and reorganization energies that rival the lowest known in biology."
} | 215 |
24430581 | PMC4106282 | pmc | 6,470 | {
"abstract": "The growth of anodic electroactive microbial biofilms from waste water inocula in a fed-batch reactor is demonstrated using a three-electrode setup controlled by a potentiostat. Thereby the use of potentiostats allows an exact adjustment of the electrode potential and ensures reproducible microbial culturing conditions. During growth the current production is monitored using chronoamperometry (CA). Based on these data the maximum current density ( j max ) and the coulombic efficiency ( CE ) are discussed as measures for characterization of the bioelectrocatalytic performance. Cyclic voltammetry (CV), a nondestructive, i.e . noninvasive, method, is used to study the extracellular electron transfer (EET) of electroactive bacteria. CV measurements are performed on anodic biofilm electrodes in the presence of the microbial substrate, i.e . turnover conditions, and in the absence of the substrate, i.e. nonturnover conditions, using different scan rates. Subsequently, data analysis is exemplified and fundamental thermodynamic parameters of the microbial EET are derived and explained: peak potential ( E p ), peak current density ( j p ), formal potential ( E f ) and peak separation (Δ E p ). Additionally the limits of the method and the state-of the art data analysis are addressed. Thereby this video-article shall provide a guide for the basic experimental steps and the fundamental data analysis.",
"introduction": "Introduction The elucidation of the fundamentals of microbial extracellular electron transfer (EET) and its exploitation in engineered systems is a vital and rapidly increasing field of research and development 1-3 . The study of these electroactive (or bioelectrocatalytic or electrochemically active ) microorganisms, including pure strains as well as defined co-cultures and complex consortia, requires a complex arsenal of techniques, methods and protocols 4 . These methods derive from diverse scientific disciplines, e.g. electrochemistry, materials science, microbiology, and provide insights on different hierarchical levels, i.e . from the entire microbial biofilm to single molecules. Thereby, electrochemistry and electrochemical methods represent the fundament of all activities. Traditionally, fuel cell type setups were often used for the growth and maintenance of electroactive microbial cultures in the archetype of these engineered systems: microbial fuel cells (MFCs) 5 . Unfortunately, these types of MFCs often did not allow monitoring or even controlling the potential of a single electrode and thus, only limited insights in the electrode processes were possible- only the cell voltage was reported. Now it is more and more acknowledged that the individual monitoring and control of the potentials of single electrodes in MFCs represent a clear advantage, not only for fundamental research but also for engineering. Furthermore, with the diversification of the applications of microbial electrochemical technologies that now include, e.g. remediation, desalination, syntheses, and even biocomputing, in so-called microbial bioelectrochemical systems (BES) 2,6,7 an external control of individual electrode potentials is often substantial. This control is usually achieved by using external power sources or potentiostats. Thereby the use of potentiostats allows- in contrast to other types of setups- an exact adjustment of the individual electrode potential. This is of high importance as the electrode represents the terminal microbial electron acceptor (for anodes) or electron donor (for cathodes) of the extracellular electron transfer. Figure 1 depicts the current state of knowledge on the microbial EET at anodes- see for details e.g. 2,4,6,8,9 The EET at the cathode is still mainly untapped 10 . Thus the control of the electrode potential enables not only the use of a reproducible microbial culturing conditions but also its tailoring in terms of EET thermodynamics 11 . Within this article it is demonstrated how the characteristic parameters of electroactive microbial biofilms can be extracted from fed-batch reactor experiments using chronoamperometry (CA) and cyclic voltammetry (CV). This includes the performance parameters maximum current density ( j max ) and coulombic efficiency ( CE ) as well as EET characteristics. Here the identification of the formal potentials ( E f ) of possible and actual electron transfer sites will be in focus. The extraction of these parameters is shown on the example of waste water derived mixed culture biofilms that are dominated by Geobacter spec . 12 These waste water derived biofilms are gained by a simple electrochemical selection procedure as demonstrated by numerous research groups around the world 13-16 . \n Dynamic electrochemical techniques to study microbial extracellular electron transfer \n For a thermodynamic characterization of the EET of electroactive microbial cultures dynamic electrochemical techniques are needed. In general these techniques include potential controlled, i.e. voltammetric, and current controlled, i.e . galvanodynamic, techniques 17 . Thereby the potential controlled techniques are more prevalent and include for instance, square wave voltammetry and linear sweep voltammetry 18 . Most popular, however, is cyclic voltammetry (CV) 19,20 . CV is well-known and widespread in different fields of electrochemistry ranging from battery research via materials science to enzyme bioelectrochemistry 21 . Its application to living microbial cells can only be dated back more than one decade 22 and increases significantly during the last years 23,24 . Especially the combination of CV and spectroscopic methods has provided unprecedented insights in the EET mechanisms and its molecular nature, e.g. 25,26 In parallel the theoretical framework for the extraction of thermodynamic and theoretical characteristics of the microbial EET was broadened and deepened 27-30 . In contrast, however, experimental mishandling or inadequate data-analysis often hamper the appropriate application of CV in the field of microbial bioelectrochemistry. Therefore, in addition to workshops ( e.g. at the EU-ISMET 2012) and tutorials 19 , this video-article shall provide a basic guide for the conduction of cyclic voltammetric experiments on electroactive bacteria. Thereby it is focusing on the basic experimental steps and the fundamental data analysis and is not intended to provide the latest protocols of kinetic analysis. The most important termini for the following discussion are summarized in Box 1 (adapted from Harnisch and Freguia 19 and modified).",
"discussion": "Discussion The presented protocol shows an easy and straightforward way for growing electroactive microbial biofilms from waste water inoculate using a three-electrode setup in a fed-batch reactor. Chronoamperometry enables the growth and selection of electroactive microbial biofilms from diverse inocula as well as the fundamental characterization of the biofilms in term of maximum current density and coulombic efficiency. Cyclic voltammetry, being a noninvasive technique, allows performing fundamental studies on the EET thermodynamics of the microbial biofilm and thus to identify possible and actual EET transfer sites. Together, these parameters allow i) characterizing and comparing different microbial biofilms for identical conditions as well as ii) studying the impact of different operational conditions on identical electroactive microbial biofilms. However, these experiments can only be a starting point. On the one hand the multifaceted field of microbial bioelectrochemistry requires further and in-depth exploitation of electrochemical methods and protocols. This includes, for instance, models for extracting kinetic and mechanistic data as well as further methods like electrochemical impedance spectroscopy 34,35 . On the other hand electrochemical methods can provide no information on the molecular nature, the microbial composition, the spatial microbial organization, etc . Here further techniques being either noninvasive and coupled to electrochemistry or invasive are needed 4 . Box 2 provides a short discussion on the critical steps when performing the experiments as well as on initial troubleshooting. From the engineering perspective it has to be taken into consideration that the electroactive biofilm anode is only one component of the current microbial bioelectrochemical systems and a multitude of other factors has to be analyzed for full systems 36 . Yet, we hope that this video-article will help to understand and tailor the core elements of BES, i.e. the microbial electrodes, in future."
} | 2,159 |
30899758 | PMC6416169 | pmc | 6,471 | {
"abstract": "Autotrophic hydrogenotrophic methanogens use H 2 /CO 2 as sole carbon and energy source. In contrast to H 2 , CO 2 is present in high concentrations in environments dominated by methanogens e.g., anaerobic digesters (AD), and is therefore rarely considered to be a limiting factor. Nonetheless, potential CO 2 limitation can be relevant in the process of biomethanation, a power-to-gas technology, where biogas is upgraded by the addition of H 2 and ideally reduce the CO 2 concentration in the produced biogas to 0–6%. H 2 is effectively utilized by methanogens even at very low concentrations, but little is known about the impact of low CO 2 concentrations on methanogenic activity. In this study, CO 2 consumption and CH 4 production kinetics under low CO 2 concentrations were studied, using a hydrogenotrophic methanogen, Methanobacterium congolense , as model organism. We found that both cellular growth and methane production were limited at low CO 2 concentrations (here expressed as Dissolved Inorganic Carbon, DIC). Maximum rates ( V max ) were reached at [DIC] of 100 mM (extrapolated), with a CO 2 consumption rate of 69.2 f mol cell −1 d −1 and a CH 4 production rate of 48.8 f mol cell −1 d −1 . In our experimental setup, 80% of V max was achieved at [DIC] >9 mM. DIC half-saturation concentrations ( K m ) was about 2.5 mM for CO 2 consumption and 2.2 mM for CH 4 production. No CH 4 production could be detected below 44.4 μM [DIC]. These data revealed that the limiting concentration of DIC may be much higher than that of H 2 for a hydrogenotrophic methanogen. However, DIC is not a limiting factor in ADs running under standard operating conditions. For biomethanation, the results are applicable for both in situ and ex situ biomethanation reactors and show that biogas can be upgraded to concentrations of 2% CO 2 (98% CH 4 ) while still retaining 80% V max at pH 7.5 evaluated from M. congolense . Since DIC concentration can vary significantly with pH and p CO 2 during biomethanation, monitoring DIC concentration through pH and p CO 2 is therefore important for keeping optimal operational conditions for the biomethanation process.",
"conclusion": "Conclusions Although CO 2 affinity of M. congolense is many times higher than H 2 affinity, CO 2 concentrations will only become severely limiting for biomethanation at very low [DIC] concentrations. Experiments were only carried out on a single methanogenic strain here and further testing of other methanogens will reveal if they elicit a similar affinity for CO 2 .",
"introduction": "Introduction Methanogenic archaea play a key role in the production of biogas from anaerobic digesters (AD), yielding a product gas with 50–75% CH 4 and 25–50% CO 2 (Plugge, 2017 ). Methanogenic archaea here produce CH 4 from either H 2 /CO 2 (hydrogenotrophic methanogensis, 4H 2 + CO 2 → CH 4 + 2H 2 O) or acetate (acetoclastic methanogensis, CH 3 COOH → CH 4 + CO 2 ). Hydrogenotrophic methanogens are ubiquitous in natural anaerobic environments other than engineered AD systems, e.g., the gastrointestinal tracts, flooded soils, and anoxic lake and marine sediments (Whitman et al., 2014 ). In anaerobic environments, H 2 is an intermediate produced by fermentative and syntrophic bacteria, where it undergoes rapid turnover and its concentration is extremely low (Lin et al., 2012 ). Use of H 2 as an electron donor is however not restricted to hydrogenotrophic methanogens, but other anaerobic microorganisms, e.g., sulfate reducers and acetogens compete for available H 2 with methanogens in anoxic environments (Robinson and Tiedje, 1984 ; Cordruwisch et al., 1988 ; Kotsyurbenko et al., 2001 ). Therefore, many studies have been committed to the understanding of H 2 uptake kinetics of hydrogenotrophic methanogens through either pure cultures or the whole microbial community in environmental samples (e.g., Conrad, 1999 ; Kotsyurbenko et al., 2001 ; Eecke et al., 2012 , 2013 ). In an AD, dissolved H 2 concentration is usually low [0.5–3 μM, (Frigon and Guiot, 1995 )]. Low H 2 concentration limits methane production through hydrogenotrophic methanogenesis, which has been verified by many studies devoted to biogas upgrading by injecting H 2 directly into the AD (Luo and Angelidaki, 2012 ; Agneessens et al., 2017 ). Above studies showed that the addition of H 2 to ADs greatly increases methane concentration in the biogas, while decreasing CO 2 concentration—a process known as biomethanation. Through biomethanation the CH 4 concentration is increased to as high as natural gas quality (>95%), and thus this process dramatically alters the standard operational conditions in AD because CO 2 concentration is correspondingly reduced to lower than 5%. Such low CO 2 concentration is rarely seen in natural anaerobic environments where methanogens are present, so it is not clear whether such low CO 2 concentration affects the activities of hydrogenotrophic methanogens. However, CO 2 is known to be an important substrate for hydrogenotrophic methanogens, as it serves as both electron acceptor for energy production and (sole) carbon source for biosynthesis through the Wood-Ljungdahl pathway (Berg, 2011 ; Borrel et al., 2016 ). To the best of our knowledge, there is limited knowledge about CO 2 uptake kinetics of methanogens in literature. Nevertheless, understanding CO 2 uptake kinetics of methanogens could consequently be crucial when dealing with the concept of biomethanation, which aims at upgrading the CH 4 concentrations in biogas to >95% by consuming CO 2 to as low concentration as possible. Previous work gives some insights about limitation of CO 2 consumption rate and methanogenic rate at low CO 2 concentrations during biomethanation (Luo et al., 2012 ; Garcia-Robledo et al., 2016 ; Agneessens et al., 2017 ). Here it was shown that H 2 consumptions rate decreased when headspace CO 2 concentrations was lower than 12% during H 2 pulse injection batch experiments in bioreactors (Agneessens et al., 2017 ), while an inhibition of H 2 consumption rate was found when CO 2 concentration was below 6% in a methanogenic manure samples (Garcia-Robledo et al., 2016 ). These results thus indicate that CO 2 uptake rate and methanogenic rate is limited at low CO 2 concentrations with great implications for the limits of the biomethanation technology. However, the exact impact of CO 2 on the methanogenic activity was not clearly depicted by those studies, as they were conducted on complex microbial communities that include both CO 2 consumers and producers—including homoacetogens that compete with methanogens for H 2 and CO 2 . Therefore, a thorough understanding of methanogenic reaction kinetics at low CO 2 concentrations seems necessary in order to find out under which conditions hydrogenotrophic methanogenic rates will be reduced or even inhibited during biomethanation. This will enable us to optimize the efficiency of biomethanation. In this study, we present the first trial to examine CO 2 uptake kinetics of a hydrogenotrophic methanogen, Methanobacterium congolense , by studying its CH 4 production and CO 2 consumption rates at low CO 2 concentrations with surplus of H 2 . We chose to study a model organism from the genus Methanobacterium because methanogens of this genus were found to increase substantially after pulse H 2 injections in reactors with mesophilic sludge (Agneessens et al., 2017 ). M. congolense was used as test strain because it is a mesophilic methanogen originally isolated from a mesophilic anaerobic digester, and also because it solely utilizes H 2 and CO 2 as substrates for growth and methane production (Cuzin et al., 2001 ).",
"discussion": "Discussion Using batch-culture experiments, we provided the first estimation of CO 2 /DIC uptake kinetics of an autotrophic hydrogenotrophic methanogen, M. congolense . We found that the affinity for DIC was dramatically lower than that of H 2 , the other reactant involved in hydrogenotrophic methanogenesis. With a K m of 2.2–2.5 mM, the affinity for DIC was shown to be a few tens to thousands times lower than the K m of H 2 , 0.44–66 μM, as previously reported for other hydrogenotrophic methanogens (Kotsyurbenko et al., 2001 ; Karadagli and Rittmann, 2007 ). Likewise, DIC threshold of 44.4 μM, at which concentration the methanogenic activity could no longer be detected for M. congolense , was also hundreds to thousands times higher than reported H 2 thresholds of 6–70 nM, observed for methanogens (Lin et al., 2012 ). Such high K m and threshold of DIC might be related to the CO 2 fixation pathway used by methanogens. M. congolense utilizes the Wood-Ljungdahl pathway for CO 2 fixation, which has previously been shown to have the highest K m of DIC among the six autotrophic inorganic carbon assimilation pathways (Raven et al., 2012 ). From an evolutionary perspective, poor affinity for DIC is in accordance with the ubiquitous distribution of methanogens in habitats with high CO 2 concentrations, such as anaerobic digesters, animal guts and sediments. Maximum CH 4 production rate ( V max−CH 4 , 48.8 f mol CH 4 cell −1 d −1 ) of M. congolense , estimated from the kinetic model here is comparable to methanogenic rates found for methanogens from other complex environments incubated at a comparable temperature (35°C): 108-135 f mol CH 4 cell −1 d −1 in anaerobic reactors (Li and Noike, 1992 ) and 31.5 f mol CH 4 cell −1 d −1 in lake sediments (Lay et al., 1996 ). Addition of different amounts of CO 2 gas at the beginning of the batch culture experiments caused the decrease of media pH due to the dissolution of CO 2 gas into the medium, where pH decreased more in bottles receiving higher amounts of CO 2 . A further challenge was that the CO 2 concentration changed constantly during the incubation, due to continuous CO 2 consumption. Therefore, we controlled the amount of CO 2 added to keep pH within an optimal range for M. congolense (pH 5.9–8.2) (Cuzin et al., 2001 ), so that growth and methanogenic activity of M. congolense were not affected during our trials. A previous study on an obligate hydrogenotrophic autotrophic methanogen, Methanocaldococcus strain JH146, showed that pH did not affect methanogenic activity when it was within the range for optimal growth (Eecke et al., 2013 ). In our experiments, addition of 2–40 mL CO 2 lowered pH from 7 to 6.44–6.94 in the mineral medium, which has lower buffering capacity but methanogenic rates kept increasing with CO 2 amount and reached a rate of ~44 f mol CH 4 cell −1 d −1 with 40 mL CO 2 (pH = 6.44). Moreover, specific ∑CO 2 consumption rates and CH 4 production rates determined from all three different experimental setups here were in good agreement with respect to [DIC], regardless of buffering capacity or incubation time ( Figure 2 ). Thus, pH seems to have little impact on the rates within the experimental range. However, addition of excessive CO 2 beyond the buffering capacity was shown to greatly inhibit methanogenic activity: addition of 60 mL CO 2 into the mineral medium decreased pH to 6.01 and resulted in a dramatic reduction of methanogenic activity to ~12 f mol CH 4 cell −1 d −1 . Our study also shows that DIC concentration influences the microbial growth rate. This is revealed by a clear reduction of growth rate from 1.12 d −1 at 5.13 mM [DIC] to <0.02 d −1 at the lowest DIC concentrations tested (0.44 mM) in the long-term batch culture experiment ( Table 1 ). A previous study showed that the mixotrophic methanogen, Methanosarcina barkeri , has very slow growth and low methanogenic rate when incubated with only H 2 but lacking CO 2 (Weimer and Zeikus, 1978 ). Here we showed that growth of M. congolense was limited when [DIC] was lower than 1.6 mM, although methanogenesis still continued at low rates. Methanogens fix CO 2 autotrophically into biomass through the Wood-Ljungdahl pathway, with which its methanogenesis pathway is associated (Berg, 2011 ). Therefore, whether the lowered growth rate at low [DIC] was due to reduced assimilation of carbon for biomass formation, or due to a reduced energy generation from methanogenesis remains unknown. Nonetheless, cell growth yield at the end of long-term incubation seems to be consistent for all DIC concentration tested: the methanogen converts approximately four moles of CO 2 into CH 4 for each mole of CO 2 incorporated into biomass ( Table 1 ). Similar fraction of CO 2 was assimilated into biomass by late exponential growth phase cells during our short-term batch incubation, as ∑CO 2 consumption rate was about 1.3 times higher than methane production rate for all bottles ( Figure 2C ). The ratio of CO 2 used in dissimilatory and assimilatory metabolisms were thus independent of the [DIC] concentration. As p CO 2 concentrations in anaerobic digesters is often high (25–50%), our data showed that inorganic carbon availability might not limit methanogenic activity of M. congolense under standard operating conditions of an anaerobic digester. Fermentation processes in the sludge will furthermore supply CO 2 to the hydrogenotrophic methanogens and hereby decrease the likelihood of CO 2 limitation under standard conditions. This study is of primary importance for biomethanation, a power-to-gas technology used for biogas upgrading to increase CH 4 concentration in the produced biogas through reduction of the CO 2 concentration. Knowledge on methanogen's CO 2 /DIC kinetics is relevant as low CO 2 concentrations (<2%) are required to fulfill criteria for injection of upgraded biogas to the natural gas grid. If M. congolense is treated as a representative of hydrogenotrophic methanogens, the biogas can be upgraded to >98% CH 4 (<2% CO 2 ) at 80% V max−CH 4 when slurry pH in the reactor is >7.5 ( Figure 3C ) and 50% V max−CH 4 at pH >6.8 ( Figure 3B ). Our result suggests that the availability of bioavailable inorganic carbon under low CO 2 concentration might not greatly decrease methanogenic activity during biogas upgrading, but other factors, such as pH, might have greater impact on methanogenic rates. A decrease in methanogenic activities to 50% of the maximum was shown in previous studies at different CO 2 concentrations: 2.9% CO 2 (59 mM [DIC] in methanogenic manure samples, Garcia-Robledo et al., 2016 ) and 10% CO 2 (257 mM [DIC] in anaerobic digestate, Agneessens et al., 2017 ). These [DIC] concentrations were higher than the K m values reported here for M. congolense , which would indicate that the organisms in these studies either had lower affinities for [DIC]/CO 2 than M. congolense , or that their methanogenic activities were inhibited by other factors like NH 3 or pH. The pH levels of 8.2 (Garcia-Robledo et al., 2016 ) and 8.3 (Agneessens et al., 2017 ), were close to the value of 8.5 reported to be inhibitory to the biomethanation process (Angelidaki et al., 2018 ). As bicarbonate is the dominant buffering system in anaerobic slurries, it is often difficult to separate effects by high pH from effects by low CO 2 concentrations here, as these are inversely related. Through the pure culture study on M. congolense reported here, it was possible to separate the direct pH effect from low concentrations of CO 2 and hereby elucidate microbial physiological limitations to the process of biomethanation of a methanogenic type strain. The results are applicable to both in situ methanation, where H 2 and CO 2 are converted by methanogens in the main reactor, and separate ex situ reactor harboring specialized methanogenic communities."
} | 3,937 |
22235327 | PMC3250475 | pmc | 6,472 | {
"abstract": "Photosynthesis uses light as a source of energy but its excess can result in production of harmful oxygen radicals. To avoid any resulting damage, phototrophic organisms can employ a process known as non-photochemical quenching (NPQ), where excess light energy is safely dissipated as heat. The mechanism(s) of NPQ vary among different phototrophs. Here, we describe a new type of NPQ in the organism Rhodomonas salina , an alga belonging to the cryptophytes, part of the chromalveolate supergroup. Cryptophytes are exceptional among photosynthetic chromalveolates as they use both chlorophyll a/c proteins and phycobiliproteins for light harvesting. All our data demonstrates that NPQ in cryptophytes differs significantly from other chromalveolates – e.g. diatoms and it is also unique in comparison to NPQ in green algae and in higher plants: (1) there is no light induced xanthophyll cycle; (2) NPQ resembles the fast and flexible energetic quenching (qE) of higher plants, including its fast recovery; (3) a direct antennae protonation is involved in NPQ, similar to that found in higher plants. Further, fluorescence spectroscopy and biochemical characterization of isolated photosynthetic complexes suggest that NPQ in R. salina occurs in the chlorophyll a/c antennae but not in phycobiliproteins. All these results demonstrate that NPQ in cryptophytes represents a novel class of effective and flexible non-photochemical quenching.",
"introduction": "Introduction Photosynthetic organisms are often exposed to varying environmental conditions, such as excessive irradiation. Over-excitation by surfeit light can produce harmful reactive oxygen intermediates detrimental to pigments, proteins and lipids [1] , [2] . Several protective mechanisms can be stimulated when light absorption exceeds its utilisation in photosynthesis. One of these, non-photochemical quenching (NPQ), is a feed-back regulatory mechanism in which excessive light irradiation is dissipated as heat (reviewed in [3] , [4] ). As the process of NPQ involves the de-excitation of chlorophyll molecules from their exited states, NPQ is usually detected indirectly by analysing chlorophyll fluorescence [5] , rather than directly by monitoring heat emissions [6] . Mechanism of NPQ is best characterized in higher plants [3] , [7] . The process of energy dissipation in NPQ is triggered by low pH in the thylakoid lumen and modulated by several factors including zeaxanthin [8] and the PsbS protein [9] , [10] . The search for the molecular photophysical mechanism of NPQ remains unresolved as several quenching mechanism have been already suggested including quenching by lutein [11] , chlorophyll to zeaxanthin charge transfer quenching [12] , by chlorophyll pairs [13] or quenching by excitonic carotenoid–chlorophyll states [14] . The understanding of NPQ is complex as several processes, each with different kinetics, are involved. On the beginning, the fast energetic quenching (qE) is triggered by lumen acidification, that is further stimulated under prolonged irradiation by zeaxanthin [15] . Long-term exposition (hours and days) to excessive irradiation then finally results in photoinhibitory quenching - qI which exact mechanism is still matter of debate [4] , [16] . Although mainly documented in plants, NPQ has also been studied in several oxygenic phototrophs; mosses, algae and cyanobacteria. The basic principle of NPQ, the safe dissipation of excessive light irradiation as heat, is identical across all organisms; however, crucial differences exist in its regulation and structural mechanisms. For example, NPQ in diatoms [17] is located in fucoxanthin–chlorophyll a/c antennae [18] , [19] , that are non-homologous to chlorophyll a/b antennae of higher plants. In addition to the structure differences, these two antennae also differ in their sensitivity to protonation, as only chlorophyll a/b antennae are able to be reversibly protonated [20] , [21] but such a effect is questionable in diatoms [17] , [22] . On the regulatory level, PsbS is known to be active in NPQ of higher plants, but not in diatoms [23] , [24] or green algae [25] . Instead another group of light-harvesting proteins from the LHCSR (formerly LI818) family, which are missing in higher plants [25] are involved in NPQ in green algae and in diatoms [26] , [27] . Additional differences exist also in the ability of transthylakoid ΔpH to trigger NPQ; the capacity of ΔpH to induce quenching is decreased in some green algae when compared to higher plants [28] . There are also different xanthophyll cycles, a violaxanthin cycle found in green algae [29] and a diadinoxanthin cycle in diatoms [30] . Furthermore, in cyanobacteria completely different mechanism of NPQ, regulated by the OCP protein, operates in the phycobilisomes [31] . Compared to higher plants, the understanding of NPQ in various algal groups is still much more fragmented or missing completely. This is especially true for chromalveolate algae involving diatoms, brown algae and cryptophytes [32] . The chromalveolate group is thought to have originated from a secondary endosymbiosis, when a chimeric organism was formed from two eukaryotic cells, a non-photosynthetic host and a photosynthetic endosymbiont of red algal origin [33] . Cryptophytes are exceptional among photosynthetic chromalveolates [32] as they are the only phototrophs to use for light harvesting both membrane-bound chlorophyll a/c proteins and phycobiliproteins that are firmly embedded in the thylakoid lumen [34] . Thus cryptophytes represent a unique evolutionary intermediate between ancestor of all chromalveolates - red algae, which contain phyobiliproteins but lack chlorophyll c , and diatoms, that have diversified more “recently” from their red algae ancestor and which contain chlorophyll c but not phycobiliproteins [23] . Moreover, the light harvesting antennae found in the cryptophytic thylakoid membrane are formed by unique chlorophyll a/ c proteins known as CAC antennae [35] . These proteins are distinct from chlorophyll a/b binding antennae of green algae and higher plants and also from chlorophyll c antennae of chromalveolates; this includes the peridinin-chlorophyll proteins of dinoflagellates and the fucoxanthin-chlorophyll proteins of diatoms [36] , [37] . For chromalveolate algae, diatoms are almost the only model organism used for intensive studding of NPQ mechanism [38] . It has been already found that NPQ in diatoms is a pH-dependent process closely associated with the diadinoxanthin cycle [17] , localised at either the fucoxanthin-chlorophyll proteins [19] or the PSII reaction centre [39] . Diatoms aside, few publications exist on NPQ activity in chromalveolates, these are limited to studies on brown algae [40] , the recently discovered apicomplexan Chromera velia \n [41] and the all chromalveolate ancestor, red algae [42] , [43] . The mechanism of NPQ in red algae is still rather enigmatic, we only know that non-photochemical quenching of fluorescence in red algae is a pH-dependent process, precise NPQ locus is not known [42] , [43] . A possible role of energy dissipation in PSII reaction centre [43] and physical phycobilisome decoupling [44] have been already discussed as possible energy dissipation pathways. Even less we know about protective mechanisms in cryptophytes. It has been shown that none of the usual xanthophyll cycle pigments (e.g. zeaxanthin, diadinoxanthin, diatoxanthin) are present at detectable level during stimulation of NPQ in Guillardia Theta \n [45] . Here we describe NPQ mechanism in the cryptophytic algae representative, Rhodomonas salina in all its details that allowed us to compare it with the same process in diatoms and in higher plants. All our results have demonstrated that NPQ in cryptophytes represents a novel class of effective non-photochemical quenching. We have showed that the process of NPQ in cryptophytes is not accompanied by the cycling of xanthophyll pigments in line with previous results and moreover its kinetics resembles the rapid and reversible energetic quenching (qE) found in higher plants. The similarity of NPQ in cryptophytes with qE of plants was further confirmed with isolated antennae complexes, that showed involvement of their protonation in the cryptophytic NPQ process. Therefore, NPQ in cryptophytes is localised to the membrane-bound CAC protein that can be triggered to quenching mode by lumen acidification.",
"discussion": "Discussion Cryptophyte algae represent a unique evolutionary link between red algae, which lack chlorophyll c but contain phycobilisomes, and diatoms, which contain chlorophyll a/c antennae but lack phycobiliproteins. Using R. salina as a model organism, we have demonstrated efficient NPQ ( Figure 1 ) operates in cryptophytes and that the regulation of this NPQ is distinct from both red algae and diatoms. First, in strict contrast to the crucial role of the xanthophyll cycle in diatoms [17] , cryptophytes have no photoprotective de-epoxidation/epoxidation cycling of xanthophyll pigments ( Table 1 ) in line with previous results [45] . Second, we have identified specific chlorophyll a/c antennae of PSII as the site of NPQ in R. salina ( Figures 4 , 5 and 6 ). Since in red algae chlorophyll a/c antennae of photosystem II are missing [36] and a dominant NPQ occurs rather in the reaction centres [42] , [43] , the cryptophytes operate new and evolutionary distinct type of NPQ. Fast kinetics of NPQ in cryptophytes implies that it represents so-called energetic type of quenching (qE; Figure 1 ). This type of NPQ is already well described for higher plants [54] and is characterised by its rapid stimulation on exposure to actinic light (in tens of seconds) and fast relaxation in dark. The immediate response to changes in irradiation is due to the ΔpH dependency of qE, as the ΔpH across the thylakoid membrane is rapidly formed in the light and quickly dissipated in dark [8] . The NPQ dependency on lumen acidification has been also demonstrated in diatoms. However, there the low lumenal pH is crucial rather for triggering of the fast diadinoxanthin to diatoxanthin de-epoxidation [51] and lumen acidification alone is not sufficient to induce NPQ [17] . As R. salina has no light-induced xanthophyll cycle ( Table 1 ) lumen acidification must play a critical role in NPQ induction (see Figure 3A ). Therefore NPQ in cryptophytes is closely related to the qE observed in higher plants, rather than the slowly reversible qI observed in diatoms [26] . Additionally, periods of prolonged excessive irradiation therefore causes photoinhibitory damage of PSII in cryptophytes (data not shown) and does not involve an increase in the diatoxanthin pool as seen in diatoms [51] . Lumen acidification in cryptophytes appears to play a direct role in switching antennae to a quenched state by reversibly protonating CAC proteins ( Figure 5 ). The importance of protonation for induction of NPQ has been demonstrated several times in higher plants using isolated light-harvesting antennae [20] , [21] , [55] . This is due to DCCD binding to carboxy amino residues located in the hydrophobic domains of light harvesting antenna that can reverse acid-induced fluorescence quenching [21] . We have performed a similar experimental procedure [55] with isolated CAC antennae, to demonstrate that the quenching of their variable chlorophyll fluorescence is pH dependent ( Figure 5B ). Moreover, we found the effect of low pH is reversible by using DCCD to deprotonate residues on the CAC proteins ( Figure 5B ), as described for light-harvesting antennae from higher plants [55] . However, the reversible part of fluorescence quenching from Fraction I (CAC proteins) and Fraction II (CAC complexes with photosystems) is small in comparison to results obtained for LHC proteins of higher plants [21] , [56] . There are several possible explanations: the limited number of protonable residues in CAC, the necessity of some other factors than low pH for maximal quenching (e.g. Ca 2+ binding to antennae [57] ) or higher importance of aggregation of CAC proteins in quenching (note the relatively pronounced decrease in fluorescence before lowering pH). On the other hand, our approach confirmed an inhibitory effect of DCCD on NPQ in vitro ( Figure 5 ) and also in vivo ( Table 2 ), which suggests presence of pH sensing mechanisms in cryptophytes CAC antennae similarly to higher plants LHCs [21] . These results are in contrast with the situation in diatoms, where the DCCD treatment stimulates NPQ, that has resulted in speculation that the FCP proteins of diatoms may not have protonable residues [17] . The CAC proteins thus appear to be the first example of chromalveolate antennae where the protonable residues play a role in NPQ stimulation. 10.1371/journal.pone.0029700.t002 Table 2 Effect of different DCCD (N,N′-dicyclohexyl-carbodiimide) concentrations on the maximal efficiency of PSII photochemistry (F V /F M ) and on NPQ. 0 µM 2 µM 5 µM 10 µM \n NPQ \n 1.17 0.64 0.74 0.37 \n F V /F M \n 0.75 0.71 0.73 0.65 F V /F M values were calculated for dark adapted sample, NPQ was detected after 120 s irradiation by orange light (620 nm, 600 µmol m −2 s −1 ). Our results suggest that lumen acidification is sufficient for the formation of relatively high NPQ (around 1.5) in cryptophytes, disposing of the necessity for xanthophyll de-epoxidation ( Table 1 ). This is in contrast to green algae, in which NPQ was found to be rather weak (below 1) in a case of low violaxanthin de-epoxidation to zeaxanthin [58] . Therefore, it has been concluded that zeaxanthin is necessary for stimulation of higher NPQ values in green algae [59] , [60] . In higher plants, the occasional absence of zeaxanthin can be overcome by PsbS protein that can stimulate NPQ to relatively high values (to about 1.5) even in Arabidopsis mutant without zeaxanthin [61] . PsbS protein is also required for the rapid stimulation of higher plants NPQ; its absence results in slower and less flexible NPQ, where it takes over an hour for NPQ to reach its maximal value [10] . Presently it is not known if, like higher plants, R. salina has a protonable PsbS-like protein. However, the rapid NPQ found in cryptophytes can result from either fast protonation of CAC antenna (see Figure 5 ) or higher lumen acidification. It is important to note that NPQ in R. salina is activated (see Figure 2 ) only after saturation of the Calvin-Benson cycle (above ∼150 µmol m −2 s −1 , see Figure S1 ), which causes limited ADP regeneration [62] . In contrast the PsbS protein stimulates NPQ at all light intensities, even when Calvin-Benson cycle is not saturated. Therefore the action of only one mechanism, such as lumen acidification resulting from Calvin-Benson cycle saturation, would explain the observed light dependency of NPQ in cryptophytes; therefore rendering the presence of PsbS unnecessary. Using spectroscopic and biochemical approaches, we localized the NPQ in R. salina to the CAC[c] oligomer, likely in vivo associated with PSII. First, we used spectrally resolved fluorescence induction [47] to calculate the in vivo spectral dependency of NPQ(λ) ( Figure 4 ). These results exclude quenching of phycoerythrin fluorescence, as judging by the light-induced changes in chlorophyll emission spectra above 640 nm ( Figure 4 ), all dissipation occurs in the chlorophyll antenna. Accordingly light absorbed by lumenal phycoerythrins is efficiently transferred to CACs, which is in line with observation that R. salina phycoerythrins form a very efficient light-harvesting system [63] , [64] . Second, we isolated CAC[c] complexes to demonstrate that the two features of in vivo NPQ, pH dependency and fast reversibility, are detectable in vitro ( Figure 5 ). In combination, our results lead to conclusion that NPQ in R. salina mainly operates in the CAC antennae of cryptophytes. As the chlorophyll a/c antenna oligomer CAC[c] with molecular mass ∼150 kDa, has been suggested as a main NPQ locus (see previous paragraph), we analysed the organization of CAC antennae using 2D clear-native/SDS-electrophoresis ( Figure 6A ). This antennae complex that dissociated during native eletrophoresis from photosystems (mostly from PSII super-complexes), is composed of at least two different CAC proteins ( Figure 6A ) consistent with previous observations [65] , [66] . Furthermore, our data show the absorption pattern of the CAC complex mirrors previous results from Choomomas sp. \n [67] and Cryptomonas maculata \n [68] . Additionally, we anticipate the CAC[c]-PSII super-complex could be homologous to Rhodomonas CS24 , a cryptophyte alga, PSII super-complexes [69] . Using single particle analysis, these authors have shown Rhodomonas CS24 PSII super-complexes are composed of four monomeric CAC proteins bound to one side of the PSII core dimer [69] . The oligomeric organization of antennae complexes in cryptophytes could affect properties of NPQ. For instance, in higher plants antennae trimers and minor light-harvesting antennae have been suggested to act as two different NPQ loci [53] , [70] . A two-site quenching mechanism has been also suggested for diatoms [19] , where a trimer of fucoxanthin-chlorophyll proteins represents typical antennae protein organization [71] . However, observations on the organization of CAC proteins in PSII supercomplexes of cryptophytes [69] suggest that CAC trimers are absent in cryptophytes, removing the possibility of a second NPQ loci. Based on these data, we speculate NPQ in R. salina resembles the quenching found in the minor chlorophyll a/b antennae CP24 and CP26 of higher plants [70] . It would be very interesting to compare the NPQ that we described here with the NPQ in red algae, as the LhcR antennae of red algae are the closest relatives to CAC antennae of cryptophytes [36] . Since the NPQ in red algae showed several similarity with NPQ in cryptophytes (e.g. pH-dependency [42] , [43] , low importance of xanthophylls cycle [72] , [73] , [74] ), it indicates that also the NPQ mechanisms seems to be evolutionary related. Here we have demonstrated that NPQ cryptophytes represents a novel class of effective NPQ that proceeds on a level of chlorophyll a/c antennae (CAC) and not in phycobiliproteins and its important properties differ significantly from NPQ described in diatoms and in higher plants. For example, the typical carotenoid quenchers found in higher plants (lutein and zeaxanthin) or in diatoms (diatoxanthin) are absent in cryptophytes. The observed absence of NPQ in phycobiliproteins means that periods of excessive irradiation absorbed by phycobiliproteins have to be ‘managed’ by its rapid transfer to CAC antennae for a safe dissipation. Thus, the cryptophytes, and in particular R. salina , represents a new model organism for the study of photoprotection and NPQ, which is going to be facilitated by the imminent completion genome sequence for a cryptophytes representative."
} | 4,789 |
25432941 | PMC4986455 | pmc | 6,473 | {
"abstract": "Mitochondria are the energy-producing organelles of our cells and derive from bacterial ancestors that became endosymbionts of microorganisms from a different lineage, together with which they formed eukaryotic cells. For a long time it has remained unclear from which bacteria mitochondria actually evolved, even if these organisms in all likelihood originated from the α lineage of proteobacteria. A recent article (Degli Esposti M, et al. 2014. Evolution of mitochondria reconstructed from the energy metabolism of living bacteria. PLoS One 9:e96566) has presented novel evidence indicating that methylotrophic bacteria could be among the closest living relatives of mitochondrial ancestors. Methylotrophs are ubiquitous bacteria that live on single carbon sources such as methanol and methane; in the latter case they are called methanotrophs. In this review, I examine their possible ancestry to mitochondria within a survey of the common features that can be found in the central and terminal bioenergetic systems of proteobacteria and mitochondria. I also discuss previously overlooked information on methanotrophic bacteria, in particular their intracytoplasmic membranes resembling mitochondrial cristae and their capacity of establishing endosymbiotic relationships with invertebrate animals and archaic plants. This information appears to sustain the new idea that mitochondrial ancestors could be related to extant methanotrophic proteobacteria, a possibility that the genomes of methanotrophic endosymbionts will hopefully clarify.",
"conclusion": "Conclusion—A Methanotrophic Past for Mitochondria? The new information presented in this review ( figs. 2 , 3 , and 5 and Supplementary figs. S1–S3 , Supplementary Material online) supports and refines recent indications that extant methylotrophs may include the relatives of proto-mitochondria ( Degli Esposti et al. 2014 ). This proposition contrasts with the majority of previous studies that had indicated different bacteria as the possible ancestors of mitochondria. Frequently, such studies have supported the possibility that proto-mitochondria originated from Rickettsia and its relatives ( Andersson et al. 1998 , 2003 ; Fitzpatrick et al. 2006 ; Williams et al. 2007 ; Sassera et al. 2011 ; Ferla et al. 2013 ). However, the possible mitochondrial origin from within the Rickettsiales, which include only obligate endosymbionts, is viewed with increasing scepticism ( Esser et al. 2004 ; Atteia et al. 2009 ; Abhishek et al. 2011 ; Gray 2012 ; Müller et al. 2012 ), because it suffers from the major problem of long-branch attraction distorting phylogenetic analysis. Moreover, in bioenergetic terms, all Rickettsiales have lost the elements of N-metabolism that are still present in mitochondria from fungi and heterokonts ( Takaya 2009 ), while often retaining the noneukaryotic bd -type ubiquinol oxidase ( Degli Esposti et al. 2014 ). This situation is similar to that encountered in various transitions from free-living to symbiotic lifestyles among extant bacteria ( fig. 4 ), thereby suggesting phenomena of convergent evolution related to endocellular symbiosis. Finally, the proponents of the Rickettsiales origin for proto-mitochondria have as yet to provide a plausible answer to the obvious question: Which organisms were they a parasite of? Methylotrophs are a general definition for chemiolithotrophic bacteria that utilize C1 compounds as their carbon and energy source and include both methane and ammonia-oxidizing organisms ( Chistoserdova et al. 2009 ). Methane oxidation arose when ambient oxygen levels increased dramatically on the planet, approximately at the time in which β- and γ-proteobacteria separated from the ancestors of current α-proteobacteria ( Battistuzzi et al. 2004 ). Although methylotrophs or methanotrophs had not been considered before as possible ancestors of mitochondria, they now appear to be good candidates for proto-mitochondria, for the following reasons:\n they are evolutionary ancient, as just discussed, consistent with the accepted notion that proto-mitochondria evolved early after the separation of the γ- and β-lineages ( Williams et al. 2007 ); they are related to autotrophic organisms forming permanent symbioses with invertebrate animals living in deep-sea habitats ( Dubilier et al. 1999 ; Fujiwara, Takai, et al. 2000 ; Cavanaugh et al. 2013 ), as well as with mosses of northern wetlands ( Kip et al. 2011 ; Dedysh 2011 ); they have a wide metabolic versatility which includes the cycles of glyoxylate, citrate, and serine that in eukaryotic lineages are segregated within organellar compartments that may also derive from mitochondria ( Mohanty and McBride 2013 ); they have a bioenergetically efficient respiratory chain, even under low oxygen concentrations, for the presence of multiple terminal oxidases ( Dam et al. 2012 ; Degli Esposti et al. 2014 ); finally, they characteristically contain “ICMs” derived from invaginations of their inner membrane ( Hagen et al. 1966 ; Watson and Mandel 1971 ; Lynch et al. 1980 ; Reed et al. 1980 ; Fujiwara, Takai, et al. 2000 ; Dunfield et al. 2010 ; Dedysh 2011 ; Kip et al. 2011 ; Cavanaugh et al. 2013 ). Such tightly packed membranes resemble mitochondrial cristae ( fig. 5 ) and are equally required to expand the surface available to plug in respiratory complexes that provide bioenergy, together with membrane-bound methane mono-oxygenase ( Chistoserdova et al. 2009 ). \n It is surprising that such evident structural similarities between methanotrophs and mitochondria have passed unnoticed for almost 50 years ( Hagen et al. 1966 ). However, the overlooked cristae-like structures of methanotrophs may constitute yet another example of how science has become compartmentalized in specialized fields, in this case a niche of microbiology and classical bioenergetics. No doubt, Lynn Margulis has encountered examples of the same kind in her pioneering work that in time established the endosymbiotic theory of eukaryotic cell evolution ( Sagan 1967 ). Taking together the timeline for bacterial evolution ( Battistuzzi et al. 2004 ) with the restricted distribution of methanotrophic symbionts to α- and γ-proteobacteria ( Fujiwara, Takai, et al. 2000 ; Kip et al. 2011 ; Dedysh 2011 ) and the list of rare bioenergetic features shared by proteobacteria and mitochondria, which are more common between α- and γ- than between α- and β-proteobacteria ( table 1 ), it seems possible that proto-mitochondria have evolved from the common ancestors of methanotrophic α- and γ-proteobacteria. The phylogenetic trees of COX proteins ( fig. 2 ) support this possibility. The invention of the metabolic pathways of methane and ammonia oxidation immediately followed the large increase in ambient oxygen that occurred around 2 billion years ago, allowing autotrophic ways of life which are now retained by the abovementioned methanotrophs ( Battistuzzi et al. 2004 ; Chistoserdova et al. 2009 ; Vlaeminck et al. 2011 ). These bacteria also possess the largest variety of COX operons and molecular forms of their catalytic subunits ( supplementary figs. S1 and S2 , Supplementary Material online), as a result of multiple events of operon and gene duplication. Indeed, the reconstruction of the molecular evolution of COX 3 proteins and their binding strength for oxygen-modulating phospholipids seems to recapitulate a progressive adaptation to increasing levels of O 2 , gauged in terms of decreasing oxygen affinity to maintain maximal efficiency of the oxidase reactions ( Riistama et al. 1996 ; Degli Esposti et al. 2014 ). These considerations refer to the symbiont independently on the host organism that was involved in the eukaryogenic event leading to the evolution of our cells. Recent evidence points to an archaeal nature for such a host ( Williams et al. 2013 ) without defining ecological and metabolic features that may help indicating the type of symbiosis that it may favor. Certainly, this host was adapted to anaerobic conditions, because the evolution of proto-mitochondria and the earliest eukaryotic cells occurred when oxygen levels were still very low in the oceans ( Johnston et al. 2009 ; Müller et al. 2012 ). It is therefore plausible that mitochondrial progenitors derive from organisms that had experimented with a wide variety of oxygen-reacting systems and thus retained great plasticity in their adaptation to environments poor in oxygen, a trait that is partially retained in eukaryotes adapted to anaerobic environments ( Mentel et al. 2014 ). Hence, those progenitors were probably related to the ancestors of extant proteobacterial methanotrophs, many of which have the capacity of establishing endosymbiosis with “primitive” animals such as gutless worms, as well as primitive plants such as mosses. A methanotrophic past for mitochondria is thus looming; future research will clarify its validity and evolutionary implications.",
"introduction": "Introduction That mitochondrial organelles derive from ancestral bacteria ( Sagan 1967 ; Margulis 1996 ) are now widely accepted, but the question of their precise origin remains open ( Gray 2012 ). The endosymbiotic transition that transformed these ancestral bacteria (or proto-mitochondria) into current mitochondria has been fundamental in the evolution of life ( Lane and Martin 2010 ). According to the current consensus, this event occurred only once, at least 1.5 billion years ago, and was followed by divergent evolutionary paths leading to altered forms of the organelles adapted to anaerobic lifestyles ( Müller et al. 2012 ; Mentel et al. 2014 ). Consistent evidence has indicated that proto-mitochondria emerged from the α lineage of proteobacteria ( Andersson et al. 1998 , 2003; Williams et al. 2007 ; Gray 2012 ; Müller et al. 2012 ). This α lineage occupies a central position in “phylum” proteobacteria, because it originated after the separation of the predominantly anaerobic δ and ε branches but before the split of the (predominantly) facultatively anaerobic γ and β branches ( Battistuzzi et al. 2004 ). Proto-mitochondria evolved soon after, when current oxygen levels were established in the atmosphere, emerged land, and the photic zone of Proterozoic oceans, while the rest of the oceans remained fundamentally anoxic ( Johnston et al. 2009 ; Müller et al. 2012 ). Because at this time all life forms were restricted to the oceans, proto-mitochondria evolved in one of the following ecological environments: (1) photic surface of the ocean; (2) oxic–anoxic interface in oceanic or other aquatic regions; and (3) medium deep ocean, including hydrothermal vents and cold seeps. In environment (1), the ancestors of mitochondria might have been equipped with the protective pigments of purple nonsulfur photosynthetic bacteria such as Rhodospirillum ( Esser et al. 2004 ) or were already adapted to the oligotrophic pelagic lifestyle of the bacteria which are dominant in current seas, for example, Pelagibacter ( Thrash et al. 2011 ). In environment (2), the ancestors of mitochondria should have been facultatively anaerobic, as in the case of extant aquatic bacteria that are free-living (e.g., Magnetococcus ; Schübbe et al. 2009 ) or predatory (e.g., Micavibrio ; Davidov et al. 2006 ). Finally, in environment (3), the ancestors of mitochondria must have had additional metabolic capacities to survive under oligotrophic conditions and eventually exploit, via chemiosynthesis, local areas enriched in chemical nutrients such as H 2 S, nitrate, reduced metals, and methane. Currently, such metabolic pathways are present in α- and γ-proteobacteria that have become endocellular symbionts of deep-sea invertebrates inhabiting hydrothermal vents, cold seeps, and fallen wood ( Dubilier et al. 1999 ; Blazejak et al. 2006 ; DeChaine and Cavanaugh 2006 ; Cavanaugh et al. 2013 ), or of peat bog mosses ( Dedysh 2011 ; Kip et al. 2011 ). From a theoretical standpoint, a wide metabolic versatility enabling adaptation to all the above environments would produce maximal probability for being close to the mitochondrial ancestors. At the moment, such a versatility seems to be present in a few families of cultivated α- and γ-proteobacteria and also some uncultivated organisms discovered in metagenomic surveys ( Wrighton et al. 2012 ). However, the requirement for metabolic versatility is not a strict condition for defining the ancestors of mitochondria, which might have been confined to only one of the abovementioned habitats. Another factor that deserves consideration is bacterial adaptation to endocellular symbiosis. Proto-mitochondria that had evolved in Proterozoic environments must have reduced their metabolic versatility during the transition to symbiotic lifestyle. Although we can only guess the details of this process, we can deduce its contours by comparison with various examples of metabolic adaptation to symbiosis or parasitism that have been documented for extant microorganisms ( Vannini et al. 2007 ; Moran et al. 2009 ; Manzano-Marín et al. 2010 ; Sachs et al. 2011 ; Caffrey et al. 2012 ). In general, the stability of the host environment reduces evolutionary pressure on organisms that have undergone the transition from free-living to endocellular symbionts, resulting in genome reduction and erosion affecting energy metabolism also ( Moran et al. 2009 ; Caffrey et al. 2012 ). The original metabolic capacity of mitochondrial ancestors has thus been lost ( Huynen et al. 2013 ; Degli Esposti et al. 2014 ), contrary to popular reconstructions which depict their bioenergetic capacity as equivalent to that of mammalian mitochondria ( Emelyanov 2001 ; Gabaldón and Huynen 2003 ). Recently, the progressive gene loss leading to the current bioenergetic capacity of mitochondria has been reconstructed by using a systematic genomic analysis of extant α-proteobacteria and protists’ mitochondria ( Degli Esposti et al. 2014 ). The analysis led to the novel indication that a group of proteobacteria, the methylotrophs, may contain the extant relatives of proto-mitochondria ( Degli Esposti et al. 2014 ). Interestingly, some of these methylotrophs such as Methylocystis possess a metabolic versatility suitable for all environments (1)–(3) mentioned above ( Dam et al. 2012 ). They are also related to the methanotrophic endosymbionts of Spagnum mosses dominating peat bogs and northern wetlands ( Raghoebarsing et al. 2005 ; Dedysh 2011 ; Kip et al. 2011 ; Belova et al. 2013 ). The work of Degli Esposti et al. (2014) has excluded from mitochondrial ancestry many bacteria that had been previously proposed as mitochondrial relatives or ancestors, for example, Rhodospirillum rubrum ( Esser et al. 2004 ; Abhishek et al. 2011 ; Thiergart et al. 2012 ) and Rickettsia ( Andersson et al. 1998 ; Sicheritz-Pontén et al. 1998 ; Williams et al. 2007 ; Abhishek et al. 2011 ; Ferla et al. 2013 ). Therefore, this work will inevitably generate controversy in the fields of molecular evolution, microbiology, and bioenergetics. Herein, I will examine major issues that may help resolving such controversy within an overview of the common bioenergetic features of proteobacteria and mitochondria."
} | 3,813 |
19209338 | null | s2 | 6,475 | {
"abstract": "This paper describes a method for prefabricating screw, pneumatic, and solenoid valves and embedding them in microfluidic devices. This method of prefabrication and embedding is simple, requires no advanced fabrication, and is compatible with soft lithography. Because prefabrication allows many identical valves to be made at one time, the performance across different valves made in the same manner is reproducible. In addition, the performance of a single valve is reproducible over many cycles of opening and closing: an embedded solenoid valve opened and closed a microfluidic channel more than 100,000 times with no apparent deterioration in its function. It was possible to combine all three types of prefabricated valves in a single microfluidic device to control chemical gradients in a microfluidic channel temporally and spatially."
} | 210 |
26404584 | PMC4582590 | pmc | 6,478 | {
"abstract": "Marinobacter sp. CP1 was isolated from a self-regenerating and self-sustaining biocathode biofilm that can fix CO 2 and generate electric current. We present the complete genome sequence of this strain, which consists of a circular 4.8-Mbp chromosome, to understand the mechanism of extracellular electron transfer in a microbial consortium."
} | 85 |
30990672 | PMC6992424 | pmc | 6,480 | {
"abstract": "DNA\norigami nano-objects are usually designed around generic single-stranded\n“scaffolds”. Many properties of the target object are\ndetermined by details of those generic scaffold sequences. Here, we\nenable designers to fully specify the target structure not only in\nterms of desired 3D shape but also in terms of the sequences used.\nTo this end, we built design tools to construct scaffold sequences de novo based on strand diagrams, and we developed scalable\nproduction methods for creating design-specific scaffold strands with\nfully user-defined sequences. We used 17 custom scaffolds having different\nlengths and sequence properties to study the influence of sequence\nredundancy and sequence composition on multilayer DNA origami assembly\nand to realize efficient one-pot assembly of multiscaffold DNA origami\nobjects. Furthermore, as examples for functionalized scaffolds, we\ncreated a scaffold that enables direct, covalent cross-linking of\nDNA origami via UV irradiation, and we built DNAzyme-containing\nscaffolds that allow postfolding DNA origami domain separation.",
"conclusion": "Conclusion With the tools and methods\npresented herein, researchers can now\nfully specify a target structure not only in terms of desired 3D shape\nand dimensions but also in terms of the sequences used. There is no\nlonger a need to design objects around generic scaffold sequences\nas in the original DNA origami procedures. We demonstrated the potential\nof these tools and methods with a set of synthetic-sequence scaffolds\nwhich we used to explore the effects of sequence redundancy and sequence\ncomposition on the self-assembly of DNA origami, which is important\ninput for guiding the construction of design-specific scaffolds. We\nbuilt mini scaffolds as short as 1024 bases and a set of fully orthogonal\nscaffolds that enable efficient one-pot multiscaffold assembly of\nDNA origami comprising up to ∼38000 base pairs. We also made\nscaffolds containing functional motifs that enable DNAzyme-driven\nlinearization and bisection of scaffolds or folded structures, which\ncan enable constructing for example interlocked machine-like objects.\nInterlocked parts of these objects could be released by DNAzyme cleavage\ntriggered by Zn 2+ -addition. We demonstrate that functional\nsequence motifs like DNAzymes, which are too long for staple strand\nsynthesis, can be integrated in the scaffold sequence. We also constructed\na CpG-free scaffold with presumably lower immunogenicity for future in vivo applications. Finally, we produced a customized\nscaffold with AA motifs spaced in intervals of 8 base pairs, which\nenables constructing square-lattice like single- or multilayer DNA\norigami that can be covalently cross-linked via UV\npoint-welding right after folding. This scaffold can be considered\nas a demonstration of a fully design-specific scaffold, but the design\nwas done such that the resulting scaffold can be used modularly in\nmany other DNA origami designs. With the currently available\ncommercial gene synthesis services,\nour method allows constructing an entirely custom scaffold for less\nthan 1000 € synthesis cost and requiring about 2 weeks of manual\nlabor. We deposited precursor plasmids for all of our scaffolds at\nAddgene to make them available for the use by other researchers, along\nwith the helper plasmids needed to produce the actual scaffold ssDNA.\nWe also deposited a designated target plasmid containing the split-ori\ncassette, allowing other researchers to easily create their own custom\nscaffolds. Synthetic genes or gene fragments can be introduced into\nour target plasmid using a convenient and robust one-step Golden Gate\ncloning protocol. 50 With custom-sequence\nscaffolds, DNA origami designers may rationally\nexploit sequence composition as a design parameter. Here, we produced\nmostly scaffold variants having a total nearest-neighbor energy higher\nthan the conventional M13 variants, which led to assembly at temperatures\nhigher than those of the M13-scaffolded object. It may be beneficial\nto explore whether the sequence composition may be tuned to push productive\nassembly temperature intervals down to physiological temperatures\nand without requiring a prior denaturation step. Furthermore, with\nfull control over sequence design, sets of orthogonal scaffolds may\nnow be produced that enable the direct and efficient assembly of oligomeric\nsuperstructures in one pot. For optimized designs such as the 126hb,\nwe observed virtually perfect assembly yield in a one-pot reaction\ncontaining multiple scaffolds, which underlines the great potential\nof the multiscaffold strategy. DNA origami applications often\nrely on the positioning of functionalities\nthat typically consist of or are attached to specific ssDNA sequences.\nWhen conventional M13 scaffolds or natural sequences are used, these\nfunctional sequences must be introduced as extensions of staple strands.\nThe incorporation yield of these extended staple strands may vary\nand can be unsatisfyingly low ( e.g. , 48%). 8 If, on the other hand, the desired functional\nsequences are included in the scaffold strand, the incorporation yield\ninto a folded DNA origami is 100%. As we demonstrated, custom scaffolds\ncan be designed and produced to include functional sequences at user-defined\npositions. An extreme example is the welding scaffold that contained\nhundreds of custom AA sites while excluding undesired TT sites. As\nan example, we integrated self-excising DNAzyme cassettes as functional\nmotifs into our scaffolds. Assembly of mechanically interlocked DNA\norigami mechanisms 39 , 51 should become much easier with\nsuch bisectable scaffolds because detachment and component release\ncan be achieved through Zn 2+ -induced excision of the DNAzyme\ncassettes. Self-linearizing scaffolds should be useful for designing\nmultilayer DNA origami with odd-numbered helices and for making objects\nwith applications such as nanopore translocation 52 , 53 or for tethered fluorophore motion assays 54 that require a linear scaffold. Future custom scaffolds might be\ndesigned to include other functional sequence motifs, such as aptamers,\nrecognition sites for DNA-binding proteins, and indicator sites for\ncomplementary DNA strands as needed, for example, for DNA paint super-resolution\nmicroscopy. 55 Another attractive\naspect of creating design-specific scaffolds\nis that they lower the barrier to making DNA origami at larger scales.\nPreviously, we reported how to biotechnologically mass produce pools\nof staple strands. 21 The synthesis of the\nnecessary plasmids with many interleaved self-cleaving DNAzyme cassettes\nposes an initial obstacle, which may render this method somewhat unattractive\nat intermediate scales and in situations where design variants will\nneed to be iterated. However, precursor plasmids for custom-sequence\nscaffolds are easily synthesized as they do not, by default, contain\nrepetitive sequences. Hence, the DNA origami concept can now be inverted:\none fixed pool of staple strands could be mass-produced biotechnologically\nin a lab-scale (or even industrial scale) bioreactor. Then, different\ncustom-sequence scaffolds can be made in shake flasks that fold the\nset of fixed-sequence staple strands into different structures, thereby\nallowing to iterate through design versions at scales inaccessible\nwith DNA reagents produced via chemical synthesis.\nA variant of this idea has been tested presented previously with the\ngoal to reuse chemically produced DNA oligonucleotides. 19",
"discussion": "Results and Discussion Sequence\nDesign and Strand Production To construct\ndesign-specific custom scaffold sequences, we created a design tool\ntermed “scaffold smith” ( Figure 1 A and Supporting Information Note S1 ). The tool integrates with the conventional DNA origami\ndesign workflow at the point when the user has produced a caDNAno\nstrand diagram. 22 The scaffold smith generates\nthe scaffold string that users will then use subsequently to generate\nthe staple sequences. The user can define sequence motifs that will\nbe excluded entirely, and the user can specify a list of sequence\nstrings to be placed in the scaffold at desired locations in the target\nobject. The tool also enables constructing a design-specific scaffold\nstring for direct, modification-free UV cross-linking of the target\nobject. 9 To this end, the tool automatically\nidentifies all scaffold base indices located at staple termini and\nat crossovers and places “A” or “AA”,\nrespectively, at those positions in the scaffold string. Also, scaffolds\nmay be produced that have fixed scaffold motifs at staple termini\nso that residual overhangs of staples that are produced biotechnologically\nand DNAzyme-digested as previously described 21 can directly pair with the scaffold. The scaffold smith can either\ngenerate sequences de novo or operate on existing\nscaffold strings to create new variants of them that include desired\nmotifs at desired locations. For de novo construction,\nthe sequence is built base-by-base with a stochastic Monte Carlo process\nbeginning at a user-defined site in the strand diagram. The algorithm\ncontrols the scaffold sequence composition in terms of the statistical\nweights of base pair steps ( e.g. , how often A should\nbe followed by A, C, G, or T, respectively), which gives the user\ncontrol over the thermodynamic properties of the scaffold to be built.\nIt also enables directly reducing or avoiding entirely the occurrence\nof known immunogenic or UV-radiation-sensitive motifs such as CG or\nTT, respectively. The tool considers the degree of sequence redundancy\nthat emerges during sequence construction and can build (pseudo-)\nDe Bruijn sequences of user-defined order. It can generate sequences\nwhere all strings of a user-defined length (for example 8 bases) appear\nonly as often as the user accepts it in the entire scaffold sequence\n(for example, not more than once). Finally, the tool computes the\noverall statistics of the generated scaffold string with respect to\ncomposition and redundancy. The user may then adjust parameters and\nrepeat the sequence construction. To summarize, the scaffold sequence\nconstruction with the scaffold smith has a deterministic and a stochastic\npart. The user can define properties, which will be strictly realized,\nsuch as exclusion and site-directed inclusion of user-defined sequence\nmotifs. All remaining sites ( i.e. , sites where the\nuser makes no specific demands) will be filled up stochastically;\nhowever, the user has control over the overall statistics of the sequence\nbuilt in terms of composition and redundancy. The underlying algorithms\nare described in more detail in Supporting Information Note S1 . We created a stand-alone graphical user interface\n(GUI) for the scaffold smith, but it should be straightforward to\nintroduce the underlying concepts into future caDNAno versions or\ninto future variants of automated design solutions such as DAEDALUS, 23 PERDIX, 24 TALOS, 25 or vHelix. 26 Figure 1 Design-specific\nscaffold sequences in minimum-constraint vectors\nfor making fully user-defined DNA origami. (A) Schematic diagram of\ninput for the scaffold smith used for creating custom scaffold sequences:\nexemplary caDNAno design diagram with scaffold strand indicated in\nblue and staple strands in multiple colors (I), user-specific constraints\n(II), and weighting factors for a stochastic base distribution (III).\n(B) Illustration of scaffold production with helper-plasmid system\nusing phagemids with a split-ori approach (top) and a modified split-ori\napproach where the backbone sequence is flanked by self-cleaving DNAzymes\n(bottom). Zn 2+ addition leads to excision of the backbone\nand linearization. Black, constant parts for each type of scaffold;\ngray, user-definable parts; light green, backbone present only in\nthe double-stranded plasmid and not in the single-stranded product;\nred, self-cleaving DNAzymes. We now focus on the question of how to practically make fully\nsequence-customized\nDNA single strands. A scalable solution for ssDNA production makes\nuse of bacteriophages with fast growing Escherichia\ncoli ( E. coli ) cells\nas host, but phage-based ssDNA scaffolds inevitably contain cassettes\nwith sequences that cannot be altered because they are required for\nthe phage production. User-defined insert sequences can only be added\nto these fixed parts. In fully customizable scaffolds, the length\nof the fixed part should be negligible compared to the total length\nof the scaffold. However, in the conventional M13 phage production\nmethod, 18 , 27 the fixed part is approximately 6000 bases\nlong, which is not negligible at all. Phagemids, in combination with\nhelper phages 28 or helper plasmids, 29 allow producing ssDNA with fixed backbones of\n∼2000 bases, which is still not negligible. Our goal was thus\nminimizing the fixed-sequence cassettes to maximize the freedom to\ndesign custom scaffold sequences while maintaining the possibility\nfor efficient production in bacterial cultures. To this end, we developed\nand tested several methods with minimized constant-sequence cassettes\n( Supporting Information Note S2 ). The production method used for most of our custom scaffolds relies\non a split origin of replication (split-ori) that was originally developed\nto produce microphages containing comparably short 221 bases long\nssDNA, in combination with helper phages. 30 Here, we integrated our design-specific scaffold sequences as custom\ninserts into the split-ori system ( Figure 1 B) and identified a suitable helper plasmid\nthat allows producing pure target ssDNA without contamination of helper\nphage DNA or other unwanted DNA species ( Supporting Information Figure S5 ). The thus-produced ssDNA scaffold strands\nare circular with a minimal constant-sequence backbone of 234 bases\n( Figure 1 B). This residual\nbackbone can then also be removed entirely via Zn 2+ -dependent digestion when flanking self-excising DNAzyme\ncassettes 21 are added during sequence preparation\nfor gene synthesis. As a result, the user obtains linear scaffold\nmolecules with virtually 100% custom sequence (except for two and\nseven base residuals at the two termini). In support of the robustness\nof the split-ori/helper-phage approach, we note that, concurrent to\nour work, Douglas and co-workers produced scaffolds for DNA origami\nby inserting coding genes or parts of the lambda phage genome into\na split-ori backbone, although Douglas et al. used\na different helper plasmid. 31 Sequence Redundancy\nand Sequence Composition Rules The commonly used M13-phage-based\nscaffolds have a comparably high\ndegree of sequence redundancy, and others have speculated that this\nredundancy may negatively influence the self-assembly behavior of\nDNA origami. 6 On the other hand, it has\nalso been speculated that the M13-based sequences were particularly\nwell-behaved and thus especially suited for DNA origami. 32 In addition, the influence of sequence composition\n( e.g. , AT vs GC content) on self-assembly\nremains in the dark. For designing synthetic scaffolds, it is important\nto understand the impact of sequence redundancy and sequence composition\non self-assembly in order to arrive at relevant sequence construction\ncriteria. To study these parameters, we constructed five synthetic\n7560 bases long scaffolds (SC2–6) and compared them to a popular\nM13-based scaffold variant (SC1) of the same length ( Figure 2 ). The designed portions of\nthe custom scaffolds SC2–6 were low redundancy de Bruijn sequences\nof order 7, which means that sequence strings with length 7 occur\nexactly once or not at all. 33 All of these\nscaffolds could be produced in shake flasks with yield and purity\nsimilar to that in conventional M13-based production ( Supporting Information Note S2 and Figures S4–S6 ).\nFour of the scaffolds (SC2, SC4, SC5, SC6) have insert sequences that\nare orthogonal to each other and to the conventional M13-based scaffolds.\nResidual sequence overlaps between these four individual scaffolds\nare determined by details of the constant-sequence cassettes in the\nphagemids and have lengths between 180 and 426 bases, which is small\ncompared to the total length (7560) of the scaffold variants. Scaffold\nvariant SC3 had a longer 1387 bases long sequence fragment taken from\nthe M13 genome; SC3 has thus a degree of sequence redundancy which\nfell between the low-redundant de Bruijn scaffolds and the highly\nredundant M13. Figure 2 Influence of base composition and sequence redundancy\nof custom\nscaffolds on DNA origami self-assembly. Blue indicates M13-based scaffolds;\norange, magenta, red, cyan, and green indicate custom scaffolds. (A)\nSchematic representations of six different 42-helix bundles folded\nusing the six different scaffolds. SC1, M13-based scaffold; SC2, reduced\nbackbone phagemid scaffold with CpG-free de Bruijn insert sequence;\nSC3, conventional phagemid with high duplicity fragment and de Bruijn\ninsert sequence; SC4, conventional phagemid with de Bruijn insert\nsequence; SC5 and SC6, split-ori based scaffold with de Bruijn sequence;\nL, length; GC, GC content of the corresponding scaffold. (B) Electrophoretic\nmobility analysis of self-assembly reactions of the 42-helix bundles\nshown in (A) at different temperatures and salt concentrations. SC,\nscaffold reference; C50 and C20, assembly reactions containing 50\nnM (C50) or 20 nM (C20) scaffold, 200 nM staples, and 20 mM MgCl 2 that were subjected to an annealing ramp from 60 to 44 °C\n(1 h per °C); temperature screen, assembly mixtures as in C50\nbut subjected to annealing ramps covering the temperature intervals\nindicated above each lane (1 h per °C); magnesium screen, assembly\nreactions containing 50 nM scaffold, 200 nM staples, and MgCl 2 concentrations between 5 mM (M5) and 30 mM (M30). P, pocket;\nF, folded 42-helix bundle. All samples were loaded onto the gel at\nan approximate scaffold concentration of 20 nM. All temperature ramps\ncontained an initial denaturation step at 65 °C for 15 min. Laser\nscanned fluorescent images of the electrophoretic analysis were autoleveled.\n(C) Statistics of sequence duplicates of different scaffold variants\nas a function of fragment length. Colors as in (A). (D) Experimentally\nobserved optimal folding temperature intervals of the 42-helix bundles\nplotted against total NN energy of corresponding scaffold variant.\nTotal NN energy was calculated using nearest-neighbor free energy\nparameters, 36 ignoring edge effects. Dots\nin red indicate upper, and dots in blue indicate lower limit of the\nhighest folding temperature interval where the sample appeared fully\nfolded. Solid lines represent linear fits. To test our custom scaffolds, we used them as templates for\nvariants\nof a previously described brick-like 42-helix bundle (42hb) 34 and synthesized the corresponding sets of staple\noligonucleotides ( Figure 2 A). We analyzed the assembly behavior of the different 42hb\nvariants at different temperatures and salt concentrations using a\nstandardized folding screen. 35 The assembly\nreactions yielded well-folded products for all six scaffold sequence\nvariants of the 42hb object, as manifested by sharp leading bands\nin gel electrophoresis ( Figure 2 B). Contrary to what has been speculated previously, 6 we did not observe systematic quality differences\nbetween the scaffold variants with higher or lower degree of sequence\nredundancy. In particular, we did not detect a beneficial effect on\nassembly behavior when using the low-redundancy de Bruijn sequences\ncompared to the conventional, much more redundant M13-based scaffold\nvariant ( Figure 2 C\nand Supporting Information Figure S7 ).\nSimilarly, we could not detect any drawbacks of synthetically designed\nscaffold sequences that are not M13-based. Sequence composition,\nhowever, did have noticeable effects on self-assembly\nbehavior. For example, well-folded objects self-assembled already\nat lower salt concentrations for sequence variants with higher GC\ncontent ( Figure 2 B,\nright). As seen previously for other DNA origami objects, 34 each sequence variant assembled successfully\nin narrowly defined temperature intervals. For our 42hb variants,\nwe found that the sequence composition of the scaffold variant determined\nthe temperature intervals in which the objects folded successfully\n( Figure 2 A,B). In particular,\nthe temperature intervals that yielded the highest folding quality\ncorrelated strongly with the scaffold sequence composition in terms\nof the total nearest-neighbor energy ( Figure 2 D). 36 In the SC2\nsequence, C is never followed by G. As the CG base pair step has a\nparticularly strong stacking energy, the omission of this base pair\nstep leads to a substantially reduced nearest-neighbor energy. Only\nlooking at GC content as predictor is too coarse: SC2 has the lowest\ntemperature interval but the second-lowest GC percentage (44%), whereas\nSC1 (=M13) has the lowest GC content but does not fold in the lowest\ntemperature interval. Hence, the sequence composition should be considered\nduring sequence construction at the level of base pair step composition.\nOur design tool scaffold smith was thus built accordingly. Smaller\nDNA Origami Depending on the target application,\nscaffolds shorter than the conventional M13 variants (∼8000\nbases) may be desirable. With the scaffold smith, scaffold sequence\nstrings of any length may now be designed. However, the scaffold production\nmethod must be adapted according to the length of the target strand.\nWe thus tested the split-ori approach for its capacity to produce\nshort scaffolds in the ∼1000 bases length range. To this end,\nwe built a circular, 1317 bases long mini-scaffold ( Supporting Information Figures S5 and S6D,E ). We found that\nthe ssDNA amount per culture volume for this short scaffold was substantially\nlower (0.38 mg/L) compared to the yields obtained for target strands\nwith lengths between ∼3000 (3.6 mg/L) and ∼9000 bases\n(2.6 mg/L). We therefore developed an alternative method for the convenient\nbiotechnological production of short linear scaffolds with completely\nuser-definable sequences. The method builds on our recently reported\nstrategy for the biotechnological production of staple strands. 21 We integrated multiple copies of the same target\nscaffold sequence in one phagemid and interleaved them with Zn 2+ -dependent, self-excising DNAzyme “cassettes”.\nThe resulting multi-insert circular DNA single strands have a total\nsize comparable to that of the conventional M13 genome, which is presumably\nfavorable for DNA packaging and phage particle production. Indeed,\nthe multi-insert phagemids can be produced with satisfying yields.\nUpon incubation with Zn 2+ , the DNAzyme cassettes become\ncatalytically active and the circular ssDNA is digested into excised\nDNAzyme snippets, residual backbone, and multiple copies of the linear\nsingle-stranded target scaffold ( Figure 3 A). Thus, the multi-insert excision approach\neffectively allows mass producing homotypic pools of DNA oligonucleotides\n(as opposed to heterotypic pools as in our previous work 21 ). We used our multi-insert excision approach\nto produce three scaffold variants with lengths of 1024, 1512, and\n2048 bases and used them to assemble 13-helix bundles of different\nlengths. All 13-helix bundle variants self-assembled with excellent\nyield into the desired shape, as corroborated by gel electrophoresis,\ntransmission electron microscopy (TEM) imaging, and reference-free\nclass averaging ( Figure 3 A,B). For making scaffolds with lengths between ∼3000 and\n∼9000 bases, we found the conventional phagemid approach to\nbe well-suited. As an example, we produced an additional series of\nsynthetic-sequence scaffolds with lengths of 2873, 4536, 6048, and\n9072 bases ( Figure S6D,E ). These variants\nexpand the currently available set of generic scaffolds 17 , 32 that is available to the community and that may be used to produce\nDNA origami with corresponding sizes. Figure 3 DNA origami objects with sizes ranging\nbetween 1024 bp (633 kDa)\nand 37800 bp (23.4 MDa) can be assembled using mini-scaffolds or in\none-pot assembly reactions containing multiple scaffolds. Blue indicates\nM13-based scaffolds; orange, green, cyan, and red indicate custom\nscaffolds. (A) Schematic representation of a circular DNA single strand\n(top left) that, in the presence of Zn 2+ , cleaves itself\nto yield four copies of a short, linear scaffold (top right) that\ncan subsequently be used to assemble a small DNA origami object (bottom).\n(B) Schematic representation (top) and average TEM images of 13-helix\nbundle (13hb) variants assembled from linear mini-scaffolds comprising\n1024 (I), 1536 (II), or 2048 bases (III). Scale bar: 20 nm. (C) Electrophoretic\nmobility analysis of mini-scaffolds and 13-helix bundle variants described\nin (B). (D) Schematic representations, single TEM images, and average\nTEM images (from top to bottom) of a 42-helix bundle assembled with\nfive scaffolds in one-pot reactions. Scale bar: 50 nm. (E) Schematic\nrepresentations, single TEM images, and average TEM images (from top\nto bottom) of an improved 42-helix bundle design with five interlocked\nscaffolds. Scale bar: 50 nm. (F) Electrophoretic mobility analysis\nof the two 42-helix bundle versions shown in (D,E). (G) Schematic\nrepresentation (top), average TEM images with corresponding model\nviews (left), and gel electrophoretic analysis (right) of a 126-helix\nbundle (126hb) assembled with two interlocked scaffolds. Scale bar:\n50 nm. (H) Overlay of a cryo-EM density map fragment and the corresponding\nscaffold routing diagram. Blue and orange paths indicate the two orthogonal\nscaffolds. Laser scanned fluorescent images of the electrophoretic\nanalyses were autoleveled. P, pocket; sta, staples. Larger DNA Origami Many applications\nof DNA origami\nrequire objects whose sizes exceed the dimensions of conventional\nM13 scaffolds. 16 , 37 − 41 Researchers have thus invested effort into building\nlarger DNA origami to achieve greater overall dimensions and to integrate\nmore features. 41 , 42 One possibility to build larger\nDNA origami with sizes beyond 10000 base pairs is to use increasingly\nlong scaffold chains. Consequently, other researchers have reported\nup to 50000 bases long scaffold strands that were constructed from\nbiological sequences, including E. coli genomic sequences and lambda phage sequences. 16 , 43 However, for scaffold lengths beyond 10kb assembly, cloning and\nplasmid handling become challenging. Moreover, when we compared the\nyield of production of scaffolds of different lengths, a trend emerged\nindicating that the yield drops for lengths approaching 10000 ( Supporting Information Figure S6F ), although\nthe data are not entirely conclusive. A second possibility for making\nlarger objects is to form higher-order assemblies from separately\nfolded DNA origami subunits. 40 , 41 , 44 , 45 Oligomerization of individually\nassembled DNA origami objects can be achieved using sticky-end interactions 37 , 40 or via shape-complementary surface features and\nstacking interactions. 38 , 39 , 41 When following these routes, the individual building blocks must\nbe produced separately and usually require some type of purification,\nwhich in addition to manual labor can negatively affect the overall\nyield. Here, we thus pursued a third, complementary strategy\nto make larger DNA origami which considers the usage of multiple scaffold\nchains in one-pot assembly reactions, which has been used already\nexemplarily in our own previous work 41 and\nin those of others. 31 , 46 For one-pot assembly of multiscaffold\nDNA origami, we anticipate that the scaffold sequences must be sufficiently\ndistinct (“orthogonal”) to achieve productive folding\nof the target object. We tested these requirements experimentally\nand found that successful one-pot coassembly does indeed require orthogonal\nscaffold sequences ( Supporting Information Figures S8 and S9 ). To enable one-pot coassembly with multiple scaffolds,\nwe thus designed four 7560 bases long scaffolds (SC2, 4, 5, 6, compare Figure 2 ) that are orthogonal\nto each other and to the conventional M13-based scaffold (SC1). As\na proof-of-concept, we designed a long pentameric 42-helix bundle\nobject ( Figure 3 D)\nthat self-assembled in a one-pot folding reaction mixture containing\nthe five scaffold chains with distinct sequences and the several hundred\nstaple oligonucleotides. Direct imaging with negative-staining TEM\nrevealed the expected 42-helix bundle pentamers without visible seams\nbetween the subunits containing the individual scaffolds ( Figure 3 D). Reference-free\nclass averages indicated a global twist deformation along the helical\naxis, which is consistent with recent findings concerning the occurrence\nof residual twist in honeycomb DNA origami. 41 TEM imaging further revealed higher-order branched networks in which\nwell-folded 42hb pentamers were connected with other 42hb pentamers\n( Supporting Information Figure S10 ). We\nattributed these connected pentamers to design flaws: For this initial\ndemonstration, we simply designed staple strands that connect the\nindividual single-scaffold 42hb blocks across the helical interface.\nSome of these connecting staple strands featured long binding segments\nthat presumably cause the undesired branched connections. We thus\nmade a second, distinct 42hb pentamer design in which we changed the\nrouting of the five scaffold chains to better interlock the individual\nchains. We also corrected right-handed twist using base pair deletions\nin the design, and we included an asymmetric feature. The thus-revised\nobject self-assembled in the expected shape as seen by TEM ( Figure 3 E), now with reduced\ntwist, and it appears as a single discrete species as seen in gel\nelectrophoresis ( Figure 3 F). The extent of aggregates was substantially reduced compared to\nthe variant without interlocked scaffolds. Importantly, the folding\nreaction mixtures for both 42hb pentamer design variants yielded only\nthe pentameric target object in addition to a design-dependent extent\nof aggregates of intact pentamers, as seen in gel electrophoresis\n( Figure 3 F). Incomplete\npentamers were absent in both design versions. To achieve complete\npentamers as a single folding product, the scaffold concentrations\nmust be adjusted such that they appear in exactly equivalent amounts\nin the folding reaction mixture. To illustrate the excellent\npotential of using multiple orthogonal\nscaffold chains for efficiently constructing larger DNA origami with\nhigh yield and high quality, we designed a barrel-like 126-helix bundle\n(126hb) comprising 15120 base pairs distributed over two orthogonal\nscaffolds that are interlocked in the helical direction ( Figure 3 G). When the relative\nscaffold concentrations were properly adjusted, the object formed\nsuccessfully with close to 100% yield and virtually no side products,\nas seen in gel electrophoretic mobility analysis and TEM imaging ( Figure 3 G,H and Figure S11 ). Reference-free class averages from\nsingle-particle micrographs were in very good agreement with the designed\nshape. Due to the high quality of the object, we were able to solve\na structure of this object using cryo-electron microscopy, in which\nnearly all of the 126 constituent helices were resolved in such detail\nthat the grooves of double helices and all connecting crossovers could\nbe discerned. We analyzed the map with respect to systematic differences\nat scaffold–scaffold seams and could not find any differences\nbetween seams containing one or both scaffolds ( Figure 3 G). Therefore, given a suitable scaffold\nrouting and properly calibrated strand concentrations, multiscaffold\nDNA origami objects can be assembled with the high yield and the high\nquality known from well-behaved single-scaffold DNA origami designs.\nOne-pot assembly of multiscaffold objects represents thus a powerful\nroute for building larger DNA origami. Functional Scaffolds: Catalytic\nMotifs and Covalent Cross-Linking The design of fully synthetic\nscaffolds enables exclusion of undesired\nmotifs and the inclusion of specific sequence motifs that serve user-defined\npurposes. As a demonstration for motif exclusion, we built a synthetic\nde Bruijn scaffold on the order of 7 that lacks CG base pair steps\n(SC2 from Figure 2 ).\nThe absence of these CpG motifs could potentially circumvent Toll-like\nreceptor-9-mediated immunogenic reactions in organisms. 7 This CG-free scaffold could be particularly advantageous\nwhen exploring in vivo applications of DNA origami.\nAs a demonstration for the site-directed functionalization of synthetic\nscaffolds with functional sequences, we built two scaffolds that contain\ncatalytic sequence motifs. We included one or two self-excising DNAzyme\ncassettes during sequence construction. Upon incubation with Zn 2+ , the DNAzymes become catalytically active, causing excision\nof the DNAzyme cassettes and thus linearization or bisection of the\nscaffold. Including these 132 bases long DNAzyme cassettes into the\nscaffold sequence ensures incorporation into every assembled DNA origami. To illustrate the functionality, we used the self-bisecting scaffold\nto assemble a variant of a previously published DNA origami switch\nobject ( Figure 4 A). 38 , 47 The switch object consists of two rigid beams that are flexibly\nlinked by a single scaffold crossover at the center. The switch features\ndouble-helical shape-complementary protrusions and recessions that\ncan dock into each other, stabilizing a closed state of the switch via base stacking interactions. Due to the electrostatic\nrepulsion of the negatively charged DNA arms, the switch will predominantly\noccupy its open state at low salt concentrations. At higher salt concentrations,\nthe electrostatic repulsion is shielded, and the stacking interactions\nare sufficient to stabilize the closed state. In our bisectable switch\nvariant, we placed the self-excising DNAzyme cassettes directly at\nthe pivot point, where the scaffold chain crosses from one switch\narm to the other ( Figure 4 A). The thus-designed objects self-assembled with high yield\nand predominantly populated an open state at <10 mM MgCl 2 and a closed state at >10 mM MgCl 2 , as expected. When\nincubated with Zn 2+ , the switch objects are cut at the\npivot point due to the excision of the DNA enzyme cassettes ( Figure 4 B). Gel electrophoretic\nmobility analysis ( Figure 4 C,D) reveals that the bisection reaction goes to completion,\nand that the kinetics of bisection strongly depends on the state of\nthe switch: at high salt (closed state), the reaction is substantially\nslower, which we attribute to activity-reducing conformational constraints\non the DNAzyme cassettes. A simple Mg 2+ dependence of the\nreaction kinetics can be ruled out because the reaction speed is the\nsame in the presence of 1.4 or 5 mM MgCl 2 . The cleavage\nreaction was also faster when residual staple oligonucleotides were\nremoved by PEG precipitation 48 prior to\nincubation with Zn 2+ ( Figure 4 D). Figure 4 Self-cleaving DNA origami. (A) Schematic representations\nof circular\nscaffolds containing two self-excising DNAzyme cassettes (top left)\nthat can be cleaved into two linear scaffolds (bottom left) or assembled\ninto a switch object (top right). Individual switch arms (bottom right)\ncan be obtained by cleavage of assembled switch objects or assembly\nusing cleaved linear scaffolds. (B) Electrophoretic analysis of reaction\nkinetics of scaffold cleavage. Controls: cleaved scaffold (lane 1),\nundigested sample (lane 2), and switch arms assembled separately (lane\n7) using cleaved scaffold. (C) Field-of-view TEM images of uncleaved\n(left) and cleaved (right) switch objects. (D) Electrophoretic analysis\nof cleavage reactions containing unpurified (lanes 1 and 5) and PEG-purified\n(lanes 2–4, 6–8) switch objects at 1.4, 4, 10, or 20\nmM MgCl 2 . Laser scanned fluorescent images of the electrophoretic\nanalysis were autoleveled, and the highlighted region was autoleveled\nindividually. P, pocket; U, undigested species; D, digested species.\nScale bar: 100 nm. Synthetic scaffold design\nalso allows integrating hundreds of user-defined\nmotifs site-specifically into a DNA origami, which can be exploited,\nfor example, for sequence-programmable, chemical-modification-free\ncovalent cross-linking of DNA origami objects, 9 termed UV point-welding. UV point-welded DNA origami objects are\nsubstantially more durable compared to nontreated objects and can\nremain stable at temperatures up to 90 °C and in pure double-distilled\nwater with no additional cations present. In our previous work, covalent\ncross-linking was achieved by placing additional thymidine bases in\nthe staple strand sequences at all termini and at all double-crossover\npositions. 9 Irradiation of such objects\nwith 310 nm light induces the formation of covalent cyclobutane pyrimidine\ndimer (CPD) bonds between colocalized thymidine bases. As a result,\ndouble-helical domains become topologically trapped, and the constituent\nstrands of thus-treated DNA origami can no longer dissociate, unless\ncovalent bonds are broken. The possibility of making fully customized\nscaffolds offers an elegant way to realize the formation of UV-induced\nCPD bonds at desired sites while suppressing the formation of CPD\nbonds at undesired sites. Using the scaffold smith tool, a scaffold\nsequence can be designed that does not exhibit any TT motifs and that\nfeatures AA only at desired crossover sites and strand termini as\nspecified in the strand diagram. As a demonstration, we constructed\na semigeneric scaffold that\ncan be used to create UV-cross-linkable single- or multilayer DNA\norigami objects in square lattice packing. In this scaffold, AA sites\nsimply appear in regular intervals of eight bases. Given appropriate\nscaffold routing, all staple crossover sites feature AA motifs on\nthe scaffold, which therefore leads to thymidines in staple strands\nthat can be cross-linked ( Figure 5 A). We produced the corresponding 7560 bases long welding\nscaffold using the backbone excision split-ori method described in Figure 1 B and used it to\nassemble a variant of a previously reported multilayer DNA origami\nobject known as the pointer. 49 The UV-welding-ready\npointer object self-assembled with satisfyingly high yield, as judged\nby electrophoretic mobility analysis ( Figure 5 C, lanes 2 and 12) and TEM imaging ( Figure 5 BI). We then irradiated\nthe pointer object at 310 nm in the presence of 30 mM magnesium chloride.\nTEM images of the pointer acquired directly after exposure to UV light\ncompared very well to those acquired prior to irradiation ( Figure 5 BII), indicating\nthat the object retained its structure. We then incubated the irradiated\nsample for 48 h in physiological (low) ionic strength conditions (PBS\nbuffer) at 40 °C ( Figure 5 BIII). Under such low ionic strength conditions, nonirradiated\ncontrol pointer objects immediately dissociated into staple strands\nand scaffold strand as seen in gel electrophoresis ( Figure 5 B, left). By contrast, the\nirradiated samples remained fully intact, as indicated by the fact\nthat the electrophoretic mobility did not change and by the absence\nof dissociated staple strand bands ( Figure 5 C, right). TEM imaging of the 48 h long PBS-incubated\nUV-welded pointer reveals well-folded objects consistent with the\ndesigned shape ( Figure 5 B, right). We thus conclude that the UV point-welding via scaffold-templated CPD bonds of the pointer was successful. Figure 5 UV point-welding\nof DNA origami with a custom scaffold. (A) Section\nof a multilayer DNA origami strand diagram with a customized scaffold\nfeaturing AA motifs every 8 base pairs, which results in adjacent\nThymidines in separate staple strands that may be UV-cross-linked.\nBlue lines, scaffold strand; gray lines, staple strands. (B) Schematic\nrepresentation (left) and average TEM images of the pointer object\nassembled with the welding scaffold. Average images of the pointer\nas obtained in the presence of 30 mM MgCl 2 before irradiation\n(I), after irradiation for 2 h at 310 nm (II) in the presence of 30\nmM MgCl 2 , and after irradiation for 2 h at 310 nm and 48\nh long incubation in low ionic strength phosphate-buffered saline\n(PBS) at 40 °C (III). (C) Electrophoretic analysis of nonirradiated\nand irradiated pointer objects incubated over time in PBS at 40 °C.\nL, 1kB Ladder; NI, not irradiated; RT, room temperature; P, pocket;\nF, folded species; sta, staples. Scale bar: 50 nm."
} | 10,126 |
33668875 | PMC7996495 | pmc | 6,481 | {
"abstract": "Methylotrophic bacteria (non-methanotrophic methanol oxidizers) consuming reduced carbon compounds containing no carbon–carbon bonds as their sole carbon and energy source have been found in a great variety of environments. Here, we report a unique moderately thermophilic methanol-oxidising bacterium (strain LS7-MT) that grows optimally at 55 °C (with a growth range spanning 30 to 60 °C). The pure isolate was recovered from a methane-utilizing mixed culture enrichment from an alkaline thermal spring in the Ethiopia Rift Valley, and utilized methanol, methylamine, glucose and a variety of multi-carbon compounds. Phylogenetic analysis of the 16S rRNA gene sequences demonstrated that strain LS7-MT represented a new facultatively methylotrophic bacterium within the order Hyphomicrobiales of the class Alphaproteobacteria . This new strain showed 94 to 96% 16S rRNA gene identity to the two methylotroph genera, Methyloceanibacter and Methyloligella. Analysis of the draft genome of strain LS7-MT revealed genes for methanol dehydrogenase, essential for methanol oxidation. Functional and comparative genomics of this new isolate revealed genomic and physiological divergence from extant methylotrophs. Strain LS7-MT contained a complete mxaF gene cluster and xoxF1 encoding the lanthanide-dependent methanol dehydrogenase (XoxF). This is the first report of methanol oxidation at 55 °C by a moderately thermophilic bacterium within the class Alphaproteobacteria . These findings expand our knowledge of methylotrophy by the phylum Proteobacteria in thermal ecosystems and their contribution to global carbon and nitrogen cycles.",
"conclusion": "4. Conclusions The novel moderately thermophilic bacterium, strain LS7-MT, was recovered from a methane-utilizing mixed-culture originating from a thermal spring sediment in the Ethiopia Rift Valley. In addition to methanol, strain LS7-MT grew on a range of one-carbon compounds and sugars. Interestingly, it can also grow on a wide selection of sugars, including disaccharides, hexoses and pentoses. Strain LS7-MT is also able to grow on methylamine. Strain LS7-MT is, to our knowledge, the first moderately thermophilic, non-pigmented and facultatively methylotroph, which is affiliated with the order Rhizobiales ( Hyphomicrobiales ) of the family Hyphomicrobiaceae in the class Alphaproteobacteria , to be isolated from a hotspring environment. Based on the initial characterization of the strain LS7-MT, we suggest the name Methylothermalis aethiopiae (methyl-, pertaining to the methyl radical; thermalis meaning heat; aethiopiae meaning “of Ethiopia”) defining its methylotrophic nature, its affiliation with thermophilic environments, and country of origin. The genome of strain LS7-MT will also provide new insights into the diversity of biological methanol oxidation and on the adaptation of this process to thermophilic conditions. Due to its thermophilic nature, this facultative methylotroph might be an interesting candidate for biotechnological applications and further studies on its molecular biology and biochemistry are needed to investigate potential uses in industry.",
"introduction": "1. Introduction In geothermal habitats, methane and other natural gases (such as short-chain alkanes) enter the Earth’s atmosphere through gas venting, seeps, and degassing of spring water. In the Ethiopian Rift Valley region, hot spring sediments from thermal springs may promote microbial community structure and diversity. Thermal environments are suitable habitats for moderately thermophilic methylotrophs, and they may play an important role in the global methane cycle [ 1 , 2 ]. Biological oxidation of methanol to CO 2 by methylotrophs in terrestrial environments reduces methanol emissions to the atmosphere and has an important effect on methanol concentrations in the atmosphere [ 3 ]. Aerobic methane- and methanol-oxidising bacteria are a unique set of Gram-negative bacteria that use reduced carbon compounds containing no carbon–carbon bonds (methane, methanol, methylated amines, etc.) as their sole carbon and energy source and make a considerable contribution to the biogeochemical cycling of C1 compounds in different ecosystems [ 4 ]. Most of the methanol-oxidizing bacteria are defined as facultative or strictly aerobic, and taxonomical studies of these bacteria have defined several different phyla of Proteobacteria, Verrucomicrobia, Cytophagales, Bacteriodetes, Firmicutes and Actinobacteria that grow on methanol. Several of the non-methanotrophic methanol oxidizers have a facultative methylotrophic lifestyle, although some species are limited to C 1 substrates (for example Methylophilus, Methylovorus, Methylophaga and Methylobacillus ) [ 3 , 5 ]. In the process whereby methanol is oxidized to formaldehyde, two distinct methanol dehydrogenase (MDH) enzymes are known; a calcium-dependent MxaFI type and a lanthanide-containing XoxF type MDH. Especially in methylotrophic Proteobacteria , the key MDH enzyme (a pyrroloquinoline quinone dependent methanol dehydrogenase enzymes) is present. The genes encoding the MDH subunits ( mxaFI ) and the cytochrome c electron acceptor ( mxaG ) were initially studied in Methylobacterium extorquens (reviewed in [ 5 , 6 ]). Functional molecular marker genes such as mxaF (encoding the large alpha-subunit of MDH), are highly conserved among the methylotrophs and have been used as functional genes in environmental studies to identify methylotrophs in numerous habitats [ 7 , 8 ]. xoxF encodes the alternative MDH, and has also been found in many methylotrophs [ 9 , 10 ] and can also be used as a functional gene marker in environmental studies [ 11 , 12 ]. The family Hyphomicrobiaceae belongs to the order Rhizobiales ( Hyphomicrobiales ) of the class Alphaproteobacteria , which is a morphologically, metabolically, and ecologically diverse group [ 13 ]. A total of 28 genera of this family have validly been described ( www.bacterio.net/-classifgenerafamilies.html # Hyphomicrobiaceae , (accessed on 9 May 2020)). Most of these genera are aerobic chemoheterotrophs and facultatively methylotrophs, whereas some can grow anaerobically by denitrification or fermentation. Research on Hyphomicrobiaceae has mainly focused on low to moderate temperature ecosystems, and the majority of these described species are found to be mesophilic and neutrophilic, and have been successfully recovered from marine and non-marine habitats, including saline environments [ 14 , 15 ]. The species Dichotomicrobium thermohalobium within the family Hyphomicrobiaceae , was defined as a chemoorganotroph and moderately thermophile (growth temperatures between 35 and 55 °C). This budding bacterium with dichotomously branching hyphae is halotolerant, and several Dichotomicrobium -like bacteria have been isolated from the hypersaline Solar Lake [ 16 ]. However, at a lower temperature (4.5 °C), similar bacterial morphotypes have also been observed in Lake Constance at between 10 and 150 m depth. However, cultivation of these bacteria was not successful [ 17 ]. A limited number of facultative methanol-oxidizers within the family Hyphomicrobiaceae of the order Rhizobiales has been described. Only one moderately thermophilic species, Methyloceanibacter caenitepidi , with a growth temperature of 19 to 43 °C (optimum 35 °C), has been reported, and this is a facultative methylotroph, which was isolated from marine sediments near a hydrothermal vent [ 18 ]. In 2013, two non-pigmented moderately halophilic bacteria— Methyloligella halotolerans and Methyloligella solikamskensis —were isolated from saline environments (temperature range: 10 to 40 °C). Both isolates can grow on methanol and were designated obligately methylotrophic [ 19 ]. All species within the genus Hyphomicrobium are metabolically flexible, and grow on methanol, methylamine, di- and trimethylamine, dichloromethane and methylsulfate as their carbon and energy source, as well as some organic compounds. They are facultatively methylotrophs and are mostly distributed in soils and aquatic habitats [ 14 , 20 ]. Most species within the Alphaproteobacteria have distinct metabolic properties (chemolithoautotrophic, strictly organotrophic or facultatively chemolithoorganotrophic, photoautotrophic and some contain bacteriochlorophyll), and exhibit mesophilic growth below 45 °C. A small number of validly described moderately thermophilic genera have been isolated from hot springs. These include Rubellimicrobium thermophilum (45 to 54 °C), Chelatococcus sambhunathii (37 to 42 °C), Tepidamorphus gemmatus (45 to 50 °C), Elioraea thermophila (55 °C) and Porphyrobacter tepidarius (40 to 48 °C) [ 21 , 22 , 23 , 24 , 25 ]. However, none of these bacteria were reported to be obligate or facultative methanol oxidizers (presumably because they lack the enzymes for the oxidation of methanol to formaldehyde). The isolation of moderately thermophilic and facultative methanol-oxidizers (optimally growth >50 °C) from extreme environments is challenging. In methane enrichments, methane oxidizers can produce methanol or other substrates (such as acetate), and thus cross-feeding can occur, stimulating methanol oxidizers and other non-methylotophic microorganisms to grow. It can be time-consuming to cultivate a pure culture of methanol oxidizers and also to separate these from potential methane oxidizers. An obligately methylotrophic bacterium (growing on methane and methanol at optimal temperatures of 50 to 55 °C) in the family Methylococcaceae of the class Gammaproteobacteria was isolated from an alkaline thermal spring in the Ethiopian Rift Valley and recently described [ 2 ]. Here, we present the isolation, characterization, physiology and genomic features of a moderately thermophilic and facultatively methylotrophic bacterium, which was recovered from a co-culture with a gammaproteobacterial methanotroph recovered from an alkaline thermal spring sample in the Ethiopian Rift Valley. The strain probably represents a new methylotrophic genus within the Alphaproteobacteria .",
"discussion": "3. Results and Discussion 3.1. Isolation of a Moderately Thermophilic Methylotroph Following two weeks of incubation at 55 °C with methane as the only carbon source, the microbial growth on KNO 3 (LMM) was observed and further confirmed by phase-contrast microscopy. After three weeks of incubation under the same conditions, no growth was found on either NH 4 Cl (LMM-AC) or (NH 4 ) 2 SO 4 (LMM-AS) media. Two different types of colonies appeared on the Gelrite plates. One type consisted of small white colonies about 0.6 to 0.8 mm in diameter (comprised of coccoid cells) and the other very shiny white colonies about 1.2 to 1.5 mm in diameter (comprised of small rod-shaped cells). Five colonies of each type were selected and transferred to fresh liquid LMM (KNO 3 ) with methane as the only carbon source. We observed that all five small white colonies sustained grown with methane, whereas the other five shiny white colonies did not show any indication of growth with methane even after four weeks of incubation. The small white colonies, containing the coccoidal cells growing on methane were further characterized and a description of these methanotrophs is now published [ 2 ]. The shiny white colonies that did not grow on methane were subjected to further investigation, described here. We tested the potential of these isolates to grow with methanol as their sole carbon and energy source and (as a control) for growth on R2A agar plates. The isolate recovered from a co-culture with a methanotrophic bacterium (LS7-MC) [ 2 ] was termed strain LS7-MT, and could use methanol, but not methane, for growth. Strain LS7-MT could also grow on acetate, pyruvate, glucose, methylamine, trimethylamine and R2A agar plates, indicating that strain LS7-MT is an aerobic facultative methylotroph. Strain LS7-MT also produced shiny white colonies on Gelrite plates that had no added carbon, which indicated that this strain was able to scavenge trace carbon from the medium. Heterotrophic contaminants were not observed during re-streaking on R2A agar plates, corroborating the purity of the isolate. Methanol, glucose and R2A agar plates were routinely inoculated and incubated at 55 °C for further purity tests. 3.2. Physiological Properties of Strain LS7-MT The fastest growth rate of strain LS7-MT occurred between 50 and 55 °C with an initial pH of 7.0. The temperature range for growth was between 30 and 60 °C, and no growth was observed at 25 or 62 °C. The pH range for growth was 6.0 to 9.3 (optimum 7.0 and 7.5); it did not grow at pH 5.0 or 9.5. Growth did not occur under aerobic conditions in the absence of methanol or under anaerobic conditions in the presence of methanol and nitrate, indicating that this strain could probably not denitrify. Strain LS7-MT was capable of growth at up to 0.5% ( v / v ) methanol. When comparing nitrogen sources for growth, using growth on LMM (KNO 3 ) with methanol or glucose as controls, no growth of LS7-MT was observed in media containing NH 4 Cl (LMM-AC) or (NH 4 ) 2 SO 4 (LMM-AS). This indicates that nitrate is an essential inorganic nitrogen source for growth. The same observation was also seen in the gammaproteobacterial methanotrophic strain LS7-MC [ 2 ]. Growth in nitrogen-free LMM (without KNO 3 ) was not observed. Strain LS7-MT was tested for methylotrophic and heterotrophic growth on a range of C 1 compounds, organic acids and sugars. Strain LS7-MT grew on all multicarbon substrates tested ( Table 1 ). No growth was observed without vitamins, indicating that vitamins in LMM are necessary for growth. Strain LS7-MT did not require additional NaCl for growth in LMM but was able to grow on a medium containing a NaCl concentration up to 0.5% ( w / v ). Therefore, strain LS7-MT is not a halophilic methylotroph. The doubling time (g) and specific growth rate (μ) of strain LS7-MT at 55 °C in LMM containing 0.15% ( v / v ) methanol were 15 h and 0.046 h −1 , respectively. Growth was inhibited by all the tested antibiotics. Major characteristics of the facultatively methylotrophic strain LS7-MT and comparisons with other related alphaproteobacterial strains (thermophiles, moderately thermophiles and mesophiles) are provided in Table 1 . 3.3. Identification of Phospholipid Fatty Acids (PLFA) PLFA profiles can be used as biomarkers for active methylotrophic bacteria in situ [ 34 ]. The PLFA composition for strain LS7-MT showed a unique fatty acid profile compared with other related alphaproteobacterial species ( Table 2 ). The major components of the PLFA compliment of strain LS7-MT were C18:0 (40.45%), C18:1w7c (24.20%) and C19:0 cyc w8c (20.77%). A high level of C18:1w7c fatty acid is a very common feature within the alphaproteobacterial methylotrophs. The content of C18:0 fatty acid was significantly higher in strain LS7-MT than any other methylotrophs reported [ 18 ]; however, it is important to point out that the fatty acid composition can change with different growth conditions and that the data with other methylotrophs could also change with different growth conditions. 3.4. Microscopic Observations Only non-motile, rod-shaped cells were observed using phase-contrast microscopy. In a methanol-grown culture, the cells of strain LS7-MT occurred individually or in aggregates with a length of 0.5 to 1.5 µm and a diameter of 0.2 to 0.5 µm ( Figure 1 ). Cells became elongated (0.4 to 1.5 µm) when growing on glucose as the only carbon source. Flagella were not apparent by electron microscopy. Intracytoplasmic membrane (ICM), carboxysome-like structures or vesicular membranes were also absent, which is consistent with strain LS7-MT being non-methanotrophic. Electron microscopy of ultrathin sections of strain LS7-MT cells revealed a typical Gram-negative cell wall structure and large poly-β-hydroxybutyrate granules (PHB). Hyphae, prosthecae or dichotomously branching hyphae (i.e., monopolar or bipolar filamentous which are common in some genera of the family Hyphomicrobiaceae ) were not observed during growth on methanol, glucose and R2A agar plates. 3.5. Phylogeny of Strain LS7-MT Analysis of the 16S rRNA gene sequence of strain LS7-MT using BLAST showed that the most closely related cultivated strains were Methyloceanibacter caenitepidi Gela4 T (96.3% sequence identity), which is a facultative methylotrophic bacterium isolated from a methane-utilizing mixed culture of marine sediment near a hydrothermal vent [ 18 ]. The sequence also showed 94.5 and 94.1% sequences similarity to M. halotolerans C2 T and M. solikamskensis SK12 T , respectively [ 19 ]. Both of these strains are non-pigmented halotolerant obligately methylotrophic bacteria and were isolated from saline environments [ 19 ]. Moreover, low sequence identities (86 to 91.1%) were found to facultative methylotrophic bacteria such as Methylobacterium nodulans, Methylobacterium organophilum, Methylobacterium extorquens, Hyphomicrobium denitrificans, Hyphomicrobium methylovorum, and Hyphomicrobium vulgare . These findings suggest that the facultative methylotroph strain LS7-MT is most probably a new genus within the Alphaproteobacteria . The phylogenetic tree of the 16S rRNA gene indicates that the strain LS7-MT clusters together with uncultured bacteria from the order Rhizobiales ( Hyphomicrobiales ) of the class Alphaproteobacteria ( Figure 2 ). The 16S rRNA gene tree analysis was also supported by the whole-genome sequencing analysis using the automated multi-locus species tree (autoMLST) pipeline. The estimated average nucleotide identity (ANI) values of strain LS7-MT with genomes of different closely related strains were 76.08% with Methyloceanibacter stevinii and 75.93% with Methyloceanibacter caenitepidi GEla4 T . The average amino acid identity (AAI) values of strain LS7-MT were 67.76% with Methyloceanibacter stevinii and 68.51% with Methyloceanibacter caenitepidi GEla4 T . Pairwise comparisons using TYGS showed dDDH sequence identities of 20.2% to Methyloceanibacter marginalis R-67177 T , 19.8% to Methyloceanibacter superfactus R-67175 T and 19.4% with Methyloceanibacter caenitepidi Gela4 T . Strain LS7-MT clusters separated from these closest relatives, which indicates that it belongs to a new genus ( Figure 3 ). A comparison of the major characteristics of the strain LS7-MT and other genera of moderately thermophilic methylotrophs is shown in Table 1 . Genes encoding the mxa gene cluster essential for methanol oxidation using the PQQ-dependent methanol dehydrogenase were clustered on the genome of strain LS7-MT ( Figure 4 A). Genes encoding xoxF1 and its cognate gene xoxJ1 were also found together in the genome of strain LS7-MT ( Figure 4 B). Phylogenetic analyses of mxaF and xoxF1 gene sequences from strain LS7-MT revealed that these genes were most closely related to mxaF from Methyloceanibacter superfactus and Methyloceanibacter caenitepidi and to xoxF1 from Methyloceanobacter marginalis , respectively ( Figure 4 C). 3.6. Genome Assembly and Annotation To obtain a better understanding of methanol metabolism in strain LS7-MT, we sequenced and assembled the draft genome. The draft genome of strain LS7-MT comprised a single circular molecule of 2,954,375 bp sequence consisting of 194 contigs, with a total number of genes of 3152 and 3098 protein-coding genes. The genome is 97.16% complete and 1.11% contaminated. The mol% G + C content of DNA was 65.9%. Genome features are summarized in Table 3 . 3.7. Methanol Metabolism Genes encoding all the steps of methanol oxidation to carbon dioxide were detected in the draft genome of the isolate. Genes encoding sMMO (soluble methane monooxygenase) and pMMO (particulate methane monooxygenase) were absent from the genome of the strain LS7-MT. In methylotrophs, there are generally two types of methanol dehydrogenases. The MxaF is a calcium-dependent, pyrroloquinoline quinone (PQQ)-linked heterotetrameric enzyme, whereas XoxF is a lanthanide-dependent homodimer. A single gene cluster ( mxaFJGIRSACKLD ) encoding the large subunit and small subunit of MDH, MxaF and MxaI and accessory genes were clustered on the genome ( Figure 4 A). The lanthanide-dependent methanol dehydrogenase XoxF1 was also present in the genome of LS7-MT ( Figure 4 B). No gene encoding the enzyme MDH2 (an alternative type of MDH) was found in the genome of LS7-MT. Screening the genome of LS7-MT revealed the presence of genes encoding enzymes of the tetrahydromethanopterin (H 4 MPT) and tetrahydrofolate (H 4 F) pathways for formaldehyde oxidation. These are crucial steps in methylotrophic metabolism since formaldehyde is a cytotoxic compound. For the tetrahydromethanopterin (H 4 MPT) pathway, formaldehyde-activating enzyme ( fae ), methenyl-H 4 MPT cyclohydrolase ( mch ), NAD(P)-dependent methylenetetrahydromethanopterin dehydrogenase ( mtdB ) and a formylmethanofuran-tetrahydromethanopterin formyltransferase ( ffsA ) were present. For the tetrahydrofolate (H 4 F) pathway, genes encoding enzymes involved in two pathways, including the methylene-H 4 F dehydrogenase and the methenyl-H 4 F cyclohydrolase enzymes were present. The genome of strain LS7-MT contains genes encoding the bifunctional enzyme FolD (5,10-methylene-tetrahydrofolate dehydrogenase and 5,10-methylene-tetrahydrofolate cyclohydrolase) encoding methylene-H 4 F dehydrogenase and methylene-H 4 F cyclohydrolase activities. The genome of strain LS7-MT also contains fhs , encoding formate-tetrahydrofolate ligase enzyme, metF encoding the methyl-H 4 F reductase enzyme and genes encoding the enzymes methylene-H 4 F dehydrogenase ( mtdA ) and methenyl-H 4 F cyclohydrolase ( fchA ). The genes encoding an NAD-dependent formate dehydrogenase were also present. Genes encoding key enzymes of the serine pathway hydroxypyruvate dehydrogenase ( hpr ) and serine-glyoxalate aminotransferase ( sga ) were present, but the genome of LS7-MT lacked hps which encodes hexulose monophosphate synthase, a key enzyme of the ribulose monophosphate pathway. The absence of genes encoding ribulose-1,5 bisphosphate carboxylase/oxygenase (RuBisCO) suggests, therefore, that carbon assimilation in strain LS7-MT is via the serine cycle. Interestingly, the LS7-MT draft genome contains parts of the methanogenesis pathway for folate biosynthesis. The enzyme formylmethanofuran dehydrogenase subunit C converting formylmethanofuran to CO 2 and methanofuran was detected. Also, the ffsA gene formylmethanofuran-tetrahydromethanopterin formyltransferase was detected previously reported in Hyphomicrobium sp (Strain MC1). This protein catalyzes the transfer of a formyl group from 5-formyl tetrahydromethanopterin (5-formyl-H 4 MPT) to methanofuran (MFR) so as to produce formylmethanofuran (formyl-MFR) and tetrahydromethanopterin (H 4 MPT) (UniProtKB). The formylmethanofuran dehydrogenase enzyme represents the initial reversible step (i.e., found in thermophilic archaea) that produces formylmethanofuran from CO 2 . It is considered that a complex of formyltransferase and formylmethanofuran dehydrogenase converts N5-Formyl H4MPT to formate in many methylotrophic bacteria (as in Methylobacterium extorquens AM1) [ 37 ]. Genes encoding the MO-Fe-nitrogenase were not detected, indicating that strain LS7-MT is unable to fix N 2 , which confirms our earlier growth studies. Genes encoding glutamine synthetase ( glnA ) and glutamate synthase ( gltBD ) are present, suggesting that ammonia assimilation is via the GS/GOGAT pathway. No genes encoding methylamine dehydrogenase were found but genes encoding the N-methylglutamate pathway ( gmaS ) were present, suggesting that strain LS7-MT assimilates methylamine via this pathway. Trimethylamine is probably metabolized via TMAO since tmm , encoding trimethylamine monooxygenase, is present in the genome of strain LS7-MT. Description of Methylothermalis aethiopiae Strain LS7-MT gen. nov. sp. nov. Methylothermalis (Me.thy.lo.ther’mal.is) N.L. n. methyl, the methyl group; N.L. pref. methylo, relating to the methyl radical N.L. masc, subst. from Gr. adj. thermalis referring to an alkaline thermal spring, and aethiopiae (ae.thi.o.pi.ae) referring to the territory where the bacterium was found. Gram-stain-negative, non-motile, aerobic and small rod-shaped cells that reproduce by binary fision. Cells are of a diameter of 0.3 to 0.5 µm and a length of 0.4–1.5 µm. Very shiny white colonies on gelrite plates were 1.2 to 1.5 mm in diameter. It is a moderate thermophile and facultative methylotroph utilizing methanol and a variety of multi-carbon compounds in addition to sugar via the serine pathway. C18:0, C18:1w7c, C19:0cyc w8c are the dominant cellular fatty acids. It does not grow on methane, and it utilizes nitrate as a nitrogen source. It is not capable of fixing atmospheric nitrogen. Vitamins are required for its growth. It contains MDH, but not pMMO- or sMMO genes. The gene xoxF1 is present. Optimum growth occurs at 55 °C (range spanning 30 to 60 °C), at pH 6.0 to 9.3 and in the presence of 0.5% ( w / v ) NaCl. Phylogenetically, strain LS7-MT belongs to the order Hyphomicrobiales of the class Alphaproteobacteria . The most closely related extant genera are Methyloceanibacter and Methyloligella within the family Hyphomicrobiaceae . The accession number (in the JGI IMG/ER) of the deposited genome sequences is 2517572012. Genome sequencing revealed a genome size of 2.95 Mbp and a DNA G + C content of 65.9%. The strain LS7-MT was isolated from a terrestrial alkaline hydrothermal spring located in the Ethiopian Rift Valley."
} | 6,434 |
35300488 | PMC8921678 | pmc | 6,483 | {
"abstract": "Soil microorganisms such as Bacteria and Archaea play important roles in the biogeochemical cycling of soil nutrients, because they act as decomposers or are mutualistic or antagonistic symbionts, thereby influencing plant growth and health. In the present study, we investigated the vertical distribution of soil metagenomes to a depth of 1.5 m in Swiss forests of European beech and oak species on calcareous bedrock. We explored the functional genetic potential of soil microorganisms with the aim to disentangle the effects of tree genus and soil depth on the genetic repertoire, and to gain insight into the microbial C and N cycling. The relative abundance of reads assigned to taxa at the domain level indicated a 5–10 times greater abundance of Archaea in the deep soil, while Bacteria showed no change with soil depth. In the deep soil there was an overrepresentation of genes for carbohydrate-active enzymes, which are involved in the catalyzation of the transfer of oligosaccharides, as well as in the binding of carbohydrates such as chitin or cellulose. In addition, N-cycling genes (NCyc) involved in the degradation and synthesis of N compounds, in nitrification and denitrification, and in nitrate reduction were overrepresented in the deep soil. Consequently, our results indicate that N-transformation in the deep soil is affected by soil depth and that N is used not only for assimilation but also for energy conservation, thus indicating conditions of low oxygen in the deep soil. Using shotgun metagenomics, our study provides initial findings on soil microorganisms and their functional genetic potential, and how this may change depending on soil properties, which shift with increasing soil depth. Thus, our data provide novel, deeper insight into the “dark matter” of the soil.",
"conclusion": "Conclusion The metagenomic analysis of deep soil layers of beech and oak forests in Switzerland revealed that tree genus and forest site factors have only a minor effect on the composition of the microbial gene repertoire, while soil depth had strong effects. Soil depth decisively changes chemical conditions and resource availability leading to more favorable conditions for Archaea than for Bacteria. The strong increase in the archaeal domains in the deep soil layers also indicates that biogeochemical processes and cycling are likely to be affected by soil depth. In deep soils, genes for carbohydrate-active enzymes involved in catalyzing the transfer of saccharide moieties are overrepresented, as are enzymes involved in binding carbohydrates such as chitin or cellulose. The greater abundance of these genes in the deep soil demonstrates that greater efforts have been made for enzymatic hydrolysis, especially for insoluble substrates. Furthermore, overrepresented genes of the N cycle in the deep soils are involved in the degradation and synthesis of N compounds, in nitrification, in denitrification, and in nitrate reduction. Consequently, the entire N-cycle is affected and N is not only used for assimilation but also for energy conservation, indicating conditions of low oxygen, even if the soils were not hydromorphic. Overall, metagenomics proved to be an appropriate approach to gain insights into biogeochemical processes that may change with altered soil properties, such as those associated with greater soil depths. Our study is one of the first to offer insight into the functional diversity of heterogeneous microbial assemblages in deep forest soils. Our data on the functional genetic potential of the soil microbiomes in deep soils provide information about their metabolic capacities, enabling modeling of depth-specific biogeochemical processes that may change as a result of altered soil conditions.",
"introduction": "Introduction In agricultural and forested areas, studies on soil microbial communities rarely include soil depths exceeding 30 cm ( Yost and Hartemink, 2020 ). Most of the organic C in soils from various biomes is located in the topsoil at 0–30 cm soil depth ( Crowther et al., 2019 ). Jobbágy and Jackson (2000) estimated in a worldwide study that 50% of the organic C in forests is in the top 20 cm. Xu et al. (2013) similarly calculated that about 70% of the soil microbial C and N in forested ecosystems is located in the upper 30 cm of the soils. In a few recent studies on soils, however, measurement went beyond the topsoil and included the deep soil down to a depth of 1 m in investigations of extracellular enzyme activities ( Dove et al., 2020 ) or metagenomic attributes of novel bacterial taxa ( Brewer et al., 2019 ). However, studies of the soil microbiome including soils at depths greater than 1 m are still particularly rare, leaving this soil compartment largely unexplored as a terra incognita ( Andújar et al., 2017 ). Deep soil layers harbor poorly known bacterial and fungal communities ( Brewer et al., 2019 ; Robin et al., 2019 ; Liu et al., 2020 ; Frey et al., 2021 ). It is assumed that living conditions in deeper soils for soil organisms and plant roots are harsher in deeper soils, as a result of higher soil density, lower oxygen concentrations, and lower carbon (C) and nutrient availability ( Lennon, 2020 ). Because soil processes, soil properties, and microbial communities are depth-dependent, soils should be studied at greater depths for a more comprehensive understanding of their relationship and interaction ( Yost and Hartemink, 2020 ). In addition, it is important to understand deep soil microbial communities because deep soils are poorly accounted for in current models of biogeochemical processes. Microorganisms are the key drivers of both C- and nutrient-cycling processes in the soil ( Falkowski et al., 2008 ). Most soil microorganisms in forest ecosystems gain their energy by decomposing different types of C from soil organic C (SOC), which vary in degradability and require different mechanisms for decomposition ( Lladó et al., 2019 ). Carbon and nutrients typically are more available in the topsoil, mainly because of larger input of leaf and fine root litter and root exudates, as well as higher biotic activity ( Eldridge et al., 2016 ; Liu et al., 2020 ). Consequently, microbial biomass per unit soil mass is usually one to two orders of magnitude lower in the deep soil than in the topsoil ( Eilers et al., 2012 ; Spohn et al., 2016 ). Nevertheless, the total biomass of Bacteria and fungi inhabiting deeper soil horizons can be as large as in the topsoil, and these microorganisms play similarly important roles in biogeochemical processes ( Brewer et al., 2019 ), although we know very little about the microbial genetic potential including soil C and nutrient cycling, soil formation, iron redox reactions, and pollutant degradation. Because C usually occurs in lower amounts in deeper soil layers compared to the topsoil, chemolithotrophic processes could become prominent in deep soil layers. This means that microorganisms could gain their energy from the oxidation of inorganic compounds instead of organic compounds ( Kelly and Wood, 2013 ). The most common substrates that are reduced are nitrogen (N), sulfur and iron compounds as well as hydrogen ( Thiel, 2011 ). Besides of inorganic C, oxygen concentrations also tend to be lower in deeper soil layers. Thus, anaerobic processes can also occur, where energy is generated through the reduction of nitrate, nitrite or sulfate ( Hooper and Dispirito, 2013 ). Some microorganisms are also able to use CO 2 as a C source. In this case, the resulting products are methane and acetic acid ( Hooper and Dispirito, 2013 ). Ammonia-oxidizing Bacteria (AOB) and ammonia-oxidizing Archaea (AOA) are central players in the global N cycle. These microbial groups can perform the first part of the nitrification process, ammonia oxidation. This is an important rate-limiting step in the various N-cycling processes, including N fixation, mineralization, nitrification, and denitrification ( Kowalchuk and Stephen, 2001 ; Zhang et al., 2013 ). Since AOB gain only little energy by oxidizing ammonia, a lower soil NH 4 + -N content in the deep soil leads to a decrease in AOB abundance, whereas AOA appear to be less constrained by the availability of N substrates and prosper in oligotrophic environments in the deep soil ( Martens-Habbena et al., 2009 ; Trivedi et al., 2019 ). In deeper soil layers, weathering processes can additionally contribute to the presence of essential nutrient elements such as Mg, P and Fe, through the mineralization of the bedrock ( Uroz et al., 2009 , 2015 ; Xi et al., 2018 ). Bacteria and fungi play a role in rock weathering by producing organic acids, hydrogen cyanide or siderophores ( Frey et al., 2010 ; Brunner et al., 2011 ; Wongfun et al., 2014 ; Xi et al., 2018 ; Wang et al., 2019 ). In addition, specific environmental enzymes involved in S and Fe metabolisms could be relevant ( Jiang et al., 2019 ; Li et al., 2020 ). Genes related to such weathering processes could be those involved in oxalate biosynthesis, in cyanide synthesis, and in siderophore synthesis and transport ( Varliero et al., 2021 ). In a first survey of the deep soil horizons in Swiss beech and oak forests, we observed the occurrence of poorly known bacterial taxa belonging to Nitrospirae, Chloroflexi, Rokubacteria, Gemmatimonadetes, and Firmicutes ( Frey et al., 2021 ). Furthermore, archaeal phyla such as Thaumarchaeota and Euryarchaeota were more abundant in the deep soil than in the topsoil. Previous research has indicated that members of Chloroflexi might be adapted to nutrient-poor environmental conditions ( Rime et al., 2016 ; Adamczyk et al., 2019 ) whereas Gemmatimonadetes might be specifically adapted to low-moisture availability ( DeBruyn et al., 2011 ; Rime et al., 2015 ). It is noteworthy, that some members of Euryarchaeota produce methane ( Bodelier and Steenbergh, 2014 ), but also oxidize methane, fix N, and reduce nitrates ( Cabello et al., 2004 ). The aim of the present study was to improve our understanding of the metabolic capabilities of the microbial communities in deep soil layers. While we focused on changes in taxonomic composition with increasing soil depth in our first survey ( Frey et al., 2021 ), here we used shotgun metagenomics to assess changes in microbial C- and N-cycling potential and link these data to the properties in deep soil layers. In particular, we hypothesized that in deep soil layers: (1) the genus of the dominant tree species in a site does not play a crucial role in shaping microbial metabolic capabilities in soil, (2) the biogeochemical and chemolithotrophic capabilities of the microbial communities are enhanced relative to shallower soil, and (3) soil N cycling is particularly affected by the metabolic capabilities of the microbial communities. We specifically focused on genes in the Carbohydrate-Active enZymes (CAZy) database, which are responsible for the catabolism of various C-complexes with varying decomposability, ranging from labile C (e.g., monosaccharides and polysaccharides) to recalcitrant C (e.g., lignin). Our study is one of the first to characterize the functional genetic potential and metabolic capabilities of microorganisms in deep soil layers and contributes to an integrated perspective on the soil microbial communities of beech and oak forests.",
"discussion": "Discussion Altered Microbial Communities in Deep Soil Layers In deep soil layers, the relative abundance of contigs assigned to Archaea was 5–9 times higher than in the topsoil, while the relative abundance of contigs assigned to Bacteria was not influenced by soil depth. Similarly, both Bacteria and Archaea were unaffected by the tree genus. In deep soil layers, the classes of the chemoautotrophic nitrifying Nitrososphaeria of the archaeal phylum Thaumarchaeota and the anaerobic Methanomicrobia of the archaeal phylum Euryarchaeota were particularly abundant. Archaea have previously been regarded as rare microorganisms that play a limited role in biogeochemical cycles in non-extreme environments ( Moissl-Eichinger et al., 2018 ). Recently, however, Archaea have been found to be distributed widely and comprise a broad diversity of gene repertoires and lifestyles ( Martínez-Núñez and Pérez-Rueda, 2016 ). These authors suggested that Archaea contain more promiscuous enzymes, i.e., the ability of an enzyme to catalyze a random side reaction in addition to its main reaction, providing them with an enzymatic repertoire that enables them to face multiple ecological challenges in harsher environments such as the deep soil. It is not unexpected to find Nitrososphaera, a class within the phylum Thaumarchaeota, in deep soils. Thaumarchaeota are generally negatively correlated with clay and C org . Our deep soil layers are characterized by a high sand and low C org. content, indicating well-drained and oligotrophic conditions. However, ecological niche preferences for ammonia-oxidizing Archaea (AOA) are only found in the literature for topsoil (e.g., Saghaï et al., 2021 ) and data from deep soils are not available as they are currently very poorly explored. Among the Bacteria, the phyla Nitrospirae, Chloroflexi, and Actinobacteria were significantly more abundant in deep soil. In general, our shotgun metagenomics data are consistent with the results of Frey et al. (2021) , who used the same forest sites as in the present study using amplicon sequencing of 16S rRNA genes. The increased occurrence of some groups of less-known prokaryotes, e.g., Archaea, Chloroflexi, or Nitrospirae at greater soil depths indicates that living conditions for microorganisms change to an oligotrophic and a more oxygen limited environment in deep soil. Such organisms gain their energy from the oxidation of inorganic atoms or molecules as a growth-supporting reductant such as ammonia or nitrite ( Hooper and Dispirito, 2013 ; Kelly and Wood, 2013 ). Taxa of Thaumarchaeota couple ammonia oxidation with C fixation ( Könneke et al., 2014 ), and taxa of Nitrospirae are able to oxidize nitrite to nitrate or even facilitate the complete cycle from ammonia to nitrite to nitrate (commamox; Daims et al., 2015 ). Moreover, AOA are favored by oligotrophic soil conditions ( Ollivier et al., 2011 ). Taxa of Methanomicrobia are able to produce methane by using CO 2 ( Enzmann et al., 2018 ), and taxa of Actinobacteria form one of the few bacterial groups able to degrade lignin ( Abdel-Hamid et al., 2013 ). However, it also indicates that deep soil microorganisms have to invest more effort into the mobilization of C and N than microorganisms in the topsoil, because they gain their energy from the oxidation of inorganic compounds (lithotrophs), which has a higher cost than required to gain energy from the oxidation of organic compounds (organotrophs) ( Hooper and Dispirito, 2013 ). Functional Gene Structure and Differentially Abundant Genes Using shotgun metagenomics, we primarily investigated the potential of functional profiles. The functional gene structure of all predicted genes showed a clear separation between the metagenomes of the topsoil and the deepsoil, and similarly, for the EggNOG, CAZy, and NCyc datasets. Moreover, most of the differentially abundant genes contributed to the difference in functional gene structure observed between the deep soil and the topsoil, with the majority of genes being overrepresented in the deep soil samples (>5 × 10 5 genes). In contrast, the comparison of the two tree genera beech and oak showed only a few over- or underrepresented genes when all genes were considered, as well as for the genes annotated with the above-mentioned databases. The three forest sites also differed in functional gene structure when compared to each other, but the differentially abundant genes were more than ten times less abundant than when comparing the two soil depths. Therefore, we concluded that the comparison between the topsoil and the deepsoil is the most relevant. Functional genes annotated against the EggNOG database, which maps predicted genes of general metabolic and cellular functions, showed that the majority of the functional gene categories were significantly overrepresented in the deep soil. In particular, genes of the functional categories such as cell cycle control, nucleotide transport, or amino acid transport and metabolism were highly significantly more abundant. This overall could indicate that in the deep soil altered cellular and metabolic processes occur in the deep soil, which affect the growth of cells and the mobilization of nutrients. Among the most overrepresented genes of the carbohydrate and amino acid transport and metabolism categories were ABC-type amino acid transporters, peptidases, synthetases, transferases, kinases, hydrolases, dehydrogenases, lyases, and mannosidases. Such a dominance of overrepresented functional gene categories is surprising but not unusual, as Perez-Mon et al. (2021) found a similar pattern when comparing permafrost soils with active-layer soils in a high mountain soil in the Swiss Alps. Likewise, in a comparison of cultivated and uncultivated chernozems in Russia, the majority of the functional gene categories were overrepresented in the virgin soil ( Gorbacheva et al., 2018 ). Similarly, Chen et al. (2021) reported, by using the KEGG (Kyoto Encyclopedia of Genes and Genomes) database, that microorganisms in bare soils, compared with those in vegetated soils, have greater relative abundances of genes associated with metabolic functions. They concluded that bare soils harbor a higher proportion of genes that are not available in public reference databases. This is in accordance to our findings that microbial communities in deepsoils are less studied compared to those of topsoils. We assume that oligotrophic microbial communities in deep soils harbor more unannotated genes than copiotrophic microbial communities. These results can be attributed to the fact that oligotrophs are less readily culturable than copiotrophs, and thus their taxonomic and functional information are underrepresented in current reference databases. Differentially Abundant Genes of C and N-Transforming Processes Specific information on metabolic properties can be gained from the differentially abundant genes of the CAZy and NCyc databases. The composition of C-degradation genes can reflect the ability of microorganisms to utilize various C-complexes, thereby affecting the accumulation of SOC pools. Gene relative abundance is an adequate predictor of the associated enzyme activity ( Trivedi et al., 2016 ). With the CAZy database we identified a significant increase in the deep soil in genes for glycosyl transferases (GT) and the carbohydrate binding modules (CBM). The GT genes play essential roles in the biosynthesis pathways of oligo- and polysaccharides, as well as in protein glycosylation and the formation of valuable natural products, and the CBM genes are involved in the binding of carbohydrates, mainly starch, pectin, chitin, and cellulose. It has been considered that CBM genes enhance enzymatic hydrolysis, especially for insoluble substrates ( Sidar et al., 2020 ). In addition, there was a distinct dominance of overrepresented genes in the CAZy database in the deep soil, indicating high metabolic activity of anabolic and catabolic processes. The degradation of oligosaccharides, chitin, pectin, starch, cellulose, and hemicellulose were very common and dominant in the deep soil, whereas those for degradation of lignin did not differ from the topsoil, with the exception of overrepresented genes for multicopper oxidases (AA1, e.g., laccase). It seems that lignin degradation mainly occurs in the topsoil, where most of the fungi live and are active (e.g., Žifčáková et al., 2017 ; Frey et al., 2021 ), if we consider fungi as the main lignin decomposers. Thus, we conclude that Bacteria and fungi in deep soil horizons mainly access easily degradable carbohydrates for their survival and growth, as oxygen and nutrients may be limited and at the edge of suitable concentrations (e.g., Lennon, 2020 ). Microorganisms cannot only promote the release of C into the atmosphere through their catabolic activities but also synthesize labile C into a stable form through their anabolic functions ( Liang et al., 2017 ). We observed that genes for microbial anabolic activities (GTs) were more abundant in the deep soil, however, further studies are required to investigate the contribution of microbial-derived C to SOC sequestration in the deep soil. With the NCyc database we found a significant increase in the deep soil in genes involved in organic degradation and synthesis. The significant increase in the abundance of N-fixing genes, however, can be neglected because the number of DNA reads was extremely low and, therefore, not relevant. Overrepresented genes in the deep soil were mainly involved in the degradation and synthesis of N compounds such as urea or glutamate, in nitrification and denitrification, and in nitrate reduction. Interestingly, genes involved in nitrification via ammonia monooxygenases ( amo ) were dominant, with all three subunit types ( A, B, C ) of Archaea and two subunit types ( B, C ) of Bacteria overrepresented in the deep soil. It has previously been reported that ammonia-oxidizing Archaea rather prefer areas with low ammonium and have a higher survival rate under conditions of low oxygen than ammonia-oxidizing Bacteria ( He et al., 2018 ). This finding was also confirmed by our quantitative PCR analysis, which indicated that the deep soil harbors significantly higher gene abundances of archaeal amoA . However, such strong seasonal shifts in ammonia and pH are not expected in the investigated calcareous deep soil layers. In the deep soil, genes for both assimilatory and dissimilatory nitrate reduction were overrepresented. Thus, nitrate is used not only for the assimilation but also for the conservation of energy, indicating that it is used as an electron acceptor for respiration in the (near) absence of oxygen ( Kamp et al., 2015 ). Moreover, dissimilatory nitrate reduction and nitrate storage are physiological life traits that provide the microorganisms with flexibility and resource independence when environments are temporarily anoxic and/or nitrate free ( Kamp et al., 2015 ). Clearly, the overrepresentation of the dissimilatory pathway is an indication of environments that are limited in oxygen even that the investigated soils did not show redoximorphic features. Consequently, microorganisms living in deeper soil horizons have to put more efforts into the mobilization, transformation, and formation of N compounds, which results in greater abundance of genes which can use nitrate not only as a N source but also in energy conservation. Overall, there are several indications that soils in deep layers may be limited in oxygen and therefore represent a challenging environment for oxic-adapted microbial life ( MacDonald et al., 1993 ). Among the functional genes involved in methane cycling, no methyl-coenzyme M reductase genes (the primary gene involved in methanogenesis, mcrA ) were detected in the metagenomes. By using quantitative PCR, however, we found an overall low abundance of mcrA genes at both soil depths, with a higher abundance of mcrA genes in the topsoil. Methanogens use CO 2 as an electron acceptor during anaerobic respiration and produce methane. Therefore, these low abundances of mcrA genes could indicate oxygen-limited but not fully anaerobic conditions ( Frey et al., 2011 ). This corresponds to the findings that the abundance of the gene for methane monooxygenase ( pmoA ), an enzyme that metabolizes methane for C-metabolism and energy, was also significantly less abundant in the deep soil. Our findings suggest that methane as a substrate for pmoA may not be produced at higher concentrations in the deep soils studied, although methane could be oxidized anaerobically using alternative electron acceptors, e.g., nitrite, nitrate, and ferric iron ( Cai et al., 2018 ; Shen et al., 2019 ). As no measurements of oxygen concentrations in topsoils and deepsoils were carried out, one can only speculate about the interpretation of the complex interactions between methane-cycling organisms, AOA and oxygen concentrations. However, two points need to be considered. Firstly, due to the sandy and well-drained soils in deep soils, it can be assumed that deepsoils are not strongly oxygen-limited and therefore contain a lower abundance of mcrA genes but a higher abundance of AOA due to the more oligotrophic conditions. Such a condition is not unusual. Secondly, due to the higher microbial activity in topsoils resulting in higher oxygen consumption, and in combination with a high clay content, there are more anaerobic microniches in the topsoil. Thus, a higher abundance of mcrA genes can be expected in topsoils. As for methanogens, which are strictly anaerobic organisms, the presence of mcrA genes has previously been shown in oxic soils in anaerobic microniches ( Frey et al., 2011 ; Li et al., 2021 ). In addition, anaerobic methanotrophs have been reported that can oxidize methane produced by methanogens ( Luesken et al., 2012 ). Anaerobic methanotrophs might be present in the deeper soil layer, most likely due to their sensitivity to oxygen. This also explains the relative high abundance of pmoA genes in both top and deep soils."
} | 6,365 |
26300390 | PMC4547134 | pmc | 6,485 | {
"abstract": "High-density living is often associated with high disease risk due to density-dependent epidemic spread. Despite being paragons of high-density living, the social insects have largely decoupled the association with density-dependent epidemics. It is hypothesized that this is accomplished through prophylactic and inducible defenses termed ‘collective immunity’. Here we characterise segregation of carpenter ants that would be most likely to encounter infectious agents (i.e. foragers) using integrated social, spatial, and temporal analyses. Importantly, we do this in the absence of disease to establish baseline colony organization. Behavioural and social network analyses show that active foragers engage in more trophallaxis interactions than their nest worker and queen counterparts and occupy greater area within the nest. When the temporal ordering of social interactions is taken into account, active foragers and inactive foragers are not observed to interact with the queen in ways that could lead to the meaningful transfer of disease. Furthermore, theoretical resource spread analyses show that such temporal segregation does not appear to impact the colony-wide flow of food. This study provides an understanding of a complex society’s organization in the absence of disease that will serve as a null model for future studies in which disease is explicitly introduced.",
"conclusion": "Conclusion Through the incorporation of dyadic- and network-level social interactions, individual movement data, and the timing of social interactions, we have gained insights into how colony organization is accomplished in C. pennsylvanicus colonies. The standing organization in an ant society, exemplified by the carpenter ant colonies studied here, appears to be more nuanced than previously imagined. Measuring social and spatial segregation in tandem is important because pathogens may have alternate means of gaining entry to, and transmitting within, social insect societies ( Table S1 ) and it remains unclear to what extent spatial distance is a proximate mechanism underlying social distance. The timing of social interactions may provide an additional layer of disease prophylaxis in social insect societies. This adds evidence for the growing argument that temporal information and meaningful behavioural interactions should be included into social network analyses if we are to make biologically accurate conclusions 27 54 . The temporal component of social interactions is especially worth future investigation because it is unclear to what extent pathogen infectivity and/or infective dose is reduced over time or multiple trophallaxis passages. Thus, social insect societies which employ living food ‘silos’ 39 may indeed be making use of such temporal protection. Analyzing carpenter ant colony organization and functioning in the absence of disease and environmental heterogeneity provides a useful null model; we are now primed to study how these complex systems react to perturbation. Future experiments in which laboratory infections are combined with integrated social, spatial, and temporal approaches will further inform how social insect colony organization and individual behaviour dynamically interact to reduce disease transmission. Social insects are host to a range of both generalist and specialist parasites 13 , some of which can change the behaviour of the infected host (ie. Ophiocordyceps ) or potentially alter the interactions between healthy and infected nest mates (ie. Beauveria, Metarhizium ), and some of which cause no discernible change in behaviour. Such studies will also afford us the ability to synchronize theoretical predictions about disease transmission in societies from agent-based and SIR modeling approaches 55 56 with empirical data from experiments in which disease can actually be introduced.",
"discussion": "Discussion Interacting with foragers, both socially and spatially, is a necessary risk for ant colonies. Theory predicts that interactions between potentially exposed foragers (actively returning foragers and inactive foragers that have foraged in the recent past) and other classes (i.e. nest workers) should be minimized 18 , but an integrated understanding of colony organization has remained elusive due to the inherent difficulties of observing within-colony social dynamics. Our behavioural analyses show that active foragers engage in more trophallaxis interactions than nest workers (approximately 2 additional interactions, Fig. 1a,b ). Static social network analyses complement these findings; in addition to engaging in more trophallaxis events (higher degree), foragers (active and inactive) exchange food with a greater number of unique individuals ( SI Table 2 ), indicating that their contact redundancy is lower than theory would predict 18 25 . Re-analyzing the static network data though ant functional group networks ( Fig. 2a,b ), in which trophallaxis connections are represented between functional groups rather than individuals, allows broader patterns of colony social organization to emerge 8 . Importantly, it also facilitates network comparison across nights and colonies and likely reflects a scale of analysis more biologically relevant to whole colony functioning 8 . Two network subgraphs predominate, accounting for 87% of those observed: inactive-nest-queen and forager-inactive-nest closed triad ( Fig. 2c,d ); these represent only two out of 59 possible patterns of functional group connection. From these subgraphs, it appears that inactive foragers may play an important role as brokers of trophallaxis in carpenter ant colonies, and that active foragers are interacting with nest workers more than might be expected.This becomes even clearer when ant-time standardization is taken into account. When the higher abundance of nest workers is taken into account, active and inactive foragers spend a greater per-capita percentage of their total in-nest time budget actually engaged in trophallaxis ( Table S4 ). While this is an intuitive finding should they only be spreading beneficial resources, this is less intuitive given the variety of material that can be spread through intimate social contact ( Table S1 ) in social insect colonies. Thus, this finding sheds light on the balance of constraints that have shaped the structure of carpenter ant social structure over evolutionary time. This suggests that under conditions in which perturbation is not present through disease or resource competition, organizational immunity is not accomplished through simply reducing trophallaxis with potentially exposed members. Future studies in which the balance is empirically ‘tipped’ in favor of disease transmission will shed light on how malleable this social structure is in the face of acute perturbation. In addition to the social position of foragers within the colony, we were also interested in how they spatially occupied their nest environment. Analysis of nest spatial usage showed that foragers (active and inactive) use more nest area relative to both nest workers and the queen ( Fig. 4 ). While the queen’s lack of movement synchronizes well with predictions from social immunity (i.e. to be secluded) the expansive movement of the foragers is counterintuitive. It is reasonable to assume that foragers should avoid internal areas of the nest 18 20 but we did not observe this ( Fig. 4 ). However, we found evidence in both colonies that foragers could be modulating their speed in response to their social environment ( Table S4 ). When foraging ants were in the same chamber as the queen, they moved faster than their nest worker counterparts. Such speed modulation could potentially reduce the impact of pathogen transmission to which foragers may have been exposed by moving faster near the most important individuals (i.e. queen, younger workers). This has not been previously considered as a mechanism to mitigate disease spread within the nest. Future studies that specifically address whether different ant classes alter their speed in response to different social environments inside the nest and what, if any, biological impact this has for potential disease transmission are needed. The static network analyses of colony social organization and the spatial movement of foragers reveal that active and inactive foragers engage in more trophallaxis interactions and occupy a great spatial area within the nest than their nest worker and queen counterparts. This would appear counter to the verbal models of social immunity where intuition, without within-nest behavioural data, has suggested a stronger segregation between worker types, especially foragers 18 . In considering social immunity, however, time has been a previously neglected component 20 . In our data, when the timing and order of trophallaxis interactions are taken into account, active and inactive foragers and the queen never interact in a way that could lead to the biologically meaningful transfer of disease (i.e. after a forager has come back into the nest after a foraging trip, carrying some pathogen that might transfer to the queen via either oral food exchange or prolonged physical contact). Thus, the timing of social interactions provides additional evidence for nuanced organization within C. pennsylvanicus colonies. Infectious agents are not the only pressure that ant colonies face. Since trophallaxis interactions are a conduit for disease and food (and other material, see Introduction), there is a fundamental tradeoff in optimizing food flow while minimizing disease spread, both of which are brought into the colony by foragers 13 . This tradeoff is of interest because it could impact colony functioning even in the absence of disease; the temporal segregation of foragers could prolong the time it takes for incoming food to reach other colony members. Simulations of resource spread over the empirically observed trophallaxis networks ( Fig. 5 ) provide an understanding of the flow tradeoff that could result from temporal segregation of foraging ants. In both colonies the mean fraction of the network reached a high of 20–25% after 20 minutes. In colony 1, this was achieved in food originating from active and inactive foragers, showing that their temporal segregation did not appear to impact the potential flow of food. In colony 2, food saturated at this percentage regardless of what ant type it originated from. Our study also highlights the need to consider an ant’s behavioural past when considering its present and future role in colony functioning and the introduction of disease risk. Active foragers who have just returned from outside the nest are clearly capable of introducing potential pathogens into the colony ( Table S1 ). What remains unclear is for how long they remain capable of transmitting disease- at what point does a potentially infected incoming forager transition to an inactive forager no longer posing a threat? While the classification of inactive forager used here was defined by the methods of the study, there are clear behavioural differences between these ants and nest workers who have never been observed to leave the nest. Had they been included within the nest worker categorisation, this would have obscured the network and spatial signals observed. We advocate that future studies of disease transmission in social insect societies follow individuals over a time period that will capture past exposure, and thus the continuum of foraging behaviour and pathogen risk."
} | 2,876 |
19840862 | null | s2 | 6,486 | {
"abstract": "Integrated approaches utilizing in silico analyses will be necessary to successfully advance the field of metabolic engineering. Here, we present an integrated approach through a systematic model-driven evaluation of the production potential for the bacterial production organism Escherichia coli to produce multiple native products from different representative feedstocks through coupling metabolite production to growth rate. Designs were examined for 11 unique central metabolism and amino acid targets from three different substrates under aerobic and anaerobic conditions. Optimal strain designs were reported for designs which possess maximum yield, substrate-specific productivity, and strength of growth-coupling for up to 10 reaction eliminations (knockouts). In total, growth-coupled designs could be identified for 36 out of the total 54 conditions tested, corresponding to eight out of the 11 targets. There were 17 different substrate/target pairs for which over 80% of the theoretical maximum potential could be achieved. The developed method introduces a new concept of objective function tilting for strain design. This study provides specific metabolic interventions (strain designs) for production strains that can be experimentally implemented, characterizes the potential for E. coli to produce native compounds, and outlines a strain design pipeline that can be utilized to design production strains for additional organisms."
} | 361 |
33925249 | PMC8125737 | pmc | 6,487 | {
"abstract": "Intelligent materials, also known as smart materials, are capable of reacting to various external stimuli or environmental changes by rearranging their structure at a molecular level and adapting functionality accordingly. The initial concept of the intelligence of a material originated from the natural biological system, following the sensing–reacting–learning mechanism. The dynamic and adaptive nature, along with the immediate responsiveness, of the polymer- and fiber-based smart materials have increased their global demand in both academia and industry. In this manuscript, the most recent progress in smart materials with various features is reviewed with a focus on their applications in diverse fields. Moreover, their performance and working mechanisms, based on different physical, chemical and biological stimuli, such as temperature, electric and magnetic field, deformation, pH and enzymes, are summarized. Finally, the study is concluded by highlighting the existing challenges and future opportunities in the field of intelligent materials.",
"introduction": "1. Introduction The unique properties of polymers have long been gaining attention and investigation from both academia and industry. The characteristics of different polymers rely on how the long chains of the molecules repeat themselves and bond with each other [ 1 ]. The inherent structure, along with the way molecules arrange themselves and cross-link, aids materials’ responsiveness to trivial environmental changes [ 2 , 3 , 4 ]. Intelligent polymers, as the word ’intelligent’ implies, are the polymers that are responsive to single or multiple stimuli, which could be either chemical or physical [ 2 , 5 ]. It is essential at the beginning of this review to define what an intelligent polymer is. To clarify the term “intelligent” with more accurate standards, we look back at the origin of it, which is borrowed from science-fiction literature as the opposite of those that are obtuse to environmental changes, that have no such ability to make choices and that cannot respond or adapt to comprehend complex situations or learn from the past. As well-established terminology in human intelligence and artificial intelligence [ 6 , 7 ], it is the intellectual power that gives human cognitive capabilities along with self-awareness to reason and understand, think and resolve, innovate and design, plan and predict, communicate and interact with each other. In a word, human intelligence can be seen as the ability to define, solve and learn from problems in various scenarios [ 8 , 9 , 10 ]. Similarly, from the deduction of parallel interpretation, the “intelligence” of a material could be considered to be the capability of property changes responsive to environmental stimuli with corresponding molecular-level structural rearrangement [ 11 , 12 , 13 ]. These external stimuli, as seen in Figure 1 a, could be referring to changes in light intensity, temperature, pH, electricity or magnetic field, mechanical deformation or pressure, biological stimuli, etc. [ 14 , 15 , 16 ]. The prototype of an intelligent polymer could be defined as a material that comprehends experiences, is self-aware and responds purposefully. Such ability to be aware of environmental changes allows intelligent polymers to adapt to ensure future improved behaviors in similar situations and in certain applications [ 17 , 18 , 19 , 20 ]. On the other hand, some researchers have also worked on implementing artificial-intelligence methodologies, like machine learning (ML), into the development of polymers, such as the ML model for polymer swelling in liquids [ 21 ], the prediction of point defects in materials [ 22 ] and sustainable material synthesis [ 23 ]. Keeping an eye on the increasing demands in developing and promoting smart materials in various types of applications in both academia and industry, polymer- and fiber-based materials are of the most interest [ 24 ]. With Figure 1 b illustrating the trends of major progress and publications in intelligent polymers, the increasing amount of research works in several directions. Figure 2 shows the historical tendency of major engineering material systems to transition from structural to functional with a prospective future of low-carbon sustainable intelligent materials [ 25 ]. Major preoccupations have seen materials starting from Stone Age primitive materials and the rudimentary use of chemistry to treat natural rubber and metals, followed by electrochemistry in the last century, with numerous materials developed or discovered in between [ 26 , 27 ]. Nowadays, the wide use of polymers and ceramics makes industrial and daily-life products feasible, affordable and suitable for mass production [ 28 , 29 , 30 ]. Piezoelectric, semiconducting and thermoelectric materials are seen in many state-of-the-art applications [ 31 , 32 , 33 , 34 ] and intelligent devices based on their unique characteristics. Nevertheless, these materials still hold a certain level of limitations regarding the degree of intelligence, as most of them lack certain functionalities such as self-control, decision-making, self-learning or ease of recycling, which are critical to the demanding sophisticated modern way of life [ 25 ]. In this review, features of polymer- and fiber-based materials are discussed, followed by applications in various fields and comparisons and summaries of the properties, characteristics and mechanisms of intelligent materials from a perspective of functionalities due to triggering stimuli, which range from physical (temperature, electric and magnetic field and deformation), chemical (pH) and biological (enzyme) basis. Section 2 focuses on the basic working mechanisms of several widely used intelligent polymers, followed by their respective characteristics and comparisons of performance in various applications. After investigations on shape-memory polymers (SMPs) in Section 2.1 and self-healing polymers in Section 2.2 , Section 2.3 focuses on multiresponsive polymers that are magnetic-, humidity- and pH-responsive. Section 3 gives a brief review on the following developed fields of polymer applications: drug-delivery systems, smart textiles and polymer-based healthcare wearables. Section 4 summarizes the discussion on intelligent polymers, points out the current research gaps and provides possible future research directions on intelligent polymers."
} | 1,603 |
38366943 | PMC10926325 | pmc | 6,488 | {
"abstract": "Abstract The Gram-negative betaproteobacterium Cupriavidus necator is a chemolithotroph that can convert carbon dioxide into biomass. Cupriavidus necator has been engineered to produce a variety of high-value chemicals in the past. However, there is still a lack of a well-characterized toolbox for gene expression and genome engineering. Development and optimization of biosynthetic pathways in metabolically engineered microorganisms necessitates control of gene expression via functional genetic elements such as promoters, ribosome binding sites (RBSs), and codon optimization. In this work, a set of inducible and constitutive promoters were validated and characterized in C. necator , and a library of RBSs was designed and tested to show a 50-fold range of expression for green fluorescent protein ( gfp ). The effect of codon optimization on gene expression in C. necator was studied by expressing gfp and mCherry genes with varied codon-adaptation indices and was validated by expressing codon-optimized variants of a C12-specific fatty acid thioesterase to produce dodecanoic acid. We discuss further hurdles that will need to be overcome for C. necator to be widely used for biosynthetic processes.",
"introduction": "Introduction \n Cupriavidus necator , previously known as Ralstonia eutropha and Alcaligenes eutrophus , is a Gram-negative betaproteobacterium. The C. necator genome is distributed into two chromosomes (4 049 965 and 2 912 457 bp) and a mega-plasmid (452 139 bp) with an overall guanine or cytosine (GC) content of 66.36% (Little et al., 2019 ). Cupriavidus necator is a facultative chemolithotroph with an ability to grow on a wide range of substrates, including sugars, lipids, and organic acids via the Entner–Doudoroff (ED) pathway and the tricarboxylic acid (TCA) cycle (Budde et al., 2011 ; Cram, 2008 ; Lu et al., 2013 ; Yan et al., 2003 ). It can also grow autotrophically using hydrogen under aerobic conditions to power assimilation of carbon dioxide via the Calvin–Benson–Bassham (CBB) cycle (Jefffke et al., 1999 ). This metabolic versatility makes C. necator an attractive metabolic engineering chassis, especially for biochemical production from C1 substrates. Cupriavidus necator has been developed industrially to leverage its native ability to store, during nutrient limited conditions, a large amount of organic carbon as polyhydroxybutyrate (PHB), a biodegradable polyester, to about 70%–80% of its dry cell weight (Lu et al., 2016 ). PHB is the archetypal polyhydroxyalkanoate (PHA), a class of biopolymers that are biodegradable, can have properties similar to traditional plastics derived from fossil fuels, and can be made from renewable feedstocks. Metabolic engineers now desire to shift this synthetic ability from PHA to other higher value products such as alcohols, methyl ketones, terpenoids, and alkenes (Brigham et al., 2013 ; Crepin et al., 2016 ; Krieg et al., 2018 ; Lu et al., 2012 ; Muller et al., 2013 ). To achieve these metabolic engineering goals, a synthetic biology toolbox, consisting of vectors, genome engineering protocols, and gene expression control strategies, must be developed. Early PHA work helped domesticate C. necator as a laboratory host by establishing stable plasmid transformation techniques and rudimentary recombineering protocols (Reinecke & Steinbüchel, 2009 ; Sato et al., 2013 ). However, progress in metabolic engineering has been slow because of the limited availability of well-established synthetic biology tools, including methods to control gene expression via (i) functional genetic elements such as promoters, both inducible and constitutive, and ribosome binding sites (RBSs), (ii) media composition, and (iii) codon optimization (Schmidt et al., 2023 ). Constitutive promoters characterized to date in C. necator mostly include the native promoters related to PHB synthesis (P phaC1 ), pyruvate metabolism (P pdhE ), acetyl-CoA synthesis (P acoE ), and translation (P rr sC ), or a promoter library created by altering these native promoters (Priefert & Steinbüchel, 1992 ; Delamarre & Batt, 2006 ; Li & Liao, 2015 ). P phaC1 is the most widely used constitutive promoter. These native promoters are relatively weak compared to the heterologous promoters like P lac and its derivatives. P l ac and its derivative promoters can work as constitutive promoters in C. necator because of the absence of native lacI and lacY homologs in the genome (Fukui et al., 2011 ). To have more effective control over the gene expression, inducible promoters are preferred. The arabinose inducible promoter system (AraC/P araBAD ) is the most widely used promoter in engineering studies of C. necator (Fukui et al., 2002 ). It can generate strong transcriptional activity but also has been reported to cause growth defects due to leaky expression (Fukui et al., 2009 ). The rhamnose inducible promoter system (RhaRS/P RhaBAD ) also works well in C. necator with strong induction and lower leaky expression (Alagesan et al., 2018 ). Induction systems using cumate (CymR/P j5-cmt ) and anhydrotetracycline (TetR/P rrsC-tetO ) have been found to have lower leaky expression and cause only slight growth defects (Gruber et al., 2016 ; Li & Liao, 2015 ). Other inducible promoter systems such as acrylate (AcuR/P acuRI ), m -toluic acid (PM/P xyls ), and itaconate (YpItcR/P ccl ) are found to work in C. necator but show high background or leaky expression levels (Alagesan et al., 2018 ; Bi et al., 2013 ; Hanko et al., 2018 ). A few native inducible promoters such as P cbbl , induced in lithoautotrophic growth conditions, P phaP , induced in phosphate limiting conditions, and hydrogenase promoters, P SH and P MBH , induced by glycerol and fructose, respectively, have been identified (Dangel & Tabita, 2015 ; Lutte et al., 2012 ; York et al., 2001 ). Along with the promoter, an RBS also has a significant impact on protein synthesis (Mutalik et al., 2013 ) and can govern product biosynthesis in an engineered microorganism. The level of enzyme production in metabolic pathways can be balanced using RBSs with varied strengths that help to achieve different levels of protein synthesis for individual genes of an operon driven by a single promoter. This helps in maintaining efficient carbon flux, removing bottlenecks, and increasing titers. Another important strategy for improving gene expression is codon optimization, the importance of which is often underappreciated. Codon usage bias is an essential feature of both prokaryotic and eukaryotic genomes (Ikemura, 1985 ; Plotkin & Kudla, 2011 ). Although most of the amino acids can be specified by more than one codon, only a subset of the codons is preferred and frequently used in highly expressed genes (Zhou et al., 2016 ). Codon usage can affect gene expression both at the transcription level and at the translation level. Different organisms have a distinct codon usage pattern that can be summarized using a codon usage table, which is defined by using a set of highly expressed genes as a reference (Sharp & Li, 1987 ). The codon adaptation index (CAI) is then calculated as the geometric mean of the frequencies of all the codons in a gene relative to the most often used synonymous codon, which is calculated from a set of highly expressed genes (Sharp & Li, 1987 ; Jansen et al., 2003 ). CAI is an effective way of predicting the expression level of a gene based on its codon sequence (Sharp & Li, 1987 ). The GC content of the genome is one of the determinants of the codon usage bias (Li et al., 2015 ). Codon optimization of a gene for heterologous expression can be done by selectively replacing codons in the original sequence with preferred codons of the new host as well as maintaining the GC content of the target host without changing the amino acid sequence of the protein. This can be done via two different ways: (i) by replacing all the native codons in the target gene with the most preferred codons in the target host organism or (ii) by replacing the native codons in the gene with the synonymous codon whose frequency in the target host most closely matches the frequency of the original codon in the native host, also known as codon harmonization (Angov et al., 2008 ). As varied CAI values are representative of the varied frequency of the most preferred codons in a target gene, it can be used to study the correlation between codon optimization and gene expression. In this study, we report our characterization of some commonly used promoters in C. necator along with some novel constitutive and inducible promoters. We have also investigated the expression of green and red fluorescent proteins with different codon variants/CAIs in C. necator . Based on the correlation derived from the fluorescent gene expression versus CAI tests, we also investigated the activity of C12-specific thioesterase ( BTE ) from bay laurel (Voelker & Davies, 1994 ) with different codon variants in C. necator and demonstrated dodecanoic acid (C12FA) production. We also outlined key hurdles to large-scale production of fatty acids in C. necator referring to the fact that it harbors several paralogs of β-oxidation genes that remain uncharacterized.",
"discussion": "Discussion Chemolithotrophs, such as C. necator , have the potential to be developed as a platform strain to produce bioproducts derived from a variety of carbon sources as well as carbon dioxide; however, continued genetic tool development is necessary to make full use of its potential as a biosynthetic platform. In this work, we tested and validated a comprehensive set of genetic elements that show meaningful differences to their performance in E. coli , and they can now be used to control the expression of heterologous pathways in C. necator . The Anderson library promoters that we tested had completely different expression strengths in C. necator from what was defined for E. coli . This indicates that the strength of promoters and other functional genetic elements cannot be predicted for C. necator based on their characterization in E. coli , suggesting that both have inherently different transcription machinery given their large evolutionary distance. Marionette promoters have been developed in E. coli for lower background activity, higher sensitivity, and limited crosstalk among the regulators; therefore, they presented a strong initial array of promoters to be tested in a new organism. The different promoters from the Marionette Sensor Collection show smaller fold changes in expression as compared to E. coli , indicating difference in regulation. In this work, eight promoters from the Marionette Sensor Collection were validated and added to the repertoire of characterized promoters in C. necator . In E. coli , P T r c confers roughly 90% stronger expression than P T ac owing to a single base pair addition in the spacer between –35 and –10 elements (Brosius et al., 1985 ). In C. necator , P T ac gives a fivefold change in expression during induction, while P T rc gives a 160-fold change in expression, mirroring the trend from E. coli but to a much larger extent, possibly suggesting that –35 to –10 spacer distance is especially important for C. necator. Additionally, RBS sequence was observed to be important for gene expression in C. necator . We obtained GFP fluorescence that varied over a 50-fold range using varied sequences of SD in the RBS. In this study, we also validated codon optimization and elucidated the relationship between gene expression and CAI for C. necator . Different CAI values indicate the presence of varied frequencies of preferred codons for a specific organism, that is, high CAI denotes higher frequency of more preferred codons and vice versa. CAI tells a general level of frequency calculated on average for a whole open reading frame but does not distinguish between codon optimization (using the most frequent synonymous codon) and harmonization (using the synonymous codon that best matches the frequency of the original codon in the native host). Also, it is worth mentioning that CAI is only an approximate indication of the suitability of the codon usage within a gene and does not consider the distribution or order of codons (Sharp & Li, 1987 ). Nevertheless, we find a strong relationship between the calculated CAI and gene expression for C. necator . The CAI would suggest whether it is of any benefit to chemically synthesize a new gene and to include more preferred codons. We found by fluorescence assay of codon-optimized variants of gfp and mCherry that 0.7–0.8 CAI (calculated using a codon-usage table of C. necator ) is a good range for gene expression in C. necator . This CAI can be used when synthesizing new genes or to infer whether existing sequences will be well expressed in this host. In a previous study, it was found that gene expression had a strong correlation with codon usage bias via translation and could also be a function of GC content, particularly in organisms that prefer G or C over A or T (Zhou et al., 2016 ). However, in Neurospora spp., it was found that at the genome-wide level GC contents (of whole transcripts) showed no or a weak negative correlation with gene expression (Zhou et al., 2016 ), but GC content at the third position of codons (%3GC) was found to show a strong positive correlation with gene expression. To test this phenomenon for C. necator , we calculated the %3GC for the codon-optimized variants of gfp and mCherry and found a strong positive correlation between %3GC content and gene expression (Fig. 4C and D ). However, in contrast to the previous study, we also found a similar correlation between gene expression and total GC content of the reporter genes ( Fig. S4 ). It might be due to the reason that C. necator is an organism with a high GC content (∼66%), whereas Neurospora sp. has ∼50% GC content in its genome and hence preference of either G or C at the third position of the codon can be easily observed in C. necator when compared to the gene expression in Neurospora . As overall GC content is high for C. necator , the possibility of G or C at the first and second positions of the codon is also very high. With the results obtained from our earlier characterization of C. necator tools, we demonstrated C12-fatty acid production in C. necator using two codon-optimized variants of the thioesterase, BTE , and were able to show that the level of codon optimization affects product biosynthesis in an engineered organism. Although we were able to produce ∼100 mg/L of dodecanoic acid, we still observed fatty acid consumption, which led to a decrease in the titers after 24 or 48 hr. As the oleochemical products were being catabolized at later time points even in the strain H16-6895, with β-oxidation operons A0459–A0464 and A1526–A1531 deleted, identification of other β-oxidation genes and deleting them remains critical. In a recent study, it was observed that growing the H16-6895 strain in media containing medium-chain-length fatty acids leads to the expression of many other β-oxidation genes, which was validated through RNAseq (Strittmatter et al., 2023 ). Although the possibilities of developing C. necator as a chassis for the production of fatty acids and their derivatives are currently limited by the large number of possible β-oxidation genes, the comprehensive toolbox of genetic elements that we characterized will be helpful in any metabolic engineering strategy applied in C. necator . Especially, in the current scenario of depleting fossil fuels, the power of C. necator to produce complex chemicals from carbon dioxide cannot be undermined."
} | 3,942 |
38366943 | PMC10926325 | pmc | 6,488 | {
"abstract": "Abstract The Gram-negative betaproteobacterium Cupriavidus necator is a chemolithotroph that can convert carbon dioxide into biomass. Cupriavidus necator has been engineered to produce a variety of high-value chemicals in the past. However, there is still a lack of a well-characterized toolbox for gene expression and genome engineering. Development and optimization of biosynthetic pathways in metabolically engineered microorganisms necessitates control of gene expression via functional genetic elements such as promoters, ribosome binding sites (RBSs), and codon optimization. In this work, a set of inducible and constitutive promoters were validated and characterized in C. necator , and a library of RBSs was designed and tested to show a 50-fold range of expression for green fluorescent protein ( gfp ). The effect of codon optimization on gene expression in C. necator was studied by expressing gfp and mCherry genes with varied codon-adaptation indices and was validated by expressing codon-optimized variants of a C12-specific fatty acid thioesterase to produce dodecanoic acid. We discuss further hurdles that will need to be overcome for C. necator to be widely used for biosynthetic processes.",
"introduction": "Introduction \n Cupriavidus necator , previously known as Ralstonia eutropha and Alcaligenes eutrophus , is a Gram-negative betaproteobacterium. The C. necator genome is distributed into two chromosomes (4 049 965 and 2 912 457 bp) and a mega-plasmid (452 139 bp) with an overall guanine or cytosine (GC) content of 66.36% (Little et al., 2019 ). Cupriavidus necator is a facultative chemolithotroph with an ability to grow on a wide range of substrates, including sugars, lipids, and organic acids via the Entner–Doudoroff (ED) pathway and the tricarboxylic acid (TCA) cycle (Budde et al., 2011 ; Cram, 2008 ; Lu et al., 2013 ; Yan et al., 2003 ). It can also grow autotrophically using hydrogen under aerobic conditions to power assimilation of carbon dioxide via the Calvin–Benson–Bassham (CBB) cycle (Jefffke et al., 1999 ). This metabolic versatility makes C. necator an attractive metabolic engineering chassis, especially for biochemical production from C1 substrates. Cupriavidus necator has been developed industrially to leverage its native ability to store, during nutrient limited conditions, a large amount of organic carbon as polyhydroxybutyrate (PHB), a biodegradable polyester, to about 70%–80% of its dry cell weight (Lu et al., 2016 ). PHB is the archetypal polyhydroxyalkanoate (PHA), a class of biopolymers that are biodegradable, can have properties similar to traditional plastics derived from fossil fuels, and can be made from renewable feedstocks. Metabolic engineers now desire to shift this synthetic ability from PHA to other higher value products such as alcohols, methyl ketones, terpenoids, and alkenes (Brigham et al., 2013 ; Crepin et al., 2016 ; Krieg et al., 2018 ; Lu et al., 2012 ; Muller et al., 2013 ). To achieve these metabolic engineering goals, a synthetic biology toolbox, consisting of vectors, genome engineering protocols, and gene expression control strategies, must be developed. Early PHA work helped domesticate C. necator as a laboratory host by establishing stable plasmid transformation techniques and rudimentary recombineering protocols (Reinecke & Steinbüchel, 2009 ; Sato et al., 2013 ). However, progress in metabolic engineering has been slow because of the limited availability of well-established synthetic biology tools, including methods to control gene expression via (i) functional genetic elements such as promoters, both inducible and constitutive, and ribosome binding sites (RBSs), (ii) media composition, and (iii) codon optimization (Schmidt et al., 2023 ). Constitutive promoters characterized to date in C. necator mostly include the native promoters related to PHB synthesis (P phaC1 ), pyruvate metabolism (P pdhE ), acetyl-CoA synthesis (P acoE ), and translation (P rr sC ), or a promoter library created by altering these native promoters (Priefert & Steinbüchel, 1992 ; Delamarre & Batt, 2006 ; Li & Liao, 2015 ). P phaC1 is the most widely used constitutive promoter. These native promoters are relatively weak compared to the heterologous promoters like P lac and its derivatives. P l ac and its derivative promoters can work as constitutive promoters in C. necator because of the absence of native lacI and lacY homologs in the genome (Fukui et al., 2011 ). To have more effective control over the gene expression, inducible promoters are preferred. The arabinose inducible promoter system (AraC/P araBAD ) is the most widely used promoter in engineering studies of C. necator (Fukui et al., 2002 ). It can generate strong transcriptional activity but also has been reported to cause growth defects due to leaky expression (Fukui et al., 2009 ). The rhamnose inducible promoter system (RhaRS/P RhaBAD ) also works well in C. necator with strong induction and lower leaky expression (Alagesan et al., 2018 ). Induction systems using cumate (CymR/P j5-cmt ) and anhydrotetracycline (TetR/P rrsC-tetO ) have been found to have lower leaky expression and cause only slight growth defects (Gruber et al., 2016 ; Li & Liao, 2015 ). Other inducible promoter systems such as acrylate (AcuR/P acuRI ), m -toluic acid (PM/P xyls ), and itaconate (YpItcR/P ccl ) are found to work in C. necator but show high background or leaky expression levels (Alagesan et al., 2018 ; Bi et al., 2013 ; Hanko et al., 2018 ). A few native inducible promoters such as P cbbl , induced in lithoautotrophic growth conditions, P phaP , induced in phosphate limiting conditions, and hydrogenase promoters, P SH and P MBH , induced by glycerol and fructose, respectively, have been identified (Dangel & Tabita, 2015 ; Lutte et al., 2012 ; York et al., 2001 ). Along with the promoter, an RBS also has a significant impact on protein synthesis (Mutalik et al., 2013 ) and can govern product biosynthesis in an engineered microorganism. The level of enzyme production in metabolic pathways can be balanced using RBSs with varied strengths that help to achieve different levels of protein synthesis for individual genes of an operon driven by a single promoter. This helps in maintaining efficient carbon flux, removing bottlenecks, and increasing titers. Another important strategy for improving gene expression is codon optimization, the importance of which is often underappreciated. Codon usage bias is an essential feature of both prokaryotic and eukaryotic genomes (Ikemura, 1985 ; Plotkin & Kudla, 2011 ). Although most of the amino acids can be specified by more than one codon, only a subset of the codons is preferred and frequently used in highly expressed genes (Zhou et al., 2016 ). Codon usage can affect gene expression both at the transcription level and at the translation level. Different organisms have a distinct codon usage pattern that can be summarized using a codon usage table, which is defined by using a set of highly expressed genes as a reference (Sharp & Li, 1987 ). The codon adaptation index (CAI) is then calculated as the geometric mean of the frequencies of all the codons in a gene relative to the most often used synonymous codon, which is calculated from a set of highly expressed genes (Sharp & Li, 1987 ; Jansen et al., 2003 ). CAI is an effective way of predicting the expression level of a gene based on its codon sequence (Sharp & Li, 1987 ). The GC content of the genome is one of the determinants of the codon usage bias (Li et al., 2015 ). Codon optimization of a gene for heterologous expression can be done by selectively replacing codons in the original sequence with preferred codons of the new host as well as maintaining the GC content of the target host without changing the amino acid sequence of the protein. This can be done via two different ways: (i) by replacing all the native codons in the target gene with the most preferred codons in the target host organism or (ii) by replacing the native codons in the gene with the synonymous codon whose frequency in the target host most closely matches the frequency of the original codon in the native host, also known as codon harmonization (Angov et al., 2008 ). As varied CAI values are representative of the varied frequency of the most preferred codons in a target gene, it can be used to study the correlation between codon optimization and gene expression. In this study, we report our characterization of some commonly used promoters in C. necator along with some novel constitutive and inducible promoters. We have also investigated the expression of green and red fluorescent proteins with different codon variants/CAIs in C. necator . Based on the correlation derived from the fluorescent gene expression versus CAI tests, we also investigated the activity of C12-specific thioesterase ( BTE ) from bay laurel (Voelker & Davies, 1994 ) with different codon variants in C. necator and demonstrated dodecanoic acid (C12FA) production. We also outlined key hurdles to large-scale production of fatty acids in C. necator referring to the fact that it harbors several paralogs of β-oxidation genes that remain uncharacterized.",
"discussion": "Discussion Chemolithotrophs, such as C. necator , have the potential to be developed as a platform strain to produce bioproducts derived from a variety of carbon sources as well as carbon dioxide; however, continued genetic tool development is necessary to make full use of its potential as a biosynthetic platform. In this work, we tested and validated a comprehensive set of genetic elements that show meaningful differences to their performance in E. coli , and they can now be used to control the expression of heterologous pathways in C. necator . The Anderson library promoters that we tested had completely different expression strengths in C. necator from what was defined for E. coli . This indicates that the strength of promoters and other functional genetic elements cannot be predicted for C. necator based on their characterization in E. coli , suggesting that both have inherently different transcription machinery given their large evolutionary distance. Marionette promoters have been developed in E. coli for lower background activity, higher sensitivity, and limited crosstalk among the regulators; therefore, they presented a strong initial array of promoters to be tested in a new organism. The different promoters from the Marionette Sensor Collection show smaller fold changes in expression as compared to E. coli , indicating difference in regulation. In this work, eight promoters from the Marionette Sensor Collection were validated and added to the repertoire of characterized promoters in C. necator . In E. coli , P T r c confers roughly 90% stronger expression than P T ac owing to a single base pair addition in the spacer between –35 and –10 elements (Brosius et al., 1985 ). In C. necator , P T ac gives a fivefold change in expression during induction, while P T rc gives a 160-fold change in expression, mirroring the trend from E. coli but to a much larger extent, possibly suggesting that –35 to –10 spacer distance is especially important for C. necator. Additionally, RBS sequence was observed to be important for gene expression in C. necator . We obtained GFP fluorescence that varied over a 50-fold range using varied sequences of SD in the RBS. In this study, we also validated codon optimization and elucidated the relationship between gene expression and CAI for C. necator . Different CAI values indicate the presence of varied frequencies of preferred codons for a specific organism, that is, high CAI denotes higher frequency of more preferred codons and vice versa. CAI tells a general level of frequency calculated on average for a whole open reading frame but does not distinguish between codon optimization (using the most frequent synonymous codon) and harmonization (using the synonymous codon that best matches the frequency of the original codon in the native host). Also, it is worth mentioning that CAI is only an approximate indication of the suitability of the codon usage within a gene and does not consider the distribution or order of codons (Sharp & Li, 1987 ). Nevertheless, we find a strong relationship between the calculated CAI and gene expression for C. necator . The CAI would suggest whether it is of any benefit to chemically synthesize a new gene and to include more preferred codons. We found by fluorescence assay of codon-optimized variants of gfp and mCherry that 0.7–0.8 CAI (calculated using a codon-usage table of C. necator ) is a good range for gene expression in C. necator . This CAI can be used when synthesizing new genes or to infer whether existing sequences will be well expressed in this host. In a previous study, it was found that gene expression had a strong correlation with codon usage bias via translation and could also be a function of GC content, particularly in organisms that prefer G or C over A or T (Zhou et al., 2016 ). However, in Neurospora spp., it was found that at the genome-wide level GC contents (of whole transcripts) showed no or a weak negative correlation with gene expression (Zhou et al., 2016 ), but GC content at the third position of codons (%3GC) was found to show a strong positive correlation with gene expression. To test this phenomenon for C. necator , we calculated the %3GC for the codon-optimized variants of gfp and mCherry and found a strong positive correlation between %3GC content and gene expression (Fig. 4C and D ). However, in contrast to the previous study, we also found a similar correlation between gene expression and total GC content of the reporter genes ( Fig. S4 ). It might be due to the reason that C. necator is an organism with a high GC content (∼66%), whereas Neurospora sp. has ∼50% GC content in its genome and hence preference of either G or C at the third position of the codon can be easily observed in C. necator when compared to the gene expression in Neurospora . As overall GC content is high for C. necator , the possibility of G or C at the first and second positions of the codon is also very high. With the results obtained from our earlier characterization of C. necator tools, we demonstrated C12-fatty acid production in C. necator using two codon-optimized variants of the thioesterase, BTE , and were able to show that the level of codon optimization affects product biosynthesis in an engineered organism. Although we were able to produce ∼100 mg/L of dodecanoic acid, we still observed fatty acid consumption, which led to a decrease in the titers after 24 or 48 hr. As the oleochemical products were being catabolized at later time points even in the strain H16-6895, with β-oxidation operons A0459–A0464 and A1526–A1531 deleted, identification of other β-oxidation genes and deleting them remains critical. In a recent study, it was observed that growing the H16-6895 strain in media containing medium-chain-length fatty acids leads to the expression of many other β-oxidation genes, which was validated through RNAseq (Strittmatter et al., 2023 ). Although the possibilities of developing C. necator as a chassis for the production of fatty acids and their derivatives are currently limited by the large number of possible β-oxidation genes, the comprehensive toolbox of genetic elements that we characterized will be helpful in any metabolic engineering strategy applied in C. necator . Especially, in the current scenario of depleting fossil fuels, the power of C. necator to produce complex chemicals from carbon dioxide cannot be undermined."
} | 3,942 |
38366943 | PMC10926325 | pmc | 6,489 | {
"abstract": "Abstract The Gram-negative betaproteobacterium Cupriavidus necator is a chemolithotroph that can convert carbon dioxide into biomass. Cupriavidus necator has been engineered to produce a variety of high-value chemicals in the past. However, there is still a lack of a well-characterized toolbox for gene expression and genome engineering. Development and optimization of biosynthetic pathways in metabolically engineered microorganisms necessitates control of gene expression via functional genetic elements such as promoters, ribosome binding sites (RBSs), and codon optimization. In this work, a set of inducible and constitutive promoters were validated and characterized in C. necator , and a library of RBSs was designed and tested to show a 50-fold range of expression for green fluorescent protein ( gfp ). The effect of codon optimization on gene expression in C. necator was studied by expressing gfp and mCherry genes with varied codon-adaptation indices and was validated by expressing codon-optimized variants of a C12-specific fatty acid thioesterase to produce dodecanoic acid. We discuss further hurdles that will need to be overcome for C. necator to be widely used for biosynthetic processes.",
"introduction": "Introduction \n Cupriavidus necator , previously known as Ralstonia eutropha and Alcaligenes eutrophus , is a Gram-negative betaproteobacterium. The C. necator genome is distributed into two chromosomes (4 049 965 and 2 912 457 bp) and a mega-plasmid (452 139 bp) with an overall guanine or cytosine (GC) content of 66.36% (Little et al., 2019 ). Cupriavidus necator is a facultative chemolithotroph with an ability to grow on a wide range of substrates, including sugars, lipids, and organic acids via the Entner–Doudoroff (ED) pathway and the tricarboxylic acid (TCA) cycle (Budde et al., 2011 ; Cram, 2008 ; Lu et al., 2013 ; Yan et al., 2003 ). It can also grow autotrophically using hydrogen under aerobic conditions to power assimilation of carbon dioxide via the Calvin–Benson–Bassham (CBB) cycle (Jefffke et al., 1999 ). This metabolic versatility makes C. necator an attractive metabolic engineering chassis, especially for biochemical production from C1 substrates. Cupriavidus necator has been developed industrially to leverage its native ability to store, during nutrient limited conditions, a large amount of organic carbon as polyhydroxybutyrate (PHB), a biodegradable polyester, to about 70%–80% of its dry cell weight (Lu et al., 2016 ). PHB is the archetypal polyhydroxyalkanoate (PHA), a class of biopolymers that are biodegradable, can have properties similar to traditional plastics derived from fossil fuels, and can be made from renewable feedstocks. Metabolic engineers now desire to shift this synthetic ability from PHA to other higher value products such as alcohols, methyl ketones, terpenoids, and alkenes (Brigham et al., 2013 ; Crepin et al., 2016 ; Krieg et al., 2018 ; Lu et al., 2012 ; Muller et al., 2013 ). To achieve these metabolic engineering goals, a synthetic biology toolbox, consisting of vectors, genome engineering protocols, and gene expression control strategies, must be developed. Early PHA work helped domesticate C. necator as a laboratory host by establishing stable plasmid transformation techniques and rudimentary recombineering protocols (Reinecke & Steinbüchel, 2009 ; Sato et al., 2013 ). However, progress in metabolic engineering has been slow because of the limited availability of well-established synthetic biology tools, including methods to control gene expression via (i) functional genetic elements such as promoters, both inducible and constitutive, and ribosome binding sites (RBSs), (ii) media composition, and (iii) codon optimization (Schmidt et al., 2023 ). Constitutive promoters characterized to date in C. necator mostly include the native promoters related to PHB synthesis (P phaC1 ), pyruvate metabolism (P pdhE ), acetyl-CoA synthesis (P acoE ), and translation (P rr sC ), or a promoter library created by altering these native promoters (Priefert & Steinbüchel, 1992 ; Delamarre & Batt, 2006 ; Li & Liao, 2015 ). P phaC1 is the most widely used constitutive promoter. These native promoters are relatively weak compared to the heterologous promoters like P lac and its derivatives. P l ac and its derivative promoters can work as constitutive promoters in C. necator because of the absence of native lacI and lacY homologs in the genome (Fukui et al., 2011 ). To have more effective control over the gene expression, inducible promoters are preferred. The arabinose inducible promoter system (AraC/P araBAD ) is the most widely used promoter in engineering studies of C. necator (Fukui et al., 2002 ). It can generate strong transcriptional activity but also has been reported to cause growth defects due to leaky expression (Fukui et al., 2009 ). The rhamnose inducible promoter system (RhaRS/P RhaBAD ) also works well in C. necator with strong induction and lower leaky expression (Alagesan et al., 2018 ). Induction systems using cumate (CymR/P j5-cmt ) and anhydrotetracycline (TetR/P rrsC-tetO ) have been found to have lower leaky expression and cause only slight growth defects (Gruber et al., 2016 ; Li & Liao, 2015 ). Other inducible promoter systems such as acrylate (AcuR/P acuRI ), m -toluic acid (PM/P xyls ), and itaconate (YpItcR/P ccl ) are found to work in C. necator but show high background or leaky expression levels (Alagesan et al., 2018 ; Bi et al., 2013 ; Hanko et al., 2018 ). A few native inducible promoters such as P cbbl , induced in lithoautotrophic growth conditions, P phaP , induced in phosphate limiting conditions, and hydrogenase promoters, P SH and P MBH , induced by glycerol and fructose, respectively, have been identified (Dangel & Tabita, 2015 ; Lutte et al., 2012 ; York et al., 2001 ). Along with the promoter, an RBS also has a significant impact on protein synthesis (Mutalik et al., 2013 ) and can govern product biosynthesis in an engineered microorganism. The level of enzyme production in metabolic pathways can be balanced using RBSs with varied strengths that help to achieve different levels of protein synthesis for individual genes of an operon driven by a single promoter. This helps in maintaining efficient carbon flux, removing bottlenecks, and increasing titers. Another important strategy for improving gene expression is codon optimization, the importance of which is often underappreciated. Codon usage bias is an essential feature of both prokaryotic and eukaryotic genomes (Ikemura, 1985 ; Plotkin & Kudla, 2011 ). Although most of the amino acids can be specified by more than one codon, only a subset of the codons is preferred and frequently used in highly expressed genes (Zhou et al., 2016 ). Codon usage can affect gene expression both at the transcription level and at the translation level. Different organisms have a distinct codon usage pattern that can be summarized using a codon usage table, which is defined by using a set of highly expressed genes as a reference (Sharp & Li, 1987 ). The codon adaptation index (CAI) is then calculated as the geometric mean of the frequencies of all the codons in a gene relative to the most often used synonymous codon, which is calculated from a set of highly expressed genes (Sharp & Li, 1987 ; Jansen et al., 2003 ). CAI is an effective way of predicting the expression level of a gene based on its codon sequence (Sharp & Li, 1987 ). The GC content of the genome is one of the determinants of the codon usage bias (Li et al., 2015 ). Codon optimization of a gene for heterologous expression can be done by selectively replacing codons in the original sequence with preferred codons of the new host as well as maintaining the GC content of the target host without changing the amino acid sequence of the protein. This can be done via two different ways: (i) by replacing all the native codons in the target gene with the most preferred codons in the target host organism or (ii) by replacing the native codons in the gene with the synonymous codon whose frequency in the target host most closely matches the frequency of the original codon in the native host, also known as codon harmonization (Angov et al., 2008 ). As varied CAI values are representative of the varied frequency of the most preferred codons in a target gene, it can be used to study the correlation between codon optimization and gene expression. In this study, we report our characterization of some commonly used promoters in C. necator along with some novel constitutive and inducible promoters. We have also investigated the expression of green and red fluorescent proteins with different codon variants/CAIs in C. necator . Based on the correlation derived from the fluorescent gene expression versus CAI tests, we also investigated the activity of C12-specific thioesterase ( BTE ) from bay laurel (Voelker & Davies, 1994 ) with different codon variants in C. necator and demonstrated dodecanoic acid (C12FA) production. We also outlined key hurdles to large-scale production of fatty acids in C. necator referring to the fact that it harbors several paralogs of β-oxidation genes that remain uncharacterized.",
"discussion": "Discussion Chemolithotrophs, such as C. necator , have the potential to be developed as a platform strain to produce bioproducts derived from a variety of carbon sources as well as carbon dioxide; however, continued genetic tool development is necessary to make full use of its potential as a biosynthetic platform. In this work, we tested and validated a comprehensive set of genetic elements that show meaningful differences to their performance in E. coli , and they can now be used to control the expression of heterologous pathways in C. necator . The Anderson library promoters that we tested had completely different expression strengths in C. necator from what was defined for E. coli . This indicates that the strength of promoters and other functional genetic elements cannot be predicted for C. necator based on their characterization in E. coli , suggesting that both have inherently different transcription machinery given their large evolutionary distance. Marionette promoters have been developed in E. coli for lower background activity, higher sensitivity, and limited crosstalk among the regulators; therefore, they presented a strong initial array of promoters to be tested in a new organism. The different promoters from the Marionette Sensor Collection show smaller fold changes in expression as compared to E. coli , indicating difference in regulation. In this work, eight promoters from the Marionette Sensor Collection were validated and added to the repertoire of characterized promoters in C. necator . In E. coli , P T r c confers roughly 90% stronger expression than P T ac owing to a single base pair addition in the spacer between –35 and –10 elements (Brosius et al., 1985 ). In C. necator , P T ac gives a fivefold change in expression during induction, while P T rc gives a 160-fold change in expression, mirroring the trend from E. coli but to a much larger extent, possibly suggesting that –35 to –10 spacer distance is especially important for C. necator. Additionally, RBS sequence was observed to be important for gene expression in C. necator . We obtained GFP fluorescence that varied over a 50-fold range using varied sequences of SD in the RBS. In this study, we also validated codon optimization and elucidated the relationship between gene expression and CAI for C. necator . Different CAI values indicate the presence of varied frequencies of preferred codons for a specific organism, that is, high CAI denotes higher frequency of more preferred codons and vice versa. CAI tells a general level of frequency calculated on average for a whole open reading frame but does not distinguish between codon optimization (using the most frequent synonymous codon) and harmonization (using the synonymous codon that best matches the frequency of the original codon in the native host). Also, it is worth mentioning that CAI is only an approximate indication of the suitability of the codon usage within a gene and does not consider the distribution or order of codons (Sharp & Li, 1987 ). Nevertheless, we find a strong relationship between the calculated CAI and gene expression for C. necator . The CAI would suggest whether it is of any benefit to chemically synthesize a new gene and to include more preferred codons. We found by fluorescence assay of codon-optimized variants of gfp and mCherry that 0.7–0.8 CAI (calculated using a codon-usage table of C. necator ) is a good range for gene expression in C. necator . This CAI can be used when synthesizing new genes or to infer whether existing sequences will be well expressed in this host. In a previous study, it was found that gene expression had a strong correlation with codon usage bias via translation and could also be a function of GC content, particularly in organisms that prefer G or C over A or T (Zhou et al., 2016 ). However, in Neurospora spp., it was found that at the genome-wide level GC contents (of whole transcripts) showed no or a weak negative correlation with gene expression (Zhou et al., 2016 ), but GC content at the third position of codons (%3GC) was found to show a strong positive correlation with gene expression. To test this phenomenon for C. necator , we calculated the %3GC for the codon-optimized variants of gfp and mCherry and found a strong positive correlation between %3GC content and gene expression (Fig. 4C and D ). However, in contrast to the previous study, we also found a similar correlation between gene expression and total GC content of the reporter genes ( Fig. S4 ). It might be due to the reason that C. necator is an organism with a high GC content (∼66%), whereas Neurospora sp. has ∼50% GC content in its genome and hence preference of either G or C at the third position of the codon can be easily observed in C. necator when compared to the gene expression in Neurospora . As overall GC content is high for C. necator , the possibility of G or C at the first and second positions of the codon is also very high. With the results obtained from our earlier characterization of C. necator tools, we demonstrated C12-fatty acid production in C. necator using two codon-optimized variants of the thioesterase, BTE , and were able to show that the level of codon optimization affects product biosynthesis in an engineered organism. Although we were able to produce ∼100 mg/L of dodecanoic acid, we still observed fatty acid consumption, which led to a decrease in the titers after 24 or 48 hr. As the oleochemical products were being catabolized at later time points even in the strain H16-6895, with β-oxidation operons A0459–A0464 and A1526–A1531 deleted, identification of other β-oxidation genes and deleting them remains critical. In a recent study, it was observed that growing the H16-6895 strain in media containing medium-chain-length fatty acids leads to the expression of many other β-oxidation genes, which was validated through RNAseq (Strittmatter et al., 2023 ). Although the possibilities of developing C. necator as a chassis for the production of fatty acids and their derivatives are currently limited by the large number of possible β-oxidation genes, the comprehensive toolbox of genetic elements that we characterized will be helpful in any metabolic engineering strategy applied in C. necator . Especially, in the current scenario of depleting fossil fuels, the power of C. necator to produce complex chemicals from carbon dioxide cannot be undermined."
} | 3,942 |
38366943 | PMC10926325 | pmc | 6,489 | {
"abstract": "Abstract The Gram-negative betaproteobacterium Cupriavidus necator is a chemolithotroph that can convert carbon dioxide into biomass. Cupriavidus necator has been engineered to produce a variety of high-value chemicals in the past. However, there is still a lack of a well-characterized toolbox for gene expression and genome engineering. Development and optimization of biosynthetic pathways in metabolically engineered microorganisms necessitates control of gene expression via functional genetic elements such as promoters, ribosome binding sites (RBSs), and codon optimization. In this work, a set of inducible and constitutive promoters were validated and characterized in C. necator , and a library of RBSs was designed and tested to show a 50-fold range of expression for green fluorescent protein ( gfp ). The effect of codon optimization on gene expression in C. necator was studied by expressing gfp and mCherry genes with varied codon-adaptation indices and was validated by expressing codon-optimized variants of a C12-specific fatty acid thioesterase to produce dodecanoic acid. We discuss further hurdles that will need to be overcome for C. necator to be widely used for biosynthetic processes.",
"introduction": "Introduction \n Cupriavidus necator , previously known as Ralstonia eutropha and Alcaligenes eutrophus , is a Gram-negative betaproteobacterium. The C. necator genome is distributed into two chromosomes (4 049 965 and 2 912 457 bp) and a mega-plasmid (452 139 bp) with an overall guanine or cytosine (GC) content of 66.36% (Little et al., 2019 ). Cupriavidus necator is a facultative chemolithotroph with an ability to grow on a wide range of substrates, including sugars, lipids, and organic acids via the Entner–Doudoroff (ED) pathway and the tricarboxylic acid (TCA) cycle (Budde et al., 2011 ; Cram, 2008 ; Lu et al., 2013 ; Yan et al., 2003 ). It can also grow autotrophically using hydrogen under aerobic conditions to power assimilation of carbon dioxide via the Calvin–Benson–Bassham (CBB) cycle (Jefffke et al., 1999 ). This metabolic versatility makes C. necator an attractive metabolic engineering chassis, especially for biochemical production from C1 substrates. Cupriavidus necator has been developed industrially to leverage its native ability to store, during nutrient limited conditions, a large amount of organic carbon as polyhydroxybutyrate (PHB), a biodegradable polyester, to about 70%–80% of its dry cell weight (Lu et al., 2016 ). PHB is the archetypal polyhydroxyalkanoate (PHA), a class of biopolymers that are biodegradable, can have properties similar to traditional plastics derived from fossil fuels, and can be made from renewable feedstocks. Metabolic engineers now desire to shift this synthetic ability from PHA to other higher value products such as alcohols, methyl ketones, terpenoids, and alkenes (Brigham et al., 2013 ; Crepin et al., 2016 ; Krieg et al., 2018 ; Lu et al., 2012 ; Muller et al., 2013 ). To achieve these metabolic engineering goals, a synthetic biology toolbox, consisting of vectors, genome engineering protocols, and gene expression control strategies, must be developed. Early PHA work helped domesticate C. necator as a laboratory host by establishing stable plasmid transformation techniques and rudimentary recombineering protocols (Reinecke & Steinbüchel, 2009 ; Sato et al., 2013 ). However, progress in metabolic engineering has been slow because of the limited availability of well-established synthetic biology tools, including methods to control gene expression via (i) functional genetic elements such as promoters, both inducible and constitutive, and ribosome binding sites (RBSs), (ii) media composition, and (iii) codon optimization (Schmidt et al., 2023 ). Constitutive promoters characterized to date in C. necator mostly include the native promoters related to PHB synthesis (P phaC1 ), pyruvate metabolism (P pdhE ), acetyl-CoA synthesis (P acoE ), and translation (P rr sC ), or a promoter library created by altering these native promoters (Priefert & Steinbüchel, 1992 ; Delamarre & Batt, 2006 ; Li & Liao, 2015 ). P phaC1 is the most widely used constitutive promoter. These native promoters are relatively weak compared to the heterologous promoters like P lac and its derivatives. P l ac and its derivative promoters can work as constitutive promoters in C. necator because of the absence of native lacI and lacY homologs in the genome (Fukui et al., 2011 ). To have more effective control over the gene expression, inducible promoters are preferred. The arabinose inducible promoter system (AraC/P araBAD ) is the most widely used promoter in engineering studies of C. necator (Fukui et al., 2002 ). It can generate strong transcriptional activity but also has been reported to cause growth defects due to leaky expression (Fukui et al., 2009 ). The rhamnose inducible promoter system (RhaRS/P RhaBAD ) also works well in C. necator with strong induction and lower leaky expression (Alagesan et al., 2018 ). Induction systems using cumate (CymR/P j5-cmt ) and anhydrotetracycline (TetR/P rrsC-tetO ) have been found to have lower leaky expression and cause only slight growth defects (Gruber et al., 2016 ; Li & Liao, 2015 ). Other inducible promoter systems such as acrylate (AcuR/P acuRI ), m -toluic acid (PM/P xyls ), and itaconate (YpItcR/P ccl ) are found to work in C. necator but show high background or leaky expression levels (Alagesan et al., 2018 ; Bi et al., 2013 ; Hanko et al., 2018 ). A few native inducible promoters such as P cbbl , induced in lithoautotrophic growth conditions, P phaP , induced in phosphate limiting conditions, and hydrogenase promoters, P SH and P MBH , induced by glycerol and fructose, respectively, have been identified (Dangel & Tabita, 2015 ; Lutte et al., 2012 ; York et al., 2001 ). Along with the promoter, an RBS also has a significant impact on protein synthesis (Mutalik et al., 2013 ) and can govern product biosynthesis in an engineered microorganism. The level of enzyme production in metabolic pathways can be balanced using RBSs with varied strengths that help to achieve different levels of protein synthesis for individual genes of an operon driven by a single promoter. This helps in maintaining efficient carbon flux, removing bottlenecks, and increasing titers. Another important strategy for improving gene expression is codon optimization, the importance of which is often underappreciated. Codon usage bias is an essential feature of both prokaryotic and eukaryotic genomes (Ikemura, 1985 ; Plotkin & Kudla, 2011 ). Although most of the amino acids can be specified by more than one codon, only a subset of the codons is preferred and frequently used in highly expressed genes (Zhou et al., 2016 ). Codon usage can affect gene expression both at the transcription level and at the translation level. Different organisms have a distinct codon usage pattern that can be summarized using a codon usage table, which is defined by using a set of highly expressed genes as a reference (Sharp & Li, 1987 ). The codon adaptation index (CAI) is then calculated as the geometric mean of the frequencies of all the codons in a gene relative to the most often used synonymous codon, which is calculated from a set of highly expressed genes (Sharp & Li, 1987 ; Jansen et al., 2003 ). CAI is an effective way of predicting the expression level of a gene based on its codon sequence (Sharp & Li, 1987 ). The GC content of the genome is one of the determinants of the codon usage bias (Li et al., 2015 ). Codon optimization of a gene for heterologous expression can be done by selectively replacing codons in the original sequence with preferred codons of the new host as well as maintaining the GC content of the target host without changing the amino acid sequence of the protein. This can be done via two different ways: (i) by replacing all the native codons in the target gene with the most preferred codons in the target host organism or (ii) by replacing the native codons in the gene with the synonymous codon whose frequency in the target host most closely matches the frequency of the original codon in the native host, also known as codon harmonization (Angov et al., 2008 ). As varied CAI values are representative of the varied frequency of the most preferred codons in a target gene, it can be used to study the correlation between codon optimization and gene expression. In this study, we report our characterization of some commonly used promoters in C. necator along with some novel constitutive and inducible promoters. We have also investigated the expression of green and red fluorescent proteins with different codon variants/CAIs in C. necator . Based on the correlation derived from the fluorescent gene expression versus CAI tests, we also investigated the activity of C12-specific thioesterase ( BTE ) from bay laurel (Voelker & Davies, 1994 ) with different codon variants in C. necator and demonstrated dodecanoic acid (C12FA) production. We also outlined key hurdles to large-scale production of fatty acids in C. necator referring to the fact that it harbors several paralogs of β-oxidation genes that remain uncharacterized.",
"discussion": "Discussion Chemolithotrophs, such as C. necator , have the potential to be developed as a platform strain to produce bioproducts derived from a variety of carbon sources as well as carbon dioxide; however, continued genetic tool development is necessary to make full use of its potential as a biosynthetic platform. In this work, we tested and validated a comprehensive set of genetic elements that show meaningful differences to their performance in E. coli , and they can now be used to control the expression of heterologous pathways in C. necator . The Anderson library promoters that we tested had completely different expression strengths in C. necator from what was defined for E. coli . This indicates that the strength of promoters and other functional genetic elements cannot be predicted for C. necator based on their characterization in E. coli , suggesting that both have inherently different transcription machinery given their large evolutionary distance. Marionette promoters have been developed in E. coli for lower background activity, higher sensitivity, and limited crosstalk among the regulators; therefore, they presented a strong initial array of promoters to be tested in a new organism. The different promoters from the Marionette Sensor Collection show smaller fold changes in expression as compared to E. coli , indicating difference in regulation. In this work, eight promoters from the Marionette Sensor Collection were validated and added to the repertoire of characterized promoters in C. necator . In E. coli , P T r c confers roughly 90% stronger expression than P T ac owing to a single base pair addition in the spacer between –35 and –10 elements (Brosius et al., 1985 ). In C. necator , P T ac gives a fivefold change in expression during induction, while P T rc gives a 160-fold change in expression, mirroring the trend from E. coli but to a much larger extent, possibly suggesting that –35 to –10 spacer distance is especially important for C. necator. Additionally, RBS sequence was observed to be important for gene expression in C. necator . We obtained GFP fluorescence that varied over a 50-fold range using varied sequences of SD in the RBS. In this study, we also validated codon optimization and elucidated the relationship between gene expression and CAI for C. necator . Different CAI values indicate the presence of varied frequencies of preferred codons for a specific organism, that is, high CAI denotes higher frequency of more preferred codons and vice versa. CAI tells a general level of frequency calculated on average for a whole open reading frame but does not distinguish between codon optimization (using the most frequent synonymous codon) and harmonization (using the synonymous codon that best matches the frequency of the original codon in the native host). Also, it is worth mentioning that CAI is only an approximate indication of the suitability of the codon usage within a gene and does not consider the distribution or order of codons (Sharp & Li, 1987 ). Nevertheless, we find a strong relationship between the calculated CAI and gene expression for C. necator . The CAI would suggest whether it is of any benefit to chemically synthesize a new gene and to include more preferred codons. We found by fluorescence assay of codon-optimized variants of gfp and mCherry that 0.7–0.8 CAI (calculated using a codon-usage table of C. necator ) is a good range for gene expression in C. necator . This CAI can be used when synthesizing new genes or to infer whether existing sequences will be well expressed in this host. In a previous study, it was found that gene expression had a strong correlation with codon usage bias via translation and could also be a function of GC content, particularly in organisms that prefer G or C over A or T (Zhou et al., 2016 ). However, in Neurospora spp., it was found that at the genome-wide level GC contents (of whole transcripts) showed no or a weak negative correlation with gene expression (Zhou et al., 2016 ), but GC content at the third position of codons (%3GC) was found to show a strong positive correlation with gene expression. To test this phenomenon for C. necator , we calculated the %3GC for the codon-optimized variants of gfp and mCherry and found a strong positive correlation between %3GC content and gene expression (Fig. 4C and D ). However, in contrast to the previous study, we also found a similar correlation between gene expression and total GC content of the reporter genes ( Fig. S4 ). It might be due to the reason that C. necator is an organism with a high GC content (∼66%), whereas Neurospora sp. has ∼50% GC content in its genome and hence preference of either G or C at the third position of the codon can be easily observed in C. necator when compared to the gene expression in Neurospora . As overall GC content is high for C. necator , the possibility of G or C at the first and second positions of the codon is also very high. With the results obtained from our earlier characterization of C. necator tools, we demonstrated C12-fatty acid production in C. necator using two codon-optimized variants of the thioesterase, BTE , and were able to show that the level of codon optimization affects product biosynthesis in an engineered organism. Although we were able to produce ∼100 mg/L of dodecanoic acid, we still observed fatty acid consumption, which led to a decrease in the titers after 24 or 48 hr. As the oleochemical products were being catabolized at later time points even in the strain H16-6895, with β-oxidation operons A0459–A0464 and A1526–A1531 deleted, identification of other β-oxidation genes and deleting them remains critical. In a recent study, it was observed that growing the H16-6895 strain in media containing medium-chain-length fatty acids leads to the expression of many other β-oxidation genes, which was validated through RNAseq (Strittmatter et al., 2023 ). Although the possibilities of developing C. necator as a chassis for the production of fatty acids and their derivatives are currently limited by the large number of possible β-oxidation genes, the comprehensive toolbox of genetic elements that we characterized will be helpful in any metabolic engineering strategy applied in C. necator . Especially, in the current scenario of depleting fossil fuels, the power of C. necator to produce complex chemicals from carbon dioxide cannot be undermined."
} | 3,942 |
24616719 | PMC3934378 | pmc | 6,491 | {
"abstract": "Although magnetotactic bacteria (MTB) are ubiquitous in aquatic habitats, they are still considered fastidious microorganisms with regard to growth and cultivation with only a relatively low number of axenic cultures available to date. Here, we report the first axenic culture of an MTB isolated in the Southern Hemisphere (Itaipu Lagoon in Rio de Janeiro, Brazil). Cells of this new isolate are coccoid to ovoid in morphology and grow microaerophilically in semi-solid medium containing an oxygen concentration ([O 2 ]) gradient either under chemoorganoheterotrophic or chemolithoautotrophic conditions. Each cell contains a single chain of approximately 10 elongated cuboctahedral magnetite (Fe 3 O 4 ) magnetosomes. Phylogenetic analysis based on the 16S rRNA gene sequence shows that the coccoid MTB isolated in this study represents a new genus in the Alphaproteobacteria ; the name Magnetofaba australis strain IT-1 is proposed. Preliminary genomic data obtained by pyrosequencing shows that M. australis strain IT-1 contains a genomic region with genes involved in biomineralization similar to those found in the most closely related magnetotactic cocci Magnetococcus marinus strain MC-1. However, organization of the magnetosome genes differs from M. marinus .",
"introduction": "Introduction Magnetotactic bacteria (MTB) are a morphologically, metabolically, and phylogenetically diverse group of prokaryotes that share the ability to synthesize intracellular, nano-sized magnetic particles called magnetosomes. Each magnetosome consists of a magnetite (Fe 3 O 4 ) or greigite (Fe 3 S 4 ) crystal enveloped by a lipid-bilayer membrane derived from the cytoplasmic membrane (Bazylinski and Frankel, 2004 ). Magnetosomes are generally organized in linear chains and orient the cell body along geomagnetic field lines while flagella actively propel the cells, resulting in so-called magnetotaxis (Bazylinski and Frankel, 2004 ; Schüler, 2008 ). MTB from the Southern Hemisphere swim antiparallel to the vertical component of the geomagnetic field toward the South and are termed South-seeking MTB (SS-MTB). In contrast, MTB from the Northern Hemisphere swim parallel to the vertical component of the geomagnetic field lines and are predominantly North-seeking (NS-MTB) (Blakemore et al., 1980 ). The inclination of the geomagnetic field lines is believed to direct cells downwards away from toxic concentrations of oxygen in surface waters, thereby helping them locate and maintain an optimal position in vertical gradients which is usually at or near the oxic-anoxic interface (OAI) (Blakemore, 1982 ; Frankel and Bazylinski, 1994 ; Bazylinski and Frankel, 2004 ). However, there are reports of SS-MTB and NS-MTB in both hemispheres (Simmons et al., 2006 ). MTB are considered fastidious microorganisms (Schüler, 2008 ), although there has recently been a considerable increase in available cultures, including the first cultivation of a greigite producer (Lefèvre et al., 2011 ). The recent availability of MTB cultures has contributed to a better characterization of the physiology and biochemistry of these microorganisms. It has also contributed to an improved understanding of the evolution of MTB and of the biomineralization processes involved since differences in the sequences of magnetosome biomineralization genes in different MTB, particularly the mam genes, revealed a strong correlation between these magnetotaxis-related genes and phylogeny based on the 16S rRNA gene (Lefèvre et al., 2013a ). Studies of magnetosome biomineralization genes in uncultivated MTB require unique approaches (Abreu et al., 2011 ; Jogler et al., 2011 ) that do not usually reveal the complete organization of biomineralization genes or genes involved in magnetotactic behavior unless the entire genome is sequenced. Moreover, because not all the magnetosome-related genes may be recognized, a direct correlation with phylogeny based on 16S rRNA gene sequences cannot be made with total accuracy. The most characterized cultivated MTB strains are phylogenetically affiliated with the Alphaproteobacteria and include Magnetococcus marinus strain MC-1 (Bazylinski et al., 2013a ), Magnetovibrio blakemorei strain MV-1 (Bazylinski et al., 2013b ), the magneto-ovoid bacterium strain MO-1 (Lefèvre et al., 2009 ), Magnetospirillum magneticum strain AMB-1, Magnetospirillum gryphiswaldense strain MSR-1, Magnetospirillum magnetotacticum strain MS-1, Magnetospira thiophilla strain MMS-1 (Williams et al., 2012 ) and Magnetospira sp. QH-2 strain 1 (Ji et al., 2014 ). Cultivated strains belonging to Deltaproteobacteria include the sulfate-reducer Desulfovibrio magneticus strain RS-1, (Sakaguchi et al., 2002 ), Candidatus Desulfamplus magnetomortis strain BW-1 (Lefèvre et al., 2011 ) and enrichment cultures of the magnetotactic multicellular prokaryotes Candidatus Magnetoglobus multicellularis (Abreu et al., 2013 ). Two cultivated strains, BW-2 and SS-5, both belonging to Gammaproteobacteria , have also been reported (Lefèvre et al., 2012 ). The biomineralization of magnetosomes is controlled by a set of highly conserved genes in magnetite-producing MTB (Richter et al., 2007 ; Jogler and Schüler, 2009 ; Jogler et al., 2009 ) and, as demonstrated more recently, in greigite-producing MTB as well (Abreu et al., 2011 , 2013 ; Lefèvre et al., 2011 , 2013b ). In some species, the magnetosome biomineralization genes are clustered on a genomic magnetosome island (MAI), which partially supports the hypothesis of horizontal gene transfer (HGT) between various MTB presumably leading to the wide distribution of these genes among members of different phylogenetic groups (Jogler and Schüler, 2009 ; Jogler et al., 2009 ; Abreu et al., 2011 ). However, certain components of typical genomic islands (transposases, t-RNA sequences, integrases), such as those observed in M. magneticum strain AMB-1, M. gryphiswaldense strain MSR-1 and D. magneticus RS-1, are not universally shared within the MAI of all MTB (e.g., M. marinus ; Schübbe et al., 2009 ). Moreover, phylogenetic analysis based on the amino acid sequences of magnetosome proteins from MTB are congruent with the phylogenetic tree based on the 16S rRNA gene sequences of the same microorganisms (Lefèvre et al., 2013a ). Therefore, the evolution and divergence of magnetosome proteins and the 16S rRNA gene occurred similarly, suggesting that magnetotaxis originated monophyletically in the Proteobacteria phylum (Lefèvre et al., 2013a ). Additional genome sequences and culture of MTB species are necessary to understand the evolution of biomineralization in Bacteria . Moreover, the availability of new cultures of MTB allows a better characterization of the physiology and biochemistry of these microorganisms, enabling the correlation of these features to magnetosome formation. Despite being the most prevalent and diverse morphotype of MTB in the environment (Spring et al., 1998 ; Schübbe et al., 2009 ), there are currently only two cultivated strains of magnetotactic cocci: M. marinus strain MC-1 (Bazylinski et al., 2013a ) and the magneto-ovoid bacterium strain MO-1 (Lefèvre et al., 2009 ). The complete genome sequence of the NS-MTB M. marinus has been reported (Schübbe et al., 2009 ), but further study is required to better understand the full diversity of the magnetotactic cocci as well as the ecological function and evolution of magnetosome biomineralization in the Alphaproteobacteria . Here, we describe both the isolation in axenic culture and the characterization of a new magnetotactic coccus, provisionally named Magnetofaba australis strain IT-1 that represents a new genus. We also conducted whole genome sequencing and functional annotation of genes related to magnetosome formation to gain insight into the phylogeny, physiology and biochemistry of this SS-MTB. This strain is the first cultivated SS-MTB, and the genomic data presented here are the first report of biomineralization genes in magnetotactic cocci capable of synthesizing elongated cuboctahedral magnetosomes.",
"discussion": "Discussion The number of MTB isolated in culture has recently increased (from 1978 to 2009, 11 MTB were available in axenic cultures; in 2012 this number was 25; Lefèvre and Long-Fei, 2013 ). However, all cultured MTB were isolated in the Northern Hemisphere and originally showed NS magnetotaxis. This work presents the first isolation of a SS-MTB from the Southern Hemisphere. The new isolate is phylogenetically affiliated with the Alphaproteobacteria class of the Proteobacteria phylum, a division that contains almost all known Fe 3 O 4 -producing MTB (DeLong et al., 1993 ; Spring et al., 1998 ), and clearly represents a new genus based on 16S rRNA gene sequence similarities. This new coccus represents a third phylogenetic group of MTB occurring in the Itaipu Lagoon (Spring et al., 1998 ). M. australis strain IT-1 is distinct from all the other cultivated magnetotactic cocci examined to date because of its South-seeking polar magnetotactic behavior, it has “faba bean” cell morphology and elongated cuboctahedral magnetite magnetosomes. Based on its 16S rRNA gene sequence, M. australis is more related to an uncultured magnetotactic coccus found in the intertidal sediments of the Yellow Sea in China (93% similarity; Zhang et al., 2012 ). This uncultured bacterium also shows a bean-like morphology and produces magnetite magnetosomes (Zhang et al., 2012 ). However, magnetosome crystal morphology, size, shape factor, magnetosome number and swimming speed in M. australis are different from the coccus described by Zhang et al. ( 2012 ). The close phylogenetic relationships may not be significantly associated to the biomineralization genes, which may result in variations in the regulation of crystal morphology between these MTB. Hopefully, physiological studies and genomic analysis of these MTB will result in information that advances the understanding of biomineralization in bean-like magnetotactic cocci. Magnetofaba australis strain IT-1 has a swimming speed similar to that observed in strain MO-1 (Lefèvre et al., 2009 ), higher than speeds found in other magnetotactic cocci (Zhang et al., 2012 ). Possibly, a highly coordinated flagella rotation is necessary to allow this high swimming speed. The high swimming speed would be advantageous for the survival of M. australis strain IT-1 because it would enable the cell to escape quickly from unfavorable environment conditions. Most cells of M. australis strain IT-1 (over 80%) has a South-seeking behavior when observed in hanging drop assays under oxic conditions, but we have also found North-seeking cells in the culture flasks. Further studies are necessary to compare the swimming behavior and orientation of magnetotactic cocci, along with their flagellar apparatus at the genetic and structural levels. We believe that such studies can now be performed because of the available SS-MTB cultures. The role of biomineralization and magnetotaxis genes in MTB is not only key in the determination of how magnetosomes are formed in MTB but also important in understanding the evolution of magnetotaxis (Lefèvre and Bazylinski, 2013 ). Although several recent reports have addressed this issue (Lefèvre et al., 2013a , b ), only a relatively small number of MTB species have been considered thus far. However, advances in the culturing of new strains promises to improve the low number of species available for evolutionary studies. M. australis strain IT-1 is the first MTB isolated in axenic culture that produces cuboctahedral magnetite magnetosomes whose magnetosome biomineralization genes have been sequenced. New data on the magnetosome biomineralization genes of coccoid or ovoid MTB increases our understanding of the biomineralization processes in MTB in general. For example, M. marinus and M. australis share several hypothetical proteins, not found in other MTB that may have key functions in biomineralization or magnetotaxis like the hypothetical protein between MamE and MamK (locus 02790), the hypothetical protein between MmsF and the Amino acid carrier protein (locus 02801), the Amino acid carrier protein (locus 02803), a hemerythrin-like (locus 02811), and a ferritin-like (locus 02816). The analysis of the putative functions of mam genes is also important in the interpretation of the evolution of magnetotaxis. Variations in both the order and sequence of mam genes between M. australis and the closely related M. marinus could explain differences between magnetosome crystal morphology in the two species. The MamC predicted protein sequence of M. australis is more similar to that of M. magneticum strain AMB-1, which is particularly interesting because cultivated Magnetospirillum species described thus far produce cuboctahedral magnetite crystals that are not elongated (Amann et al., 2007 ). Scheffel et al. ( 2008 ) showed that the protein MamC and other proteins in the same operon ( mam GFDC) are not essential for magnetosome formation but are involved in controlling crystal size and morphology in M. gryphiswaldense . In M. australis, mamC is organized in a mam CXZ operon, similar to M. blakemorei . The other proteins involved in the size and shape of magnetosomes (MamD, MamF, Mms6, and MmsF) are more closely related to those found in M. marinus . Therefore, the fact that M. australis MamC is related to cuboctahedral magnetite-producing bacteria suggests that this protein might be responsible for crystal morphology in this case. Additionally, based on the similarity of mam XZC gene organization between M. australis and M. blakemorei , we speculate that gene organization and/or preferential expression of mam CXZ could be involved in crystal elongation. MmsF has been shown to be involved in the geometry of magnetosome maturation, as the deletion of mmsF resulted in elongated magnetosomes in M. magneticum strain AMB-1 (Murat et al., 2012 ). However, we did not identify a close similarity between MmsF from M. australis strain IT-1 and other MTB that synthesize elongated octahedral crystals. The expression level of MmsF may influence crystal morphology, which could explain how closely related mam genes from different species (i.e., M. australis and M. marinus ) produce magnetosomes with different characteristics. Variation in the expression level of the mam GFDC operon in M. gryphiswaldense resulted in crystals exceeding the size of those of the wild-type (Scheffel et al., 2008 ). The absence of mamT in M. australis strain IT-1 reveals a new group of 19 genes common to cultivated magnetotactic Alphaproteobacteria : mamA, B, C, D, E, F, H, I, K, L, M, N, O, P, Q, R, S, X and Z , in addition to the mms6 and mmsF genes. Although mamT is present in the Alpha - and Deltaproteobacteria , it is not essential for biomineralization. Proteins with similar function (MamP or MamE) are likely sufficient to control the balance between Fe 2+ and Fe 3+ in the magnetosome. In M. magneticum (Murat et al., 2010 ) and M. gryphiswaldense (Lohβe et al., 2011 ) mamT is not essential for magnetosome synthesis. Considering that both M. australis strain IT-1 and M. marinus strain MC-1 have a common magnetotactic ancestor and that biomineralization proteins apparently evolved together in both strains, it is reasonable to assume that a common ancestor exists among all freshwater and marine MTB from the Magnetococcales order. No non-MTB belonging to the Magnetococcales order has ever been reported, but this fact does not preclude HGT among Alphaproteobacteria because strains phylogenetically closer to Magnetospirillum do not have the magnetotactic phenotype (Jogler and Schüler, 2009 ). Thus, magnetosome biomineralization genes common to all MTB ( mamABEIKMPQ ) might have been acquired from an ancestor common to all MTB (Abreu et al., 2011 ; Lefèvre et al., 2013a ). However, genes such as mamCDF, mamL, mamXZ, mms6 , and mmsF could have been acquired by descent of magnetotactic Alphaproteobacteria and magnetotactic cocci, which appear to emerge as the most basal lineage of the Alpha - and Gammaproteobacteria (Singer et al., 2011 ; Lefèvre and Bazylinski, 2013 ). mamG, mamR, mamV, mamU, and mamY genes were likely acquired recently by Magnetospirillum species, given that the magnetotactic cocci studied so far, M. marinus strain MC-1 and M. australis strain IT-1, do not contain these genes. Differences observed in the biomineralization genes between M. australis strain IT-1, M. marinus strain MC-1 and the other Alphaproteobacteria are possibly a result of gene rearrangements, deletions or insertions of new genes through the evolution or a post-acquisition of the biomineralization genotype among MTB. Culture and sequencing of new species of magnetotactic cocci from freshwater or marine water are needed to improve the understanding the evolutionary events that occurred in the Alphaproteobacteria and magnetotactic cocci and will more precisely define the Magnetococcaceae family in the Magnetococcales order as either the earliest diverging order in the Alphaproteobacteria class or as a new class of Proteobacteria , as proposed by Singer et al. ( 2011 ). M. australis strain IT-1 is now the third cultivated magnetotactic coccus that represents a second new genus in the Magnetococcaceae family and is the first cultivated SS-MTB."
} | 4,393 |
30488036 | PMC6246695 | pmc | 6,492 | {
"abstract": "Metabolism is one of the attributes of life and supplies energy and building blocks to organisms. Therefore, understanding metabolism is crucial for the understanding of complex biological phenomena. Despite having been in the focus of research for centuries, our picture of metabolism is still incomplete. Metabolomics, the systematic analysis of all small molecules in a biological system, aims to close this gap. In order to facilitate such investigations a blueprint of the metabolic network is required. Recently, several metabolic network reconstructions for the model organism Caenorhabditis elegans have been published, each having unique features. We have established the WormJam Community to merge and reconcile these (and other unpublished models) into a single consensus metabolic reconstruction. In a series of workshops and annotation seminars this model was refined with manual correction of incorrect assignments, metabolite structure and identifier curation as well as addition of new pathways. The WormJam consensus metabolic reconstruction represents a rich data source not only for in silico network-based approaches like flux balance analysis, but also for metabolomics, as it includes a database of metabolites present in C. elegans , which can be used for annotation. Here we present the process of model merging, correction and curation and give a detailed overview of the model. In the future it is intended to expand the model toward different tissues and put special emphasizes on lipid metabolism and secondary metabolism including ascaroside metabolism in accordance to their central role in C. elegans physiology.",
"conclusion": "Conclusion We have shown that there is discrepancy between the model metabolites and metabolites detected by different metabolomics approaches. Specific metabolites from the WormJam model have not been detected so far. Although several are simply not stable enough or have a high turnover rate will be probably never be detected, others might be not accessible with current approaches to low concentrations. On the other side, many detected and reported metabolites could not be found in the model. This is of major interest, since either the identifications are wrong or the model is incomplete. Several examples shown in this paper highlight that we might actually miss several aspects in the metabolism and biology of C. elegans . Based on some of our findings the community as whole can now start to explore new metabolic pathways or links to other aspects and their link to metabolism (e.g., epigenetics).",
"introduction": "Introduction Metabolism is a key mediator of the biological processes underlying living organisms. Metabolic changes are at the frontline of the cellular response to environmental or physiological changes, and altered metabolism is a hallmark and driver of the pathologies accompanying conditions such as aging and cancer (Finkel, 2015 ). Nevertheless, our understanding of all the complexities of metabolic processes in different conditions remains incomplete. The model organism Caenorhabditis elegans is emerging as a key resource for the study of metabolism in multicellular organisms, as while it shares much of its central metabolic pathways with humans, it is easy to culture in laboratory conditions, can be grown in large populations of isogenic individuals in order to study purely environmental differences, and has a short life span enabling rapid longitudinal data acquisition even across multiple generations (Tissenbaum, 2015 ; Maglioni and Ventura, 2016 ; Shen et al., 2018 ). Metabolomics evaluates the metabolic state of a given sample by measuring the concentrations of a large number of small molecules simultaneously, allowing metabolic differences between different conditions to be evaluated. Advances in metabolomics involve the development of methods and standards to more accurately detect, distinguish and quantify small molecules from a broad range of pathways in increasingly smaller quantities of sample material. In C. elegans the currently detectable metabolome encompasses >1,000 distinct metabolites (not counting lipids), but this is continuously evolving, and the estimated size of the full metabolome under ordinary conditions may be even > 10,000 distinct molecules based on recent metabolomics work uncovering new metabolites (Artyukhin et al., 2018 ). Whole-genome metabolic reconstructions are in silico representations of all the metabolic reactions in a given organism as a network of metabolites and the reactions in which they are produced or consumed, with associated genes. They are representations of metabolic knowledge in a given organism abstracted to the level of a single cell. These reconstructions allow sophisticated mathematical analysis techniques to make predictions about the dynamic intracellular fluxes under different conditions. In particular, Flux Balance Analysis (FBA) and its derivatives permit the use of whole-genome reconstructions together with experimental molecular phenotypes and biological objective functions in order to obtain optimal fluxes landscapes at steady-states (O'Brien et al., 2015 ). One can then observe how these landscapes evolve upon mutations and in different environments, or to predict drug targets and biomarkers. For C. elegans , several such metabolic reconstructions exist (Büchel et al., 2013 ; Gebauer et al., 2016 ; Yilmaz and Walhout, 2016 ; Ma et al., 2017 ) (see Figure 1 ). We have been working with the whole community to reconcile and develop a single model, representing the best consensus of known metabolism in C. elegans . This community effort has been christened “WormJam” (Hastings et al., 2017 ). Figure 1 Overview of published C. elegans metabolic reconstructions and their relation to consensus models described in this manuscript. Metabolic reconstructions and metabolomics characterization in different organisms typically proceed independently; the metabolites that are included in a metabolic reconstruction may be different to the metabolites that can confidently be identified in cutting-edge metabolomics investigations in that organism. This is largely due to the different sources of technical complexity and opportunities in the different types of investigation. The gap that ensues between model and metabolomics is one of the specific areas that we are aiming to address with the WormJam effort, which includes participants both from the metabolomics and metabolic modeling communities. In this paper, we describe the work we have done to develop and extend the consensus model, including merging pre-existing models and curation of novel pathways for C. elegans , and the work that is currently ongoing to bridge between the model and the metabolites that can be detected with metabolomics."
} | 1,700 |
26692227 | PMC4687164 | pmc | 6,493 | {
"abstract": "Background Clostridium autoethanogenum is an acetogenic bacterium capable of producing high value commodity chemicals and biofuels from the C1 gases present in synthesis gas. This common industrial waste gas can act as the sole energy and carbon source for the bacterium that converts the low value gaseous components into cellular building blocks and industrially relevant products via the action of the reductive acetyl-CoA (Wood-Ljungdahl) pathway. Current research efforts are focused on the enhancement and extension of product formation in this organism via synthetic biology approaches. However, crucial to metabolic modelling and directed pathway engineering is a reliable and comprehensively annotated genome sequence. Results We performed next generation sequencing using Illumina MiSeq technology on the DSM10061 strain of Clostridium autoethanogenum and observed 243 single nucleotide discrepancies when compared to the published finished sequence (NCBI: GCA_000484505.1), with 59.1 % present in coding regions. These variations were confirmed by Sanger sequencing and subsequent analysis suggested that the discrepancies were sequencing errors in the published genome not true single nucleotide polymorphisms. This was corroborated by the observation that over 90 % occurred within homopolymer regions of greater than 4 nucleotides in length. It was also observed that many genes containing these sequencing errors were annotated in the published closed genome as encoding proteins containing frameshift mutations (18 instances) or were annotated despite the coding frame containing stop codons, which if genuine, would severely hinder the organism’s ability to survive. Furthermore, we have completed a comprehensive manual curation to reduce errors in the annotation that occur through serial use of automated annotation pipelines in related species. As a result, different functions were assigned to gene products or previous functional annotations rejected because of missing evidence in various occasions. Conclusions We present a revised manually curated full genome sequence for Clostridium autoethanogenum DSM10061, which provides reliable information for genome-scale models that rely heavily on the accuracy of annotation, and represents an important step towards the manipulation and metabolic modelling of this industrially relevant acetogen. Electronic supplementary material The online version of this article (doi:10.1186/s12864-015-2287-5) contains supplementary material, which is available to authorized users.",
"conclusion": "Conclusions The whole genome sequence of C. autoethanogenum presented here-in represents a correction of the sequencing errors present in the previously published closed genome sequence generated primarily from an early iteration of PacBio sequencing technology. It was annotated via an automated pipeline and further curated manually to ensure the quality of annotation. This has resulted in the generation of the most accurate closed-genome sequence of the industrially relevant acetogen C. autoethanogenum to date and is an important step forward for academic institutions and industrial companies that wish to study and / or manipulate this organism for the purposes of high-value chemical production.",
"discussion": "Discussion The current greatest technical challenge for creating single closed whole genome sequences is the presence of long stretches of repetitive DNA within those sequences, which hinders the assembly of shorter DNA reads into larger scaffolds and finished whole genome sequences. Many of the current technologies, including Illumina MiSeq, Ion Torrent and 454 GS FLX+ Titanium give read lengths in the region of 100–1000 base pairs, which compared with repetitive sequence lengths commonly found in bacteria of 5–7 Kb [ 44 ], is insufficient to create a single closed sequence without manual finishing, which can be costly and time-consuming. The PacBio RS II sequencing system, used by Brown et al. [ 16 ] for generation of a closed WGS of C. autoethanogenum , was until recently the only long-read single-molecule sequencer available, and is capable of simplifying the process of genome assembly due to greatly increased read lengths [ 45 ]. Reads in excess of 15 Kb have been reported utilising the PacBio system [ 45 ], compared with Illumina MiSeq generating average read lengths of 250 base pairs in this study. Thus, the utilisation of PacBio systems for the generation of closed WGS’s from organisms that do not currently have such a sequence is highly advantageous in terms of both time and cost. However, it has been found that the error rate for PacBio sequencing is relatively high when compared to Illumina sequencing data [ 46 , 47 ], especially concerning homopolymer regions between two and fourteen base pairs in length [ 48 ]. In our study, we demonstrated a heavy bias towards under-calling of homopolymer regions, which in this example led to ~240 erroneous deletions from the ~4.35 Mb genome of C. autoethanogenum . This high error rate is in-line with previous findings on long-read assemblies [ 45 ], and in recent years improvements to the algorithms used by PacBio have had the consequence of reducing the overall error rate significantly. However, it may still be the case that the PacBio system should ideally be used in conjunction with other forms of sequencing following PacBio assembly, such as Illumina MiSeq and Sanger sequencing, to ensure accuracy of the data, certainly for assemblies performed with earlier iterations of the PacBio technology, as is the case with the dataset in question here. The recently released Oxford Nanopore technology has potential to further revolutionise the field of genome sequencing over the coming years, allowing label-free, ultra-long reads (10 4 –10 6 bases), with the capability for extremely high throughput, and low material requirement [ 49 ]."
} | 1,469 |
24976386 | PMC4074835 | pmc | 6,495 | {
"abstract": "Despite the extensive use of in-vitro models for neuroscientific investigations and notwithstanding the growing field of network electrophysiology, all studies on cultured cells devoted to elucidate neurophysiological mechanisms and computational properties, are based on 2D neuronal networks. These networks are usually grown onto specific rigid substrates (also with embedded electrodes) and lack of most of the constituents of the in-vivo like environment: cell morphology, cell-to-cell interaction and neuritic outgrowth in all directions. Cells in a brain region develop in a 3D space and interact with a complex multi-cellular environment and extracellular matrix. Under this perspective, 3D networks coupled to micro-transducer arrays, represent a new and powerful in-vitro model capable of better emulating in-vivo physiology. In this work, we present a new experimental paradigm constituted by 3D hippocampal networks coupled to Micro-Electrode-Arrays (MEAs) and we show how the features of the recorded network dynamics differ from the corresponding 2D network model. Further development of the proposed 3D in-vitro model by adding embedded functionalized scaffolds might open new prospects for manipulating, stimulating and recording the neuronal activity to elucidate neurophysiological mechanisms and to design bio-hybrid microsystems.",
"discussion": "Discussion We presented a new experimental in-vitro platform constituted of 3D engineered neuronal cultures coupled to MEAs for network electrophysiology. The use of microbead scaffolds was specifically optimized and tailored to be integrated with planar MEA in order to investigate the functional properties (i.e. electrical activity) of 3D hippocampal networks that were never presented before. We carefully compared the spontaneous and evoked electrophysiological activity of 3D cultures with the traditional network dynamics from homogeneous-uniform 2D cultures. First, in the developed 3D engineered hippocampal assemblies, cell morphology, connectivity (see also Figure 2 ) and, potentially, extracellular matrix, are much closer to the in-vivo situation 1 4 28 . Second, we observed striking differences with respect to uniform 2D networks, and we quantified evidences of a wide dynamic repertoire of activity patterns possibly recapitulating various activity dynamics of in-vivo brain regions 29 30 . Quasi-synchronous network bursting activity of high density 2D neuronal networks 9 18 31 32 was maintained in 3D cultures but global frequency was decreased (<1 Hz) and global synchrony was lost (spatial segregation of bursting - see Figure 3 ). Even if the recorded dynamics were very different, the average connectivity (i.e., 600 connections per neuron) was found to be the same in both 2D and 3D networks and the proportion between GABAergic and glutammatergic populations was likely to remain similar in both 2D and 3D models (with about a 1:5 ratio, see also Supplementary – Fig. S5 a–b ) 24 33 34 . In the 3D architecture, we can speculate that, with the enhanced dendritic development, the resulting network was more complex, and that cell morphology might also contribute to such increased complexity. We can hypothesize that the different GABAergic interneuron populations could gain a considerable increase of the functional surface (volume) compared to those grown in the 2D cultures. These possible wider and longer interactions with other neurons could contribute to desynchronize and temporally differentiate the network activity, hence producing bursting activities confined to sub-populations and global asynchronous activity patterns 35 . The addition of BIC induced in the 3D network model a more synchronized state (similar to what happen in uniform 2D networks), indicating a possible more synchronized activity in the upper layers, and thus determining a reduced impact of the electrophysiological signals coming from above (see Supplementary – Fig. S2 ). Along this direction, a systematic study of the spontaneous activity of 3D networks during development with the specific labeling of interneurons, could contribute to the understanding of the significance of the balance between excitation and inhibition with specific reference to the role of GABAergic populations 35 36 37 , distribution of hubs 38 and network synchronization 39 . Although such variable spontaneous activity together with the analysis of signal propagation is a convincing hallmark of the inherent contribution of the 3D structure to the network dynamics, the ultimate evidence that we are playing with actual 3D functionally interconnected neuronal networks is given by the results obtained upon the application of an external electrical stimulation. 2D interconnected networks stimulated by different electrodes, showed a relatively fast and synchronized response depending on the stimulating site. The site of stimulation causes an almost uniform response (for the high-connectivity of the network) in all the active sites (see Supplementary – Fig. S6a–b ). The response time is relatively fast and confined below the first 250 ms ( Figure 7d ). 3D networks stimulated from the bottom layer exhibit various types of synchronized response (see also Supplementary – Fig. S6c–d ) often generating a uniformly distributed response delay ( Figure 7e ). In general, the evoked activity can be thought as the combination of two types of response. The first type is generated by subsets of neurons in the read-out layer that are connected with subgroups of neurons in the same layer. The second type of response is generated by subsets of neurons in the read-out layer connected with sub-networks of neurons in the upper layers. The first type of response is fast; the second type is slow. Because of the different described types of activated circuits, the network response is not globally synchronized. When stimulating 3D networks from the top layer, the stimulation needs to be transmitted, through different neurons of the various layers, to neurons of the read-out layer coupled to the MEA generating time delays greater than 500 ms (cf. Figure 7f ). These long delays could be explained by a less effective connectivity in the z-direction and by a possible decrease in the synaptic density from the bottom to the top layer. The administration of synaptic blockers to the culture impairs the recording of evoked responses in the bottom layer, thus demonstrating that activity propagates through synaptic reverberation and amplification in the different layers. The enhanced variability in the time delays of the evoked responses (cf. Figure 7h ) is an additional proof we are facing heterogeneous functionally interconnected 3D cultured networks. In summary, the developed experimental paradigm can constitute the basis for a next class of experimental models to study neurophysiology in in-vitro systems, for investigating the computational properties of neuronal networks, and for developing new bio-hybrid microsystems."
} | 1,746 |
26859772 | PMC5029162 | pmc | 6,496 | {
"abstract": "Over the past decade, high-throughput short-read 16S rRNA gene amplicon sequencing has eclipsed clone-dependent long-read Sanger sequencing for microbial community profiling. The transition to new technologies has provided more quantitative information at the expense of taxonomic resolution with implications for inferring metabolic traits in various ecosystems. We applied single-molecule real-time sequencing for microbial community profiling, generating full-length 16S rRNA gene sequences at high throughput, which we propose to name PhyloTags. We benchmarked and validated this approach using a defined microbial community. When further applied to samples from the water column of meromictic Sakinaw Lake, we show that while community structures at the phylum level are comparable between PhyloTags and Illumina V4 16S rRNA gene sequences (iTags), variance increases with community complexity at greater water depths. PhyloTags moreover allowed less ambiguous classification. Last, a platform-independent comparison of PhyloTags and in silico generated partial 16S rRNA gene sequences demonstrated significant differences in community structure and phylogenetic resolution across multiple taxonomic levels, including a severe underestimation in the abundance of specific microbial genera involved in nitrogen and methane cycling across the Lake's water column. Thus, PhyloTags provide a reliable adjunct or alternative to cost-effective iTags, enabling more accurate phylogenetic resolution of microbial communities and predictions on their metabolic potential.",
"introduction": "Introduction Enabled by the advent of polymerase chain reaction (PCR) in 1983, the small subunit (SSU or 16S) ribosomal RNA gene has become the most widely used marker for performing phylogenetic analyses allowing the classification of novel bacterial and archaeal taxa. In addition to providing taxonomic information, cultivation-independent 16S rRNA gene profiling has transformed the study of microbial ecology and human health, enabling quantitative insights into microbial community diversity in natural and engineered ecosystems including our own bodies (e.g. Giovannoni et al. , 1990 ; Muyzer et al. , 1993 ; Janssen, 2006 ; Turnbaugh et al. , 2007 ; Bolhuis and Stal, 2011 ; Kembel et al. , 2012 ; Yatsunenko et al. , 2012 ). Expanding exponentially over the past three decades, the public 16S rRNA gene databases have however been faced with the challenge of accurately placing sequences into a given reference tree. This challenge is particularly prominent for environmental 16S rRNA gene sequences, which are marked by high numbers of novel taxa without cultivated representatives. Massive individual and institutional efforts have been made to standardize classification of environmental 16S rRNA sequences through dedicated database development and custom analysis tools ( Giovannoni et al. , 1990 ; Muyzer et al., 1993 ; Desantis et al. , 2006 ; Janssen, 2006 ; Turnbaugh et al. , 2007 ; Bolhuis and Stal, 2011 ; Pagani et al. , 2011 ; Pruitt et al. , 2011 ; Kembel et al. , 2012 ; Pruesse et al. , 2012 ; Quast et al. , 2012 ; Yatsunenko et al. , 2012 ; Fish, 2013 ). Despite these improvements, reference sequences with low read accuracy, chimeric sequences and partial rRNA gene sequences with reduced phylogenetic resolution generated on short-read sequencing platforms such as 454 and Illumina remain problematic, resulting in incorrect or less accurate classification of environmental sequences. Although read lengths on these platforms continue to improve, only full-length (FL) or near FL 16S rRNA sequences have been proven adequate for tree construction necessary for precise phylogenetic placement ( Kim et al. , 2011 ; Yarza et al. , 2014 ). This reality poses a serious analytic challenge given that the majority of contemporary 16S rRNA sequence information emanates from short-read sequencing platforms ( Tringe and Hugenholtz, 2008 ; Yarza et al. , 2014 ). Environmental 16S rRNA gene profiles were first performed using Sanger sequencing, which could provide accurate, near FL sequences. However, this process remains costly and at low throughput, involving the cloning of PCR products before paired-end sequencing. Thus, Sanger-based profiles generally involved relatively few samples with sequence information for less than tens to hundreds of clones per sample ( Giovannoni et al. , 1990 ). Today, microbial community profiles generated on the Sanger platform are scarce and unlikely to capture complete community diversity as estimated from richness analyses, rendering them adjunctive to short-read sequencing data sets ( Youssef et al. , 2009 ). The first commercially available next-generation sequencer, the Roche/454 FLX pyrosequencer, offered high-throughput technology at roughly 1/10th the cost of Sanger sequencing. To adopt this technology for microbial community profiling, Sogin et al. (2006) PCR-amplified the V6 variable region of the bacterial 16S rRNA gene and generated ~118 000 ‘16S pyrotags' averaging 100 bp read length in a single run, orders of magnitude more sequences than any previous Sanger study ( Sogin et al. , 2006 ). The use of barcodes enabled multiplexing of different samples within a single run further increasing the statistical power of the 454 platform ( Parameswaran et al. , 2007 ; Hamady et al. , 2008 ). Lazarevic et al. (2009) ported this sequencing paradigm to the Illumina platform (Illumina, Inc., San Diego, CA, USA) by amplification and sequencing of the V5 loop region, providing even greater depth of coverage and a reduced price point. Currently, the most common approach for microbial community profiling uses V4, V3–V4 or V4–V5 primers on Illumina platforms to generate the so-called Illumina V4 16S rRNA gene sequences (iTags) averaging ~250–430 bp read length ( Caporaso et al. , 2012 ; Takahashi et al. , 2014 ; Parada et al. , 2015 ). Indeed, most 16S rRNA gene sequences in GenBank were generated on Illumina platforms because of their economy of scale (>10 million reads in a single MiSeq run) and high base-calling accuracy ( Lazarevic et al. , 2009 ; Claesson et al. , 2010 ). Despite the ease and quantitative power of short-read amplicon sequencing, the representation of microbial community diversity at different taxonomic levels based on partial 16S rRNA gene sequences has been received with skepticism, as the specific combination of primer choice, read length, environmental source, reference database and assignment method influence both taxon abundance estimates and placement precision on the tree of life ( Soergel et al. , 2012 ; Yarza et al. , 2014 ). Optimal primer selection for short-read sequencing requires comparisons with other data sets and recruitment to FL 16S rRNA gene sequences to assign accurate taxonomy to incomplete sequences ( Liu et al. , 2007 ; Walters et al. , 2011 ; Soergel et al. , 2012 ). Pacific Biosciences (PacBio) has recently developed a long-read sequencing technology, which for the first time in sequencing history has the capacity to cost-effectively sequence FL 16S rRNA genes at comparatively high throughput. A resurgence of FL sequences used as ‘gold standards' has the potential to yet again transform microbial community studies, increasing the accuracy of taxonomic assignments for known and novel branches in the tree of life on previously unobtainable scales. Here we directly address current limitations associated with partial 16S rRNA gene sequencing, through the application of PacBio's long-read, single-molecule real-time (SMRT) sequencing technology for high-resolution phylogenetic microbial community profiling. As PacBio sequencing performance has improved in recent years, its average read lengths now exceed 8 kb at ~87% read accuracy ( Koren and Phillippy, 2015 ). In theory, such read lengths should provide high-quality sequences for 1.5 kb 16S rRNA gene amplicons via circular consensus sequencing, yet this method has only been used for a few environmental surveys ( Babauta et al. , 2014 ; Mosher et al. , 2014 ). To test and validate this approach, we generated PacBio shotgun sequences as well as PacBio FL (PhyloTags) and iTags from a defined mock community of 23 cultivated bacterial strains ( Supplementary Table 1 ). We then used this same approach to assess the microbial diversity of Sakinaw Lake on the Sunshine coast of British Columbia, Canada, a meromictic lake rich in candidate phyla.",
"discussion": "Discussion We here demonstrate that PhyloTags do not need technical replication and strongly correlate with shotgun metagenome sequences. PhyloTags overall showed comparable results to traditional iTag sequences for the relatively simple mock community, as well as the more complex environmental sample with PCR and/or primer bias being likely the chief driver for differences in community profiles between platforms. A comparison between FL and in silico generated partial amplicon data in the environmental sample, however, showed that multiple phyla were completely missed by short-read sequences, community structure was significantly shifted at the genus level and that several dominant microbial genera across the water column of Sakinaw Lake could only be resolved via PhyloTags. The 16S rRNA gene surveys have been radically changing our view of microbial evolution and diversity. FL 16S rRNA gene sequences are known to be more effective than partial gene sequences in inferring phylogenetic affiliation among and between microbial community members ( Liu et al. , 2007 ; Walters et al. , 2011 ; Soergel et al. , 2012 ). Hence, near FL sequences generated on the Sanger sequencing platform have remained the gold standard for a long time. However, while Sanger sequencing is associated with the trouble and expense of low-throughput cloning into host cells, PacBio has recently been offering a cost-effective, high-throughput alternative that produces long reads (2–15 kb), which can be used to generate FL 16S rRNA gene sequences. Few 16S rRNA gene sequence studies have taken advantage of the long reads that the PacBio platform offers. Although recently Babauta et al. (2014) sequenced the V1–V3 region of a microbial mat community to successfully track composition changes during enrichment for microelectrode interactions, Mosher et al. (2014) concluded that 16S rRNA gene sequences >1400 bp allowed enhanced phylogenetic and taxonomic resolution to the species level in environmental samples compared with the 454 platform. Our study complements these efforts by evaluating pros and cons for various types of community analyses, including known simple and unknown complex communities with phyla abundantly and minimally represented in the database. It is the first benchmark study using FL 16S rRNA gene sequences generated on the PacBio platform and provides a comprehensive comparison between current iTag and emerging PhyloTag 16S rRNA sequencing paradigms, highlighting the impact of both short- and long-read sequencing platforms on microbial community profile interpretations. Our benchmarked 16S rRNA gene sequence analysis pipeline for use with SMRT sequencing technology was consistently reproducible. Although composition analysis of the mock community exhibited a marginally higher correlation between shotgun data and iTags, analysis of environmental samples indicated superior phylogenetic resolution of PhyloTags. We attribute the slightly higher correlation between iTags and shotgun sequence data to lower primer/PCR bias in the V4 primers and the resulting shorter amplicons, as compared with the FL amplification products. Moreover, the mock community was composed of few, mostly distantly related organisms, which are well represented in the 16S rRNA gene databases. Therefore, accurate taxonomic placement was not problematic for either FL or partial 16S rRNA gene sequences. The resolving power of PhyloTags in our data sets was more apparent in samples with complex microbial communities and when reference sequences in the database were scarce. Misclassifications and inability to classify sequences due to read length alone impaired interpretation of community function inferred from community diversity information at different taxonomic levels. From species to phylum, ~12–25% more FL than V4 sequences were unambiguously classified. Thus, FL sequences provide a more complete picture of community composition needed to accurately link microbial players with important biogeochemical cycles within the given ecosystem. Indeed, FL sequences enabled the identification of abundant genera known to participate in methane and nitrogen cycling in Sakinaw Lake, which were under-represented in the V4 sequences. Since the generation of PhyloTags does not require amplification during the sequencing step, sequencing platform-specific bias is predicted to be generally reduced compared with other platforms. PhyloTag sequencing also offers the highest contig accuracy without discrimination against GC-rich or -poor regions, which further reduces bias in amplicon-based profiling ( Quail et al. , 2012 ). The raw error rate in PacBio sequences is ⩽15% and dominated by indels, which are more difficult to correct than substitutions (B Bushnell, personal communication). For this study, shorter reads were used representing the consensus of many passes over the same molecule. These consensus reads had an error rate around 0.5% relative to the original genomic sequence. This is adequate to confidently assign OTUs at the species level using a 97% identity threshold, as two reads with 0.5% error from the same sequence will retain 99% identity. However, differentiation between strains or quantification of the 16S rRNA copy number of an organism remains difficult at this point. PhyloTag error rates can be further reduced in a number of ways: first, by selecting an inter-read consensus after cluster generation. This requires new algorithm development, as the consensus program we tested did not produce adequate results (typically yielding chimeras between different 16S rRNA copies). Second, longer movies (capturing image information of the SMRT cell) will allow more passes over a molecule, increasing the intra-read consensus quality. Third, PacBio chemistry, software and calibration improvements will directly result in more accurate sequences. Finally, structural modeling of the folded RNA may aid in differentiating between genetic variation and sequencing error, allowing better error correcting or filtering of high-error-rate reads. PacBio has been directing efforts towards improving their technology considering exactly these parameters ( Supplementary Figure 10 ), so that approaching the quality of Sanger amplicon sequencing appears realistic over time. Although the use of V4 iTags for microbial community profiling has multiple advantages including cost-efficiency (lowest cost per base at 0.11$/Mb), high-throughput multiplexing, the possibility of using universal primers that target archaeal and bacterial taxa simultaneously and the opportunity to get a deep insight into the rare biosphere, these do come at the expense of taxonomic resolution. Accurately extending the microbial 16S rRNA gene catalogue will be challenging if only partial 16S rRNA gene sequences are considered and evaluated as short-read sequences can potentially lead to both inflation of diversity and missing diversity, for example, with respect to new candidate ranks at various taxonomic levels. Moreover, comparison between data sets generated with different primers may lead to classification discrepancies, which limit the accuracy of microbial community profiling. This limitation can be mitigated if FL 16S rRNA gene sequencing at high throughput as alternative to Sanger sequencing becomes the new standard, or at minimum complementary to Illumina 16S rRNA gene surveys. Using PhyloTags to assess microbial community diversity in environmental samples allows us to fill important gaps in the tree of life while improving classification and microbial community profiling accuracy with important implications for inferred metabolic potential and biogeochemical roles of uncultivated microorganisms in natural and human engineered ecosystems."
} | 4,076 |
35720594 | PMC9198353 | pmc | 6,497 | {
"abstract": "Plant beneficial bacteria, defined as plant growth-promoting rhizobacteria (PGPR), play a crucial role in plants’ growth, stress tolerance and disease prevention. In association with the rhizosphere of plants, PGPR facilitate plant growth and development either directly or indirectly through multiple mechanisms, including increasing available mineral nutrients, moderating phytohormone levels and acting as biocontrol agents of phytopathogens. It is generally accepted that the effectiveness of PGPR inoculants is associated with their ability to colonize, survive and persist, as well as the complex network of interactions in the rhizosphere. Despite the promising plant growth promotion results commonly reported and mostly attributed to phytohormones or other organic compounds produced by PGPR inoculants, little information is available on the potential mechanisms underlying such positive effects via modifying rhizosphere microbial community and soil functionality. In this review, we overviewed the effects of PGPR inoculants on rhizosphere microbial ecology and soil function, hypothesizing that PGPR may indirectly promote plant growth and health via modifying the composition and functioning of rhizosphere microbial community, and highlighting the further directions for investigating the role of PGPR in rhizosphere from an ecological perspective.",
"conclusion": "Conclusions and Future Perspectives PGPR have been considered as the key elements of rhizosphere engineering for their ability to promote plant growth and fitness under abiotic and biotic stress conditions. In the past 2–3 decades, hundreds of PGPR strains have been isolated, characterized and used to promote the growth and development of a variety of different plants under normal and stressful conditions. With a better understanding of how various PGPR contribute to plant growth, scientists have paid more attention to the effects of PGPR inocula on underground soil microbial community. Although an increasing number of studies have concluded that PGPR inoculation affect rhizosphere microbiomes, it remains unclear whether or how subsequent changes in rhizosphere microbiome contribute to improving the growth and stress resistance of host plants. PGPR inocula may directly affect the composition of rhizosphere microbiome, or they may indirectly affect rhizosphere microbiome composition via interfering root exudation patterns, which in both ways can alter the functional activity of the rhizosphere mcirobiome and finally facilitate plant growth and fitness. This suggests the need for a deeper understanding of the mechanisms underlying PGPR-induced plant growth promotion in the rhizosphere. In this regard, the three-way interactions among the PGPR inocula, indigenous rhizosphere microbiome and plant roots need to be integratively studied to understand the plant growth-promoting process. Root exudates can provide the first insights into plant-microbe interactions, and the role of exudates for shaping rhizosphere microbiome has been readily confirmed. Nowadays, new approaches have been developed and allow us to get a deeper insight into PGPR-roots-rhizosphere microbiome interactions. For example, metabolomics approach, especially untargeted can provide detailed information about the composition of root exudates and how they are affected by rhizosphere microbes, which allows us to find key compounds modulating plant–microbes interactions. By combining with metabolomics, plant transcriptomics and rhizospheric bacterial community integrative analyses can provide deeper insights into how inoculants promote plant growth and stress resistance. Furthermore, to get a better understanding of mechanisms underlying PGPR-induced plant growth promotion in the rhizosphere, the specific PGP traits of the used PGPR inoculants should also be addressed, since the influence of PGPR inoculation on rhizosphere microbiome is strain-specific. However, the PGPR inoculants used in most studies possess multiple PGP activities and it is difficult to figure out which activity is more important. In the future, more efforts can be taken in uncovering whether and which specialized molecules or metabolites produced by PGPR are involved in the modification of rhizosphere microbiome using wild-type versus PGP mutants. For example, studies using bacterial mutants impaired in ACC deaminase production have demonstrated that the expression of ACC deaminase can readily impact the colonization of other microorganisms present in the rhizosphere, including symbionts ( Nascimento et al., 2018 ). Ultimately, understanding how PGPR modify rhizosphere microbiome and subsequently feedback on plant phenotypic traits will enable the development of rhizosphere engineering strategies using specific PGPR or signals to modify rhizosphere functioning for a given soil and environment.",
"introduction": "Introduction Plants depend upon the beneficial interactions between roots and microorganisms for nutrient acquisition, growth promotion and disease control under often rapidly changing environments. Plant roots in natural environments are in constant and complex interactions with diverse microbes that inhabit in their vicinity (the soil layers of 0.5–4 mm immediately surrounding the roots), known as the rhizosphere ( Lundberg et al., 2012 ; Kuzyakov and Razavi, 2019 ). The rhizosphere is one of the most complex ecosystems on earth, considered as a hotspot of plant-microbe interactions. The plant and rhizosphere microbiome have co-evolved for mutual benefits ( Eichmann et al., 2021 ). Plants feed the rhizosphere microbiome with carbon and nitrogen metabolites through root exudation. In turn, beneficial microbes contribute to the nutrient uptake, phytohormone regulation, and biotic and abiotic stress resistance of plant. Rhizosphere-inhabiting bacteria that have the ability to facilitate plant growth and health are collectively defined as plant growth promoting rhizobacteria (PGPR). PGPR include the rhizospheric bacteria that are free-living (e.g., Pseudomonas spp., Bacillus spp., Streptomyces spp., Burkholderia spp., Azospirillum spp., etc.), and the bacteria that form specific symbiotic relationships with plants (e.g., Rhizobium spp. and Frankia spp.; Glick, 2012 ). PGPR can directly improve plant growth and development via increasing available mineral nutrients (e.g., N, P, and Fe) or modulating phytohormone levels, such as auxin, ethylene, cytokinin, abscisic acid, gibberellic acid, etc. ( Figure 1 ). In addition, PGPR can indirectly facilitate plant growth and fitness through their suppressive activity against phytopathogens ( Glick, 2012 ; Kong and Glick, 2017 ). Typically, the beneficial effects of PGPR on plant growth and health are more pronounced when plants were grown in poor and/or stressed soils ( Kong and Glick, 2017 ). Thus, PGPRs have been extensively studied for their beneficial traits and the potential use in bioaugmentation, biostimulation or biocontrol as microbial inoculums. In particular, PGPR inoculation has been considered as an important strategy for sustainable agriculture, as the successful use of this practice enables to reduce or even eliminate the use of pesticides and/or fertilizers without yield loss ( Ambrosini et al., 2016 ). Figure 1 The mechanisms of PGPR improving plant growth and fitness. It is generally accepted that the effectiveness of PGPR inoculants is associated with their ability to colonize, survive and persist, as well as the interactions with native microbial community in the rhizosphere. Thus, increasing attention has been paid to how PGPR inoculation affects the indigenous microbial community and activity within either rhizospheric soil or bulk soil ( Table 1 ). Since PGPR inoculation can significantly affect root development and exudation ( Vacheron et al., 2013 ; Ray et al., 2018 ; Alzate et al., 2020 ; He et al., 2022 ), it can be expected that PGPR inoculants would modify the community composition of rhizosphere microbiome. Moreover, it is known that the total number of microorganisms can be up to 10 8 –10 12 per gram of soil ( Bloem et al., 1995 ). A relatively high concentration of PGPR (10 6 –10 12 CFU/kg soil or 10 6 –10 8 CFU per seedling or seed) inoculants is always introduced to achieve its effectiveness ( Table 1 ), which can induce changes to rhizosphere characteristics within a certain period ( Ambrosini et al., 2016 ). Furthermore, plant roots can produce a wide range of metabolites which play an important role in shaping the rhizosphere microbiome ( Jacoby et al., 2020a ). On the other hand, root metabolism and exudation can also change according to the rhizosphere microbiome structure and assembly ( Korenblum et al., 2020 ); even small changes in the microbial community structure might result in large alterations of host phenotypes ( Brinker et al., 2019 ). However, there is still limited information concerning how inoculated PGPR affect rhizosphere microbiome or how subsequent changes in rhizosphere microbiome contribute to improving plant growth and fitness. Table 1 The effects of PGPR inoculants on soil microbial community composition and activity. PGPR inoculants Isolation sources PGP traits Plants Inoculant dose Stress Plant growth duration Soil type Results Refs Neorhizobium huautlense T1-17 Heavy metal-contaminated soil IAA, siderophore, ACC deaminase Chinese cabbages and radishes 5*10 12 CFU/kg soil Cd, Pb 2 months R Significantly increased the ratio of IAA-producing bacteria Wang et al. (2016) (G) Pseudomonas sp. SUT 19; Brevibacillus sp. SUT 47 Roots of forage corn IAA, N 2 -fixing, ACC deaminase, P solubilization only for SUT19 Forage corn 10 8 CFU per seedling/seed / 1–2 months R No influence on microbial community structure Piromyou et al. (2011) (L and F) Pseudomonas sp. S2-3 and UW4; Burkholderia sp. S6-1 Farmland polluted by acidic mine drainage IAA, P solubilization, Ammonia production, ACC deaminase for S6-1 and UW4, siderophore only for S6-1 \n Brassica juncea \n 3.75*10 9 CFU/kg soil Cu, Pb, and Zn 1–100 days B Significantly changed the bacterial community composition 1 day after inoculation, with minor changes continuing to be observed 10 days after inoculation; increased the complexity and stability of co-occurrence network Kong et al. (2019) (L) Burkholderia phytofirmans PsJN Onion roots ACC deaminase, IAA Maize Seeds incubated in 10 9 CFU/ml for 90 min Cd, Pb and Zn 69 days R Affected rhizosphere microbiome diversity only to a minor extent Touceda-González et al. (2015) (G) Mix culture of Bacillus aryabhattai and Bacillus megaterium Culture collection P solubilization \n B. juncea \n 1.67*10 9 CFU/ kg soil Cd 1–8 weeks R Significantly changed the species diversity and richness indices of microbial community Jeong et al. (2013) (L) Azospirillum brasilense Sp6 Katholieke Universiteit Leuven, Belgium IAA Quailbush 1.2*10 6 CFU/seed Metals 15, 30 and 60 days R Induced a significant change in the DGGE profiles of rhizosphere microbial community De-Bashan et al. (2010) (G) Sinorhizobium meliloti 4H41 and/or Rhizobium gallicum 8a3 Common bean nodules N 2 -fixing Common bean 10 8 CFU/plant / 0, 1 and 2 months after _inoculation B Significantly affected the composition of the bacterial and Rhizobiaceae communities Trabelsi et al. (2011) (F) Clavibacter sp. MTR-21A, Rhodanobacter sp. MTR-45B, and Arthrobacter sp. K4-10C and MTR-44 Rhizosphere of quailbush plants IAA and siderophores for all strains; P-solubilization for MTR-21A and MTR-44; ACC deaminase only for MTR-44 Quailbush and buffalo grass 2*10 7 CFU/seed for alginate-encapsulation Metals 75 days R Significantly influenced the development of the rhizosphere community structure Grandlic et al. (2009) (G) Azospirillum lipoferum CRT1 Commercial inoculants / Maize 3*10 7 CFU/seed / 7, 35 and 65 days R Modified the composition of the resident bacterial community of the rhizosphere Baudoin et al. (2009) (F) Paracoccus versutus NM01 and Aeromonas caviae NM04 As-polluted soils IAA, siderophore, and P solubilization Fern / As 4 weeks R Displayed higher bacterial diversity indices (ACE and Chao1) Marwa et al. (2020) (G) Pseudomonas fluorescens MC46 Rhizosphere of Vigna unguiculata subsp. sesquipedalis Ammonia production, P-solubilization, siderophore, IAA, EPS Mung bean 2 × 10 8 CFU per pot Triclocarban 5 weeks B Enhanced soil enzyme activities Sipahutar et al. (2018) (G) A. brasilense 40 M and 42 M Roots of field-grown maize / Rice 6*10 9 CFU/kg seed / 35 and 117 days R Significantly increased the percentage of microaerophilic diazotrophs; significantly changed the taxonomic structure and the functional diversity of rhizosphere microbial community de Salamone et al. (2010) (F) Enterobacter 15S; Pseudomonas 16S Horticultural soils IAA, P solubilization, siderophores production Tomato 10 6 CFU per seedling / 40 days R Induced a deterministic effect on the functional diversity of rhizosphere microbiome Alzate et al. (2020) (G) Enterobacter 15S; Pseudomonas 16S Horticultural soils IAA, P solubilization, siderophores production Tomato 10 6 CFU per seedling NaCl 40 days R Increased the content of ROS-scavenging and antioxidant compounds, and improved the facilitation of Fe acquisition by inoculation of Pseudomonas 16S Alzate et al. (2021) (G) \n A. brasilense \n Maize rhizosphere IAA, siderophores, ACC deaminase Maize 10 11 CFU/kg seed / 62 and 132 days R Increased the number of microaerophilic nitrogen fixing microorganisms; modified the physiology of the rhizosphere microbial communities Di Salvo et al. (2018a) (F) A. brasilense 40 M and 42 M Roots of field-grown maize / Wheat 5.55*10 9 CFU/kg seed / 88 and 133 days R Modified both physiology and genetic structure of rhizosphere microbial communities. Di Salvo et al. (2018b) (F) A. brasilense Az1 and Az2, Pseudomonas fluorescens Pf Commercial inoculants / Wheat 5.5–10.5*10 9 CFU /kg seed / 106, 136, 155 days R No influence on culturable actinomycetes and bacteria, but changed the number of culturable fungi and the carbon-source utilization activities of microbial communities Naiman et al. (2009) (F) Acinetobacter pittii and Escherichia coli Culture collection P solubilization Solanum nigrum L. 4–5 × 10 10 CFU/plant Cd 30 and 60 days R Enriched dominant microbial taxa with plant growth promotion function and keystone taxa related to Cd mobilization; up-regulated the expression of genes related to bacterial mobility, amino acid metabolism, and carbon metabolism among rhizobacterial community He et al. (2022) (G) Bacillus subtilis , Paenibacillus polymyxa Commercial inoculants / Wheat 30 kg ha −1 High P At regreening, flowering, and harvest stages R Significantly enriched various bacterial genera Chen et al. (2021) (F) Bacillus amyloliquefaciens FH-1 Rhizosphere of tea tree N 2 -fixing, inorganic P, K solubilization, siderophore, ACC deaminase Cucumber 10 8 CFU/ g soil Coastal saline-alkali soil 35 days R Reduced the rhizosphere bacterial diversity, increased Proteobacteria, and decreased Acidobacteria; increased bacteria-bacteria interactions Wang et al. (2021) (L) Soil type: R, rhizospheric soil; B: bulk soil. Experimental conditions are designated (L) for more controlled laboratory conditions, (G) for greenhouse conditions, or (F) for field trials. Here, the effects of PGPR inoculation on the microbial properties and functioning of rhizosphere is reviewed and discussed. The effect of PGPR inoculants on the microbial structural and functional diversity and chemical diversity in the rhizosphere are described in detail. Ultimately, understanding the modification effects of PGPR inoculation on rhizosphere microbiome, and their subsequent role in the rhizosphere functioning is key to a deep sight into the plant growth promotion (PGP) mechanisms of PGPR. It is essential for the establishment of strategic plant-microbe partnership improving plant health and fitness under rapidly changing environments."
} | 4,007 |
35145274 | PMC8967799 | pmc | 6,498 | {
"abstract": "Microbial communities inhabit spatial architectures that divide a global environment into isolated or semi-isolated local environments, which leads to the partitioning of a microbial community into a collection of local communities. Despite its ubiquity and great interest in related processes, how and to what extent spatial partitioning affects the structures and dynamics of microbial communities is poorly understood. Using modeling and quantitative experiments with simple and complex microbial communities, we demonstrate that spatial partitioning modulates the community dynamics by altering the local interaction types and global interaction strength. Partitioning promotes the persistence of populations with negative interactions but suppresses those with positive interactions. For a community consisting of populations with both positive and negative interactions, an intermediate level of partitioning maximizes the overall diversity of the community. Our results reveal a general mechanism underlying the maintenance of microbial diversity and have implications for natural and engineered communities.",
"introduction": "Introduction Microbial communities are critical to natural ecological processes, such as biogeochemical cycling 1 , animal and human health 2 , 3 , and engineering applications 4 , 5 . Microbial community structure, meaning species identities and their abundance, is a primary feature that defines the functioning of microbial communities 6 . Along with internal factors, such as growth rate, death rate, and interactions, external factors, such as ecological factors and chemical environments also modulate microbial community structures 7 . However, our knowledge is still limited regarding what factors impact microbial community structures in a scalable and general manner and how they operate. Survey-based studies of complex microbial communities using sequencing technologies provide large amounts of high-quality data and empirical insights 8 , 9 but causal and mechanistic links are often missing between external factors and community structure 10 . In contrast, controlled assembly of a few species can provide mechanistic interpretations since specific variables related to community structure can be manipulated. These studies have investigated the contributions of different factors that are biological 10 – 13 , chemical 14 , 15 , or physical 16 – 18 . However, how the learned insights scale up to more complex communities, where diverse interaction types and higher-order interactions may be present, is difficult to test and remains unclear 10 . Among these factors, spatial partitioning is ubiquitous yet mostly overlooked for microbial communities. Spatial partitioning describes the physical separation of a community into local communities. For example, the physical architectures of the gut 19 , plant root 20 , and soil 21 all partition microbial communities into distinct local communities that are separated to different extents ( Fig. 1a ). Due to the complexity of the physical architecture of microbial communities, the partitioning can be mostly complete, such as the microbiota in two different animals or the local microbial communities in two separate droplets. It can also be partial, resulting from the cell mobility or dispersal across local environments 18 or diffusion of signaling molecules across local communities 16 . In the simplest case, where partitioning is complete, local environments each consists of only a subset of all members and partitioning restricts interactions within local communities. In general, spatial partitioning reduces the overall strength of interactions in the global microbial communities and lowers the number of interacting species for each individual member 22 . Moreover, the type of interactions experienced by a member can vary drastically depending on the random assembly of local environment 23 . In other words, spatial partitioning can modulate the dynamics of a microbial community by globally modulating the type and strength of interactions experienced by each member. This emphasis on interactions, derived from studying microbial communities, differs substantially from research in multicellular organisms, which places much greater emphasis on dispersal between local communities, abiotic factors, and neutral dynamics 24 . Focusing on interactions therefore has potential to contribute to the historically organismal-level study of spatial effects on local and global community diversity. It is yet unclear whether the effect of spatial partitioning is highly system specific or whether it follows general rules. Beyond the challenges of distilling causal mechanisms and general rules, defining spatial partitioning in a relevant and quantitative manner is also challenging. To address this question and overcome these challenges, we first established a theoretical framework to explain the mechanisms by which spatial partitioning affects community structure. Based on the theoretical framework, we formed a hypothesis that spatial partitioning reduces biodiversity for negative interaction dominated community and increases biodiversity for positive interaction dominated community, and biodiversity peaks at an intermediate partitioning level for communities with both positive and negative interactions. We then tested our hypothesis using precisely controlled top-down experiments of simple communities and scaled up to complex natural communities. The ability to control microbial community structures through modulation of spatial partitioning can address a wide range of challenges we face with natural and engineered microbial communities for ecological, medical, and engineering purposes.",
"discussion": "Discussion Our study reveals a simple principle that dictates how spatial partitioning modulates microbial community structure through global modulation of interactions. Although spatial partitioning modulates the growth of populations receiving negative versus positive interactions in opposite directions, it is robust that complex communities with both negative and positive interactions reach the highest biodiversity at an intermediate partitioning level. Previous studies that use precisely controlled assembly primarily focus on characterizing pairwise interactions, which limits the scalability of the experiments 23 . In contrast, we leveraged seeding stochasticity and community-level interaction characteristics to demonstrate the robustness and scalability of this principle. Further, this community-level principle is the result of the collective response of single species in the community. For experimentalists, our study suggests the importance of the explicit consideration of physical arrangement of communities in experimental designs. For example, serial dilutions not only modulate the initial density of communities but also increase the spatial partitioning level of the community. Our study also suggests that to maintain the highest biodiversity of a natural community, beyond the design of chemical environment, the design of the physical environment is also crucial. For natural microbial communities that arise from complex spatial partitioning environments, spatial design of lab cultures can be especially important for maintaining their structure and biodiversity. Engineering of habitat by spatial partitioning can be an effective strategy to modulate and control microbial community structures. Beyond using microtiter plates, other engineering methods can also be used to impose spatial partitioning that has not been fully tested yet, such as encapsulation 40 and inkjet printing 41 . The modulation of community structure can happen at three levels. First the relative abundance of a single population can be modulated based on archetype. Second, the proportions of interactions can be modulated through increasing or decreasing partitioning. Third, the biodiversity can be modulated or maintained through the modulation of spatial partitioning level or a mixed level of partitioning. Beyond being a general and robust modulator, spatial partitioning, as a physical factor, is orthogonal to chemical and biological factors and can be used in parallel. The role of space in maintaining biodiversity is a central question beyond microbial ecology, as exemplified by classic Island Biogeography Theory 42 , debate regarding the optimal design of biodiversity reserves 43 , 44 , and the subsequent flowering of metacommunity ecology 24 , 45 , 46 . Many specific aspects of partitioning, such as nestedness 47 , stochastic extinction 48 , and migration rate 49 have been investigated. However, most studies largely overlook interactions, in particular cooperation. Our general principle of spatial partitioning accounts for both types of interactions, addressing a critical gap in our understanding of spatial mechanisms for the maintenance and promotion of biological diversity. This helps to clarify the role of spatial mechanisms alongside temporal mechanisms of biodiversity maintenance and spatiotemporal mechanisms, such as the intermediate disturbance hypothesis 50 . Finally, the insights presented here and offers a fresh perspective for interpreting, screening, and controlling microbial community structures and the relative abundance of individual population."
} | 2,326 |
30674794 | PMC6318631 | pmc | 6,499 | {
"abstract": "We describe the creation of hollow tubular hydrogels in which different zones along the length of the tube are composed of different gels. Our method to create these gels is adapted from a technique developed previously in our lab for creating solid hybrid hydrogels. The zones of our tubular gel are covalently bonded at the interfaces; as a result, these interfaces are highly robust. Consequently, the tube can be picked up, manipulated and stretched without suffering any damage. The hollow nature of these gels allows them to respond 2–30-fold faster to external stimuli compared to a solid gel of identical composition. We study the case where one zone of the hybrid tube is responsive to pH (due to the incorporation of an ionic monomer) while the other zones are not. Initially, the entire tube has the same diameter, but when pH is changed, the diameter of the pH-responsive zone alone increases (i.e., this zone bulges outward) while the other zones maintain their original diameter. The net result is a drastic change in the shape of the gel, and this can be reversed by reverting the pH to its original value. Similar localized changes in gel shape are shown for two other stimuli: temperature and solvent composition. Our study points the way for researchers to design three-dimensional soft objects that can reversibly change their shape in response to stimuli.",
"conclusion": "3. Conclusions In this paper, we have showcased a new approach for making hybrid tubular gels that have zones corresponding to different polymers along the length of the tube. Each zone retains its unique individual properties. The zones are connected by a robust interface, with polymer chains across the interface being covalently bonded to each other. The hollow nature of the tubes allows them to swell much faster than their solid counterparts. By choosing stimuli-responsive monomers for distinct zones, we can engineer a tube that shows a significant change in shape when exposed to particular stimuli. The three stimuli that were studied here are pH, temperature, and solvent composition. Shape-changes occur because distinct zones of the tube swell (or shrink) to different extents in response to the stimulus. The shape changes are reversed when the stimulus is restored to its initial value. Such reversible shape-changes have been demonstrated with both two-zone and three-zone hybrids, and this concept can be easily extended to even more complex structures with multiple zones. Overall, our study shows how researchers can build a hydrogel in a particular 3-D shape and have it transform into another 3-D shape in a controlled fashion.",
"introduction": "1. Introduction Recently, our lab has been interested in devising new polymeric hydrogels that exhibit different properties over distinct zones [ 1 , 2 ]. For example, we have created gels in which one zone or portion is responsive to a particular stimulus, say temperature, while adjacent zone(s) are unresponsive to this stimulus (and possibly responsive to a different stimulus such as solution pH) [ 1 , 2 ]. In addition, we have designed gels in which adjacent zones have very different mechanical properties: e.g., a stiff, brittle zone can be next to a soft, extensible zone [ 1 ]. The motivation for these studies arose from our realization that many gel-like systems in nature, including both aquatic and terrestrial animals, tissues or organs in our body, and plant parts, are inhomogeneous and have multiple zones with distinct properties [ 3 , 4 ]. Moreover, we noted that current approaches to create novel hydrogels were mainly focused on tuning the chemical structure of the monomers used in gel synthesis [ 5 , 6 ]. In comparison, physical approaches to creating hybrid hydrogels have been relatively unexplored [ 7 , 8 , 9 , 10 ], and we have therefore focused our efforts in this direction. Another direction in the hydrogel field that has proven popular among researchers is in designing hydrogels that change their shape in response to a stimulus [ 11 , 12 , 13 ]. The natural inspiration for shape-changing materials comes from a variety of plant-based structures such as seed pods, awns, and leaves, all of which exhibit changes in shape under specific conditions. Most studies on shape-changing gels begin with a flat film or strip of a gel, which folds, i.e., rolls up, to form a particular shape, most typically an open tube [ 11 , 12 , 13 ]. Folding has been induced in response to external stimuli such as temperature [ 14 ], pH [ 15 ], or light [ 16 ], or upon the addition of aqueous solutes such as enzymes [ 17 ]. Gels that fold usually have a hybrid design, where the flat sheet is itself composed of multiple zones or layers. This is done by cross-linking monomers using ultraviolet (UV) light that is sent through a photolithographic mask corresponding to a specific pattern. The ability of a gel to fold hinges on the differential swelling of various zones in the gel when placed in water; this in turn creates stresses in the material that lead to folding. To our knowledge, gels that fold are always a flat sheet at the outset, not a three-dimensional (3-D) object. In this paper, we report the creation of hybrid polymer gels in a tubular geometry. Unlike the solid hybrids reported earlier from our lab (which were either cylinders, discs, or cuboids) [ 1 , 2 ], the hybrids in this study are cylindrical tubes –with a hollow core and a wall that is thin compared to the cylinder diameter. Along its length, each tube has at least two zones, which correspond to different polymer gels. The properties of the overall tube thus depend on the chemistry of each zone. We will discuss the responses of specific tubes to three external stimuli: temperature, pH, and solvent composition. The main result here is that our tubes exhibit shape changes, which is due to the differences in swelling between the distinct zones of the tube. Thereby, this study, for the first time demonstrates reversible changes from one 3-D shape to another in a polymer gel . Additionally, our study shows that hollow cylinders exhibit a change in their volume and shape much faster than solid ones, which is a crucial benefit of working with the former.",
"discussion": "2. Results and Discussion 2.1. Synthesis of Hybrid Gel Tubes Our method for preparing hybrid tubular gels is illustrated in Figure 1 . This method was developed previously in our lab [ 1 ] and has been subsequently copied and extended by others [ 9 , 10 ]. The key to this method is to stack the pre-gel solutions when their viscosities are sufficiently high, so as to drastically minimize convective mixing between the solutions [ 1 ]. To prepare a tubular hybrid, we use a mold so that the center of the gel remains hollow. The simplest version of the mold is made by arranging a small cylinder (vial or tube) concentrically inside a bigger cylinder, as shown in Panel 1. The small vial here is filled with water to increase its inertia and sealed with tape. The pre-gel solutions are then pipetted in the annular space between the two vials, one on top of the other. We first introduce pre-gel A, a mixture of monomer, cross-linker, initiator, accelerant, and rhodamine B dye up to half the vial height (Panel 2). Here, the monomer is acrylamide (AAm) at a concentration of 1 M (~7% by wt) and the cross-linkers are laponite (LAP) nanoparticles (4 wt %). Pre-gel A is already a viscous solution at the outset. Next, pre gel B, a solution of 1 M AAm and 0.1 wt % N,N′ -methylene-bis(acrylamide) (BIS) along with the same initiator and accelerant, is introduced on top of pre-gel A (Panel 3). Note that the two pre-gels in this case have the same monomer, but different cross-linkers; also, pre-gel B is colorless whereas pre-gel A has a pink color from the dye. The high viscosity of pre-gel A prevents convective mixing at the interface between the pre-gels [ 1 ]. We then leave the system to polymerize at room temperature. Afterward, the gel is removed from the mold and is shown to be a hollow tube with a diameter of ~25 mm, length of ~50 mm and a wall thickness of ~ 5 mm (Panel 4). Note that the inner vial diameter dictates the wall thickness and the outer vial diameter dictates the outer diameter of the tube. The tube shows well-separated regions of the two gels, i.e., AAm/LAP is the pink zone and AAm/BIS is the colorless zone. As expected, the zone that is cross-linked by LAP is more compliant and stretchable than the adjacent zone that is cross-linked by BIS (Panel 5) [ 1 , 18 ]. Note that the LAP serves as chemical cross-linkers here: i.e., when AAm monomers and LAP particles are in the presence of free-radicals generated by the initiator, polymer chains of AAm are induced to grow from the surfaces of LAP particles [ 18 , 19 ]. The higher stretchability of LAP gels is believed to be because the chain segments between adjacent particles (i.e., cross-links) are longer than in a conventional BIS cross-linked gel [ 1 , 16 ]. It is important to note that the zones of the hybrid tube are connected by a strong and robust interface. As a result, the tube does not rupture on twisting or stretching (Panel 5). The robust interface is a consequence of our synthesis method [ 1 ]. When viscous pre-gels A and B are brought into contact (Panel 3), chains (oligomers) of A will be able to diffuse from Zone A into Zone B and vice versa. As a result, some covalent linkages of A and B chains will occur at the interface, which are crucial in ensuring that the interface is robust [ 1 ]. If the pre-gels are not viscous, they will undergo considerable mixing and one will end up with a gel that is a copolymer of A and B, rather than well-separated zones. Also, if we fully polymerize Gel A and Gel B and thereafter bring them into contact, the A/B interface will be very weak and the sample will be ripped apart under moderate stretching [ 1 ]. 2.2. Swelling Kinetics for Solid Cylinders vs. Hollow Tubes How does the rate of swelling compare between solid and hollow cylinders? To study this, we made gels in the form of solid cylinders and hollow tubes with the same monomer composition (both gels were not hybrids). The monomer was a mixture of AAm (nonionic) and 2-(dimethylamino)ethyl methacrylate (DMEM) (cationic), with the AAm:DMEM molar ratio fixed at 90:10. The total monomer content was 1 M and the cross-linker was 0.1% BIS. Due to its ionic nature, a gel with this composition is expected to swell significantly in water at pH 7 [ 5 , 6 ]. Figure 2 shows two comparisons of solid and hollow cylinders, which were each placed in a reservoir of water at time t = 0. In Figure 2 a, the two cylinders have an outer diameter of 25 mm and a length of 50 mm, with the wall thickness of the hollow tube being 5 mm. The solid cylinder swells to a diameter of 40.5 mm in about 150 h. The hollow tube swells to a slightly larger diameter of 43.4 mm, but more importantly in only about 50 h, i.e., in one-third the time. In Figure 2 b, the three cylinders all have an outer diameter of 15 mm and a length of 40 mm. Two hollow tubes are studied, with wall thicknesses of 1.8 and 1.2 mm, respectively. The two tubes swell to a diameter of ~30 mm within about 4 h. The solid cylinder, on the other hand, swells to a slightly lower diameter of 28.4 mm, but takes more than 120 h to do so (30-fold longer). The above data clearly show the faster swelling of hollow tubes compared to their solid cylinder counterparts. This is due to the fact that the smallest dimension pertinent to swelling of the hollow gels is the wall thickness, which is 1 to 5 mm. The relevant counterpart for the solid gels is the outer diameter of the cylinder, which is either 15 or 25 mm, i.e., a much larger dimension. Diffusion in the main mode by which water is transported into the gel, allowing it to swell. It is well-known that the diffusive timescale τ will vary with the smallest dimension a of the gel as per the Einstein-Smoluchowski equation [ 20 ]:\n (1) τ ~ a 2 Ɗ \nwhere Ɗ is the diffusivity of the species that is diffusing, which in this case is water. Equation (1) shows that the smaller the length a to be diffused through, the lower the time τ for diffusion. 2.3. Stimuli-Responsive Gel Tubes (Two-Zone Hybrids) We proceeded to investigate hollow hybrid tubes having two zones with different stimuli-responsive properties. First, we created tubes with one ionic and one nonionic zone and studied the effect of pH on these tubes. Two such tubes are shown in Figure 3 . The nonionic zone in each tube is made using 1 M of N , N ′-dimethylacrylamide (DMAA) with 7.5% LAP as the cross-linker. For the tube in Figure 3 a, the ionic zone is composed of AAm:DMEM in a molar ratio of 90:10 (the total monomer being 1 M) and with 0.1% BIS as the cross-linker. As noted in Figure 2 , DMEM is a cationic monomer and imparts ionic properties to its zone. The reason for the high LAP content in the nonionic DMAA zone was to inhibit its swelling. Figure 3 a, panel 1 shows the initial hybrid tube, which has dimensions identical to the tube in Figure 1 , i.e., 25 mm outer diameter, 5 mm wall thickness, and overall height 50 mm. The nonionic and ionic zones are each about half the height of the tube, i.e., 25 mm each. The above tube is then placed in water at ambient pH and temperature (Panel 2), and it is left to swell for more than a day. Thereafter, the swollen tube is removed and placed vertically next to a vial for size comparison (the vial is 25 mm × 55 mm, i.e., the size of the initial tube) (Panel 3). We observe substantial swelling in the ionic zone of the tube compared to the nonionic zone. The ionic zone has increased to about 4× its original diameter and 2× its original height whereas the nonionic zone is only slightly larger than its original dimensions. The overall gel thus assumes the shape of a bottle with a small neck relative to its body. Similar results are obtained for a different hybrid tube where we use the anionic monomer sodium acrylate (SA) instead of the cationic DMEM ( Figure 3 b). This tube has the same DMAA/LAP zone and a zone of 1 M AAm:SA in a 90:10 (1 M total monomer) cross-linked with 0.1% BIS. When this tube is placed in water at ambient pH, it also shows substantial swelling of its ionic zone relative to its nonionc zone (Panel 3). Thus, the tube again transforms to a bottle shape, and here the diameter of the ionic zone reaches about 3× its original diameter. In both the above cases, the original dimensions of the tube can be recovered by altering pH. For the tube in Figure 3 a, it has to be placed in water at pH 10 or greater, whereupon the DMEM-bearing chains lose their charge. The ionic zone then deswells and reverts to its initial size. For the tube in Figure 3 b, similar deswelling occurs when it is placed in water at pH 3 or lower, whereupon the SA units lose their charge. Next, we studied a hybrid tube that is responsive to solvent composition ( Figure 4 ). In this case, the hybrid has two zones, one of 1 M AAm cross-linked with 3% LAP and another of 1 M DMAA cross-linked with 4% LAP (Panel 1). It is known that AAm gels shrink in water containing acetone above a critical level (~50%) whereas DMAA gels are not sensitive to acetone [ 5 , 6 ]. This behavior derives from the fact that poly(AAm) chains are soluble in water but insoluble in acetone. We placed the AAm/DMAA hybrid tube in a 50/50 acetone/water solution at room temperature (Panel 2). As shown by Panels 3 and 4, the AAm zone shrinks and turns opaque, while the DMAA zone slowly starts swelling to absorb the solvent. After a day, the AAm zone is considerably smaller than its initial size while the DMAA portion is swollen appreciably. Thus, the initial tube is transformed into a funnel shape with a significant difference in diameter between the two zones. This shape change can be reversed by placing the tube back in water. We then explored a temperature-responsive hybrid tube ( Figure 5 ). This again had two zones, one of 1 M N -isopropylacrylamide (NIPA) cross-linked with LAP (3%) and another of 1 M DMAA cross-linked with LAP (4%) (Panel 1). NIPA gels are known to be thermo-responsive: specifically, they shrink when heated above their lower-critical solution temperature (LCST), which is 32 °C [ 5 , 6 ]. DMAA gels, in contrast, are not responsive to temperature. Panels 2 and 3 show the result of placing the NIPA/DMAA tube in hot water (~45 °C), above the LCST of NIPA. The NIPA zone turns opaque and shrinks, whereas the DMAA zone remains clear and expands slightly. Thus, the tube is again transformed into a funnel shape. On placing the gel back in water at room temperature (23 °C, below the LCST of NIPA), the NIPA portion reverts to its initial clear state and the gel recovers its symmetric tubular shape. The shape-changing properties of the above tubular gels can be potentially exploited for certain applications. We illustrate one such idea in Figure 6 . For this, we created a hybrid tube with one zone of DMAA cross-linked with 7.5% LAP and the other of NIPA cross-linked with 3% LAP. The inner diameter of the tube was designed to be slightly more than 15 mm. A vial of 15 mm outer diameter was then inserted through the center of the tube. In this state, the tube is not able to grasp the vial ( Figure 6 a). But when the tube-vial combination is immersed in hot water at 50 °C, the shrinking of the NIPA zone allows this portion to contract around the vial, grasping it tightly. This happens within minutes after immersion. The DMAA zone remains free and unadhered to the vial. When the tube is now pulled up by its DMAA zone, it is able to lift the grasped vial off the ground cleanly and without any slippage ( Figure 6 b). Note that the DMAA portion is stiffer and less elastic due to the higher cross-linker content, which is ideal for the lifting zone. The key result here is that the responsive tube is able to alter its shape to conform to the shape of the encapsulated object. As a result, the tube is able to grasp the object tightly, allowing it to be picked up and manipulated. This ability could be potentially useful in soft robotics or related areas. 2.4. Stimuli-Responsive Gel Tubes (Three-Zone Hybrids) The procedure outlined here to create hybrid tubular gels can be extended in many ways. One extension that we will now discuss is to create a tube with more than two zones. This is shown by the tube in Figure 7 , which has three adjacent zones, of which only one is pH-responsive. The zone compositions are as in Figure 3 b: the left and right zones are each made of 1 M DMAA cross-linked with 7.5% LAP, while the middle zone is made of 1 M AAm:SA 90:10 cross-linked with 0.1% BIS. This tube is synthesized by the same procedure as in Figure 1 , but modified to allow for three zones instead of two. As synthesized, the overall tube length is 50 mm, with the DMAA zones each having a length of 20 mm each while the AAm:SA zone has a length of 10 mm (Panel 1). We then placed this tube in water at pH 7 and left it to swell for more than a day. Thereafter, the swollen gel is removed and placed next to a ruler for size comparison with the initial tube (Panel 2). At a pH of 7, the middle zone (AAm:SA) swells due to the anionic nature of the SA groups in it, but the other two zones are nonionic and hence do not swell as much. Due to this differential swelling, the initial tube is transformed into a shape with a central bulge. The diameter and length of the middle zone (the bulged region) are about 3× their original values at their central point. On the other hand, the diameter and length of the flanking zones are only slightly larger than their initial values. This shape change can also be reversed by placing the gel in pH 3 water. Figure 7 shows that multi-zone hybrid tubes can be used to engineer more complex shape-changes than are possible with two-zone hybrids."
} | 4,974 |
39650251 | PMC7617082 | pmc | 6,500 | {
"abstract": "Trees within farmers’ fields can enhance systems’ longer-term productivity e.g., via nutrient amelioration, which is indispensable to attain sustainable agroecosystems. While arbuscular mycorrhizal fungi (AMF) are known to improve plant access to soil nutrients, the potential of AMF to mediate nutrient uptake of tree-derived N by crops from beyond the crops’ rooting zones is unclear. We hypothesized that AMF quantitatively contribute to the crop uptake of tree-derived N. We set up root and AMF exclusion and control plots around faidherbia trees ( Faidherbia albida ) and used the 15 N natural abundance technique to determine the magnitude of AMF-mediated uptake of tree-derived N by maize from beyond its rooting zone in smallholder fields. We further tested whether AMF-mediated N uptake decreases with distance-from-tree. We show that within one cropping season, maize obtained approximately 35 kg biologically fixed N ha -1 from faidherbia. One third of tree-derived N in maize leaves was attributed to AMF-mediated N uptake from beyond the maize rooting zone and two thirds to N from tree leaf litter, regardless of distance-from-tree. As hypothesized, maize grown close (1 m) to faidherbia obtained significantly more tree-derived N than at further distances (4 and 5 m). Thus, the faidherbia-AMF association can enhance agroecosystem functioning.",
"discussion": "Discussion Incorporating faidherbia trees in agroecosystems can benefit crop yields through microdose-fertilization N 2 -fixing trees provide high-quality above- and belowground organic matter inputs to the soil e.g. in the form of tree leaf litter input, root exudates, and root turn-over 2 – 4 . Faidherbia leaf litter input alone can provide 50 to 80 kg N ha -1 to the soil under faidherbia trees within a given season 18 , 19 . However, how much of this tree leaf litter-derived N is mineralized and subsequently incorporated into crop biomass is yet unknown. The distinct isotopic N signature i.e., 15 N: 14 N ratio of N 2 -fixing faidherbia allows distinguishing between tree-derived biologically fixed N 2 and residual soil N 23 , 24 . Because maize does not biologically fix N 2 , its isotopic N signature is largely determined by the residual soil N and thus, the 15 N natural abundance technique allows tracing the isotopically distinct tree-derived, biologically fixed N 2 into maize 23 , 24 . Specifically, determining the δ 15 N of maize leaf, paired soil samples and faidherbia reference samples and using an isotope mixing model allowed calculating the proportion of tree-derived N in maize. It is to note that the 15 N natural abundance technique does not detect any non-biologically fixed N2 derived from the trees, resulting in an underestimation of the tree-derived N in maize. We found that in total, over the course of one season, tree-derived N accounts for 35 kg N ha -1 in maize, which corresponds to about 30 % of the total N in maize. Therefore, our results confirm the importance of faidherbia trees in improving the N budget of crops in farmers’ fields. The broad-scale recommended rate of N fertilization in Malawi is 96 kg N ha -1 , but on average only 18 kg N ha -1 is applied by farmers 25 , 26 and many farmers, as those in our study region, lack access to fertilizer and thus, do not apply any fertilizer to their field (based on conversations with the farmers we worked with). We demonstrate that faidherbia provides more than one third of the recommended dose of fertilizer and almost twice the amount that is on the average applied by subsistence farmers. Microdose-fertilization has been shown to result in significant yield increases e.g. microdose-fertilization of 24 kg N ha -1 resulted in a 64 % increase in maize grain yield relative to an unfertilized control 27 . We found maize yields within 5 m of faidherbia were approximately 50 % greater compared to yields of maize plants away from faidherbia, the latter was determined in a previous study 32 . Specifically, maize grain yield was 3.7 ± 0.4 t ha -1 under faidherbia compared to 2.5 ± 0.6 t ha -1 at about 35 m away from faidherbia. We note that the yield determined away from faidherbia was based on green cob dry weight 32 , while yield under faidherbia was determined from mature cobs, after the plants had fully matured, which might have resulted in a slight underestimation of yield away from faidherbia and therefore a slight overestimation of the approximate 50 % increase in yield under relative to away from faidherbia. Nevertheless, our estimates are in line with previous findings 27 . Furthermore, our results show that foliar N concentration was greater within the immediate vicinity (1-2 m) of faidherbia compared to further distances (4-5 m). The same holds true for tree-derived N in maize leaves as indicated by the increase in δ 15 N in maize leaves with distance-from-tree. We conclude that faidherbia trees are effective in providing N microdose-fertilization to crops in subsistence farmers’ fields and, therefore, may contribute to increased yields. Mycorrhizal fungi increase uptake of tree-derived N by maize Greater crop yields and soil nutrient contents previously observed around N 2 -fixing trees within agricultural fields have been mostly ascribed to high quality organic matter inputs to the soil 14 – 21 . The contribution of AMF in making these inputs available to crops has gained much less attention. There has been some evidence that AMF facilitate N transfer from trees to surrounding plants 8 , but verification of this mechanism under field conditions on smallholder farms has been lacking. Faidherbia has been shown to associate with AMF, both in topsoil and great soil depth 22 . Our experimental design and methods did not allow to provide evidence for the existence of direct linkages between tree and maize via a mycorrhizal mycelia. Therefore, we cannot differentiate between direct root-to-root and indirect soil-to-root mediated N transfer enabled by AMF. However, combining the 15 N natural abundance technique and root and AMF exclusion plots allowed us to quantify the effect of AMF-mediated uptake of tree-derived N by maize from beyond its rooting zone whether via direct or indirect transfer, in farmers’ fields. While we cannot exclude the possibility that other filamentous fungi may have contributed to the uptake of tree-derived N by maize from beyond its rooting zone 28 , 29 , we assume that most of the uptake was AMF-mediated. We estimated that the AMF-mediated uptake of tree-derived N by maize plants accounted for 28 % of the total tree-derived N in maize leaves within 5-m around faidherbia trees ( Table 2 ). Tree litter was responsible for most, i.e., about two-thirds, of the tree-derived N in maize and the presence of tree roots within the rooting zone of maize (if at all present) had a negligible effect on the uptake of tree-derived N by maize ( Fig. 2 , Table 1 & 2 ). The experimental plots were located under the tree crown (average crown radius of 5 ± 0.4 m) and therefore, it was expected that the proportion of tree-derived N obtained by maize from tree litter input was the same within the 5 m radius around faidherbia ( Fig. 2 ). Similarly, the δ 15 N and total N concentration of the surface soil must have been mostly affected by tree leaf litter input which, given homogenous tree leaf litter input across the plots, may explain why we observed no increase of soil δ 15 N and total N concentration with distance-from-tree. The contribution of AMF-mediated N transfer from tree to crops (direct or indirect) versus uptake via root-to-root contact and direct uptake of N from tree root exudates by crop roots to the crops’ N budget likely depends on the architecture of the tree root system and the distance-from-tree. Specifically, we expected a decrease in the contribution of AMF- and root-mediated uptake of tree-derived N by maize with distance-from-tree due to an increasing distance between the maize and tree rooting zones. Indeed, given the maize foliar δ 15 N but not the soil δ 15 N increased with distance-from-tree, our data provides some evidence for this hypothesis. However, the proportion of tree-derived N obtained by maize via litter-, AMF-, and root-mediated processes was not significantly affected by distance-from-tree ( Fig. 2 ). We did not examine the tree root system, but observed no fine tree roots within a radius of 5 m from faidherbia (at a depth of 0 to 50 cm) which is in line with previous findings about faidherbia’s deep taproot development 30 . Even if maize roots usually grow deeper than 50 cm in the absence of our experimental plots, the lack of fine tree roots within the top 50 cm and across the 5 m from the base of the tree suggests that root-to-root contact between faidherbia and maize is typically minimal and explains why we found no additional increase in tree-derived N obtained by maize plants grown in the Litter&AMF&Roots plots relative to those grown in the Litter&AMF plots ( Table 2 ). Therefore, given the apparent separation of faidherbia and maize roots, our results highlight the potential importance of AMF in connecting the soil volume between the rooting zone of faidherbia and maize for maize to gain access to a larger pool of tree-derived N. Despite the contribution of AMF to the proportion of tree-derived N in maize, maize shoot biomass and grain yield were not significantly increased ( Table 2 ). This is probably linked to the fact that total foliar N content was not affected by plot type, i.e., type of interaction, between tree and maize ( Table 2 ). Total tree-derived N in maize leaves was also not significantly different between plot types but the data follow the same trend as the proportion of tree-derived N ( Table 2 ). While AMF-mediated uptake of tree-derived N by maize from beyond its rooting zone may not increase the N content in maize, an AMF-mediated uptake of tree-derived N from beyond the rooting zone of maize may improve internal N cycling within the agroecosystem. In conclusion, this study provides insight into the underlying ecological process through which N input from trees may be made accessible to crops. It appears that AMF connect the space between the rooting zones of trees and crops via mycelia and as such, increase the amount of tree-derived N accessible to crops. Especially in low-input cropping systems such as those in Malawi, N microdose-fertilization by faidherbia trees and AMF-mediated uptake of tree-derived N by crops could enhance sustainability of agroecosystems in the longer-term."
} | 2,640 |
39650251 | PMC7617082 | pmc | 6,500 | {
"abstract": "Trees within farmers’ fields can enhance systems’ longer-term productivity e.g., via nutrient amelioration, which is indispensable to attain sustainable agroecosystems. While arbuscular mycorrhizal fungi (AMF) are known to improve plant access to soil nutrients, the potential of AMF to mediate nutrient uptake of tree-derived N by crops from beyond the crops’ rooting zones is unclear. We hypothesized that AMF quantitatively contribute to the crop uptake of tree-derived N. We set up root and AMF exclusion and control plots around faidherbia trees ( Faidherbia albida ) and used the 15 N natural abundance technique to determine the magnitude of AMF-mediated uptake of tree-derived N by maize from beyond its rooting zone in smallholder fields. We further tested whether AMF-mediated N uptake decreases with distance-from-tree. We show that within one cropping season, maize obtained approximately 35 kg biologically fixed N ha -1 from faidherbia. One third of tree-derived N in maize leaves was attributed to AMF-mediated N uptake from beyond the maize rooting zone and two thirds to N from tree leaf litter, regardless of distance-from-tree. As hypothesized, maize grown close (1 m) to faidherbia obtained significantly more tree-derived N than at further distances (4 and 5 m). Thus, the faidherbia-AMF association can enhance agroecosystem functioning.",
"discussion": "Discussion Incorporating faidherbia trees in agroecosystems can benefit crop yields through microdose-fertilization N 2 -fixing trees provide high-quality above- and belowground organic matter inputs to the soil e.g. in the form of tree leaf litter input, root exudates, and root turn-over 2 – 4 . Faidherbia leaf litter input alone can provide 50 to 80 kg N ha -1 to the soil under faidherbia trees within a given season 18 , 19 . However, how much of this tree leaf litter-derived N is mineralized and subsequently incorporated into crop biomass is yet unknown. The distinct isotopic N signature i.e., 15 N: 14 N ratio of N 2 -fixing faidherbia allows distinguishing between tree-derived biologically fixed N 2 and residual soil N 23 , 24 . Because maize does not biologically fix N 2 , its isotopic N signature is largely determined by the residual soil N and thus, the 15 N natural abundance technique allows tracing the isotopically distinct tree-derived, biologically fixed N 2 into maize 23 , 24 . Specifically, determining the δ 15 N of maize leaf, paired soil samples and faidherbia reference samples and using an isotope mixing model allowed calculating the proportion of tree-derived N in maize. It is to note that the 15 N natural abundance technique does not detect any non-biologically fixed N2 derived from the trees, resulting in an underestimation of the tree-derived N in maize. We found that in total, over the course of one season, tree-derived N accounts for 35 kg N ha -1 in maize, which corresponds to about 30 % of the total N in maize. Therefore, our results confirm the importance of faidherbia trees in improving the N budget of crops in farmers’ fields. The broad-scale recommended rate of N fertilization in Malawi is 96 kg N ha -1 , but on average only 18 kg N ha -1 is applied by farmers 25 , 26 and many farmers, as those in our study region, lack access to fertilizer and thus, do not apply any fertilizer to their field (based on conversations with the farmers we worked with). We demonstrate that faidherbia provides more than one third of the recommended dose of fertilizer and almost twice the amount that is on the average applied by subsistence farmers. Microdose-fertilization has been shown to result in significant yield increases e.g. microdose-fertilization of 24 kg N ha -1 resulted in a 64 % increase in maize grain yield relative to an unfertilized control 27 . We found maize yields within 5 m of faidherbia were approximately 50 % greater compared to yields of maize plants away from faidherbia, the latter was determined in a previous study 32 . Specifically, maize grain yield was 3.7 ± 0.4 t ha -1 under faidherbia compared to 2.5 ± 0.6 t ha -1 at about 35 m away from faidherbia. We note that the yield determined away from faidherbia was based on green cob dry weight 32 , while yield under faidherbia was determined from mature cobs, after the plants had fully matured, which might have resulted in a slight underestimation of yield away from faidherbia and therefore a slight overestimation of the approximate 50 % increase in yield under relative to away from faidherbia. Nevertheless, our estimates are in line with previous findings 27 . Furthermore, our results show that foliar N concentration was greater within the immediate vicinity (1-2 m) of faidherbia compared to further distances (4-5 m). The same holds true for tree-derived N in maize leaves as indicated by the increase in δ 15 N in maize leaves with distance-from-tree. We conclude that faidherbia trees are effective in providing N microdose-fertilization to crops in subsistence farmers’ fields and, therefore, may contribute to increased yields. Mycorrhizal fungi increase uptake of tree-derived N by maize Greater crop yields and soil nutrient contents previously observed around N 2 -fixing trees within agricultural fields have been mostly ascribed to high quality organic matter inputs to the soil 14 – 21 . The contribution of AMF in making these inputs available to crops has gained much less attention. There has been some evidence that AMF facilitate N transfer from trees to surrounding plants 8 , but verification of this mechanism under field conditions on smallholder farms has been lacking. Faidherbia has been shown to associate with AMF, both in topsoil and great soil depth 22 . Our experimental design and methods did not allow to provide evidence for the existence of direct linkages between tree and maize via a mycorrhizal mycelia. Therefore, we cannot differentiate between direct root-to-root and indirect soil-to-root mediated N transfer enabled by AMF. However, combining the 15 N natural abundance technique and root and AMF exclusion plots allowed us to quantify the effect of AMF-mediated uptake of tree-derived N by maize from beyond its rooting zone whether via direct or indirect transfer, in farmers’ fields. While we cannot exclude the possibility that other filamentous fungi may have contributed to the uptake of tree-derived N by maize from beyond its rooting zone 28 , 29 , we assume that most of the uptake was AMF-mediated. We estimated that the AMF-mediated uptake of tree-derived N by maize plants accounted for 28 % of the total tree-derived N in maize leaves within 5-m around faidherbia trees ( Table 2 ). Tree litter was responsible for most, i.e., about two-thirds, of the tree-derived N in maize and the presence of tree roots within the rooting zone of maize (if at all present) had a negligible effect on the uptake of tree-derived N by maize ( Fig. 2 , Table 1 & 2 ). The experimental plots were located under the tree crown (average crown radius of 5 ± 0.4 m) and therefore, it was expected that the proportion of tree-derived N obtained by maize from tree litter input was the same within the 5 m radius around faidherbia ( Fig. 2 ). Similarly, the δ 15 N and total N concentration of the surface soil must have been mostly affected by tree leaf litter input which, given homogenous tree leaf litter input across the plots, may explain why we observed no increase of soil δ 15 N and total N concentration with distance-from-tree. The contribution of AMF-mediated N transfer from tree to crops (direct or indirect) versus uptake via root-to-root contact and direct uptake of N from tree root exudates by crop roots to the crops’ N budget likely depends on the architecture of the tree root system and the distance-from-tree. Specifically, we expected a decrease in the contribution of AMF- and root-mediated uptake of tree-derived N by maize with distance-from-tree due to an increasing distance between the maize and tree rooting zones. Indeed, given the maize foliar δ 15 N but not the soil δ 15 N increased with distance-from-tree, our data provides some evidence for this hypothesis. However, the proportion of tree-derived N obtained by maize via litter-, AMF-, and root-mediated processes was not significantly affected by distance-from-tree ( Fig. 2 ). We did not examine the tree root system, but observed no fine tree roots within a radius of 5 m from faidherbia (at a depth of 0 to 50 cm) which is in line with previous findings about faidherbia’s deep taproot development 30 . Even if maize roots usually grow deeper than 50 cm in the absence of our experimental plots, the lack of fine tree roots within the top 50 cm and across the 5 m from the base of the tree suggests that root-to-root contact between faidherbia and maize is typically minimal and explains why we found no additional increase in tree-derived N obtained by maize plants grown in the Litter&AMF&Roots plots relative to those grown in the Litter&AMF plots ( Table 2 ). Therefore, given the apparent separation of faidherbia and maize roots, our results highlight the potential importance of AMF in connecting the soil volume between the rooting zone of faidherbia and maize for maize to gain access to a larger pool of tree-derived N. Despite the contribution of AMF to the proportion of tree-derived N in maize, maize shoot biomass and grain yield were not significantly increased ( Table 2 ). This is probably linked to the fact that total foliar N content was not affected by plot type, i.e., type of interaction, between tree and maize ( Table 2 ). Total tree-derived N in maize leaves was also not significantly different between plot types but the data follow the same trend as the proportion of tree-derived N ( Table 2 ). While AMF-mediated uptake of tree-derived N by maize from beyond its rooting zone may not increase the N content in maize, an AMF-mediated uptake of tree-derived N from beyond the rooting zone of maize may improve internal N cycling within the agroecosystem. In conclusion, this study provides insight into the underlying ecological process through which N input from trees may be made accessible to crops. It appears that AMF connect the space between the rooting zones of trees and crops via mycelia and as such, increase the amount of tree-derived N accessible to crops. Especially in low-input cropping systems such as those in Malawi, N microdose-fertilization by faidherbia trees and AMF-mediated uptake of tree-derived N by crops could enhance sustainability of agroecosystems in the longer-term."
} | 2,640 |
39650251 | PMC7617082 | pmc | 6,501 | {
"abstract": "Trees within farmers’ fields can enhance systems’ longer-term productivity e.g., via nutrient amelioration, which is indispensable to attain sustainable agroecosystems. While arbuscular mycorrhizal fungi (AMF) are known to improve plant access to soil nutrients, the potential of AMF to mediate nutrient uptake of tree-derived N by crops from beyond the crops’ rooting zones is unclear. We hypothesized that AMF quantitatively contribute to the crop uptake of tree-derived N. We set up root and AMF exclusion and control plots around faidherbia trees ( Faidherbia albida ) and used the 15 N natural abundance technique to determine the magnitude of AMF-mediated uptake of tree-derived N by maize from beyond its rooting zone in smallholder fields. We further tested whether AMF-mediated N uptake decreases with distance-from-tree. We show that within one cropping season, maize obtained approximately 35 kg biologically fixed N ha -1 from faidherbia. One third of tree-derived N in maize leaves was attributed to AMF-mediated N uptake from beyond the maize rooting zone and two thirds to N from tree leaf litter, regardless of distance-from-tree. As hypothesized, maize grown close (1 m) to faidherbia obtained significantly more tree-derived N than at further distances (4 and 5 m). Thus, the faidherbia-AMF association can enhance agroecosystem functioning.",
"discussion": "Discussion Incorporating faidherbia trees in agroecosystems can benefit crop yields through microdose-fertilization N 2 -fixing trees provide high-quality above- and belowground organic matter inputs to the soil e.g. in the form of tree leaf litter input, root exudates, and root turn-over 2 – 4 . Faidherbia leaf litter input alone can provide 50 to 80 kg N ha -1 to the soil under faidherbia trees within a given season 18 , 19 . However, how much of this tree leaf litter-derived N is mineralized and subsequently incorporated into crop biomass is yet unknown. The distinct isotopic N signature i.e., 15 N: 14 N ratio of N 2 -fixing faidherbia allows distinguishing between tree-derived biologically fixed N 2 and residual soil N 23 , 24 . Because maize does not biologically fix N 2 , its isotopic N signature is largely determined by the residual soil N and thus, the 15 N natural abundance technique allows tracing the isotopically distinct tree-derived, biologically fixed N 2 into maize 23 , 24 . Specifically, determining the δ 15 N of maize leaf, paired soil samples and faidherbia reference samples and using an isotope mixing model allowed calculating the proportion of tree-derived N in maize. It is to note that the 15 N natural abundance technique does not detect any non-biologically fixed N2 derived from the trees, resulting in an underestimation of the tree-derived N in maize. We found that in total, over the course of one season, tree-derived N accounts for 35 kg N ha -1 in maize, which corresponds to about 30 % of the total N in maize. Therefore, our results confirm the importance of faidherbia trees in improving the N budget of crops in farmers’ fields. The broad-scale recommended rate of N fertilization in Malawi is 96 kg N ha -1 , but on average only 18 kg N ha -1 is applied by farmers 25 , 26 and many farmers, as those in our study region, lack access to fertilizer and thus, do not apply any fertilizer to their field (based on conversations with the farmers we worked with). We demonstrate that faidherbia provides more than one third of the recommended dose of fertilizer and almost twice the amount that is on the average applied by subsistence farmers. Microdose-fertilization has been shown to result in significant yield increases e.g. microdose-fertilization of 24 kg N ha -1 resulted in a 64 % increase in maize grain yield relative to an unfertilized control 27 . We found maize yields within 5 m of faidherbia were approximately 50 % greater compared to yields of maize plants away from faidherbia, the latter was determined in a previous study 32 . Specifically, maize grain yield was 3.7 ± 0.4 t ha -1 under faidherbia compared to 2.5 ± 0.6 t ha -1 at about 35 m away from faidherbia. We note that the yield determined away from faidherbia was based on green cob dry weight 32 , while yield under faidherbia was determined from mature cobs, after the plants had fully matured, which might have resulted in a slight underestimation of yield away from faidherbia and therefore a slight overestimation of the approximate 50 % increase in yield under relative to away from faidherbia. Nevertheless, our estimates are in line with previous findings 27 . Furthermore, our results show that foliar N concentration was greater within the immediate vicinity (1-2 m) of faidherbia compared to further distances (4-5 m). The same holds true for tree-derived N in maize leaves as indicated by the increase in δ 15 N in maize leaves with distance-from-tree. We conclude that faidherbia trees are effective in providing N microdose-fertilization to crops in subsistence farmers’ fields and, therefore, may contribute to increased yields. Mycorrhizal fungi increase uptake of tree-derived N by maize Greater crop yields and soil nutrient contents previously observed around N 2 -fixing trees within agricultural fields have been mostly ascribed to high quality organic matter inputs to the soil 14 – 21 . The contribution of AMF in making these inputs available to crops has gained much less attention. There has been some evidence that AMF facilitate N transfer from trees to surrounding plants 8 , but verification of this mechanism under field conditions on smallholder farms has been lacking. Faidherbia has been shown to associate with AMF, both in topsoil and great soil depth 22 . Our experimental design and methods did not allow to provide evidence for the existence of direct linkages between tree and maize via a mycorrhizal mycelia. Therefore, we cannot differentiate between direct root-to-root and indirect soil-to-root mediated N transfer enabled by AMF. However, combining the 15 N natural abundance technique and root and AMF exclusion plots allowed us to quantify the effect of AMF-mediated uptake of tree-derived N by maize from beyond its rooting zone whether via direct or indirect transfer, in farmers’ fields. While we cannot exclude the possibility that other filamentous fungi may have contributed to the uptake of tree-derived N by maize from beyond its rooting zone 28 , 29 , we assume that most of the uptake was AMF-mediated. We estimated that the AMF-mediated uptake of tree-derived N by maize plants accounted for 28 % of the total tree-derived N in maize leaves within 5-m around faidherbia trees ( Table 2 ). Tree litter was responsible for most, i.e., about two-thirds, of the tree-derived N in maize and the presence of tree roots within the rooting zone of maize (if at all present) had a negligible effect on the uptake of tree-derived N by maize ( Fig. 2 , Table 1 & 2 ). The experimental plots were located under the tree crown (average crown radius of 5 ± 0.4 m) and therefore, it was expected that the proportion of tree-derived N obtained by maize from tree litter input was the same within the 5 m radius around faidherbia ( Fig. 2 ). Similarly, the δ 15 N and total N concentration of the surface soil must have been mostly affected by tree leaf litter input which, given homogenous tree leaf litter input across the plots, may explain why we observed no increase of soil δ 15 N and total N concentration with distance-from-tree. The contribution of AMF-mediated N transfer from tree to crops (direct or indirect) versus uptake via root-to-root contact and direct uptake of N from tree root exudates by crop roots to the crops’ N budget likely depends on the architecture of the tree root system and the distance-from-tree. Specifically, we expected a decrease in the contribution of AMF- and root-mediated uptake of tree-derived N by maize with distance-from-tree due to an increasing distance between the maize and tree rooting zones. Indeed, given the maize foliar δ 15 N but not the soil δ 15 N increased with distance-from-tree, our data provides some evidence for this hypothesis. However, the proportion of tree-derived N obtained by maize via litter-, AMF-, and root-mediated processes was not significantly affected by distance-from-tree ( Fig. 2 ). We did not examine the tree root system, but observed no fine tree roots within a radius of 5 m from faidherbia (at a depth of 0 to 50 cm) which is in line with previous findings about faidherbia’s deep taproot development 30 . Even if maize roots usually grow deeper than 50 cm in the absence of our experimental plots, the lack of fine tree roots within the top 50 cm and across the 5 m from the base of the tree suggests that root-to-root contact between faidherbia and maize is typically minimal and explains why we found no additional increase in tree-derived N obtained by maize plants grown in the Litter&AMF&Roots plots relative to those grown in the Litter&AMF plots ( Table 2 ). Therefore, given the apparent separation of faidherbia and maize roots, our results highlight the potential importance of AMF in connecting the soil volume between the rooting zone of faidherbia and maize for maize to gain access to a larger pool of tree-derived N. Despite the contribution of AMF to the proportion of tree-derived N in maize, maize shoot biomass and grain yield were not significantly increased ( Table 2 ). This is probably linked to the fact that total foliar N content was not affected by plot type, i.e., type of interaction, between tree and maize ( Table 2 ). Total tree-derived N in maize leaves was also not significantly different between plot types but the data follow the same trend as the proportion of tree-derived N ( Table 2 ). While AMF-mediated uptake of tree-derived N by maize from beyond its rooting zone may not increase the N content in maize, an AMF-mediated uptake of tree-derived N from beyond the rooting zone of maize may improve internal N cycling within the agroecosystem. In conclusion, this study provides insight into the underlying ecological process through which N input from trees may be made accessible to crops. It appears that AMF connect the space between the rooting zones of trees and crops via mycelia and as such, increase the amount of tree-derived N accessible to crops. Especially in low-input cropping systems such as those in Malawi, N microdose-fertilization by faidherbia trees and AMF-mediated uptake of tree-derived N by crops could enhance sustainability of agroecosystems in the longer-term."
} | 2,640 |
39650251 | PMC7617082 | pmc | 6,501 | {
"abstract": "Trees within farmers’ fields can enhance systems’ longer-term productivity e.g., via nutrient amelioration, which is indispensable to attain sustainable agroecosystems. While arbuscular mycorrhizal fungi (AMF) are known to improve plant access to soil nutrients, the potential of AMF to mediate nutrient uptake of tree-derived N by crops from beyond the crops’ rooting zones is unclear. We hypothesized that AMF quantitatively contribute to the crop uptake of tree-derived N. We set up root and AMF exclusion and control plots around faidherbia trees ( Faidherbia albida ) and used the 15 N natural abundance technique to determine the magnitude of AMF-mediated uptake of tree-derived N by maize from beyond its rooting zone in smallholder fields. We further tested whether AMF-mediated N uptake decreases with distance-from-tree. We show that within one cropping season, maize obtained approximately 35 kg biologically fixed N ha -1 from faidherbia. One third of tree-derived N in maize leaves was attributed to AMF-mediated N uptake from beyond the maize rooting zone and two thirds to N from tree leaf litter, regardless of distance-from-tree. As hypothesized, maize grown close (1 m) to faidherbia obtained significantly more tree-derived N than at further distances (4 and 5 m). Thus, the faidherbia-AMF association can enhance agroecosystem functioning.",
"discussion": "Discussion Incorporating faidherbia trees in agroecosystems can benefit crop yields through microdose-fertilization N 2 -fixing trees provide high-quality above- and belowground organic matter inputs to the soil e.g. in the form of tree leaf litter input, root exudates, and root turn-over 2 – 4 . Faidherbia leaf litter input alone can provide 50 to 80 kg N ha -1 to the soil under faidherbia trees within a given season 18 , 19 . However, how much of this tree leaf litter-derived N is mineralized and subsequently incorporated into crop biomass is yet unknown. The distinct isotopic N signature i.e., 15 N: 14 N ratio of N 2 -fixing faidherbia allows distinguishing between tree-derived biologically fixed N 2 and residual soil N 23 , 24 . Because maize does not biologically fix N 2 , its isotopic N signature is largely determined by the residual soil N and thus, the 15 N natural abundance technique allows tracing the isotopically distinct tree-derived, biologically fixed N 2 into maize 23 , 24 . Specifically, determining the δ 15 N of maize leaf, paired soil samples and faidherbia reference samples and using an isotope mixing model allowed calculating the proportion of tree-derived N in maize. It is to note that the 15 N natural abundance technique does not detect any non-biologically fixed N2 derived from the trees, resulting in an underestimation of the tree-derived N in maize. We found that in total, over the course of one season, tree-derived N accounts for 35 kg N ha -1 in maize, which corresponds to about 30 % of the total N in maize. Therefore, our results confirm the importance of faidherbia trees in improving the N budget of crops in farmers’ fields. The broad-scale recommended rate of N fertilization in Malawi is 96 kg N ha -1 , but on average only 18 kg N ha -1 is applied by farmers 25 , 26 and many farmers, as those in our study region, lack access to fertilizer and thus, do not apply any fertilizer to their field (based on conversations with the farmers we worked with). We demonstrate that faidherbia provides more than one third of the recommended dose of fertilizer and almost twice the amount that is on the average applied by subsistence farmers. Microdose-fertilization has been shown to result in significant yield increases e.g. microdose-fertilization of 24 kg N ha -1 resulted in a 64 % increase in maize grain yield relative to an unfertilized control 27 . We found maize yields within 5 m of faidherbia were approximately 50 % greater compared to yields of maize plants away from faidherbia, the latter was determined in a previous study 32 . Specifically, maize grain yield was 3.7 ± 0.4 t ha -1 under faidherbia compared to 2.5 ± 0.6 t ha -1 at about 35 m away from faidherbia. We note that the yield determined away from faidherbia was based on green cob dry weight 32 , while yield under faidherbia was determined from mature cobs, after the plants had fully matured, which might have resulted in a slight underestimation of yield away from faidherbia and therefore a slight overestimation of the approximate 50 % increase in yield under relative to away from faidherbia. Nevertheless, our estimates are in line with previous findings 27 . Furthermore, our results show that foliar N concentration was greater within the immediate vicinity (1-2 m) of faidherbia compared to further distances (4-5 m). The same holds true for tree-derived N in maize leaves as indicated by the increase in δ 15 N in maize leaves with distance-from-tree. We conclude that faidherbia trees are effective in providing N microdose-fertilization to crops in subsistence farmers’ fields and, therefore, may contribute to increased yields. Mycorrhizal fungi increase uptake of tree-derived N by maize Greater crop yields and soil nutrient contents previously observed around N 2 -fixing trees within agricultural fields have been mostly ascribed to high quality organic matter inputs to the soil 14 – 21 . The contribution of AMF in making these inputs available to crops has gained much less attention. There has been some evidence that AMF facilitate N transfer from trees to surrounding plants 8 , but verification of this mechanism under field conditions on smallholder farms has been lacking. Faidherbia has been shown to associate with AMF, both in topsoil and great soil depth 22 . Our experimental design and methods did not allow to provide evidence for the existence of direct linkages between tree and maize via a mycorrhizal mycelia. Therefore, we cannot differentiate between direct root-to-root and indirect soil-to-root mediated N transfer enabled by AMF. However, combining the 15 N natural abundance technique and root and AMF exclusion plots allowed us to quantify the effect of AMF-mediated uptake of tree-derived N by maize from beyond its rooting zone whether via direct or indirect transfer, in farmers’ fields. While we cannot exclude the possibility that other filamentous fungi may have contributed to the uptake of tree-derived N by maize from beyond its rooting zone 28 , 29 , we assume that most of the uptake was AMF-mediated. We estimated that the AMF-mediated uptake of tree-derived N by maize plants accounted for 28 % of the total tree-derived N in maize leaves within 5-m around faidherbia trees ( Table 2 ). Tree litter was responsible for most, i.e., about two-thirds, of the tree-derived N in maize and the presence of tree roots within the rooting zone of maize (if at all present) had a negligible effect on the uptake of tree-derived N by maize ( Fig. 2 , Table 1 & 2 ). The experimental plots were located under the tree crown (average crown radius of 5 ± 0.4 m) and therefore, it was expected that the proportion of tree-derived N obtained by maize from tree litter input was the same within the 5 m radius around faidherbia ( Fig. 2 ). Similarly, the δ 15 N and total N concentration of the surface soil must have been mostly affected by tree leaf litter input which, given homogenous tree leaf litter input across the plots, may explain why we observed no increase of soil δ 15 N and total N concentration with distance-from-tree. The contribution of AMF-mediated N transfer from tree to crops (direct or indirect) versus uptake via root-to-root contact and direct uptake of N from tree root exudates by crop roots to the crops’ N budget likely depends on the architecture of the tree root system and the distance-from-tree. Specifically, we expected a decrease in the contribution of AMF- and root-mediated uptake of tree-derived N by maize with distance-from-tree due to an increasing distance between the maize and tree rooting zones. Indeed, given the maize foliar δ 15 N but not the soil δ 15 N increased with distance-from-tree, our data provides some evidence for this hypothesis. However, the proportion of tree-derived N obtained by maize via litter-, AMF-, and root-mediated processes was not significantly affected by distance-from-tree ( Fig. 2 ). We did not examine the tree root system, but observed no fine tree roots within a radius of 5 m from faidherbia (at a depth of 0 to 50 cm) which is in line with previous findings about faidherbia’s deep taproot development 30 . Even if maize roots usually grow deeper than 50 cm in the absence of our experimental plots, the lack of fine tree roots within the top 50 cm and across the 5 m from the base of the tree suggests that root-to-root contact between faidherbia and maize is typically minimal and explains why we found no additional increase in tree-derived N obtained by maize plants grown in the Litter&AMF&Roots plots relative to those grown in the Litter&AMF plots ( Table 2 ). Therefore, given the apparent separation of faidherbia and maize roots, our results highlight the potential importance of AMF in connecting the soil volume between the rooting zone of faidherbia and maize for maize to gain access to a larger pool of tree-derived N. Despite the contribution of AMF to the proportion of tree-derived N in maize, maize shoot biomass and grain yield were not significantly increased ( Table 2 ). This is probably linked to the fact that total foliar N content was not affected by plot type, i.e., type of interaction, between tree and maize ( Table 2 ). Total tree-derived N in maize leaves was also not significantly different between plot types but the data follow the same trend as the proportion of tree-derived N ( Table 2 ). While AMF-mediated uptake of tree-derived N by maize from beyond its rooting zone may not increase the N content in maize, an AMF-mediated uptake of tree-derived N from beyond the rooting zone of maize may improve internal N cycling within the agroecosystem. In conclusion, this study provides insight into the underlying ecological process through which N input from trees may be made accessible to crops. It appears that AMF connect the space between the rooting zones of trees and crops via mycelia and as such, increase the amount of tree-derived N accessible to crops. Especially in low-input cropping systems such as those in Malawi, N microdose-fertilization by faidherbia trees and AMF-mediated uptake of tree-derived N by crops could enhance sustainability of agroecosystems in the longer-term."
} | 2,640 |
37679373 | PMC10485025 | pmc | 6,502 | {
"abstract": "Division of labor is a hallmark characteristic of social insect colonies. While it is understood that worker differentiation is regulated through either the queen or her brood, the understanding of the physiology behind task regulation varies within social species. Studies in eusocial insects have shown that juvenile hormone (JH) is associated with division of labor and the onset of foraging tasks. Although, outside of a few key species, this interaction has yet to be elucidated in the red imported fire ant, Solenopsis invicta . In this study, we evaluated the role of a JH analog, S-hydroprene in worker task transition in Solenopsis invicta . S-hydroprene was applied to nurses to observe behavioral changes. S-hyroprene application to nurses did not affect phototaxis, but there was a shift in behavior from internal, nest-based behaviors to external, foraging-based behaviors. These results show that JH may be implicated in worker task transition in S. invicta and may function similarly as it does in other eusocial insects.",
"introduction": "Introduction Division of labor occurs when individuals within a colony perform certain tasks associated with growth and maintenance of the whole colony. It is considered a hallmark characteristic of social insect structure 1 . Distinctions of task allocation can sometimes be indicated by polymorphic individuals that have morphological characters distinct to a specific caste. Social insects can also be monomorphic where the differences between individuals performing different tasks can be physiologically based and associated with nutrition, colony needs and age polyethism determine the task that is performed by workers 2 , 3 . Understanding the physiological basis of division of labor can provide insight on the evolutionary trajectory of eusocial colony structure. In insects, juvenile hormone (JH) is typically associated with metamorphosis, development and reproduction 4 . However, there has been much research among both solitary and social insects, especially that of insects within the order Hymenoptera, that show its role in behavioral transition and task determination in insects 4 – 8 . In the honey bee Apis mellifera , high JH titers are associated with foraging behavior while low titers are associated with nursing behavior 8 , 9 . In some ant species such as Pogonomyrmex californicus , Myrmicaria eumenodies , and Harpegnathos saltator , JH titers were significantly higher in ants displaying extranidal behavior such as foraging and nest defense 10 , 11 , and topical applications of JH or JH-analogs induced foraging-like physiological changes 12 . The red imported fire ant, Solenopsis invicta , provides a good model to further study the hormonal basis of work task transition, specifically the correlation of JH on task transition. The worker caste is comprised of sterile females which perform all tasks necessary for colony survival, growth, and maintenance 13 . For S. invicta , the most well-known aspect of JH is its function in queen development and behavior, often reflecting the reproductive status of the individual queens and their social form 14 – 16 . However, the role of JH in S. invicta workers remains understudied. Previously, the effect of the application of the JH analog S-hydroprene on the expression of genes such as vitellogenin and hexamerin , which are possibly co-opted for task allocation in workers, was evaluated. The application of JH analog to nurses caused shifts in expression of hexamerins to be similar to that of a forager, indicating a possible role in the shift from nurses to foragers 17 , 18 . However, these studies did not evaluate if the application also resulted in changes in the behavior of the ants. In this study, we provide a behavioral assay to evaluate the prolonged effect of the application of the JH analog, S-hydroprene, on worker behavior, specifically in medium nurses. Nurses were treated with the analog or shams for a 7-day period, and the effect of the application was observed on phototaxis preferred placement in a microcolony (nest-like or foraging-like).",
"discussion": "Discussion In insects, juvenile hormone titers are important in the development and reproduction of both solitary and social insects 4 , 22 . The results from our study show that the JH analog can cause a behavioral shift in S. invicta workers. JH has been shown to influence worker behavior and task in many other insect species, however the extent of this influence has yet to be fully understood. Our results show that the direct application of the JH analog does not influence worker phototaxis or preference to light (Fig. 3 ). In some ant species, phototaxis can be used as a proxy for foraging. For example, in the study of the effect of JH in the behavior of Acromyrmex octospinosus workers, an increased attraction towards light was tied to behaviors relating to leaving the nest, typically that of foraging 23 . However, S. invicta workers are more influenced by temperature than light when it comes to foraging 24 , 25 . Therefore, the results of the phototaxis assay paired with that of the behavioral assay in the microcolonies indicate that light plays a small role if any in influencing foraging in S. invicta , as more workers were found within foraging conditions when treated with S-hydroprene compared to sham or acetone treatments (Fig. 5 ). Several factors such as age or nutritional status of the worker could affect its propensity to switch from nursing to foraging. Indeed, it is known that age in eusocial insects like Bombus impatiens 26 , A. mellifera 3 and even in S. invicta 27 plays a role in the behavior the insect performs. In this study, the age of the workers was not controlled, instead, nurse ants interacting with the brood were randomly selected to perform the experiment. Despite this, our results show that the effect of the application of a JH analog on S. invicta worker behavior and task was consistent with those found in other ant, wasp, bee, and termite species when the insects were treated with a JH analog 7 , 8 , 10 , 11 , 28 . Future experiments could assess whether these other factors influence the effect of the juvenile hormone analog on the behavior of fire ant workers. Overall, this study supports the notion that the role of JH might be conserved in S. invicta as a major regulator for division of labor."
} | 1,595 |
37971327 | PMC10711437 | pmc | 6,503 | {
"abstract": "Abstract Bacterial transformation is an important mode of horizontal gene transfer that helps spread genetic material across species boundaries. Yet, the factors that pose barriers to genome-wide cross-species gene transfer are poorly characterized. Here, we develop a replacement accumulation assay to study the effects of genomic distance on transfer dynamics. Using Bacillus subtilis as recipient and various species of the genus Bacillus as donors, we find that the rate of orthologous replacement decreases exponentially with the divergence of their core genomes. We reveal that at least 96% of the B. subtilis core genes are accessible to replacement by alleles from Bacillus spizizenii . For the more distantly related Bacillus atrophaeus , gene replacement events cluster at genomic locations with high sequence identity and preferentially replace ribosomal genes. Orthologous replacement also creates mosaic patterns between donor and recipient genomes, rearranges the genome architecture, and governs gain and loss of accessory genes. We conclude that cross-species gene transfer is dominated by orthologous replacement of core genes which occurs nearly unrestricted between closely related species. At a lower rate, the exchange of accessory genes gives rise to more complex genome dynamics.",
"introduction": "Introduction Horizontal gene transfer (HGT) is an important factor in bacterial evolution. It plays a major role in providing non-sexually reproducing organisms with genetic variability. Phylogenetic studies have shown that a surprisingly large fraction of bacterial genomes and gene classes have been affected by HGT over the course of evolution ( 1–4 ). However, the genome-wide dynamics of HGT are poorly characterized. In bacteria, one of the main mechanisms mediating HGT is natural transformation. In this process, cells take up DNA from the surrounding and integrate it into their genome in a process called homologous recombination. Bacillus subtilis is among the 80 species known to be competent for transformation ( 5 , 6 ). After external DNA has been taken up into the cytoplasm, the recombination machinery performs a homology search on the genome ( 7 ). Microhomologies as short as 8 nt are recognized and further binding of neighboring nucleotides can initiate branch invasion of the genome ( 8 ). If branch migration becomes stabilized, a heteroduplex can form as a three-stranded D-loop ( 9 ). Several studies report the formation of mosaic patterns between donor and recipient alleles resulting from this process ( 10–14 ). It remains unclear by which mechanism and how frequently these mosaic patterns are formed. Various processes limit the efficiency of HGT by transformation. At the level of DNA uptake, competence for transformation is tightly controlled in some species including B. subtilis ( 15 , 16 ). Once inside the cell, DNA from different strains or species is subject to degradation by restriction modification systems ( 17 , 18 ). Furthermore, the sequence divergence between the donor and recipient alleles has been established as a main barrier to recombination. Multiple studies have approached this dependence by investigating the rate and outcome of transformation for sets of predefined, single genes ( 19–22 ). An exponential decrease in the replacement probability was found for different bacterial species ( 22 , 23 ), including B. subtilis ( 19 , 24 ) and Saccharomyces cerevisiae ( 20 ). Likewise, increasing sequence divergence was found to cause decreasing integration lengths ( 19 ). Studying the effects of sequences divergence between single orthologous genes is well suited for understanding specific details in the process, yet it falls short of capturing the whole genome transfer dynamics and effects of accessory genes. Genome-wide recombination between different strains and species has been investigated under selective conditions for B. subtilis ( 18 , 25 ), Streptococcus pneumoniae ( 13 , 14 ) and Haemophilus influenzae ( 26 ). In these studies, the probability of detecting replacement of a specific gene was dependent on local sequence identity and on its fitness effects. Their disentanglement requires extensive modeling ( 25 ). Under minimally selective conditions, the distribution of fitness effects of transformation has been investigated ( 27 ), but the underlying genome dynamics were not systematically studied. Here, we develop a replacement accumulation assay under minimal selection to investigate how the genomic distance between donor and recipient affects genome dynamics. We reveal that the exponential relation between transfer rate and sequence divergence holds for orthologous replacement within the core genomes of Bacillus species. Furthermore, we show that the dynamics of the accessory genomes are linked to the rates of orthologous replacement. By pooling the data from a large set of transformation experiments, we investigate cold spots of orthologous replacement. For two closely related Bacillus species we demonstrate that nearly the entire core genome is accessible to replacement. Taken together, our work contributes to understanding the barriers to horizontal gene transfer at the level of the entire genome.",
"discussion": "Discussion For eukaryotes, mating occurs only between individuals of the same species. This is different for bacteria where genes can be transferred horizontally between species, yet the rates and restrictions of this process are poorly understood at the whole genome level. Here, we employed a replacement accumulation assay for characterizing the effects of sequence divergence on genome-wide transfer between Bacillus species. We show that orthologous replacement dominates genome dynamics and that the sequence identity between donor and recipient is crucial for the dynamics of both core and accessory genomes. Remarkably, nearly the entire core genome of B. subtilis is accessible to orthologous replacement by B. spizizenii core genes. In the 42 pooled hybrid strains, only 4% of all core genes were never hit by replacement. In the following, we discuss all 137 cold spot genes. Lack of replacement within specific genes can be caused (i) by flanking regions that belong to the accessory genome and inhibit recombination, (ii) by the high local sequence divergence within the putative cold spot, or (iii) by lethality (or strong reduction of fitness). To address the first point, we calculated the fraction of cold spots that are flanked by accessory genome and found that 29% and 46% of the regions are flanked by accessory genome at one side or both sides, respectively (Figure S12, 7). This indicates that being flanked by accessory genome reduces the probability of orthologous replacement and we conclude that the architecture of the accessory genome affects the rate of core genome replacement. Next, we compared the distributions of mean identities of genes that have been fully replaced in at least one hybrid strain with cold spot genes (Figure S11C). The identity distribution of cold spot genes is shifted towards lower values, indicating that lower-than-average sequence identity contributes to lack of recombination within these regions. However, multiple putative cold spot genes have high sequence identity (Figure S11B). We speculated that replacement within a specific class of these genes may cause a strong reduction of fitness, but using Panther GO, we found no gene class overrepresented within the putative cold spots with a sequence identity exceeding 95%. Recent reports focused on genome-wide recombination between different strains of the same species. Even though the experimental details vary strongly between the different studies, there are multiple characteristics of orthologous replacement that tend to hold in general. For S. pneumoniae ( 13 ), H. influenzae ( 26 ), Helicobacter pylori ( 17 ) and B. subtilis ( 18 ) replacements were scattered throughout the genome without obvious hot-spots apart from the sites that were selected for. Here, using B. subtilis as recipient, we also find that gene transfer occurs across the entire genome, when B. spizizenii or B. vallismortis served as donors. By contrast, for the B. atrophaeus donor, we observed transfer only within a few genome regions. The average core genome identities of all donor-recipient pairs described above exceed 92%, whereas the average identity between B. subtilis and B. atrophaeus is considerably lower at 87%. For BatroHyb, transfer was observed only within regions of higher-than-average identity, where ribosomal genes are overrepresented. While other genes with high identity exist (Figure S7), none of them is overrepresented using a Panther GO analysis. It remains difficult to disentangle whether the high sequence identity of these essential genes is a cause or a consequence of enhanced gene transfer rate. These genes may be functional only if their sequence is highly conserved. On the other hand, frequent gene transfer across species may be involved in maintaining the integrity of the essential genes. We found that the rate of replacement decreases exponentially as a function of sequence divergence of the core genomes of the Bacillus donors. At the level of single gene replacement, it has been shown that the probability for recombination decreases rapidly as a function of sequence divergence ( 19 , 22–24 ). In those studies, the transformation rate was determined as the number of clones that developed antibiotic resistance due to replacement of a single nucleotide. In the present study, the transfer rate is defined very differently, i.e. as the fraction of recipient core genome replaced by donor genome per time. This rate depends on the local sequence divergence of the replaced segment (comparable to the previous studies), but also on the length distribution of the integrated segments as well as the architecture of the accessory genomes. Therefore, it is remarkable that the exponential relation holds for genome-wide transfer. Given the exponential relation, we can estimate a transfer rate of \\documentclass[12pt]{minimal}\n\\usepackage{amsmath}\n\\usepackage{wasysym}\n\\usepackage{amsfonts}\n\\usepackage{amssymb}\n\\usepackage{amsbsy}\n\\usepackage{upgreek}\n\\usepackage{mathrsfs}\n\\setlength{\\oddsidemargin}{-69pt}\n\\begin{document}\n$1.2 \\times {10}^{ - 4}{\\mathrm{\\ \\% }}\\,{{\\rm h}}^{ - 1}$\\end{document} for GeoHyb. This corresponds to a number of transferred bp not significantly different from zero during the whole of the 40 h experiment. Due to the large error, we cannot determine whether this donor deviates from the exponential behavior and supports the idea of an identity cut-off for transformation as previously proposed ( 48 ). Nevertheless, it is interesting to note that we observed no gene transfer across the different genera. The decrease of replacement probability with average sequence divergence between core genomes is consistent with earlier findings that show larger-than-average sequence identity close to the endpoints of the replacements ( 17 , 25 ). On the other hand, several studies report a lack of dependence between the replacement probability and sequence identity ( 13 , 18 ). This difference is particularly striking in a study by Vasileva et al. ( 18 ) that reports on recombination between B. subtilis and donor species whose sequence divergences are comparable to the ones used in our study. Vasileva et al. ( 18 ) used protoplast fusion to import donor DNA. Therefore, the substrate for recombination in that study was double-stranded DNA while single-stranded DNA is imported by transformation. It is conceivable that the substrate affects the recombination process. By comparing the results of the replacement accumulation experiment described in this study with results from our previous study ( 25 ), we can assess effects of competition between hybrids on the genome dynamics. That earlier study differed in two important aspects. We let B. subtilis transform with genomic DNA of B. spizizenii , running 21 cycles. By contrast to the present study, however, during each cycle newly formed hybrid strains were allowed to compete against each other for extended periods of time. As a consequence, the fitter hybrids were selected for. Second, during each cycle, cells were treated with UV light ( 25 ). We determined the core genome transfer rate in both studies. Here, we determined a transfer rate of \\documentclass[12pt]{minimal}\n\\usepackage{amsmath}\n\\usepackage{wasysym}\n\\usepackage{amsfonts}\n\\usepackage{amssymb}\n\\usepackage{amsbsy}\n\\usepackage{upgreek}\n\\usepackage{mathrsfs}\n\\setlength{\\oddsidemargin}{-69pt}\n\\begin{document}\n$0.21{\\mathrm{\\% }}\\,{{\\rm h}}^{ - 1}$\\end{document} for BspizHyb which is lower than the rate found by Power et. al of approximately \\documentclass[12pt]{minimal}\n\\usepackage{amsmath}\n\\usepackage{wasysym}\n\\usepackage{amsfonts}\n\\usepackage{amssymb}\n\\usepackage{amsbsy}\n\\usepackage{upgreek}\n\\usepackage{mathrsfs}\n\\setlength{\\oddsidemargin}{-69pt}\n\\begin{document}\n$0.28{\\mathrm{\\% }}\\,{{\\rm h}}^{ - 1}$\\end{document} . This indicates that high transfer rates were beneficial in the evolution experiment in agreement with the net fitness increase found in Power et al. ( 25 ). The length distributions of integrated segments were exponential in both studies, but the characteristic length was higher in the earlier study with selection and irradiation than in the replacement accumulation experiment studied here, suggesting that competition has selected for the integration of extended segments. This observation is interesting in the context of disruptive epistasis; with increasing length of the integrated segments, the probability of partially replacing operons decreases. In summary, comparison between two replacement accumulation experiments in the presence and absence of selection, we find a tendency, that selection and/or irradiation enhances the rate of orthologous replacement and the integration of extended segments. In conclusion, we show that for species with low sequence divergence, nearly all core genes are accessible to gene transfer. The gene replacement rate between Bacillus species decreases exponentially with genome divergence between donor and recipient. Recombination between orthologous genes also mediated dynamics of the accessory genomes, however that rate of insertions and deletions of accessory genome was more than a magnitude lower compared to the replacement rate. We propose that cross-species transformation affects bacterial evolution in two ways. First, there is a very frequent transfer between orthologous genes of closely related species. The fitness effects of most of these transfers are fitness-neutral, yet there is potential for beneficial transfer ( 27 ). This frequent exchange represents genomic ‘tinkering’. On the other hand, accessory genes are exchanged at a lower rate, yet we expect them to have larger fitness effects because they enable gain and loss of entire genes or operons."
} | 3,756 |
38730813 | PMC11084899 | pmc | 6,504 | {
"abstract": "We present a surface modification technique that turns CuNi foam films with a high contact angle and non-sticking property into a sticky surface. By decorating with mesh-like biaxially oriented polypropylene (BOPP) and adjusting the surface parameters, the surface exhibits water-retaining capability even when being held upside down. The wetting transition process of droplets falling on its surface were systematically studied using the finite element simulation method. It is found that the liquid filled the surface microstructure and curvy three-phase contact line. Moreover, we experimentally demonstrated that this surface can be further applied to capture underwater air bubbles.",
"conclusion": "4. Conclusions In this study, surfaces with high droplet adhesion were created by decorating CuNi foam film with mesh-like BOPP. The three-phase contact line ratio is used to judge the influence of the open area fraction on the contact angle hysteresis of the composite surface. A large three-phase contact line ratio leads to a large contact angle hysteresis and good adhesion. The three-phase contact angle ratio selected in this letter is √0.30, exhibiting the highest adhesion. The multiscale hierarchical composite roughness composed of BOPP and CuNi foam film with a different roughness and height can make the contact angle hysteresis of droplets on it greater than 90°, even if it reaches 180°. The sources and wetting process of wetting resistance were accurately described through finite element numerical calculations, further elucidating the mechanism of wetting transition in hierarchical porous CuNi composite films. In addition, the prepared surface can also realize the adhesion of air bubbles underwater.",
"introduction": "1. Introduction Surface topography has a great influence on wettability and, in particular, on the motion of droplets, with behaviors ranging from highly repellent to highly adhesive. Wetting-resistant surfaces, owing to their large water contact angle and non-sticking characteristics, have long been desired for a range of technological applications, including self-cleaning [ 1 , 2 , 3 ], anti-fogging [ 4 ], anti-icing [ 5 ], buoyance enhancement and drug reduction [ 6 ]. In terms of applications, nowadays in daily life and many industrial contexts, techniques such as spray [ 7 ], jet cooking [ 8 ], heat transfer [ 9 ] and/or microfluidics [ 10 ] require rather sticky surfaces. Recently, proper surface engineering approaches have become an important route to transfer liquid–surfaces adhesion. Decorating a flat surface with either hydrophobic or hydrophilic nanoparticles can create superhydrophilicity with high droplet adhesion [ 11 ]. Moreover, besides tailoring the adhesion properties, texturing the surface with chemical and physical heterogeneity also exerts a great influence on the in-air or underwater three-phase contact line. For example, the alternated superhydrophobic and hydrophilic strip surface produces a large contact angle hysteresis, preventing the movement of underwater bubbles. The super-hydrophobicity of a surface is generally determined by two crucial factors: high surface roughness and low surface energy [ 12 ]. Three conventional approaches exist for the fabrication of foam films with superhydrophobic interfaces [ 13 ]: (a) Surface modification techniques can enhance the hydrophilicity of metal foam films by changing their surface properties and/or energy states. For example, plasma treatment [ 14 ], UV irradiation [ 15 ], and chemical modification-induced surface roughening [ 16 ]. (b) Designing the microstructure of metal foam films involves incorporating nano-level microstructures or microporous structures and using chemical methods to generate interfaces with desired morphology and chemical functionality in situ [ 17 ], thereby enhancing the surface area and roughness to improve hydrophilicity [ 13 ]. (c) To achieve the desired morphology, it is essential to implement appropriate surface modification techniques [ 18 ]. Additionally, coating the metal foam film with hydrophilic materials such as fluoropolymers or silanes [ 19 ] can effectively enhance its surface hydrophilicity. In comparison to alternative approaches, the application of a hydrophilic coating layer onto the metal foam film offers a simple, convenient, and cost-effective method. Moreover, by precisely controlling the material composition and thickness of the coating layer, it becomes feasible to tailor the hydrophilicity according to specific application requirements. However, it is crucial to acknowledge that coatings can be influenced by factors such as friction, aging, or chemical damage, necessitating regular maintenance and repair. Consequently, the selection of an appropriate coating material becomes important in ensuring the required performance and reliability in specific applications. It has been demonstrated that this sticky phenomenon, also known as “petal effect”, is a consequence of significant contact pining, by which the droplets are affected in a complicated way by many surface topographies such as roughness factors and chemistries [ 20 , 21 , 22 , 23 ]. Pining of the contact line primarily prevents droplets from moving, and hence a sticky state describes systems with high contact angle hysteresis. In general, contact angle hysteresis is relevant to the tortuosity of the three-phase contact line and the maximum contact length scale. With proper surface engineering approaches, it is possible to manipulate the contact line structure including shape, length, continuity, and amount of contact. For example, microfabricated reentrant architecture creates a finger-like contact line, enabling highly wetting behavior for liquid/surface combinations that are typically non-wettable [ 24 ]. Rough surfaces can derive their low contact angle hysteresis from discontinuous contact [ 25 ]. The aforementioned studies have proved that manipulating surface topography should serve as a quick and efficient way for the design and engineering of surfaces with tailored adhesion properties. There are few direct methods to observe the wetting state of a droplet on a microstructure surface, mainly limited to partial structures beneath the droplet. It is difficult to observe the internal wetting state within the droplet or to fully observe the entire wetting process of the droplet. Similarly, observing the wetting process of a droplet on a BOPP-CuNi metal composite film surface also presents challenges. In recent years, many studies have employed simulation methods to analyze the changes in the wetting state of droplets on surface microstructures. The most widely used numerical calculation methods are molecular dynamics simulation, lattice Boltzmann method, and computational fluid dynamics (CFD) simulation. These methods are, respectively suitable for microscale, mesoscale, and macroscale. Since the wetting process of real-sized droplets on specially structured surfaces involves macroscopic field changes, the computational fluid dynamics method (CFD) is more applicable for such studies [ 26 , 27 , 28 , 29 , 30 , 31 ]. The dynamics of the droplet impact is the main research area of CFD methods, with few studies focusing on the wetting state of droplets on microstructure surfaces. CFD methods are also commonly used in the field of microflow simulation, and the application of macroscopic-scale CFD methods to microscopic-scale research is a novel approach that can overcome the spatial and temporal limitations of molecular dynamics and lattice Boltzmann methods when calculating the wetting behavior of millimeter-scale droplets [ 32 ]. In this study, the CFD method was used to simulate the wetting process of droplets on a BOPP-CuNi metal composite film surface, investigating the specific wetting process of droplets on complex microstructure surfaces in a near-real size state. This study aims to address the shortcomings of existing research on the wetting process of liquids in near-real size and provide support for the “lotus effect” wetting transition theory. It is expected to provide empirical references for other related studies. In this work, we propose a novel methodology of turning traditional non-sticking metallic foam film surface sticky by simply manipulating the surface parameter. The 3D CuNi foam films are prepared by one-step hydrogen bubble-assisted galvanostatic electrodeposition, while biaxially oriented polypropylene (BOPP) mesh with dissimilar surface roughness topologies, height and open area is used to decorate the Cu-Ni foam film surfaces. Recent developments in using the hydrogen template-assisted electrodeposition method have led to the synthesis of many foam films including Cu, CuNi, and Ni. These films typically contain macropores and, depending on the synthetic routes, nano porous dendritic walls, hence exhibiting a hierarchical porosity [ 33 , 34 , 35 , 36 ]. In this context, we demonstrated that the CuNi metallic foams, with a contact angle greater than 150, show non-stick properties to water droplets. In this study, composite surfaces configured of mesh-like BOPP and CuNi foam that constitute the “open area” are prepared. Contact angle and contact angle hysteresis were measured for each composite surface. It is also shown that the fraction of open area, and roughness of the BOPP can be adjusted to modify contact line pining and the adhesion properties. The correlation between these surface topographies and contact angle hysteresis is revealed.",
"discussion": "3. Results and Discussion 3.1. CuNi Foam Film (before BOPP Modification) Experimental results elucidate that the interplay between contact angle and sliding angle is linked to surface morphology [ 39 ]. High-resolution SEM micrographs distinctly depict the as-fabricated CuNi foam films manifesting a three-dimensional, interconnected porous architecture. Within this structure, the dendritic walls present a hierarchical porosity, as illustrated in Figure 4 a. Figure 4 b delineates the wettability of 2 μL water droplets on the CuNi foam film surface, where the measured contact angle approaches 158°, signifying a pronounced Superhydrophobicity. It is imperative to underscore that the propensity for a droplet’s mobility on a substrate is governed more by the contact angle hysteresis than the contact angle alone [ 40 ]. Sequential images in Figure 4 c elucidate the forces exerted by the syringe on the water droplet as it disengages from both the syringe and the CuNi film surface. Remarkably, the droplet neither permeates nor diffuses over the coating. Increasing the droplet’s volume to 10 μL leads to its detachment from the syringe due to gravitational forces. Yet, upon contact with the CuNi coating, it manifests a rolling behavior, and does not establish a permanent position on a CuNi foam film angled at 2°. The randomness in pore positioning, resultant from the hydrogen bubble template during the electrodeposition process [ 41 ], hinders the establishment of a continuous solid–liquid contact line. This engenders the facile rolling-off of droplets upon minimal surface inclination. Such observations reinforce the conclusion that superhydrophilicity embodies not only high contact angle but also low contact angle hysteresis [ 42 ]. 3.2. Mesh-like BOPP-Modified CuNi Foam Film Surface Quantitative measurements illustrated in Figure 5 exhibit the variation in sliding angles across the mesh-like BOPP-modified CuNi foam films differing in area fraction and height. Notably, for an area fraction of 0.30, the sliding angle first increases before diminishing with increasing BOPP height. Conversely, with an open area fraction of 0.15, the sliding angle gradually decreases while the height increases. In explicit terms, a surface having an area fraction of CuNi foam films and BOPP film of 0.3 and a height of 0.04 mm exemplifies optimal droplet adhesion, i.e., sample 5, whereas with an open area fraction of 0.15, a BOPP height of 0.02 mm realizes the most advantageous adhesion. Moreover, when the height is consistent, the adhesion of the open area fraction of 0.30 is greater than that of 0.15. But it has been experimentally verified, the graded roughness composed of the height and roughness values selected in this work does not exceed the allowed critical value of graded roughness. When the graded roughness is less than 14.2 µm, increasing the roughness can increase the contact angle hysteresis and increase the wetting step jump, improving the adhesion of droplets on solid surfaces [ 43 ].Therefore, choosing sample 7 to represent the roughness change of the BOPP surface, the BOPP was further polished with 240# sandpaper, and the corresponding images of the droplet on its surface when it is horizontal, vertical, and upside-down are shown in Figure 6 . It is seen that the roughness of BOPP results in pronounced contact angle hysteresis, wherein the droplet remains steadfast, even upon inverting the composite surface. The contact angle hysteresis intricately intertwines with area fraction, geometric characteristics, and spatial distribution [ 44 ]. In terms of the open area fraction, a more robust metric, the three-phase line ratio, emerges as a suitable descriptor for contact angle hysteresis. Within a model framework of square pillar distribution, the relationship between open area fraction and the three-phase line ratio is articulated as follows: g = √f (3) \nwhere ‘g’ represents the three-phase line ratio and ‘f’ symbolizes the open area fraction [ 45 ]. Experimental measurements suggest that an elevated three-phase line ratio increases the hysteresis, consistent with results derived from open area fraction evaluations. Therefore, a composite surface having an open area fraction of 0.30 emerges as the most propitious to engender adhesion to droplets. Conversely, the influence of BOPP’s height on the surface contact angle hysteresis does not exhibit a consistent pattern. Based on solid–liquid contact line theory, a droplet exhibits steadfastness on the surface when the pinning force at the contact line counterbalances the body force (attributable to the droplet’s weight) [ 46 ]. 3.3. Fem-Simulation of Wetting Dynamics To offer a succinct analysis, water droplets on the mash-like BOPP modified CuNi surface have been strategically simplified, effectively rendering the problem quasi two-dimensional. As shown in Figure 7 , CuNi foam films manifest dual roughness dimensions: one at a nanometer scale offered by the dendritic wall—articulated in terms of groove width G1’, step width W1’, and groove depth H1’—and another at a microscale, typified by a three-dimensional porous configuration, denoted by groove width G1, step width W1, and step height H1. Polished BOPP exhibits bi-dimensional roughness: the micron-scale roughness post-polishing with 240# sandpapers is defined by groove width G’, step width W’, and groove depth H’; another dimension is the millimeter-scale roughness, defined by groove width G, step width W, and step height H. Based on this model, Figure 8 shows a series of dynamic images illustrating the droplet deposition process across distinct temporal intervals, as calculated through the phase field method. As the droplet descends, it engages with the microstructured surface, wherein the blue zone signifies the liquid phase, and the white represents the gaseous phase. Through computational simulations, the intricate interplay between the droplet and the microstructures becomes discernible. The entire wetting process embodies a series of intricate wetting states. Initially, at t = 0 s, the droplet epitomizes a quintessential spherical contour. Subsequently, governed by gravitational forces, the droplet begins to move, overcoming its initial state of inertia. At t = 0.003 s, the bottom of the droplet comes into contact with the bulge and wets the protruding microstructure surface, resulting in the Wenzel wetting state. Above the three-phase contact line, the region is wetted, while below, it remains non-wetted. By this juncture, the droplet has fully wetted the protruding microstructure, exhibiting a slight rebound at t = 0.006 s. The enlarged view of the following states is shown in Figure 9 . Starting from t = 0.0067 s, the droplet progressively wets the surface of each protruding microstructure until its manifestation as the Wenzel wetting state at t = 0.00714 s. During this interval, the droplet is subjected to the cumulative forces of surface tension, inertia, and pinning dynamics, inducing a translocation of its center of mass and noteworthy deformation. The droplet, in this scenario, is rendered incapable of sustaining its pristine spherical interface, undergoing temporal oscillations [ 47 , 48 , 49 ]. Figure 8 reveals that, following these oscillations, the droplet enters a transient equilibrium state at t = 0.0345 s. Unwetted regions within the microstructures trap some gas, exhibiting the Cassie state. A brief equilibrium wetting state appears at t = 0.1460 s. Under the influence of oscillations and gravity, the droplet progressively wets the recessed microstructures. When the droplet contacts a small cylindrical protrusion, it quickly makes contact with the bottom surface of the microstructure, and sequentially wets the remaining small cylinders and the entire grooves. This causes the gas to escape, eventually leading to a fully wetted stable state, where the three-phase contact line is anchored at the bottom of the microstructure. From the state at t = 0.1417 s shown in Figure 8 , the droplet is seen to wet the small cylinders of the recessed microstructure, rapidly moisten a portion of the substrate, and then penetrate the microstructures along its path. At t = 0.1600 s, the entire plane becomes wetted and stabilizes. The droplet stays pinned to the microstructure surface until the simulation concludes at t = 0.2000 s, without any subsequent changes. Wetting phenomena on solid surfaces are conventionally articulated through the paradigms of “energy concept” and “pressure concept”. Specifically, the “energy concept” is particularly suitable for predicting non-uniform wetting states on rough surfaces, while the “pressure concept” is more suitable for analyzing the process of wetting transitions in hierarchical multiscale microstructures. It involves the expansion of the three-phase contact line and is based on the theory of pressure-induced wetting transitions. When the hydrostatic pressure within the liquid phase continuously increases and exceeds the breakthrough pressure, as defined by the following equation, the changes in the gas–liquid interface commence [ 50 , 51 , 52 ].\n (4) p h − p 0 > p b r e a k \nwhere p 0 represents the air pressure at the bottom of the microstructure, p h represents the hydrostatic pressure, and p b r e a k represents the breakthrough pressure. When the pressure difference surpasses a critical value, the wetting transition initiates, and the contact line begins to expand. In the wetting process of hierarchical multiscale microstructures, the pressure-induced wetting transition plays a crucial role. Upon the droplet’s engagement with the microstructures, the pressure within the droplet increases due to the confinement and compression of the liquid phase. Such a surge amplifies the wetting force, fostering a progressive dilation of the contact line to subsume the microstructural surfaces. The pressure-induced wetting transition can be further understood by considering the balance among the interfacial tension, the capillary pressure, and the pressure within the droplet. When the pressure within the droplet exceeds a certain threshold, the force balance undergoes perturbation, leading to the expansion of the three-phase contact line and the transition to a new wetting state. Overall, the pressure concept is particularly relevant for analyzing the wetting transition process in hierarchical multiscale microstructures, where the pressure difference between the liquid and gas phases determines the occurrence of wetting transitions and the expansion of the three-phase contact line. The wetting process of droplets on microstructures can be categorized by the dynamics of the three-phase contact line. There are two primary modes of breakthrough at the gas–liquid interface, each triggered by a critical pressure difference. The first mode, known as the Canthotaxis effect, involves the three-phase contact line advancing along the surface, with the gas–liquid interface descending. In the second mode, called the Laplace breakthrough, the three-phase contact line remains anchored at sharp geometric edges while the gas–liquid interface expands. Figure 10 illustrates the alterations in the gas–liquid interface caused by varying pressures in these two scenarios. As per the Laplace law, the curvature radius of the gas–liquid interface is dictated by the pressure difference and the inherent contact angle of the interface. When the three-phase contact line is anchored to protrusions on the microstructure, a continuous increase in the droplet’s hydrostatic pressure results in the gas–liquid interface intruding into the recessed microstructure. Based on the formula for curvature radius, assuming that the intrusion of the gas–liquid interface does not result in compression of the air inside the microstructure, the pressure in this region does not increase. As a result, the gas–liquid interface forms a cap-like shape.\n (5) p C , b r e a k = 4 σ s i n θ i S In the Laplace breakthrough mode, the expression for the breakthrough pressure is the following: (6) p L , b r e a k = 4 σ S \nwhere S denotes the spacing between microstructures, σ represents the surface tension of the droplet, and θ i is the intrinsic contact angle between the solid and liquid. From the breakthrough equation, it is evident that p L , b r e a k is significantly greater than p C , b r e a k . In this model, the wetting process of the droplet is predominantly governed by the intrinsic contact angle. The surface of the microstructure’s protrusions has an intrinsic contact angle that is hydrophilic. Therefore, when the droplet wets the edges of the microstructure, it results in the pinning effect of the three-phase contact line. This pinning continues until the gas–liquid interface reaches the convex pillar situated in the recessed region of the microstructure, signaling the termination of the pinning effect. Consequently, the droplet, in a near-steady state, briefly undergoes wetting in the p L mode before swiftly wetting the entire microstructure in the p C mode. The graph depicted in Figure 11 presents both the static water pressure and the pressure variations as the droplet wets the microstructure. It can be observed that the static water pressure of the droplet incrementally rises from bottom to top which is attributable to gravitational effects. The pressure on the droplet’s outer surface is lower than the internal pressure distribution, which is an outcome of surface tension forces. This observation aligns with the insights provided by the pressure distribution graph. When the static water pressure within the droplet surpasses the air pressure confined at the base of the microstructure, and their difference exceeds the breakthrough pressure, the three-phase contact line begins to shift. In this scenario, however, the gas phase trapped in the recessed part of the microstructure is compressed by the droplet, leading to an elevation in air pressure. Consequently, as the three-phase contact line expands, the gas–liquid interface does not assume the cap-shaped form discussed in the two modes. Instead, it undergoes an initial uniform wetting of the recessed morphology, achieving a temporary equilibrium state, before swiftly transitioning to non-uniform wetting. This behavior can be attributed to the trapped air, which acts as a pressure buffer opposing consistent wetting. In the pressure-induced wetting transition process, characterized by a hydrophilic intrinsic contact angle, capillary forces drive the initial downward infiltration of the droplet into the cavity, creating a concave fluid interface. As the static water pressure keeps rising, the three-phase contact line descends along the sidewall. A transient equilibrium flat-interface morphology occurs when counterbalanced by the confined air, which is subsequently followed by non-uniform wetting. In summary, during the quasi-steady state, the droplet operates in the Laplace breakthrough mode. However, as it stabilizes, the droplet transitions from the uniform wetting state, undergoing a wetting shift, and ultimately aligns with the Canthotaxis effect mode. This theoretical analysis and the computational results of the transition from the Cassie state to the Wenzel state are consistent with one another. This congruence offers an alternative explanation for the pronounced contact angle hysteresis exhibited by droplets on hierarchical multiscale microstructure surfaces. Similarly, the fabricated surface demonstrates a capacity to seal an air layer underwater to some extent. Figure 12 shows the ability of sample 7 to retain air bubbles when submerged in water. The camera’s perspective is aligned with the direction in which bubbles adhere to the surface. When bubbles are introduced onto this surface and agitated by a magnetic stirrer, they remain stable on the surface, even when disrupted at rotations of 200 r/min and 500 r/min. This demonstrates that the fabricated surface provides adhesion not only for water droplets but also for air bubbles."
} | 6,398 |
35098218 | PMC8790729 | pmc | 6,505 | {
"abstract": "The sustainability\nof current and future plastic materials is a\nmajor focus of basic research, industry, government, and society at\nlarge. There is a general recognition of the positive impacts of plastics,\nespecially packaging; however, the negative consequences around end-of-life\noutcomes and overall materials circularity are issues that must be\naddressed. In this perspective, we highlight some of the challenges\nassociated with the many uses of plastic components and the diversity\nof materials needed to satisfy consumer demand, with several examples\nfocused on plastics packaging. We also discuss the opportunities provided\nby conventional and advanced recycling/upgrading routes to petrochemical\nand bio-based materials and feedstocks, along with overviews of chemistry-related\n(experimental, computational, data science, and materials traceability)\napproaches to the valorization of polymers toward a closed-loop environment.",
"introduction": "Introduction Plastics play an indispensable\nrole in every aspect of modern life\nfrom materials for aircraft, cars, and buildings to clothing and shoes,\nfood and beverage packaging, and biomedical devices. Globally, ∼390\nmillion metric tons of plastic products, including resins, were produced\nin 2016, and that amount is expected to double in approximately 20\nyears under various production scenarios. 1 , 2 This\nincreasing demand is a direct result of the burgeoning need for high\nstrength to weight ratio, multifunctional materials. 3 Unfortunately, less than 10% of plastics waste is collected\nworldwide for recycling, 4 and even less\nis actually recycled, exacerbating the environmental impacts associated\nwith landfill usage, degradation byproducts, and aquatic pollution,\namong other examples, with the weighting of the different impacts\nvarying by region. 5 , 6 Considered a critical tool in\nthe repurposing pathway, mechanical recycling has several challenges\nwith respect to circularity and practical end-of-life outcomes. Similar\nchallenges exist as research efforts expand to incorporate (bio)degradation\nas a sustainability plastics pathway. 7 − 13 While plastics are often viewed as monolithic, the drive toward\ndurable, lightweight, and functional materials has led to the production\nof multicomponent systems of increasing complexity and diversity.\nThis trend is most obvious in plastics packaging, 14 which demands features such as oxygen barrier properties,\nmechanical protection, and sensing, and requires several polymeric\ncomponents, often in a multilayer configuration—along with\nadditives (organic and inorganic fillers) to seamlessly incorporate\nthe requisite features. 11 , 15 These multicomponent\nplastics necessitate an understanding of how the combination of macromolecules\nand small molecules contribute to plastics waste and affect downstream\nsorting and life-cycle approaches."
} | 711 |
19109881 | null | s2 | 6,506 | {
"abstract": "In this post-genomic era, our capacity to explore biological networks and predict network architectures has been greatly expanded, accelerating interest in systems biology. Here, we highlight recent systems biology studies in prokaryotes, consider the challenges ahead, and suggest opportunities for future studies in bacterial models."
} | 83 |
35687874 | PMC9851154 | pmc | 6,507 | {
"abstract": "Biocatalysis\nis a key tool in both green chemistry and biorefinery\nfields. NOV1 is a dioxygenase that catalyzes the one-step, coenzyme-free\noxidation of isoeugenol into vanillin and holds enormous biotechnological\npotential for the complete valorization of lignin as a sustainable\nstarting material for biobased chemicals, polymers, and materials.\nThis study integrates computational, kinetic, structural, and biophysical\napproaches to characterize a new NOV1 variant featuring improved activity\nand stability compared to those of the wild type. The S283F replacement\nresults in a 2-fold increased turnover rate ( k cat ) for isoeugenol and a 4-fold higher catalytic efficiency\n( k cat / K m )\nfor molecular oxygen compared to those of the wild type. Furthermore,\nthe variant exhibits a half-life that is 20-fold higher than that\nof the wild type, which most likely relates to the enhanced stabilization\nof the iron cofactor in the active site. Molecular dynamics supports\nthis view, revealing that the S283F replacement decreases the optimal\np K a and favors conformations of the iron-coordinating\nhistidines compatible with an increased level of binding to iron.\nImportantly, whole cells containing the S283F variant catalyze the\nconversion of ≤100 mM isoeugenol to vanillin, yielding >99%\nmolar conversion yields within 24 h. This integrative strategy provided\na new enzyme for biotechnological applications and mechanistic insights\nthat will facilitate the future design of robust and efficient biocatalysts.",
"conclusion": "Concluding Remarks In this work,\n35 variants were rationally designed, constructed,\nand tested for activity. The S283F variant was selected for further\ninvestigations on the basis of increased selectivity and catalytic\nrates for isoeugenol compared to those of wild-type enzymes. This\nvariant also showed an enhanced kinetic (operational) thermostability\ndue to enhanced stabilization of the iron cofactor inside the catalytic\ncavity. Incidentally, the primary molecular determinant of NOV1 kinetic\nstability is suggested to be iron deletion of the active site. MD\nanalyses supported increased iron retention in the active site of\nS283F and, therefore, enhanced kinetic stability. Biotransformation\nof the plant-derived phenylpropanoid compound, isoeugenol, using whole\ncells overproducing the improved variant showed remarkable levels\nof conversion to vanillin at concentrations of ≤100 mM, at\nroom temperature, and in relatively short periods. The deactivation\nand stability of the enzymes under harsh conditions are some of the\nmain limitations of the industrial application of biocatalysts. Therefore,\nthe S283F variant, which shows increased activity and stability, is\nan exciting candidate for industrial bioprocesses targeted in valorizing\nlignin-related phenolics for biovanillin production in the lignocellulosic\nbiorefinery realm.",
"discussion": "Results and Discussion Active Site Design: Construction and Characterization\nof Variants The analysis of the binding of vanillin and resveratrol\nto NOV1\n(PDB entries 5J55 and 5J54 ,\nrespectively) suggested that isoeugenol can be docked into the NOV1\nactive site with the hydroxyl group pointed toward Y101 and K134 residues\nand the methoxy substituent pointed toward the pocket created by N120,\nT121, and L475 ( Figure 1 A–C, region A). In this position, the isoeugenol aromatic\nring plane sits parallel to the side chain of F59. Moreover, the reactive\ndouble bond of isoeugenol is located right above the iron center.\nIn contrast, the terminal methyl group is hosted in a large and hydrated\nniche in the rear of the cavity close to F281, S283, F307, and F354\n( Figure 1 A–C,\nregion B). The alignment of NOV1 with P. nitroreducens isoeugenol monooxygenase shows a phenylalanine residue at position\nS283 in this enzyme. 16 Furthermore, analysis\nof the X-ray crystal structure supported the reasoning that the S283F\nreplacement could improve isoeugenol catalysis by filling the free\nspace left in the active site upon binding the single-ring isoeugenol\nsubstrate. Mutations N120L, F281W, S283Q, F307H, F307W, and\nL475S were suggested by sequence comparisons and biochemical information\nfrom the literature. 14 , 16 , 18 , 19 Positions 120, 121, 281, 283, 307, 354,\n473, and 475 were submitted to mutagenesis using Rosetta and filtered\nby protein–ligand docking (see the computational methods section\nfor further details). Thirty-five variants based on the rational and\ncomputational approaches were constructed using site-directed mutagenesis\nand characterized ( Tables S1 and S3 ). The\nenzymatic activity of isoeugenol was tested in crude cell extracts\nin 96-well microplates. The results showed that mutations at positions\n120, 121, 473, and 475 impaired isoeugenol activity. Three double\nvariants, F281M/S283T, F281M/S283V, and F281C/S283I, showed specific\nactivities comparable to that of the wild type, and single variant\nS283F stood out with almost 2-fold higher enzymatic activity compared\nto that of the wild type. Biochemical and Kinetic Characterization The wild type\nand variant S283F were produced at an Erlenmeyer scale and purified.\nBoth enzymes showed an optimal pH of 9 and optimal temperatures of\n∼28 °C for the wild-type enzyme and 32 °C for the\nS283F variant ( Figure 2 A,B). Purified enzyme preparations displayed approximately 0.5 mol\nof iron/mol of protein and were partially iron-depleted, similar to\nthose observed in isoeugenol oxygenases from P. putida IE27 16 and P. nitroreducens IEM. 18 Figure 2 pH and temperature profiles. (A) pH–activity\nprofile of\nwild-type (●) and S283F (■) NOV1. Reactions were performed\nusing Britton Robinson buffer (in the pH range from 3 to 11) in the\npresence of 1 mM isoeugenol at room temperature. (B) Temperature dependence\nof enzymatic activity in reactions performed in 100 mM Tris-HCl buffer\n(pH 9). The steady-state kinetic analysis\nrevealed that the S283F variant\nfeatures a k cat 2-fold higher than that\nof the wild type, and a slightly higher K m value for isoeugenol, leading to a catalytic efficiency ( k cat / K m ) comparable\nto that of the wild type ( Table 1 ). The activity using resveratrol as a substrate was\nalso investigated; the S283F variant showed a 5-fold lower k cat and a 4-fold higher K m , resulting in a sharp 20-fold decrease in k cat / K m , compared to that of\nthe wild type. Notably, the kinetic analysis for O 2 in\nthe presence of isoeugenol showed that the S283F variant displayed\nenhanced binding to molecular oxygen, with an ∼2-fold lower K m and a 4-fold higher catalytic efficiency compared\nto those of the wild type ( Table 1 and Figure S3 ). This is\nan important asset for overcoming the usually limiting levels of soluble\nO 2 in large-scale industrial processes; indeed, enhancing\nO 2 binding and catalysis has been a critical challenge\nin the application of oxygenases. 37 , 38 The results\nobtained supported the prediction that the introduction of the bulky\nand hydrophobic phenylalanine at position 283 impairs resveratrol\noxidation by shifting the specificity of NOV1 toward smaller substrates\nsuch as isoeugenol and molecular oxygen. Table 1 Apparent\nSteady-State Kinetic Parameters\nof Wild-Type and S283F NOV1 for Isoeugenol, Molecular Oxygen (in the\npresence of isoeugenol), and Resveratrol a isoeugenol O 2 (isoeugenol) resveratrol wild type k cat (s –1 ) 7.3 ± 0.2 10.9 ± 0.8 0.38 ± 0.02 K m (mM) 0.6 ± 0.1 (0.7 ± 0.1) × 10 –2 0.06 ± 0.01 k cat / K m (M –1 s –1 ) (12.2 ± 2) × 10 3 (15.6 ± 4) × 10 5 (6.3 ± 0.2) × 10 3 S283F k cat (s –1 ) 14.5 ± 0.8 17.3 ± 0.6 0.078 ± 0.004 K m (mM) 0.8 ± 0.1 (0.29 ± 0.04) × 10 –2 0.227 ± 0.002 k cat / K m (M –1 s –1 ) (18.1 ± 4) × 10 3 (59.7 ± 10) × 10 5 (0.34 ± 0.02) × 10 3 a Kinetic assays were performed\nat room temperature in 0.1 M Tris-HCl (pH 9). Enzymatic Vanillin Bioproduction Bioconversion assays\nconfirmed that purified S283F converts isoeugenol to vanillin more\nefficiently than does the wild type. A >99% conversion of 10 mM\nisoeugenol\nafter reaction for 1.5 h was achieved, which favorably compares with\nthe 67% conversion obtained using the wild-type enzyme ( Figure 3 A). The reaction product was\nconfirmed to be vanillin by HPLC ( Figure S4 ) in accordance with the previous identification. 14 Further addition of isoeugenol (10 mM) resulted in 96%\nand 74% conversion yields after 24 h for S283F and the wild type,\nrespectively. The total turnover numbers (TTNs), defined as the total\nmoles of vanillin produced per mole of enzyme over the entire length\nof the reaction, are 5.1 and 2.2 for S283F and the wild type, respectively.\nRemarkably, the k cat of the S283F variant\nfor isoeugenol is 2–10 times higher than the values previously\nreported for isoeugenol oxygenases, 16 , 18 , 19 , 39 further endorsing this\nenzyme as a superior biocatalyst at nonlimiting substrate concentrations,\ntypical of industrial setups. We set up time-course bioconversion\nassays using whole cells that overproduced the S283F variant to reduce\nthe costs associated with enzyme purification ( Figure 3 B,C). 40 , 41 Our results revealed\nthat excellent molar conversion yields (>99%) were achievable within\n24 h of reaction using ≤100 mM isoeugenol in the presence of\nsmall amounts (3.5%) of ethanol, a biosolvent, in the whole cell catalysis\nmixture. Figure 3 Bioconversions of isoeugenol to vanillin. (A) Time course of vanillin\nproduction using 1 unit mL –1 wild-type (blue) and\nS283F NOV1 (red) purified enzymes; reactions started with 10 mM substrate,\nand additional supplementation with 10 mM isoeugenol occurred after\nreaction for 1.5 h (arrow). (B) Time course of vanillin production\nusing recombinant E. coli whole cells overproducing\nthe S283F NOV1 variant (final OD 600 of 2) in reaction mixtures\ncontaining initial concentrations of 10 (yellow), 25 (green), 50 (red),\nand 100 (blue) mM isoeugenol. (C) Molar conversion yields after 24\nh for reaction mixtures containing initial concentrations of 10 (yellow),\n25 (green), 50 (red), and 100 (blue) mM isoeugenol. Reactions were\nperformed in glycine-NaOH buffer (pH 9) at room temperature and 150\nrpm. Notably, the obtained conversion\nyields were similar to those obtained\nwith Pseudomonas isoeugenol monooxygenases in the\npresence of organic solvents or enzyme aggregates 17 , 20 and higher than those obtained with oxygenases from Herbaspirillum\nseopedicae and Rhodobacteraceae bacterium ( Table S4 ). 39 Furthermore, substrate concentrations of ≤100 mM were tolerated\nby the NOV1 system in contrast to other enzymatic systems inhibited\nby isoeugenol and force reactions to be performed at significantly\nlower substrate concentrations. 39 , 42 NOV1 Stability and Iron\nIncorporation The kinetic or\noperational stability is relevant for assessing the biocatalyst performance\nunder specific operating conditions, for example, at a given temperature,\nand for studying pathways that lead to the formation of irreversibly\ninactivated states of the enzyme. 43 The\nthermal inactivation assays revealed that the S283F amino acid replacement\ndrastically improves the enzyme kinetic stability. While wild-type\nNOV1 displayed inferior stability with a half-life ( t 1/2 ) of ∼1 h at 25 °C, variant S283F exhibited\na half-life of ∼29 h ( Figure 4 ). Notably, incubation of the wild type with iron increased\nthe half-life 10-fold but not that of the variant, indicating that\n(i) binding of the iron cofactor seems to be the critical determinant\nof thermostability of the NOV1 enzyme and (ii) the wild-type enzyme\nloses iron more quickly than the S283F variant. The chemical unfolding\nof the wild type and S283F was assessed by using the fluorescence\nemission of tryptophan residues. Figure 4 Kinetic stability. Stability of wild-type\n(circles) and S283F (squares)\nNOV1 at 25 °C in the absence (empty symbols) and presence of\n100 equiv of FeSO 4 (filled symbols). The inset shows the\nlinear regression of logarithm activity vs time. In the absence of\niron, the half-lives at 25 °C were 1.3 ± 0.2 and 29 ±\n3.4 h for the wild type and S283F variant, respectively. The addition\nof iron increased 10-fold the half-life (11.4 ± 0.7 h) of the\nwild type, whereas the stability of S283F remained similar (30.4 ±\n1.2 h). The folded and unfolded states\nwere the only states that accumulated\nin significant amounts. A two-stage process accurately fit the unfolding\nprocess ( Figure 5 A),\nand both enzymes displayed similar stability. The guanidinium hydrochloride\nmidpoint concentration is ∼0.6 M (where 50% of molecules are\nunfolded), and the native-state free energy is 3.5 kcal mol –1 . In thermal unfolding experiments, the fluorescence emission from\ntryptophan residues increased in the range of 30–40 °C\n( Figure S5 ), indicating the iron cofactor’s\nrelease, a known fluorescence quencher. 44 Incubation of the wild type with 2000 molar equivalents of EDTA\nabolished this effect ( Figure S5A ), in\ncontrast to S283F, which shows a persisting “iron quenching\neffect” even after incubation with EDTA, suggesting a significantly\nhigher affinity for iron ( Figure S5B ),\nin line with the longer half-lives of kinetic stability. The apparent\nmelting temperatures ( T m ) are very similar\nin both enzymes at 57–59 °C ( Figure 5 B); static light scattering at 500 nm revealed\na strong aggregation tendency with an onset of aggregation ( T agg ) at 47 °C before enzyme unfolding ( Figure S6 ). These results showed that both enzymes\nshare a relatively high thermal robustness of the enzymes’\nnative state. The higher kinetic thermostability observed in the S283F\nvariant compared to that of the wild type most likely results from\nan enhanced stabilization of the iron cofactor inside the catalytic\ncavity. Figure 5 (A) Fraction of wild-type (●) and S283F (□) NOV1\nunfolded by guanidinium chloride as measured by fluorescence emission\nof tryptophyl residues at 340 nm. Measurements were performed by reading\nthe fluorescence at excitation wavelengths of 296 nm and emission\nwavelengths of 340 nm. The solid line is the fit according to the\nequation f U = exp(−Δ G °/ RT )/[1 + exp(−Δ G °/ RT )], which assumes the N ↔\nU equilibrium. 25 (B) Thermal unfolding\nfollowing fluorescence emission of tryptophan residues at 340 nm ( T m = 57–59 °C) for the wild type\n(●) and S283F variant (□). Structural Characterization of the S283F Variant The\ncrystal structure of NOV1-S283F was determined at 2.9 Å using\nsynchrotron radiation ( Table S2 ), revealing\nthe recognizable electron density of a phenylalanine side chain at\nposition 283 ( Figure 6 A–C). The aromatic ring of F283 was found to occupy the active\nsite, cluttering the catalytic cavity. The Fe(II)-O 2 complex,\nrequired for activity, is coordinated by four histidine residues:\nH167, H218, H284, and H476 ( Figure 6 B,C). F283 is predicted to reach out and interact with\nisoeugenol through hydrophobic contacts ( Figure 1 C), whereas K134 and Y101 create the proper\nhydrogen bonding environment with the 4-hydroxy group of the substrate\n( Figure 6 C,D). Figure 6 (A) Crystals\nof the S283F NOV1 variant. (B) Weighted 2 F o – F c electron density\nof the active site. The contour level is 1.2σ. The side chain\nof F283 is quite recognizable with its aromatic ring close to the\nHis-coordinated iron. The difference Fourier F o – F c map [contoured at\nthe 3.0σ level (green)] showed a residual electron density interpreted\nas bound oxygen. (C) Oxygen-bound active site structure of the final\nmodel. (D) S283F NOV1 crystal structure with the reaction product\n(vanillin, carbons colored purple) modeled in the cavity. The model\nwas generated using the structure of the complex between the wild-type\nenzyme and vanillin as a reference (carbons colored green, PDB entry 5J55 ). F283 interacts\nwith the edge of the substrate ring, whereas F59 is involved in π–π\nstacking with the aromatic ring of the substrate. Molecular Dynamics Simulations and Substrate Docking Starting\nfrom the experimental wild-type (PDB entry 5J55 ) and S283F (this\nwork) structures, the computational analysis showed that a comparable\nnumber of interactions is present in both wild-type and S283F enzymes\nand the active site cavity along the MD trajectories (800 ns), which\nremained in a relatively stable conformation with a constant volume\nin both structures ( Figure S7 ). However,\nthe S283F replacement resulted in local rearrangement of a sizable\nhydrophobic core present in the wild type to create small hydrophobic\npatches near the active site ( Figure 7 ); the average cavity volume is expanded in S283F where,\nadditionally, longer distances were measured between molecular oxygen\nand F59 ( Figure S8A,B ). Consequently, a\nhigher number of water molecules was identified inside the active\nsite of the mutant ( Figure S8C ). The more\nexpanded shape of the S283F active site could make the catalytic center\nmore accessible to the solvent and, by extension, other small molecules,\nsuch as molecular oxygen and isoeugenol. Figure 7 Inter-residue interaction\nanalysis performed with PIC (Protein\nInteraction Calculator) using the X-ray structures of wild-type NOV1\n(PDB entry 55j5 ) and the S283F variant (this work). The side chain–side chain\nhydrophobic interactions that appear in the wild type but not in the\nvariant (left) or the variant but not in the wild type (right) are\nshown as blue and orange spheres. The S283F mutation is highlighted\nwith green spheres. The gray spheres and sticks comprise interactions\nshared by both systems. Furthermore, the presence\nof the bulky phenylalanine at the active\nsite seems to distort the resveratrol conformation significantly when\napproaching precatalytic states ( Figure 8 A,B and Table S5 ). In contrast, the distortion of the isoeugenol structure when approaching\nprecatalytic binding modes appeared only slightly higher in the variant\nthan in the wild type. These data support the experimental observation\nthat resveratrol is a poorer substrate for the variant than is the\nwild type (4-fold higher K m and 5-fold\nlower k cat ). Furthermore, MD trajectories\nof the ligand-, oxygen-, and iron-free systems of the wild type showed\nthat the four iron binding histidines are kept in a stable conformation\nthat is not compatible with iron coordination, particularly for H167\nand H218 ( Figure S9 ). This was different\nfrom the case for S283F, where the presence of phenylalanine in the\niron neighborhood increased the fluctuation of the His residues, which\nvisited conformations compatible with iron binding ( Figure S9 ), indicating a lower relative transition energy\nbarrier between iron-free and iron-bound forms in S283F. This increased\nflexibility of the iron-coordinating histidines could be related to\nalterations in the H-bond network of the active site, where three\nconserved carboxylate residues (E135, E353, and E418) are responsible\nfor stabilizing H218, H284, and H476. 14 , 45 In addition,\nlower p K a values for all histidine side\nchains, particularly H167 and H218, were found in the S283F variant\n( Figure 8 C), suggesting\nthat their dissociation equilibrium favors the unprotonated state\nrequired to bind the iron metal. These electrostatic calculations\nsupport a higher affinity of iron for the S283F variant with kinetic\nstability and thermal unfolding/EDTA chelation, which showed that\nthe wild type loses iron more readily than does S283F ( Figure 4 and Figure S5A,B ). Figure 8 (A) Dihedral (Δ E ) and binding energies\n(Δ G ) estimated for ensemble docking of isoeugenol\nand resveratrol\nto wild-type NOV1 and the S283F variant. Δ E dihedral measures how strong the ligand distortion is\ncompared to its protein-free form, and Δ G binding estimates the quality of ligand–protein interactions.\n(B) Representation of resveratrol binding to wild-type NOV1 (gray)\nand the S283F variant (blue). The substrate is represented as spheres,\nand the residues are represented as sticks. (C) p K a values of iron-coordinating histidines 167, 218, 284,\nand 476 were measured every 40 ps along the 800 ns MD trajectory for\nthe apo form of both the wild type (red) and the S283F mutant (blue)."
} | 5,023 |
37330657 | PMC10460858 | pmc | 6,508 | {
"abstract": "Abstract Inspired by the brilliant and tunable structural colors based on the large refractive index contrast (Δ n ) and non‐close‐packing structures of chameleon skins, ZnS–silica photonic crystals (PCs) with highly saturated and adjustable colors are fabricated. Due to the large Δ n and non‐close‐packing structure, ZnS–silica PCs show 1) intense reflectance (maximal: 90%), wide photonic bandgaps, and large peak areas, 2.6–7.6, 1.6, and 4.0 times higher than those of silica PCs, respectively; 2) tunable colors by simply adjusting the volume fraction of particles with the same size, more convenient than the conventional way of altering particle sizes; and 3) a relatively low threshold of PC's thickness (57 µm) possessing maximal reflectance compared to that (>200 µm) of the silica PCs. Benefiting from the core–shell structure of the particles, various derived photonic superstructures are fabricated by co‐assembling ZnS–silica and silica particles into PCs or by selectively etching silica or ZnS of ZnS–silica/silica and ZnS–silica PCs. A new information encryption technique is developed based on the unique reversible “disorder–order” switch of water‐responsive photonic superstructures. Additionally, ZnS–silica PCs are ideal candidates for enhancing fluorescence (approximately tenfold), approximately six times higher than that of silica PC.",
"conclusion": "3 Conclusion In summary, bio‐inspired ZnS–silica PCs with intense reflectance, wide photonic bandgaps, highly saturated colors, long‐range order, and non‐closely packed structures were prepared by the non‐close–assembling of the uniform ZnS–silica core–shell particles with large n in ETPTA with a small n , followed by the photopolymerization. The silica shell enables the strong electrostatic repulsions between ZnS–silica particles and thus guarantees the highly ordered structures. Both the large Δ n and ordered structures are the major reasons for the outstanding optical performances of ZnS–silica PCs. The non‐close‐packing structure enables tunable brilliant structural colors covering most visible colors by simply altering φ \n ZnS–silica of two ZnS–silica particles. These ZnS–silica PCs show intense reflectance with a wide range of thicknesses and a much smaller T \n th than that of the PCs prepared with silica particles. A variety of new photonic superstructures were obtained by self‐assembling ZnS–silica and silica particles into ZnS–silica/silica PCs or by selective etching silica or ZnS of these PCs and the ZnS–silica PCs. Furthermore, a water‐responsive photonic superstructure was fabricated, which shows unique disorder–order switch and thus off–on structural colors under dry‐wetted states. Based on these specific properties, a new information encryption strategy is developed by simply combing these water‐responsive photonic superstructures. The information is encrypted under normal condition but decrypted in water and the encryption–decryption switch is highly reversible. It is shown that ZnS–silica PCs are excellent platform for enhancing fluorescence, showing ≈10‐fold enhancement by matching the reflection and fluorescent wavelengths, approximately six times higher than that of silica PC. This work not only offers direct non‐close‐packing–based brilliant structural colors but also develops derived PC superstructures, which open a new avenue to fabricate advanced photonic materials and may facilitate structural color related applications in displays, anti‐counterfeiting, and optical devices.",
"introduction": "1 Introduction Structural colors, [ \n \n 1 \n , \n 2 \n , \n 3 \n , \n 4 \n , \n 5 \n , \n 6 \n \n ] existing in natural opals, butterfly wings, bird features, chameleons, etc., originate from the selective reflection of visible light by the periodically ordered structures with dielectric contrast. For instance, in the plum‐throated cotinga ( Cotinga maynana [ C. maynana ]), [ \n \n 7 \n \n ] the back feather barbs show non‐iridescent colors but faint turquoise‐blue color due to the nearly random close‐packed spherical air cavities ( Figure \n \n 1 a,b ). In striking contrast, chameleons [ \n \n 8 \n \n ] show bright structural colors due to the strong coherent scattering of light caused by the large refractive index contrast (Δ n ) between the non‐closely packed guanine nanocrystals and superficial iridophores (Figure 1c,d ). In addition, the colors of chameleons can be altered through altering the distances between guanine nanocrystals due to the non‐close‐packing structures. Particularly, artificial chameleon skins [ \n \n 9 \n , \n 10 \n , \n 11 \n , \n 12 \n , \n 13 \n \n ] based on the colloidal photonic crystals (PCs) have been extensively investigated because of their potential applications in displays, [ \n \n 14 \n , \n 15 \n , \n 16 \n , \n 17 \n , \n 18 \n \n ] printings, [ \n \n 19 \n , \n 20 \n , \n 21 \n , \n 22 \n \n ] sensing, [ \n \n 23 \n , \n 24 \n , \n 25 \n , \n 26 \n , \n 27 \n \n ] rewritable paper, [ \n \n 28 \n , \n 29 \n , \n 30 \n \n ] anti‐counterfeiting, [ \n \n 31 \n , \n 32 \n , \n 33 \n \n ] and optical devices. [ \n \n 34 \n , \n 35 \n , \n 36 \n \n ] \n Figure 1 Fabrication and characterizations of ZnS–silica PCs. a) Male Plum‐throated Cotinga ( Cotinga maynana , Cotingidae). b) Sphere‐type β ‐keratin and air nanostructure from back contour feather barbs of C. maynana . c) Digital photo of a chameleon. d) SEM image of the skin of the Chameleon. e) TEM image of the ZnS–silica particles. The average size of ZnS–silica particle is 158 nm (core: 110 nm and shell: 24 nm). f) Schematic illustration of the fabrication of PC through the self‐assembly of the ZnS–silica particles. g,h) Reflection spectra of the silica and ZnS–silica PCs of g) experimental and h) simulated results. i) Enhanced factor of the FWHM, reflectance, and peak area of the PC compared to those of the PCS. j) SEM image and k) angle‐resolved spectra of the PC. l) Spatial reflection spectra of the ZnS–silica PC from the point A to B. m) TEM images of ZnS–silica particles (178, 212, and 248 nm) with the same ZnS (142 nm) core but different silica shell thicknesses (18, 35, and 53 nm) and the corresponding n) ZnS–silica PC with φ \n ZnS–silica fixed to 30%. o) Reflectance of the ZnS–silica PC as a function of φ \n ZnS–silica . The thicknesses of all PCs are fixed to 57 µm. The images of the (a,b) bird and (c,d) chameleon were reprinted with permission from refs. [ 7 , 8 ], respectively. a,b) REproduced with permission. [ \n \n 7 \n \n ] Copyright 2009, Royal Society of Chemistry. c,d) Reproduced with permission. [ \n \n 8 \n \n ] Copyright 2015 Springer Nature. To achieve brilliant and tunable structural colors, large Δ n and non‐closely packed structures are necessary. In addition, the thicknesses and order degree require extra attention. Direct constructing non‐closely packed particles/polymer PCs with a large Δ n will be an ideal solution to fulfill the above requirements. Recent works proved that non‐close‐packing structures can be obtained by assembling silica particles in polymers. [ \n \n 37 \n , \n 38 \n , \n 39 \n , \n 40 \n \n ] However, the small Δ n (0.02–0.06) between the silica ( n \n silica = 1.46) particles and polymers ( n = 1.44‐1.52) leads to the low reflectance (0–40%) and weak colors in most cases. [ \n \n 13 \n , \n 18 \n , \n 32 \n , \n 41 \n , \n 42 \n \n ] Even worse, the reflectance of the silica PC decreases greatly (<10%) when its thickness is below 100 µm. [ \n \n 26 \n \n ] Using particles with large n will be a promising and effective way to address this problem. So far, particles including Cu 2 O [ \n \n 43 \n , \n 44 \n \n ] ( n \n Cu2O = 2.7), CdS [ \n \n 45 \n \n ] ( n \n CdS = 2.5), CeO 2 \n [ \n \n 46 \n \n ] ( n \n CeO2 = 2.20), Fe 3 O 4 \n [ \n \n 16 \n , \n 47 \n \n ] ( n \n Fe3O4 = 2.42), and ZnS [ \n \n 48 \n , \n 49 \n \n ] ( n \n ZnS = 2.37) with high n have been prepared. Nevertheless, the inherent colors of Cu 2 O, CdS, CeO 2 , and Fe 3 O 4 particles will affect the color purity and color saturation of PCs. Very recently, non‐close‐packed PCs [ \n \n 49 \n \n ] with bright colors were prepared by etching the silica parts of the closely packed ZnS–silica/polymer composite PC with elaborate fabrications. Despite the large Δ n (0.4–0.6), the PC's reflectance (45%) is not as high as expected probably due to disturbance of the order degree by the etching processes. Therefore, it is still a big challenge to fabricate PCs with high reflectance, non‐closely packed ordered structures, brilliant and tunable colors with wide thicknesses. In this work, inspired by the characteristics of chameleon skins and the previous works, PCs addressing the above drawbacks were fabricated by the direct non‐close‐assembling of ZnS–silica core–shell particles with high n in trimethylolpropane ethoxylate triacrylate (ETPTA) with a low n (Figure 1e,f ). The ZnS–silica PCs show highly saturated colors with wide thicknesses due to the large Δ n (0.142–0.230) between the ZnS–silica particles and ETPTA ( n = 1.470). The reflectance of PCs ranges at 70–98%, much higher than those of silica PCs (20‐40%) and conventional PCs. Benefiting from the non‐close‐packing structure, the colors of ZnS–silica PCs can be altered through adjusting the volume fraction of the particles with the same sizes, which is different from the traditional way of altering particle sizes. The silica shell is used to enhance the electrostatic repulsion between ZnS particles while retaining the high content of ZnS, which is crucial to obtain both non‐closely packed structures and bright colors. Based on the core–shell structure of the ZnS–silica particles and non‐closely packed structures of the PCs, a variety of new and complex derived PC superstructures can be obtained by co‐assembling the ZnS–silica and silica particles together into ZnS–silica/silica PC or by selective etching silica or ZnS of ZnS–silica/silica and silica PCs. Among these superstructures, HF‐etched ZnS–silica superstructures show unique water‐responsive “disorder–order” switch, giving rise to the reversible off–on colors under the dry and wetted state, respectively. Based on these characteristics, a new information encryption strategy was developed by simply combining these HF‐etched ZnS–silica superstructures with different optical performances. Moreover, it is shown that ZnS–silica PCs are excellent platforms which can enhance the fluorescent intensity by a factor of ≈10, approximately six times higher than that of silica PC. This work offers a simple, convenient, and robust strategy to fabricate highly brilliant PCs and derived advanced photonic structures and shows their potential application in fluorescence enhancement. These will upgrade the basic understanding of structural color and structures of PCs, and advance other applications such as displays, anti‐counterfeiting, optical devices, photocatalysis, and solar energy.",
"discussion": "2 Results and Discussion 2.1 ZnS–Silica PC with Brilliant Color Highly brilliant PCs were fabricated by 1) the preparation of ZnS–silica particles; 2) self‐assembling these particles in ETPTA to form long‐range ordered and non‐closely packed structures; and 3) fixing the ordered structures by photopolymerization (Figure 1e ). Here, ZnS has a high n , which can efficiently improve the Δ n and thus reflectance of the PC. The silica shell coated on the surface of the ZnS particle is used to enhance the electrostatic repulsion between particles, thereby facilitating the self‐assembly of ZnS–silica particles in ETPTA. The small Δ n between silica ( n = 1.460) and ETPTA ( n = 1.470) can effectively reduce the incoherent scattering and thus enhance the color saturation of the PC. The usage of ETPTA can ensure a fast polymerization speed because each ETPTA molecular has three —CH=CH 2 groups, therefore, the photopolymerization has little influence on the order degree of non‐close‐packing structures. To prepare PC film with visible colors, uniform ZnS–silica particles (polydispersity index: 0.011) with an average diameter of 158 nm (Figure 1e , core: 110 nm and shell: 24 nm) were used. The transmission electron microscope (TEM) shows the nonuniform contrast of the ZnS because of its discontinuous crystal structure of the polycrystalline ZnS particle. Therefore, the n of the polycrystalline ZnS (1.910 [ \n \n 49 \n \n ] ) is similar to that of guanine crystal in chameleon skins but much lower than that of ZnS single crystals ( n = 2.37). The ζ ‐potential value of the ZnS–silica nanospheres ( ζ = −43 mV in ethanol) is much larger than that of the original ZnS nanospheres ( ζ = −7 mV in ethanol) so that the electrostatic repulsions between particles can be greatly enhanced. Therefore, the ZnS–silica particles could simultaneously possess good colloidal stability in solvents and a high n ( n = 1.612) compared to the widely used silica ( n = 1.460) particles. Briefly, these ZnS–silica particles were mixed with ethanol and ETPTA to form a uniform solution. After evaporation of ethanol, a liquid showing brilliant colors was obtained, indicating the ordered packing of ZnS–silica particles in ETPTA. This liquid was exposed to UV light to polymerize the ETPTA, thus fixing the ordered structures to obtain a solid and free‐standing PC film. The volume fraction of the ZnS–silica particles ( φ \n ZnS–silica ) and ETPTA ( φ \n E ) is 20% and 80%, respectively, implying the non‐close‐packing structures since the φ \n ZnS–silica is far below that (74%) of the closely packed one. Without specific statement, the thicknesses of PCs are fixed to 57 µm. The as‐fabricated ZnS–silica PC film possesses a highly brilliant green color with an intense reflection peak position located at 530 nm (Figure 1g ). In comparison, silica PC ( φ \n silica : 20%) based on a similar particle size (154 nm, Figure S1 , Supporting Information) only shows weak green owing to its weak reflectance located at 526 nm. The reflection wavelength of the ZnS–silica PC is larger than that of the silica PC due to the larger n of ZnS–silica particles. These experimental results are similar to those of the results (Figure 1h ) obtained by finite‐difference time‐domain (FDTD) simulations, suggesting the reflectance of PCs can be dramatically enhanced when ZnS–silica particles are used as the building blocks. The full width at half maximum (FWHM), reflectance, and reflection peak area of the ZnS–silica PC are 1.6, 2.6, and 4.0 higher than those of silica PC (Figure 1i ), respectively, which can be explained by their different Δ n . Generally, the bandgap broadens, reflectance improves, and reflection peak area increases when Δ n increases. [ \n \n 50 \n , \n 51 \n , \n 52 \n \n ] The Δ n of PCs can be calculated by Equation ( 1 ), where n \n c and n \n e are the n of the colloids and ETPTA, respectively. The average n of ZnS–silica particle ( n \n ZnS–silica ) can be calculated by Equation ( 2 ). n \n ZnS and n \n silica present the n of the ZnS core and silica shell, respectively. D \n core and D \n core–shell are the average diameter of the ZnS core and silica shell, respectively. The Δ n of the ZnS–silica PC is 0.142, 14.2 times higher than that of the silica PC (0.010), which greatly enhances the efficiency of light scattering at the interface of two dielectric materials and leads to a wider bandgap, higher reflectance, and larger peak area. Therefore, compared with silica PC, more saturated structural colors can be produced by the ZnS–silica PC which reflects more light into naked eyes. 2.2 Non‐Closely Packed Structure Except for the brilliant color, the as‐fabricated PC has a non‐closely packed structure. As shown in scanning electron microscope (SEM, Figure 1j ) top view image, ZnS–silica particles are non‐closely packed into face‐centered cubic structures with a long‐range order. The surface‐to‐surface distance between neighboring particles ( D \n s–s ) is measured to be 50 nm. In addition, the cross‐sectional SEM images (Figure S2 , Supporting Information) of the central and rim regions of the same sample show similar results, demonstrating the good uniformity of non‐closely packed structures across the whole PC. Besides, the reflection wavelength of the PC blueshifts as the incident and detection angles increase simultaneously (Figure 1j ), further verifying its long‐range ordered structures. The highly ordered structures can be attributed to the strong electrostatic repulsion between the particles. [ \n \n 13 \n , \n 53 \n \n ] Along with the evaporation of ethanol, the ZnS–silica colloidal solution concentrates and the average distance between the particles decreases accordingly. The particles begin to self‐assemble into ordered structures when the electrical double layers of particles start to overlap. A new balance was reached after evaporating almost all ethanol, leading to the highly ordered structures thanks to the strongly repulsive forces between the silica shells of ZnS–silica particles. In comparison, white solution was obtained when ZnS particles with poor electrostatic repulsions are used as the building block, proving the critical role of the silica shell in manipulation of the assembly behavior of ZnS particles. The non‐closely packed structure also can be confirmed by reflection spectra. D \n s–s can be calculated by Bragg's law (Equation ( 3 )) and Equation ( 4 ), where m and λ are the diffraction order and reflection wavelength, respectively. D \n id is the interparticle spacing between neighboring particles. n is the refractive index of the PC, and θ is the angle between the reflected beam and the normal. n \n i and φ \n i are the refractive indexes and volume fractions of each component of PCs. The D \n s–s between neighboring particles can be calculated by Equation ( 5 ), where D \n c is the diameter of colloidal particles. The D \n s–s of the ZnS–silica PC is calculated to be 58 nm, consistent with the result obtained from the SEM image. The uniformity of structural color plays an important role for the applications of PCs. Here, we use spatial reflection spectra to characterize the uniformity of the PC by collecting the continuous reflection spectra from point A to B. As presented in Figure 1l , the small variation of the reflection wavelength and reflectance of the PC from point A to B suggest its good uniformity. These results demonstrated that the PC with the long‐range order, non‐closely packed structures, high reflectance, broad bandgap, and highly brilliant structural color can be fabricated based on the direct non‐close‐packing of ZnS–silica particles in ETPTA.\n \n (1) \n Δ n = n c − n e \n \n \n (2) \n n ZnS − silica = n ZnS D core / D core − shell 3 + n silica 1 − D core / D core − shell 3 \n \n \n (3) \n m λ = 1.633 D id n 2 − sin 2 θ \n \n \n (4) \n n 2 = ∑ n i 2 φ i \n \n \n (5) \n D s − s = D id − D c \n \n Thanks to the high n of ZnS core, ZnS–silica particles with a wide range of shell thicknesses can be used for constructing highly reflective PCs. Here, by fixing the ZnS core to 142 nm, ZnS–silica particles (Figure 1m and Figure S3 , Supporting Information) with silica shell of 18, 35, and 53 nm and corresponding n of 1.690, 1.595, and 1.545 were prepared. Correspondingly, these ZnS–silica particles possess ζ ‐potential values of −40, −46, and −49 mV, implying the enhanced charge separation by a thicker silica shell. Then, PCs with φ \n ZnS–silica of 30% were fabricated based on the non‐close–assembling strategy. Figure 1n and Figure S4 , Supporting Information, show the high reflectance (≈90%), brilliant colors, and long‐range order of all these ZnS–silica PCs. Obviously, a thicker silica shell will induce a longer wavelength of the PC. It is surprising that these PCs show similar reflection intensity because ZnS–silica PC with a thinner silica shell is supposed to exhibit a higher reflectance due to its larger Δ n . This might be explained by the slight difference in ζ ‐potential values. ZnS–silica particles with a large ζ ‐potential value will assemble into more ordered structures, leading to a higher reflectance. Thus, the balance between the increase in reflectance by a large Δ n and the decrease in reflectance by a small ζ ‐potential value induces the similar reflectance of these PCs. In this regard, the shell thickness has negligible effect on the reflectance and there is no optimized shell thickness (18–53 nm) for ZnS–silica particles. The change of ZnS‐particle size by altering shell thicknesses is more convenient than changing the size of ZnS core in practical applications. In addition to Δ n , the φ \n ZnS–silica is also a crucial parameter to the reflectance. For non‐close–assembling, there is a threshold of φ \n ZnS–silica (13%, Figure 1o ), over which ZnS–silica particles start to assemble into ordered structure. The ZnS–silica PC can be divided into: crystal regions with ZnS–silica particles packed into ordered structures and amorphous regions with particles randomly moved due to Brown motion. When the φ \n ZnS–silica is below 13%, no colloidal crystals were formed, resulting in a flat reflectance. When the φ \n ZnS–silica is slightly larger than 13%, only a small number of particles assemble into crystals and the density of crystal region is low, leading to a weak reflectance. As φ \n ZnS–silica gradually increases to 35%, more and more particles participate into crystal region; therefore, reflectance increases accordingly. ZnS–silica particles become too crowded for ordered packing after further increasing φ \n ZnS–silica (40–45%), thereby leading to the decrease in reflectance. In addition, no structural color but white powders can be obtained when φ \n ZnS–silica exceeds 45%. Thus, φ \n ZnS–silica of 20–40% should be the good choice for achieving PCs with high reflectance. Compared to other ZnS–silica PCs, the decrease in the reflectance of ZnS–silica PC with the φ \n ZnS–silica of (40–45)% can be attributed to the decrease in order degree since the refractive index contrast is the same for all samples. The order degree depends on the electrostatic repulsions between ZnS–silica particles and ionic strength. A strong electrostatic repulsion and low ionic strength are favorable for a high order degree and intense reflectance. For ZnS–silica PCs, with a φ \n ZnS–silica < 35%, the increase in φ \n ZnS–silica decreases the interparticle distance ( D \n id ) of ZnS–silica particles, leading to enhanced electrostatic repulsions and thus the increased tendency in reflectance. In contrast, the increase in φ \n ZnS–silica will cause the increase in ionic strength, leading to the decreased tendency in reflectance. As the former one dominates the order degree, as a result, the reflectance increases slightly along with the increase in φ \n ZnS–silica . However, when φ \n ZnS–silica > 35%, the further increase in ionic strength will decrease the electrostatic repulsions because the electrical double layers of ZnS–silica particles were significantly compressed by the absorption of increased counterions, causing the decrease in reflectance. It should be noted that no coffee ring was observed for the ZnS–silica PC by the non‐close–assembling strategy. The coffee‐ring effect usually originates from the selective deposition of colloids on the outmost layer of the colloid/solvent solution when conventional self‐assembly methods, such as drop‐casting and dip‐coating, are used. For these approaches, it is difficult to avoid the coffee ring effect and achieve excellent uniformity since the formation and fixation of ordered structures occur almost simultaneously, both of which depend on solvent species, substrates, temperature, and humidity. In this work, the colloidal/solvent solution consists of silica/ethanol/ETPTA. After selective evaporation of ethanol, silica particles were uniformly packed in ETPTA into a long‐range order which was then fixed by UV polymerization. Unlike the traditional ways, the formation and fixation of uniform structures can be independently and efficiently controlled, thus avoiding the coffee ring effect. Additionally, the gaps between silica particles fulfilled by ETPTA also can prevent unfavorable cracks compared to conventional PCs. 2.3 Tunable Structural Colors Different from the closely packed PCs requiring tens of particle sizes to tune the structural colors, almost all visible colors of ZnS–silica PCs can be obtained by simply altering the φ \n ZnS–silica based on limited particle sizes. Here, four ZnS–silica particles with sizes ranging from 128 to 248 nm ( Figure \n \n 2 a and Figure S5 , Supporting Information) were used as candidates for fabricating PCs. As shown in Figure 2b , for the same φ \n ZnS–silica, the structural color red shifts as the particle size increases due to the increase of D \n id (Figure S6 , Supporting Information). For the same particle size, the structural color blue shifts as the φ \n ZnS–silica increases owing to the decrease in D \n id (Figure S7 , Supporting Information). According to Bragg's law, the increase or decrease in D \n id will cause the redshift or blueshift of wavelengths and structural colors, respectively. Compared to other PCs, 158 and 184 nm ZnS–silica PCs show highly brilliant colors, covering most of visible colors. To evaluate the color saturation of these PCs, their reflection spectra are converted into the black points in the CIE chromaticity diagram (Figure 2c ). The hue of the PC can be identified directly through observing the color where the black point locates at. The color saturation is high and weak when the black point locates near the edge and center, respectively. The CIE diagram shows that all these black points are close to the edges, verifying the highly saturated structural colors of these PCs in human eyes. Figure 2 Tunable structural colors by altering φ \n ZnS–silica and ZnS–silica particle sizes. a) TEM images of ZnS–silica particles with different size of 128 (core: 92 nm and shell: 18 nm), 158 (core: 110 nm and shell: 24 nm), 184 (core: 142 nm and shell: 21 nm), and 248 nm (core: 142 nm and shell: 53 nm). b) Digital photos of ZnS–silica PCs with different φ \n ZnS–silica (20‐40%) and with particle sizes of 128–248 nm. The diameter of each sample is 1 cm. c) CIE diagram of the PCs with the ZnS–silica particle size of 158 and 184 nm. d) Reflection spectra of the ZnS–silica PCs with different φ \n ZnS–silica (20–40%) and with particle sizes of 128–248 nm. e) Reflection wavelength tuning range of the ZnS–silica PCs relative to the size ZnS–silica particles. f–h) Reflection spectra of the ZnS–silica and silica PCs. The size of ZnS–silica particle in (f), (g), and (h) is 158 ( φ \n ZnS–silica : 40%), 184 ( φ \n ZnS–silica : 30%), and 184 nm ( φ \n ZnS–silica : 20%), respectively. The size of silica particle in (f), (g), and (h) is 160 ( φ \n silica : 40%), 186 ( φ \n silica : 30%), and 186 nm ( φ \n silica : 20%), respectively. The thicknesses of all PCs are 57 µm. To determine the color tuning range of the PC by varying φ \n ZnS–silica , their reflection spectra were collected. Figure 2d shows that most PCs show high reflectance (60–81%) corresponding to the brilliant colors due to their large Δ n . The slight difference in reflectance can be attributed to the difference in order degree. In addition, the change in reflection wavelength as the function of φ \n ZnS–silica is well consistent with the variation of colors. Taking the 184 nm ZnS–silica PC as the example, its reflection blueshift from 619 to 538 nm with a wavelength tuning range (Δ λ ) of 81 nm when φ \n ZnS–silica increases from 20 to 40%, in good agreement with the color change from red to green. The peak variation of ZnS–silica PCs due to the change of φ \n ZnS–silica or particle size is consistent with the calculated results (Tables S1 and S2 , Supporting Information). Correspondingly, the transmission (Figure S8a , Supporting Information) of these PCs is almost zero and not very high when the wavelength is shorter and longer than stopbands, respectively. This can be ascribed to the incoherent scattering of light by the large Δ n that enhances the light scattering efficiency and strong absorption of ZnS–silica particles at short‐wavelength regions (Figure S8b , Supporting Information). The Δ λ of ZnS–silica PCs depends on the particle sizes. The Δ λ increases from 54 to 135 nm (Figure 2e ) when ZnS–silica particle increases from 128 to 248 nm accordingly. This can be attributed to the gradual increase in D \n s–s (from 47 to 92 nm accordingly). A larger D \n s–s is more favorable for a large shift of wavelength when increasing φ \n ZnS–silica , thus, leading to a larger Δ λ . The FWHM, reflectance, and peak area of the typical blue, yellow, and red ZnS–silica PCs are 2.8–3.6, 2.7–3.5, and 6.5–8.8 times higher than those of silica PCs (Figure 2f–h ) with similar particle sizes (Figure S9 , Supporting Information). It is worth noting that highly brilliant yellow (size: 184 nm and φ \n ZnS–silica : 30%) has been obtained despite its broad peak profile, which is a big challenge for silica PCs. Therefore, compared to the conventional way, the non‐close–assembling strategy can efficiently and conveniently achieve most visible colors with only two different sizes, which will facilitate their practical applications. Except these, one may find that 248 nm ZnS–silica PCs with φ \n ZnS–silica of 20, 25, and 30% show dim green, lake blue, and blue colors, probably due to the Mie scattering of these ZnS–silica particles. The reflection peak position of Mie scattering can be roughly calculated by λ \n Μ = n \n c \n D \n id , where λ \n Μ is resonant wavelength. Here, the λ \n Μ of the 20%, 25%, and 30% samples is calculated to be 525, 494, and 472 nm, respectively, in good agreement with their colors. Unfortunately, no obvious resonant reflective peaks can be detected. Such phenomenon requires extra investigations and efforts to reveal the inherent mechanism. 2.4 Optimized Thickness ( T ) The reflectance of PCs is not only dependent on the Δ n but also positively related to the periodic number of ordered structures. For PCs, there is a threshold of the thickness ( T \n th ), with which the PCs possess the maximal reflectance. For T < T \n th , the increase in T will lead to more efficient coherent and incoherent scattering of light by the increased periodic structures and increased defects, respectively. At this stage, the incoherent scattering of light is not strongly enough to influence the color visibility of PCs. Therefore, the reflectance increases when the T increases. For T > T \n th , despite the increase of T , the reflectance will not increase but the incoherent scattering increases due to the further increase of the number of defects, causing the pale and whitish colors of PCs. To investigate the influence of T on the reflectance, ZnS–silica particles with the size of 242 nm (core: 120 nm and shell: 45 nm, Figure S10 , Supporting Information) were used to fabricate the PCs with diverse thicknesses. As shown in Figure \n \n 3 a , a wide range of thicknesses (14–138 µm) can be easily achieved by simply altering the thickness of the interval using the non‐close–assembling approach, which is exceedingly difficult for conventional assembly methods. For all samples, φ \n ZnS–silica is fixed to 35%. For comparison, silica PCs were also fabricated by replacing the ZnS–silica particles with silica particles (Figure 3b , φ \n silica fixed to 35%). Compared to silica PCs, ZnS–silica PCs show much brilliant red colors (Figure 3b ) and more saturated colors (Figure S11 , Supporting Information) with a wide range of thicknesses. For ZnS–silica PCs, their reflectance and peak areas first increase and then maintain nearly constant once the T increases over 57 µm (Figure 3c,d ), suggesting the T \n th of the PC is or near 57 µm. However, the case is quite different for silica PCs whose reflectance and peak areas increase gradually when T increases, indicating the T \n th of the silica PC is larger than 138 µm. According to the previous work, [ \n \n 54 \n \n ] the T \n th of the silica PC might be located at around 270–360 µm. The striking differences in T \n th between the ZnS–silica and silica PCs can be attributed to their different efficiency in coherent scattering of light, which is proportional to the Δ n . For silica PCs, their Δ n are small, leading to weak reflectance, narrow bandgaps, and the low efficiency in reflecting light. For ZnS–silica PCs, their Δ n are large, resulting in intense reflectance, wide bandgaps, and high efficiency of coherent scattering of light. Therefore, compared to silica PCs, light can be reflected more efficiently by ZnS–silica PCs with the same thickness, which induces the small T \n th . It is worth noting that the ZnS–silica PC still shows high reflectance (≈50%) when the PC is as thin as 14 µm, which is 7.6 times higher than that of silica PC with the same thickness. Additionally, the saturation thickness of the ZnS–silica PC is similar (≈57 µm, Figures S12–S14 , Supporting Information) regardless of the ZnS core diameter, silica shell thickness, and ZnS–silica particle volume fraction. Figure 3 Effect of thicknesses. a) Cross‐sectional SEM images of ZnS–silica PCs with different thicknesses. b) Digital photos of ZnS–silica and silica PCs with different thicknesses. c,d) Reflection spectra of c) ZnS–silica PCs and d) silica (213 nm) PCs. e) Reflectance and f) Peak areas of the PCs as a function of thickness. ZnS‐SiO 2 : 210 nm (core: 120 nm and shell: 45 nm). One may find that the FWHM of the 14 µm ZnS–silica PC is much larger than others. For a PC, its FWHM depends on the order degree and refractive index contrast (Δ n ). A higher order degree and a smaller Δ n will be favorable for a small FWHM. For ZnS–silica PCs, their Δ n is constant, therefore, it is reasonable to infer that the large FWHM of the 14 µm ZnS–silica PC might be attributed to the decrease in order degree. It should be noted that the shoulder peaks at 565 nm originate from the spectrometer and will be amplified when overlapping with reflection wavelengths (Figure S15 , Supporting Information) due to the light modulation effect by photonic bandgaps. Overall, the high reflectance of the PC with a broad thickness will be useful in some applications such as optical devices and smart windows. 2.5 Derived Photonic Superstructures Thanks to the core–shell structures of ZnS–silica particles and the non‐close‐packing structure, a variety of new photonic superstructures ( Figure \n \n 4 \n ) can be fabricated through 1) co‐assemble ZnS–silica and silica particles into ZnS–silica/silica PCs, and 2) selective etching the ZnS or silica from ZnS–silica/silica PCs and ZnS–silica PCs. Here, 172 nm ZnS–silica particles (core of 142 nm and shell of 15 nm, Figure S16 , Supporting Information) and silica particles (180 nm) with similar sizes are used to fabricate ZnS–silica/silica PC through self‐assembly strategy. A thin silica shell is favorable for etching. φ of particles is 30% with φ \n ZnS–silica : φ \n silica = 1:1 so that nearly equal numbers of ZnS–silica and silica particles is introduced into PCs. ETPTA is a hydrophobic polymer, which hinders the etching. Therefore, the hydrophilic poly(ethylene glycol) diacrylate (PEGDA, Figure S17 , Supporting Information) is selected to replace ETPTA to fabricate PCs. Figure 4 Schematic illustration of the fabrication of derived photonic superstructures. In this figure, the size of ZnS–silica particle is 172 nm (ZnS core: 142 nm and shell: 15 nm) and the size of silica particle is 180 nm. Under SEM ( Figure \n \n 5 a and Figure S18 , Supporting Information), ZnS–silica particles with high brightness and silica particles with low brightness are alternatively packed into long‐range ordered and non‐closely packed structures similar to that of ZnS–silica PC. This ZnS–silica/silica PC features a green color and a strong photonic bandgap at 533 nm (Figure 5b ). Although there are two Δ n in the film, n is a certain value for the PC according to Equation ( 4 ); therefore, ZnS–silica/silica PC shows a single green color and a certain reflection wavelength according to Bragg's law. After etching by HCl, ZnS was removed selectively from ZnS–silica particles, leaving silica particles with hollow structures. Thus, HCl etched PC (Figure 5c ) with solid silica and hollow silica particles alternatively packed can be observed. Compared to the pristine PC, the reflection wavelength of the HCl etched PC blueshifts nearly 30 nm with a lake blue (Figure 5d ) due to decrease in n . Noticeably, its reflectance is still intense, because the ordered structure is still retained after etching. In contrast, a different derived photonic superstructure was obtained when the silica was selectively etched by HF. As shown in Figure 5e , silica particles and the silica shells of ZnS–silica particles are removed away, leading to the break of long‐range order and only some short‐range order can be observed. Thus, the HF etched PC exhibits a weak reflectance and neglectable color (Figure 5f ). Despite the low reflectance, its reflection wavelength is still angle‐dependent (Figure S19 , Supporting Information). However, the reflectance decreases nearly to 0 when the incident and detection angles are large than 20° simultaneously, suggesting a decrease in the angle‐dependence. Similarly, compared to the pristine PC, the blueshift of the reflection peak position also can be attributed to the decrease of n . Figure 5 Photonic superstructures. a,c,e) SEM images and b,d,f) reflection spectra and photos of the a,b) ZnS–silica/silica PC, c,d) corresponding HCl etched PC, and e,f) HF etched PC. g,i,k) SEM images and h,j,l) reflection spectra and photos of the g,h) ZnS–silica PC, i,j) corresponding HCl etched PC, and k,l) HF etched PC. The scale bar in photos is 1 mm. In this figure, the size of ZnS–silica particle is 172 nm (ZnS core: 142 nm and shell: 15 nm) and the size of silica particle is 180 nm. The thicknesses of all PCs are fixed to 57 µm. Additional two derived PCs also have been fabricated through selective etching the core or the shell of ZnS–silica PC. As shown in Figure 5g , ZnS–silica particles are non‐closely packed, possessing a reflection signal located at 534 nm and a typical green color (Figure 5h ). After etching by HCl, all ZnS were removed and hollow silica particles were non‐closely packed (Figure 5i ) in etched PC, causing a large blueshift of reflection wavelength and a blue color (Figure 5j ). When the ZnS–silica PC was etched by HF, a new derived superstructure (Figure 5k ) can be generated. However, it is hard to compare the D \n s‐s before and after etching due to the difficulty in recognizing the thickness of the silica shell. The etching of silica shells from ZnS–silica particles can be confirmed by the variation of the reflection signal (Figure 5l ). The replacement of silica shell by air causes the slight blueshift of reflection wavelength, while the decrease in Δ n induces the decrease of reflectance. As the order degree was retained, the reflectance of the photonic superstructure is still intense. Its reflection wavelength is angle‐dependent (Figure S20 , Supporting Information), further proving the ordered structure after etched by HF. ZnS–silica particles are non‐closely packed with polymers filled between neighboring particles, resulting in a dramatic decrease in the resolution of SEM. Therefore, no obvious voids can be observed from the SEM images of the HF‐etched samples. ZnS–silica PCs with thicker silica shells will be favorable to show the voids. Here, the ZnS–silica (core of 142 nm and shell of 35 nm, Figure S21a , Supporting Information) PC was fabricated, which possesses highly ordered structures (Figure S21b , Supporting Information) and high reflectance at 640 nm (Figure S21c , Supporting Information). After etching in HF, the reflectance of the PC decreases dramatically to almost zero due to the break of order degree and the voids can be clearly seen (yellow arrows, Figure S21d , Supporting Information). This HF‐etched PC, named water‐responsive photonic superstructure, was then immersed in water, interestingly, an intense reflection peak located at 681 nm appears, suggesting the recovery of order degree due to the move of ZnS particles. Compared to the pristine PC, its wavelength redshift can be ascribed to the increase in lattice distance caused by the swelling of PEGDA in water. These results demonstrate that the ZnS particles are movable in voids. We have prepared five photonic superstructures including one ZnS–silica/silica PC by co‐assembly strategy and four etched PCs from ZnS–silica/silica PC and ZnS–silica PC. It is difficult to fabricate these derived photonic superstructures using similar strategies from the conventional PCs based on silica or polystyrene particles. Although brilliant colors have been achieved by conventional dip‐coating, drop‐casting, and fluidic cells approaches using ZnS [ \n \n 49 \n , \n 51 \n , \n 55 \n , \n 56 \n \n ] and CeO 2 \n [ \n \n 46 \n \n ] particles as building blocks, the non‐close–assembling and the selective etching strategy has following advantages in: 1) fabricating non‐closely packed structures in a straightforward and efficient way; 2) avoiding the “coffee ring” effect; 3) tuning structural colors by simply altering φ \n ZnS–silica ; 4) a wide tuning range of thicknesses; 5) constructing abundant PC derived superstructures; and 6) unique water‐responsive colors. These PCs and derived superstructures may advance the manipulation of light in different ways and may facilitate the applications of PCs in displays, sensing, anti‐counterfeiting, optical devices, and so on. 2.6 Information Encryption and Fluorescence Enhancement A new information encryption strategy is developed by taking the advantages of the unique “off–on” switch of reflection wavelengths of the water‐responsive photonic superstructures. Here, ZnS–silica PCs ( φ \n ZnS–silica : 30%) with reflection wavelengths located at 606 and 472 nm (Figure S22 , Supporting Information) have been prepared when ZnS–silica particles with size of 200 nm (core of 140 nm and shell of 30 nm) and 154 nm (core of 105 nm and shell of 24.5 nm) were used, respectively. After selective etching by HF, for easy discussion, corresponding HF‐etched PCs were named as PC 200 and PC 154 , both of which show negligible reflectance (Figure S23 , Supporting Information). Eleven PC 200 are packed into “A” and four PC 154 are packed as the background, thus forming a triangle pattern ( Figure \n \n 6 a ). As expected, under normal condition, this pattern shows white appearance and the “A” is encrypted and invisible (Figure 6b ) due to the loss of ordered structures. In contrast, the red “A” with reflection wavelength at 632 nm (Figure S24 , Supporting Information) can be decrypted from the blue background with a peak position located at 493 nm when this pattern is soaked in water because of recovery of long‐range order. The encryption–decryption process is highly reversible (Figure S25 , Supporting Information). This encryption strategy with “off–on” color switch is different from conventional encryption methods based on the color switch from one to another, which will facilitate new applications in anti‐counterfeiting, displays, sensing, and so forth. Figure 6 Photonic superstructures for information encryption and PCs for PL enhancement. a) Schematic illustration of the information encryption strategy based on photonic superstructures. b) Digital photos of the pattern under the dry and wetted states. c) Reflection spectra of the ZnS–silica and silica PCs. d) PL intensity of the Eu(TTA) 3 /ETPTA film covered on ZnS–silica PC, silica PC, and ETPTA films, respectively. e) Reflection of ZnS–silica PCs with different reflection wavelengths. f) Enhanced factor of the fluorescence when ZnS–silica PCs with different reflection wavelengths are used. The thicknesses of all PCs are fixed to 57 µm. Except for information encryption, significant fluorescence enhancement can be realized when ZnS–silica PCs were used as substrates. For fluorescence enhancement, since the bandgap of the conventional silica PCs is narrow, the reflection peak position must be specifically altered to match the fluorescent wavelength, which could be inconvenient and difficult in practical applications. In addition, the low reflectance of conventional PCs could lead to the limited enhancement of fluorescence of dyes. In contrast, ZnS–silica PCs possess broad photonic bandgaps and high reflectance, which make them ideal candidates for enhancing fluorescence. We design a double layered films with a fluorescent film covered on a PC to investigate the fluorescence enhancement. The freestanding fluorescent film was prepared by sandwiching the Eu(TTA) 3 /ETPTA solution (fluorescent peak: 612 nm) between two glasses, followed by a photopolymerization. ZnS–silica and silica PCs with reflection wavelengths located at 612 nm matching the fluorescent wavelength were used (Figure 6c ). The thicknesses of the fluorescent and the PC films are ≈14 and ≈50 µm, respectively. When excited by the 405 nm, the fluorescent film shows a weak intensity on the ETPTA film (Figure 6d ). Interestingly, the PL intensity is enhanced by a factor of ≈10 when the fluorescent film is covered on the ZnS–silica PC with reflection signal located at 612 nm. The fluorescence enhancement is mainly attributed to enhanced extraction of light generated in the PC, which can serve as the dielectric cavity and act as a local resonance mode for the emission propagation. [ \n \n 57 \n , \n 58 \n , \n 59 \n , \n 60 \n \n ] In contrast, there is only 1.7‐fold enhancement at the maximum emission wavelength of fluorescent film on the silica PC. The significant difference in enhancement can be attributed to the difference in reflectance between the ZnS–silica and silica PCs. In addition, the fluorescent intensity was enhanced by a factor of 6–7 when the reflection wavelengths of ZnS–silica PCs were located out of the emission of Eu(TTA) 3 (Figure 6e,f ), probably due to the scattering of light. Overall, the maximal enhancement of the fluorescence was obtained when the stopband overlaps the maximum emission wavelength of Eu(TTA) 3 ."
} | 11,605 |
34039603 | PMC8153729 | pmc | 6,509 | {
"abstract": "Mitochondria-bearing microeukaryotes produce energy through anaerobic pathways to succeed in the face of ocean deoxygenation.",
"introduction": "INTRODUCTION The extent and intensity of oxygen-depleted waters are expanding in the oceans ( 1 , 2 ). The biological and ecological impacts of this ongoing “deoxygenation” are not fully realized, but it is clear that organisms in affected habitats will need to adapt to these ongoing environmental changes or face extirpation or even extinction. The stress and loss of taxa in newly hypoxic or anoxic zones will alter and compromise the efficiency of local food webs and could have marked impacts on the carbon cycle and other intimately linked cycles (e.g., nitrogen and metals). At this time, it is a prerequisite to decipher biological mechanisms underlying the resilience and adaptation to deoxygenation to predict the biological responses to the ongoing oxygen loss and the ensuing impacts on oceanic biogeochemical cycling, which are paramount to predictive global climate modeling. Foraminifera, among the most abundant and diverse protists (Rhizaria), are essential organisms within the ocean and its sediments ( 3 ), ranging from high latitude to tropical regions. Further, foraminifera can comprise most of the eukaryotic biomass in deep-sea settings. Certain species of benthic foraminifera are highly abundant in oxygen-depleted, sulfidic sediments where they can be ~2 orders of magnitude more abundant than foraminiferal assemblages of oxygenated sediments ( 4 , 5 ). Some benthic foraminifers store and respire nitrate (NO 3 − ) and reportedly perform complete denitrification ( 6 ). Estimates suggest that these foraminifera account for up to 70% of total denitrification in some sediments ( 6 , 7 ), making these microbial eukaryotes important players in anoxic settings and in marine biogeochemical cycling of nitrogen. Furthermore, experimental results showed that environmental hydrogen peroxide increased adenosine triphosphate concentration in some benthic foraminifera from oxygen-depleted sediments ( 8 ). Symbioses are widely reported in foraminifera inhabiting oxygen depleted to anoxic sediments, or the redoxcline, some hosting symbiotic bacteria ( 4 , 9 , 10 ), and others sequestering diatom chloroplasts (kleptoplasts) ( 11 – 13 ). Furthermore, an unidentified organelle-like structure, the so-called electron opaque bodies common to most benthic foraminifera ( 14 ), can assimilate nitrate, ammonium, and, perhaps, sulfate ( 15 , 16 ), thereby making foraminifera cogs in carbon (C), nitrogen (N), and sulfur (S) cycling. Foraminifera have large repetitive genomes with hundreds of megabases, complicating attempts to produce quality genome assemblies and reliable metabolic predictions ( 17 , 18 ). Key genes in the denitrification pathway were identified in foraminiferal congeners Globobulimina turgida and Globobulimina auriculata ( 19 ) and recently from metatranscriptome analyses of bulk sediment containing foraminifera ( 20 ). The phylogeny of foraminiferal denitrification genes appears to differ from the known denitrification genes in fungi, the only other eukaryote known to perform denitrification ( 21 ). However, with few draft genomes and transcriptomes, our knowledge on the potential metabolic capacity of foraminifera living in anoxic and hypoxic sediments remains largely uncharted territory. We investigated the metabolic adaptations of two highly successful benthic foraminiferal species inhabiting hypoxic or anoxic-sulfidic sediments of the Santa Barbara Basin (SBB) (CA, USA). To identify their respective metabolic adaptations, we sequenced libraries of host-enriched mRNA (transcriptomes) and libraries of the host plus any prokaryotic associates (holobiont; metatranscriptomes) from field-collected specimens and after laboratory incubations under different geochemical conditions. The kleptoplastidic Nonionella stella Cushman & Moyer, 1930 typically occurs in anoxic and sulfidic sediments, with abundances of >200 individuals cm −3 of sediment (fig. S1A) ( 5 ). Bolivina argentea Cushman, 1926 (fig. S1B) typically inhabits hypoxic sediments with abundances of >35 individuals cm −3 of sediment ( 5 ), lacks endobionts, and has chloroplasts that are usually digested instead of sequestered ( 7 ). Our transcriptome analyses of both foraminiferal species identified previously unknown candidate genes involved in anaerobic energy metabolism and ammonium (NH 4 + ) assimilation or recycling. In addition, a comprehensive analysis of denitrification genes expressed in our samples provided phylogenetic inference of a putative oxygenic denitrification pathway. Further, a functional N. stella kleptoplasty was inferred by the expression of genes related to a complete diatom plastid genome, although hosts live far below the euphotic zone. This metabolic variety suggests that at least some species of this diverse protistan group will withstand severe deoxygenation and likely play major roles in oceans affected by climate change.",
"discussion": "DISCUSSION Both species of benthic foraminifera were resilient to all experiment regimes, spanning from well-aerated to euxinic conditions. While our experiment treatments lasted only 3 days, the prevalence of these species in their respective collection sites demonstrates their ability to thrive in redoxcline sediments. Given that the foraminiferal community, including these two species, dominates the biovolume of the SBB eukaryotic benthic ecosystem ( 4 ), it is likely that this protistan taxon will play a major role in response to expansion of low oxygen to anoxic benthic habitats. Our metatranscriptome analysis revealed that all plastid-encoded transcripts in N. stella were derived from diatoms ( Fig. 1A ), likely belonging to the genus Skeletonema , a result consistent with Grzymski et al. ’s ( 12 ) findings that were based on plastid 16 S ribosomal RNA gene sequences. The consistently high expression of some key plastid-encoded genes like rbcL , rbcS, and photosystem proteins in N. stella confirms plastid integrity ( Fig. 1B ). While the kleptoplasts could perhaps assimilate inorganic carbon in situ, our specimens were preserved after brief exposure to light, so transcription of light-regulated genes may be a methodological artifact. Previous isotopic labeling analyses to demonstrate photosynthetic activity of foraminiferal kleptoplasts were inconclusive: A close relative to N. stella also collected from an aphotic habitat lacked photosynthetic activity and exhibited light-induced kleptoplast degradation ( 13 ); another study demonstrated detectable photosynthetic activity ( 11 ). Our results support transcriptional viability, suggesting the importance of kleptoplasty to the trophic strategy of SBB N. stella , which typically lacks food vacuoles with evidence of digestion products (fig. S1) and thrives at water depths exceeding 560 m, far below the euphotic zone ( 5 ). Horizontal gene transfer may also play a role in kleptoplasty. The consistent expression of diatom-related genes encoding FCP, across all our replicates and treatments for N. stella, but not B. argentea, strongly suggests that FCP in N. stella is not an artifact or contaminant ( Fig. 1B ). The activity of fucoxanthin was previously reported in SBB N. stella using a different method ( 12 ); it was hypothesized then that plastid proteins and fucoxanthin remain stable for over 1 year due to slow turnover rates in anoxic environments and extremely low irradiance at >550-m depth ( 12 , 35 ). As relatively few additional diatom genes were detected at similar breadth and depth of coverage, it is unlikely that the presence of undigested but active diatom nuclei could explain the presence of FCP. Therefore, our observations point toward a horizontal transfer of the FCP gene from the nucleus of an engulfed diatom to the N. stella nucleus. Horizontal gene transfer of plastid-related genes from the nucleus of the photosynthetic eukaryote to the nucleus of the secondary eukaryotic host (eukaryote-eukaryote symbiosis) is widespread throughout diverse lineages of eukaryotes, ultimately leading to secondary and tertiary endosymbiosis events ( 36 ). Hence, there is a mounting body of evidence for kleptoplasty as a major evolutionary step toward the establishment of permanent algal endosymbionts ( 37 ). However, examples of kleptoplasty that support the evolutionary transition toward permanent endosymbiosis have been documented in species restricted to the photic zone such as in dinoflagellates that retain haptophyte chloroplasts and the symbiosis between the sea slug Elysia chlorotica and Vaucheria litorea plastids ( 38 – 40 ). The nuclear-encoded FCP in N. stella represents new evidence supporting this hypothesis beyond the photic zone. In summary, retaining chloroplasts in a functional state and the detection of nuclear-encoded plastid-directed genes of diatom origin in N. stella further support the hypothesis that N. stella actively uses kleptoplasts to survive in the euxinic SBB seafloor, far below the euphotic zone ( 12 ). Analyses of transcriptomes indicate that both species likely assimilate ammonium through different pathways and that neither performs nitrate assimilation ( Fig. 2, A and B ). These findings contradict previous expectations alluding to kleptoplasts as nitrate and ammonium assimilatory organelles in kleptoplastidic foraminifera ( 12 , 13 ). Transmission electron microscopy–nanoscale secondary ion mass spectrometry (TEM-nanoSIMS) isotopic studies of other foraminiferal species demonstrated that the assimilation of both nitrate and ammonium localize to “electron-opaque bodies” rather than kleptoplasts ( 13 , 16 ). In N. stella, ammonium assimilation is likely via the GS-GOGAT pathway, a route for assimilating ammonium produced from nitrate reduction in photosynthetic eukaryotes and fungi ( 22 , 23 ). Our phylogenetic analyses illustrate that N. stella GOGAT belongs to NADH-GOGAT ( 22 , 41 ) (fig. S2). NADH-GOGAT can also be localized to chloroplasts as in some plants ( 24 ), while in diatoms and fungi, it is localized to the mitochondria ( 42 , 43 ). Thus, we expect the GS-GOGAT pathway in N. stella to occur outside kleptoplasts. The activity of GS-GOGAT is driven by the high affinity of GS for ammonium and glutamate ( 41 , 44 ). As noted, because N. stella vacuoles are typically “empty” rather than filled with food particles (fig. S1, C and D), reassimilation of ammonium or urea could play a major role in sustaining N. stella ’s overall nitrogen demand. In contrast, B. argentea had low-to-absent expression of GS-GOGAT but high expression of mitochondrial GDH ( Fig. 2B ). GDH has low affinity for ammonium and is involved with regulating the flux between the Krebs cycle and nitrogen metabolism, rather than ammonium assimilation ( 43 ). Possible roles of GDH in ammonium assimilation in heterotrophic nonsymbiotic species similar to B. argentea were predicted in a recent study ( 45 ). The consistent expression of genes related to anaerobic respiration and energy metabolism (denitrification and pyruvate oxidation via PFOR) suggests that they are essential for sustaining the cellular activity of our two foraminiferal species regardless of oxygen concentration. Even foraminifera incubated under aerated (i.e., oxygenated) conditions expressed most of these genes as highly as they did under anoxic incubations. Our findings are in line with those of Orsi et al. ( 20 ), which demonstrated a significant increase in expression of genes involved in protein synthesis, intracellular protein trafficking, and modification of the cytoskeleton in anoxic sediments bearing foraminifera, whereas genes assigned to energy production and metabolism were consistently transcribed across different oxygen levels. Our analysis confirms that these specific pathways are consistently expressed, bolstering support for the hypothesis of their constitutive expression. Biogeochemical studies have shown that some benthic foraminifera can perform complete denitrification ( 6 , 7 , 19 , 20 ). Although nitrate was depleted at the end of our incubations (table S1), it is established that benthic foraminifera store nitrate and that the nitrate turnover rate is on the order of a few days ( 46 ). Denitrification in foraminifera can be mediated by endosymbiotic bacteria ( 9 ) or performed by the host ( 7 , 19 ). We show that both of our foraminifera express two key denitrification proteins, NirK and Nor, and two types of nitrate/nitrite transporters ( Fig. 2 ). Our phylogenetic and domain structure analyses showed that the denitrification proteins from our foraminifera and Globobulimina spp. ( 19 ) group together, forming a lineage distinct from bacteria, archaea, and fungi ( Fig. 3C and figs. S3 and S5). This grouping suggests that a long-diverged denitrification pathway may be common to redoxcline foraminifera, with denitrification genes likely acquired from ancestral bacteria and/or archaea early in foraminiferal evolution and independently from other eukaryotes ( 19 ). Extensive searches of our data revealed that neither species expressed the typical eukaryotic NR. The MOSC-containing protein in foraminifera differs from the previously described eukaryotic NRs ( 47 – 49 ), yet it has all the essential domains for nitrate reduction, with domain composition similar to the NADPH nitrate reductase but with different organization ( Fig. 3A ). In addition, gene expression of the MOSC-containing protein was comparable to other denitrification genes. Thus, here, we describe a previously unidentified pNR homolog in foraminifera and provide its complete sequence (data S1). Future biochemical studies will be required to determine its specific role. We detected multiple copies of the Nor gene in both species, with an expression level 10-fold higher than the expression of NirK. The high expression of Nor suggests biological relevance to these foraminifera. Phylogenetic and domain structure analysis showed that foraminiferal Nor are more closely related to Nod than to qNor and NorB ( Fig. 3, C and D ) ( 50 ), favoring its potential role in oxygenic denitrification. However, despite the relative similarity between our putative foraminiferal Nor and known Nod genes, there remains substantial sequence divergence. This sequence divergence—combined with overall uncertainty in the relationships between the qNor, Nor-related, and Nod genes—necessitates future dedicated studies. In particular, isotope labeling studies are needed to identify and localize the end products, whether NO is reduced to N 2 O, a potent greenhouse gas, or directly to N 2 and O 2 . As previously reported in bacterial methanotrophs ( 27 , 50 ), being committed to oxygenic denitrification is preferable to avoid the detrimental effects of switching electron acceptors if the respiratory chain were not simultaneously activated ( 51 , 52 ). On the other hand, acetylene inhibition experiments in foraminifera argue against oxygenic denitrification ( 53 ) because acetylene blocks the conversion of N 2 O to N 2 , revealing N 2 O as a common intermediate within foraminiferal cells, indicative of Nor-type denitrification. Together, the genetic basis of dissimilatory nitrate reduction is highly unusual in N. stella and B. argentea , as exemplified by a domain-level rearrangement in a pNR and divergent NirK sequences. Eukaryotes that are adapted to prolonged periods of anoxia require energy through anaerobic energy metabolism. The expression of anaerobic fumarate reduction and ethanol-producing fermentation pathways, for example, has recently been reported by Orsi et al. ( 20 ). Besides concurring with these observations, we identified a previously unrecognized anaerobic energy generation pathway in N. stella and B. argentea that involves PFOR and [FeFe]-hydrogenases ( Figs. 2 and 4 and fig. S6). This metabolism has been described in eukaryotes that have hydrogenosomes and mitosomes or in anaerobic H 2 -producing mitochondria, currently referred to as mitochondrion-related organelles (MROs) ( 54 ). The facultative anaerobic energy metabolism and hydrogen production via PFOR and [FeFe]-hydrogenase were also associated with chloroplasts in diatoms and green algae when exposed to anoxia, a process deemed “dark fermentation” ( 30 ). Such fermentation may be assumed to occur in N. stella kleptoplasts. The fact that B. argentea performs the same fermentation but does not host intact chloroplasts suggests that the metabolism is not localized in or supported by kleptoplasts. In both N. stella and B. argentea, the predicted domain structure of the [FeFe]-hydrogenase is most similar to those of Stygiella incarcerate (Jakobid flagellate), Naegleria gruberi (amoeba/flagellate), and Acanthamoeba , belonging to the M3-type [FeFe]-hydrogenase, which is markedly distinct from the trimeric [FeFe]-hydrogenase in ciliates. The latter has two additional FeS clusters at the C terminus, a single thioredoxin-like [2Fe2S] and [4Fe4S]-cluster motif corresponding to NuoF (fig. S7). The N. gruberi [FeFe]-hydrogenase has been experimentally shown to be active, to produce molecular hydrogen even when grown under aerobic conditions, and to localize to the cytosol, as similarly noted for Giardia and Entamoebae ( 55 ). On the basis of the similarity of [FeFe]- hydrogenases of N. stella and B. argentea to N. gruberi , we suggest with caution their ability to produce hydrogen gas. Further, the observation that both PFOR and [FeFe]-hydrogenase were expressed across all tested conditions highlights their capacity to perform anaerobic energy metabolism under conditions with different oxygen concentrations ( Fig. 2B ). However, the localization of both anaerobic metabolism genes whether in the mitochondria or the cytoplasm and the production of molecular hydrogen by the [FeFe]-hydrogenase remain unclear. Now, there are no data on the foraminiferal mitochondrial genome or the function of electron opaque bodies ( 14 ). However, ultrastructural studies have long shown that mitochondria are present in foraminifera, albeit sometimes with atypical structure to the classic eukaryotic mitochondria ( 14 ). Recent studies have shown that the MROs in free-living anaerobic and microaerophilic protists are not discrete categories but rather represent a functional continuum between mitochondria and mitosomes ( 56 ). Documentation of active anaerobic metabolic pathways in our two species suggests that these benthic foraminifera have H 2 -producing mitochondria, which have an electron transport chain but use an electron acceptor other than oxygen ( 54 , 57 ). Previous findings showing that two other foraminifera (allogromiids) survive incubation with cyanide and salicylhydroxamic acid, inhibitors of mitochondrial complex III and IV ( 58 ), further support the assertion that some foraminiferal mitochondria function anaerobically. In summary, our gene expression analysis of two benthic foraminiferal species, thriving in hypoxic and anoxic sediments, demonstrated great metabolic versatility. These foraminifera are well poised to play major roles in response to oceanic deoxygenation, resulting in expansion of oxygen-depleted seafloor sediments. In addition, the multiple adaptive metabolic strategies identified are changing our classical view on the evolution and diversity of eukaryotes, which hypothesizes that the rise of oxygen in Earth’s system led to the acquisition of oxygen-respiring mitochondria. These foraminifera represent a model of “typical” mitochondrial-bearing eukaryotes, yet they are adapted to and can thrive in anoxic environments, often with high levels of sulfide; respire nitrate over oxygen ( 53 ); and produce energy using the same metabolism performed by anaerobic eukaryotes. Benthic foraminifera represent truly successful microbial eukaryotes with diverse and sophisticated metabolic adaptive strategies that we are just beginning to discover."
} | 5,025 |
34471289 | null | s2 | 6,510 | {
"abstract": "Extracellular electron transfer by Geobacter species through surface appendages known as microbial nanowires"
} | 27 |
32019917 | PMC7000784 | pmc | 6,511 | {
"abstract": "Most mono- and co-culture bioprocess applications rely on large-scale suspension fermentation technologies that are not easily portable, reusable, or suitable for on-demand production. Here, we describe a hydrogel system for harnessing the bioactivity of embedded microbes for on-demand small molecule and peptide production in microbial mono-culture and consortia. This platform bypasses the challenges of engineering a multi-organism consortia by utilizing a temperature-responsive, shear-thinning hydrogel to compartmentalize organisms into polymeric hydrogels that control the final consortium composition and dynamics without the need for synthetic control of mutualism. We demonstrate that these hydrogels provide protection from preservation techniques (including lyophilization) and can sustain metabolic function for over 1 year of repeated use. This approach was utilized for the production of four chemical compounds, a peptide antibiotic, and carbohydrate catabolism by using either mono-cultures or co-cultures. The printed microbe-laden hydrogel constructs’ efficiency in repeated production phases, both pre- and post-preservation, outperforms liquid culture.",
"introduction": "Introduction Microbial production of value-added products ranging from small molecules to complex proteins is becoming increasingly attractive and effective across industry and academia 1 – 4 . Recent advances in synthetic biology have further enabled microbial production to be modular and distributed across multiple organisms, thus creating synthetic consortia that can reduce metabolic loads and afford more robust cell populations 5 – 7 . Though commonly employed in bioreactor and cellular interaction studies, the limitations of liquid culture systems are especially poignant when attempting to control the dynamics of a multi-organism consortium. Specifically, repeated (and sometimes even single) batch liquid co-cultures typically fail over time without sophisticated genetic control systems or particular nutrient conditions that seek to minimize the competitive growth bias that often occurs when utilizing disparate microorganisms 8 – 11 . Immobilized cell technologies, wherein microbes are encapsulated within a polymeric matrix, have been developed as an alternative to suspension cell culture 12 – 16 . These microbe-laden matrices have been used to investigate quorum sensing between microbial species 14 , and as 3D architected ‘living materials’ 15 – 17 . Calcium alginate and other polysaccharides are the most common matrices used for immobilizing cells, despite the sensitivity of the ionic crosslinks to the presence of charge-bearing molecules and the pH of the medium 18 . Various other hydrogel materials, comprised of synthetic or modified naturally occurring polymers, have been explored but fail to produce a material that is simultaneously readily processable, mechanically robust, and inert to the chemicals and biochemicals present in the media 19 – 29 . Herein, we demonstrate a platform that utilizes a hydrogel system to compartmentalize microbes to build spatially segregated microbial consortia. To this end, Nelson and co-workers previously reported shear-thinning hydrogels based on F127-dimethacrylate (F127-DMA) polymer 16 . The F127-bisurethane methacrylate (F127-BUM) hydrogels employed in this study also exhibit a temperature-dependent sol–gel transition (~17 °C), which was used to immobilize yeast cells. The temperature response of the material facilitated the facile incorporation of the cells homogeneously throughout the hydrogel, while the shear-thinning behavior facilitated the extrusion of the cell-laden hydrogel from a nozzle. In this work, we show that this F127-BUM hydrogel can be used to compartmentalize the various constituent organisms of an engineered microbial consortium that would otherwise be incompatible in a traditional liquid suspension culture. In doing so, we show that mono- and co-culture systems immobilized within hydrogels can be extrusion printed to form solid-state bioreactors capable of producing small molecules and antimicrobial peptides for multiple, repeated cycles of use. Interestingly, the microbe-laden hydrogels can also be preserved via lyophilization, stored in a dried state, and then rehydrated at a later time for on-demand chemical and pharmaceutical production (Fig. 1 ) in a manner that outperforms a traditional liquid-based culture format. Moreover, for co-culture systems, the spatial compartmentalization of the microbes enabled precise control of the consortium composition and dynamics without the need for genetically encoded mutualism. Fig. 1 Overview of microbe-laden, extrusion-printed hydrogels for on-demand production. The hydrogel encapsulation and on-demand production process is divided into three parts. In the Gel Preparation stage, the printed and UV-cured microbial hydrogels are transferred to culture medium for cell outgrowth. While this initial outgrowth can also be used for production, the resulting cell-laden living materials can proceed to either the Gel Storage or On-Demand Production phase depending on user needs. In the Gel Storage stage, the microbial gels are treated with different types of preservation methods for storage and future use. The preserved gels are subsequently rehydrated and incubated in fresh medium to perform on-demand production, with iterative re-uses as desired.",
"discussion": "Discussion We have developed a microbe-laden hydrogel platform that can compartmentalize and spatially organize individual microbial populations and consortium members into hydrogel constructs for the production of both small molecules and active peptides. The approach enables repeated re-use and preservation through refrigeration or lyophilization, thus enabling on-demand production of these molecules in a manner that is unmatched by traditional liquid-based culturing. The ability to enable long-term stability of cells and consortia (up to 1 year in the continuous fermentation of yeast to produce ethanol and at least 3 months in the case lyophilized gel storage and subsequent use for betaxanthins) provides a niche for preserving catalytic function in industrial bioprocesses. Moreover, the ability to control consortium dynamics simply by changing the amount of hydrogel ink printed provides a newfound capacity for plug-and-play synthetic consortia. Looking forward, this strategy enables a portable, reusable, and on-demand capacity for small molecule and pharmaceutical production from a variety of microorganisms."
} | 1,631 |
34177820 | PMC8222582 | pmc | 6,512 | {
"abstract": "Bacterial biofilms play a key role in metal biosorption from wastewater. Recently, Enterobacter asburiae ENSD102, Enterobacter ludwigii ENSH201, Vitreoscilla sp. ENSG301, Acinetobacter lwoffii ENSG302, and Bacillus thuringiensis ENSW401 were shown to form air–liquid (AL) and solid–air–liquid (SAL) biofilms in a static condition at 28 and 37°C, respectively. However, how environmental and nutritional conditions affect biofilm formation; production of curli and cellulose; and biosorption of copper (Cu), nickel (Ni), and lead (Pb) by these bacteria have not been studied yet. In this study, E. asburiae ENSD102, E. ludwigii ENSH201, and B. thuringiensis ENSW401 developed the SAL biofilms at pH 8, while E. asburiae ENSD102 and Vitreoscilla sp. ENSG301 constructed the SAL biofilms at pH 4. However, all these strains produced AL biofilms at pH 7. In high osmolarity and ½-strength media, all these bacteria built fragile AL biofilms, while none of these strains generated the biofilms in anaerobic conditions. Congo red binding results showed that both environmental cues and bacterial strains played a vital role in curli and cellulose production. Calcofluor binding and spectrophotometric results revealed that all these bacterial strains produced significantly lesser amounts of cellulose at 37°C, pH 8, and in high osmotic conditions as compared to the regular media, at 28°C, and pH 7. Metal biosorption was drastically reduced in these bacteria at 37°C than at 28°C. Only Vitreoscilla sp. ENSG301 and B. thuringiensis ENSW401 completely removed (100%) Cu and Ni at an initial concentration of 12.5 mg l –1 , while all these bacteria totally removed (100%) Pb at concentrations of 12.5 and 25 mg l –1 at pH 7 and 28°C. At an initial concentration of 100 mg l –1 , the removal of Cu (92.5 to 97.8%) and Pb (89.3 to 98.3%) was the highest at pH 6, while it was higher (84.7 to 93.9%) for Ni at pH 7. Fourier transform infrared spectroscopy results showed metal-unloaded biomass biofilms contained amino, hydroxyl, carboxyl, carbonyl, and phosphate groups. The peak positions of these groups were shifted responding to Cu, Ni, and Pb, suggesting biosorption of metals. Thus, these bacterial strains could be utilized to remove Cu, Ni, and Pb from aquatic environment.",
"conclusion": "Conclusion Biofilm formation and biofilm matrix compounds such as curli and cellulose production in E. asburiae ENSD102, E. ludwigii ENSH201, Vitreoscilla sp. ENSG301, A. lwoffii ENSG302, and B. thuringiensis ENSW401 were remarkably affected by different environmental and nutritional conditions. Only Vitreoscilla sp. ENSG301 and B. thuringiensis ENSW401 completely removed (100%) Cu and Ni at an initial concentration of 12.5 mg l –1 , while all these strains totally removed (100%) Pb at initial concentrations of 12.5 and 25 mg l –1 at pH 7 and 28°C. FTIR study showed that Cu, Ni, and Pb could be sorbed by carbonyl, carboxyl, and phosphate groups of the biomass biofilms of E. asburiae ENSD102, Vitreoscilla sp. ENSG301, A. lwoffii ENSG302, and B. thuringiensis ENSW401, while phosphate groups and P–O of the (C–PO 2 –3 ) moiety have no role in Cu removal by E. ludwigii ENSH201. Thus, all these bacterial strains can be utilized in biosorption of heavy metals from wastewater.",
"introduction": "Introduction Metals at molecular densities greater than 5 g/cm 3 are known as heavy metals ( Weast, 1984 ). Heavy metal release from various industries [such as steel, leather, electroplating, mine tailings, paints, wastewater treatment plants, and agricultural operations (fertilizers, pesticides, and irrigations)] is one of the major causes of environmental pollution. Some heavy metals like copper (Cu), zinc (Zn), iron (Fe), cobalt (Co), chromium (Cr), and nickel (Ni) are required for growth and metabolism of organisms when they are present in trace amount, known as trace elements or micronutrients. However, they become toxic when the concentration increases. Conversely, non-essential heavy metals including lead (Pb), cadmium (Cd), mercury (Hg), and arsenic (As) are toxic even at very low concentrations. Accumulation of such heavy metals in soils and water bodies poses threat to human health (including potential carcinogenicity), other living organisms, and eventually overall biodiversity ( Naser et al., 2014 ; Ahmed et al., 2016 ; Alam et al., 2017 ; Burakov et al., 2018 ). Various physicochemical technologies such as reverse osmosis, filtration, electro-dialysis, flocculation, ion exchange, activated carbon, and chemical precipitation are being practiced to remove the heavy metals from aqueous systems. However, all these methods have some disadvantages like being expensive, having high energy and reagent requirements, not being appropriate for the removal of low concentrations (1–50 mg l –1 ) of heavy metals, releasing of chemical sludge, and being less practical under natural environmental conditions ( Ahluwalia and Goyal, 2007 ; Pan et al., 2009 ; Edwards and Kjellerup, 2013 ; Redha, 2020 ). Biosorption is one of the bioremediation technologies and uses fungi, bacteria, algae, and plants to sequester heavy metals ( Nies, 1999 ). In the biosorption process, microbes adsorb metals on the cellular surface through attachment/linkage onto many anionic functional groups ( Lo et al., 2014 ). Indeed, biosorption technique offers several benefits over the physicochemical methods in terms of economic aspects, high metal-binding capacity, eco-friendliness, and regeneration of biosorbents with the possibility of the recovery of metals ( Kratochvil and Volesky, 1998 ; Sag and Kutsal, 2001 ; He and Chen, 2014 ; Santhosh et al., 2016 ). Compared to fungi, algae, and plants, bacterial strains were found effective to remove heavy metals from aquatic environment ( Oves et al., 2013 ; Banerjee et al., 2015 ; Wei et al., 2016 ; Yang et al., 2017 ; El-Naggar et al., 2018 ). Though free-living bacterial cells have a greater capacity for metal removal from aquatic environment ( Malik, 2004 ; Zeng et al., 2009 ), their survival is less likely due to decreased protection and the low metabolic activity ( von Canstein et al., 2002 ). Hence, it is urgently needed to find out effective indigenous bacterial biosorbents that can survive even under toxic environmental conditions along with diverse metabolic states. Bacterial biofilms are highly structured, surface-associated cells, enclosed in a matrix of self-produced extracellular polymeric substances (EPS) ( Costerton et al., 1999 ). Compared to free-living planktonic counterparts, bacterial biofilms provide numerous benefits, including protection of cells from adverse environmental stresses (e.g., toxic chemicals, pH change, dehydration, and predation), the ability to communicate by expression of quorum-sensing molecules, exchange of genetic material (e.g., horizontal gene transfer) and nutrient availability from waste products, and persistence in different metabolic functions with respect to electron acceptor reduction ( Teitzel and Parsek, 2003 ; Boles et al., 2004 ; Vu et al., 2009 ; Haque et al., 2012 , 2017 ; Edwards and Kjellerup, 2013 ). The bacterial EPS is composed of different high-molecular-weight biopolymers including proteins, cellulose-rich polysaccharides, nucleic acids, and lipids ( Flemming and Wingender, 2010 ; Mosharaf et al., 2018 ). Bacterial surface appendages including the flagella, pili, and aggregative fimbriae/curli were also reported to stabilize the biofilm matrices ( Flemming et al., 2007 ). Several researchers have shown that negatively charged functional groups/ligands of EPS serve as a trap for heavy metal ions ( Sutherland, 1984 ; Deng et al., 2007 ; Wei et al., 2016 ). Enzymatic activities in EPS play a key role in detoxification of heavy metals by transformation and subsequent precipitation in the polymeric mass ( Pal and Paul, 2008 ). Both living and dead biomass biofilms can be applied to remove heavy metals from the wastewater. Among them, living biofilms were found effective to remove heavy metals from both in the continuous treatment effluents ( Gadd, 2009 ) and in the real industrial and municipal effluents ( Kotrba et al., 2011 ). Both biosorption and bioaccumulation simultaneously take place in living bacterial biofilms. Nevertheless, several cellular mechanisms including synthesis of specific enzymes and action of cytoplasmic or membrane proteins were shown to express in the living bacterial biofilms ( Kumar et al., 2007 ). Therefore, instead of free-living planktonic bacteria/dead bacterial biofilms, growing (living) bacterial biofilms have been appreciated for several bacterial-dominated processes and recommended for the removal of heavy metals from the environment ( Singh et al., 2006 ; Pal and Paul, 2008 ; Edwards and Kjellerup, 2013 ; Meliani and Bensoltane, 2016 ; Balan et al., 2020 ). Enterobacter asburiae ENSD102, Enterobacter ludwigii ENSH201, Vitreoscilla sp. ENSG301, and Acinetobacter lwoffii are Gram-negative bacteria that are positive to catalase and acetoin tests but are negative to gelatin liquefaction, methyl red, and indole tests. Some strains of E. asburiae degraded polyethylene plastic ( Sato et al., 2016 ) and augmented crop growth ( Dolkar et al., 2018 ; Mahdi et al., 2020 ). Vitreoscilla sp. was reported to synthesize hemoglobin used in metabolic engineering ( Wakabayashi et al., 1986 ). On the other hand, Bacillus thuringiensis is a Gram-positive spore-forming bacterium and well known for the production of insecticidal crystalline (Cry) proteins. Recently, all these bacterial strains were isolated from the wastewater of Bangladesh and reported to form biofilms by expression of curli (a proteinaceous component of the EPS) and nanocellulose fibers ( Mosharaf et al., 2018 ). Important environmental applications of these bacterial strains are summarized in Table 1 . TABLE 1 Environmental applications of bacterial strains used in this study. Bacteria Important environmental applications References E. asburiae Degradation of polythene, detoxification of carcinogenic dyes, removal of heavy metals, solubilization of nutrients, fixation of nitrogen, crop growth promoter, utilization as biofertilizer, and reduction of metal toxicity in crop plants. Jetiyanon, 2015 ; Kang et al., 2015 ; Paul and Mukherjee, 2016 ; Ren et al., 2019 ; Mahdi et al., 2020 ; Haque et al., 2021a E. ludwigii Degradation of dyes, biosorption of heavy metals, functions as nematicides, solubilization of nutrients, reduction of cadmium stress in plants, plant growth enhancer, and prevention of drought and salinity stress in crop plants. Shoebitz et al., 2009 ; Gontia−Mishra et al., 2016 ; Radwan et al., 2017 ; Adhikari et al., 2020 ; Haque et al., 2021b Vitreoscilla sp. Decolorization, degradation, and detoxification of textile dyes; hemoglobin technology used in bio-product synthesis; bioremediation; and enhancement of tolerance of submergence, oxidative, and nitrosative stress in plants. Stark et al., 2015 ; Zelasco et al., 2006 ; Haque et al., 2021a , b A. lwoffii Detoxification of dyes, biodegradation of diesel, bioremediation of heavy metals, and solubilization of phosphate. Sadiq et al., 2013 ; Mindlin et al., 2016 ; Imron and Tttah, 2018 ; Haque et al., 2021a , b B. thuringiensis Decolorization of azo dyes; degradation of naproxen, ibuprofen, and chlorpyrifos; removal of heavy metals; used in insect control; plant growth activator; solubilization of nutrients; fixation of nitrogen; and mitigation of drought stress in plants. Oves et al., 2013 ; Armada et al., 2014 ; Aceves-Diez et al., 2015 ; de Maagd, 2015 ; Marchlewicz et al., 2016 ; Martins et al., 2018 ; Haque et al., 2021a , b Several studies have shown that environmental conditions affect biofilm formation ( Rinaudi et al., 2006 ; Liang et al., 2010 ; Haque et al., 2015 ; Ross et al., 2018 ) and the expression of curli and cellulose in different bacterial strains ( Gerstel and Römling, 2003 ; Barnhart and Chapman, 2006 ). Initial metal concentration, temperature, pH, and contact time were shown to affect the biosorption of heavy metals ( Oves et al., 2013 ; Yang et al., 2017 ; El-Naggar et al., 2018 ; Redha, 2020 ). Among the factors, pH plays a key role in the metal speciation, metal solubility, and dissociation of functional groups present in the bacterial cell wall ( Esposito et al., 2002 ). Metal ions in solution undergo hydrolysis as the pH increases. However, the extent of hydrolysis at different pH values differs with each metal, but the usual sequence of hydrolysis is the formation of hydroxylated monomeric species followed by the formation of polymeric species and then the formation of crystalline oxide precipitates after aging ( Bacs and Mesmer, 1976 ). Therefore, adsorption of metals on interfaces is highly pH-dependent. For example, Cu can be present in solution as three different species: Cu 2+ , CuOH + , and Cu(OH) 2 . Cu 2+ and CuOH + are more favorable Cu species under lower pH conditions ( Yang et al., 2017 ). Cu, Ni, and Pb are frequently found in industrial wastewater, rivers, sediments, fish, and vegetables in Bangladesh ( Naser et al., 2014 ; Ahmed et al., 2016 ; Mosharaf et al., 2018 ; Uddin and Jeong, 2021 ). Concentrations of these metals were also reported to increase day by day in the environment of Bangladesh ( Uddin and Jeong, 2021 ). Thus, it is urgently needed to study the biosorption of Cu, Ni, and Pb from the environment. How environmental factors affect biofilm formation, the expression of biofilm matrix components (e.g., curli and cellulose), and the biosorption of Cu, Ni, and Pb has never been investigated in E. asburiae ENSD102, E. ludwigii ENSH201, Vitreoscilla sp. ENSG301, A. lwoffii ENSG302, and B. thuringiensis ENSW401. Therefore, it is aimed to quantify the effects of different environmental cues such as temperature, pH, osmolarity, oxygen tension, and nutritional strength on biofilm formation and production of curli and cellulose in these bacterial strains. It is also intended to evaluate these bacterial strains for their efficacies to remove Cu, Ni, and Pb from aqueous solutions in response to initial metal concentration, temperature, and pH. Furthermore, it is aimed to identify the chemical functional groups/ligands present in both metal-unloaded and metal-loaded biomass biofilms produced by these bacterial strains using Fourier transform infrared (FTIR) spectroscopy. The study will contribute toward understanding the mechanisms and potential of these bacterial strains in biosorption of heavy metals from aquatic environment.",
"discussion": "Discussion Environmental Conditions Affect Bacterial Biofilm Formation In this study, it was observed that environmental cues play a vital role in biofilm formation ( Figure 1 ), production of curli and cellulose ( Figures 2 – 4 ), and biosorption of Cu, Ni, and Pb ( Figures 5 – 7 ) by E. asburiae ENSD102, E. ludwigii ENSH201, Vitreoscilla sp. ENSG301, A. lwoffii ENSG302, and B. thuringiensis ENSW401. E. asburiae ENSD102 and Vitreoscilla sp. ENSG301 produced the SAL biofilms both at pH 4 ( Figure 1E ) and pH 8 ( Figure 1F ), while E. ludwigii ENSH201 and B. thuringiensis ENSW401 constructed the SAL biofilms only at pH 8 ( Figure 1F ). However, all these bacterial strains developed the AL biofilm at pH 7 ( Figure 1D ). Furthermore, only Vitreoscilla sp. ENSG301 and B. thuringiensis ENSW401, but not other bacterial strains, generated the SAL biofilms in high osmotic condition ( Figure 1G ). Thus, temperature, pH, and osmolarity and bacterial strains might play an important role in SAL and AL biofilm formation. Numerous researchers have differentiated AL and SAL biofilm genetically, enzymatically, and based on cultural conditions ( Friedman and Kolter, 2004 ; Yap et al., 2005 ; Haque et al., 2012 ). Bacterial biofilm formation in different bacterial strains was shown to be influenced by the pH ( Hostacká et al., 2010 ; Ramli et al., 2012 ; Nguyen et al., 2014 ; Zhou et al., 2014 ; Haque et al., 2017 ), high osmolarity ( Lebeer et al., 2007 ; Hou et al., 2014 ; Kavamura and de Melo, 2014 ; Haque et al., 2017 ), and oxygen tension ( Gerstel and Römling, 2001 ; Bjergbæk et al., 2006 ; Ahn and Burne, 2007 ; Liang et al., 2010 ; Wu et al., 2013 ). Except temperature ( Mosharaf et al., 2018 ), effect of pH, osmolarity, and oxygen tension on biofilm formation by E. asburiae ENSD102, E. ludwigii ENSH201, Vitreoscilla sp. ENSG301, A. lwoffii ENSG302, and B. thuringiensis ENSW401 was not studied by any other contemporary researches. Bacterial Biofilm Formation as Affected by Nutritional Factors In ½-strength SOBG media, all these bacterial strains generated thin and fragile AL biofilms ( Figure 1H ) as compared to those of regular SOBG media ( Figure 1D ). Shen et al. (2018) reported that at higher nutrient concentrations, biofilms are thicker and denser than under nutrient-poor conditions. Bacterial biofilm formation was also shown to be activated by different divalent cations (such as Mg 2+ and Ca 2+ ) through their effect on electro-static interactions ( Patrauchan et al., 2005 ). Among the divalent cations, Ca 2+ impacts the mechanical properties of biofilms, as well as cross linkers ( Patrauchan et al., 2005 ). On the other hand, Mg 2+ increased initial attachment in P. fluorescens ( Song and Leff, 2006 ) by reducing the repulsive force between the negatively charged bacterial and substratum surfaces and between negative functional groups of the polysaccharides ( Koechler et al., 2015 ). Haque et al. (2012) have reported that a low concentration (10 μM) of Mg 2+ increases the AL biofilm formation of Dickeya dadantii 3937 in a PhoP-PhoQ-dependent manner. AL biofilm formation controlled by the CytR homolog in Pectobacterium carotovorum subsp. carotovorum PC1 was also shown to be increased by the different divalent cations, e.g., Mg 2+ , Ca 2+ , Cu 2+ , Zn 2+ , and Mn 2+ ( Haque et al., 2017 ). In this study, E. asburiae ENSD102, E. ludwigii ENSH201, Vitreoscilla sp. ENSG301, A. lwoffii ENSG302, and B. thuringiensis ENSW401 also produced the dense and stout AL biofilms by addition of different divalent cations (0.009 M) including Mg 2+ , Ca 2+ , Cu 2+ , and Zn 2+ (data not shown). Thus, not only environmental cues but also nutritional conditions might play a crucial role in the formation of a thick and robust AL biofilm. Environmental and Nutritional Cues Affect Curli and Cellulose Expression in Bacteria Protein filaments, known as curli, and cellulose played a pivotal role in AL biofilm/pellicle formation in E. coli ( Prigent-Combaret et al., 2000 ) and Salmonella enterica serovar Enteritidis ( White et al., 2003 ). The production of curli in Enterobacteriaceae was shown to be regulated by temperature. For example, curli in Salmonella usually is visible under 30°C ( Gerstel and Römling, 2003 ; Bokranz et al., 2005 ), but some strains such as S. typhimurium can express at 37°C ( Olsén et al., 1998 ). Conversely, in clinical isolates of the E. coli , the expression of the curli at 37°C is a rarely visible phenomenon ( Barnhart and Chapman, 2006 ). In the present study (in Congo red binding assays), E. asburiae ENSD102 and B. thuringiensis ENSW401 produced the curli both at 28 and 37°C ( Figures 2A,B ), while E. ludwigii ENSH201 expressed only curli at 28°C ( Figure 2A ). Curli was not synthesized by Vitreoscilla sp. ENSG301 both at 28°C ( Figure 2A ) and 37°C ( Figure 2B ), while this bacterial strain generated the curli at pH 4 at 28°C ( Figure 2C ). The production of cellulose was also found to be dramatically reduced by high temperature (i.e., 37°C), pH 8, high osmolarity, and anaerobic conditions in E. asburiae ENSD102, E. ludwigii ENSH201, Vitreoscilla sp. ENSG301, A. lwoffii ENSG302, and B. thuringiensis ENSW401 ( Figures 3B–D , 4 ). The expression of the curli fimbriae and cellulose in Salmonella spp. is most intense at temperatures under 30°C, in low osmolarity, limited availability of nutrients, and aerobic conditions ( Gerstel and Römling, 2003 ; Solomon et al., 2005 ; Steenackers et al., 2012 ). Thus, a variation of biofilm formation in E. asburiae ENSD102, E. ludwigii ENSH201, Vitreoscilla sp. ENSG301, A. lwoffii ENSG302, and B. thuringiensis ENSW401 in response to environmental and nutritional conditions might be due to differential expression of curli and cellulose. Mechanisms of Heavy Metal Tolerance in Bacteria The determination of MTC in different heavy metals is of particular interest, when bacterial strains are applied for biosorption. In this study, the MTC of Cu, Ni, and Pb was remarkably higher in B. thuringiensis ENSW401 as compared to other bacterial strains tested ( Table 2 ). E. asburiae ENSD102, E. ludwigii ENSH201, Vitreoscilla sp. ENSG301, and A. lwoffii ENSG302 are Gram-negative bacteria, while B. thuringiensis ENSW401 is a Gram-positive bacterium. Generally, the cell wall of both types encompass a peptidoglycan layer that is rich in carboxylate groups and completely surrounds the cell. The peptidoglycan layer of Gram-positive bacterium is thicker (three layers) than the peptidoglycan layer of Gram-negative bacterium (two layers). Thus, cell wall structure might play a pivotal role in MTC of Cu, Ni, and Pb in these bacterial strains. Roane et al. (2009) have reported that (i) binding of metals to extracellular materials of the bacterial cells immobilizes the metals and prevents them from entering into the bacterial cells, (ii) several bacterial strains produced siderophore (iron-complexing, low-molecular-weight organic compounds) complexes that increase metal tolerance, (iii) bacterial strains also generated biosurfactant (excreted from the bacterial cells) complexes of metals that are non-toxic to the cells, and (iv) numerous plasmid-encoded genes [e.g., cusCBA (resistance to copper), cnrCBA (cobalt–nickel resistance), and pbrA (encoding lead resistance)] conferred higher levels of metal tolerance in different bacteria. Thus, several mechanisms might be used in these bacterial strains for the tolerance to Cu, Ni, and Pb. Mechanisms of Heavy Metal Toxicity in Bacteria The growth of E. asburiae ENSD102, E. ludwigii ENSH201, Vitreoscilla sp. ENSG301, A. lwoffii ENSG302, and B. thuringiensis ENSW401 was severely inhibited with the increase of the concentration of Cu, Ni, and Pb after MTC (data not shown). Bacterial growth, morphological characteristics, and biochemical processes were reported to be disrupted due to toxicity of heavy metals ( Roane et al., 2009 ). Koechler et al. (2015) stated that high concentrations of heavy metals including Cu, Ni, and Pb directly or indirectly generate reactive oxygen species (ROS) upon reacting with DNA, resulting in damaged bases or strand breaks, lipid peroxidation, or protein modification. Roane et al. (2009) reported that metals bind to many cellular ligands and displace essential metals from their native binding sites due to ionic interactions. Moreover, they have shown that metals affect the oxidative phosphorylation and membrane permeability. Some metals can inhibit cellular activity because they present a structural homology with enzyme substrates leading to the metal toxicity. Heavy metal can also cause ion imbalance by adhering to the cell surface and entering through ion channels or transmembrane carriers ( Chen et al., 2014 ). Therefore, future studies should focus on the mechanism of the toxicity of E. asburiae ENSD102, E. ludwigii ENSH201, Vitreoscilla sp. ENSG301, and B. thuringiensis ENSW401 in response to higher concentrations of Cu, Ni, and Pb. Biofilm Formation in Relation to Heavy Metal Uptake in Bacteria Biofilm production in E. asburiae ENSD102, Vitreoscilla sp. ENSG301, and A. lwoffii ENSG302 was reported to be affected by 500 to 2,000 mg l –1 of CuSO 4 .5H 2 O, Pb(NO 3 ) 2 , or NiCl 2 ( Mosharaf et al., 2018 ). In this study, biofilm production was not remarkably varied in E. asburiae ENSD102, E. ludwigii ENSH201, Vitreoscilla sp. ENSG301, A. lwoffii ENSG302, and B. thuringiensis ENSW401 in response to 12.5 to 200 mg l –1 Cu, Ni, or Pb ( Supplementary Figure 1 ). Accordingly, biosorption capacity was not significantly varied in these bacterial strains in response to 12.5, 25, and 50 mg l –1 Cu, Ni, and Pb ( Figure 5 ). Interestingly, only Vitreoscilla sp. ENSG301 and B. thuringiensis ENSW401 completely removed (100%) Cu and Ni at an initial concentration of 12.5 mg l –1 , while all these bacterial strains totally removed (100%) Pb at initial concentrations of 12.5 and 25 mg l –1 at pH 7 and 28°C. However, Vitreoscilla sp. ENSG301 and B. thuringiensis ENSW401 removed much more Cu, Ni, or Pb as compared to E. asburiae ENSD102, E. ludwigii ENSH201, and A. lwoffii ENSG302 in response to 100, 150, and 200 mg l –1 ( Figure 5 ). Thus, biosorption might be dependent on both concentrations of the heavy metals (Cu, Ni, and Pb) and bacterial strains. At present, various ordinance, laws, rules, acts, and policies have been made to control environmental pollution in Bangladesh. The Department of Environment of Bangladesh also set safety limit of different heavy metals in industrial effluent [ DoE (Department of Environment), 2008 ]. The World Health Organization [ WHO (World Health Organization), 2017 ], European Union [ European Union (EU), 2002 ], United States Environmental Protection Agency [ USEPA (United States Environmental Protection Agency), 2012 ], and Bangladesh [ DoE (Department of Environment), 2008 ] prescribed the maximum acceptable concentrations at (mg l –1 ) 2.0, 0.5, 0.2, and 0.5, respectively, for Cu; 0.02, 0.5, 0.2, and 1.0, respectively, for Ni; and 0.01, 0.5, 0.05, and 0.10, respectively, for Pb. Thus, treated wastewater by these bacterial strains at an initial concentration of 12.5 mg l –1 for Cu and 12.5 and 25.0 mg l –1 for both Ni and Pb are within the safety limit set by the abovementioned organizations, while concentrations of Cu, Ni, and Pb are higher than the maximum acceptable values in all other treated samples. Cellular Structure, EPS, and Enzymes on Metal Biosorption in Bacteria Bacterial cellular structure, EPS, and extracellular enzyme play a vital role in metal biosorption. Both Gram-positive and Gram-negative bacterial cell wall contain peptidoglycan that determines the metal-binding capability. However, teichoic acids are present only in Gram-positive bacteria that provide an overall negative charge to the cell wall, due to the presence of phosphodiester bonds. On the other hand, lipopolysaccharides (LPS) are only present in Gram-negative bacteria that confer an overall negative charge to the cell wall of Gram-negative bacteria. The anionic functional groups present in the cell wall of Gram-positive and Gram-negative bacteria are the key contents primarily responsible for the anionic character and metal-binding or biosorption capacity of the cell wall ( Moat et al., 2002 ). Notably, bacterial biofilm EPS also play a key role in metal biosorption ( Pal and Paul, 2008 ; Li and Yu, 2014 ). Among the contents of the EPS, proteins form complexes with heavy metal ions ( Mejáre and Bülow, 2001 ), while polysaccharides cross-link with metals ( Li and Yu, 2014 ). Biofilm EPS matrix has abundant chemical functional groups such as amino, carboxyl, carboxylate, phosphate, and hydroxyl groups ( Mosharaf et al., 2018 ). It was reported that negatively charged functional groups present in the EPS matrix formed organometallic complexes with multivalent metal cations via electrostatic interactions and subsequent metal removal ( Gutnick and Bach, 2000 ). Numerous extracellular enzymes (e.g., protease, peptidase, endo-cellulase, α-glucosidase, β-glucosidase, peroxidase, etc.) have been detected in bacterial biofilms ( Flemming and Wingender, 2010 ). Many of them were reported to degrade the contents of EPS ( Flemming and Wingender, 2010 ) and detoxify the heavy metals ( Pal and Paul, 2008 ). However, extracellular enzymes synthesized by E. asburiae ENSD102, E. ludwigii ENSH201, Vitreoscilla sp. ENSG301, and B. thuringiensis ENSW401 and their involvement in detoxification of heavy metals are yet to be examined. Physicochemical Conditions Alter Metal Biosorption in Bacteria Removal of heavy metals from aqueous solution by bacteria is a complex process due to the effect of different physicochemical factors such as initial metal concentration, temperature, pH, time, ionic strength, and metal chemistry ( Gabr et al., 2008 ; Hassan et al., 2009 ). In this study, increasing the concentrations of Cu, Ni, and Pb decreased the metal biosorption ( Figure 5 ). Higher metal biosorption at lower concentrations of heavy metals was reported to be due to the availability of free metal-binding sites, while lower metal biosorption at higher concentrations is due to lack of free metal-binding sites ( Kaduková and Virèíková, 2005 ; Oves et al., 2013 ; Kirova et al., 2015 ). Cellulose-based materials including cellulose gels, cellulose composites, cellulose derivatives, functionalized cellulose, and nano-crystaline cellulose are widely used for the adsorption of heavy metals from wastewater ( Jamshaid et al., 2017 ). E. asburiae ENSD102, E. ludwigii ENSH201, Vitreoscilla sp. ENSG301, A. lwoffii ENSG302, and B. thuringiensis ENSW401 were shown to produce nanocellulose that is amorphous in nature ( Mosharaf et al., 2018 ). In this study, cellulose production ( Figure 4 ) and biosorption of Cu, Ni, and Pb by these bacterial strains were dramatically reduced at 37°C as compared with 28°C ( Figure 6 ). Bacterial cellulose production was shown to be linked with biosorption of metals ( Teitzel and Parsek, 2003 ; Li and Yu, 2014 ). Thus, reduction of the removal of Cu, Ni, and Pb by these bacterial strains at 37°C might be due to a lower production of cellulose. Redha (2020) has shown that metal biosorption is not highly affected in temperatures ranging from 20 to 35°C. On the other hand, Tas̨ar et al. (2014) have reported that increaseing temperature from 20 to 40°C decreases the surface activity of biosorbents such as peanut shells leading to decreased biosorption of Pb. In this study, pH levels regulated the biosorption of Cu, Ni, and Pb ( Figure 7 ). The difference in metal biosorption at different pH was associated with the effect of both the chemistry of the functional groups and the chemistry of metal ions ( Wei et al., 2016 ). At low pH, functional groups present in the biofilm EPS tightly bound with hydronium ions leading to restricting the binding of metal cations due to repulsive force. Conversely, with increasing pH, various functional groups including carbonyl, carboxyl, phosphate, and amino start experiencing negative charges due to deprotonation leading to binding with metal cations and thus increasing the biosorption capacity ( Oves et al., 2013 ; Abdi and Kazemi, 2015 ). In this study, the chemical functional groups present in the biofilm EPS of E. asburiae ENSD102, E. ludwigii ENSH201, Vitreoscilla sp. ENSG301, A. lwoffii ENSG302, and B. thuringiensis ENSW401 were determined using FTIR in both metal-loaded and -unloaded (control) samples at an initial Cu, Ni, and Pb concentration of 100 mg l –1 , pH 7, and at 28°C. Several functional groups including –OH, –NH, –CH, C = O, COO-, and P-O were detected in the samples ( Figures 8A,B and Supplementary Figures 2 – 4 ). FTIR results revealed that Cu, Ni, and Pb could be sorbed by carbonyl, carboxyl, and phosphate groups of E. asburiae ENSD102, Vitreoscilla sp. ENSG301, A. lwoffii ENSG302, and B. thuringiensis ENSW401, while phosphate groups and P–O of the (C–PO 2 –3 ) moiety have no role in Cu removal by E. ludwigii ENSH201. The chemical functional groups in metal-loaded biofilm EPS in E. asburiae ENSD102, E. ludwigii ENSH201, Vitreoscilla sp. ENSG301, A. lwoffii ENSG302, and B. thuringiensis ENSW401 were not reported by any other contemporary researches. Future Perspective and Scale Up Future studies should focus on the biosorption of heavy metal from real wastewater by these bacterial strains. The mechanisms involved in metal toxicity in these bacterial strains should be studied. Extracellular enzymes produced in the EPS matrix of these bacterial strains and their role in the detoxification of heavy metals should also be examined. Nevertheless, genetic engineering tools should be used to construct the engineered E. asburiae ENSD102, E. ludwigii ENSH201, Vitreoscilla sp. ENSG301, A. lwoffii ENSG302, and B. thuringiensis ENSW401 with higher metal sorption capacity. For scale up, large amounts of biomass biofilm can be produced in less expensive growth media by using these bacterial strains. Currently, bacterial biofilm biomass is being used in different types of bioreactors including fixed bed reactor, packed bed reactor, and fluidized bed reactor to remove heavy metals from wastewater. Thus, biomass biofilm produced by E. asburiae ENSD102, Vitreoscilla sp. ENSG301, A. lwoffii ENSG302, and B. thuringiensis ENSW401 can be utilized in the ex situ conditions for different engineered bioreactor systems. This will require an interdisciplinary approach with the integration of metallurgical, chemical, mathematical, and civil engineering skills along with sorption and wastewater treatment plan to combat heavy metal pollution from the aquatic environment. The last but not the least, in order to get the best out of the results obtained and to get this technology used for heavy metal pollution mitigation, it needs to be integrated into the policy simultaneously by the concerned government and international donor agencies."
} | 8,490 |
39827174 | PMC11742803 | pmc | 6,513 | {
"abstract": "Coastal wetlands are rich in terrestrial organic carbon. Recent studies suggest that microbial consortia play a role in lignin degradation in coastal wetlands, where lignin turnover rates are likely underestimated. However, the metabolic potentials of these consortia remain elusive. This greatly hinders our understanding of the global carbon cycle and the “bottom-up” design of synthetic consortia to enhance lignin conversion. Here, we developed two groups of lignin degrading consortia, L6 and L18, through the 6- and 18-month in situ lignin enrichments in the coastal East China Sea, respectively. Lignin degradation by L18 was 3.6-fold higher than L6. Using read-based analysis, 16S rRNA amplicon and metagenomic sequencing suggested that these consortia possessed varied taxonomic compositions, yet similar functional traits. Further comparative metagenomic analysis, based on metagenomic assembly, revealed that L18 harbored abundant metagenome-assembled genomes (MAGs) that encoded diverse and unique lignin degradation gene clusters (LDGCs). Importantly, anaerobic MAGs were significantly enriched in L18, highlighting the role of anaerobic lignin degradation. Furthermore, the generalist taxa, which possess metabolic flexibility, increased during the extended enrichment period, indicating the advantage of generalists in adapting to heterogenous resources. This study advances our understanding of the metabolic strategies of coastal prokaryotic consortia and lays a foundation for the design of synthetic communities for sustainable lignocellulose biorefining. Supplementary Information The online version contains supplementary material available at 10.1186/s13068-025-02605-w.",
"conclusion": "Conclusions In conclusion, this genome-centric metagenome analysis revealed the metabolic potential of lignin degradation in coastal intertidal zones. Microbial LDGCs were discovered across 13 bacterial phyla, covering depolymerization and catabolism under aerobic/anaerobic conditions. Investigating the genomic potential of the L18 consortia with higher lignin degradation capacity broadens our understanding of the metabolic strategies used by natural microbial consortia in the highly disturbed ecosystem, with versatile abiotic gradients. Moreover, it provides inspiration for the “bottom-up” design of synthetic microbial communities in lignin valorization. On one hand, the knowledge about MAGs and LDGCs guides us to employ members from the Bacteroidia , Verrucomicrobiae and Spirochaetia classes with dypB and laccase for lignin depolymerization. Species from the class Alphaproteobacteria could be the well candidates for aerobic degradation of lignin-derived aromatic compounds, whiles Desulfobacteria and Desulfarculia species might be considered for anaerobic lignin-derived aromatic compound degradation. On the other hand, the cooperative strategies in natural consortia, e.g., aggregation, DOL and metabolic flexibility, lay foundations to design inter-species interactions within synthetic communities. Considering the complex and amorphous structures of lignin, understanding the various metabolic strategies for different lignin types would provide more accurate and comprehensive clues for lignin bioconversion in the near future.",
"discussion": "Discussion In this study, we developed two groups of lignin degrading consortia via 6- and 18-month in situ enrichment in coastal intertidal zones, respectively. They exhibited great differences with respect to lignin degradation (7.06% vs 25.19%). Genomic potential analysis of the complex degradation process of lignin via metagenomics is usually restricted due to the limited number of genes that can be annotated via the widely used CAZy and KEGG databases. For instance, 63 gene families from the cultured communities in the nearshore sediments of the East and South China Seas, 75 gene families from the Amazon River microbiomes and 13 gene families from a North American forest consortium were revealed to participate in lignin degradation, respectively [ 3 , 4 , 9 ]. Here, we employed our recently developed lignin degradation functional gene database (LCdb) [ 30 ] and revealed that the 414 gene families in the L6 and L18 consortia were involved in lignin degradation via the metagenomic gene-centric analysis, with the similar the abundances and compositions. In contrast, they exhibit significant taxonomic variations, in which the taxon varied with environmental factors (Fig. 2 , Fig. S2, Fig. S8 and Table S3). Our previous study also reported that environmental factors, pH, temperature, salinity and DO, all significantly influenced the variations of community composition in the in situ enrichment experiment ( r = 0.28–0.47, p = 0.001) [ 11 ]. It coincided with the recent studies that environmental selects functional traits, instead of species [ 20 ]. Importantly, it revealed that individual functional traits just indicated the metabolic routes, but were not strongly associated with the lignin metabolism capacity. In contrast, LDGC-based genome-centric analysis uncovered the genomic potential differences between L6 consortia and L18 consortia. L6 consortia harbored 12 LDGCs that participated in 9 complete or nearly complete lignin-derived aromatic compound pathways, whiles L18 consortia contained 16 LDGCs, involved in 12 aromatic compound pathways (Fig. 4 , Fig. S6 and Table S6). Specifically, the des in the aerobic syringate degradation pathway, bcr and bam in the benzoyl-CoA degradation pathway, and pad in the anaerobic phenylacetate degradation pathway were specifically detected in L18 consortia (Figs. 4 , 5 ). Moreover, the abundance of these MAGs was significantly higher in L18 than in L6, especially for the MAGs from the classes Bathyarchaeia and Desulfobacteria (Fig. 3 ). Archaeal Bathyarchaeia are reported to utilize lignin as the sole carbon source under anaerobic conditions [ 13 ]. Anaerobic lignin degradation is commonly accompanied by nitrate, iron and sulfate reduction, which allow for electron transfer during the degradation process [ 36 , 74 , 75 ]. Coinciding with this, the relative abundance of Desulfobacteria , as sulfate reducers [ 70 , 76 ], was increased in L18, although sulfur in Kraft lignin also could contribute to their enrichment (Fig. S5). Moreover, a higher abundance of the mtgB gene family was observed in L18, which encodes methyltransferase and was reported to be a critical step in anaerobic lignin degradation [ 14 ]. This indicated that L18 employed more anaerobic members to enhance the metabolic capacity. The LDGCs differences were also associated with the fluctuating coastal intertidal zones. Compared to the LDGCs in aerobic G- and H-type lignin unit degradation pathways, lig in the aerobic S-type pathway showed significantly associated with the environmental factors (Fig. S8). Moreover, more LDGCs in anaerobic degradation pathways exhibited significant correlation with the environmental factors, in contrast to the aerobic LDGCs (Fig. S8). Together, L18 consortia recruited more abundant MAGs that encoded diverse and unique LDGCs. Aggregation is a general strategy that microbial communities utilize to efficiently perform complex tasks [ 77 ]. Multiple members could perform the same function, and thus enhance efficiencies when forming a union. In this study, the aggregation strategy was used by both L6 and L18 consortia, where each LDGC was encoded by several MAGs for lignin degradation, including lignin depolymerization, and aerobic/anaerobic aromatic compound degradation (Fig. 4 and Fig. S6). Moreover, L18 consortia exhibited a cooperative advantage over L6 consortia. They recruited more MAGs to enhance the LDGCs abundances and further efficiently accomplish each task. For instance, the L6 group recruited 14 MAGs with 1.81% abundance that encoded dypB , the dominate lignin oxidizing gene in coastal regions (Fig. 2 C) [ 11 , 30 , 78 ], whereas the L18 group employed 17 MAGs that harbor dypB , with a 4.08% abundance. Similarly, 4 MAGs, at 0.24% abundance, in L6 harbored the pca in aerobic PCA 3,4-cleavage, while 6 MAGs with 0.42% abundance in L18 contained the complete pathway. In particular, Desulfobacteria and Desulfarculia MAGs were significantly increased in L18 (Fig. S5-S6), which not only participated in anaerobic degradation of 4-hydroxybenzoic acid, benzoyl-CoA and phenylacetate, but also were responsible for electron transfer during anaerobic lignin degradation. Besides aggregation, division of labor (DOL) is another cooperative strategy evolved by microbial communities to accomplish complex tasks, reduce metabolic burden, accelerate the metabolite catabolism, and increase toxic compound resistance [ 79 – 81 ]. One of the potential mechanisms for DOL is complementing differences in gene content [ 82 ]. Here, complementary functions were observed in the L6 and L18 consortia. MAGs from the classes Bacteroidia , Verrucomicrobiae and Spirochaetia are mainly responsible for lignin depolymerization. The dypBs are mostly encoded by Bacteroidia , whereas laccases are generally encoded by Verrucomicrobiae and Spirochaetia (Fig. 4 ). In contrast, Alphaproteobacteria encode numerous LDGCs that participate in aerobic degradation of lignin-derived aromatic compounds, while Desulfobacteria and Desulfarculia encode LDGCs involved in anaerobic lignin degradation. Although Bacteroides species from the class Bacteroidia are well-known members of the gut microbiota, members of the phylum Bacteroidetes are prevalent in a variety of marine environments, e.g., coastal, offshore, sediments and hydrothermal vents [ 83 ]. Marine Bacteroidetes members commonly attach to particles and increase in abundance during phytoplankton blooms, as they encode gene families of carbohydrate-active enzymes (CAZymes) to degrade polysaccharides, e.g., cellulose [ 83 ]. Recent studies also revealed they play a role in degradation of lignin-derived aromatic compounds in the Pearl River [ 15 ]. Here, we revealed that members from the Bacteroidia encode dypB , laccase and mnsod for lignin depolymerization (Fig. 4 ). Marine Verrucomicrobiota are thought to excel at the degradation of complex polysaccharides, including digesting fucoidan from brown macroalgae [ 84 ], as well as sulfated and fructose-containing polysaccharides during diatom blooms [ 85 ]. The class Spirochaetia in the phylum Spirochaetota was reported to be more abundant in environments with high concentrations of hydrocarbons, as they harbor polycyclic aromatic hydrocarbon ring-hydroxylating dioxygenase (PAH-RHDGNɑ) [ 86 ]. Here, we expanded their roles to the degradation of aromatic carbon polymers, revealing they also encode dypB and/or laccase to depolymerize lignin. In addition, the class Alphaproteobacteria from phylum Pseudomonadota is widely distributed in terrestrial and aquatic ecosystems and harbors various types of metabolic processes [ 87 , 88 ]. Previous research has suggested that Alphaproteobacteria associates with wood rot fungi to decompose wood [ 89 ]. In the Pearl River freshwater environment, Alphaproteobacterial MAGs harbor genes for the degradation of ferulic acid, syringyl fragment and diarylpropane biphenyl fragment [ 15 , 90 ]. Here, we further demonstrated that Alphaproteobacteria in coastal intertidal zones contain multiple LDGCs involved in the aerobic degradation of a wide range of lignin-derived aromatic compounds, including syringate, aryl ether, vanillin, benzoate, benzoyl-CoA, protocatechuic acid and 3-hydroxycinnamin acid (Fig. 4 and Fig. S6). In contrast to aerobic lignin degradation, MAGs affiliated with Desulfobacteria and Desulfarculia exhibited the genomic potential for anaerobic lignin degradation. They harbored dissimilatory sulfate reduction pathways to allow elemental sulfur as an electron acceptor during anaerobic lignin degradation (Fig. 4 and Fig. S5, S6). Besides their well-known role in sulfur reduction [ 70 , 71 , 76 ], they also carried LDGCs for the anaerobic catabolism of lignin-derived aromatic compounds (4-hydroxybenzoate, phenylacetate and benzoyl-CoA), demonstrating their central multi-role in lignin degradation under anaerobic/anoxic environments. Interestingly, the archaeal class Bathyarchaeia , affiliated with Desulfobacteria and Desulfarculia , was also enriched in the L6 and L18 consortia. Bathyarchaeia has been isolated from estuarine sediments and are reported to mediate anaerobic lignin degradation [ 13 ]. A recent study further suggested that it is widely distributed in multiple anoxic marine ecosystems, particularly anoxic coastal sediments and encodes methyltransferases ( mtgBs ) for O -demethylation of lignin monomers [ 14 ]. However, the Bathyarchaeia MAGs recovered in the current study did not contain mtgB , although the gene family was detected in both the L6 and L18 consortia by gene-centric analysis (Fig. 2 C). This indicated that unrecognized genes or LDGCs with anaerobic lignolytic activity might be present in the Bathyarchaeia MAGs. Together, the complementary gene contents suggested that a task division strategy should exist in the enriched communities, in which each taxon performs a specific job. Furthermore, metabolic flexibility contributes to the execution of these cooperative strategies. We observed that L18 exhibited an increased abundance in generalist taxa, e.g., Alphaproteobacteria E_bin.143 and Alphaproteobacteria E_bin.95 (Fig. 4 ). Moreover, additional generalists were specifically recruited by L18, e.g., Bacteroidia E_ bin.12, Anaerolineae E_ bin.30 and Alphaproteobacteria E_bin.147 (Fig. 4 ). The enriched generalists in L18 suggested that metabolic flexibility should a key factor to perform lignin degradation in coastal intertidal zones. Lignin is a highly heterogeneous aromatic polymer that varies from plant to plant, and a variety of aromatic compounds are released during lignin depolymerization [ 22 , 91 ]. Generalists with metabolic flexibility could well utilize the available heterogenous compounds. A similar situation was observed in a bioreactor system where the glucose and xylose co-utilizing generalist outcompeted specialists [ 92 ]. Furthermore, the coastal intertidal zone is a highly disturbed ecosystem [ 93 ]. The specialization-disturbance hypothesis suggests that disturbances positively stimulate generalists while being detrimental to specialists [ 94 , 95 ]. Consequently, Anaerolineae E_ bin.30, as a typical generalist that shows genomic potential for both aerobic and anaerobic metabolism, was expectedly increased in L18 in response to the strong fluctuations of dissolved oxygen (Figs. 4 and 5 )."
} | 3,693 |
37953278 | PMC10641076 | pmc | 6,516 | {
"abstract": "Networks of random-assembled gold clusters produced in the gas phase show resistive switching (RS) activity at room temperature and they are suitable for the fabrication of devices for neuromorphic data processing and classification. Fully connected cluster-assembled nanostructured Au films are characterized by a granular structure rich of interfaces, grain boundaries and crystalline defects. Here we report a systematic characterization of the electroforming process of the cluster-assembled films demonstrating how this process affects the interplay between the nano- and mesoscale film structure and the neuromorphic characteristics of the resistive switching activity. The understanding and the control of the influence of the resistive switching forming process on the organization of specific structures at different scales of the cluster-assembled films, provide the possibility to engineer random-assembled neuromorphic architectures for data processing task.",
"conclusion": "Conclusions We have characterized the physical processes underlying the resistive switching forming of cluster-assembled nanostructured Au films: local thermal effects and electromigration induced by the current flowing in the systems cause a structural reorganization at the nano and mesoscale. The thermal dissipation properties of the substrates determine the relative weights of the contribution of these processes and hence the resulting reorganization of the network. The reorganization of the grain-boundaries during the forming process, considered the physical phenomena at the base of the RS activity in Au cluster-assembled devices 26 – 29 , is of particular relevance together with the mesoscale formation of bridges between different nanoscale islands. Fine differences in the network reorganization on this scale correspond to specific temporal correlation of resistive switching events; the hierarchical and modular structure of the Au cluster-assembled thin film on each specific scale makes the system complexity great and ordered enough to promote an organized current redistribution, subsequent to RS events due to grain boundary dynamic, resulting in time correlated electrical activity. Our results stress the role of the morphological complexity in the resulting non-linear electrical activity of Au cluster-assembled based devices; the choice of substrates with different thermal properties and the tuning of electrical parameters during the RS activation open the way to the engineering of the resistive switching dynamics, with defined time correlations and RS amplitude, aiming at the development of bottom-up neuromorphic hardware technologies with suited electrical properties for specific data processing tasks .",
"introduction": "Introduction Over the last three decades the advent of the “big data” paradigm in science, business, media, and telecommunications urged the search for solutions of the inability of traditional CMOS-based computing platforms to perform massive real-time data processing at a sustainable hardware and energy cost 1 . The race for extreme miniaturization, beyond the physical limits of CMOS technology, suggested the possibility of developing radically different approaches to computation, based on nanoscale objects such as molecules, nanowires, quantum dots 2 – 5 . The development of massive fabrication technologies of nanometer-scale logic devices collided with the much higher energetic and manufacturing cost of the top-down lithographic techniques compared to bottom-up approaches and with the nature of nanoscale components usually affected by defects, structural flaws and variability in performances 6 , 7 . Pursuing the analogy with biological neural networks, the memristor was identified as an artificial alter ego of the neuron and of the synapse thus providing the elemental building block for the fabrication of hardware supporting artificial neural networks at reduced energetic costs 8 , 9 . The use of memristors, in the mainstream research and technological effort, however is not driving a complete adoption of the mammalian brain architectures 10 , since memristors are organized in deterministic crossbar arrays fabricated with CMOS technology and coupled with traditional microelectronic components 9 , thus making many problems related to CMOS technology still waiting to be solved. In order to cope with these limitations, several architectures based on defect tolerance, redundancy and reconfigurability were proposed, taking inspiration from the architecture of the mammalian brains 11 – 14 . Brain activity is based on networks composed by neurons organized according to redundant structure and connected by non-linear synapses, whose organization and activity infer them spatio-temporal correlations 15 – 18 . The fabrication of interconnected redundant networks based on standard electronic components is beyond the capabilities of top-down technologies 19 – 21 . Alternative fabrication approaches of hardware based on redundant interconnectivity can rely on random-assembling strategies 11 , 22 , 23 . In the last decade, random-assembled networks composed by a large number of non-linear nanoscale junctions between nanoparticles and nanowires have been proposed 2 , 13 , 23 – 25 . Among random-assembled neuromorphic devices, it has been recently demonstrated that the assembling of gold nanoparticles produced in the gas phase results in nanostructured films exhibiting non-ohmic electric behaviour and complex Resistive Switching (RS) activity 26 – 29 . These random-assembled systems manifest interesting features as resistive switching events characterized by power law dynamics and long-range interactions 11 , 26 , 28 . Resistive switching in Au cluster-assembled devices have been recognized as the result of grain boundaries dynamic reorganization under an applied external voltage 27 , 28 . However, the phenomena which drive the forming of the reconstructed granular networks and which induce a reconfiguration of the structural and electrical properties of the system from an ohmic to a non-linear electrical behaviour have not been identified yet. Furthermore, the interplay between the morphology and the structure of the activated random-assembled network, from the nano to the mesoscale, and the non-linear electrical behaviour has never been explored systematically. Here we report the characterization of the influence of the electroforming process on the resistive switching activity with time correlations of cluster-assembled Au nanostructured films (ns-Au). The control of the forming processes acting on the nanoscale and mesoscale re-organization of the nanostructured films allows also to gain a deeper understanding of the interplay between structural and functional electric properties of these systems.",
"discussion": "Results and discussion As-deposited morphology and resistive switching forming process The morphology of the ns-Au thin films can be affected by the nanoscale features characterizing the interface of the substrates, i.e. oxidized silicon (SiO x ) and glass. To test this hypothesis, we evaluated the root-mean-square roughness (Rq) of the substrates, calculated by AFM images as the ones reported in Fig. 3 a,b; Rq results 0.34 ± 0.05 nm for SiO x and 0.88 ± 0.3 nm for glass. The morphology of the ns-Au film, a typical AFM image of the as-deposited cluster-assembled gold film is shown in Fig. 3 c, appears as a granular and porous matrix composed by a huge number of few nanometres large clusters 26 , 41 , impinging on the substrate with kinetic energy low enough to prevent them from a post-deposition fragmentation upon the surface 41 . The roughness of the deposited ns-Au film, which increases with the thickness (t) of the film according to a power law (Rq ⁓ t β ) 41 , is 3.5 ± 0.5 nm. The as-deposited ns-Au films appear with the same granularity and result in same roughness values on both SiOx and glass substrates. Figure 3 AFM images of the ( a ) SiO x substrate, ( b ) the glass and ( c ) the as-deposited ns-Au thin film on a glass substrate. As-deposited gold thin films in the thickness range of 15–40 nm showed ohmic electrical behaviour, as for each explored thickness above the percolation threshold 26 . The resistive switching forming procedure consisted in circulating high current densities up to 10 9–10 A/m 2 , for about 90 ± 11 s for ns-Au/SiO x and 30 ± 3 s for ns-Au/glass; this caused the modification of the film morphology, structure, and electrical behaviour 26 , 42 , 43 . By high current circulating through the film, we observed a first resistance decrease for granular network reorganization (with a reduction of up the 50% of the initial value), in contrast to the expected ordinary joule-effect related resistance increase observed in metals, followed by a gradual increase of the resistance (up to the double of the initial resistance value in about 1 s) with a final abrupt resistance increase of about 1–3 orders of magnitude 27 . At this point the cluster-assembled films started to exhibit a stable resistive switching activity and we observed non-linear electrical behaviour 26 , 28 , 29 . The described RS forming procedure resulted in different electrical and structural evolutions depending on the type of substrates, as reported here after. The ns-Au deposited on ultra flat SiO x and subjected to RS forming procedure remains ohmic even after circulating current densities up to 10 10–11 A/m 2 . Reorganization of the network structure Mutually correlated interfacial phenomena, as electro-migration and joule heating, occurring at the interface with the ns-Au film can lead the metallic network to a reorganization of its structure and morphology 42 , 43 . In Table 1 the structural parameters extracted from XRD patterns are reported for the as deposited ns-Au sample, the switched ones on glass and on SiOx and for the ns-Au/SiO x ultra flat sample which manifested ohmic electrical behaviour even after the procedure to trigger the RS activity. It is important to note here that all the XRD measurements were done in ambient conditions and not in-situ during the electrical treatment. The error bars denote the uncertainty of the analysis done by the whole powder pattern fitting program MStruct on measured data. Table 1 Structural parameters calculated from XRD measurements for the as-deposited ns-Au, the switched ns-Au/SiO x and switched ns-Au/glass sample and for the ns-Au/SiO x ultra flat sample after unsuccessful application of the forming process. Samples Mean size of crystallites (nm) Microstrain (%) Stacking faults (%) As deposited ns-Au 5.1 ± 0.8 0.7 ± 0.2 13.4 ± 2 Ns-Au switched on SiO x 10.5 ± 0.5 0 5.8 ± 0.7 Ns-Au switched on glass 11.7 ± 1.4 0.4 ± 0.2 6.7 ± 1 Ns-Au formed on ultra flat SiO x 9.8 ± 0.5 0 5.2 ± 0.6 The RS forming procedure causes an increase in the mean size of crystallites and a decrease in the number of stacking faults, comparable for the systems on both substrates. In the case of ns-Au/SiO x samples after the forming procedure the microstrain, i.e., the distribution of interplanar spacings, vanishes completely pointing to more ordered crystal structure compared to the system on glass substrate. Conversely the persistence of microstrains in the ns-Au/glass system suggests a still granular and rough morphology characterizing the network at the nanoscale. In fact, the surface is known to induce microstrain in the nanostructure and so smaller structures result in enhanced microstrain 44 – 46 . Figure 4 a–d reports the SEM images of the as-deposited ns-Au sample (we reported the one deposited on glass, which is similar to the one deposited on SiO x ), the two formed ones and the ns-Au unsuccessfully formed on ultra flat SiO x ; the image of the formed ns-Au/glass sample confirms qualitatively the hypothesis evidenced by XRD measurements. The formed networks are characterized by a complex and highly interconnected network at the mesoscale, while the paths of gold on ns-Au/SiO x sample are more flattened and looks less granular at the nanoscale compared to the ns-Au/glass system. Figure 4 ( a ) SEM images of the ns-Au as-deposited on the glass substrate, ( b ) on the same substrate after the forming process, ( c ) ns-Au/SiO x substrate after the forming process and ( d ) SEM images of the ns-Au deposited on the ultra flat SiO x substrate after unsuccessfully RS forming process, in particular after the application of 35 V. In order to evaluate the differences in the gold networks formed on glass and on SiO x substrate at the mesoscale, we have performed an analysis of the SEM images by home-made MATLAB routines, described in Materials and Method section, to evaluate the connectivity of the complex structure, on more than five images for each sample. The morphologies of the two formed and switching networks have almost comparable surface coverage (54 ± 1% on SiO x and 61 ± 5% on glass), but they are composed by different density of holes (191 ± 17 #/µ \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\mathrm{m}}^{2}$$\\end{document} m 2 on SiO x and 236 ± 49 #/µ \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\mathrm{m}}^{2}$$\\end{document} m 2 on glass), of endpoints (142 ± 20 #/µ \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\mathrm{m}}^{2}$$\\end{document} m 2 on SiO x and 300 ± 84 #/µ \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\mathrm{m}}^{2}$$\\end{document} m 2 on glass) and of nodes (854 ± 71 #/µ \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\mathrm{m}}^{2}$$\\end{document} m 2 on SiO x and 1222 ± 171 #/µ \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\mathrm{m}}^{2}$$\\end{document} m 2 on glass). In general, the morphology at the mesoscale of ns-Au formed on SiO x is more compact and well connected, compared to the ns-Au/glass system which appears more fragile and further from the continuous layer. This nicely corresponds to the findings from XRD analysis, by which we observed in general more ordered crystal structure for the ns-Au system on SiO x substrate. Interestingly, the network of the ns-Au/SiOx ultra flat at the mesoscale is more compact (Fig. 4 d), since the density of endpoints (75 ± 10), nodes (395 ± 30) and holes (145 ± 35) decreases compared to the ns-Au morphology on glass and on SiO x . The coverage of ns-Au switched on SiO x ultra flat is ⁓ 55 ± 3%. It is worth reporting that just minor microstructural differences calculated from XRD measurements were observed between samples deposited on the two types of SiO x substrates, as shown in Table 1 . The minor decrease of the overall stacking faults density indicated slightly better ordered crystal structure which again corresponds to more compact structure. The distribution of surface heights in greyscale of the four SEM images (Fig. 5 ) confirms the layered morphology of ns-Au/SiO x samples at the nanoscale, while more broaden distributions characterize the as-deposited and the ns-Au/glass morphology at this scale. Figure 5 Grey-scale distributions of the SEM images shown in Fig. 4 . Electrical characterization Non-linear electrical properties of resistive switching Au cluster-assembled devices have been characterized, according to the analysis strategy described in the Materials and Method Section, on three film thicknesses (15 nm, 23 nm, 40 nm). Figure 6 a,b reports the resistive switching electrical behaviour manifested by the 23 nm thick Au cluster-assembled films deposited on SiO x and glass substrates respectively. The samples characterized by this thickness has been chosen as representative of all the thicknesses explored, since no difference in morphology as also in the time correlation of the electrical response appears related to a change in thickness. The resistive switching activity of ns-Au/SiO x samples (Fig. 6 a) is observed only for applied voltages greater than 5 V, whereas for ns-Au/glass, RS is already observed at voltages larger than 1 V, showing lower stability compared to ns-Au/SiO x for comparable resistance values and so lower current density (this aspect is further investigated in the next paragraph). Furthermore, the activation of resistive switching leads the samples to different resistance values, R ns-Au/SiOx ~ kΩ and R ns-Au/Glass ~ kΩ-GΩ. Moreover, the amplitude of the switching events can be as high as the 100% of the resistance value on glass while on SiO x substrates the switching events are rarely greater than the 10% of the resistance. We have further investigated the discrepancy in the temporal distribution of the switching events in the analysed time window. RS events, marked by red circles in Fig. 6 a,b, are grouped for the ns-Au/SiO x samples in burst periods followed by silence periods (no switching activity) of different lengths. On the contrary, the RS events observed for ns-Au/glass samples seem not to be grouped in burst periods but are more uniformly distributed on the time sequence. Figure 6 RS activity over a 400 s time window at constant voltages of 15 V for a 23 nm thick ns-Au/SiO x ( a ) and ns-Au/glass ( b ) samples. The switching event (red circle) is identified within the entire set of resistance data (blue data) when two consecutive resistance values differ each other more than 4 times the relative standard deviation of the resistance values computed in a low-noise interval. ( c , d ) ISI distributions (blue) in log–log scale fitted with power law line shape (orange) calculated from RS activity on the two samples type, respectively. ( e , f ) Distributions of the number of switching events per 1 s intervals of shuffled and unshuffled data series for ns-Au/SiO x and ns-Au/glass samples, respectively. In order to quantify the differences of the RS activity time distribution and to evaluate the time correlations of the switching activity, the Inter Switching Interval (ISI) 24 , 33 analysis has been performed following the protocol reported in Materials and Methods section. ISI distributions for ns-Au/SiO x and ns-Au/glass samples are respectively shown in Fig. 6 c,d in logarithmic scale from which the mentioned differences are quantitatively evaluated. The RS events grouped in small time windows contribute to the left side of the distribution, while the right side of the distributions considers the events separated by longer time periods; the ns-Au/SiO x samples have a distribution tail much more extended than ns-Au/glass. To quantify the time correlation degree systematically, six different ISI distributions were computed for six different voltage values (± 5 V, ± 15 V, ± 25 V) for each sample. Then the obtained distributions were fitted both with exponential and power law line shapes. The power law distribution manifests a heavy tail which indicates the presence of time correlations, on the contrary the exponential distribution represents independent uncorrelated events 47 . Thus, comparing the R 2 coefficients obtained by the two fitting procedures it was possible to observe a higher correlation degree with power law for ns-Au/SiO x samples than for ns-Au/glass. No significant differences within each of the two systems emerge from the electrical behaviour at different constant voltages, for both the polarities, as well as for different thicknesses. Quantitatively, the subtraction of the power law and exponential R 2 fitting coefficients is systematically greater for ns-Au/SiO x samples compared to ns-Au/glass ones indicating a better agreement to a power law distribution for the former system, as reported in Table 2 . Table 2 Average R 2 coefficients of the fitting procedures (for all the applied voltage values: ± 5 V, ± 15 V, ± 25 V) for power law and exponential line shapes both for ns-Au/SiO x and ns-Au/glass samples. R 2 power law (ISI) R 2 exponential (ISI) ΔR 2 (ISI) Discrepancy Ns-Au/SiO x 0.73 ± 0.07 0.23 ± 0.01 0.5 ± 0.01 0.5 ± 0.05 Ns-Au/glass 0.65 ± 0.01 0.3 ± 0.011 0.35 ± 0.01 0.28 ± 0.03 Difference of the R 2 coefficients and discrepancies between the shuffled and unshuffled distributions of the number of switching events per 1 s large bin are reported. We also performed a further analysis following Karsai et al. method 35 , also reported in Materials and Methods section. The distribution of the number of switching events belonging to intervals of 1 s are reported in Fig. 6 e,f for the two different substrates, both for shuffled and unshuffled time series. For unshuffled time series, the switching events are uniformly arranged thus the resulting number of events per time interval distribution does not show the tail expected for grouped events as for the case of unshuffled ns-Au/SiO x samples data series which indicates the presence of correlations. On the contrary, for ns-Au/glass samples the distributions of the unshuffled data do not show a significant departure from the shuffled series. To quantify the difference between shuffled and unshuffled distributions the discrepancy was defined as the difference of the normalized distributions, which is also reported in Table 2 . The discrepancies computed for all the analysed data series for each sample strengthen the evidence of a higher degree of time correlations for ns-Au/SiO x samples and a lower correlation for ns-Au/glass samples. We argue that the correlation degree discrepancy is due to the morphological and structural differences of the Au cluster-assembled films due to the forming process previously reported and further discussed in the following. In situ thermal characterization of the RS forming process We characterized the evolution of the temperature of the nanostructured films during the resistive switching forming process with a thermo-camera to gain a deeper insight in the mechanisms responsible for the network reorganization. The main electrical and thermal features characterizing the switching triggering of the ns-Au deposited on glass and on SiO x , obtained by electrical measurements at constant voltage and by the IR-video acquired by the thermocamera, are reported in Table 3 . Table 3 Thermal and electrical quantities (time, temperature, current density, voltage and energy) measured during the forming of the switching activity for ns-Au/SiO x and ns-Au/glass samples. Substrate t sw [s] T sw [°C] J sw [A/m 2 ] V sw [V] t sw *P diss /s f [J/nm] Ns-Au/SiO x 90 ± 11 248 ± 8 (3 ± 0.2) ·10 10 33 ± 3 40 ± 5 Ns-Au/glass 30 ± 3 430 ± 14 (4 ± 1) ·10 9 8 ± 1 0.6 ± 0.1 Each value is obtained by averaging over all the tested samples. t sw is defined as the time in which a resistance variation was observed; T sw , J sw , V sw are the pointwise temperature, current density and voltage at the time of the appearance of the switching, while the last column represents the energy employed during the procedure to form the switching activity normalized by the cluster-assembled film thickness. The thermo-camera measurements show that SiO x substrates better dissipate the heat than glass, as it was expected. This is clearly demonstrated by the temperature measurements of the nanostructured film area as a function of the dissipated electrical power, computed as P = I · V, where I and V are the applied current and voltage during the switching procedure, respectively, showing that much higher temperatures are reached on the glass substrates even at lower dissipated power (Fig. 7 a). Moreover, due to the different thermal diffusion coefficients, the heat dissipated is much more localized close to the nanostructured thin film when considering glass substrates, (see Fig. 7 c,d for comparison). Heat localization was quantified by computing the spatial derivatives in a region perpendicular and next to the nanostructured film, as schematically reported in Fig. 7 b. Spatial derivatives were evaluated from the graph showed in Fig. 6 e,f in which the substrate temperature is showed as a function of time and distance from the nanostructured films. These are proportional to the spatial gradients and in the case of SiO x (see Fig. 7 e) they are almost constant, meanwhile the functional shape of the glass spatial derivatives are power laws, which indicates a much stronger heat localization due to lower heat conductivity of glass. Furthermore, time derivatives of the glass substrates have similar functional shape compared to SiO x but they are 2–4 times higher, pointing out that SiO x substrates need more time to heat up and to cool down due to the higher value of its thermal conductivity. Probably, this extra time could also enable the better ordering of crystal structure, which is observed for the SiO x substrate. Figure 7 ( a ) Average temperature reached by the ns-Au film area as a function of the electrical dissipated power for glass and SiO x substrates. ( b ) Optical image of SiO x substrate equipped with electrodes bridged by the ns-Au film (shaded stripe in the middle), the red bar (1 mm) represents the area from which figure ( e ) and figure ( f ) were computed. ( c,d ) IR-images of the cluster assembled films respectively deposited on SiO x and glass over which the same temperature range is observed at 25 V and 3 V respectively. Heat dissipation is much more localized in the case of glass. ( e,f ) Temperature of the substrates along the red stripe shown in (b) as a function of the time and distance from the ns-Au film for SiO x and glass, respectively. At time t = 0 s, the power supply is turned on and the sample starts to heat up until the voltage source is turned off and the samples start to cool down. The well localized heat spot is evident in the glass case as like as the faster heating and cooling resulting from different thermal conductivity of the substrates. The differences of the substrates thermal properties cause the discrepancies observed in the RS forming process suggesting that the onset of the RS activity is due to different relative magnitudes of the thermal phenomena involved. In the glass case the onset of the RS activity was obtained in relatively small times, as reported in Table 3 , compared to SiO x . Moreover, the temperatures reached on glass substrates were much higher and localized compared to SiO x and the current densities were at least one order of magnitude lower than for SiO x . Glass provides the nanostructured film much higher temperatures in less time, modifying the structural properties of the cluster-assembled film mostly as a fast-annealing effect on more localized regions. On the other hand, SiO x dissipates better heat and so higher currents and longer times are needed to trigger the RS activity. In this case, electromigration is probably more relevant, compared to thermic effects due to the lower temperatures reached. Discussion on structural and electrical interplay In order to understand the effect of the resistive switching forming process on the different electrical properties of ns-Au/glass and ns-Au/SiO x , reported in Fig. 8 a,b, we investigated the corresponding morphological and structural organization of the two networks. It is worth to remember that the resistive switching in Au cluster-assembled films is a consequence of the grain boundaries reorganization due to current-induced local Joule heating, which modifies continuously the nanostructure of the system, as demonstrated in previous works 26 – 29 . Figure 8 ( a , b ) Constant voltage characterization (15 V) of Au cluster-assembled films deposited on glass and SiO x substrates, respectively. The former shows more uniformly distributed RS events, the latter shows multiple metastable resistance levels and burst activity. ( c ) HRTEM image of a typical region of the as-deposited cluster-assembled Au film on Si 3 N 4 TEM grid. Each white dot is a single atomic column, and parallel dots lines are the lattice planes. The zones where several parallel dots lines, i.e., lattice planes, are observed correspond to crystal domains. The current flow changes grain boundaries orientation giving rise to RS events. ( d ) Schematic representation of ns-Au network evinced by SEM images reported in Fig. 4 b: yellow dots represent crystal grains; black lines represent possible current paths. Ns-Au network on glass substrate manifests high density of branchpoints, endpoints and a single hierarchical level defined by the nanoscale features characterized by the typical electrical behaviour ( a ). ( e ) ns-Au network on SiO x substrate is better connected than on glass and manifests two hierarchical levels defined by nanoscale grains and mesoscale reorganization, responsible of the electrical behaviour ( b ). ( f ) Ns-Au network on defect-free surface SiO x substrate present even thicker features preserving ohmic conduction channels. The inset reports the I-V curve of ohmic ns-Au on this flat and low surface defects SiO x substrate. Figure 8 c reports a HRTEM image of ns-Au in which grain boundaries are visible. The modification of the grain boundaries changes the local resistivity of the film and consequently the current redistributes in the neighbour junctions of the metal film 26 , 28 , 41 , in a non-trivial recurrent manner. In fact, the redistribution of the current occurs not only as a function of the nanoscale features of clusters, but it also depends on the morphology of the network at the mesoscale, which influences the spatial propagation of the current, redistributed through the ns-Au thin film, and thus the global film resistance dynamic 26 . We took into consideration the mesoscale reorganization of the clusters network to explain the different electrical behaviours reported in section \" Electrical characterization \", because there were no notable differences in the nanoscale characteristics of the two types of samples, such as grain dimensions and nanoscale defects that emerged from the XRD measurements. Ns-Au films deposited on SiO x substrates are better connected than on glass, since they manifest bigger connections among islands at the mesoscale. On the other hand, despite the coverage of the two samples is approximately the same, ns-Au/glass samples show more holes, more branchpoints and more ending points, stressing a more fragile and intricated structure composed by thinner and more tangled connections, compared to ns-Au/SiO x samples. Thus, the Joule heating effect due to a further application of constant voltage could be more detrimental for the small connections of Au clusters on glass, leading to severe junction rupture and continuous current redistribution to the neighbours in several pathways, which determines the great continuous resistance changes observed (up to 100% of the film resistance value, Figs. 6 b and 8 a). Conversely on ns-Au/SiO x samples the lower number of branchpoints and the slightly larger size of the paths promote a correlated current redistribution and probably allow the perturbation propagation toward more distant regions, enhancing the mutual interaction of distinct parts of the system, resulting in the observed correlated resistance changes (up to 10% of the film resistance value, Figs. 6 a and 8 b). Moreover, we observed more frequent resistance changes for ns-Au/glass samples compared to ns-Au/SiO x and this could be again a consequence of the less compact and badly connected network at the mesoscale for the former system, schematically shown in Fig. 8 d. In fact, smaller morphological features, characterized previously by SEM analysis and shown in Fig. 5 , require less current and less time to be modified resulting in a continuous current redistribution due to grain boundary modification resulting in frequent resistance changes. On the contrary, Au clusters films connections on SiO x substrate, schematically reported in Fig. 8 e, are more compact and well-organized thus much more current and time are needed to significantly modify the mesoscale junctions leading to longer periods of time in which no resistance changes above the noise level. The close interplay between structural and electrical properties of cluster-assembled gold films is further confirmed by the study of a ns-Au sample deposited on flat and less defective SiO x substrates. This ns-Au/SiO x ultra flat sample manifested ohmic electrical behaviour even after the procedure to trigger the RS activity because the mesoscale connections are even bigger than in the SiO x case so preserving ohmic conduction channels. In ns-Au/SiO x samples the nanoscale structure made of grain of 10 nm size and a mesoscale morphology characterized by islands and larger mutual connections than those of the ns-Au/glass samples, define a structural hierarchy which translates in a temporal hierarchy the resistive switching events; namely the time required to modify the nanoscale structural features could be lower compared to the time required to modify the mesoscale structural features, thus justifying the observed power-law distribution of the electrical time series 25 . Conversely, cluster networks on glass manifest less defined hierarchical levels separation, due to the smaller size of the mesoscale features (~ 10 nm), thus resulting in uniformly distributed RS events, namely in uncorrelated electrical activity. Moreover, the ns-Au network on ultra-flat SiO x , Figs. 4 d and 8 f, present even thicker morphological features compared to ns-Au/SiO x network, Figs. 4 c and 8 e, thus not showing resistance variations and preserving ohmic conduction channel. Interestingly, in literature 48 there are reported other examples of networks which by presenting more hierarchical levels manifest complex critical behaviour thus being characterized by power laws distributions in their dynamics as like the ones we observed in the temporal distributions of the RS activity. The hierarchical levels of the cluster-assembled film deposited on SiO x organized into greater complexity promotes the critical 48 and therefore the more correlated behaviour 47 of the system, compared to the ns-Au/glass sample. This evidence stresses not only that the ns-Au/SiO x devices RS activity exhibits similar statistical properties as the one observed in biological neuronal networks 16 – 18 , 49 , but more interestingly that this activity is related to the ns-Au/SiO x network connectivity which is higher than in the case of ns-Au/glass network. The latter is more disordered and with less hierarchical levels separation, morphology that brings it far from the mammalian brain network which manifests in a hierarchical topology 50 , 51 .\n\nDiscussion on structural and electrical interplay In order to understand the effect of the resistive switching forming process on the different electrical properties of ns-Au/glass and ns-Au/SiO x , reported in Fig. 8 a,b, we investigated the corresponding morphological and structural organization of the two networks. It is worth to remember that the resistive switching in Au cluster-assembled films is a consequence of the grain boundaries reorganization due to current-induced local Joule heating, which modifies continuously the nanostructure of the system, as demonstrated in previous works 26 – 29 . Figure 8 ( a , b ) Constant voltage characterization (15 V) of Au cluster-assembled films deposited on glass and SiO x substrates, respectively. The former shows more uniformly distributed RS events, the latter shows multiple metastable resistance levels and burst activity. ( c ) HRTEM image of a typical region of the as-deposited cluster-assembled Au film on Si 3 N 4 TEM grid. Each white dot is a single atomic column, and parallel dots lines are the lattice planes. The zones where several parallel dots lines, i.e., lattice planes, are observed correspond to crystal domains. The current flow changes grain boundaries orientation giving rise to RS events. ( d ) Schematic representation of ns-Au network evinced by SEM images reported in Fig. 4 b: yellow dots represent crystal grains; black lines represent possible current paths. Ns-Au network on glass substrate manifests high density of branchpoints, endpoints and a single hierarchical level defined by the nanoscale features characterized by the typical electrical behaviour ( a ). ( e ) ns-Au network on SiO x substrate is better connected than on glass and manifests two hierarchical levels defined by nanoscale grains and mesoscale reorganization, responsible of the electrical behaviour ( b ). ( f ) Ns-Au network on defect-free surface SiO x substrate present even thicker features preserving ohmic conduction channels. The inset reports the I-V curve of ohmic ns-Au on this flat and low surface defects SiO x substrate. Figure 8 c reports a HRTEM image of ns-Au in which grain boundaries are visible. The modification of the grain boundaries changes the local resistivity of the film and consequently the current redistributes in the neighbour junctions of the metal film 26 , 28 , 41 , in a non-trivial recurrent manner. In fact, the redistribution of the current occurs not only as a function of the nanoscale features of clusters, but it also depends on the morphology of the network at the mesoscale, which influences the spatial propagation of the current, redistributed through the ns-Au thin film, and thus the global film resistance dynamic 26 . We took into consideration the mesoscale reorganization of the clusters network to explain the different electrical behaviours reported in section \" Electrical characterization \", because there were no notable differences in the nanoscale characteristics of the two types of samples, such as grain dimensions and nanoscale defects that emerged from the XRD measurements. Ns-Au films deposited on SiO x substrates are better connected than on glass, since they manifest bigger connections among islands at the mesoscale. On the other hand, despite the coverage of the two samples is approximately the same, ns-Au/glass samples show more holes, more branchpoints and more ending points, stressing a more fragile and intricated structure composed by thinner and more tangled connections, compared to ns-Au/SiO x samples. Thus, the Joule heating effect due to a further application of constant voltage could be more detrimental for the small connections of Au clusters on glass, leading to severe junction rupture and continuous current redistribution to the neighbours in several pathways, which determines the great continuous resistance changes observed (up to 100% of the film resistance value, Figs. 6 b and 8 a). Conversely on ns-Au/SiO x samples the lower number of branchpoints and the slightly larger size of the paths promote a correlated current redistribution and probably allow the perturbation propagation toward more distant regions, enhancing the mutual interaction of distinct parts of the system, resulting in the observed correlated resistance changes (up to 10% of the film resistance value, Figs. 6 a and 8 b). Moreover, we observed more frequent resistance changes for ns-Au/glass samples compared to ns-Au/SiO x and this could be again a consequence of the less compact and badly connected network at the mesoscale for the former system, schematically shown in Fig. 8 d. In fact, smaller morphological features, characterized previously by SEM analysis and shown in Fig. 5 , require less current and less time to be modified resulting in a continuous current redistribution due to grain boundary modification resulting in frequent resistance changes. On the contrary, Au clusters films connections on SiO x substrate, schematically reported in Fig. 8 e, are more compact and well-organized thus much more current and time are needed to significantly modify the mesoscale junctions leading to longer periods of time in which no resistance changes above the noise level. The close interplay between structural and electrical properties of cluster-assembled gold films is further confirmed by the study of a ns-Au sample deposited on flat and less defective SiO x substrates. This ns-Au/SiO x ultra flat sample manifested ohmic electrical behaviour even after the procedure to trigger the RS activity because the mesoscale connections are even bigger than in the SiO x case so preserving ohmic conduction channels. In ns-Au/SiO x samples the nanoscale structure made of grain of 10 nm size and a mesoscale morphology characterized by islands and larger mutual connections than those of the ns-Au/glass samples, define a structural hierarchy which translates in a temporal hierarchy the resistive switching events; namely the time required to modify the nanoscale structural features could be lower compared to the time required to modify the mesoscale structural features, thus justifying the observed power-law distribution of the electrical time series 25 . Conversely, cluster networks on glass manifest less defined hierarchical levels separation, due to the smaller size of the mesoscale features (~ 10 nm), thus resulting in uniformly distributed RS events, namely in uncorrelated electrical activity. Moreover, the ns-Au network on ultra-flat SiO x , Figs. 4 d and 8 f, present even thicker morphological features compared to ns-Au/SiO x network, Figs. 4 c and 8 e, thus not showing resistance variations and preserving ohmic conduction channel. Interestingly, in literature 48 there are reported other examples of networks which by presenting more hierarchical levels manifest complex critical behaviour thus being characterized by power laws distributions in their dynamics as like the ones we observed in the temporal distributions of the RS activity. The hierarchical levels of the cluster-assembled film deposited on SiO x organized into greater complexity promotes the critical 48 and therefore the more correlated behaviour 47 of the system, compared to the ns-Au/glass sample. This evidence stresses not only that the ns-Au/SiO x devices RS activity exhibits similar statistical properties as the one observed in biological neuronal networks 16 – 18 , 49 , but more interestingly that this activity is related to the ns-Au/SiO x network connectivity which is higher than in the case of ns-Au/glass network. The latter is more disordered and with less hierarchical levels separation, morphology that brings it far from the mammalian brain network which manifests in a hierarchical topology 50 , 51 ."
} | 10,905 |
22023727 | null | s2 | 6,518 | {
"abstract": "The new generation of silicon-based multielectrodes comprising hundreds or more electrode contacts offers unprecedented possibilities for simultaneous recordings of spike trains from thousands of neurons. Such data will not only be invaluable for finding out how neural networks in the brain work, but will likely be important also for neural prosthesis applications. This opportunity can only be realized if efficient, accurate and validated methods for automatic spike sorting are provided. In this review we describe some of the challenges that must be met to achieve this goal, and in particular argue for the critical need of realistic model data to be used as ground truth in the validation of spike-sorting algorithms."
} | 181 |
36920783 | PMC10281605 | pmc | 6,519 | {
"abstract": "Summary As a finite and non‐renewable resource, phosphorus (P) is essential to all life and crucial for crop growth and food production. The boosted agricultural use and associated loss of P to the aquatic environment are increasing environmental pollution, harming ecosystems, and threatening future global food security. Thus, recovering and reusing P from water bodies is urgently needed to close the P cycle. As a natural, eco‐friendly, and sustainable reclamation strategy, microalgae‐based biological P recovery is considered a promising solution. However, the low P‐accumulation capacity and P‐removal efficiency of algal bioreactors restrict its application. Herein, it is demonstrated that manipulating genes involved in cellular P accumulation and signalling could triple the Chlamydomonas P‐storage capacity to ~7% of dry biomass, which is the highest P concentration in plants to date. Furthermore, the engineered algae could recover P from wastewater almost three times faster than the unengineered one, which could be directly used as a P fertilizer. Thus, engineering genes involved in cellular P accumulation and signalling in microalgae could be a promising strategy to enhance P uptake and accumulation, which have the potential to accelerate the application of algae for P recovery from the water body and closing the P cycle.",
"introduction": "Introduction Being a finite and non‐renewable resource, phosphorus (P) is primarily mined from rock P reserves, which are limited to a small number of geographical regions (Cordell et al ., 2009 ; MacDonald et al ., 2011 ), and about 82% of P rock extracted globally is for P fertilizers (Cordell and White, 2014 ). Off‐site release of P due to anthropogenic activity increases environmental pollution, including release from industrial wastewater, municipal sewage effluent, and agricultural run‐off (Cordell and White, 2014 ). P loss from agriculture contributed more than 38% of the P load in freshwater systems (Mekonnen and Hoekstra, 2018 ), and about 30–50% of P in P fertilizers is exported via wind and water erosion into water bodies (Cordell and White, 2014 ; Liu et al ., 2016 ). Increasing P release into the environment, the rate‐limiting nutrient for phytoplankton growth, has promoted toxic algae blooms that seriously damage livings in aquatic ecosystems (Elser and Bennett, 2011 ; Schindler et al ., 2016 ). Thus, reducing off‐site P release to adjacent ecosystems is key to reducing the eutrophication (Schindler et al ., 2016 ). Due to extensive P mining, massive loss of P sources in agricultural and industrial production, and a lack of efficient P‐recycling approaches, the broken P cycle is threatening the ecosystems and global food security (Elser and Bennett, 2011 ; Gilbert, 2009 ; Withers, 2019 ); thus, closing the P cycle is of increasing concern and appeal (Cong et al ., 2020 ). To ensure that P resources are adequate for food security and to reduce environmental pollution, sustainable approaches to P recovery from waste and recycling it for use in agriculture are required. As an environmentally friendly and sustainable alternative to energy‐intensive and conventional biological treatment processes, enhanced biological P removal (EBPR) is increasingly used in P recovery from wastewater (Li et al ., 2019a ; Solovchenko et al ., 2016 ; Xu et al ., 2020 ). EBPR is usually executed by polyphosphate (polyP)‐accumulating organisms (PAO), commonly bacteria and algae. Bacteria‐based EBPR systems have been used extensively to remove nutrients from wastewater, but it usually causes a large carbon and N 2 O emission, and the recovered P is often difficult to reuse easily (Li et al ., 2019a ). Like bacteria, algae preferentially absorb and utilize P as inorganic phosphate (Pi), and excess Pi is stored as polyP in algal vacuoles (also called acidocalcisomes) (Aksoy et al ., 2014 ). Algae can perform sustained luxury P uptake (i.e., they take up more P than required for their immediate growth), grow fast, use nutrients in wastewater, and produce biomass suitable for algae‐based fertilizer production. Thus, given the low operational cost and avoidance of sludge‐handling problems, algae‐based EBPR systems offer attractive nutrient‐removal alternatives (Li et al ., 2019a ). Recently, improved algae‐based EBPR systems that incorporate membrane bioreactors (Qin et al ., 2020 ) or optimize processes (Abeysiriwardana‐Arachchige et al ., 2021 ) were proposed. However, two main issues limit the development of the algae‐based EBPR system (Li et al ., 2019a ; Solovchenko et al ., 2016 ; Xu et al ., 2020 ): (1) compared with bacteria‐based EBPR systems, its P‐removal efficiency still needs to be improved (summarized in Table S1 ); and (2) although the total P concentration in algae is much greater than that in land plants, ranging from 10–30 mg/g dry matter (Procházková et al ., 2014 ) (as measured and shown in Table S2 ), increasing their P‐accumulation capacity is still warranted. The recent discoveries of the major transcription factor for P signalling in the model green alga Chlamydomonas reinhardtii and its tonoplast‐located P‐efflux transporter have enhanced the understanding of the cellular P accumulation and signalling mechanisms in algae. These findings showed that P‐deficiency responses are regulated mainly by the MYB transcription factor Phosphate Starvation‐Responsive 1 ( PSR1 ) in Chlamydomonas reinhardtii (Moseley et al ., 2006 ; Wykoff et al ., 1999 ), and loss‐of‐function of PSR1 caused compromised P signalling and a reduced total cellular P concentration (Bajhaiya et al ., 2016 ; Wang et al ., 2020 ). Moreover, the loss‐of‐function of a tonoplast‐located Pi‐efflux transporter, Phosphate Transporter C1 (CrPTC1), an SPX‐SLC protein with both SPX (named after SYG1/Pho81/XPR1) and SLC (named after permease solute carrier 13) domains causes excess polyP accumulation in vacuoles and strongly induces P‐starvation response in C. reinhardtii (Wang et al ., 2021 ). To improve the P‐accumulation capacity and P‐removal efficiency of algal bioreactors and further accelerate the application of algae for water treatment to recover P and tighten the P cycle, herein, engineering genes involved in cellular P accumulation and signalling in C. reinhardtii is reported to generate modified algae with greater luxury P uptake and greater P‐accumulation capacity. The engineering strategy is based on knocking out the PTC1 gene and overexpressing the PSR1 gene, respectively, alone or in combination. Engineered strains were further assessed and employed to recover P from artificial and industrial wastewater. All engineered strains accumulated more P and showed a greater ability to remove P without compromising biomass production than the wild‐type (WT). These approaches have the potential to accelerate the application of algae for water treatment to recover P as algal fertilizer and tighten the P cycle.",
"discussion": "Discussion With increasing urbanization and the demand for agricultural production, increasingly more P is lost to the environment through effluent discharge, and P recovery is urgently needed to tighten the P cycle to ensure food security and reduce environmental pollution (Elser and Bennett, 2011 ; Gilbert, 2009 ; Peñuelas and Sardans, 2022 ). As an environmentally friendly and sustainable alternative, P removal by algae or their aggregates is increasingly employed in wastewater treatment (Mohsenpour et al ., 2021 ). Much progress has been made in previous studies to maximize P recovery of algae‐based EBPR systems, including coordinating algae growth conditions that affect P‐removal efficiency (Powell et al ., 2009 ), enhancing P entrapment and resistance to environmental stress by microalgae–bacteria interactions (Xu et al ., 2020 ), and integrating with algal biofilm reactors or immobilized‐algae EPBR designs (Solovchenko et al ., 2016 ). However, the relatively low P‐removal efficiency in current algae‐based EPBR systems limits its potential for a widely used cycle that takes P removal from waste streams to crop plant fertilizer and algal strains with a high capacity of taking up and storing large quantities of P in their cells are anticipated (Nie et al ., 2020 ; Solovchenko et al ., 2016 ). In addition, the area required by P‐recovery plants depends on their P content and areal biomass production, and P recovery by algae with high P concentration was proposed as the most area‐saving and P‐recycling strategy compared with recovery by other aquatic plants (Shilton et al ., 2012 ). In this study, by increasing PSR1 expression under the Crptc1 background, the P concentration of the algal biomass could be increased more than doubled, up to 6.83%, which is comparable to the stoichiometric proportion of total P in microbes (about 5–10%) (Yao et al ., 2016 ; Zhang and Elser, 2017 ). Thus, it is indicated that engineering genes involved in P accumulation and signalling to enhance luxury P uptake and accumulation in algae per se would be a fundamental and promising approach for improving algae‐based EBPR systems. Here, three improved approaches involving genetic engineering of SPAO were proposed (Figure 4 ): (1) genetic operation of genes controlling vacuolar P accumulation. Down‐regulation (or loss‐of‐function) of SPX‐SLC proteins to enhance P and polyP accumulation in vacuoles and further increase the P‐removal capacity in SPAO; (2) increasing the expression of the core regulator of the Pi‐starvation response – PSR1 to further enhance Pi acquisition through directly up‐regulating the expression of P‐starvation‐induced genes (PSIGs), including Pi transporters that are responsible for Pi absorption from the environment, and ALPs which liberate Pi from dissolved organic P compounds; (3) combining the above two approaches – enhancing Pi‐starvation signalling and accumulating P in vacuoles. Based on assessment in Chlamydomonas in this study, the engineered SPAOs through the third approach enhance the P‐removal capacity from artificial wastewater to almost three times that of WT (complete P‐removal time for SPAO23 vs. WT: 60 h vs. 216 h). Figure 4 The proposed model for SPAO design and closing the P cycle. The proposed model for SPAO design is shown on the left. Compared with conventional PAO (wild‐type microalgae), SPAO presents greater polyP accumulation and higher P‐removal capacity. Three approaches for genetic engineering of SPAO to enhance the P‐removal capacity are suggested: (1) genetic operation of genes controlling the vacuolar P accumulation. Down‐regulation (or loss‐of‐function) of SPX‐SLC proteins to enhance P accumulation in vacuoles and further increase the P‐removal capacity in SPAO; (2) increase the expression of PSR1, which enhances Pi acquisition through directly up‐regulating the expression of P‐starvation‐induced genes (PSIGs); (3) combining the above two approaches – enhancing P‐starvation signalling and accumulating P in vacuoles. After being applied to recover P from the water system, SPAO algae with greater polyP accumulation can be used as a slow‐release P fertilizer to close the P cycle. Species of several algal genera have been assessed and employed for P removal from wastewater, such as Chlorella , Scenedesmus , Oocystis , and Ankistrodesmus (Mohsenpour et al ., 2021 ). In this study, it is shown that engineering the genes involved in the Pi signalling and accumulation could enhance the luxury P uptake and enable the development of species or strains that are more efficient at P removal from wastewater. PSR1 is conserved to regulate the Pi‐starvation signalling in green plants and algae (Jia et al ., 2021 , 2023 ; Rubio et al ., 2001 ). The previous study also demonstrated that SPX‐SLC proteins are widely found in green algae and responsible for the P accumulation in green algae (Wang et al ., 2021 ). Specifically, these conservative mechanisms of Pi signalling and accumulation are widespread in green algae. Thus, although this study takes the model green alga C. reinhardtii as an example, it is possible to broadly produce genetically engineered microalgae to enhance their ability to remove P from wastewater. In summary, this study reported the genetic engineering approaches in microalgae that can increase the concentrations of total P more than three times in algal cells, which made P‐removal capacity from wastewater more efficient as the engineered strains removed P three times quicker than the unengineered algae did. Given the conservation of PSR1 and SPX‐SLC proteins in green algae (Jia et al ., 2021 ; Rubio et al ., 2001 ; Wang et al ., 2021 ), the approach reported in this study is likely to function in other microalgae species for wastewater treatment to recover P as algal fertilizer and tighten the P cycle."
} | 3,222 |
38363832 | PMC10871537 | pmc | 6,520 | {
"abstract": "Serving as the “eyes” and “ears” of the Internet of Things, optical and acoustic sensors are the fundamental components in hardware systems. Nowadays, mainstream hardware systems, often comprising numerous discrete sensors, conversion modules, and processing units, tend to result in complex architectures that are less efficient compared to human sensory pathways. Here, a visual-audio photodetector inspired by the human perception system is proposed to enable all-in-one visual and acoustic signal detection with computing capability. This device not only captures light but also optically records sound waves, thus achieving “watching” and “listening” within a single unit. The gate-tunable positive, negative, and zero photoresponses lead to highly programmable responsivities. This programmability enables the execution of diverse functions, including visual feature extraction, object classification, and sound wave manipulation. These results showcase the potential of expanding perception approaches in neuromorphic devices, opening up new possibilities to craft intelligent and compact hardware systems.",
"introduction": "INTRODUCTION Vision and hearing, being pivotal information sources for humans to perceive the ambient environment, hold great importance across diverse applications such as security monitoring, smart home, unmanned driving, and human-robot interaction ( 1 – 4 ). In contemporary sensor hardware systems, the task of detecting optical and acoustic signals is often assigned to different sensors. Furthermore, the detection and processing modules are physically separated. The detection module captures external stimuli and transforms them into analog data. These analog data necessitate conversion through an analog-digital converter before being transmitted to the processing module, which operates based on the von Neumann computing architecture, thereby conducting computations ( 5 , 6 ). Although this strategy offers remarkable stability and maturity, massive discrete detectors and accompanying peripheral equipment occupy considerable space. In addition, the raw data acquired by the detection module are typically unstructured and carry extraneous background information. This situation imposes substantial burdens on data conversion, transmission, and storage, consequently impeding the operation efficiency of the processing module ( 7 , 8 ). To address these challenges, the adoption of neuromorphic sensors, drawing inspiration from the human retina, has emerged as a promising solution ( 9 , 10 ). In the human retina, the various functional layers and neural circuits not only receive visual signals but also conduct preliminary preprocessing ( 11 ). These processes occur before information transmission to the brain ( 12 , 13 ), resulting in the elimination of useless data and mitigating delays caused by extensive information transfer, thereby accelerating cognitive computation within the brain. Recently, notable advancements have been achieved in retinal mimicry devices, showcasing exceptional performance. These devices have evolved from bulk materials ( 5 , 14 ) to two-dimensional materials ( 7 , 15 , 16 ) and further toward van der Waals heterojunctions ( 17 – 19 ). Simultaneously, their capabilities have expanded from the amalgamation of perception and preprocessing ( 11 , 20 ) to encompass nonvolatile storage ( 7 , 21 ), visual adaptation ( 16 ), target recognition ( 15 ), motion detection ( 17 , 22 ), and broadband sensing ( 18 ). However, the mainstream focus of retinal mimicry technology remains confined to visual signal detection, while the migration of this technology to facilitate auditory signal perception has yet to be considered and realized because of the distinct detection mechanism of visual and audio signals. Recently, the perception of acoustic vibrations through light sensing has been demonstrated ( 23 – 25 ), especially using invisible light to augment detection confidentiality. This strategy offers a unique opportunity for amalgamating the detection process of visual and auditory stimuli. For instance, the capture of sound waves can be realized by recording surface fluctuations with a high-speed camera ( 23 ) or using a position-sensitive detector to monitor light spot displacements ( 24 , 25 ). However, it should be highlighted that these optical approaches represent an indirect process, imposing substantial demands on detector responsivity. Furthermore, considering the requirement to encompass, and possibly surpass, the audible frequency range of the human ear, detectors must have rapid response speeds. While the hearing sensors have demonstrated proficiency in sound wave detection, the gap lies in their inability to conduct in-chip signal processing. Consequently, a subtle architecture with high sensitivity and compactness remains to be explored that integrates the perception and preprocessing of both visual and auditory information. This article presents a bionic visual-audio photodetector (VAPD), built upon a vertically stacked graphene-germanium (Gra-Ge) hybrid field-effect phototransistor, effectively merging visual and auditory perception while performing on-chip signal preprocessing capability. By managing the interplay of source-drain and gate leakage currents via gate voltage control, the device achieves continuously tunable positive photocurrent (PPC) and negative photocurrent (NPC) with comparable amplitudes and response speeds to each other. This phenomenon resembles sensory cell signal acquisition and effectively mimics the “excitation” and “inhibition” akin to synaptic behavior. Crucially, the device exhibits commendable responsivity and operation speed, rendering it proficient in capturing light source alterations triggered by acoustic vibrations. The gate-programmable photoresponse also extends the versatility of the device to not only preprocess visual images and execute target recognition tasks but also modulate the intensity of sound waves. This ingenious all-in-one device architecture design can greatly simplify hardware, offering an alternative promising inspiration for developing intelligent and compact sensor systems.",
"discussion": "DISCUSSION In summary, this study reports a VAPD that senses both visual and auditory signals while concurrently executing preprocessing and modulation tasks. This innovation is accomplished by ingeniously harnessing the potential of gate leakage current in field-effect phototransistors to produce PPC and NPC with comparable intensity and switching speed. Exploiting this bidirectional photocurrent, the device exhibits highly reconfigurable responsivities, which enables a series of signal preprocessing operations, including edge enhancement, object classification, and sound wave modulation. The outcomes of this research contribute to the burgeoning field of neuromorphic sensors, while expanding horizons for the design and implementation of intelligent optoelectronic devices."
} | 1,738 |
37938727 | PMC9723582 | pmc | 6,521 | {
"abstract": "Microbial communities are inter-connected systems of incredible complexity and dynamism that play crucial roles in health, energy, and the environment. To better understand microbial communities and how they respond to change, it is important to know which microbes are present and their relative abundances at the greatest taxonomic resolution possible. Here, we describe a novel protocol (RoC-ITS) that uses the single-molecule Nanopore sequencing platform to assay the composition of microbial communities at the subspecies designation. Using rolling-circle amplification, this methodology produces long-read sequences from a circular construct containing the complete 16S ribosomal gene and the neighboring internally transcribed spacer (ITS). These long reads can be used to generate a high-fidelity circular consensus sequence. Generally, the ribosomal 16S gene provides phylogenetic information down to the species-level, while the much less conserved ITS region contains strain-level information. When linked together, this combination of markers allows for the identification of individual ribosomal units within a specific organism and the assessment of their relative stoichiometry, as well as the ability to monitor subtle shifts in microbial community composition with a single generic assay. We applied RoC-ITS to an artificial microbial community that was also sequenced using the Illumina platform, to assess its accuracy in quantifying the relative abundance and identity of each species.",
"introduction": "Introduction The bacterial ribosomal RNA operon (rrn) typically produces a polycistronic precursor RNA containing the 5S, 16S, and 23S rRNA genes along with an internal transcribed spacer (ITS) region [ 1 ]. While there are exceptional circumstances, for example with the recent observation of 16S genes unlinked from the other ribosomal genes [ 2 ], these cases are the exception rather than the rule. After expression, the precursor RNA is cleaved by a cascade of RNases to generate the individual gene products, and the ITS region is degraded [ 3 ]. The individual genes produce key RNA products that are involved in protein production and thus these genes, or their homologs, are found in all free-living organisms. Individual genomes may have one or more ribosomal operons [ 4 ] and the presence of multiple rrn copies is thought to allow cells to quickly respond to favorable growth conditions by increasing growth rates [ 5 ]. Although multiple rrn copies are often homogenized by concerted evolution, there are many instances of intragenomic rRNA heterogeneity [ 4 , 6 ]. The 16S gene has become the focus of modern microbial phylogenetics by virtue of its length and mix of highly conserved and variable regions. The conserved regions make it amenable for polymerase chain reaction (PCR) amplification, while the variable regions make it a useful phylogenetic marker [ 7 ]. As a result, it has a long history of use in phylogenetics and is widely used to taxonomically survey microbial populations [ 8 , 9 ]. While longer, the 23S gene has a lower density of informative markers and is therefore rarely used for general phylogenetic purposes in eubacteria; [ 7 ] the 5S gene is small and relatively rapidly evolving, so has seen occasional use as a phylogenetic marker [ 10 – 12 ]. The ITS region is the most rapidly evolving part of the rrn [ 13 ], likely because it has no defined functional role except for the occasional tRNA genes found within) [ 1 ]. This poor conservation and variability in length has made it difficult to use as a phylogenetic marker. However, the position of the ITS region between the highly conserved portions of the 16S and 23S genes means that it can be readily amplified with conserved primers. It has therefore been used as a high-resolution phylogenetic marker [ 14 – 17 ] or more generically as a DNA fingerprinting in a technique called ARISA [ 18 – 20 ]. Due to its ease of amplification and the density of informative sites, the 16S gene has long been the primary target for phylogenetic and taxonomic study. The full-length 16S sequence can be used for phylogenetic resolution down to the species level [ 21 ] and can be readily acquired using Sanger sequencing. As 454 [ 22 , 23 ] and later Illumina [ 24 ] short-read sequencing technologies became available at much lower costs per base, 16S sequencing shifted to focus on individual or small subsets of the nine recognized variable regions [ 22 , 25 ]. Depending on the organisms involved, this smaller number of regions was sufficient to classify microbes taxonomically to the genus or species level but lacked the phylogenetic resolution of the full-length gene. Recently, strategies that look at multiple variable regions in parallel have been developed to improve the resolution of these variable region-based approaches [ 26 ]. On the other hand, metagenomic studies of random genomic regions have revealed tremendous genetic and functional diversity within species [ 27 – 29 ], indicating that short-read sequencing of 16S fails to reflect much of the within-species variation. However, the low cost of short-read sequencing platforms resulted in an explosion of microbial data, along with new databases and tools for analyzing, profiling, and comparing these enormous datasets [ 30 , 31 ]. New developments in sequencing technologies have ushered in attempts to combine quantity with quality and to recover the richness of the full-length 16S gene and beyond, including the entire rRNA and various portions of it including the ITS, 5S and 23S genes [ 32 – 39 ]. Long-read, single molecule sequencing methods including the Pacific Biosciences and Oxford Nanopore Technologies sequencing platforms have made it possible to sequence thousands and even tens of thousands of base pairs in with a single read [ 40 , 41 ]. These sequencing techniques have a higher error rate than Illumina short reads, but recent innovations both in how the sequencing is performed and in the reliability of the base calling have greatly improved the quality of the reads. PacBio HiFi reads use a circular consensus strategy to get 99% accuracy on inserts from 15 to 20 kb. Creative use of Oxford Nanopore Technologies (hereafter “Nanopore”) long reads to repetitively sequence a circularized template have similarly resulted in “consensus reads” with reduced error rates; [ 19 , 42 – 46 ] on-going improvements to Nanopore base-calling software (e.g., guppy) [ 47 ] have achieved 99% accuracy on single pass sequencing data. Here, we describe a new high-throughput sequencing strategy that relies on rolling-circle amplification coupled with Nanopore long-read single molecule sequencing to capture the entire 16S and ITS region. By sequencing the entire 16S region our method provides high-quality phylogenetic information, including resolution at the species level. The inclusion of the ITS region also allows for the ability to distinguish between sub-species including among individual rrn copies within a genome. Together, these tools allow for resolution between microbial strains, leading to more complete characterization of microbial populations and their dynamics. We describe the steps necessary to prepare and sequence a library of reads using our method, which we call RoC-ITS (pronounced like the word rockets), and detail a computational pipeline that can quickly and effectively analyze the data. We applied RoC-ITS to an artificial community of eubacteria that have also been sequenced with Illumina short-reads in order to demonstrate its effectiveness.",
"discussion": "Discussion As demonstrated here, increased resolution through the inclusion of the less-constrained ITS region can greatly enhance compositional analysis of microbial communities. This allows for strain-level identification, which could be used to correlate subtle shifts in community composition with environmental factors or experimental outcomes. By examining the utility of long-read data to identify individual rrns, this method extends work by other researchers who have used long-read sequencing strategies to examine the entire ribosomal RNA to obtain strain level identifications [ 32 , 66 ]. The UMI-PCR-based approach advocated in these papers has the advantage of simplicity, but it does require a bottlenecking step such that the population of starting molecules is considerably smaller than the sequencing depth. While reducing the effective depth of sequencing, this results in each UMI being sequenced multiple times resulting in a consensus sequence with a low potential for error. While RoC-ITS can be enhanced with bottlenecking, this approach was not employed here and is not essential as each sufficiently long read confirms itself and provides actionable information. Instead, the UMIs were used to identify and avoid double-counting potential PCR-induced over-amplification; however, this did not appear to be a serious issue in these circumstances with only a limited depth of sequencing. Furthermore, the ability to discern individual rrns within organisms means that for many species, multiple independent markers can be used to assess and correlate quantification between the markers, thereby providing increased accuracy and sensitivity. Note that this increased sensitivity comes at the price of increased complexity, especially when the number of rrns is not known in advance and the tools to make use of this information do not exist. Analysis of the 16S gene alone shows that individual rrns can, generally, be identified but that there is a much greater information content (roughly tenfold more) in the ITS region. Note that some of these differences, for example in Bacillus and Escherichia , are due to the insertion of entire tRNA genes which could be viewed as a singular event. While the full rrn can be sequenced [ 32 ] and doubtless contains additional markers for distinguishing individual rrns and strains of microbes, it is not clear whether this would provide sufficient benefit to justify the potential noise introduced with long PCR especially when applied to complex and potentially poorly characterized microbial communities. The work outlined here only demonstrates that such an approach would be feasible, as real-world cases would likely be far more complex, possibly with more subtle strain-level variation. Fortunately, existing tools, such as dbOTU3 [ 67 ], are well suited to this role of “grouping” sequences that are highly correlated across large numbers of different samples. The explicit knowledge of these markers could be used to target particular strains for isolation and genome sequencing to better understand the specific genomic differences that underly these population shifts. In turn, this could lead to better functional characterization of the underlying genes and their roles in a community. Ultimately, a holistic approach may produce more robust models describing how microbial communities function and respond to other microbes and to changes in the environment. We demonstrated that the inclusion of the ITS region can provide valuable resolving power to interrogate microbial communities; however, many technical challenges remain. The stringent criteria used here to retain reads for analysis meant that 80–90% of the Nanopore reads were left unused. The majority of these reads were abandoned because they were too short and therefore did not generate the minimum number of sub-reads ( N = 5) that we required, so improvements to the production of longer rolling-circle products would be a boon. There are already products that select longer DNA fragments, 10 kb or larger, that might work with minor changes to the protocols described herein. Furthermore, improvements to the Nanopore flow cells and base-calling software could also improve the reliability of the read data, allowing the shorter reads to be useful. For example, multiple sequence alignment is key to organizing and structuring RoC-ITS sequences into clusters. Integrating the depth of coverage supporting any given RoC-ITS sequence to provide a quality score could, with the development of new software, be used to produce better error correction and consensus calling. Alternative sequencing approaches, such as the PacBio platform, can automatically iterate over the template to produce higher-quality reads [ 32 , 68 ]. Clever Illumina library construction strategies (such as that used by LoopSeq) can allow sequencing of the entire 16 S gene and may be amenable to sequencing of the ITS and 16S gene [ 44 ]. Given the relatively high cost of single-molecule sequencing technologies, it may be more cost-effective to invest in cataloging the ITS diversity so that in the future, given a sufficiently rich database of ITS sequences with clear taxonomic assignments, reliance on the 16S may be pared down or eliminated altogether allowing high-resolution taxonomic assignment through the use of Illumina-based protocols focused on the ITS region alone. A significant number of reads were also lost due to template swapping that likely occurred during the PCR steps, resulting in chimeric reads. It is unclear if this problem is exasperated by the length of the template, the high conservation of the template, or is just more apparent due to specifics of the experiment performed with discrete isolates. Many of the chimera are easily detected because they were the result of template jumping to or from the most abundant organisms, Flavobacteria and Pseudomonades , and the fact these jumps do not seem to be restricted to the conserved 16S portion of the reads. Undoubtedly some of the noise associated with the sequence analysis was also due to the formation of within-genus chimera, which can be much harder to detect. Fine-tuning the molecular biology may be able to minimize this effect, but it will likely remain a problem. Developing a much more comprehensive database of rrns in the future would allow the use of existing fast tools for identifying and excluding chimeras, even within genera. While there are large databases of 16S and 16S variable regions already available, these are relatively low-resolution databases that do not come close to capturing the full richness of the microbial world. Increasing resolution using highly variable segments like the eubacterial ITS region makes it feasible to start to track strains even in complex communities. High-throughput single-molecule techniques make this a possibility, and our work highlights the power of applying such techniques to microbial populations. Further software development will likely be needed to deal with larger volumes of data and to organize it into distinct taxonomic groups in the absence of prior information. Our current approach relies on the strategic use of computationally expensive approaches like MSA to ensure a high confidence consensus RoC-ITS sequence. In particular, we use MSA to generate consensus sequences either during the production of a RoC-ITS sequence from its underlying sub-reads or when we generate a consensus from a set of RoC-ITS sequences representing a distinct 16S-ITS operon. In the first case, the number of sub-reads that contributes to a RoC-ITS sequence is inherently limited by the technology and all the sequences should be nearly identical limiting the computational burden. In the second instance, the number of sequences is somewhat unbounded and given a larger dataset this could prove computationally problematic. In these cases, as these are already highly similar sequences, we would recommend capping the number of sequences used to generate the RoC-ITS consensus sequence to limit the computational complexity of the alignment. The overall clustering steps rely on much faster k-mer-based techniques such as those implemented in the cd-hit package. However, with more comprehensive 16S-ITS databases, even more rapid approaches relying on k-mers may be able to assign sub-reads to known reference sequences and establish a probability that the sub-reads all are derived from a known 16S-ITS reference. Then, the more expensive MSA-based steps would only be needed when no 16S-ITS reference could be discovered. There are other refinements or improvements that might follow directly from this work; we did not check whether the primers used here would work universally in Archaea, but since universal 16S primers already exist, it’s likely that similarly universal primers can be identified in the 23S gene. By using a wholly different pair of primers, this technique could be applied to the ITS regions from fungal organisms or used to characterize large loci from any organism. Overall, there is much promise for high-throughput, high-resolution approaches that can elucidate microbial composition and correlate composition with functional and environmental variables."
} | 4,207 |
25567007 | PMC4286733 | pmc | 6,523 | {
"abstract": "One of challenges for using microtubules (MTs) driven by kinesin motors in microfluidic environments is to control their direction of movement. Although applying physical biases to rectify MTs is prevalent, it has not been established as a design methodology in conjunction with microfluidic devices. In the future, the methodology is expected to achieve functional motor-driven nanosystems. Here, we propose a method to guide kinesin-propelled MTs in multiple directions under an electric field by designing a charged surface of MT minus ends labeled with dsDNA via a streptavidin-biotin interaction. MTs labeled with 20-bp or 50-bp dsDNA molecules showed significantly different trajectories according to the DNA length, which were in good agreement with values predicted from electrophoretic mobilities measured for their minus ends. Since the effective charge of labeled DNA molecules was equal to that of freely dispersed DNA molecules in a buffer solution, MT trajectory could be estimated by selecting labeling molecules with known charges. Moreover, the estimated trajectory enables to define geometrical sizes of a microfluidic device. This rational molecular design and prediction methodology allows MTs to be guided in multiple directions, demonstrating the feasibility of using molecular sorters driven by motor proteins.",
"discussion": "Discussion A method was established for predicting A values based on the cantilever model 11 , and it was demonstrated that these values could be decreased by labeling DNA molecules. The optimization of the A value is critical for a molecular system such as a sorter that separates target molecules carried by MTs 8 9 48 . When designing MTs for a molecular sorter, the ratio of A values between cargo-carrying MTs and the absolute A value to achieve the designated curvature is critical. The former reflects the relative distance between trajectories of two MT groups labeled with different molecules; obtaining an optimum ratio requires adjusting μ e,⊥ and E y I values in equation (4) , because μ EOF , E , and 〈 d 〉 are considered constants in a FC. In this experiment, μ e,⊥ values were varied; E y I can be also altered by changing MT-polymerizing conditions 34 36 or MT-associated proteins 39 49 . GMPCPP-polymerized MTs have a higher E y I than GTP-polymerized MTs (~8 × 10 −24 N m 2 vs. ~2.5 × 10 −24 N m 2 ) 34 . A large difference in A values allows the separation of MT groups according to effective charge with high precision. Thus, MTs can be designed with a wide range of ratios of A values by adopting large μ e,⊥ and small E y I for one group of MTs with small A , and small μ e,⊥ and large E y I for another group with large A . Once ratios of A values have been defined for two MT groups, absolute A values can be optimized by adjusting E and 〈 d 〉 to obtain appropriate curvature in the molecular sorter, with the objective of guiding two labeled MT groups toward two areas spaced apart denoted by the y coordinates y (∞) = Aπ /2, that MTs ultimately reach in an electric field. Optimizing parameters E y I , μ e,⊥ , E , and 〈 d 〉 for two labeled MT groups according to the proposed steps determines MT trajectories, and provides a design strategy for further developments in the construction of a molecular sorter. For example, when E is altered to 4 kV m −1 and E y I for 50-bp DNA(R)-G-MT to 2.5 × 10 −24 N m 2 by polymerization in the presence of GTP 34 , the difference in values of y (∞) between 20-bp DNA(G)-R-MT and 50-bp DNA(R)-G-MT in FC-mix will increase from 16.6 µm to 59.4 µm. Here, we calculated these values using the A value for MB-R-MT in FC-mix (36.4 ± 16.4 µm, n = 96). This difference is large enough to sort two DNA-labeled MB-MT groups in a typical microfluidic device with channel width of several micrometers to several hundred micrometers. In summary, a methodology was presented for designing MTs whose trajectories can be predicted, and whose gliding directions on a kinesin-coated surface when loaded with cargo can be controlled under an electric field. DNA labeling that locally increased surface charge density or electrophoretic mobility at MT tips decreased the A value, which was consistent with that estimated by the cantilever model. More importantly, even after conjugation, the tubulin dimer and DNA molecules retained their effective charges, which reflected their electrophoretic mobilities; thus, MT trajectories can be specified by selecting a cargo molecule with known charge. Further optimization of parameters other than electrophoretic mobility—i.e. bending stiffness, field intensity, and kinesin density—were also discussed with respect to a molecular sorter. The strategy described herein can serve as a guideline for any molecular shuttle-based nanosystem."
} | 1,206 |
22701450 | PMC3373147 | pmc | 6,525 | {
"abstract": "Genetic diversity of phylogenetic or functional markers is widely used as a proxy of microbial diversity. However, it remains unclear to what extent functional diversity (FD), gene sequence diversity and community functioning are linked. For a range of denitrifying bacteria, we analyzed the relationships between (i) the similarity of functional traits evaluated from metabolic profiles (BIOLOG plates) or from N 2 O accumulation patterns on different carbon sources and (ii) the similarity of phylogenetic (16S rRNA gene) or functional ( nir gene) markers. We also calculated different proxies for the diversity of denitrifier community based on taxa richness, phylogenetic (16S rRNA gene) or functional similarities (based either on metabolic profiles or N 2 O accumulation patterns), and evaluated their performance in inferring the functioning of assembled denitrifying communities. For individual strains, the variation in the 16S rRNA gene sequence was weakly correlated with the variation in metabolic patterns (ρ = 0.35) and was not related to N 2 O accumulation. The latter was correlated with the similarity of nitrite reductase residues. When nir genes were analyzed separately, the similarity in amino acids coded by the nirS genes was highly correlated with the observed patterns of N 2 O accumulation (ρ = 0.62), whereas nirK amino acid residues were unrelated to N 2 O accumulation. For bacterial assemblages, phylogenetic diversity (average similarity among species in a community) and mean community dissimilarity (average distance between species) calculated using 16S rRNA gene sequences, and FD measures associated with metabolic profiles, poorly predicted the variation in the functioning of assembled communities (≤15%). In contrast, the proxies of FD based on N 2 O accumulation patterns performed better and explained from 23 to 42% of the variation in denitrification. Amongst those, community niche was the best metric, indicating the importance of complementarity for resources in the context of bacterial community functioning.",
"introduction": "Introduction As biodiversity is threatened by global changes, understanding the relationship between diversity and the functioning of ecosystems or biological communities has received increasing attention (Hooper et al., 2005 ). Microbial communities are key drivers of soil ecosystem processes. Yet, as 10 g of soil might contain 10 4 –10 6 distinct taxa (Torsvik and Ovreas, 2002 ; Gans et al., 2005 ), a complete understanding of microbial diversity is still a challenge. Despite this overwhelming prokaryotic diversity, the potential loss of diversity at the microbial scale is also of concern. In this context, it remains unclear to what extent microbial community structure, especially when based on the 16S rRNA gene as phylogenetic marker, can be used to explain differences in ecosystem processes related to carbon and nitrogen cycles (Wertz et al., 2006 ; Fierer et al., 2007 ; Le Roux et al., 2008 ; Philippot et al., 2010 ). Overall, from a biodiversity-ecosystem functioning point of view, it remains unclear the extent to which microbial community composition can be used to predict its impact on ecosystem processes and how community composition should be characterized in this context. Given the high degree of redundancy observed for microorganisms, changes in microbial community structure might not lead to changes microbial community function. Functional redundancy is indeed commonplace and especially for broad functions such as those associated with carbon metabolism, community composition may not influence community functioning (Wohl et al., 2004 ; Wertz et al., 2006 ). In contrast, it has been recently shown that differences in microbial community structure led to changes in mineralization rates (Strickland et al., 2009 ). Contradictory results have also been observed for other less broad functions, such as denitrification in soils (Cavigelli and Robertson, 2000 ; Wertz et al., 2006 ; Salles et al., 2009 ). This inconsistency in results might be due to differences in the methodological approaches and genes targeted to predict the effect of community structure on ecosystem functioning. For some processes, the genetic diversity of the phylogenetic marker is not related to the functional traits that influence the functioning of the ecosystem (Jones et al., 2008 ; Baelum et al., 2010 ). In that case, taxa identity might not provide enough information on whether an organism is able to carry out a given process, at what rates or under which environmental conditions. For instance, physiological studies have shown that the enzymatic activities among species from different bacterial classes might be more comparable than between species belonging to the same genus (Carlson and Ingraham, 1983 ) whereas very similar taxa can strongly differ from a functional point of view (Salles et al., 2009 ). Therefore, in order to better predict community functioning, it is crucial to understand the relationship between microbial phylogeny and physiology (Allison and Martiny, 2008 ). In the context of bacteria associated with the nitrogen cycle, the congruence between taxonomic phylogenies, which are mostly based on 16S rRNA gene sequences, and the phylogenies based on functional gene sequences is process-dependent. For instance, ammonium oxidation is carried out by a relatively small number of prokaryotic taxa and experimental evidence suggests a congruence in the phylogeny of ribosomal (16S rRNA) and amo genes for both bacterial and archaeal ammonia oxidizers (Prosser and Nicol, 2008 ). Among denitrifiers, the enzyme nitrite reductase, which mediates the reduction of nitrite to nitric oxide, is present in all denitrifying bacterial species. Furthermore, two functionally redundant but structurally distinct nitrite reductases are found among denitrifiers: a copper (Cu-Nir) and a cytochrome cd1 (Cd-Nir) nitrite reductase, coded by the nirK and nirS genes, respectively. In contrast to nitrifiers, denitrifying bacteria are phylogenetic diverse and are distributed over 60 genera (Zumft, 1997 ). When comparing the phylogeny of the nir genes with the 16S rRNA gene, it was shown that only the nirS and 16S rRNA gene phylogeny was congruent, suggesting that nirK might have been mainly acquired through horizontal gene transfer (Heylen et al., 2006 ). The use of either phylogenetic or functional genes has improved our knowledge about the ecology of microbial functional groups. For instance, a good congruence seems to exist between the phylogeny (at the genus level) and functional traits of bacterial nitrite oxidizers (Attard et al., 2010 ). In the case of denitrification, the use of functional genes might provide evidence that nirK- and nirS -harboring bacteria are ecologically distinct. For instance, a study focusing on a short-term restoration chronosequence indicated that the abundance of the major nirS populations vary similarly with time after disturbance, since sites that were restored at the same time shared higher similarities in nirS communities. Conversely, nirK populations were characterized by three independent response groups, suggesting higher sensitivity to environmental gradients (Smith and Ogram, 2008 ). Furthermore, it has been proposed that nirK denitrifiers respond to a range of environmental parameters, whereas denitrifiers harboring nirS are mainly driven by nitrate availability (Jones and Hallin, 2010 ). The ecologically distinct role of nirK and nirS communities has been also observed in the rhizosphere of grain legumes where nirK and not nirS gene transcripts could be detected (Sharma et al., 2005 ), and in cropping systems where changes in denitrification were related to the abundance of nirK - rather than nirS -harboring bacteria (Attard et al., 2011 ). Despite our increasing knowledge about the ecological distribution of these functional groups and the mechanisms underlying community assembly, an issue that remains open is to what extent the information based on the genetic diversity of phylogenetic or functional markers reflect the diversity of traits among functional groups, and furthermore, how both these markers ultimately influence ecosystem functioning. In this study we used denitrifiers as a model group to address these relationships. We focused on denitrification because it is a function performed by taxonomically diverse species, whose activity is regulated in particular by the quality and quantity of organic compounds. Moreover, the two key functional genes coding for nitrite reductase ( nirK and nirS ) could provide different results due to their different functionalities and to the importance of horizontal transfer for nirK . In order to test the extent to which functional similarity (similarity of functional traits) is linked to the variation in the sequence of phylogenetic or functional marker genes, we analyzed a set of 29 denitrifying strains according to the following attributes. Genetic diversity was calculated for the phylogenetic (16S rRNA) and functional markers (nir). Additionally, functional trait diversity was determined according to the metabolic profiles of the strains by measuring their activity over a range of 95 carbon sources under denitrifying conditions and measuring their N 2 O accumulation patterns in presence of six different carbon sources. We then assessed to what extent the genetic similarities (16S rRNA gene or nir genes) could be used to infer the similarity in functional traits. In this context, we hypothesize that the 16S rRNA phylogenetic marker would be weakly correlated with the similarity in metabolic profiles, but not correlated with the N 2 O accumulation patterns. Moreover, we predict that the genetic diversity of the functional markers (nir genes) would correlate with the N 2 O accumulation patterns. Under this hypothesis, we also predicted that this correlation could be weaker for genes highly submitted to horizontal transfer, such as nirK . In addition, to examine the link between bacterial diversity and denitrification, we calculated a range of diversity proxies widely used in general ecology (Heemsbergen et al., 2004 ; Petchey and Gaston, 2006 ; Cadotte et al., 2009 ; Petchey et al., 2009 ; Mouchet et al., 2010 ). We also determined which diversity proxies, based on taxa richness, phylogenetic (16S rRNA gene) or functional similarities (based either on metabolic profiles or N 2 O accumulation patterns), were more relevant for predicting the effect of bacterial diversity on denitrification. For that, we used recently published data (Salles et al., 2009 ) on denitrification rates for a range of assembled denitrifying bacterial communities. We hypothesize that diversity proxies that take into account functional attributes would be the best predictors of community functioning. The observed relationships between the functional traits and phylogenetic/genetic relatedness and a range of diversity proxies widely used in microbial ecology are discussed in the context of predicting the effect of bacterial diversity on community functioning.",
"discussion": "Discussion Many studies evaluating the effects of biodiversity on ecosystem functioning have focused on species richness, despite the fact that ecosystem functioning is not governed by the phylogenetic content of its organisms but rather by the functional traits of the individuals present, the distribution and abundance of these individuals, and their biological activity (Hooper et al., 2002 ; Naeem and Wright, 2003 ; Giller et al., 2004 ; Salles et al., 2009 ). In this context, the choice of relevant functional traits for macro-organisms has been compared to the search for the holy grail (Lavorel and Garnier, 2002 ). When considering microorganisms, one could argue that this search for appropriate functional traits represents a more feasible task, as the functional genes coding for the well-studied functions such as denitrification are mostly known (Philippot and Hallin, 2005 ; Philippot et al., 2007 ). Defining the groups involved in each process sets a good foundation toward the quest for the functional “micro grail,” even though the sheer microbial diversity might still limit this quest. Indeed such an approach has been extensively used in microbial ecology, and the diversity within functional groups has been assessed by quantifying the genetic diversity of either phylogenetic markers such as 16S rRNA gene (e.g., Stephen et al., 1996 ) or functional markers such as key functional genes involved in the process of interest (e.g., Poly et al., 2008 ). Despite the usefulness of these approaches, the issues that remain open are (i) to what extent does the genetic variation observed for phylogenetic or functional markers correspond to the variation of functional traits within microbial groups, and (ii) what effect does this genetic variation, functional or phylogenetic, and diversity of functional traits have on community and ecosystem functioning. We have addressed these issues for a range of denitrifying bacteria using two approaches. Firstly, by looking at the relationship between the similarity of functional traits evaluated from metabolic profiles that were obtained on BIOLOG plates or from N 2 O accumulation patterns on different C sources, and the similarity of phylogenetic (16S rRNA gene) or functional ( nir gene) markers. Secondly, we computed proxies of phylogenetic diversity and FD among denitrifying bacterial assemblages, which were then used to assess how well they could explain the functioning (denitrification) of assembled communities. Phylogenetic signal and physiological traits The relationship between ecological and phylogenetic similarities defines the phylogenetic signal (Losos, 2008 ), and this notion was supported in our metabolic profile data: closely related strains were able to utilize carbon substrates in a similar fashion, whereas distantly related strains exhibited a greater variation in their metabolic resemblance. However, the strength of the phylogenetic signal to explain metabolic profiles was low, as the Mantel test exhibited a ρ = 0.35 when considering the whole set of strains. In fact, in some cases, unrelated strains shared higher similarities in their metabolic profiles (as shown by smaller Euclidean distances) than closely related ones. Similar conclusions have been recently reported for natural soil microbial communities, from which 39% of the catabolic profiles could be explained by the phylogenetic content of the communities, when considering two sites (Fierer et al., 2012 ). Interestingly, our findings indicate a higher consistency in terms of metabolic profiles among members of the α-proteobacteria class, which showed higher phylogenetic signal in metabolic profiles when compared to γ-proteobacteria class. This is consistent with previous results reporting that there is an ecological coherence of bacterial groups at deeper branches of bacterial taxonomy, such as bacterial classes (Fierer et al., 2007 ; Philippot et al., 2010 ). For instance, the comparison between the metabolic plasticity of soil bacterial communities facing addition of different C compounds and their phylogeny suggests that closely related species do not always use resources in a similar manner, although some bacterial families seem to be more consistent in their carbon metabolism (Goldfarb et al., 2011 ). Our results are consistent with the conclusion of Cohan ( 2006 ) who stated that bacterial systematics fails to provide species labels allowing predictions about the biology of the members of a given bacterial species, given the great diversity in the metabolic capabilities of closely related strains. Actually, our results indicate that the use of phylogeny to infer bacterial community composition or diversity might not be meaningful from an ecosystem function perspective. This conclusion is even stronger if phylogenetic traits are used to infer denitrification (here, N 2 O production), a process known to be weakly related to phylogeny (Philippot and Hallin, 2005 , see section below). Indeed, we observed no phylogenetic signal when evaluating the patterns of N 2 O production, even after correcting these values for the number of cells (an indication of specific activity). Functional markers and N 2 O production In order to evaluate the diversity of denitrifying bacterial strains in environmental sample, most studies use the genetic diversity of sequences of key genes involved in the denitrification step of interest, rather than phylogenetic markers (Patra et al., 2005 ; Sharma et al., 2005 ; Heylen et al., 2006 ; Smith and Ogram, 2008 ; Hallin et al., 2009 ). In our study, the patterns of N 2 O accumulations (for a range of carbon sources) did not show a phylogenetic signal but were highly correlated with the identity of nitrite reductase residues. Interestingly, the patterns differed among nitrite reductase genes. The variation in the identity of the partial NirS protein was highly correlated with the variation observed for N 2 O accumulation profiles for nirS -harboring bacteria; in contrast, the variation observed for N 2 O accumulation profiles was not significantly correlated with variation in the identity of the NirK protein for nirK -harboring bacteria. It has been suggested that the nirS gene is less prone to horizontal gene transfer than nirK (Heylen et al., 2006 ; Jones et al., 2008 ), which could explain the higher congruency observed between functional gene sequence similarity and functional trait similarity for nirS -harboring bacteria. These findings have great implications for studies that use nitrite reductase genes to link community composition and denitrification rates, indicating that although the genetic diversity associated with both nirK and nirS can be used to study shifts in the composition of denitrifying communities, the changes in the genetic structure of nirK-harboring denitrifier communities are not well related to changes in functional traits relevant for denitrification. In our study we have opted for focusing on nir genes, since those are present in all denitrifying species (Mahne and Tiedje, 1995 ). One could argue that our analyses were performed on partial amino acid sequences from both proteins (116 and 143 amino acid residues from NirK and NirS, respectively) and that the use of the whole protein could partly influence our results. Nevertheless, the stretch of DNA coding for nitrite reductase used here corresponds to those commonly used to study the diversity of denitrifiers (Heylen et al., 2006 ; Smith and Ogram, 2008 ; Hallin et al., 2009 ). Additionally, considering that other genes involved in the denitrification processes do not always explain denitrification patterns in a similar fashion as nir gene (Kandeler et al., 2006 ; Hallin et al., 2009 ) it would be interesting to determine how the identity of genes such as nar , nor , or nos , would predict denitrification rates. These comparisons might indicate the most appropriate functional marker to describe the structure of denitrifying communities. Moreover, our results show that the regulation mechanisms of targeted functions and the evolutionary history of bacterial taxa have to be accounted for to steer us towards our quest for the functional grail in microbial ecology. Characterizing denitrifier diversity from a functional perspective The quest to understand and predict the effects of biodiversity on ecosystem functioning has led to the development of a range of diversity measures. These have been used to quantify the extent to which the different aspects of biodiversity, such as species richness, phylogenetic distance or FD, influence ecosystem processes, and services (Petchey and Gaston, 2002 ; Petchey, 2004 ; Mouillot, 2007 ; Cadotte et al., 2009 ; Mouchet et al., 2010 ). Likewise, the need to predict microbial-mediated ecosystem processes has spurred on the development of a range of methodological approaches that focus on the genetic information contained on phylogenetic or functional gene markers, and also on physiological trait like metabolic patterns, substrate induced respiration, and enzyme activities, to mention a few. However, a range of proxies is rarely systematically studied, especially for a range of bacterial species that can then be examined as assembled communities. This limits our ability to infer which diversity proxies are the most useful to explain community functioning. We tackled these issues by calculating indexes based on phylogenetic and functional diversity that are often used to predict the effect of the diversity of higher organisms on ecosystem processes (Petchey and Gaston, 2002 ; Petchey, 2004 ; Cadotte et al., 2009 ) for a range of denitrifying bacterial strains. Furthermore, we inferred how valuable each one of them was for predicting community functioning (denitrification), by relating these indexes to the level of community functioning reported by Salles et al. ( 2009 ) for denitrifying bacterial assemblages. Despite the fact that diversity metrics based on phylogenetic distance or the number of species in a given community are often poorly related to community functioning in general (Hooper et al., 2005 ) and microbial (Salles et al., 2009 ) ecology, they are often used to assess microbial diversity in environmental samples. When used to predict the functioning of denitrifying bacterial assemblages, both the metrics based on phylogenetic marker (PD and Diss) and on species richness (S) performed poorly. Considering the issues discussed in the previous section, the results observed for PD were not surprising, and probably reflect the low phylogenetic signal associated with denitrification. The weak predictive power of species richness should be carefully considered, especially in experiments addressing the relationship between microbial diversity and functioning, in which species richness is often the explanatory variable considered (Bärlocher and Corkum, 2003 ; Setäla and McLean, 2004 ; Wohl et al., 2004 ; Bell et al., 2005 ; Tiunov and Scheu, 2005 ; Jiang, 2007 ). Contrary to measures based on phylogenetic distance or species richness, measures associated with functional traits are more meaningful in the biodiversity-ecosystem functioning context, as they integrate organismal traits that directly influence a given process. We therefore determined functional diversity for two types of traits, metabolic diversity and N 2 O production patterns, each one quantifying a different aspect of denitrifier functionality. For both trait types, we used different metrics based on multivariate strategy, FAD and FD, and compared their ability to predict denitrification to the one of CN (Salles et al., 2009 ). Interestingly, the diversity measures based on metabolic diversity explained no or little variation in denitrification, whereas those based on N 2 O production patterns performed better. This suggests that both the choice of traits and diversity metric(s) are important to properly infer community functioning. More specifically, a diversity measure based on overall carbon metabolism (FAD BIOLOG ) poorly predicted denitrification, whereas applying the same diversity metric but based on N 2 O production patterns doubled its explanatory power, highlighting the importance of selecting for the appropriate traits. FD metrics also differ in their explanatory power. For instance, multivariate diversity measures based on N 2 O production patterns (F D N 2 O and FA D N 2 O ) explained the denitrification rates equally well, which could be partially explained by the degree of correlation between them ( R = 0.63). However, their performance remained inferior to CN. This could be attributed to the fact that CN reflects the complementarity effect among strains (accounting for the performance of each strain on single carbon sources, it considers the sum of the highest performance on each individual carbon source possible for a given community) rather than diversity per se , as calculated for FA D N 2 O and F D N 2 O . Thus, the superior performance of CN as compared to the other proxies presented here supports previous evidence (Hooper et al., 2005 ) of the importance of complementarity for resource use among taxa in the context of bacterial community functioning. The information retained in the phylogenetic marker might be ecologically meaningful for studies focusing on microbial community composition and distribution. However, our results confirm the often cited hypothesis that, for broad functions such as denitrification, the diversity of functional gene sequences are better predictors of functioning than the diversity of sequences of phylogenetic markers (Philippot and Hallin, 2005 ). Moreover, our results suggest that characterizing the genetic diversity of nir gene fragments, as is often done to analyze the relationship between the diversity and functioning of denitrifiers, might be more meaningful for nirS - than nirK- harboring communities. Further studies focusing on the whole denitrification machinery and considering full gene sequences are necessary to confirm our hypothesis. Nevertheless, we provide evidence that findings based on the relationship between nir K genetic diversity and denitrification rates should be considered with caution. More generally, our results show that when analyzing the link between the diversity and functioning of microbial communities, indexes based on the diversity of phylogenetic or functional marker genes, or on functional trait diversity, might be useful. But they might remain inferior to indexes that more explicitly reflect complementarity effects among populations rather than the diversity per se . Overall, our results spur the use of diversity indexes based on relevant functional traits and the development of diversity proxies that integrate complementarity effect."
} | 6,517 |
38736443 | PMC11085264 | pmc | 6,527 | {
"abstract": "Arbuscular mycorrhizal fungi (AMF) are obligate symbionts that interact with the roots of most land plants. The genome of the AMF model species Rhizophagus irregularis contains hundreds of predicted small effector proteins that are secreted extracellularly but also into the plant cells to suppress plant immunity and modify plant physiology to establish a niche for growth. Here, we investigated the role of four nuclear-localized putative effectors, i.e. , GLOIN707, GLOIN781, GLOIN261, and RiSP749, in mycorrhization and plant growth. We initially intended to execute the functional studies in Solanum lycopersicum , a host plant of economic interest not previously used for AMF effector biology, but extended our studies to the model host Medicago truncatula as well as the non-host Arabidopsis thaliana because of the technical advantages of working with these models. Furthermore, for three effectors, the implementation of reverse genetic tools, yeast two-hybrid screening and whole-genome transcriptome analysis revealed potential host plant nuclear targets and the downstream triggered transcriptional responses. We identified and validated a host protein interactors participating in mycorrhization in the host. S. lycopersicum and demonstrated by transcriptomics the effectors possible involvement in different molecular processes, i.e. , the regulation of DNA replication, methylglyoxal detoxification, and RNA splicing. We conclude that R. irregularis nuclear-localized effector proteins may act on different pathways to modulate symbiosis and plant physiology and discuss the pros and cons of the tools used.",
"introduction": "1 Introduction Plants are sessile organisms that are exposed to various biotic and abiotic stresses against which they have developed sophisticated defense mechanisms ( He et al., 2018 ; Gull et al., 2019 ). Biotic invaders, such as microbial pathogens, must overcome the multilayered plant immune system and change the plant’s physiology to successfully colonize the plant’s tissues and exploit the plant’s nutritional resources ( Jones and Dangl, 2006 ; Dodds and Rathjen, 2010 ). To this end, pathogens often secrete so-called effector proteins that act on the outside or inside of plant cells ( Lo Presti et al., 2015 ). Intracellularly, they interfere with numerous plant molecular pathways by binding host plant macromolecules or through the alteration of their biological activity ( Lo Presti et al., 2015 ). This interplay occurs in different subcellular compartments, among which the nucleus, where effectors induce transcriptional reprogramming by binding the promoter region of specific plant genes or participate in posttranscriptional processing of specific mRNAs ( Fu et al., 2007 ; Canonne and Rivas, 2012 ; Kim et al., 2020 ). Additionally, plant symbionts use effectors to modulate host plant defense mechanisms and physiology. The arbuscular mycorrhizal (AM) symbiosis is one of the most well-characterized mutualistic relationships between roots of a wide range of land plants and AM fungi (AMF) belonging mainly to the Glomeromycotina subphylum ( Parniske, 2008 ; Spatafora et al., 2016 ). Under phosphate-limiting conditions, plant roots accommodate the fungus that forms highly branched hyphal structures, the so-called arbuscules, inside plant cortical cells, in which the two partners exchange nutrients ( Pimprikar and Gutjahr, 2018 ). Plants benefit from the fungal delivery of water and inorganic nutrients, mainly phosphorus, and, in return, favor fungal growth by transferring sugars and lipids ( Jiang et al., 2017 ; Lanfranco et al., 2018 ). The genome of the AMF model species Rhizophagus irregularis encodes approximately 300 putative in silico predicted secreted effector proteins, implying their potential importance in symbiosis establishment and maintenance ( Lin et al., 2014 ; Sędzielewska Toro and Brachmann, 2016 ; Kamel et al., 2017 ; Maeda et al., 2018 ; Zeng et al., 2018 ). However, to date, merely five of the predicted effector proteins, i.e. , SP7, SIS1, RiCRN1, RiSLM, and RiNLE1, have been functionally characterized. Understanding the role of these effectors is a challenging task because the fungus is recalcitrant to efficient genetic modification, making it difficult to individuate the genetic evidence of function. Hence, reverse genetic tools are often the only way to address the problem. For the effector proteins mentioned above, a role in mycorrhization has been demonstrated by means of Medicago truncatula (barrel medic) composite plants with transgenic roots overexpressing and/or silencing the effector proteins ( Kloppholz et al., 2011 ; Tsuzuki et al., 2016 ; Voß et al., 2018 ; Zeng et al., 2020 ; Wang et al., 2021 ). Of these five characterized R. irregularis effector proteins, three are nucleus compartmentalized. The targeted plant proteins and subsequent host pathways have only been identified for SP7 and RiNLE1, which possibly modulate M. truncatula host defense responses by two different strategies. SP7 interacts with the pathogenesis-related ETHYLENE RESPONSE FACTOR19 (ERF19) in the plant nucleus, where it regulates the expression of plant defense genes to boost AMF accommodation ( Kloppholz et al., 2011 ), whereas RiNLE1 relies on the epigenetic regulation of HISTONE 2B ( H2B ), altering the expression of several host genes involved in immunity ( Wang et al., 2021 ). Thus, although nearly 1/4 th of the putative R. irregularis effectors has a predicted nuclear localization ( Zeng et al., 2018 ; Aparicio Chacón et al., 2023 ), the interacting host plant nuclear proteins and the triggered downstream transcriptional responses responsible for the plant host performance are largely unknown. Here, we investigated four putative nuclear-localized effector proteins from R. irregularis , i.e. , GLOIN707, GLOIN781, GLOIN261, and RiSP749, using Solanum lycopersicum (tomato), a host plant of economic interest not previously used for AMF effector biology, M. truncatula , a well-known host and the non-host Arabidopsis thaliana (Arabidopsis) We found that they are potentially secreted by the fungus, localized in the plant host nucleus, and expressed during symbiosis in tomato. We identified their influence on plant growth and mycorrhization by ectopic expression of the effectors in Arabidopsis and M. truncatula , respectively. To gain insight into their molecular mode of action during mycorrhization, we examined which tomato nuclear plant proteins are targeted and which downstream transcriptional responses are triggered. Finally, we discuss the potential roles of these R. irregularis nuclear-localized effector proteins during AM symbiosis and consider the pros and cons of the tools used.",
"discussion": "4 Discussion Studying the involvement of fungal genes in arbuscular mycorrhization is not an easy task because the fungus remains recalcitrant to genetic modification, hampering genetic studies that are often the final proof of the protein’s functionality ( Helber and Requena, 2008 ). AMF are expected to secrete hundreds of effectors outside or inside plant cells to change plant immunity and physiology, allowing fungal accommodation inside the plant cells to establish a functional symbiosis. Therefore, to unravel the role of possible plant nuclear-localized effectors in growth and mycorrhization, as well as their corresponding plant nuclear protein targets and transcriptional responses in tomato roots, we used different biochemical and genetic approaches, following the recently reported stepwise pipeline ( Aparicio Chacón et al., 2023 ). These tools brought us closer to the possible function of four fungal effectors but also shed light on the associated drawbacks of the used methods. Based on existing lists of putative effectors, we identified four candidates possibly encoding fungal effectors. We found that GLOIN707, GLOIN781 and GLOIN261 share homology with hypothetical nuclear effector-like proteins from other AMF, suggesting potential roles during AM symbiosis. RiSP749 displayed a broader homology with predicted effectors and ribonucleoproteins from AMF and other organisms. All four effectors were expressed during symbiosis in tomato, revealing a higher expression for GLOIN261 and RiSP749 when comparing the expression in root tissue enriched for functional arbuscules (identified through PT4 expression) to the non-enriched root tissue. Also in the stage-specific dataset of Zeng et al., GLOIN261 demonstrated induced expression in arbuscules in M. truncatula , while the other effectors are not detected ( Zeng et al., 2018 ). In the future, it would be interesting to complement these expression studies with stage-specific expression analysis specifically in tomato, by for instance state-of-the-art single-cell analysis or by in situ hybridization as was done for RiNLE1 ( Wang et al., 2021 ). However, expression does not ensure that the putative effector acts intracellularly, and besides the YST assay and tagging with fluorescent proteins for subcellular localization studies that we have done in this work, future experiments such as immunolocalization ( Wang et al., 2021 ) or (single-cell) proteomics, which is an upcoming field in effector research, should be performed ( Kelly, 2020 ; Miltenburg et al., 2022 ). Nevertheless, intracellular effectors are expected to be secreted outside the fungus and translocated inside the plant cytoplasm. All four proteins were predicted to have a functional SP, although prediction for RiSP749 differed between different versions of the SignalP tool (SignalP v4 predicted one, while SignalP v5.0 did not). Possible functionality of the SP was demonstrated using the YST system, which is an easy system based on yeast growth via an SP-driven invertase secretion ( Lee and Rose, 2012 ). The predicted SP of GLOIN707, GLOIN781 and RiSP749 were indeed able to secrete the yeast invertase, allowing colony growth indicative for functional SPs, irrespective of mixed outcomes of the predictive programs for RiSP749. In accordance, the GLOIN707 and GLOIN781 full-length CDS also allowed yeast growth, while this was not the case for the full-length RiSP749. RiSP749 is the largest protein and hence the large size might hinder the invertase activity of the secreted fusion protein. Alternatively, the RiSP749 CDS might contain information impeding secretion. In contrast to the other three effectors, the predicted GLOIN261 SP invertase fusion was not sufficient to guarantee growth of the transformants, while the full-length version did, indicating the involvement of additional protein regions or protein structures that may be implicated in secretion, possibly via a non-conventional pathway ( Stuer et al., 2023 ). Although the YST assay is broadly used in fungal effector research to validate effectors’ secretion, the biological context in which these effectors are secreted is far more complex and might be influenced by plant-derived molecules that are lacking in the yeast experimental set-up. Therefore, besides validating whether the proteins were properly expressed in the yeast cells, also alternative approaches such tomato roots treated with synthetic effector peptides or immunolocalization on symbiotic tissue would help to unravel the effectors internalization. Furthermore, expression in transformable tomato root-interacting fungi such as Fusarium solani could be tried to test their secretion and translocation in planta ( Skiada et al., 2019 ). The four effectors have been selected based on the presence of possible NLSs. Fluorescence microscopy analysis indeed confirmed the nuclear subcellular localization of all protein fusions studied, but more detailed research will be required to verify the involvement of the in silico -predicted NLS in the import of the effectors into the plant nucleus, because some of the fusion proteins were also detected within other subcellular compartments, such as the cytosol. It might be interesting to test whether the NLS of the investigated effectors is the sole responsible for the effector’s translocation into the nucleus via the canonical α/β-importin pathway or whether, in contrary, effectors can passively diffuse through the nuclear pores, as they display molecular weights below 40 kDa ( Liu and Coaker, 2008 ; Harris et al., 2023 ). A common analysis used to study the effect of fungal effectors on plant physiology is through ectopic expression ( Kloppholz et al., 2011 ; Zeng et al., 2020 ; Wang et al., 2021 ), but this also has some limitations, such as the transgene may be silenced, or the observed general phenotype may not correspond to the true function due to misexpression. We generally experienced a high level of silencing when constitutively expressing the fungal effector genes in tomato, making it difficult to analyze their effect on mycorrhization in tomato composite plants. Therefore, we substituted composite plants with another host, M. truncatula . In addition, we used the non-host Arabidopsis, because we hypothesized that if the effector targets a conserved pathway in plant development, we might also observe the effect in this easily transformable plant, regardless of whether it is a host or not. Phenotyping the effect of the ectopic expression of the effectors and silencing of the tomato proteins on AM colonization was inferred with the Trouvelot method and via expression of RiEF1α and MtPT4 expression, but no consistent conclusions could be made at the molecular level. This discrepancy might be due to the variation observed among the phenotypes of various transgenic roots with differences in their levels of effector expression, and the roots utilized for qRT-PCR analysis. Increasing the sample size, and the use of alternative approaches such as the magnified intersections method, might be helpful in the future to investigate the effect on mycorrhization in more detail ( McGonicle et al., 1990 ). While studying the involvement of GLOIN707 in AM symbiosis, we observed a negative effect on the growth of Arabidopsis plants constitutively expressing the GLOIN707 effector. This reduction in plant growth may also explain the observed negative effect on the mycorrhization frequency and intensity in M. truncatula . This effect is rather unexpected, because effectors should contribute to symbiosis, not hinder it. Hence, these phenotypes reflect a major drawback of ectopic expression strategies and should be interpreted with caution, because observed phenotypes may be indirectly caused by general overexpression, by the fact that effectors might not act alone during symbiosis, or because they might need specific environmental conditions to exert their beneficial role in AM symbiosis. By using an inducible expression system in tomato hairy root cultures, we could investigate the effect of short-term overexpression of GLOIN707 on the transcriptome. This analysis resulted in differential expression of more than 200 genes for GLOIN707, with a significant overrepresentation of genes involved in cell-cell junction assembly and defense response to other organisms. It is tempting to speculate that activation of defense might explain the negative effect on plant development and symbiosis, although, future experiments should validate this mode-of-action. The phenotypic and transcriptional data suggest that GLOIN707 interferes with key cellular processes in the host plant, under which response to (a)biotic factors, metabolism and transport. Another way to address the function of the effector is through reverse genetic approached to find the plant proteins with whom they interact. Y2H analysis followed by rBiFC validation revealed a strong interaction between GLOIN707 and the tomato protein Sl296, encoding a CHP zinc-finger protein-like protein that is mainly restricted to plants of the Solanaceae family, suggesting a specialized role within that family. This interaction may redirect Sl296 from the cytosol to the nucleus, where both proteins were found to associate in the nucleolus, the high activation site of ribosomal rRNA synthesis and ribosome biogenesis ( Kalinina et al., 2018 ). Zinc-finger proteins are nuclear proteins involved in transcriptional or translation regulation of RNA, DNA or proteins upon (a)biotic stresses ( Han et al., 2021 ). Reduced expression of Sl296 decreased the arbuscular abundance in mycorrhized root fragments measured by microscopical imaging, suggesting that it may be required for proper arbuscular development, although we could not confirm this with RiEF1α . Expression of Sl296 was AM responsive, especially at early stages, similarly to the expression of GLOIN707 . How GLOIN707 affects the Sl296 action needs further analysis, but given the possible Sl296 RNAi-impaired AM phenotype and the nucleolar site of the interaction, the effector may have an impact on ribosome biogenesis or rRNA transcription modulation via its interaction with Sl296 to increase the plant’s metabolism to accommodate the fungus. It would be of interest to produce stable tomato Sl296 knock-out mutants via CRISPR technology to unequivocally confirm the observed AM phenotypes and further characterize the biological relevance of this protein association. Although the ectopic expression of the nuclear-localized effector GLOIN781 negatively affected the mycorrhization frequency in M. truncatula , it had a positive effect on Arabidopsis root growth. Protein–protein interaction approaches revealed the nuclear interaction between GLOIN781 and the SlGLY protein, encoding a putative glyoxalase, present in many different plant families implying a conserved and fundamental role of GLY proteins in plant development. Indeed, glyoxalases detoxify MG, a byproduct of several metabolic pathways in plant cells that causes oxidative stress when abundant ( Thornalley, 2003 ) and acts as a signaling molecule at low concentrations, interacting with cytosolic calcium ions ( Hoque et al., 2016 ). Recently, two potential growth-promoting microbes, i.e. , Pseudomonas sp. CK-NBRI-02 and Bacillus marisflavi CK-NBRI-03, have been found to alter MG levels and, subsequently, the MG detoxification machinery in Arabidopsis to enhance plant defense responses and growth ( Kaur et al., 2022 ). SlGLY expression was pronounced in mycorrhized tomato roots at late stages, i.e ., 6 weeks, whereas GLOIN781 was more highly expressed in mycorrhized tissues (enriched and not-enriched segments) at 2 and 4 weeks. Furthermore, partial SlGLY silencing appeared to negatively affect the AM colonization frequency, although we could not confirm this with RiEF1α expression. As two of the significantly downregulated GLOIN781 target genes of tomato, i.e. , glyoxylate reductase and glyoxal oxidase, play a role in MG homeostasis, we hypothesize that GLOIN781 may regulate the nuclear MG levels through its association with SlGLY, and that this interaction might participate in the AM-dependent calcium spiking to control the initiation of symbiosis. How this regulation is achieved is currently unknown, but given that metabolic pathways are highly active during arbuscule establishment and functioning, and that both genes are expressed in colonized root fragments with or without arbuscules, a role for MG in maintaining and initiating arbuscules can be assumed and should be investigated in the future. To further confirm the potential role of GLOIN781-SlGLY interaction and MG regulation in AM, the glyoxal I enzymatic activity of SlGLY should be tested, as well as nuclear Ca2+, K+, and MG levels could be quantified in plant lines ectopically expressing GLOIN781 and in the SlGLY RNAi lines. Our experiments did not deliver much more insight into the function of GLOIN261. The expression analysis revealed a specific enrichment in arbuscule-containing root segments at later stages, and expression of GLOIN261 played a beneficial role in Arabidopsis root and leaf growth, although the mycorrhization parameters did not change significantly in mycorrhized composite lines of M. truncatula . Besides its nuclear localization, no function for GLOIN261 could be hypothesized due to lack of interacting plant proteins. Its correct protein interactors may have been missed because of their absence in the cDNA library, because this library did not contain material from mycorrhized roots, but GLOIN261 may also associate with other biomolecules, such as DNA and RNA. Subjecting GLOIN261 to chromatin immunoprecipitation (ChIP) or cross-linked RNA IP (CLIP) experiments may shed more light on its host target molecules. Previously, RiSP749 has been predicted to be a large, secreted protein without differential expression in three different host plants, i.e. , M. truncatula, Brachypodium distachyon (stiff brome), and Lunularia cruciata (crescent-cup liverwort) ( Kamel et al., 2017 ). In contrast, we found RiSP749 to be expressed in tomato mycorrhized root regions and specifically enriched in arbuscule-containing root segments at 6 weeks, highlighting the importance of studying effectors in a host plant of interest ( Lanfranco et al., 2018 ; Zeng et al., 2018 ). Just like for GLOIN261, ectopic expression of RiSP749 did not affect mycorrhization and growth which might be because of the poor ectopic expression levels we obtained. Nevertheless, it interacted with the serine/arginine-rich (SR) splicing factor RSZ22 (LOC050) ( Barta et al., 2010 ) inside the nucleus, which was also highly expressed at later stages of symbiosis. Unfortunately, no LOC050 silencing lines could be generated, hinting at an essential role in plant growth. Both RiSP749 and LOC050 exhibit RNA-binding motifs and the homology of RiSP749 to small nuclear ribonucleoproteins strongly suggests a role in mRNA splicing for example of specific genes involved in metabolic processes important for arbuscule-containing cells. Detailed investigation of the RNA targets of both LOC050 and RiSP749 in these cells by tissue-specific RNA-sequencing approaches of transgenic lines or CLIP experiments will help to validate this hypothesis. Certainly because it recently has been demonstrated that other R. irregularis effectors can also interact with specific SR splicing factors to regulate alternative splicing in potato ( Betz et al., 2023 ), and certain effectors secreted by the phytopathogen Phytophthora can reprogram their host by modulating alternative splicing of host mRNAs in tomato ( Huang et al., 2020 ). In conclusion, following our pipeline ( Aparicio Chacón et al., 2023 ), we were able to select, identify and characterize four unknown R. irregularis nuclear-localized effector proteins with different modes of action in tomato. Moreover, we used effector interactomics to identify unknown host plant genes involved in AM, reflecting how effectors can be used as fishing strategies to elucidate new AM players. However, our study revealed the drawback of ectopic expression in functional characterization, requiring loss-of-function studies, either by fungal mutagenesis or virus- and host-induced gene silencing, for a final validation of their function. Experiments that consider the combinatorial influence of effectors, as well as the likelihood that effectors also target host plant DNA and/or RNA in different plant hosts, will help to unravel the complex communication between mutualistic fungi and their hosts."
} | 5,863 |
39985718 | PMC11846762 | pmc | 6,528 | {
"abstract": "Escalating global concerns about soil degradation, driven by erosion, salinization, compaction, pollution, and organic matter loss, highlights the critical need for sustainable remediation. Biocrusts—complex communities of cyanobacteria, algae, lichens, bryophytes, and fungi—play a pivotal role in soil stabilization, erosion prevention, and nutrient cycling. This study presents recent advancements in biocrust application for soil management and restoration, focusing on artificial biocrusts as a nature-based solution biotechnology. It emphasizes their effectiveness in enhancing soil quality, biodiversity, and ecosystem functionality. Researchers are leveraging these microbial communities to develop strategies that improve soil health and rehabilitate degraded landscapes. The review concludes that biocrusts are a viable strategy for boosting soil resilience and enhancing soil health against environmental stressors. It recommends future research on their long-term ecological impacts and methods to enhance their functionality.",
"conclusion": "Conclusions and Perspectives Soil health is an integral component of ecosystem sustainability, agricultural productivity, and environmental conservation. Biocrusts—communities of cyanobacteria, algae, lichens, bryophytes, and fungi that colonize the soil surface in arid and semi-arid regions—play a pivotal role in enhancing soil health through their complex microbial interactions. Understanding the microbial ecosystems within biocrusts provides essential insights into strategies for controlling degradation and restoring degraded soils. This review examines the mechanisms by which biocrusts improve soil health, focusing on nutrient cycling, soil stabilization, carbon sequestration, and water retention. It synthesizes current research on the ecological functions of biocrusts, highlighting their potential as a sustainable solution to mitigate soil degradation and enhance ecosystem resilience. Biocrusts have shown promising results in enhancing soil health, stabilizing soils, increasing water infiltration, fixing nitrogen, and sequestering carbon, offering a sustainable approach to land rehabilitation that aligns with conservation objectives. The preservation and promotion of biocrusts in both natural and managed landscapes can significantly enhance soil fertility and resilience. Nonetheless, further research is required to elucidate the mechanisms underlying biocrust functionality, optimize their application across different environmental contexts, and evaluate their long-term impacts on soil quality and biodiversity. Future studies should investigate the roles of specific microbial taxa within biocrust communities, assess the resilience of biocrusts to changing environmental conditions, develop practical guidelines for integrating biocrusts into restoration strategies, evaluate the economic benefits of biocrust applications for soil health, and explore the interactions between biocrusts and aboveground vegetation in ecosystem processes.",
"introduction": "Introduction Global drylands are experiencing soil degradation due to land-use practices and climate change, which affects ecosystems and human livelihoods [ 1 ]. Various concerns have been raised about the possibility of using biocrusts, a prevalent component of dryland ecosystems, to improve soil fertility and stability [ 1 , 2 ]. As estimated, approximately 12% of terrestrial landscapes and 25–30% of dry and semi-arid regions are covered by biocrusts [ 3 – 5 ]. Biocrusts, a complex assemblage of microorganisms, play a significant role in maintaining soil health, preventing degradation, and promoting restoration [ 6 ]. They create a thin layer on the soil’s surface and are made up of cyanobacteria, algae, lichens, bryophytes, and fungi [ 4 ]. Biocrusts are crucial for soil stability and operation in dryland ecosystems, supporting soil fertility, water availability, and carbon sequestration. Notably, some species of cyanobacteria have the unique ability to fix atmospheric nitrogen into a form that plants can utilize. This process enriches the soil with nitrogen, which is essential for plant growth [ 2 , 5 , 7 , 8 ]. Heindel et al. [ 9 ] further demonstrated that soils harboring cyanobacteria-rich biocrusts exhibited marked improvements in nitrogen availability, which substantially benefited plant development. Moreover, biocrusts contribute to enhanced soil porosity and reduced evaporation by forming a protective layer on the soil surface, a critical function in arid regions where water scarcity is a limiting factor for vegetation [ 8 , 10 ]. Comparative studies, including those by Antoninka et al. [ 11 ] and Aqeel et al. (2023), have shown that biocrusted soils retain moisture more effectively than bare soils, primarily due to their improved structural and organic matter composition, thereby maintaining higher moisture levels post-rainfall events. It should be noted that through photosynthesis and organic matter accumulation, biocrusts contribute to carbon sequestration in soils because cyanobacteria convert carbon dioxide into organic compounds during photosynthesis, which can then be incorporated into the soil matrix as organic matter [ 11 ]. Their disappearance may have significant ecological implications, making them especially important in dry and semi-arid areas [ 12 , 13 ]. The filamentous structures of cyanobacteria and other microorganisms help bind soil particles together, reducing erosion caused by wind and water, which is vital in stabilizing and maintaining soil integrity in fragile ecosystems [ 14 – 16 ]. A study by Valentin [ 17 ] demonstrated that biocrusts can reduce soil erosion rates by up to 90% compared to bare soils. They provide habitat for various organisms including microfauna and macrofauna that depend on these communities for food and shelter (this biodiversity contributes to overall ecosystem resilience, and they create microhabitats that support a variety of life forms which can enhance ecosystem functionality. Plant ecologists have developed strategies to reduce habitat loss due to disturbance or climate change, such as a controversial method of aided migration, which involves relocating seeds or plants northward in anticipation of suitable habitat movement [ 11 ]. Another approach is “pre-storation,” which promotes restoration using species acclimated to both present and future conditions. Young et al. [ 18 ] suggest assisted migration is a potential biocrust control tactic. A study by Thomazo et al. [ 19 ] indicated that biocrusts significantly contribute to the nitrogen cycle in arid and semi-arid ecosystems by facilitating biological fixation, undergoing intense internal N transformation processes, and directly causing some losses through dissolved, gaseous, and erosional processes (Fig. 1 a, b). Due to low denitrification rates and limited respiration, biocrusts function as net exporters of ammonium, nitrate, and organic N. However, research on biocrust inoculation primarily focuses on laboratory or plot scales. Duran et al. [ 20 ] proposed three perspectives for effective biocrust application and monitoring to understand ecosystem functions and services which aims to scale up the processes (Fig. 1 c). However, biocrusts in Antarctica can operate as ecosystem engineers, boosting soil fertility and nutrient availability, which positively improves the ecophysiological performance of native Antarctic vascular plants (Fig. 1 d) [ 21 ]. This is especially important in cold climates where vascular plant growth is restricted by limited water availability, which is essential to establishing and growth of these plants. Fig. 1 Biocrust and associated nutrient cycling [ 19 ]. a Internal structure of association between cyanobacteria. b Nitrogen cycling transformation in modern biocrust [ 19 ]. c Relationship and link between biocrust and soil–plant processes [ 20 ]. d Roles on soil properties and effect on vascular plants [ 21 ]. @2018 Nature Communication (Open Access permission attached) Global soil deterioration is a significant issue that affects ecosystem health, environmental sustainability, and agricultural output [ 22 ]. It results from degrading nearly one-third of the world’s soil resources, impacting biodiversity, water quality, agricultural production, and climate resilience [ 23 ]. Causes include unsustainable farming methods, deforestation, urbanization, industry, overgrazing, and climate change, even though climate change may also positively affect the distribution of biocrusts by removing the upper vegetation that competes with them for space and light. Similarly, certain levels of overgrazing, if not severe enough to degrade the soil, may have comparable effects (Fig. 2 ). The primary effects of soil degradation include compaction, pollution, nutrient depletion, soil erosion, and loss of organic matter. Fig. 2 Schematic of global soil degradation [ 26 ]. Adapted from the global assessment of human-induced soil degradation website Agricultural production is directly affected by soil degradation, lowering crop yields and food security. Degraded soils make it harder for plants and animals to thrive, further reducing biodiversity and can lead to increased poverty, hunger, and food insecurity in vulnerable areas [ 10 , 22 ]. Additionally, erosion and fertilizer runoff from deteriorated soils can cause contamination of water bodies and aquatic habitats [ 24 ]. To address this issue, a comprehensive strategy incorporating soil conservation methods, organic farming, reforestation, agroforestry, sustainable land management practices, and restoration of degraded areas is required [ 25 ]. Soil degradation releases stored carbon into the atmosphere, resulting in greenhouse gas emissions and soil organic matter loss. Addressing these challenges necessitates a collaborative effort among governments, non-governmental organizations, researchers, and local communities to advocate for and implement sustainable soil management strategies. Increased public awareness and engagement with international frameworks such as the World Soil Day campaign, the Global Soil Partnership, and the United Nations Sustainable Development Goals are critical. Although it is commonly believed that biocrusts have a slow recovery time of hundreds or thousands of years, and the long-term viability of biocrust restoration initiatives is frequently hampered by severe abiotic conditions, recent research suggests that they can recover relatively quickly with some assistant approaches; cyanobacteria-dominated biocrusts are likely to recover within 5–10 years, while lichen- and moss-dominated crusts take 10–20 years. Biocrusts play a crucial role in soil stabilization, improving soil health and reducing erosion by binding soil particles together to form aggregating structures [ 6 , 7 ],Xiao et al., 2024). Biocrusts not only enhance plant growth and water infiltration but also restore ecosystem functionality through carbon sequestration, nutrient cycling, and the creation of microhabitats for diverse species [ 20 , 27 ]. Despite their benefits, the effective implementation of biocrust restoration faces several challenges, including the need for suitable environmental conditions, species selection, and integration with existing land management practices. Additionally, the slow growth rates of certain biocrust organisms and their vulnerability to disturbances complicate restoration efforts. Effective biocrust restoration requires collaboration among scientists, land managers, and policymakers, coupled with further research to elucidate the mechanisms of biocrust formation and their interactions within microbial ecosystems in degraded soils. This review aims to provide a comprehensive overview of biocrusts’ role in enhancing soil health, highlighting their potential in mitigating land degradation and restoring degraded landscapes, and identifying gaps in current knowledge for future research."
} | 2,994 |
24109477 | PMC3790079 | pmc | 6,529 | {
"abstract": "In marine sediments archaea often constitute a considerable part of the microbial community, of which the Deep Sea Archaeal Group (DSAG) is one of the most predominant. Despite their high abundance no members from this archaeal group have so far been characterized and thus their metabolism is unknown. Here we show that the relative abundance of DSAG marker genes can be correlated with geochemical parameters, allowing prediction of both the potential electron donors and acceptors of these organisms. We estimated the abundance of 16S rRNA genes from Archaea, Bacteria, and DSAG in 52 sediment horizons from two cores collected at the slow-spreading Arctic Mid-Ocean Ridge, using qPCR. The results indicate that members of the DSAG make up the entire archaeal population in certain horizons and constitute up to ~50% of the total microbial community. The quantitative data were correlated to 30 different geophysical and geochemical parameters obtained from the same sediment horizons. We observed a significant correlation between the relative abundance of DSAG 16S rRNA genes and the content of organic carbon ( p < 0.0001). Further, significant co-variation with iron oxide, and dissolved iron and manganese (all p < 0.0000), indicated a direct or indirect link to iron and manganese cycling. Neither of these parameters correlated with the relative abundance of archaeal or bacterial 16S rRNA genes, nor did any other major electron donor or acceptor measured. Phylogenetic analysis of DSAG 16S rRNA gene sequences reveals three monophyletic lineages with no apparent habitat-specific distribution. In this study we support the hypothesis that members of the DSAG are tightly linked to the content of organic carbon and directly or indirectly involved in the cycling of iron and/or manganese compounds. Further, we provide a molecular tool to assess their abundance in environmental samples and enrichment cultures.",
"introduction": "Introduction Archaea are widely distributed around the globe and abundant in both the terrestrial and marine realms, where they display a remarkable diversity (Robertson et al., 2005 ; Schleper et al., 2005 ; Brochier-Armanet et al., 2011 ). Although characterized isolates now assigned to this domain have been around for almost 100 years (Klebahn, 1919 ; Dassarma et al., 2010 ), the majority remain uncharacterized and are only recognized through their genetic fingerprint (Teske and Sorensen, 2008 ; Cavicchioli, 2011 ). These fingerprints, mainly obtained in the form of 16S rRNA genes, contain valuable information about the identity and abundance of organisms, but unfortunately rarely give any clues about their metabolism. This is of particular concern for uncharacterized archaeal (or bacterial) groups with a high abundance and a cosmopolitan distribution, as they might have profound influence on major geochemical cycles. Examples in this respect are anaerobic methanotrophs (ANME) (Boetius et al., 2000 ; Orphan et al., 2001 ) and ammonia oxidizing archaea (Konneke et al., 2005 ; Treusch et al., 2005 ; Leininger et al., 2006 ), two abundant groups of archaea whose metabolism only recently have been uncovered. One of the most prominent archaeal lineages in marine sediments based on 16S rRNA gene surveys is the Deep Sea Archaeal Group (DSAG). No representatives from this group have so far been cultured or otherwise metabolically characterized. The lineage was first described in 1999 by Takai and Horikoshi who obtained 16S rRNA gene signatures from a hydrothermal system, naming the cluster Deep Sea Hydrothermal Vent Crenarchaeotic Group 1 (Takai and Horikoshi, 1999 ), which was later re-named to DSAG by the same authors (Takai et al., 2001 ). A few months later Vetriani and colleagues published additional sequence information related to this group from marine sediments obtained in the Atlantic Ocean, assigning the name Marine Benthic Group B (MBG-B) to the cluster (Vetriani et al., 1999 ). Although several other nomenclatures have been applied to this group over the years (Dong et al., 2006 ; Robertson et al., 2009 ) most studies use DSAG, MBG-B or both. Based on 16S rRNA gene information the DSAG form a monophyletic cluster within the Crenarchaeota phylum, and there is currently no evidence supporting an inclusion in the newly defined Thaumarchaeal phylum (Brochier-Armanet et al., 2008 ; Spang et al., 2010 ; Pester et al., 2011 ). In order to resolve a more exact placement of this group in the tree of life, additional genome information is needed. Signatures of DSAG have been reported from a number of marine habitats, including hydrothermal vents (Takai and Horikoshi, 1999 ; Reysenbach et al., 2000 ; Teske et al., 2002 ; Nakagawa et al., 2005 ; Hirayama et al., 2007 ), microbial mats and filaments from different marine settings (Knittel et al., 2005 ; Omoregie et al., 2008 ; Robertson et al., 2009 ; Reigstad et al., 2011 ), seep systems (Heijs et al., 2007 ; Dang et al., 2010 ; Orcutt et al., 2010 ), deep-sea sediments (Vetriani et al., 1999 ; Inagaki et al., 2006 ; Nunoura et al., 2009 ; Lloyd et al., 2010 ; Jorgensen et al., 2012 ), and near-shore and intertidal sediments (Inagaki et al., 2003 ; Powell et al., 2003 ; Kim et al., 2005 ). However, the DSAG is not restricted to the marine environment, as can be concluded from several recent studies reporting their occurrence in inland lakes (Dong et al., 2006 ; Jiang et al., 2008 ; Nold et al., 2010 ; Schubert et al., 2011 ), a terrestrial cave system (Chen et al., 2009 ), soil (Kasai et al., 2005 ), and fresh water iron mats (Kato et al., 2012 ). Although members of the DSAG apparently have a liberal habitat preference, there is at least one common denominator: they seem to be restricted to anaerobic or micro-aerophilic environments. Furthermore, it has been suggested that they prefer moderately saline and alkaline conditions (Jiang et al., 2008 ). DSAG signatures are often part of the dominating archaeal 16S rRNA gene pool in marine sediments (Orcutt et al., 2011 ). In an attempt to quantify their absolute abundance in environmental samples, Knittel and co-workers successfully designed and applied specific FISH probes, and were able to visualize small coccoid-shaped cells, but due to their small size (0.2–0.4 μm) they evaded enumeration (Knittel et al., 2005 ). Despite the lack of cultured representatives and metagenomic information related to the DSAG, several indirect measures gave rise to hypotheses about their metabolism. DSAG are very often found in methane-producing environments and thus have been speculated to be involved in methane cycling (Knittel et al., 2005 ; Biddle et al., 2006 ; Inagaki et al., 2006 ; Sorensen and Teske, 2006 ), and perhaps also linked to the sulfur cycle (Inagaki et al., 2006 ). However, stable isotope and subsequent lipid analysis have shown that organic carbon rather than methane-derived carbon is being assimilated in the cells (Biddle et al., 2006 ). Furthermore, DSAG are not restricted to the sulfate/methane transition zone and are found in environments that are sulfate limited, e.g., fresh water systems and sulfate depleted marine sediments (Teske and Sorensen, 2008 ). These observations make it unlikely that DSAG performs the type of sulfate-dependent anaerobic methane oxidation known from the archaeal ANME lineages (Boetius et al., 2000 ), but rather point to a heterotrophic or mixotrophic life style. Such a life style was recently supported by our own studies, in which we proposed that the DSAG could be involved in the iron cycle, using iron oxide as a terminal electron acceptor while oxidizing organic carbon (Jorgensen et al., 2012 ). This proposal was based on a tight correlation between the relative abundance of DSAG 16S rRNA genes in deep-sea sediment and concentrations of organic carbon and iron oxide. In addition a clear covariance with dissolved iron was observed. However, this covariance was not significant and it was speculated that it could be due to limited data points. In order to investigate the occurrence and possible metabolism of the DSAG in higher resolution we study here two sediment cores retrieved from the Norwegian-Greenland Sea. The cores were taken within a radius of 1 km from the area in the Arctic Mid-Ocean rift valley where our previous study was conducted. In total we investigate and analyze 52 deep-sea sediment horizons with respect to the occurrence and abundance of DSAG 16S rRNA genes by applying qPCR with newly designed primers. These quantitative results are evaluated in the context of 30 different geochemical parameters from both the solid and interstitial phase in order to examine potential covariance patterns. We also revisit the phylogeny of this group in order to uncover potential biogeographic distribution patterns.",
"discussion": "Discussion Previous findings had suggested that sediments in the study area have a compact geochemical depth profile with relatively shallow sulfate reduction and elevated concentrations of dissolved iron in combination with a high abundance of DSAG marker genes (Jorgensen et al., 2012 ). This abundance was linked to the content of both organic carbon and iron oxide and additionally showed a positive trend with dissolved iron, although not significant. Along with the special geochemical nature of the sediments, this motivated us to collect a new replicate gravity core (GC14) and a longer piston core from the same area (PC15), in order to validate some of the previous observations by specific assays and with a higher spatial resolution. Our qPCR results from these cores support a high abundance of DSAG 16S rRNA genes, constituting up to 100% of all archaeal an estimated 50% of the total microbial community in specific horizons (Figures 1C,D ). The geochemical results suggest that we recovered five geochemical redox zones; oxic, nitrogenous, manganous, ferruginous, and sulfidic (based on sulfate depletion), but the zonation appeared to be less compressed than in the cores previously studied (Jorgensen et al., 2012 ). Further, the high level of dissolved iron in the porewater was confirmed (Figure 2C ) and suggests active microbial iron reduction from the upper anoxic horizons and throughout the length of both cores. We show here that the content of iron oxide in the measured sediment horizons, as the only one of the major oxides, is significantly correlated to the relative abundance of DSAG 16S rRNA genes (Figure 4A , supplementary table 1 ). The ability to utilize various forms of iron oxide, such as poorly crystalline iron oxides, as terminal electron acceptor, is a widespread trait in Bacteria and Archaea (Lovley and Phillips, 1988 ; Lovley et al., 2004 ; Weber et al., 2006 ). Iron is released into the porewater upon microbial reduction of iron oxide and in our study the concentration of dissolved iron increased with depth displaying a fluctuating pattern, presumably due to differences in reduction and/or oxidation rate in specific sediment horizons. We were able to correlate this pattern with the relative abundance of DSAG (Figure 4B , supplementary table 1B ) but only when we excluded data from the lower half of core PC15, in which the relative proportions of Archaea and Bacteria changed dramatically (Figure 1B ). This change in ratio coincides with the transition from a fine silty material to a coarser grained homogenous sediment, deposited by a extensive glaciogenic debris flow that prevail throughout the length of the core. This is likely to cause drastic changes in the geophysical and geochemical properties of the sediment, as can also be seen from the geochemical data. When excluding the lower half we also notice a significant covariance with dissolved manganese, which might suggest that manganese oxide, likewise is a potential electron acceptor for this group, or specific members of it (supplementary table 1B ). Such a scenario would not be surprising as most of the characterized iron reducing strains also have the capability to reduce manganese (Nealson and Saffarini, 1994 ; Lovley et al., 2004 ). Notably, none of the other 30 geochemical and geophysical parameters measured showed any significant correlation with the DSAG 16S rRNA gene abundance (supplementary table 1 ). However, relative abundance of ribosomal DNA is not necessarily a good measure for activity and this has to be kept in mind when evaluating these data. Various forms of organic carbon can be used as electron donors by iron reducing microorganisms, and indirect evidence for an organotrophic lifestyle among the DSAG was provided by stable isotope experiments and subsequent lipid analysis by Biddle and co-workers (Biddle et al., 2006 ). This suggestion was supported by our previous observed correlation between the abundance of DSAG and organic carbon (Jorgensen et al., 2012 ). Here we verify this correlation for both cores (Figure 4D , supplementary table 1 ). However, beside organic carbon, both H 2 (Lovley et al., 1989 ) and ammonia (Clement et al., 2005 ) have been discussed as possible electron donors for iron reducers, although the latter still lacks final evidence. In addition, certain sulfate reducing and methanogenic microorganisms have also been reported to have the ability to reduce iron oxide and although this reduction is without the benefit of growth, this could be another explanation for the observed correlations (Roberts, 1947 ; Lovley and Phillips, 1986 ; Coleman et al., 1993 ; Lovley et al., 1993 ; Bond and Lovley, 2002 ). Beside the obvious possibility that members of the DSAG use organic carbon as electron donor, fermentation where iron oxide is used as an electron sink could also explain the observed correlations in our study. However, it has been demonstrated that only a very small part of the reducing equivalents are shuttled through to iron oxide (Lovley and Phillips, 1986 ). Further, it has been speculated that microbially mediated reduction of iron could be coupled to the anaerobic oxidation of methane, a process that theoretically yields more energy than the coupling between methane and sulfate (Caldwell et al., 2008 ; Beal et al., 2009 ). Methane measurements were conducted throughout the length of PC15. However, all measurements were below detection and thus no link between DSAG and methane could be inferred. Further, although unlikely, we cannot rule out that the observed covariance with organic carbon is due to underlying correlations, such as between hydrogen production and organic carbon concentration, implying that the DSAG perform iron and/or manganese reduction coupled to hydrogen oxidation. Considering the above-mentioned hypotheses in the light of our data, we think the most plausible metabolism for DSAG seems to be oxidation of organic carbon coupled to the reduction of iron oxide and/or manganese oxide. However, these correlations, as all correlation analysis should be evaluated with caution, as causality cannot be inferred based on this type of data alone. Thus, one or several underlying parameters might be the true reason why significant correlations are observed. The phylogenetic analysis of the DSAG indicates no apparent habitat-specific clustering of sequences as opposed to findings for other archaeal groups such as the Korarchaeota and the Marine Group I (Auchtung et al., 2006 ; Durbin and Teske, 2010 ; Reigstad et al., 2010 ; Jorgensen et al., 2012 ). However, sequences in the deepest branching DSAG group (Alpha) are exclusively of hydrothermal origin, suggesting a hydrothermal origin of the entire group, as also previously argued (Reysenbach et al., 2000 ; Teske et al., 2002 ). In this context, it is noteworthy to mention that all known archaeal groups able to perform dissimilatory iron reduction are thermophilic (Weber et al., 2006 ). It is intriguing to speculate that this trait might have been passed on from thermophilic DSAG lineages to other members of this group that evolved and adapted to a mesophilic lifestyle. An interesting finding in our qPCR study is the sudden ten-fold decrease in the ratio between archaeal and bacterial 16S rRNA gene copies (from average 0.27 to less than 0.03) in core PC15. Overall the total copy numbers stayed relatively stable from approximately 2 m below surface and throughout the core (10 7 copies/gram sediment), which suggest that the available energy is unchanged and sustains the same number of organisms. However, caution has to be taken in the interpretation as differences between copy numbers in microbial groups, the presence of extracellular DNA, along with the biases that PCR amplification and quantification can inflict means that copy numbers not necessarily reflect neither cell abundance nor activity. The underlying reason for the observed decrease in the proportion of Archaea is not known, but we note the following: The change is gradual over approximately 1 m. It is tightly coupled to changes in the geochemical composition of the sediment, reflecting a shift in the lithology from fine silty sediment to a coarser grained homogenous glaciogenic debris flow deposit. Probably, one or more bacterial taxa have an advantage under the geophysical and geochemical conditions prevailing below 6 m sediment depth, which enables them to out-compete others. We also note that the relative 16S rRNA gene abundance of both bacteria and archaea co-varies with several of the measured geochemical parameters, although none of them known to be of importance in microbial redox reactions. This could suggest that the abundances of the two domains are tightly coupled to the geochemical nature of the sediment. In sum we find that the DSAG is a dominating microbial group in these sediments and that their relative abundance is tightly linked to both the concentration of solid iron oxide and dissolved iron in the interstitial water, a strong indication of iron reducing capabilities. Further, the data indicate that the group also could be involved in the manganese cycle. We also verify that DSAG 16S rRNA gene abundance is correlated to organic carbon, an observation that falls in line with the earlier hypothesis that DSAG are organotrophic organisms. Despite these indications it is clear that further studies are needed to clarify if the correlations are of a direct or indirect nature. For future efforts to obtain enrichments and ultimately pure cultures of this group we provide a qPCR protocol to monitor the abundance of DSAG."
} | 4,633 |
34204423 | PMC8235077 | pmc | 6,531 | {
"abstract": "The exploration of nonhazardous nanoparticles to fabricate a template-driven superhydrophobic surface is of great ecological importance for oil/water separation in practice. In this work, nano-hydroxyapatite (nano-HAp) with good biocompatibility was easily developed from discarded oyster shells and well incorporated with polydimethylsiloxane (PDMS) to create a superhydrophobic surface on a polyurethane (PU) sponge using a facile solution–immersion method. The obtained nano-HAp coated PU (nano-HAp/PU) sponge exhibited both excellent oil/water selectivity with water contact angles of over 150° and higher absorption capacity for various organic solvents and oils than the original PU sponge, which can be assigned to the nano-HAp coating surface with rough microstructures. Moreover, the superhydrophobic nano-HAp/PU sponge was found to be mechanically stable with no obvious decrease of oil recovery capacity from water in 10 cycles. This work presented that the oyster shell could be a promising alternative to superhydrophobic coatings, which was not only beneficial to oil-containing wastewater treatment, but also favorable for sustainable aquaculture.",
"conclusion": "4. Conclusions Apparently, abundant biogenetic CaCO 3 with low cost is an attractive raw material for HAp preparation. Specifically, oyster shell was extensively used to synthesize HAp nanoparticle via hydrothermal treatment [ 35 ]. Therefore, oyster shell could be a promising raw material for fabricating a HAp-nanoparticle-coated superhydrophobic surface. A kind of nano-hydroxyapatite material was developed from discarded oyster shell via a simple and low-cost procedure, which was then coated on the surface of pristine sponge. The uniformly distributed nano-HAp along the PU skeleton formed a microstructure that changed the wetting properties of PU sponge from hydrophobicity to superhydrophobicity. The resultant nano-HAp/PU sponge is not only an adsorbent that can adsorb oil or organics, but also an excellent separation material that can continuously separate oil from water. It is a promising candidate for oil/water separation method for its high absorption capacity, low cost, and eco-friendliness. Additionally, some functional groups such as -NH 2 and -OH on sponge provided a chemical connection between the HAp coating and PU substrate, which improved the durability of the surface and reusability of the HAp/PU sponge. The nano-HAp/PU sponge can be used in combination with the pump to deal with an oil spill emergency by recovering oil from water. In addition, the superhydrophobic coating on PU sponge is derived from discarded oyster shell, which provides a novel strategy to solve the serious pollution problem caused by accumulation of oyster shell. These advantages make nano-HAp/PU sponge a potential separator candidate for the recovery of oil from water for environmental protection application.",
"introduction": "1. Introduction The emergence and persistence of oil pollution in aquatic and marine ecosystems have raised universal health concerns on the environment and wildlife for decades. Many operational innovations introduced by the Maritime Agreement Regarding Oil Pollution (MARPOL) to regulate the discharge of oily water have contributed greatly to a noticeable decrease in marine pollution, whereas it is well recognized that a greater effort shall be carried out to treat the discharged oil considering the more stringent requirement on maximum permissible concentration (MPC) for oil worldwide [ 1 , 2 , 3 ]. For example, the MPC for oil in the bilge water discharges through the oily water separator is the well-known 15 ppm standard enforced by MARPOL since 1983 [ 4 , 5 ]. Therefore, oil/water separation methods, including absorption [ 6 ], flocculation [ 7 ], and electroflocculation [ 8 ], have recently been applied to purify oily water and adopted as an alternative to biological treatment for its time-saving property [ 9 ]. Of these, absorption appears to be more attractive due to its universal function in removing all forms of oil from the oily water, high oil recovery efficiency, and convenient post-treatment. Usually, the absorbents used for the treatment of oily water are either natural products or close derivatives, including plant fibers [ 10 ], wool [ 11 ], organophilic clay [ 12 ], exfoliated graphite [ 13 ], starch [ 14 ], and cellulose-based materials [ 15 ], which possess eco-friendliness, low cost, and easy accessibility. However, these materials exhibited low selectivity for oil and water, inadequate buoyancy, and poor mechanical properties, resulting in a low recovery rate of oil and inconvenient recycling of absorbent [ 16 ]. Hence, the exploration of new oil absorbents exhibiting high oil/water selectivity, low density, and excellent recyclability is of great economic and ecological significance for oil pollution prevention. Superhydrophobic materials with different wetting properties between water and oil are promising to effectively separate oil from water. Many superhydrophobic materials have been fabricated for oil/water separation by using metallic mesh [ 17 ], glass [ 18 ], textile [ 19 ], and sponge [ 20 ] as substrates. Compared with other substrates, sponges are more portable and convenient to recycle owing to their low density, high flexibility, and three-dimensional architectures. In addition, high porosity and large surface area endow sponge with high absorption capacity. Additionally, sponge generally contains hydrophilic groups, such as -OH and -COOH, which offer potential for further modification [ 21 ]. Therefore, many works have been devoted to preparing superhydrophobic sponge for efficient oil/water, such as melamine sponge [ 22 ], polyurethane sponge (PU sponge) [ 21 ], and cellulose sponge [ 23 ], of which PU sponge has been considered as an ideal substrate due to its low cost, reproducibility, and compatibility [ 20 ]. Generally, the superhydrophobic surface can be acquired by introducing rough structures and polymer chemicals of low surface energy [ 24 ]. For example, polydimethylsioxane (PDMS) and poly(vinylidene fluoride) (PVDF) were introduced to reduce the surface energy and thus increase the hydrophobicity of nanoparticle surfaces [ 18 , 22 ]. Considering the toxicity of fluorinated polymers, the employment of PDMS to design and construct superhydrophobic surfaces is a better option. In addition, metals and their oxides have been widely employed to develop micro/nanostructures to increase surface roughness [ 25 ]. To some extent, the metal-derived superhydrophobic surfaces exhibited satisfactory oil/water separation. However, there are still some problems existing that seriously restrict their practical applications. First, most of the prepared micro/nanostructures of superhydrophobic surfaces are too fragile to endure mechanical forces or chemical erosion [ 24 ]. Second, the release of metal ions and nanoparticles from the metal-containing superhydrophobic material may induce toxicity to the aquatic ecosystem and human health. Therefore, it is of great significance to develop biocompatible nanomaterials with fine mechanical properties. Interestingly, hydroxyapatite (HAp) nanoparticle has been widely studied in the medical area because of its superior biocompatibility and specific bioactivity for drug, bone mineral, and gene delivery [ 26 ]. For instance, hydroxyapatite has been exploited as an orthopedic and dental implant due to its mechanical, thermal, and chemical stability [ 27 ]. HAp can be easily prepared from various natural materials, including biogenetic calcium carbonate (CaCO 3 ) and mineral-derived calcium compounds [ 28 ]. In recent years, shellfish aquaculture has achieved large development to cover the large consumption of seafood products. A large number of oyster shells that have been seriously undervalued have become waste or low-value resources, occupying some tidal flats and land, corrupting and smelling, and bringing many adverse effects to the environment [ 29 ]. Therefore, the comprehensive development and utilization of oyster shells are of great significance. How to effectively develop and utilize oyster shell resources and turn them into treasures is the research purpose of this subject. Herein, for the first time, we prepared green-based PU sponge (EP/SPU) by using hydroxyapatite prepared from oyster shells and polydimethylsiloxane (PDMS). There are few reports in the literature about the use of shell/PU sponge for oil/water separation. Of course, some researchers used oyster shells to prepare a superhydrophobic foam; however, because the main component of oyster shells is calcium carbonate, the prepared materials are easily corroded by strong acids and lose their function. It is particularly important to explore a material that can use oyster shells to prepare acid and alkali resistance. In this study, we used oyster shells to prepare a nanomaterial that can better resist acid and alkali corrosion so that the prepared superhydrophobic sponge has stronger acid and alkali resistance and can operate stably in the pH range of 4–12. This not only realizes the resource utilization of oyster shells, but also increases the possibility of superhydrophobic sponges in oil–water separation applications. Recycling and reuse of discarded oyster shell can considerably increase the profit of oyster farming and achieve sustainable development. Herein, this study aims to explore the possibility of oyster shell in preparing Hap-coated superhydrophobic material.",
"discussion": "3. Results and Discussion 3.1. Chemical Composition Analysis Hydroxyapatite obtained from oyster shells and HAp-coated PU sponge were first characterized by X-ray diffraction (XRD) analysis. As shown in Figure 2 , two peaks centered at about 31° and 27° can be corresponded to the XRD patterns of pristine sponge. Additionally, several sharp peaks at 25.8°, 28.1°, 28.9°, 32.2°, 32.9°, 34.1°, and 39.8° in Figure 2 a can be attributed to the (002), (102), (210) (112), (300), (212), and (310) planes of pure hexagonal HAp (JCPDS card No. 09-0432) [ 25 ]. These results suggest that the well-prepared HAp particles were successfully loaded on the PU sponge. The surfaces of the PU sponge before and after HAp loading were characterized by attenuated total reflectance (ATR)-FTIR to further confirm the structural and chemical changes of the samples. The pristine PU sponge exhibited typical characteristic peaks of 3726, 2879, 1743, 1616, 1103, and 632 cm −1 , which are related to the stretching vibrations of O-H, C-H, C=O, N-H, epoxy C-O, and =C-H groups, respectively ( Figure 3 b). After modifying the PU sponge with the HAp particles, several characterized peaks at 1037, 632, and 613 cm −1 can be clearly observed for the HAp/PU sponge ( Figure 3 c). These peaks are associated with the bending vibration of P-O and the stretching vibrations of P-O and P=O groups present in HAp, respectively ( Figure 3 a), which are close to the vibrations observed by Shaly et al. [ 31 ]. Moreover, two new distinct peaks appeared at 806 and 1265 cm −1 , possibly ascribing to the symmetrical stretching vibration of Si-O-Si groups and the symmetrical bending vibration of Si-CH 3 , respectively, which revealed the effect of cross-linked PDMS on surface modification. Under the alkali condition, the alkoxy groups of siloxanes molecules can be hydrolytically accelerated to form silanol groups, which can covalently react with the hydroxyl groups of HAp at a low temperature of 80 °C [ 32 ]. Thus, these FTIR results clearly demonstrate the successful covalent coating of silane molecules with HAp molecules to the PU sponge surface. 3.2. Surface Morphology Analysis The sharp three-dimensional images of HAps, original sponge, and HAp-coated PU sponge, provided by FE-SEM at different magnifications, shed light on the topography and morphology of the samples under study. It can be seen from Figure 4 c that the un-processed PU sponge has a 3D porous network structure with a pore size ranging from 200 to 600 μm, which is very helpful for the loading of nanoparticles and is essential for maintaining high adsorption capacity [ 33 ]. Magnified images of the unprocessed PU sponge ( Figure 4 c,d) present the smooth skeleton surface, which has no micro-nano structure on its surface and cannot achieve superhydrophobicity. After PDMS modification and HAp loading, the 3D porous framework of the PU sponge was still maintained ( Figure 4 e), indicating that the porous skeleton structure was not destroyed during the solution–immersion. In addition, the dense hydroxyapatite particle was randomly distributed on the PU sponge surface ( Figure 4 f), rendering the micron-scale roughness of the coated surface ( Figure 4 g). It has been shown that the special wetting behavior of the surface is closely related to its morphology and chemical composition so that the combination of high surface roughness and lower surface energy is essential for obtaining a superhydrophobic surface [ 34 ]. Both the enhanced surface roughness of PU by micro-sized and nano-sized hydroxyapatite ( Figure 4 a,b) and the lower surface energy led by PDMS give the potential to achieve improved superhydrophobicity of PU. The spectra obtained from the energy dispersive spectrometer (EDS) analysis test for hydroxyapatite, PU sponge, and nano-HAp/PU sponge are exhibited in Figure 5 . The EDS spectrum shows the presence of calcium, phosphorous, oxygen, and silicon elements of the nano-Hap/PU sponge, indicating the successful loading of HAp on the sponge and the effectiveness of the preparation method. Moreover, the emerging Si peak indicates the successful modification of PU sponge by PDMS, which is beneficial to lowering the surface energy. 3.3. Wetting Performance of the Superhydrophobic Sponge The water contact angle (WCA) is often used to measure the wettability of the modified surface, which can reflect the fluid transport selectivity during oil/water separation. It has been widely accepted that superhydrophobicity (WCA > 150°) is normally needed to realize high selectivity [ 30 ]. The water contact angle values of different samples were shown in Figure 6 . As shown in Figure 6 , after the modification of PDMS, the water contact angle of the PU sponge slightly increased from 117° to 133°, indicating that the enrichment of PDMS on the surface could improve hydrophobicity. PDMS is supposed to decrease the surface energy and increase the surface roughness [ 22 ]. Moreover, due to the low cost, easy fabrication, high durability and flexibility, and self-healing properties, PDMS was extensively used for the preparation of new superhydrophobic materials with potential applications in phase separation [ 18 ]. However, PDMS modification alone presented a negligible effect on surface roughness, which is sufficient to obtain high superhydrophobicity. Fortunately, further introduction of HAp particles largely enhanced the surface roughness, and thus the water contact angle of the nano-HAp/PU sponge increased to above 150°. In this case, PDMS acted as both a low surface energy modifier and an adhesive. As a result, the introduction of PDMS and HAp obviously changed the wettability of the PU sponge. A single water droplet depositing on the HAp/PU sponge surface presented almost a perfect sphere ( Figure 6 c), while it exhibited a hemisphere on the surface of the pristine sponge ( Figure 6 a). Due to the as-formed micro/nanostructures on the HAp/PU sponge surface, air was trapped among these hierarchical rough structures so that it possessed high superhydrophobicity of water rather than oil ( Figure 6 d) [ 33 ]. Thus, the nano-HAp/PU sponge showed a high static water contact angle and a low oil contact angle. Corrosion resistance of superhydrophobic materials is a crucial factor for their practical applications. Previous studies showed that the inert properties of microstructural constituents greatly contributed to the chemical stability of their bulk materials [ 24 ]. Indeed, the change of the microstructure on the HAp/PU surface under acid stress could influence the stabilization and wetting behavior of the PU sponge, which strongly correlated with its efficiency in practical oil–water mixture separation [ 26 ]. Thus, acid-induced behavior of water contact angles was investigated by immersing the as-prepared nano-HAp/PU sponges in simulated water at various pH for 1 h. It is encouraging that the HAp/PU sponge could remain superhydrophobic within the pH range of 2–11 ( Figure 7 ). In a strong alkaline solution, the WCA of the nano-HAp/PU sponges appeared similar as under neutral conditions, which is ascribed to the inherent stability of HAp. After being immersed in strong acidic solution, its WCA was observed to undergo a gradual change by 5°, almost retaining superhydrophobicity. The slight decrease may be due to the tiny H+ absorbed on the surface of HAp/PU sponge by electrostatic interaction. Therefore, the good chemical stability of the HAp/PU sponge can be attributed to the chemical inertness of HAp and the strong covalent bonding among HAp, PMDS, and PU sponge. Thus, the HAp/PU sponge could not be damaged, even under strong acidic and alkaline solution conditions. In order to study the effect of HAp loading on the performance of superhydrophobic sponges, we added HAp of different qualities during the sponge preparation process and measured their water contact angles. As shown in Figure 7 , the WCA of the sponge modified by PDMS alone is 130.0°, and the water contact angles of the sponges with 0.2, 0.5, 0.8, 1.0, and 1.2 g HAp added are 144.1, 156.0, 146.8, 143.0, and 141.2, respectively, indicating that the loading of HAp increases the sponge’s wetting properties, playing a key role in the preparation of superhydrophobic sponges. With the increase of HAp dosage, the water contact angle of the modified sponge is gradually increasing. This is because the increase of HAp increases the rough structure of the sponge surface and makes the sponge more hydrophobic. When the dosage of HAp is 0.5 g, the hydrophobic performance of the sponge is the best, and its water contact angle is greater than 150°. When the amount of HAp added is greater than 0.5 and gradually increases, the water contact angle of the modified sponge gradually decreases, indicating that the load of excessive HAp particles may cause the surface of the sponge to be covered by the particles in a large area, which is not conducive to the construction of rough surfaces, which reduces the water contact angle of the sponge. 3.4. Performance of Evaluation of Nano-HAp/PU Sponge as Oil Absorbent Herein, diesel and chloroform were used to study the selective absorption of oil above and below the water, respectively. As presented in Figure 8 a, once the dyed diesel floating on the surface of the water contacted the modified sponge, it was rapidly absorbed into the skeleton of the sponge. Interestingly, when the nano-HAp/PU sponge was transferred to the water/chloroform mixture, it removed chloroform droplets within 5 s without any obvious residue ( Figure 8 b and Video S1 ). The nano-HAp coated on the skeleton can be fully wetted by contacted oil, and these oils quickly diffused into the sponge with the aid of capillary force, which rendered the modified sponge both of superhydrophobicity and superoleophilicity properties in water. Subsequently, the absorbed oil without apparent water was collected through simple squeezing, which means a fast, economic, and effective separation process. Therefore, the maximum adsorption capacity of oil by nano-HAp/PU sponge was further evaluated to test its potential in practical cleanup of organic pollutants from water. In a typical adsorption measurement, soybean oil, diesel, lube, toluene, and chloroform were chosen as adsorbates. The nano-HAp/PU sponge had a wide range of absorption capacities for different adsorbates depending on the viscosity and density of the oil or organic solvents, of which the adsorption capacity for soybean oil, diesel, lube, toluene, and chloroform can reach 11.3, 9.8, 11.4, 17.1, and 22.7 g.g −1 , respectively ( Figure 9 a). In particular, the absorption capacity for chloroform is almost 23 times that of the nano-HAp/PU mass itself, which demonstrates the excellent absorption capacity of nano-HAp/PU sponge for oil/organic solvents as well as other reported oil absorbents [ 18 , 22 ]. In addition, nano-HAp/PU could maintain superhydrophobic and stable absorption capability for organics of either high or low viscosity after cycling for even 10 cycles (see in Figure 8 b). A slight fluctuation of oil-absorption capacity along the cycling test could be observed, which may be due to the residual oil remaining in the pores of the sponge. After oil extrusion, the HAp/PU sponge could quickly recover its original shape without any deformation due to the excellent compressibility and elasticity of the PU sponge. Additionally, the microstructure of HAp composition was not damaged heavily even undergoing cyclic extrusion, indicating the high stability under mechanical stress. The good mechanical stability of HAp/PU sponge is beneficial to ultrawetting materials for practical oil/water separation. 3.5. In Situ Separation of Oil/Water Mixtures Furthermore, the continuous separation performance of oil/water mixture with superhydrophobic nano-HAp/PU sponge was investigated by running a pump-driven system ( Figure 10 ). The separation performance of oil from water with pristine PU sponge was also evaluated by the same method for comparison. Once the pump works, the diesel dyed with oil red was quickly and continuously passed through the fixed nano-HAp/PU sponge and was then collected into the right glass conical flask. Video S2 showed that the superhydrophobic sponge could recover 83 mL of dyed diesel from 250 mL of the static and immiscible oil/water mixture within 50 s, while the water level in the left glass conical flask remained unchanged. The separation efficiency was calculated as 99.6% by using the volume ratio between the collected diesel oil and that initially added in the mixture. In contrast, the unmodified sponge could not effectively collect diesel from the oil/water mixture, even connected to pump-assisted separation device (see in Video S3 ). These results indicated that the presence of nano-HAp endowed the HAp/PU sponge with the excellent oil–water separation ability, which is consistent with the results of the wetting performance discussed in Section 3.3 ."
} | 5,637 |
40000631 | PMC11861941 | pmc | 6,533 | {
"abstract": "Wearable thermocells offer a sustainable energy solution for wearable electronics but are hindered by poor fatigue resistance, low fracture energy, and thermal inefficiencies. In this study, we present a high-strength, fatigue-resistant thermocell with enhanced thermoelectric performance through solvent exchange-assisted annealing and chaotropic effect-enhanced thermoelectric properties. The mechanical strength and toughness are improved by forming macromolecular crystal domains and entangling polymer chains. Guanidine ions, with strong chaotropic properties, optimize the solvation layer of redox ion couple, boosting thermoelectric efficiency. Compared to existing anti-fatigue thermocells, the current design exhibits a 20-fold increase in mechanical toughness (368 kJ m -2 ) and a 3-fold increase in Seebeck coefficient (5.4 mV K -1 ). With an ultimate tensile strength of 12 MPa, a fatigue threshold of 4.1 kJ m -2 , and a specific output power density of 714 μW m -2 K -2 , this thermocell outperforms existing designs, enabling more reliable and efficient wearable electronics and stretchable devices.",
"introduction": "Introduction With the advancement of electronic technology, the demand for environmentally friendly and sustainable power sources for flexible electronics is becoming increasingly important 1 – 3 . Stretchable thermoelectric (TE) materials, based on the Seebeck effect, have garnered significant attention due to their high stretchability, energy conversion efficiency, and capacity to harvest low-grade thermal energy from the environment for power generation 4 , 5 . However, traditional non-intrinsic TE materials, typically derived from inorganic semiconductors, are constrained by low thermopower (also known as Seebeck coefficient, S c ), mechanical brittleness, high costs, and complex manufacturing processes 6 – 9 . Intrinsically stretchable thermocells offer a more adaptable means of utilizing human thermal energy. Specifically, flexible quasi-solid thermocells show great promise in wearable electronics, due to their exceptional stretchability, high S c , and resistance to leakage. These thermocells have a S c of approximately 1.6 mV K −1 \n 10 , 11 . However, since their network structure is predominantly composed of weak hydrogen bonding or ionic cross-linking materials, they suffer from notch sensitivity, resulting in low fracture energy (<10 J m − 2 ) and fatigue threshold (<10 J m − 2 ), which significantly impacts their stability, accuracy, and service life 12 – 14 . In practical applications, long-term load cycles can induce fatigue damage and crack formation in quasi-solid thermocells, further diminishing their performance 15 – 19 . Therefore, fabricating a quasi-solid thermocell with high fatigue resistance and excellent TE properties has long been a challenging and highly sought-after goal. Indeed, many pioneering works have been carried out on fatigue resistance and thermopower enhancement of thermocells. The improvement of fatigue resistance can be categorized into two primary mechanisms. One approach focuses on structural engineering, involving techniques such as mechanical training 20 , directional freezing, and ice templates 21 , 22 . These methods aim to create anisotropic micro-nanostructures to boost the fracture energy and fatigue threshold of thermocells. However, the mechanical properties vary across different orientations, allowing for stress enhancement only in specific directions, which limits their practical applications 23 , 24 . The second mechanism involves molecular engineering, introducing double cross-linked networks 19 , 25 , 26 or hard domain phases 27 , 28 that are stronger than polymer chains (e.g., multiple hydrogen-bonding interaction regions or crystallization regions). Dense and randomly cross-linked nanonetworks or polymer crystalline domains can enhance fracture energy, reduce fatigue crack growth, and mitigate notch sensitivity. The enhanced crack propagation resistance is not directionally selective, with values rising to 3.12 kJ m −2 . However, thermopower remains relatively low, with a value below 2.5 mV K − 1 \n 29 . The anti-fatigue thermocells described above utilize [Fe(CN) 6 ] 4-/3- as a redox pair for thermal energy conversion 5 , 30 , 31 . In these systems, low solvation entropy [Fe(CN) 6 ] 4- is spontaneously oxidized to high solvation entropy [Fe(CN) 6 ] 3- , releasing electrons to the thermal electrode, which are then consumed through the external circuit 32 . In addition, the entropy difference can be further increased by controlling the interactions with the redox pair, such as introducing additives or modifying the solvent system, enabling a thermopower of up to 3.73 mV K − 1 with [Fe(CN) 6 ] 4-/3- redox pairs and guanidinium salts 15 . Thus, in the design of high-performance anti-fatigue thermocells, mechanical and crack propagation properties can be enhanced by modifying the entanglement and crystallization domain of polymer chains 23 , 24 , 33 – 36 , while the thermoelectric effect can be improved by altering the solvation shell of redox pairs 5 , 15 , 30 . With the holy dream and considerations above, we developed a proof-of-concept anti-fatigue thermocell (SAFG) with high strength, fatigue resistance, and enhanced thermopower, employing solvent exchange-assisted annealing (SA) and chaotropic effect-boosted thermoelectric strategies using [Fe(CN) 6 ] 4-/3- -GdmCl solution (FG). Replacing the high-quality solvent with a poor-quality one strengthened the interaction between polymer chains, reinforcing the polymer network. Annealing, as a post-treatment process following solvent exchange, induced the relaxation and rearrangement of polymer chains, progressively enhancing the network by regulating the chain entanglement and aggregation through macromolecule movement. In addition, guanidine ions, with their strong chaotropic cation and excellent stability in the aramid nanofibers/poly(vinyl alcohol) (ANFs/PVA) solution system, maximized the rearrangement of the solvation layer of redox couples. Compared with the highest anti-fatigue quasi-solid thermocells with mechanical training, SAFGs exhibited approximately 20-fold and 3-fold improvements in mechanical toughness (368 kJ m − 2 ) and S c (5.4 mV K − 1 ), respectively. SAFGs achieved an ultimate tensile strength of 12 MPa, a fatigue threshold of 4.1 kJ m − 2 , and a specific output power density of 714 μW m − 2 K − 2 , the highest values reported for anti-fatigue quasi-solid thermocells. Furthermore, SAFG-based TE systems can harvest low-grade thermal energy and possess temperature sensing capabilities, making them promising candidates for wearable electronics, stretchable artificial tendons, and temperature monitoring applications.",
"discussion": "Discussion An anti-fatigue thermocell (SAFG) was developed using solvent exchange-assisted annealing (SA) and chaotropic effect-boosted thermoelectric strategies using [Fe(CN) 6 ] 4-/3- -GdmCl solution (FG). The mechanical properties were enhanced by adjusting the entanglement and crystallization domains of the polymer chains, yielding a stress of 12 MPa and a fatigue threshold of 4.1 kJ m −2 . GdmCl improved thermopower by enhancing the entropy difference of the redox pair. The SAFG system achieved a high thermopower of 5.4 mV K −1 and a high specific output power density of 714 μW m −2 K −2 , the highest power density reported for anti-fatigue quasi-solid thermocells. Furthermore, the SAFG-based TE system can harvest low-grade thermal energy, has temperature sensing capabilities, and shows great promise for applications in wearable electronics, stretchable artificial tendons, and temperature monitoring. SAFGs overcome the mechanical limitations of conventional thermocells in terms of fatigue resistance without sacrificing TE performance, providing a foundation for designing efficient and reliable wearable electronics."
} | 1,975 |
29576672 | PMC5856065 | pmc | 6,535 | {
"abstract": "Abstract \n Climate change is driving range contractions and local population extinctions across the globe. When this affects ecosystem engineers the vacant niches left behind are likely to alter the wider ecosystem unless a similar species can fulfil them. Here, we explore the stress physiology of two coexisting kelps undergoing opposing range shifts in the Northeast Atlantic and discuss what differences in stress physiology may mean for future niche filling. We used chlorophyll florescence ( F \n v \n /F \n m ) and differentiation of the heat shock response (HSR) to determine the capacity of the expanding kelp , Laminaria ochroleuca , to move into the higher shore position of the retreating kelp, Laminaria digitata . We applied both single and consecutive exposures to immersed and emersed high and low temperature treatments, replicating low tide exposures experienced in summer and winter. No interspecific differences in HSR were observed which was surprising given the species’ different biogeographic distributions. However, chlorophyll florescence revealed clear differences between species with L. ochroleuca better equipped to tolerate high immersed temperatures but showed little capacity to tolerate frosts or high emersion temperatures. Many patterns observed were only apparent after consecutive exposures. Such cumulative effects have largely been overlooked in tolerance experiments on intertidal organisms despite being more representative of the stress experienced in natural habitats. We therefore suggest future experiments incorporate consecutive stress into their design. Climate change is predicted to result in fewer ground frosts and increased summer temperatures. Therefore, L. ochroleuca may be released from its summer cold limit in winter but still be prevented from moving up the shore due to desiccation in the summer. Laminaria ochroleuca will, however, likely be able to move into tidal pools. Therefore, only partial niche filling by L. ochroleuca will be possible in this system as climate change advances. \n A plain language summary is available for this article.",
"conclusion": "5 CONCLUSION Our study provides insight into whether a range expander can replace a range contractor at the interface where they coexist within a biogeographic boundary zone. We show that cumulative stress is a key factor in determining interspecific differences in stress physiology. Such knowledge serves to increase our understanding of the processes driving species redistributions, which is comparatively lacking for marine organisms. However, some caution should be taken when generalising results to the wider region. Although the magnitude of difference between species is quite striking and unlikely to arise from site specific local adaptations such factors cannot be discounted, as only a single site and time point were sampled. Moreover, we have only investigated physiological differences between species and species interactions (e.g. facilitation) have not been accounted for. Such interactions may be important in further elucidating species responses to warming.",
"introduction": "1 INTRODUCTION The world's oceans have warmed by 0.11°C per decade for the last 40 years while short term rapid increases in ocean and aerial temperatures (heatwaves) have also increased in frequency and duration (Christidis, Stott, Brown, Hegerl, & Caesar, 2005 ; Coumou & Rahmstorf, 2012 ; Hartmann, Tank, & Rusticucci, 2013 ; Lima & Wethey, 2012 ; Meehl & Tebaldi, 2004 ; Seneviratne et al., 2012a ). Together, these two types of warming are resulting in the global redistribution of marine species (Burrows et al., 2011 ; Hobday et al., 2016 ; Poloczanska et al., 2013 ; Smale & Wernberg, 2013 ), altering the structure and functioning of entire ecosystems (Doney et al., 2012 ; Hawkins et al., 2009 ; Wernberg et al., 2016 ). In marine systems, organisms generally occupy the entirety of their “thermal niche” meaning marine populations are particularly responsive to warming (Sunday, Bates, & Dulvy, 2012 ). While local conditions can cause mosaic patterns of stress intensities in the intertidal (Helmuth et al., 2016 ; Lourenço et al., 2016 ), it is still range margins that are seen to be at the forefront of warming trends, resulting in poleward range expansions (leading edge) or contractions (trailing edge) (Chen, Hill, Ohlemüller, Roy, & Thomas, 2011 ; Hampe & Petit, 2005 ; Poloczanska et al., 2013 ). Understanding the physical determinants that set these boundaries is therefore critical for predicting the structure and functioning of ecosystems as climate change progresses (Gaston, 2003 ). Kelps dominate shallow temperate rocky reefs where they support high levels of primary productivity and modify environmental conditions such as light (Wernberg, Kendrick, & Toohey, 2005 ), water flow (Rosman, Koseff, Monismith, & Grover, 2007 ), physical disturbance (Connell, 2003 ) and sedimentation rates (Eckman, Duggins, & Sewell, 1989 ), allowing rich assemblages to persist (Steneck et al., 2002 ). The biogeographic ranges of kelp species are largely controlled by temperature (Eggert, 2012 ; Lüning & tom Dieck, 1990 ). As such, increases in mean temperatures and heatwaves can alter macroalgal distributions (Edwards & Estes, 2006 ; Filbee‐Dexter, Feehan, & Scheibling, 2016 ; Smale & Wernberg, 2013 ; Smale, Wernberg, Yunnie, & Vance, 2015 ; Tuya et al., 2012 ; Voerman, Llera, & Rico, 2013 ), which can have catastrophic consequences for the associated ecosystems they underpin (Wernberg et al., 2013 , 2016 ). The British Isles is an important Northeast Atlantic biogeographical transition zone (Forbes, 1853 ). Along Britain's south coast, two structurally similar canopy‐forming kelps occur in sympatry at opposing edges of their distributional ranges. Laminaria digitata, a boreal species, approaches its trailing edge while Laminaria ochroleuca, a Lusitanian species, reaches its leading edge (Bartsch, Vogt, Pehlke, & Hanelt, 2013 ; Smale et al., 2015 ). Both coexist on moderately sheltered to moderately exposed shores where their vertical distributions overlap. Laminaria digitata dominates the low intertidal (<1 m above Chart datum ) before being replaced by L. ochroleuca further down the shore (Hargrave, Foggo, Pessarrodona, & Smale, 2016 ). These species are currently undergoing opposing poleward range migrations, with L. digitata population declines observed along both sides of the English Channel, while L. ochroleuca has proliferated at its leading range edge (Raybaud et al., 2013 ; Smale et al., 2015 ). Laminaria digitata is a key ecosystem engineer (Schultze, Janke, Krüß, & Weidemann, 1990 ) and its loss could significantly impact wider community structure, unless another functionally similar species, such as L. ochroleuca, is able to move into the vacated habitat left behind. The environmental conditions a species can survive in (fundamental niche) are often wider than where it is found (realised niche). The reason for this disparity is often due to interspecific competitive exclusion (Hutchinson, 1957 , 1978 ). It is not known to what extent L. ochroleuca's vertical distribution on the shore is dictated by an inability to tolerate greater periods of low tide stress or through L. digitata's competitive dominance at higher tidal heights. Indeed, L. ochroleuca is found in much more varied habitats at lower latitudes that are currently occupied by L. digitata in Britain (e.g. tide pools & exposed coasts) (Pereira, Engelen, Pearson, Valero, & Serrão, 2015 ). The acquisition of such knowledge will allow for more accurate predictions of the structure and functioning of these communities in a future warmer world. Optimal growth and maximal survival temperatures of macroalgae are well studied (Bolton & Anderson, 1987 ; Bolton & Lüning, 1982 ; Hargrave et al., 2016 ; Lüning, 1984 ; Lüning & Freshwater, 1988 ; Orfanidis, 1991 ; Simonson, Metaxas, & Scheibling, 2015 ; Tom Dieck, 1992 ) but the effects of consecutive low tide exposures remain relatively unknown (but see Pereira et al., 2015 ). This is surprising as low tide stress has long been known to be important in determining the vertical and latitudinal distributions of intertidal organisms (Dring, 1982 ; Evans, 1948 ) and increases in aerial temperatures can cause shifts in macroalgae distributions (Harley & Paine, 2009 ; Harley et al., 2012 ; Martínez et al., 2012 ; Ugarte, Critchley, Serdynska, & Deveau, 2009 ). Here we examined the potential for L. ochroleuca to move into niches left behind by L. digitata as its biogeographic range contracts in response to warming. Firstly, we determined the thermal tolerance of both species by subjecting individuals to a single temperature shock using a traditional metric of stress, upregulation of Hsp70 across a range of temperatures. Secondly, we used chlorophyll florescence ( F \n v \n /F \n m \n ) to investigate the resistance and resilience of each species to consecutive low tide scenarios. Understanding tolerances to consecutive low tide stress of these two species will provide important insight into how climate change can alter range edge dynamics whilst also providing foresight into the future species composition of kelp‐dominated communities on Northeast Atlantic rocky shores.",
"discussion": "4 DISCUSSION 4.1 Interspecific difference in consecutive low tide tolerance The two species exhibited varied responses to consecutive immersed heat shocks with the warmer water species, L. ochroleuca, better equipped to deal with higher temperature treatments. At 24°C, a progressive erosion of resilience was seen in L. digitata that was not observed in L. ochroleuca . By the final treatment at 28°C, all L. digitata were non‐viable whereas L. ochroleuca were able to return to control values after 3 days of recovery. Interestingly, a similar lethal response at 28°C was not observed in L. digitata during emersed treatments which is likely a product of evaporative cooling reducing the tissue temperature or desiccation inducing an ametabolic state. Both species differed in their abilities to tolerate stress related to emersion. Most strikingly, L. ochroleuca showed no tolerance to even a single freezing exposure. Indeed, anecdotal evidence supports this with reports that over mild winters, L. ochroleuca progressively advances up the shore becoming more abundant at higher shore heights, until a severe ground frost results in rapid die off of in the intertidal (G. Boalch, pers. comm.). Laminaria ochroleuca was also less equipped to tolerate elevated aerial temperatures, which seemed related to the greater water loss experienced resulting in more severe desiccation. Laminaria digitata has been shown to recover from 60% water loss (Dring, 1982 ), which was also evident in this study. Whilst desiccation tolerance levels are unavailable for L. ochroleuca , it was where water loss fell below c . 60% that recovery was not possible. Climate change in the UK is predicted to result in less summer cloud, increased air temperatures and sunny days, while winter ground frosts will become more infrequent (Jenkins et al., 2009 ). This may result in L. ochroleuca's maximum tidal height switching from being cold limited in the winter to desiccation limited in the summer. Therefore, the habitat vacated by L. digitata may remain unoccupied by L. ochroleuca as conditions will still remain outside of its fundamental niche. However, it should be noted that conditions may change asymmetrically. Night temperatures are rising faster than day temperatures (Davy, Esau, Chernokulsky, Outten, & Zilitinkevich, 2017 ) so advancement up the shore may be possible at least for a period of time. One habitat where L. ochroleuca may be able to expand into is tidal pools. Tidal pools in SW England can already reach >28°C in summer (Martins et al., 2007 ), a temperature that results in considerable stress to L. digitata but not L. ochroleuca . Currently, only L. digitata occupies these habitats suggesting L. ochroleuca is being competitively excluded. As temperatures rise and L. digitata becomes less competitively dominant or vacates these tidal pools, L. ochroleuca will face few barriers to expanding its realised niche to encompass these habitats. 4.2 Comparing the heat shock response and F \n v / F \n m (single exposures) We observed a clear disparity between the HSR and F \n v \n /F \n m as metrics for stress. No interspecific differences in thermal set points of the HSR were found but differences were observed in F \n v \n /F \n m to similar single stress treatments. For example, L. digitata were able to recover from exposures that represent T \n peak and T \n off of the HSR, while L. ochroleuca showed no deviation away from control values. This is surprising given that T \n peak and T \n off are thought to represent the very upper limits of a species tolerance (Barua & Heckathorn, 2004 ). Mismatches between chlorophyll florescence and the HSR have been observed before (Jueterbock et al., 2014 ) and may be due to the photosynthetic apparatus having other protective mechanisms in place, allowing for greater thermotolerance compared to the rest of the cell (Downs, Mueller, Phillips, Fauth, & Woodley, 2000 ). For example, the small HSP, cp‐sHSP, is known to play an important role in protecting photosystem II during heat stress, and production levels are related to thermotolerance in higher plants (Neta‐Sharir, Isaacson, Lurie, & Weiss, 2005 ; Shakeel, Haq, Heckathorn, & Luthe, 2012 ). Chlorophyll florescence may also differ from Hsp quantification as it measures different aspects of heat stress and not just symptoms of protein damage. For example, stress induced uncoupling of enzymes and metabolic pathways can cause the accumulation of reactive oxygen species that can lead to oxidative stress affecting F \n v / F \n m values (Liu & Pang, 2010 ). Plants and seaweeds can upregulate genes with anti‐oxidative functions during periods of heat stress to protect the photosystems (Collén, Guisle‐Marsollier, Léger, & Boyen, 2007 ) and as such maintain photosynthetic function. Thus, different pressures and mechanisms may affect the two metrics differently and they should not be used interchangeably. Moreover, measuring the HSR alone is not sufficient to fully determine the levels of stress affecting macrophytes as they are likely to be more tolerant than levels of T \n peak , and even T \n off would suggest. 4.3 Single vs. consecutive exposures Intertidal stress studies have overwhelmingly focussed on absolute limits from single treatments with cumulative effects largely being ignored. We identified clear interspecific differences in tolerances to low tide scenarios that were only apparent after consecutive exposures. For example, if a single treatment had been conducted on L. digitata at 28°C, we would conclude that such temperature shocks would be non‐lethal as full recovery was attained within 24 hrs. Moreover, when comparing this to L. ochroleuca we may conclude they exhibit similar tolerances. However, after the final treatment, all L. digitata were non‐viable whereas L. ochroleuca had returned to near control values. Combined with data from the HSR indicating similar tolerances, measuring a single treatment would lead to potentially misleading interpretations. Environmental Niche Models (ENMs also called distribution, envelope and bioclimatic models) are the most widely used tools to predict the impact of climate change in species distributions. The majority of ENMs determine where a species can exist in the future based on the conditions they exist in now. However, in recent years there has been an attempt to base ENMs on mechanistic cause and effect relationships with environmental variables (Jordà, Marbà, & Duarte, 2012 ; Martínez, Arenas, Trilla, Viejo, & Carreño, 2015 ; Sunday et al., 2012 ). Modellers seek to link critical temperature thresholds (e.g. CT max and CT min ) with current and future warming scenarios. Here we show an erosion of resilience leading to mortality at temperatures lower than CT max with consecutive exposures. Therefore, such models may underestimate the effect of future warming on species distributions, with local extinctions potentially occurring at lower temperatures. This is especially true for intertidal and shallow subtidal species that experience discrete consecutive exposures on a daily basis or during a set of exposures that coincide with low spring tides. In such cases, smaller increases in temperatures or discrete “heatwave” events (Hobday et al., 2016 ), could result in consecutive exposures lower than CT max but great enough to result in considerable stress and subsequent mortality. Therefore, the effect of consecutive exposures on thermal tolerance should be factored into predictive models wherever possible."
} | 4,242 |
22042361 | PMC3227202 | pmc | 6,537 | {
"abstract": "Large scale electrophysiological recordings from neuronal ensembles offer the opportunity to investigate how the brain orchestrates the wide variety of behaviors from the spiking activity of its neurons. One of the most effective methods to monitor spiking activity from a large number of neurons in multiple local neuronal circuits simultaneously is by using silicon electrode arrays 1-3 . Action potentials produce large transmembrane voltage changes in the vicinity of cell somata. These output signals can be measured by placing a conductor in close proximity of a neuron. If there are many active (spiking) neurons in the vicinity of the tip, the electrode records combined signal from all of them, where contribution of a single neuron is weighted by its 'electrical distance'. Silicon probes are ideal recording electrodes to monitor multiple neurons because of a large number of recording sites (+64) and a small volume. Furthermore, multiple sites can be arranged over a distance of millimeters, thus allowing for the simultaneous recordings of neuronal activity in the various cortical layers or in multiple cortical columns (Fig. 1). Importantly, the geometrically precise distribution of the recording sites also allows for the determination of the spatial relationship of the isolated single neurons 4 . Here, we describe an acute, large-scale neuronal recording from the left and right forelimb somatosensory cortex simultaneously in an anesthetized rat with silicon probes (Fig. 2).",
"discussion": "Discussion This paper demonstrates how to use the silicon electrode arrays to record from large population of neurons (>100) in multiple cortical areas simultaneously. In order to be successful in surgery and recording, the following issues should be considered: Introduction of the probes to a desired area: When inserting the probes in brain tissue, it is possible to cause considerable damage. This can result in a low quality of recorded units. To avoid this problem we recommend the following: (i) Introduce the probes at a certain degree angle (recommended to use a 10 degree angle). By doing this, dendritic damage of the recorded neurons can be reduced; (ii) After the probes are inserted in the brain tissue, they are initially lowered at a faster rate (approximately 50 to 100 microns per 10-30 sec) until they get closer to the desired area of recording. When the probes are about 200 microns from the target area, the position is adjusted more slowly (approximately 10 microns every 2 or 3 minutes). Stabilization of recordings: When the probes are in the designated area and considerable activity is detected (distinctive spikes in the majority of the channels), it is recommended to wait approximately 30 minutes before starting a recording. This will allow brain tissue to mechanically stabilize after the insertion of probes and ensure a more stable recording. Brain pulsation: Occasionally it is possible to see brain pulsation that can significantly reduce the quality of a recording. A small craniotomy (just enough space to fit the probe) can reduce brain pulsation in a recording area. If necessary, the cisterna magna can be punctured. This reduces cerebrospinal fluid pressure and decreases swelling and pulsation. Silicon probe configuration: Silicon probes can have a variety of shapes and recording site configurations. For example, they can vary in the number of shanks, the length and thickness of shanks, and in the arrangement of recording sites (e.g. tetrode vs. linear configuration; see: www.neuronexustech.com). The choice of probe used with a specific configuration depends on the scientific question needing to be answered. For example, if the objective is to record populations of neurons in multiple locations in one specific layer (as presented in these recordings), the best choice is to use a probe with eight shanks and a tetrode configuration. This allows for recordings of multiple single units at each tetrode and sampling of eight locations across a 1.4mm span. In another instance, if someone would like to study activity propagations across cortical layers, the best choice would be to use a probe with one shank with regularly distributed recording sites along that shank which allows recording in multiple cortical layers simultaneously 14 ."
} | 1,072 |
35105804 | PMC8832967 | pmc | 6,539 | {
"abstract": "Significance In the microbial world, it is common for previously isolated communities to come in contact with one another. This phenomenon is known as community coalescence. Despite it being a key process in the assembly of microbial communities, little is known about the mechanisms that determine its outcomes. Here we present an experimental system that allowed us to study over 100 coalescence events between previously segregated microbiomes. Our results, predicted by a mathematical model, provide direct evidence of ecological coselection: the situation where members of a community recruit one another during coalescence. Our combined experimental and theoretical framework represents a powerful tool to predict the outcomes and interrogate the mechanisms of community coalescence.",
"conclusion": "Conclusions Understanding the mechanisms underlying the responses of microbial communities to invasions is an essential but poorly understood question in microbial ecology ( 10 ). Theory has suggested that communities may exhibit an emergent cohesiveness ( 11 , 12 , 18 , 19 ), leading to members of the same community recruiting one another during community–community invasions. Our results provide direct experimental evidence of ecological coselection in a large number of community coalescence experiments and highlight the critical role that may be played by the rarer, subdominant species in the generation of community cohesiveness. Our simulations suggest that the strength and direction of ecological coselection may be modulated by the underlying cross-feeding networks that shape the structure of communities in synthetic minimal environments ( 27 , 28 ). This idea is supported by the observation that our microbial consumer-resource model captures the trends observed experimentally when we enable a large variation in the metabolic fluxes across species. The model predicts a trade-off between the strength of bottom-up coselection and the ability of dominant–dominant pairwise competition to dictate coalescence outcomes, which we have confirmed experimentally. It also suggests that rarer taxa may play a more prominent role in coselecting dominant species when the cross-feeding interactions across community members are horizontal rather than hierarchical. Testing this theoretical prediction would require one to map the cross-feeding networks of all of our communities. Keeping track of every nutrient secreted by every species in coculture and by which species they are uptaken is still a low-throughput process that is both labor intensive and expensive, but recent progress in metabolomic tools promise to help us test this hypothesis in future work. Our findings, together with previous results in different systems ( 22 ) as well as theoretical predictions ( 11 , 18 – 21 ), suggest that collective interactions of microbes with one another and with the environment should be generically expected to produce ecological coselection during community coalescence.",
"discussion": "Results and Discussion We collected eight natural microbiomes from different soil and plant environmental samples ( Fig. 1 A ) and used them to inoculate eight identical habitats containing minimal media with either glutamine or citrate as the only supplied carbon source. We chose these two carbon sources because they are metabolized through different pathways in bacteria ( 31 , 32 ), and we hypothesize that communities assembled in either resource will be supported by cross-feeding networks of distinct sets of metabolites ( 27 , 28 ), thus leading to potentially variable degrees of community cohesiveness and coalescence outcomes ( 14 , 18 , 19 , 21 ). After inoculation, all communities were serially passaged for 12 transfers (84 generations), with an incubation time of 48 h and a dilution factor of 1:100. ( Fig. 1 B and Stabilization of Environmental Communities in Simple Synthetic Environments ). In previous work we have shown that under these conditions, 12 transfers allow communities to approach a state of “generational equilibrium,” where the community composition at the end of one batch of incubation will be the same as in consecutive incubations. We isolated the dominant species of every community ( Isolation of Dominant Species ) and identified them by Sanger sequencing their 16S rRNA gene ( Determination of Community Composition by 16S Sequencing ), which correctly matched the dominant exact sequence variant (ESV) ( 33 , 34 ) found through community-level 16S Illumina sequencing ( SI Appendix , Fig. S1 ). These dominants remained at high frequency after seven additional transfers with the exception of two of the citrate communities and one of the glutamine communities (where the dominants were presumably a transiently dominating species) that were excluded from further analysis ( SI Appendix , Fig. S1 ). Similarly, pairs of communities where the dominants shared a same 16S sequence and had similar colony morphology were excluded ( SI Appendix , Fig. S1 ). Top-Down Ecological Coselection. One form of cohesiveness may arise when the subdominant members of the community depend on the dominant species. This can occur, for instance, when the dominant provides resources (or stressors) that select for (or against) the subdominant taxa ( Fig. 1 E , Left ). If communities being coalesced exhibit positive cohesiveness from the top-down, the fate of the subdominant community members will be tied to that of their dominant: If a dominant gets excluded, its ecological partners will be likely to fall with it, whereas if the dominant thrives after coalescence, its subdominant partners will be likely to follow suit. In this scenario, we would expect the outcome of community coalescence to be predicted by which of the two dominants is most competitive in pairwise competition. Likewise, competition between dominants should be affected only weakly by the presence or absence of subdominant species, which would play a passive role under top-down coselection. To test this hypothesis, we performed all pairwise competitions between dominant species in either the glutamine or the citrate environments by mixing them 1:1 on their native media and propagating the cultures for seven serial transfers, roughly 42 generations ( Coalescence, Competition, and Invasion Experiments ). We then carried out all possible pairwise community coalescence experiments by mixing equal volumes of the communities and propagating the resulting cultures for seven extra transfers ( Fig. 1 F ). The frequencies of all species in both community–community and dominant–dominant competitions were determined by 16S Illumina sequencing ( Determination of Community Composition by 16S Sequencing ). To test the effects of top-down coselection at the community level, we quantified the distances between the primary communities and the final coalesced community using the relative Bray–Curtis similarity index ( Metrics of Community Distance ) and compared them to the outcomes of the pairwise competitions between dominants alone ( Fig. 2 A ). We noticed a difference between communities assembled in the glutamine and citrate environments: For the latter, the structure of the coalesced communities tends to be strongly dictated by the result of the dominant–dominant competition ( Fig. 2 B , Right ; R 2 = 0.57 , P < 10 − 4 , N = 22). For the former, the pairwise competitive ability of a dominant is only weakly predictive of the performance of its community in coalescence ( Fig. 2 B , Left ; R 2 = 0.15 , P < 0.05, N = 34). In both cases, the data are consistent with positive, rather than negative top-down coselection ( Fig. 2 A ). Alternative quantification of the distance between communities yielded similar results, with weaker effects when the metric used accounted only for the presence/absence of specific species and not for their relative abundance ( SI Appendix , Fig. S2 ). All these metrics include the presence of the dominant species themselves. To better disentangle the effect that these dominants have on the other members of their communities, we repeated the analysis this time excluding the dominant species from the compositional data, finding that our results still hold ( SI Appendix , Fig. S3 ). We then examined whether, as predicted by the top-down cohesiveness hypothesis, the subdominants would play a passive role in the competition between dominant species. We found that, for communities assembled in the citrate environments, the relative frequency of a dominant against another in head-to-head pairwise competition is highly predictive of its relative frequency against that same other dominant when the other species are present too, i.e., during community coalescence ( Fig. 2 C , blue dots; R 2 = 0.83 , P < 10 − 8 , N = 22). This is not the case for the glutamine communities ( Fig. 2 C , red dots; R 2 = 0.04 , P > 0.05, N = 34). This suggests that, in the glutamine environments, head-to-head competition between dominants is strongly affected by interactions between those dominants and the less abundant species of the communities. On the other hand, the subdominant taxa seem to play a more passive role in the citrate environments. Together, these observations indicate that communities stabilized with citrate as the primary supplied resource display a strong degree of top-down cohesiveness, with the fates of the subdominant species determined to a large extent by dominant–dominant pairwise competition. This competition is, in turn, only weakly affected by the presence of the subdominants. For glutamine communities, although some level of top-down coselection is consistent with our data, the subdominants do not appear to just be passively responding to their dominants but rather playing an active role in community coalescence. Fig. 2. Top-down coselection in microbial community coalescence. ( A ) Experimental setup and hypothesis. The relative similarity between the coalesced and the primary community A, denoted as Q , is quantified using the Bray–Curtis similarity ( BC ) index. We hypothesize that, if top-down positive coselection was strong, the dominant that is most competitive would coselect its ecological partners and therefore a positive correlation would be observed between Q and the frequency of the dominant A in pairwise competition. Alternatively, top-down negative coselection would result in a negative correlation as the most competitive dominant would antagonize its own ecological partners. We would see no correlation if none of these forms of top-down coselection were substantial. ( B ) Coalescence outcomes are predicted by the pairwise competition between dominants in our experiments. ( Left ) Red, glutamine communities, R 2 = 0.15 , P < 0.05. ( Right ) Blue, citrate communities, R 2 = 0.57 , P < 10 − 4 . Two biological replicates per experiment are plotted individually. ( C ) Pairwise competition of dominants in the presence or absence of the subdominant taxa of the primary communities. In the horizontal axis, we plot the frequency of the dominant of community A in head-to-head pairwise competition with the dominant of community B. In the vertical axis, we plot the same relative frequency when the two species compete in the presence of their ecological partners, i.e., during community coalescence. R 2 = 0.04 , P > 0.05 for glutamine (red) and R 2 = 0.83 , P < 10 − 8 for citrate (blue). ( D ) Simulations with a microbial consumer-resource model are able to capture these trends ( R 2 = 0.22 , P < 10 − 5 ). One may hypothesize that the strong signatures of top-down coselection observed in the citrate environments are simply due to variation in average fitness across inocula. Each of the communities in our study was started from a different natural microbiome ( Stabilization of Environmental Communities in Simple Synthetic Environments ), and it is possible that taxa sharing a same origin naturally exhibit correlated fitness in the synthetic environments. This could result in apparent top-down cohesiveness if said environment was just selecting for the taxa with the highest fitness in it: Species from “high-fitness inocula” would tend to be recruited together into the coalesced communities more often than species from “low-fitness inocula.” To test this hypothesis, we isolated multiple species from each community (the dominant and between one and four subdominants) and estimated their fitness in synthetic citrate media by allowing them to grow in monoculture and quantifying their average growth rate over the first 15 h ( Isolation of Subdominant Species and Fitness Estimation ). We found no correlation between the growth of a dominant and its subdominants by themselves ( SI Appendix , Fig. S4 ). In fact, multiple subdominants were not able to grow in monoculture, evidencing a need for facilitation from their ecological partners. These observations support the idea that the environment does not just select for taxa with high fitness in isolation: Instead, as the natural microbiomes stabilize in the synthetic media, a complex interplay between the species and their habitat is established, resulting in the modification of the environment and the assembly of dense cross-feeding networks where microbes can persist by utilizing the metabolic secretions of their partners (and not necessarily the externally supplied resource) ( 27 , 28 ). Therefore, even if inocula may exhibit differences in the average fitness of their taxa, these do not appear to be inherited by the communities once stabilized, ruling out the possibility that top-down coselection emerged from inoculum variation. Box 1. A microbial consumer-resource model for community coalescence The MicroCRM ( 27 , 29 , 30 ) is a modeling framework based on the classic MacArthur’s consumer resource model ( 47 ). It encodes the dynamics of a system with S species and M resources in terms of a consumer preference matrix c and a metabolic matrix D , with an additional set of parameters controlling the species maintenance costs ( m i for species i ), the resource energy densities ( w α for resource α ), the energy to growth rate conversion factor ( g i for species i ), and the leakage fraction, i.e., the amount of energy lost as byproducts when a resource is consumed ( l α for resource α ). The element c i α of the consumer preference matrix represents the uptake rate of resource α by species i (although the relationship between c i α and the uptake rate can be more complex in modeling scenarios that are not considered here) ( 27 , 29 , 30 ). In our simulations, the elements of c are sampled from a gamma distribution and weighed so that consumers are specialized (i.e., have higher uptake rates) in a particular resource type (details in Simulations ; SI Appendix ; and refs. 29 and 30 ). Experimental evidence suggests that individual species can secrete different sets of metabolites to the environment when growing on the same primary resource ( 28 , 37 , 38 ). Thus, we define D as a three-dimensional matrix where the element D i β α represents the energy flux in the form of resource β that is secreted by species i when it metabolizes resource α . Note that D i β α need not be equal to D j β α if i ≠ j (illustration below). In the simulations, the elements of D are sampled from a Dirichlet distribution to ensure normalization, ∑ β D i β α = 1 ( 29 , 30 ). Other parameter values are set to the defaults of the Community Simulator ( 30 ), provided in SI Appendix . \n \n The following equations describe the kinetics of the abundances of the i th species (denoted as N i ) and the α th resource (denoted as R α ): [1] d N i d t = g i N i [ ∑ α ( 1 − l α ) w α c i α R α − m i ] [2] d R α d t = − ∑ j N j c j α R α + ∑ j ∑ β N j c j β R β [ l β D j α β w β w α ] . These equations can take slightly different forms in certain cases, e.g., if the primary resource is supplied continuously instead of at the beginning of each growth cycle ( 29 , 30 ). They represent a good approximation for the community dynamics between consecutive serial dilutions in our setup. Here, we assembled in silico communities by randomly sampling a set of species from a pool, then integrating Eqs. 1 and 2 , diluting the final abundances, replenishing the primary resource, and repeating the process until generational equilibrium was achieved ( Simulations ). Coalescence simulations were carried out by mixing pairs of communities as described in the main text. Next, to investigate the determinants of top-down coselection and the factors modulating its strength, we ran a set of simulations of community coalescence. We used a MicroCRM ( 27 , 29 ) as implemented in the Community Simulator package for Python ( 30 ) (Box 1 ). We chose this modeling framework because communities assembled under our experimental conditions have been shown to be sustained by dense metabolic cross-feeding networks ( 27 , 28 ) for which the MicroCRM provides a good description. We and others have previously found a strong concordance between the behavior of laboratory and natural microbial communities and the behavior of the MicroCRM ( 27 , 29 , 30 , 35 , 36 ). To reproduce our experimental protocol in silico, we first generated a library of resources and two nonoverlapping pools of species. A collection of 100 communities was generated from each pool (200 total) by randomly choosing 50 species and allowing them to stabilize through 20 growth–dilution cycles. We then mixed these stable communities in pairs to simulate our coalescence and dominant–dominant competition experiments ( Simulations and SI Appendix ). We found that the MicroCRM simulations naturally exhibit the observed correlation between the head-to-head pairwise competition of dominants and the outcome of community coalescence ( Fig. 2 D ), further supporting the idea that top-down ecological coselection consistently emerges from metabolic interactions across species. Moreover, we found that top-down coselection is observed under a wide range of different simulation conditions and cross-feeding networks ( SI Appendix , Fig. S5 ), indicating that it is a robust phenomenon. Bottom-Up Coselection during Community Coalescence. Our data indicate that the primary resource supplied to the communities can modulate the effect that the subdominants have in the dominants pairwise competition ( Fig. 2 C ) and the strength of top-down coselection ( Fig. 2 B ). The fact that our model captures these trends suggests that this might be a result of the metabolic interactions between community members, including the less abundant species. To investigate the potential role of the subdominant taxa in coalescence, i.e., whether the dominants may be coselected for or against by them ( Fig. 1 E , Right ), we ran a new set of simulations this time invading one of the communities (henceforth the nonfocal) with the dominant of the other community (henceforth the focal) alone ( Simulations ). We compared the invasion success of the focal dominants in isolation with respect to our previous simulations where they invaded accompanied by their ecological partners. The invasion success of the dominants was quantified as their relative abundance in the final stabilized communities ( Fig. 3 A ). Whenever positive bottom-up ecological coselection is strong, we expect to see dominants reaching higher invasion success with their subdominant partners than by themselves, with the strongest instances occurring when dominants are unable to invade on their own but reach high densities when invading together with the other members of their communities ( Fig. 3 A , green shaded region). Alternatively, a high degree of bottom-up antagonism would result in dominants invading more effectively alone than in the presence of their ecological partners ( Fig. 3 A , red shaded region). Finally, if bottom-up coselection is weak, we would see a similar invasion success regardless of the presence or absence of the subdominant species ( Fig. 3 A , gray shaded region). Fig. 3. Trade-offs between bottom-up and top-down ecological coselections. ( A ) Experimental setup and hypothesis. We hypothesize that three scenarios are possible regarding bottom-up coselection: Subdominant species could coselect for (green) or against (red) their dominant in coalescence, which would result in the focal dominant reaching higher (positive bottom-up coselection) or lower (negative bottom-up coselection) abundances when accompanied by its ecological partners with respect to when invading alone. Alternatively, the subdominants could have no effect in the invasion success of the dominant taxa (no bottom-up coselection, gray). ( B ) Simulations with a microbial consumer-resource model: We plot the frequency reached by the focal dominants when invading the nonfocal communities in isolation versus the same frequency when invading together with their ecological partners, i.e., in community coalescence. Simulations show either weak (gray area) or strong positive (green area) bottom-up coselection, but negative bottom-up coselection is rare. ( C ) We divided the data from our simulations into two sets according to whether positive or no bottom-up coselection was observed (that is, whether points fell into the green or gray areas of B ). Here we reproduce the plots in Fig. 2 B for each set, representing the result of the dominant head-to-head pairwise competition versus the outcome of community coalescence. ( Left ) Strong positive bottom-up coselection ( R 2 = 0.00 , P > 0.05). ( Right ) No bottom-up coselection ( R 2 = 0.34 , P < 10 − 6 ). ( D ) Experiments show that in our conditions, positive bottom-up coselection is indeed more frequent and strong than negative bottom-up coselection (red and blue dots for glutamine and citrate, respectively). ( E ) We reproduce the plots in C for our experimental data; i.e., we recreate Fig. 2 B but this time splitting our data by the strength of bottom-up coselection instead of by the carbon source supplied to the communities. ( Left ) Strong positive bottom-up coselection ( R 2 = 0.07 , P > 0.05). ( Right ) No bottom-up coselection ( R 2 = 0.37 , P < 10 − 4 ). In our simulations of the MicroCRM, we found no instances of bottom-up antagonism but multiple such instances of positive bottom-up coselection as well as no (or weak) bottom-up coselection ( Fig. 3 B ). Many dominant members of our in silico communities could not invade another community on their own (or could do so only at very low final relative abundances, below 0.1) but were able to reach high frequencies when they were accompanied by their subdominant partners in community coalescence. Notably, this behavior was contingent on the metabolic matrix being sparse and different for different families (i.e., D i β α need not be equal to D j β α for any two species i and j and resources α and β ; Box 1 ), as experiments suggest is the case in natural settings ( 28 , 37 , 38 ) ( SI Appendix , Fig. S6 ). Thus, theory indicates that positive bottom-up coselection is frequent and potentially very strong, while negative bottom-up coselection is far more uncommon. Interestingly, our simulations suggest that strong bottom-up coselection should be observed only in communities where top-down coselection is weak, while top-down coselection is seen only when bottom-up coselection is weak. To better illustrate this prediction, we divided our simulations into two subsets: The first one was composed of the instances where positive bottom-up coselection was strong (i.e., dots in the green shaded region in Fig. 3 B ), and the second set included all other cases (dots near the diagonal in Fig. 3 B ). We reexamined our original simulations and found that when bottom-up positive coselection is strong, the pairwise competition of dominants is not predictive of coalescence outcomes ( Fig. 3 C , Left ; R 2 = 0.00 , P > 0.05, N = 21), indicating that top-down coselection is weak. At the same time, when considering only those coalesced communities in the diagonal in Fig. 3 B (where bottom-up coselection is weak), our model predicts that the fates of the subdominant community members after coalescence are more strongly determined by the head-to-head competition between dominants in isolation ( R 2 = 0.34 , P < 10 − 6 , N = 79 for instances where bottom-up coselection is weak [ Fig. 3 C , Right ]; R 2 = 0.22 , P < 10 − 5 , N = 100 when all instances are considered [ Fig. 2 D ] ). We then asked whether this behavior predicted by the model was also observed in our experimental communities. To address this question, we carried out a new round of experiments where we invaded the nonfocal communities with the dominants of the focal communities alone ( Coalescence, Competition, and Invasion Experiments ). After stabilization ( Stabilization of Environmental Communities in Simple Synthetic Environments ), we quantified species abundance through 16S Illumina sequencing ( Determination of Community Composition by 16S Sequencing ). Consistent with the behavior of our model, we observed that whenever bottom-up coselection is seen, it is always positive and we do not see any instances of antagonistic coselection ( Fig. 3 D ). Interestingly, bottom-up recruitment appears to be more frequent in the glutamine environments, where top-down coselection was weak, than in the citrate ones, where top-down coselection was strong ( Fig. 2 ). We then repeated our analysis in Fig. 3 C , this time splitting our data according to the observed strength of bottom-up coselection instead of the primary carbon source as we had done in Fig. 2 B . Our findings were in line with the model prediction: Pairwise competition between dominants is predictive of coalescence outcomes only if bottom-up coselection is weak ( Fig. 3 E ; \n R 2 = 0.07 , P > 0.05, N = 14 when bottom-up coselection is strong; R 2 = 0.37 , P < 10 − 4 , N = 42 when bottom-up coselection is weak). Once the bottom-up communities are removed, both the glutamine and citrate communities display similar degrees of top-down cohesiveness ( Fig. 3 E , Right ). This suggests that the main difference between citrate and glutamine habitats from the standpoint of community coalescence is that the latter is richer in communities exhibiting bottom-up cohesiveness than the former. When this difference is factored out, both behave similarly. Understanding the Mechanisms of Ecological Coselection: A Minimal Model of Community Coalescence. In view of the success of our model in reproducing the experimentally observed trends in ecological coselection, we set out to better understand the mechanisms for its emergence. In our experimental conditions and in the MicroCRM simulations, communities are sustained by dense cross-feeding facilitation networks. These networks can have a very vertical, top-down structure if a single species (the dominant) cross-feeds the less abundant members of the community but these do not cross-feed the dominant in return. Alternatively, if the dominant is strongly cross-fed by the less abundant species in the community, the network structure would be more horizontal. In the latter scenario, positive bottom-up coselection of a dominant can take place if cross-feeding from its ecological partners allows it to persist in the final community after coalescence—even if it cannot invade successfully in isolation. We found it useful to study a minimal model of community coalescence to test these ideas ( MinimalModel ). This model is composed of two communities (focal and nonfocal) with only two species each as illustrated in Fig. 4 A . Within each community, the dominant species ( s 1 and s 1 ′ , respectively) are able to utilize the single externally supplied resource ( R 1 ). They secrete a single byproduct ( R 2 and R 2 ′ , respectively) off which the subdominants ( s 2 and s 2 ′ , respectively) can feed. Finally, these subdominants secrete an additional resource ( R 3 and R 3 ′ , respectively). The dominants’ ability to utilize their subdominants’ metabolic byproducts determines whether the structure of the cross-feeding networks of these minimal communities is vertical (if the dominants cannot utilize the subdominants’ secretions and thus are not cross-fed by them) or horizontal (in the opposite scenario). The rates controlling how effectively the dominants can metabolize said byproducts modulate the direction of the cross-feeding networks ( Fig. 4 A ). The model is thus specified by four parameters: the uptake rate of the primary resource by the focal dominant, c 11 ; the uptake rate of the primary resource by the nonfocal dominant, c 11 ′ ; the uptake rate of the byproduct R 3 by the focal dominant, c 13 ; and the uptake rate of the byproduct R 3 ′ by the nonfocal dominant, c 13 ′ . Fig. 4. A minimal model of community coalescence. ( A ) Illustration of the model structure and parameters. The primary resource ( R 1 ) is replenished after each growth–dilution cycle (red arrows). Solid arrows indicate resource consumption, and dashed arrows represent resource secretion. ( B–E ) Coalescence outcomes in the minimal model under different relations of cohesiveness between the focal and nonfocal communities. We represent the relative Bray–Curtis similarity between the focal and the coalesced communities ( Q ) as a function of the relevant model parameters. For the specific representative cases indicated by the open circles, we also show Q as a function of the frequency of the focal dominant in pairwise competition with the nonfocal dominant, as well as the frequency of the focal dominant invading alone versus invading accompanied by its subdominant partner. In the limit case when the cross-feeding networks of both communities are strictly vertical (that is, the subdominants are passively sustained by the dominants but do not cross-feed them), but also different in the resources each secretes, it is straightforward that the outcome of community coalescence will depend on the competitive ability of the dominants to grow on the single externally supplied resource. The most competitive dominant will coselect its subdominant (i.e., top-down coselection) through the secretion of specific metabolic byproducts that it can consume (but that the subdominant of the other community cannot) as shown in Fig. 4 B . If the nonfocal community is maintained by a more horizontal cross-feeding network, it can display further resistance to invasion by the vertical focal community. In this case, even if the focal dominant is more competitive for the externally supplied resource than the nonfocal dominant, the nonfocal community could still dominate in coalescence due to cross-feeding from the subdominant favoring the dominant. The stronger the bottom-up metabolic flux from the nonfocal subdominant toward its dominant, the more prominent this effect can be ( Fig. 4 C ). The situation could become more interesting when the focal ( Fig. 4 D ) or both the focal and the nonfocal communities ( Fig. 4 E ) exhibit a horizontal cross-feeding network. In both of these scenarios, cross-feeding from the focal subdominant could favor the persistence of the focal community in coalescence even when the focal dominant is less competitive for the primary resource in head-to-head pairwise competition (and therefore cannot invade in isolation). In summary, thinking through our minimal model tells us that coalescence outcomes should be contingent on the direction of the cross-feeding networks sustaining the communities in this simple setting. To verify our intuitive reasoning, we ran simulations of all scenarios described above with our minimal model of community coalescence implemented in the MicroCRM framework ( Minimal Model and SI Appendix ). In line with our initial proposition, simulations indicate that bottom-up coselection of a dominant that is unable to invade by itself is possible if said dominant is strongly cross-fed by its ecological partner ( Fig. 4 ). Community Hierarchy Regulates the Strength of Bottom-Up Coselection. How do the ideas above scale to more complex and diverse communities? In natural microbiomes and in our laboratory cultures, a large number of species can coexist and cross-feed each other, giving rise to facilitation networks that are far denser than the ones in our minimal model. To generalize the intuition gained in Fig. 4 to communities with more than two species, we introduce a hierarchy index h that quantifies how vertical a cross-feeding network is: [3] h = Δ N dom R1 Δ N dom , where Δ N dom represents the overall increase in dominant biomass within a single batch incubation for a generationally stable community, and Δ N dom R1 represents the increase in said biomass resulting from the metabolism of the primary resource ( R 1 ) only. If the dominant was utilizing just the primary resource, the cross-feeding network would be very vertical ( h ∼ 1 ), whereas if it was growing mostly on the secretions of other taxa, it would be more horizontal ( h ∼ 0 ). We quantified the hierarchies of the communities in our MicroCRM simulations, finding that h follows a bimodal distribution ( Fig. 5 A ). We therefore divided our simulations into four groups according to whether the cross-feeding networks of both focal and nonfocal communities were vertical (“high h ” when h > 0.25) or horizontal (“low h ” when h < 0.25) as shown in Fig. 5 B . For each group, we evaluated the frequency of instances of bottom-up coselection, i.e., the fraction of cases where a dominant that could not invade in isolation was successful when accompanied by its ecological partners (green area in Fig. 3 B ). We found that bottom-up ecological coselection is significantly more frequent when the focal community is nonhierarchical ( Fig. 5 C ), in line with what the minimal model anticipated ( Fig. 4 ). Fig. 5. Community hierarchy modulates the recurrence of bottom-up coselection. ( A ) Distribution of community hierarchies for our in silico communities. ( B ) We divided our coalescence simulations into four groups according to the hierarchies of the focal ( h F ) and nonfocal ( h NF ) communities as indicated by the dashed boxes. For every group, we calculated the fraction of cases where bottom-up coselection was observed; i.e., the focal dominant was unsuccessful when invading in isolation but successful when invading with its ecological partners. ( C ) Bottom-up coselection of the focal dominant during coalescence is significantly more frequent when the focal community is nonhierarchical. Error bars representing 95% confidence intervals and P values were computed by bootstrapping (**** P < 10 − 4 where indicated). Conclusions Understanding the mechanisms underlying the responses of microbial communities to invasions is an essential but poorly understood question in microbial ecology ( 10 ). Theory has suggested that communities may exhibit an emergent cohesiveness ( 11 , 12 , 18 , 19 ), leading to members of the same community recruiting one another during community–community invasions. Our results provide direct experimental evidence of ecological coselection in a large number of community coalescence experiments and highlight the critical role that may be played by the rarer, subdominant species in the generation of community cohesiveness. Our simulations suggest that the strength and direction of ecological coselection may be modulated by the underlying cross-feeding networks that shape the structure of communities in synthetic minimal environments ( 27 , 28 ). This idea is supported by the observation that our microbial consumer-resource model captures the trends observed experimentally when we enable a large variation in the metabolic fluxes across species. The model predicts a trade-off between the strength of bottom-up coselection and the ability of dominant–dominant pairwise competition to dictate coalescence outcomes, which we have confirmed experimentally. It also suggests that rarer taxa may play a more prominent role in coselecting dominant species when the cross-feeding interactions across community members are horizontal rather than hierarchical. Testing this theoretical prediction would require one to map the cross-feeding networks of all of our communities. Keeping track of every nutrient secreted by every species in coculture and by which species they are uptaken is still a low-throughput process that is both labor intensive and expensive, but recent progress in metabolomic tools promise to help us test this hypothesis in future work. Our findings, together with previous results in different systems ( 22 ) as well as theoretical predictions ( 11 , 18 – 21 ), suggest that collective interactions of microbes with one another and with the environment should be generically expected to produce ecological coselection during community coalescence."
} | 9,278 |
36039297 | PMC9418442 | pmc | 6,540 | {
"abstract": "Summary In honey bee colonies, workers generally change tasks with age (from brood care, to nest work, to foraging). While these trends are well established, our understanding of how individuals distribute tasks during a day, and how individuals differ in their lifetime behavioral trajectories, is limited. Here, we use automated tracking to obtain long-term data on 4,100+ bees tracked continuously at 3 Hz, across an entire summer, and use behavioral metrics to compare behavior at different timescales. Considering single days, we describe how bees differ in space use, detection, and movement. Analyzing the behavior exhibited across their entire lives, we find consistent inter-individual differences in the movement characteristics of individuals. Bees also differ in how quickly they transition through behavioral space to ultimately become foragers, with fast-transitioning bees living the shortest lives. Our analysis framework provides a quantitative approach to describe individual behavioral variation within a colony from single days to entire lifetimes.",
"introduction": "Introduction Social insect colonies are comprised of individual organisms that form a cooperative entity to propagate their genes ( Seeley, 1989 ; Wilson and Sober, 1989 ; Smith and Szathmary, 1995 ). To survive, grow, and reproduce, a colony must navigate the same biotic and abiotic challenges as unicellular and multicellular organisms, but coordination must now occur at the level of individual workers ( Hölldobler and Wilson, 2009 ). Social insect colonies lack centralized control, but across the ants, bees, termites, and wasps, tasks are instead self-organized among workers, whether genetically, physiologically, spatially, or temporally ( Oster and Wilson, 1978 ; Seeley, 1982 ; Porter and Tschinkel, 1985 ; Jeanne, 1986 ; Jeanne et al., 1988 ; Frumhoff and Baker, 1988 ; Robinson et al., 1989 , 2009 ; Fewell and Page, 1993 ; O’Donnell and Jeanne, 1995 ; Gordon, 1996 ; Naug and Gadagkar, 1998 ; Beshers and Fewell, 2001 ; Oldroyd and Fewell, 2007 ; Jandt and Dornhaus, 2009 ; Mersch et al., 2013 ; Baudier et al., 2020 ). Understanding how individuals combine to form a collective provides insights into the evolutionary drivers of organization across biological scales ( Smith and Szathmary, 1995 ; Davidson et al., 2021 ). A key challenge for highly integrated collective systems, such as eusocial insects, is how to allocate tasks among the individual units. While a fixed allocation strategy may be efficient in stable environments, a flexible approach allows colonies to respond to changing conditions ( Gordon, 2014 , 2016 ). Responsive (and decentralized) changes in task allocation can arise, for example, from individuals with different response thresholds for task-specific stimuli ( Bonabeau et al., 1997 ), individuals selecting tasks based on current need or availability ( Tofts, 1993 ; Jeanne, 1996 ), state-dependent probabilities to switch or remain in a current task ( Gordon, 1999 ; Goldsby et al., 2012 ), age, developmental, or physiological task engagement ( Seeley, 1982 ; Robinson et al., 1989 ; O’Donnell and Jeanne, 1993 ; Cook et al., 2019 ), or a combination of these mechanisms ( Johnson, 2010 ). These mechanisms can also depend on the type of task: non-specialized tasks may be distributed widely among colony members, whereas tasks requiring certain physiological abilities may be restricted to specific individuals ( Johnson, 2003 ; Robinson et al., 2009 ). Across social insect species, how and when tasks are allocated among individuals represents a balance between robustness and flexibility in colony function ( Charbonneau and Dornhaus, 2015 ). In colonies of the Western honey bee Apis mellifera individuals perform different tasks according to multiple factors, including developmental state, genetics, and behavioral feedback mediated by social interactions ( Huang et al., 1994 ; Beshers and Fewell, 2001 ; Robinson, 2002 ; Grozinger et al., 2007 ; Johnson, 2008b ; Cook and Breed, 2013 ; Cook et al., 2019 ; Wild et al., 2021 ). This gives rise to a general tendency for young bees to care for brood in the center of the nest, middle-age bees to perform various tasks throughout the nest, and old bees to forage outside and advertise food sites with waggle dances on the dance floor ( Seeley, 1982 ). Within these general trends, individuals may switch between tasks, or perform multiple different tasks in a day; therefore, individual behavior is better described with “task-repertoires” — groups of tasks that are similar behaviorally and/or spatially ( Seeley, 1982 ; Johnson, 2010 ). Although task repertoires vary with age, an age-based categorization does not account for variation among individuals throughout their lives, or how previous social and/or environmental experiences may influence task allocation ( Jeanson and Weidenmüller, 2014 ; Beshers and Fewell, 2001 ; Wild et al., 2021 ). While previous studies have relied on human observation to assign behavior to individuals using ethograms (e.g. Lindauer (1952) ; Seeley (1982) ; Seeley and Kolmes (1991) ; Johnson (2003) ; Siegel et al. (2013) ; Smith et al. (2017) ; Perez and Johnson (2019) ), recent advances in automated tracking make it possible to extract behavioral metrics beyond the scope and scale of human observation (e.g. continuous location and instantaneous speed) ( Mersch et al., 2013 ; Crall et al., 2015 , 2018 ; Wario et al., 2015 ; Wild et al., 2018 ; Gernat et al., 2018 ; Jones et al., 2020 ; Richardson et al., 2021 ; Bozek et al., 2021 ). This allows one to move from general trends to detailed, long-term, quantification of behavior. The use of quantitative metrics to characterize behavior enables a data-driven approach to investigate the causes and consequences of individual variability and inter-individual differences across timescales. In this study, we present data and analyze the behavior of 4,100+ honey bees across 16 age-matched cohorts tracked within an observation hive for 50 + days throughout a summer (July–October 2018). We define an analysis framework using behavioral metrics calculated from the motion data that quantify bees’ space use, detection, and movement. We use this framework to examine behavioral variation among age-matched bees, as well as variation in the behavioral trajectories of individuals over lifetimes. This analysis framework enables a quantitative comparison of the behavior of thousands of individuals at different timescales.",
"discussion": "Discussion Using individual tracking data from 4,100+ honey bees, we calculated behavioral metrics from the motion data and defined an analysis framework to describe behavioral variation at different timescales. At the timescale of a single day, bees differed their space use, detections, and movement, as quantified by the behavioral metrics shown in Figure 3 . Although some behavioral patterns are more associated with older bees (e.g. behavioral day cluster 1), and others with younger bees (e.g. behavioral day cluster 3), we see considerable overlap in the age distributions associated with different behavioral days ( Figure 4 ). Looking at the entire lives of individuals, bees predominantly differed in their movement patterns (speed/dispersion; Life-PCA 1), and the age at which they transitioned to dance floor/outside activities (Life-PCA 2) ( Figure 5 ). We found that across entire lifetimes, some individuals exhibit consistently different movement characteristics—in particular, consistently higher (or lower) dispersion across nest areas over their entire lives ( Figure 5 ). Behavioral differences among individuals may enable eusocial insect colonies to be flexible in response to changing conditions, yet robust to the maintenance of other colony functions ( Jandt and Gordon, 2016 ; Garrison et al., 2018 ). Individual tracking of bumblebees has revealed consistent differences in movement activity ( Jandt and Dornhaus, 2009 ; Crall et al., 2018 )—in particular, in the overall spatial area occupied by an individual (i.e. dispersion). Other work has shown, for example, that bumblebees differ in thermoregulation response thresholds ( Jandt and Dornhaus, 2014 ), ants show consistent differences in exploratory behavior ( Maák et al., 2020 ), and honey bees differ in dance activity in response to the same food source ( George and Brockmann, 2019 ). It is important to note that the colony response is an emergent outcome of the many individuals, where each individual also adjusts their behavior in response to the behavior of others (e.g. Ulrich et al. (2021) ). In general, the distribution of individual behavioral traits within a eusocial insect colony is expected to affect colony function, because the colony is the reproductive unit that selection acts upon ( Jeanson and Weidenmüller, 2014 ; Jandt and Gordon, 2016 ). However, the effect of inter-individual variation may depend on the specific function. For example, while the effect of inter-individual differences in response thresholds on overall bumblebee colony thermoregulation behavior is unclear ( Jandt and Dornhaus, 2014 ), variation in body size among bumble bee workers in a colony has been linked to enhanced comb production ( Holland et al., 2021 ), and other work with ants has demonstrated that the distribution of individual traits affects colony foraging behavior ( Kolay et al., 2020 ). To understand the effects of inter-individual variation on colony performance, it is therefore important to consider both the specific colony function as well as the ecological context ( Gordon, 2016 ; Davidson et al., 2021 ). It is well known that there is a genetic basis for behavior in honey bees ( Calderone and Page, 1988 , Calderone and Page, 1991 ; Robinson et al., 1989 ; Page and Robinson, 1991 ; Fewell and Page, 1993 ; Junca et al., 2019 ; George et al., 2020 ), which likely also applies to lifetime behavior. The cohorts used in this study came from naturally mated colonies; each source colony has a different queen, and while some cohorts came from the same source colony (see Figure S5 ), workers in a given source colony also represent multiple different patrilines (queens mate with 12 ± 6 drones; ( Tarpy et al., 2004 )). To examine precisely the extent to which our results have a genetic basis, future work could compare behavior from single-drone inseminated queens, or use genomic sequencing to determine each worker’s patriline ( Junca et al., 2019 ). Patriline diversity is important for colony-level function ( Jones et al., 2004 ; Seeley and Tarpy, 2007 ; Mattila and Seeley, 2007 ; Mattila et al., 2012 )); whether a diversity in “bee-lives” (i.e. differences in movement characteristics and behavioral transitioning ages; Figure 5 ) contributes to colony function is unknown. In our analysis, we find that bees differ in both movement characteristics and the age at which they transition to spending time on the dance floor and outside of the nest ( Figure 5 and S4 ). Previous work has noted how age is not the only factor that determines task allocation and behavioral transitions; social interactions, colony state, and environmental conditions also play a role ( Beshers and Fewell, 2001 ; Johnson, 2010 ; Jeanson and Weidenmüller, 2014 ; Wild et al., 2021 ). While we see differences in space use with age, in our analysis of movement characteristics, we find that average speed tends to increase with age but dispersion does not ( Figure S2) . For example, while age explains 9.2% of the variance of all metrics together, age explains only 0.6% of the variance in dispersion. The amount explained by age is 14.4% for speed, and as much as 23.1% for median exit distance (see Table 2 ). We also note that precocious foraging, which is similar to the “early-to-transition” individuals that we observe, can be induced via hormone treatments ( Robinson et al., 1989 ), infection ( Woyciechowski and Moroń, 2009 ), colony demography ( Huang and Robinson, 1996 ), or even pesticide exposure ( Hesselbach et al., 2020) , but here we see that such individuals exist even in unmanipulated colonies, similar to the study by Wild et al., 2021 . In wasps, differences in the age at which individuals transition to different tasks have also been observed ( Jeanne et al., 1988 ). Across cohorts, individuals from cohort N did tend to show more early-to-transition behavior than bees in other cohorts ( Figure S5 ) but further experiments would be needed to show whether such differences are driven by genetic or environmental factors. Our study uses a large observation hive (3-frames; 7,252 cm 2 of surface area), which is larger and can house more bees than other studies using automated tracking of honey bees (e.g. Wild et al. (2020) ; Jones et al. (2020) ; Bozek et al. (2021) ; Wild et al. (2021) . It is possible that nest size influences task allocation or transition rates; for example, workers in smaller colonies may transition between tasks more frequently ( Jeanne, 1986 ; Dornhaus et al., 2012 )). The observation hive was designed to mimic natural conditions and provide sufficient space for spatially separated comb-use areas (e.g. a dance floor that does not overlap with brood). Still, it is smaller than a natural nest (mature natural nests can have 13,369 ± 1174 cm2 of comb surface area; Smith et al. (2016) ). We note that a systematic comparison of how nest structure influences behavior should consider not only size but also nest geometry (e.g. Pinter-Wollman (2015) ). Previous work has used ethograms to define categorical age-based labels such as nurses, middle-aged bees, and foragers ( Lindauer, 1952 ; Seeley, 1982 ; Seeley and Kolmes, 1991 ; Johnson, 2008a , b , 2010 ). While such labels have the advantage of being easy to interpret, manually assigning behavioral tasks has multiple disadvantages, including: limited reproducibility (ethogram interpretations depend on the observer), behavioral descriptions must fit into pre-defined categories, and scaling issues (tracking multiple bees simultaneously, or over long time-periods, can be infeasible). Although automated tracking methods address these issues, simple trajectory data may not always be of direct biological or functional relevance ( Krause et al., 2013 ). In the current study, for example, we incorporate maps of the nest structure to extract additional biological information for a given spatial positioning (e.g. the individual is located atop brood, versus on the dance floor). With honey bees, tasks are often location-specific, such that, for example, bees found on the brood area are typically doing brood care ( Seeley, 1982 ). However, using location to infer task is an assumption, and some tasks, such as fanning, may not be location-specific. This is an inherent tradeoff with high-throughput methods like automated tracking. An important area for future work is to compare and relate the results of automated tracking methods, to approaches that use ethograms to manually assign behavior and task repertoires (e.g. cell cleaning, fanning, and waggle dances) ( Lindauer, 1952 ; Seeley, 1982 ; Mattila et al., 2012 ; Smith et al., 2017 ; Perez and Johnson, 2019 ). Recent work has combined barcode tracking with supervised machine learning methods to automatically identify specific behavioral events ( Gernat et al., 2020 ; Jones et al., 2020 ). These approaches apply convolutional neural networks (CNNs) to video data to identify a specific behavior of interest (e.g. egg-laying), which can be associated with the known identities of tracked bees through the barcode positions. Gernat et al. (2020) trained their CNN to detect trophollaxis events, and Jones et al. (2020) to detect egg-laying events and when bees exited for outside trips. These are supervised methods which require training and specified behavior to identify, and thus have focused on a few types of behavioral events which could be reliably identified. Alternatively, recent work has combined general methods of pose estimation with barcode tracking and applied this to bumblebees ( Smith et al., 2022 ); such pose estimation data could be used with unsupervised methods in order to identify complex behavioral patterns without training or a-priori specification ( Berman et al., 2014 ; Graving and Couzin, 2020 ). In contrast to these approaches, which use smaller colonies and shorter tracking periods of 2–7 days ( Gernat et al., 2020 ; Jones et al., 2020 ; Smith et al., 2022 ), in this study, we extract only trajectory data from barcode tracking, which enables the analysis of thousands of bees during their entire lifetimes in a timespan of several months. Future work can merge these approaches or choose the methods most appropriate to specific biological questions, by combining aspects of supervised identification of behavioral events, unsupervised behavioral classification from pose estimation, and behavioral metrics calculated from trajectory data. Automated tracking makes it possible to obtain long-term datasets for thousands of individuals, making it possible to investigate individual variation at an unprecedented scale. Our long-term tracking results present a detailed picture of how individuals in a colony differ in their behavior from day-to-day and over entire lifetimes, and establish an analysis framework that can quantify these differences and how they may contribute to colony function. Limitations of the study In this study, we analyzed the data of thousands of honey bees tracked using barcodes in an observation hive over an entire summer. While we examine variation among behavioral days and across the lifetimes of individual bees, we note that the metrics used to quantify behavior are restricted to quantities that can be calculated from the trajectory data (see Figure 3 for behavioral metrics). As such, these metrics do not directly represent biologically relevant behavioral patterns, such as foraging, cell cleaning, or fanning, that are typically identified with manual observation. Future work could examine how the behavioral metrics calculated from trajectory data are correlated with such manual assignments of behavior. Although we examined the behavior of thousands of bees from multiple age-matched cohorts, our data are from a single observation hive over a single summer. Given that the colony had free access to forage outside, and that behavior can change with environmental factors, we can expect results to differ quantitatively from year-to-year. Nonetheless, we expect that observed qualitative trends would be similar for such a repeated experiment. Future work would be needed to test the repeatability and robustness of the observed trends, given the colony-level sample size."
} | 4,707 |
38337180 | PMC10939414 | pmc | 6,541 | {
"abstract": "Abstract Alpha-diversity indices are an essential tool for describing and comparing biodiversity. Microbial ecologists apply indices originally intended for, or adopted by, macroecology to address questions relating to taxonomy (conserved marker) and function (metagenome-based data). In this Perspective piece, I begin by discussing the nature and mathematical quirks important for interpreting routinely employed alpha-diversity indices. Secondly, I propose a metagenomic alpha-diversity index ( M D ) that measures the (dis)similarity of protein-encoding genes within a community. M D has defined limits, whereby a community comprised mostly of similar, poorly diverse protein-encoding genes pulls the index to the lower limit, while a community rich in divergent homologs and unique genes drives it toward the upper limit. With data acquired from an in silico and three in situ metagenome studies, I derive M D and typical alpha-diversity indices applied to taxonomic (ribosomal rRNA) and functional (all protein-encoding) genes, and discuss their relationships with each other. Not all alpha-diversity indices detect biological trends, and taxonomic does not necessarily follow functional biodiversity. Throughout, I explain that protein Richness and M D provide complementary and easily interpreted information, while probability-based indices do not. Finally, considerations regarding the unique nature of microbial metagenomic data and its relevance for describing functional biodiversity are discussed.",
"introduction": "Introduction As microbial ecologists, we are interested in how microorganisms shape the world around us. No taxon single-handedly drives any given biochemical process in isolation, and so when we wish to understand how a process functions, we must consider taxa at the scale of the communities they exist in. Ultimately, we seek to explain how each respective taxon within a community contributes toward (or hinders) a given process of interest, succinctly termed as the biodiversity-ecosystem function (BEF) relationship (Manning et al. 2018 ). Typically, successful enquiries consider three avenues of investigation in combination: (i) the biodiversity of a community (alpha-diversity); (ii) the composition of that community (beta-diversity); and (iii) what makes them differ (determined via differential abundances, biochemical analyses etc). For example, we may be interested in how antibiotics can inadvertently disrupt the typical function of the gut microbiome. After seven days of clindamycin application, many commensal Bacteroidota within an individual's gut are driven to extinction (decreased alpha-diversity), a small number of Bacteroidota taxa fill the newly unoccupied niches (shifted beta-diversity), because they are antibiotic-resistant (increased abundance of antibiotic-resistance genes) (Jernberg et al. 2007 ). Thus, we can describe the impact of antibiotics on the gut microbiome, understand the consequences of local extinction (i.e. severely reduced alpha-diversity), and even make some predictions. For example, we could expect that the long-term persistence (over two years) of relatively poorly diverse communities that host elevated antibiotic-resistance genes may increase the risk of resistant pathogens becoming established (Macfarlane 2014 ). This Perspective piece will focus on the first avenue of investigation, i.e. alpha-diversity, specifically within the context of deriving measures of functional biodiversity from metagenomic data. Issues regarding the application of historical alpha-diversity indices from macroecology will be discussed. Finally, a simple index to derive the biodiversity of a set of protein-encoding genes is proposed and its application demonstrated. The examples here show how alpha-diversity indices are an essential tool for explaining how communities respond to environmental stressors, changing conditions or develop over time. The value of these indices goes beyond acting as explanatory tools, though, and ultimately have the potential to act as quantitative predictors of (un)desirable ecosystem functions (Petchey and Gaston 2006 ), e.g. an x increase in rhizosphere functional biodiversity is associated with a y increase in plant growth. An abridged discussion of alpha-diversity indices Most alpha-diversity indices are univariate metrics that measure specific qualities of the ranked Species Abundance Distribution (SAD), which represents one of the fundamental means by which ecologists consider a community (McGill et al. 2007 ). Each index has its own nuances and quirks that must be taken into consideration when comparing between communities. The simplest and most intuitive is the Richness of taxa present ( T R ), which represents the sum of unique taxa within a community. It is expressed as the length of the SAD. This metric is unbounded and can, theoretically, vary from one to an infinite number of taxa. However, T R is the metric most sensitive to sampling depth (i.e. total number of observations per sample) and sampling scale (Gotelli and Colwell 2001 ). Care must therefore be taken when defining what is meant by a ‘community’ in the context of the experiment, e.g. the community of Prokaryotes living within a 2 mg soil aggregate versus all Prokaryotes present within 500 mg of a composite soil (Szoboszlay and Tebbe 2021 ). Clearly T R will be higher in the latter. In the same fashion, it makes little sense to compare T R between environmental samples of similar properties where sampling depth differs greatly, e.g. a sample with 10 3 amplicon sequences versus a sample with 10 5 . Evenness is a measure of equality between taxon abundances, and is reflective of the slope of the SAD. Take, for example, Simpson's Diversity index as a measure of Evenness, as: \n \\documentclass[12pt]{minimal}\n\\usepackage{amsmath}\n\\usepackage{wasysym}\n\\usepackage{amsfonts}\n\\usepackage{amssymb}\n\\usepackage{amsbsy}\n\\usepackage{upgreek}\n\\usepackage{mathrsfs}\n\\setlength{\\oddsidemargin}{-69pt}\n\\begin{document}\n\\begin{eqnarray*}\nD\\ = \\ 1\\ - \\ \\sum p_i^2\n\\end{eqnarray*}\\end{document} \n Where p i is the proportional abundance of the i th taxon. (Please note that this is a simplified expression of Simpson's original index (Simpson 1949 )). Specifically, D asks ‘If I choose two individuals from a community at random, what is the likelihood that these belong to different taxa?’ D is bound between 0 and 1–1/ T R , with a value approaching 0 indicating an uneven community where there is a high chance that the two selected individuals share the same taxonomy. It follows logically that a high probability of selecting individuals from the same taxon means the community has a low biodiversity. As D approaches 1–1/ T R , this represents a perfectly even community where all taxa are equally abundant and there is a high probability of randomly selecting individuals from distinct taxa. In microbial ecology, we could expect nutrient-rich environments dominated by a small number of fast-growing, copiotrophic taxa or environments subject to extremes in temperature, pH etc . to have a D approaching 0. Unlike T R , D is a proportional metric, and is therefore less biased by sampling depth than T R . Although D was conceived to handle communities of infinite population sizes (Simpson 1949 ) in practice, the T R of microbial communities is much greater than for plants or animals, and ‘large’ numbers can render D difficult to interpret (described further below). A third example that is widespread in microbial ecology is the Shannon diversity index, as: \n \\documentclass[12pt]{minimal}\n\\usepackage{amsmath}\n\\usepackage{wasysym}\n\\usepackage{amsfonts}\n\\usepackage{amssymb}\n\\usepackage{amsbsy}\n\\usepackage{upgreek}\n\\usepackage{mathrsfs}\n\\setlength{\\oddsidemargin}{-69pt}\n\\begin{document}\n\\begin{eqnarray*}\nH^{\\prime} = \\ - \\sum \\ln \\left( {{p}_i} \\right){p}_i\n\\end{eqnarray*}\\end{document} \n Where, as above, p i is the proportional abundance of the i th taxon. H′ was not originally intended for ecological applications, yet somewhat like D , it asks a probability-based question: ‘How likely is it that the next individual in a sequence belongs to the same taxon as the current individual?’ As H′ increases, it is less likely that these two individuals belong to the same taxon. Unlike D, H′ is not strictly proportional, and so it is relatively more sensitive to sampling constraints. Furthermore, H' acts in a highly non-linear fashion, meaning that it increases rapidly in poorly diverse communities and slowly in more complex communities. In regard to the SAD, H' functions as an intermediary between Richness and Evenness. As they measure different, but unified, aspects of the SAD, it is possible to derive them as extensions of each other (Hill 1973 ). With the advent of metagenomics, alpha-diversity indices were quickly applied to discrete counts of protein-encoding genes in order to quantify the functional biodiversity of communities. The Richness of protein-encoding genes ( P R ) can correlate strongly and positively with T R across temperature gradients (Ruhl et al. 2022 ), aridity gradients (Song et al. 2019 ), and with seasonality (Galand et al. 2018 ). Similarly, the H′ of protein-encoding genes can correlate positively with taxonomic-based H′ (Fierer et al. 2013 ). Alpha-diversity measures of protein-encoding genes have also been used as one of our three pillars of investigating ecosystem processes. Such studies support general concepts such as: functional potential of host-associated microbiomes change over host developmental life-stages (e.g. early versus late growth stages of Arabidopsis rhizosphere (Chaparro et al. 2014 )); greater functional potential conveys benefits for host physiology (e.g. corals become more resistant to bleaching (Cardenas et al. 2022 )); and increased functional potential is linked to higher rates of certain ecosystem processes (e.g. increased greenhouse gas emissions from peatlands (Pavia et al. 2023 )). Tracking alpha-diversity changes also shows that it is possible to restore lost functional potential in disturbed ecosystems, e.g. re-vegetation of deforested landscapes (Guo et al. 2018 ). These are fundamentally important basic questions toward understanding BEF relationships. However, sequencing data also allows us to consider underlying genetic relationships between taxa, e.g. alpha- and beta-diversity metrics that compare (dis)similarity between taxa that share a single conserved genetic marker (Faith 1992 , Lozupone et al. 2007 ), and we should therefore not feel limited to treating genes simply as discrete counts in a series, nor to only employ indices that ask fairly abstract probability-based questions. Imagine a forest Where every tree represents a unique protein-encoding gene, e.g. pyruvate kinase, ammonia mono-oxygenase, predicted but functionally unknown proteins, and so on. Some of these trees will have long branches that spread far from the trunk, ending in many individual leaves. These branch lengths represent the dissimilarity in the gene between taxa (the leaves of the branch) that encode for the same gene (at the end of different, but connected, branches). We could speculate that these are the most interesting trees in this forest as they represent homologous genes that share a common ancestor, yet have diverged over time, and while the protein's key function is shared, they may perform optimally under different niches, e.g. low-affinity versus high-affinity particulate methane mono-oxygenase. Other trees may be very large, yet ‘stumpy’ in terms of their branch lengths. These would represent highly-conserved homologous genes that are unlikely subject to (or direct contributors toward) niche differentiation between taxa, e.g. glutamate synthase. Some short trees are more akin to shrubs—these have relatively few leaves (i.e. fewer taxa in the community encode for these proteins), yet may still carry out key functions, e.g. nitrogenase. In this analogy, the genetic diversity inherent within certain communities will give rise to dense, broad-branched leafy forests whereas others will be more like an arid shrubland. This is not to say that the genetic diversity in the imaginary arid shrubland is unimportant for that given ecosystem (Shade 2017 ), but one can reasonably expect a greater potential for unique functionality under more variable conditions in the forest. Our aim is to quantify this in a meaningful manner. Let us ask ‘What is the biodiversity amongst a set of observed protein-encoding genes?’ We have a set number of observations ( N ) that could be entire coding sequences from a collection of genomes, or predicted protein-encoding genes from a metagenome, depending on what is being analysed. These are the leaves in our forest. There are also a set number of unique protein-encoding genes, the protein Richness ( P R ), to which N are distributed amongst, acting as the trees that support each leaf. Each protein-encoding gene (leaf) that belongs to a P (tree) also differs from the other leaves, calculated as a % of dissimilarity in sequence identity ( d ) (pair-wise branch length). Therefore, the biodiversity within the i th P is simply a ratio of the sum of pair-wise dissimilarities ( d i ) to the number of pair-wise combinations amongst the protein-encoding genes in the i th P ( c i ). This is summed to give the biodiversity across all P : \n \\documentclass[12pt]{minimal}\n\\usepackage{amsmath}\n\\usepackage{wasysym}\n\\usepackage{amsfonts}\n\\usepackage{amssymb}\n\\usepackage{amsbsy}\n\\usepackage{upgreek}\n\\usepackage{mathrsfs}\n\\setlength{\\oddsidemargin}{-69pt}\n\\begin{document}\n\\begin{eqnarray*}\n\\sum \\frac{{{d}_i}}{{{c}_i}}\n\\end{eqnarray*}\\end{document} \n It should be noted that this ratio is compatible with gene clustering algorithms that report pair-wise (dis)similarities between a representative gene and all others within the homolog, including a self-comparison, and therefore in these cases c i will always be at least 1 (see Supplementary Fig. S1 for a conceptual visualization of this). This simplistic ratio will, however, lose information from so-called Orphan proteins that are only detected once (i.e. singletons). The d i of a protein-encoding gene observed only once will be 0, and so it will not contribute to the biodiversity sum. As these Orphans are protein-encoding genes that may be rare (yet potentially interesting!) within the community, or our sequencing depth may simply not be deep enough to observe its homologs, we still wish to retain information from their detection. To save the Orphans, we adjust the biodiversity ratio as so: \n \\documentclass[12pt]{minimal}\n\\usepackage{amsmath}\n\\usepackage{wasysym}\n\\usepackage{amsfonts}\n\\usepackage{amssymb}\n\\usepackage{amsbsy}\n\\usepackage{upgreek}\n\\usepackage{mathrsfs}\n\\setlength{\\oddsidemargin}{-69pt}\n\\begin{document}\n\\begin{eqnarray*}\n\\sum 1 + \\ \\frac{{{d}_i}}{{{c}_i}}\n\\end{eqnarray*}\\end{document} \n Such a value is inherently tied to P R , however, and as described above, such alpha-diversity indices are sensitive to sampling depth and scale. To improve comparability between samples (i.e. communities) we weight the overall value by our total observations N . This has the added benefit of creating upper and lower boundaries on the index. Our metagenomic alpha-diversity index ( M D ) is thus: \n \\documentclass[12pt]{minimal}\n\\usepackage{amsmath}\n\\usepackage{wasysym}\n\\usepackage{amsfonts}\n\\usepackage{amssymb}\n\\usepackage{amsbsy}\n\\usepackage{upgreek}\n\\usepackage{mathrsfs}\n\\setlength{\\oddsidemargin}{-69pt}\n\\begin{document}\n\\begin{eqnarray*}\n{M}_D = \\ \\frac{1}{N}\\ \\sum \\left( {1 + \\ \\frac{{\\ {d}_i}}{{{c}_i}}} \\right)\n\\end{eqnarray*}\\end{document} \n \n M D increases for communities with diverse functional gene homologs associated with either completely unique and/or dissimilar protein-encoding genes. Conversely, communities dominated by protein-encoding genes that are highly similar will yield a low M D . Somewhat similar to D, M D is bound between a theoretical lower limit of no biodiversity among protein-encoding genes, 1/ N , and a theoretical upper limit of ‘perfect’ biodiversity where each protein-encoding gene is absolutely unique, 1 (please consult the supplementary material for a simplified mathematical proof). Let us consider a simple example. Imagine three in silico ‘communities’ as: (i) varying Escherichia coli strains; (ii) commensal host-associated human gut taxa ( Bacteroides thetaiotaomicron, Bacteroides fragilis, Faecalibacterium prausnitzii, Clostridium butyricum, Lactobacillus acidophilus, Bifidobacterium lactis ) (Newton et al. 2013 ); and (iii) a phototrophic biological soil crust (BSC) of free-living taxa ( Microcoleus vaginatus, Stenotrophomonas maltophila, Pelomonas saccharophila, Azotobacter beijerinckii, Lactiplantibacillus plantarum, Methylobacterium aerolatum ) (Couradeau et al. 2019 ) (Table 1 ; Table S1 for genome source information). Each community has six distinct taxa ( T R ). Amino acid sequences of protein-encoding genes among genomes, N , were clustered dependent on shared kmers, and pairwise dissimilarity between clustered homologs calculated, with MMSeqs2 (Steinegger and Söding 2017 ) (although other pairwise comparative methods could be employed, such as all-vs-all BLAST (Price et al. 2008 ) or mapping predicted protein-encoding genes back to custom databases (Galand et al. 2018 )). The lower cut-off E value of homologs clustered by MMSeqs2 was ca . 10 −4 , which equates to a false discovery rate of incorrectly assigning a protein-encoding gene to a group of homologs as roughly 10 −4 (Steinegger and Söding 2017 ). A minimum sequence identity cut-off was not imposed. P R , H′ and D were calculated from the proportional sizes of clustered homologs. The log 10 P dissimilarity and M D are also reported. The indices P R , H′ and M D show expected trends of E. coli < Human Gut < BSC. As mentioned above, D suffers from the ‘large’ numbers of P R here. Table 1. Alpha-diversity index comparisons within three simplistic, in silico communities. T R = taxonomic Richness; N = total number of protein-encoding genes compared within the in silico community; P R , H′, D , Log 10 P dissimilarity and M D = respectively as Richness, Shannon, Simpson Evenness, protein dissimilarity and metagenomic diversity indices derived from protein-encoding genes within the community. Community \n T R \n \n N \n \n P R \n \n H′ \n \n D \n Log 10 P dissimilarity \n M D \n \n E. coli \n 6 28 029 5 485 8.25 0.999 3.77 0.21 Human Gut 6 20 007 10 456 8.87 0.999 4.06 0.57 Biological Soil Crust 6 26 814 15 318 9.29 0.999 4.22 0.62 While P R , H′ and M D all indicate that BSC has the greatest functional potential, I argue that the value of M D lies in its interpretability. Rather than asking an abstract, probability-based question, it specifically asks how much diversity exists amongst the observed protein-encoding genes. It is immediately apparent from the M D approaching 0 that the protein-encoding genes in the E. coli group (0.21) are highly similar to each other relative to the gut and BSC groups, i.e. there is high redundancy, poor biodiversity and ultimately lower potential for varied functionality. As the theoretical upper limit of 1 indicates that every protein-encoding gene is absolutely unique, and the lower limit is effectively 0, the BSC M D of 0.62 indicates that most of the protein-encoding genes in this group are either divergent within/between, or are completely unique to, these six taxa. P R is also quite simple to interpret, e.g. there are 3 x more unique protein-encoding genes in the BSC group than the E. coli group. Indeed, while P R and M D provide distinct information, they have a complementary interpretation—the ca . 3 x more unique protein-encoding genes in BSC versus E. coli also equates to ca . 3 x more genetic dissimilarity amongst these genes. In isolation, though, P R cannot provide information regarding genetic dissimilarity and/or potential functional redundancy among the six taxa. For example, while the P R of the BSC group is ca . 50% greater than the gut taxa, M D is only marginally higher in the BSC group, and this implies a relatively greater overlap in general functionality amongst these six free-living taxa. In contrast, H′ seems to suggest that the functional biodiversity among six E. coli strains (8.25) is not that dissimilar from the two groups comprised of distinct prokaryotes (8.87 and 9.29). Due to the highly non-linear nature of H′ , one cannot interpret this difference as ca . 10% greater diversity in the BSC versus E. coli groups. H′ can only tell us that diversity in BSC is higher than E. coli . But what about metagenomes? The following three examples of measuring functional biodiversity are from metagenomes. For specific methods of how metagenomic data was processed, please refer to the Supplementary Methods . The first example considers changes in taxonomic and functional biodiversity across a steep temperature gradient within a geothermal hotspring (Ruhl et al. 2022 ). The original study found that both T R (as operational taxonomic units) and P R (as Pfam annotated protein-encoding genes) decreased as temperature increased. The bioinformatic approaches used here differed, e.g. all protein-encoding genes were analysed and not only those that could be assigned a functional annotation. Even so, the same strong trends unifying both taxonomic and functional biodiversity are clear (Fig. 1 ). Ruhl et al., concluded that the rapid decrease in taxonomic and functional biodiversity across the gradient was a consequence of heat-stress selecting for relatively simple communities of thermophilic taxa. Additionally, the thermophilic taxa were also predicted to have on average smaller genomes than mesophiles, further contributing to the decreased functional biodiversity. From the analyses performed here (Fig. 1 ), this strong temperature-dependent trend was apparent regardless of how taxonomic biodiversity ( T R , H′, D ) or functional biodiversity ( P R , H′, M D ) was considered. Regardless, comparing P R between the coldest and hottest communities, we see that the mesophilic community has ca . 10 000 more unique protein-encoding genes, equivalent to a ca . 20% increase in Richness. Similarly, M D shows that there is a ca . 15% increase in the genetic diversity with these additional 10 000 unique protein-encoding genes present. However, as above with the E. coli example, an increase in H' from 10.77 (hottest) to 10.98 (coldest), or roughly a 1% increase, does not tell us anything about the underlying relationship between temperature and functional diversity here, other than that mesophilic communities are more diverse than thermophilic. Figure 1. Example one, geothermal hotspring temperature biodiversity gradient. Both taxonomic (ribosomal rRNA gene) and functional (all protein-encoding genes) biodiversity decreases with increasing temperature as stress selects for few heat-adapted taxa with relatively limited functionality. Linear regression slopes for each index are shown ( n = 1 per temperature point). T R = taxonomic-marker derived Richness, H′ = Shannon, D = Simpson Evenness, P R = protein Richness, M D = metagenomic diversity derived from genetic (dis)similarity of all protein-encoding genes. Example number two comes from observations during a natural, annual event: how a dramatic increase in summer daylight hours gives rise to a bloom of life in the pelagic Arctic Ocean (Puente-Sanchez et al. 2022 ). Samples were taken in March, April, May and June. In early spring a 2 m thick ice-sheet covered the sea, no photosynthetically active radiation (PAR) could reach the pelagic communities and integrated chlorophyll a was < 2 mg m −2 . Over time as the season transitioned into summer, ice-melt was prolific, sea ice was breaking apart and sufficient PAR had led to > 200 mg m −2 integrated chlorophyll a . Neither taxonomic nor functional alpha-diversity indices were reported in this study, however the seasonal change was marked by strong compositional shifts, with photosynthetic Pro- and Eukaryotes, heterotrophic Bacteroidota and Pseudomonadota (formerly Proteobacteria ) blooming in summer, and a concurrent relative decrease in Thermoproteota (formerly Thaumarchaeota ), Planctomycetota and Verrucomicrobiota (Puente-Sanchez et al. 2022 ). Derivation of alpha-diversity indices here showed that overall taxonomic biodiversity decreased in June photosynthetic communities (Fig. 2 ). Despite a lower taxonomic biodiversity, the June communities had a greater functional biodiversity, likely driven by an enrichment in functional potential (and the great repertoire of associated genetic machinery) of photosynthetic microorganisms, e.g. Photosystem I, Photosystem II, carboxysome, Calvin-Benson-Bassham cycle etc . (Rubin et al. 2015 ). Previous studies of ecological succession in oceanic diatom blooms have also demonstrated that, while a relatively small subset of Bacteroidota and Pseudomonadota heterotrophs are enriched alongside photoautotrophs, these taxa possess diverse carbohydrate active enzymes and broad oligomer and monomer substrate preferences that target diatom and cyanobacterial exopolysaccharides (Teeling et al. 2012 , Zheng et al. 2019 ). It is therefore worth emphasising that trends in taxonomic and functional biodiversity are not necessarily linked—having more unique heterotrophs in March and April, as per the 16S rRNA gene, does not necessarily mean that their genomes host a greater diversity, or functional potential, of homologous protein-encoding genes relative to the photosynthetically-active community and its associated specialist heterotrophs. As with previous examples, P R and M D give complementary information—June communities have ca . 6 000 more unique protein-encoding genes with a ca . 7% greater genetic diversity amongst them. H′ , of course, can only tell us that the June communities are more functionally biodiverse than those in March. Figure 2. Example two, seasonal comparison of Arctic Ocean communities as they transition from spring into summer. Increased daylight during the summer month of June drives a decrease in taxonomic (ribosomal rRNA gene) biodiversity as communities become dominated by photosynthetic organisms and a subset of specialist heterotrophs. However, functional (all protein-encoding genes) biodiversity is greater in the photosynthetic communities. Results of significance testing with gamma-distributed general linear models are shown where April, May or June differed from March. n = 3 for March, n = 1 for April, n = 2 for May and n = 2 for June. (*) P < 0.05 (**) P = 0.001 (***) P < 0.001. T R = taxonomic-marker derived Richness, H ′ = Shannon, D = Simpson Evenness, P R = protein Richness, M D = metagenomic diversity derived from genetic (dis)similarity of all protein-encoding genes. The third and final example involves the successional development of soil microbial (and plant) communities after volcanic eruptions at the Llaima volcano in Chile (Hernández et al. 2020a , b ). Lava flow had essentially created new substrate for colonisation at distinct geographical sites around the volcano, allowing for a successional time gradient for comparisons across ca . 50, 250 and 350 years. At the time of sampling, the ‘early’ successional stage was colonised by lichen-prokaryote symbiotic communities, while the intermediate and latter stages were colonised by understory plants. Hernández et al. 2020a , b show that as soils developed, overall T R (as operational taxonomic units) increased, with the early stage strongly dominated by ‘simplistic’ communities of autotrophic archaeal ammonia oxidisers, Cyanobacteriota , nitrogen, hydrogen and carbon monoxide-fixing Chloroflexota that transitioned to the more ‘typical’ soil communities dominated by highly diverse heterotrophic Pseudomonadota, Acidobacteriota and Actinobacteriota . Here, a significant increase in T R was noted at the intermediate successional stage, however community Evenness ( D ) actually decreased by the late successional stage, as communities shifted from primarily autotrophic to heterotroph-dominated soil assemblages (Fig. 3 ). In terms of functional biodiversity, both P R and M D identified a decreased functional potential after 371 years of ecological succession. Therefore, while taxonomic alpha-diversity indices gave somewhat inconsistent results for overall biodiversity (increased Richness yet decreased Evenness) the functional genetic information showed a consistent trend in that functional biodiversity decreased as the niche-differentiated autotrophic communities were replaced by heterotrophs that shared relatively similar functions for nutrient acquisition and metabolism of plant-derived organic substrates. Figure 3. Example three, successional development of soil communities after volcanic eruptions. Taxonomic Richness peaks at a mid-successional stage. Taxonomic Evenness, protein Richness and genetic functional biodiversity as M D ultimately decrease as niche-differentiated autotrophic communities are replaced by soil organoheterotrophs after 371 years. Results of significance testing with gaussian-distributed general linear models are shown where mid or late successional stages differed from the earliest sampled stage. (*) P < 0.05 (**) P = 0.001 (***) P < 0.001. n = 3 per successional stage. T R = taxonomic-marker derived Richness, H ′ = Shannon, D = Simpson Evenness, P R = protein Richness, M D = metagenomic diversity derived from genetic (dis)similarity of all protein-encoding genes. Some technical considerations While M D seeks to measure the functional biodiversity of a community from a different angle (i.e. genetic dissimilarity) than pre-existing alpha-diversity indices, it remains constrained by data quality and processing. Larger contigs will improve gene prediction and clustering, potentially yielding ‘more accurate’ alpha-diversity indices, and so direct comparisons between M D values should share assembly software, parameters etc . Similarly, the parameters used to cluster protein-encoding genes as homologs must also be consistent (who should be considered as belonging to a leaf on the same tree?) as these cutoffs are essential to how the pair-wise genetic dissimilarities are calculated. Secondly, due to redundancy in the genetic code, amino acid sequences are better reflective of actual protein function than nucleic acid sequences (Wang et al. 2013 ). Therefore, I suggest that amino acid sequences should preferentially be analysed when the overall goal is to investigate meaningful relationships in how functional biodiversity may inform actual BEF relationships. Thirdly, while M D is less sensitive to N (i.e. total observations) than P R or H′ , it is not a perfectly proportional metric bounded between 0 and 1, and so rarefying or randomly subsampling to a shared N will improve comparability between samples within similar ecosystems. Interestingly, unlike P R and H′, M D actually has a negative relationship with increasing N , which shows that it is redundancy/high similarity between genes that primarily drives M D downwards. Thus, a sample with N = 2 M may be resequencing/re-observing the same genes over and over, which will lower M D relative to the same sample with N = 1 M. Finally, deriving M D from host-associated communities may prove tricky—any ‘contaminant’ host genetic material that is sequenced alongside its microbiota will affect how M D is calculated. While pre-existing pipelines remove human-associated genetic material (Uritskiy et al. 2018 ) this would not be sufficient for deriving M D from, for example, a root endophyte community. A future for microbial diversity metrics The suggested M D is by no means meant to replace pre-existing alpha-diversity metrics, nor will it be the last proposed metric. However, going into the future, the following points are worth considering. As most protein-encoding genes from environmental sources cannot currently be annotated (Nayfach et al. 2021 ), functional biodiversity studies should not be limited to only analysing the relatively small fraction of genes that can currently be annotated. Galand et al. ( 2018 ) demonstrate this point very well. There are many ways one can compare the (dis)similarity of protein-encoding genes without resorting to annotation, for example local alignment-based (Schloss and Handelsman 2008 ) or kmer-based techniques (Steinegger and Söding 2017 ). Furthermore, the leap between determining a relationship between functional biodiversity and measurable ecosystem function is vast (Petchey and Gaston 2006 ) and fraught with many conflicting concepts. When only a subset of taxa are active at any one time (Shi et al. 2011 ), and indeed a lot of environmental DNA comes from necromass (Carini et al. 2017 ) and a non-negligible amount of genetic material may represent ‘pseudo-genes’ (Goodhead and Darby 2015 ), it is understandable to question the usefulness of deriving an alpha-diversity index for functional biodiversity. Care must also be taken not to conflate concepts of ‘functional biodiversity’, i.e. as described throughout this Perspective piece, with ‘functional traits’ (Escalas et al. 2019 ), which are an emergent property from collections of specific genes and/or gene variants, e.g. methanogenesis, maximum growth rate, copiotrophic or stress tolerant life-strategies etc . (Krause et al. 2014 , Malik et al. 2020 , Westoby et al. 2021 ). Once again, alpha-diversity indices provide information on only one piece of our biological puzzles, and must be considered within a greater context and systems-based understanding when investigating why communities ‘are as they are and do what they do’. Here they serve a useful explanatory purpose as a quantifiable summary of functional potential, with the benefit of M D lying in its simple interpretability, comparability between communities as a bounded metric, and that it tries to more directly address the gap between microbial metagenomic information and the overall BEF. Despite the abovementioned hurdles that must be considered and/or overcome, sensible, quantitative metrics that explain BEF relationships are a worthy goal to strive toward as they have the potential to model and predict (un)desirable ecosystem functions in the world around us.",
"discussion": "An abridged discussion of alpha-diversity indices Most alpha-diversity indices are univariate metrics that measure specific qualities of the ranked Species Abundance Distribution (SAD), which represents one of the fundamental means by which ecologists consider a community (McGill et al. 2007 ). Each index has its own nuances and quirks that must be taken into consideration when comparing between communities. The simplest and most intuitive is the Richness of taxa present ( T R ), which represents the sum of unique taxa within a community. It is expressed as the length of the SAD. This metric is unbounded and can, theoretically, vary from one to an infinite number of taxa. However, T R is the metric most sensitive to sampling depth (i.e. total number of observations per sample) and sampling scale (Gotelli and Colwell 2001 ). Care must therefore be taken when defining what is meant by a ‘community’ in the context of the experiment, e.g. the community of Prokaryotes living within a 2 mg soil aggregate versus all Prokaryotes present within 500 mg of a composite soil (Szoboszlay and Tebbe 2021 ). Clearly T R will be higher in the latter. In the same fashion, it makes little sense to compare T R between environmental samples of similar properties where sampling depth differs greatly, e.g. a sample with 10 3 amplicon sequences versus a sample with 10 5 . Evenness is a measure of equality between taxon abundances, and is reflective of the slope of the SAD. Take, for example, Simpson's Diversity index as a measure of Evenness, as: \n \\documentclass[12pt]{minimal}\n\\usepackage{amsmath}\n\\usepackage{wasysym}\n\\usepackage{amsfonts}\n\\usepackage{amssymb}\n\\usepackage{amsbsy}\n\\usepackage{upgreek}\n\\usepackage{mathrsfs}\n\\setlength{\\oddsidemargin}{-69pt}\n\\begin{document}\n\\begin{eqnarray*}\nD\\ = \\ 1\\ - \\ \\sum p_i^2\n\\end{eqnarray*}\\end{document} \n Where p i is the proportional abundance of the i th taxon. (Please note that this is a simplified expression of Simpson's original index (Simpson 1949 )). Specifically, D asks ‘If I choose two individuals from a community at random, what is the likelihood that these belong to different taxa?’ D is bound between 0 and 1–1/ T R , with a value approaching 0 indicating an uneven community where there is a high chance that the two selected individuals share the same taxonomy. It follows logically that a high probability of selecting individuals from the same taxon means the community has a low biodiversity. As D approaches 1–1/ T R , this represents a perfectly even community where all taxa are equally abundant and there is a high probability of randomly selecting individuals from distinct taxa. In microbial ecology, we could expect nutrient-rich environments dominated by a small number of fast-growing, copiotrophic taxa or environments subject to extremes in temperature, pH etc . to have a D approaching 0. Unlike T R , D is a proportional metric, and is therefore less biased by sampling depth than T R . Although D was conceived to handle communities of infinite population sizes (Simpson 1949 ) in practice, the T R of microbial communities is much greater than for plants or animals, and ‘large’ numbers can render D difficult to interpret (described further below). A third example that is widespread in microbial ecology is the Shannon diversity index, as: \n \\documentclass[12pt]{minimal}\n\\usepackage{amsmath}\n\\usepackage{wasysym}\n\\usepackage{amsfonts}\n\\usepackage{amssymb}\n\\usepackage{amsbsy}\n\\usepackage{upgreek}\n\\usepackage{mathrsfs}\n\\setlength{\\oddsidemargin}{-69pt}\n\\begin{document}\n\\begin{eqnarray*}\nH^{\\prime} = \\ - \\sum \\ln \\left( {{p}_i} \\right){p}_i\n\\end{eqnarray*}\\end{document} \n Where, as above, p i is the proportional abundance of the i th taxon. H′ was not originally intended for ecological applications, yet somewhat like D , it asks a probability-based question: ‘How likely is it that the next individual in a sequence belongs to the same taxon as the current individual?’ As H′ increases, it is less likely that these two individuals belong to the same taxon. Unlike D, H′ is not strictly proportional, and so it is relatively more sensitive to sampling constraints. Furthermore, H' acts in a highly non-linear fashion, meaning that it increases rapidly in poorly diverse communities and slowly in more complex communities. In regard to the SAD, H' functions as an intermediary between Richness and Evenness. As they measure different, but unified, aspects of the SAD, it is possible to derive them as extensions of each other (Hill 1973 ). With the advent of metagenomics, alpha-diversity indices were quickly applied to discrete counts of protein-encoding genes in order to quantify the functional biodiversity of communities. The Richness of protein-encoding genes ( P R ) can correlate strongly and positively with T R across temperature gradients (Ruhl et al. 2022 ), aridity gradients (Song et al. 2019 ), and with seasonality (Galand et al. 2018 ). Similarly, the H′ of protein-encoding genes can correlate positively with taxonomic-based H′ (Fierer et al. 2013 ). Alpha-diversity measures of protein-encoding genes have also been used as one of our three pillars of investigating ecosystem processes. Such studies support general concepts such as: functional potential of host-associated microbiomes change over host developmental life-stages (e.g. early versus late growth stages of Arabidopsis rhizosphere (Chaparro et al. 2014 )); greater functional potential conveys benefits for host physiology (e.g. corals become more resistant to bleaching (Cardenas et al. 2022 )); and increased functional potential is linked to higher rates of certain ecosystem processes (e.g. increased greenhouse gas emissions from peatlands (Pavia et al. 2023 )). Tracking alpha-diversity changes also shows that it is possible to restore lost functional potential in disturbed ecosystems, e.g. re-vegetation of deforested landscapes (Guo et al. 2018 ). These are fundamentally important basic questions toward understanding BEF relationships. However, sequencing data also allows us to consider underlying genetic relationships between taxa, e.g. alpha- and beta-diversity metrics that compare (dis)similarity between taxa that share a single conserved genetic marker (Faith 1992 , Lozupone et al. 2007 ), and we should therefore not feel limited to treating genes simply as discrete counts in a series, nor to only employ indices that ask fairly abstract probability-based questions."
} | 10,331 |
34256809 | PMC8276468 | pmc | 6,542 | {
"abstract": "Background The full biosphere structure and functional exploration of the microbial communities of the Challenger Deep of the Mariana Trench, the deepest known hadal zone on Earth, lag far behind that of other marine realms. Results We adopt a deep metagenomics approach to investigate the microbiome in the sediment of Challenger Deep, Mariana Trench. We construct 178 metagenome-assembled genomes (MAGs) representing 26 phyla, 16 of which are reported from hadal sediment for the first time. Based on the MAGs, we find the microbial community functions are marked by enrichment and prevalence of mixotrophy and facultative anaerobic metabolism. The microeukaryotic community is found to be dominated by six fungal groups that are characterized for the first time in hadal sediment to possess the assimilatory and dissimilatory nitrate/sulfate reduction, and hydrogen sulfide oxidation pathways. By metaviromic analysis, we reveal novel hadal Caudovirales clades, distinctive virus-host interactions, and specialized auxiliary metabolic genes for modulating hosts’ nitrogen/sulfur metabolism. The hadal microbiome is further investigated by large-scale cultivation that cataloged 1070 bacterial and 19 fungal isolates from the Challenger Deep sediment, many of which are found to be new species specialized in the hadal habitat. Conclusion Our hadal MAGs and isolates increase the diversity of the Challenger Deep sediment microbial genomes and isolates present in the public. The deep metagenomics approach fills the knowledge gaps in structure and diversity of the hadal microbiome, and provides novel insight into the ecology and metabolism of eukaryotic and viral components in the deepest biosphere on earth. Supplementary Information The online version contains supplementary material available at 10.1186/s13059-021-02408-w.",
"conclusion": "Conclusions We adopted a twofold strategy to investigate the microbial community structure and functions in the Challenger Deep sediment biosphere. The deep metagenomics approach reconstructed 178 MAGs from the deepest habitat, and revealed the full biosphere structure and novel biodiversity, particularly enabling and extending our study to include microeukaryotic and viral components. The largest MAG set reconstructed for the hadal microbiome established versatile community functions marked by unexpected enrichment and prevalence of mixotrophy and facultative anaerobic metabolism. On the other hand, fungi as heterotrophs had broad metabolic potentials in carbon, nitrogen, and sulfur metabolism, particularly the capability of dissimilatory nitrate reduction and sulfate reduction that had not been reported for deep ocean microeukaryotes. These findings implicated the possible roles of hadal fungi in the biogeochemical processes of the hadal trench environment. The viral components evolved with new diversities and carried AMGs important for modulating hosts’ metabolic functions, which illustrated the differentiation of viral niche to microbial hosts in the hadal habitat. The large-scale cultivation approach obtained more than 2000 isolates from the Challenger Deep sediment, and cataloged 1070 bacteria and 19 fungi by 16S rRNA gene or ITS amplicon sequencing. Many of them were likely new species specialized in the Challenger Deep habitat and showed morphological variations under elevated pressure (100 MPa). These isolates represent a small fraction of the diversity in the Challenger Deep sediment habitat based on the deep metagenomic data. They would serve as model organisms and provide new opportunities to study and understand the physiology of piezotolerant microbes.",
"discussion": "Results and discussion Hadal sediment geochemistry and deep metagenomic sequencing Sediment samples were collected using two deep-sea hadal landers from the seafloor (depth of 10840 meters) at the Challenger Deep (142° 21.7806′ E, 11° 25.8493′ N). The sediment samples were dissected into three depth segments, i.e., the surficial segment MT-1 (sediment depth 0–5 cm), the mid-segment MT-2 (5–10 cm), and the deep segment MT-3 (10–14 cm). Geochemical measurements on the samples were taken either onboard the ship ZhangJian or inland laboratories using preserved samples. The contents of total organic carbon (TOC) and total nitrogen (TN) in the sediment were measured between 0.49 and 0.55 (wt%) and between 0.05 and 0.06 (wt%), respectively (Additional file 1 : Table S1). They are within the previously reported values for the sediment of the Challenger Deep [ 13 ]. The values of δ 13 C (− 21.41 to − 21.53‰) and δ 15 N (5.42 to 6.69‰) were within the ranges of the commonly observed values for marine organic matter [ 17 ]. This agrees with the results of recent studies that marine algae were the dominant source of sedimentary organic matter in the southern Mariana Trench [ 18 , 19 ]. For nutrient ions, while the porewater SO 4 2− concentrations were constant throughout the three segments, those of NH 4 + , PO 4 3− , and NO 2 − were trending upward with increasing sediment depth (Additional file 2 : Table S2). The NO 3 − concentrations, on the other hand, decreased with depth. The dissolved major and trace elements remained relatively homogenous with little variations among the three segments (Additional file 3 : Table S3 and S 4 ). To explore the full microbiome structure in the Challenger Deep sediment, we performed deep metagenomic sequencing on the sediment samples which was designed with enhanced sensitivity, to capture the genetic contents of microbes with low abundances. Note that we took steps to minimize the impact of sample temperature increase by swiftly processing and freezing sediment samples, preserving the big-picture characteristics of the microbiome for metagenomic analysis. Three or more independent Illumina libraries were generated for each segment, and on average 22.6 Gb sequence data were obtained from each library, generating a total of 248.65 Gb raw sequence data (Additional file 5 : Table S5). Metagenomic co-assembly was carried out using all the clean data, which resulted in a metagenome of 6.65 Gb in total length with ~ 6.21 million contigs and an N50 of ~ 1.14 kb. To reveal the compositions of the hadal sediment communities, taxonomic profiling analysis was performed on the metagenomics sequences using the kaiju program and the NCBI-nr library [ 20 , 21 ]. The relative sequence abundance for bacteria and archaea in the sediment accounted for 82.07% and 6.23%, respectively, whereas that for microeukaryotes and viruses was 0.69% and 0.12%, neither of which has been reported for the Challenger Deep sediment before. Different from previous studies based on rRNA gene PCR-tag sequencing, our approach estimated the sequence abundance values via the same workflow, generating scores directly comparable within the community scope. At the phylum level, the most abundant components were Proteobacteria, Chloroflexi, Actinobacteria, Thaumarchaeotathat, Planctomycetes, Firmicutes, Bacteroidetes, Gemmatimonadetes, Acidobacteria, and Gemmatimonadetes, belonging to either bacteria or archaea (Fig. 1 A). Our results agree with previous studies relying on 16S rRNA PCR-amplicon sequencing, in which Alphaproteobacteria, Chloroflexi, and Gemmatimonadetes were the most abundant bacteria, and Thaumarchaeota the most abundant archaea found in the Challenger Deep sediment habitat [ 16 , 22 ]. However, the relative abundance of Thaumarchaeota varied largely between the different studies of the Mariana Trench habitats, ranging from 0.67 to 67% [ 23 ] and from 0.5 to 40% [ 22 ]. In a non-Mariana system, the Yap Trench, while Thaumarchaeota was similarly enriched, the abundance of Chloroflexi, Actinobacteria, Planctomycetes, and Gemmatimonadetes in its sediment microbiome was significantly lower than that of Mariana Trench sediment habitats [ 24 ]. We found that the overall relative abundance of archaea decreased with sediment depth, similar to findings in a previous study [ 13 ]. However, the relative abundance of both the microeukaryotes and marine viruses increased with sediment depth. The most abundant eukaryotes were Opisthokonta (Fungi), Alveolata, Stramenopiles, and Rhodophyta. Note the top eukaryotic phyla, Ascomycota and Basidiomycota, ranked the 16th and 17th overall in sequence abundance (Fig. 1 A). We found MT-1 and MT-2 had consistent microbial compositions compared to those of MT-3, suggesting a geochemical boundary separated them at a 10-cm depth that was previously shown to limit oxygen access in hadal sediment [ 25 ]. Albeit in a relatively low abundance, the eukaryotic and viral components were unraveled for the first time in the Challenger Deep sediment, and as integral parts of the hadal biosphere, their ecological significance was further addressed below.\n Fig. 1 Composition and phylogenetic diversity of the microbial community in the Challenger Deep sediment habitat. A The relative sequence abundance of dominant microbial groups (top 30). B Phylogenetic analysis of the 178 MAGs based on 43 conserved single-copy, protein-coding marker genes using the maximum likelihood algorithm. Bootstrap values based on 1000 replications are shown for each branch. The scale bar represents 0.1 amino acid substitution per position Phylogenetic diversity of the hadal sediment microbiome Metagenomic binning on the assembled metagenome and filtering resulted in 178 quality draft genomes, i.e., MAGs (metagenome-assembled genomes) (Additional file 6 : Table S6). These MAGs were at or above the medium-quality standards for draft genome, which required > 50% completeness and < 10% contamination (“Materials and method”) [ 26 ]. Furthermore, among them, 101 draft genomes were > 70% completeness, and 22 were > 90% completeness. The MAGs represented the most diverse metagenome reconstructed from the hadal sediment biosphere, comprising members from 26 phyla or candidate phyla. Sixteen of them had MAGs found from the Changer Deep sediment for the first time, i.e., Gemmatimonadetes (8), Verrucomicrobia (8), Nitrospinae (5), Elusimicrobia (1), Ignavibacteriae (1), Calditrichaeota (4), candidate phyla including Zixibacteria (4), Ca. Hydrogenedentes (2), Ca. Dependentiae (1), Ca. Diapherotrites (1), Ca. Doudnabacteria (2), Ca. Latescibacteria (2), Ca. Omnitrophica (3), Ca. Marinimicrobia (2), and unclassified bacteria including Bacterium SM23_31 (2), Ca. Sumeriaeota (2) and a newly defined phylum UBP7_A (1) [ 27 ]. In comparison, previous studies reported eleven and thirty MAGs that were co-assembled by combing water and sediment microbial sequences from the Mariana Trench and were affiliated with three and twelve phyla, respectively [ 28 , 29 ]. Besides, using the single-cell sequencing approach, twelve single-cell amplified genomes (SAGs) were generated for Parcubacteria from the Mariana Trench sediment [ 30 ]. Note that these earlier works had fewer MAGs despite co-assembly by mixing sample data from multiple sources. Thus, the deep metagenomics approach has significantly enhanced the coverage and sensitivity of the hadal microbiome. To illustrate the taxonomic diversity of the hadal sediment microbes, a phylogenetic tree for the 178 MAGs was constructed (Fig. 1 B), using 43 conserved single-copy, protein-coding marker genes [ 31 , 32 ]. A substantial number of uncultured microbial lineages were uncovered and classified with the phylogenetic analysis. The main bacterial groups included Alphaproteobacteria (22 MAGs), Gammaproteobacteria (19), Chloroflexi (19), Planctomycetota (14), Bacteroidota (10), Actinobacteriota (10), Gemmatimonadetes (8), Verrucomicrobia (6), Nitrospinae (4), as well as Elusimicrobia (1), Ignavibacteriae (1), Nitrospirae (1), and Calditrichaeota (4). Notably, bin.97 formed a monophyletic clade in the phylogenetic tree and was distant from the PVC groups that include Planctomycetes (Fig. 1 B). It was closely related to Planctomycetales bacterium 4484_113 based on analysis using GTDB-Tk [ 33 ], for which the Average Nucleotide Identity (ANI) to bacterium 4484_113 stood at 63.86%. Bacterium 4484_113 was originally defined as unclassified Planctomycetes, but a recent study separated it from Planctomycetes to form a new phylum UBP7_A [ 27 ]. Thus, our study uncovered a likely second member of UBP7_A from the Challenger Deep sediment habitat. The main archaea groups included Ca. Woesearchaeota (3 MAGs), Thaumarchaeota (4), and Ca . Diapherotrites (1) (Fig. 1 B). Note that Ca. Diapherotrites, represented by bin.150, was found for the first time in the Challenger Deep sediment. Intriguingly, bin.150 was situated as an outgroup to both Thaumarchaeota (TACK group) and Woesearchaeota, and had a closer relationship with bacteria in the phylogenetic tree (Fig. 1 B). Based on analysis using GTDB-Tk, bin.150 was close to the unclassified Diapherotrites archaeon UBA493 that is affiliated to DPANN group. Its ANI to UBA493, the closest relative, was 72.19%. We placed bin.150 with additional members of Ca. Diapherotrites for phylogenetic analysis and found bin.150 formed a long-branch that represents a specialized Diapherotrites clade in the Challenger Deep sediment (Additional file 17 : Fig. S1). A previous study reported that as a member of Ca. Diapherotrites, Ca. Iainarchaeum acquired anabolic genes from bacteria via horizontal gene transfer [ 34 ]. The same reason may explain why bin.150 had a close relationship with bacteria in our phylogenetic results. So by reconstructing the largest metagenome of the hadal sediment biosphere, we recovered representative genomes of all major prokaryotic lineages previously identified by 16S rRNA gene amplicon-based surveys for the Challenger Deep sediment habitat [ 35 ], providing valuable references for us to further look into details of hadal microbiome regarding genetic diversity, metabolic functions, and symbiotic relationship. While our analyses were based on constructed MAGs, we acknowledged the missing pathway components that are likely attributed in part by the incomplete assembly, and the many assembled sequences of unknown molecular functions that are knowledge gaps to be bridged with new means [ 36 ]. Versatile metabolic function of the hadal sediment microbiome Heterotrophy vs. autotrophy To investigate the metabolic potential of each component, the 178 MAGs reconstructed from the Challenger Deep sediment microbiome were assigned with metabolic functions based on KEGG annotation. To determine microbes’ lifestyle, the MAGs were first investigated for heterotrophic potential and were found to contain genes involved in various pathways for degradation of carbohydrates (all MAGs), hydrocarbons (34 MAGs affiliated with 9 phyla), and aromatic compounds (146 MAGs affiliated with 24 phyla) (Fig. 2 and Additional file 7 : Table S7). It is likely that sinking particulates from the upper ocean or terrestrial inputs, partly due to the funneling effect and earthquake-inducing landslides, were the source of the organic matter in the deepest habitat [ 37 ].\n Fig. 2 Heat-map presentation of genomic features and metabolic potential for the 178 MAGs (with the taxonomic assignment) reconstructed for the Challenger Deep sediment microbiome. Key genes involved in carbohydrate degradation (CAYzme), CO 2 fixation, aerobic respiration, anaerobic respiration, and chemolithotrophy are illustrated (refer to Additional file 7 : Table S7 for details). Abbreviations: GH, glycosidases or glycosyl hydrolases; PL, polysaccharide lyases; CE, carbohydrate esterases; GT, glycosyltransferases; AA, auxiliary activities; CBM, carbohydrate-binding modules; WL, Wood-Ljungdahl pathway; CBB, Calvin-Benson-Bessham cycle; rTCA, reverse tricarboxylic acid cycle; 3-HP: 3-hydroxypropionate bi-cycle; 3-HP/4-HB cycle, 3-hydroxypropionate/4-hydroxybuty rate cycle On the other hand, we found 69 out of 178 MAGs (39%; 16 phyla) contained pathways for inorganic carbon fixation. Six different CO 2 fixation mechanisms were found to involve various microbes (Fig. 2 ). Among them, 3-hydroxypropionate bi-cycle (3-HP) was the most prevalent in 43 MAGs, compared to the Calvin-Benson-Bassham Cycle (CBB or Calvin cycle) (11 MAGs), Wood-Ljungdahl pathway (WL pathway) (6 MAGs), the reverse TCA cycle (rTCA) (2 MAGs), the 3 hydroxypropionate-4 hydroxybutyrate cycle (3-HP/4-HB cycle) (6 MAGs), and methanogenesis (16 MAGs). Previously, 3-HP was found in Chloroflexaceae, Alpha- and Gammaproteobacteria, and SAR202 [ 38 , 39 ]. In our study, the 43 MAGs detected to have the 3-HP pathway were taxonomically assigned to eight phyla (31%) (Fig. 2 and Additional file 7 : Table S7). They include the unclassified Chloroflexi bacterium RBG_16_64_32, Alpha-, Delta-, and Gammaproteobacteria, Nitrospinae, Acidobacteria, Actinobacteria, Calditrichaeota, Ca. Hydrogenedentes, and Gemmatimonadetes. For other CO 2 fixation pathways, the key enzymes in the Calvin Cycle, ribulose-bisphosphate carboxylase ( rbcS ), and phosphoribulokinase ( prkB ) were detected in five phyla (Fig. 2 and Additional file 7 : Table S7). The rTCA cycle biomarker Acl was present in one MAG for Nitrospira and one MAG for Ca. Woesearchaeota. Further, rTCA cycle was the only CO 2 fixation pathway employed by Nitrospira and Ca. Woesearchaeota. While nitrite-oxidizing Nitrospira was previously known to use rTCA cycle for CO 2 fixation [ 40 ], this is the first reported case that Ca. Woesearchaeota may be capable of rTCA reaction. 3-HP/4-HB cycle was the most energy-efficient aerobic autotrophic pathway for CO 2 fixation [ 41 ]. The biomarker enoyl-CoA hydratase/3-hydroxyacyl-CoA dehydrogenase involved in 3-HP/4-HB or DC/4-HB cycle was found in three bacterial groups, Nitrospinae, Acidobacteria, and Planctomycetes, and one archaea, Thaumarchaeota. The WL pathway biomarkers, i.e., acetyl-CoA decarbonylase/synthase complex subunit delta ( cdhD ), acetyl-CoA synthase ( acsB ), anaerobic carbon monoxide dehydrogenase ( cooS and cooF ), and 5-methyltetrahydrofolate corrinoid/iron-sulfur protein methyltransferase ( acsE ) were found in Bacteroidetes, Alphaproteobacteria, Ca. Sumerlaeota, Chloroflexi, Gammaproteobacteria, and Planctomycetes. Moreover, we found 16 MAGs associated with 7 phyla, i.e., Proteobacteria, Bacteroidetes, Chloroflexi, Gemmatimonadetes, Nitrospinae, Planctomycetes, and Thaumarchaeota, contained the Coenzyme M biosynthesis and methanogenesis genes (Fig. 2 and Additional file 7 : Table S7), indicating the hadal microorganisms had the potential to produce methane from fixing CO 2 in the hadal zone. Overall, among the 178 MAGs reconstructed for the hadal sediment microbiome, 69 MAGs (~ 39%) were affiliated with 16 phyla and mixotrophic based on their capacity of inorganic carbon fixation (Fig. 2 and Additional file 7 : Table S7). The enrichment and significant taxonomic expansion of mixotrophic microbes we revealed for the deepest hadal microbiome may indicate an adaptive niche that mixotrophy confers to microbes living in an oligotrophic habitat like the Challenger Deep sediment. Aerobic vs anaerobic respiration We investigated the hadal microbiome for its potential to carry out aerobic or anaerobic respiration. In total, 155 MAGs (~ 87%) were found to contain aerobic respiratory genes, such as Cytochrome c oxidases (Cox/Cyd/Qox/cco/Cyo). These MAGs are associated with 24 phyla, such as Acidobacteria, Bacteroidetes, Gemmatimonadetes, and Proteobacteria (Fig. 2 and Additional file 7 : Table S7). Therefore, a large majority of the microbes in the hadal habitat can potentially use oxygen as an electron acceptor for energy generation. On the other hand, anaerobic respiration pathways were also found to be widely distributed, for which nitrate/nitrite or sulfate was the alternative electron acceptors. Dissimilatory nitrate reduction to ammonia pathway (DNRA), denitrification pathway, and sulfate reduction pathways were searched and found in a total of 145 MAGs (81%) (Fig. 2 and Additional file 7 : Table S7). These MAGs covered 21 of the 26 phyla we identified in the hadal microbiome. Specifically, the genes for DNRA ( nirB and nirD as markers), denitrification pathway ( nirK and norC ), assimilatory sulfate reduction pathway ( sat , aprAB , and dsrAB ), and dissimilatory sulfate reduction pathway ( aprA- and aprB ) were present in 64 MAGs (15 phyla), 77 MAGs (16 phyla), 135 MAGs (20 phyla), and 67 MAGs (15 phyla), respectively (Fig. 2 and Additional file 7 : Table S7). Previous studies based on 16S rRNA gene sequencing analysis suggested that dissimilatory nitrate reduction and denitrification were the dominant reactions, whereas microbial sulfate reduction was negligible in the Challenger Deep sediment [ 13 ]. However, our analysis shows that the broad distribution of both assimilatory sulfate reduction and dissimilatory sulfate reduction points to the equal importance of sulfate reduction pathways for anaerobic respiration, if not otherwise more important. In addition, for microbes capable of anaerobic respiration, many were found to have the potential for different types of fermentation for degradation of organic matters (Additional file 17 : Analysis of fermentation; Additional file 7 : Table S7). The enrichment of anaerobic or anaerobic respiration metabolism in microbial community was apparently driven by the local habitat conditions. Examples of the deep petroleum seep sediments and Guaymas Basin hydrothermal sediments illustrated MAGs enriched for anaerobic respiration in the presence of hydrocarbons, i.e., alkanes and aromatic compounds, or depleted oxygen levels in sediments [ 31 , 42 ]. We further investigated the MAGs for the potential to carry out both aerobic and anaerobic respiration and found 136 MAGs out of 178 (~ 76%), affiliated with 20 phyla (Fig. 2 and Additional file 7 : Table S7), were capable of facultative anaerobic metabolism. Compared to the microbial communities in deep petroleum seeps or Guaymas Basin hydrothermal habitat, the high proportion of facultative anaerobes in the Challenger Deep sediment, reflects the microbial metabolic versatility and the ability to adapt by the hadal microbes to endemic habitats or environmental disturbance. Chemolithotrophy Microbes are known to acquire their energy for growth and CO2 fixation from the oxidation of inorganic compounds, such as hydrogen (H 2 ), hydrogen sulfide (H 2 S), ammonia (NH 3 ), carbon monoxide (CO), and metals [ 43 ]. The nitrification gene markers were found in 53 MAGs (30%, 17 phyla) of the Challenger Deep sediment microbiome (Fig. 2 and Additional file 7 : Table S7). The key enzyme in nitrite oxidation, nitrite oxidoreductase/nitrate reductase was present in 35 MAGs (20%; 13 phyla), suggesting that nitrification is an important approach for energy production for a substantial proportion of microbes in the hadal sediment biosphere. Other genes involved in nitrification pathways, PmoA-amoA , PmoB-amoB , and PmoC-amoC , were detected in 4 MAGs associated with 3 phyla (Additional file 7 : Table S7). Hydroxylamine dehydrogenase gene ( hao ) was detected in 21 MAGs (12%; 12 phyla). Interestingly, the gene, hao , was not found in bathypelagic microbes capable of nitrification [ 44 ], reflecting the difference in microbial mediators between the bathypelagic and hadal zones in similar biogeochemical process. The genes for CO oxidation, sulfide oxidation, H 2 oxidation, and iron oxidation were also detected in the Challenger Deep sediment microbiome. Carbon monoxide dehydrogenase (CODH; cox gene) was found in 92 MAGs (52%; 15 phyla) (Fig. 2 and Additional file 7 : Table S7), indicating its broad distribution, and the importance of CO-oxidation as an energy source in the hadal habitat. Sulfide oxidation genes such as sqr and fccB were found in 33 MAGs (19%; 8 phyla), i.e., Acidobacteria, Actinobacteria, Bacteroidetes, Chloroflexi, Gemmatimonadetes, Planctomycetes, Thaumarchaeota, and Proteobacteria. For iron oxidation, we detected cyc2 gene (encoding Cytochrome c) in one MAG associated with Gemmatimonadetes, suggesting that Fe 2+ can serve as an alternative electron donor for Gemmatimonadetes. Microeukaryotic community and predominant fungal groups Microbial eukaryotes in hadal sediment were the least studied and remained largely unknown. Our analysis found that Opisthokonta (Fungi) were the predominant eukaryotes in the Challenger Deep sediment, accounting for 87.42% of the total eukaryotic sequences. Ascomycota (41.73%), Basidiomycota (26.82%), and Mucoromycota (12.64%) were the top phyla, followed by Chlorophyta (6.61%), Rhodophyta (2.44%), Bacillariophyta (2.31%), Chytridiomycota (2.21%), Zoopagomycota (1.91%), Apicomplexa (0.96%), etc. (Fig. 3 A). Our results revealed the presence of Stramenopiles, e.g., Blastocladiomycota, and Alveolata, e.g., Apicomplexa, two members of the super-group SAR (i.e., Stramenopiles, Alveolata, and Rhizaria), in the hadal sediment, albeit at low abundances. Rhizaria was, however, notably absent. Previous studies showed that the abundance of Alveolata and Stramenopiles decreased with depth in the water column of the Mariana Trench, whereas Opisthokonta had an inverse trend [ 45 ]. Our study expanded the findings into the trench sediment, illustrating the co-existence pattern of Alveolata, Stramenopiles, and Opisthokonta.\n Fig. 3 Composition of microeukaryotic community, and metabolic functions of the dominant fungal groups in the Challenger Deep sediment habitat. A The relative sequence abundance of different eukaryotic groups within total eukaryotes. B Phylogenetic relationship of major fungal groups identified in the hadal habitat, and the profiles of a carbohydrate-active enzyme family (CAZymes) and peptidase family genes. The numbers of genes detected are denoted by shade intensity. Abbreviations: GH, glycosidases or glycosyl hydrolases; PL, polysaccharide lyases; CE, carbohydrate esterases; GT, glycosyltransferases; AA, auxiliary activities; CBM, carbohydrate-binding modules. C Metabolic potentials of carbon, nitrogen, sulfur, and iron metabolism shown for the six dominant fungal groups. The presence of genes within the metabolic pathways is denoted for each group by the fan areas with color for the corresponding phylum. Gene symbols and metabolites are labeled with the KEGG designation (refer to Additional file 1 : Table S8 for details). As the dominant eukaryotic group, fungi in the Challenger Deep sediment biosphere comprised six phyla, i.e., Zoopagomycota, Mucoromycota, Ascomycota, Basidiomycota, Chytridiomycota, and Blastocladiomycota, which can be further classified into twenty-seven classes (Fig. 3 B). Fungi were known to be distributed globally in deep-sea water, including that in the Mariana Trench, as Basidiomycota and Ascomycota were the primary components of the deep-sea fungal community [ 46 , 47 ]. Basidiomycota and Ustilaginomycetes were previously detected in deep-sea sediment using a cloning library method [ 48 ]. The capability of living in anoxia conditions may be crucial for fungi to adapt to the hadal sediment environments, as Ascomycetes, Basidiomycetes, and Chytridiomycetes were reportedly capable of fermentation and anaerobic growth in the deep ocean [ 49 ]. Metabolic functions of the fungal community in the hadal sediment biosphere The details of gene contents and metabolic functions of the fungal community in both the water column and deep-sea sediment remained largely unknown, as early studies were based on 18S rRNA gene/ ITS amplicon sequencing [ 45 , 47 ]. In the current work, we explored the metabolic potential of the fungal groups using the assembled metagenome. Carbon metabolism We investigated the potential of the hadal fungi to metabolize carbohydrates and peptides in the hadal sediment by looking into genes for carbohydrate-active enzymes (CAZYmes) and peptidases. In total, we detected 262 CAZYmes (88 family) and 703 peptidases (77 family) (Fig. 3 B and Additional file 8 : Table S8). Members of CAZyme included 79 GHs (glycosidases or glycosyl hydrolases), 8 PLs (polysaccharide lyases), 51 CEs (carbohydrate esterases), 83 GTs (glycosyltransferases), 14 AAs (auxiliary activities; associated with polysaccharide and lignin degradation), and 27 CBM (carbohydrate-binding modules). The major sources of CAZymes were Eurotiomycetes (number of genes n = 20), Agaricomycetes (n = 16), Sordariomycetes (n = 38), and Malasseziomycetes (n = 30). Particularly, key CAZyme genes encoding xylanase (GH10) were found in Agaricomycetes, and those encoding cellulase (GH5) were found in Agaricomycetes, Chytridiomycetes, and Microbotryomycetes (Additional file 8 : Table S8). Peptidase genes were abundant in Endogonomycetes (n = 124), Agaricomycetes (n = 84), Sordariomycetes (n = 70), and Malasseziomycetes (n = 67) (Fig. 3 B and Additional file 8 : Table S8). Six peptidase groups, i.e., Aspartic (A), Cysteine (C), Metallo (M), Mixed (P), Serine (S), and Threonine (T) peptidases, were found in the fungal genomes. Among them, the most abundant was Metallo peptidase, consisting of 230 members that belong to 30 families, followed by Cysteine (n = 225; 21 families), Serine (n = 168; 19 families), Threonine (n = 43; 3 families), and Aspartic peptidases (n = 14; 3 families) (Fig. 3 B and Additional file 8 : Table S8). So, the hadal fungi have broad potentials in organic carbon cycling, capable of degrading a variety of carbohydrate and peptide substrates. Nitrogen metabolism . The hadal sediment fungi were found to possess the complete pathways for dissimilatory nitrate reduction and assimilatory nitrate reduction, and the partial pathway for nitrogen denitrification, but lacked the ability for nitrification or anammox (Fig. 3 C). The key enzymes for nitrate reduction, namely NarGHI/NapAB and NirBD for dissimilatory pathway, and NR/NasAB and NIT-6 for assimilatory pathway, were found in several fungal groups, indicating dissimilatory nitrate reduction is an important function for the hadal fungi. Ammonia in hadal water could be generated from decomposition of nitrogenous organic matter [ 4 ]. However, it can be limited in hadal sediment. Therefore, the nitrate reduction by the sediment fungi can be an important source of ammonia for hadal sediment microbes. The partial pathway for nitrogen denitrification was also found in the hadal sediment fungi, missing the NosZ gene that coverts nitrous oxide to nitrogen. Downstream of the NarGHI/NapAB genes (shared with the dissimilatory nitrate reduction pathway), the NirK and NorBC genes completed the pathway to covert nitrite to nitric oxide, and nitric oxide to nitrous oxide, which may be released into the hadal sediment environment (Fig. 3 C). However, unlike the bacterial community, the fungal community appeared lacking the capability of nitrification, as some key enzymes were generally missing in nitrification and anammox pathways (Fig. 3 C). Notably, denitrification pathway enzymes were found in Fusarium, Penicillium, and Aspergillus from the Challenger Deep sediment (Additional file 8 : Table S8). Some Fusarium species in the deep-sea oxygen-limiting environments were reportedly capable of denitrification [ 50 ]. In other low-oxygen habitats, such as anaerobic marine sediment, salt-tolerant Penicillium and Aspergillus species were also identified to carry out the denitrification process [ 50 ]. These data illustrate the potential of the hadal fungi in nitrogen metabolism, possibly having a significant role in the ecological process in the hadal environment. Sulfur metabolism Sulfate reduction is one of the main anaerobic respiratory pathways that many microbes living in anaerobic conditions depend on. The hadal fungi in the Challenger Deep sediment were found to possess the complete pathways for assimilatory sulfate reduction, dissimilatory sulfate reduction, and sulfide oxidation (Fig. 3 C). They were also found to contain partial pathway for the SOX system, but were incomplete for oxidation of thiosulfate. The data suggest that hadal sediment fungi—like hadal bacteria, could take part in sedimentary sulfur cycle, which has not been reported for fungal communities in deep-sea sediment. The possession of complete sulfate reduction enzymes indicated the hadal fungi had the potential of using sulfate reduction for energy production when living under anaerobic conditions (Fig. 3 C). A high copy number of genes for sulfate reduction enzymes, like Sat, CysC, CysH , and CysJ , were detected in the hadal sediment fungi (Additional file 8 : Table S8). The sulfate adenylyltransferase gene ( sat ) for catalyzing the bidirectional reactions between APS and sulfate was widely distributed in members of different phyla, e.g., Ascomycota (4 classes), Basidiomycota (3 classes), Chytridiomycota (2 classes), and Mucoromycota (4 classes). In the sulfide oxidation pathway, oxidation of sulfide to sulfite was catalyzed by DsrA/B , whose genes were detected in several classes of Basidiomycota and Ascomycota, like Malasseziomycetes and Sordariomycetes. Subsequently, sulfite was oxidized to APS by adenylylsulfate reductases. Dothideomycetes (belonging to Ascomycota) contained Apr-A gene for sulfite oxidization to APS. Agaricomycetes (belonging to Basidiomycota) had the adenylylsulfate reductase gene (glutathione), denoted as APR , for the same function. Finally, APS was oxidized to sulfate by the sat -coding enzymes, which was found in Ascomycota, Basidiomycota, Chytridiomycota, and Mucoromycota. The genes encoding taurine dioxygenase ( tauD ) for converting taurine to sulfite were found in Basidiomycota and Ascomycota, like Agaricomycetes, Dothideomycetes, and Eurotiomycetes. The genes encoding alkanesulfonate monooxygenase ( ssuD ) for converting alkanesulfonate to sulfite were found in Leotiomycetes (belonging to Ascomycota). These enzymes would produce sulfite from an organic sulfur substrate, which in turn would be fed into the sulfur metabolic pathways. The hadal sediment fungi were also found to possess sox genes, indicating their potential for thiosulfate/sulfide oxidization. For example, Endogonomycetes of Mucoromycota contained soxC encoding sulfane dehydrogenase subunits for oxidizing thiosulfate to sulfate. Malasseziomycetes of Basidiomycota and Sordariomycetes of Ascomycota were found to have SQOR (eukaryotic sulfide quinone oxidoreductase) genes that are involved in the first step of hydrogen sulfide metabolism to produce sulfane sulfur metabolites. A previous study reported that fungi could feed sulfate to sulfate-reducing bacteria (SRB) [ 50 ]. Interestingly, we also found SRB, e.g., Desulfobacterales and Desulfuromonadales (within the class Deltaproteobacteria), and Nitrospirae (class) in the Challenger Deep sediment, which corroborates the evidence for the existence of sulfide oxidation reactions and the roles that fungi that play in sulfur cycling in the hadal sediment habitat. In addition, the hydrogen sulfide metabolisms possessed by the hadal sediment fungi would counter the accumulation of sulfide (H 2 S, HS − , and S 2− ) that was generated by SRB in the anaerobic environment. Thus, the discovery of the critical metabolic genes in sulfur metabolism implicated the important role of hadal fungal community in sulfur cycling and energy transformation in the trench environments. Metavirome in the Challenger Deep sediment habitat The existence and composition of viral community in the hadal sediment habitat were an open question remaining to be answered. Our deep metagenomics approach offered a new opportunity to look into the viral components for the deepest biosphere. Viral sequence reads and their taxonomic affiliations were determined using Kaiju by mapping to the viral references from NCBI-nr database. A total of fifteen major viral families were identified in the Challenger Deep sediment (Fig. 4 A). dsDNA viruses were found most frequently, which were mainly affiliated with the order Caudovirales, also known as the tailed bacteriophages [ 52 ]. The predominant family was Myoviridae, accounting for 24.62% of the total viral sequences, followed by Siphoviridae (19.70%) and Podoviridae (9.40%). Notably, we detected a relatively high abundance of giant viruses in the Challenger Deep sediment, such as Mimiviridae (4.16%) and Phycodnaviridae (1.06%) (Fig. 4 A, red star). Giant viruses were often missed or underestimated in early studies due to the technique used to capture viral particles via filtering [ 53 ]. Our approach is indifferent to the viral components of different particle sizes.\n Fig. 4 Diversity of metavirome, virus-host association, and viral genome annotation. A The relative abundance of dominant virus groups within metavirome. B Phylogenetic analysis of Caudovirales based on TerL using the maximum likelihood algorithm. Reference viral sequences from NCBI are colored in black. Scale bar, one amino acid substitution per site. The same tree with detailed labeling is provided in Fig. S3 (Additional file 17 : Fig. S3). C Visualization of the virus-host association network. Diamonds and circles denote marine viruses and microbial hosts, respectively. Association is summarized between a virus family and a microbe class, represented by a linked grey line. D Annotation of viral genomes. COG annotation was performed using eggNOG-mapper [ 51 ]. AMGs related to carbohydrate metabolism are illustrated in the inner panel To investigate the diversity and evolution among the members of Caudovirales found in the Challenger Deep sediment, phylogenetic analysis was performed on the viral contigs, using a marker gene coding the terminase large subunit (TerL) [ 54 ]. Interestingly, for the two families, Myoviridae and Siphoviridae, there were separate branche(s) forming within each of them (Fig. 4 B). They were tentatively named MT clade I and MT clade II for Myoviridae, and MT clade III for Siphoviridae. The new diversity of Caudovirales in the Challenger Deep sediment may reflect the differentiation of viral niche to microbial hosts living in the hadal habitat of high-hydrostatic pressure and oligotrophy. Virus-host association network To infer virus and microbial host interactions, we took the use of a combination of different methods as described [ 55 ]. Possible virus-host interactions were summarized for each viral family. As a result, members from 131 bacteria and archaea classes were associated with 20 viral families (Fig. 4 C and Additional file 8 : Table S9). The association network revealed many one-to-many relationships between viruses and hosts, and vice versa. Siphoviridae was connected with the largest number of hosts that belong to 95 classes. Notably, many of the hosts were uncultured microbes that were only inferred from their presence in metagenome sequences. Unlike in epipelagic and mesopelagic ocean waters, where the most frequent hosts were Cyanobacteria and Alphaproteobacteria (mainly SAR11 [ 52 ]), the most frequent hosts in the Challenger Deep sediment were Firmicutes (mainly Bacilli) and Bacteroidetes, followed by Euryarchaeota (including uncultured marine group II/III and Diaforarchaea), unclassified Chloroflexi, and Alphaproteobacteria. A previous study reported that the dsDNA virus T7virus (belonging to Podoviridae) could infect Deltaproteobacteria, Gammaproteobacteria, Alphaproteobacteria, Firmicutes, and Cyanobacteria [ 55 ]. Our results indicated that the T7virus from the Challenger Deep sediment was also associated with an archaeal host, e.g., the Diaforarchaea group of Euryarchaeota. Functional viromics To investigate the gene contents and functions of the hadal sediment viruses, we annotated their contigs using the Clusters of Orthologous Groups (COGs) database [ 56 ]. While a large proportion of their ORFs had unknown functions, high abundance genes were found in the COG categories of “replication, recombination and repair,” “transcription,” “signal transduction mechanism,” “cell wall/membrane/envelope biogenesis,” etc. (Fig. 4 D). Besides the genes for basic viral functions, a special group of virus-encoded genes can modulate the activities of the hosts upon infection. These so-called auxiliary metabolic genes (AMGs) were a means for viruses to manipulate host metabolism, like sulfur and nitrogen cycling. We identified 40 putative AMGs from the Challenger Deep sediment viruses, having roles in carbon, sulfur, or nitrogen metabolism (Additional file 10 : Table S10). Among them, auxiliary carbohydrate metabolic genes were the most frequent. By searching against the CAZymes database, 45 carbohydrate metabolism-related genes were identified, which included 31 GHs (glycosidases or glycosyl hydrolases), six PLs (polysaccharide lyases), five GTs (glycosyltransferases), two CBM (carbohydrate-binding modules), and one AA (auxiliary activities). The presence of frequent auxiliary carbohydrate metabolic genes provided evidence that the hadal sediment viruses may heavily manipulate carbohydrate metabolism of hosts, especially promoting carbohydrate degradation on hosts in an oligotrophic habitat. AMGs for carbohydrate metabolism were also the largest group in other marine virome dataset, like the Tara Oceans Viromes [ 55 ], which concentrated, however, on different pathways, like galactose metabolism and glycosyltransferases, reflecting the differentiated viral niche to hosts in dramatically different habitats. For nitrogen metabolism, NirK that encodes nitrite reductase [ 57 ] was found in the hadal viruses, suggesting possible roles to enhance host nitrite reduction pathway and release of nitric oxide into hadal sediment. For sulfur cycling, the CysN/NodQ gene encoding ATP sulphurylase [ 58 ] that reduces sulfate to produce 3'-phosphoadenosine-5'- phosphosulfate (PAPS), the first step of assimilatory sulfate reduction, was found in the hadal viruses. Notably, both NirK and CysN/NodQ are found for the first time among AMGs for ocean viruses. Large-scale isolation of microbes from the Challenger Deep sediment habitat To catalog and characterize microbes in the hadal sediment habitat, an intensive effort was made for the isolation of microbes from the Challenger Deep sediment. We adopted a protocol previously developed for the isolation of environmental microorganisms, which used serial dilutions of samples to allow the growth of “disadvantaged” microbes that otherwise would not be isolated [ 59 ]. To broaden the diversity of microbial isolates, we employed 24 different media and combined them with various culture conditions (Additional file 11 : Table S11). Notably, the experimental conditions would enable facultative anaerobes but not strict anaerobes. As a result, more than two thousand individual isolates were obtained, for which 1089 were completed for 16S rRNA gene (for prokaryotes) or ITS amplicon (for eukaryotes) sequencing (Additional file 12 : Table S12 and S13). Bacterial isolates The taxonomy of 1070 bacterial isolates was assigned based on 16S rRNA gene sequences using SILVA database (release 123) [ 60 ]. The bacterial isolates belonged to four phyla, i.e., Proteobacteria, Bacteroidetes, Actinobacteria, and Firmicutes, which were further categorized into 7 classes, 18 orders, 25 families, and 40 genera (Additional file 17 : Fig. S2 and Additional file 12 : Table S12). They matched the microbes represented by sequences in the metagenome. Halomonas, Pseudoalteromonas, and Idiomarina were the top genus, accounting for 32.77%, 22.23%, and 9.92% of the isolates, respectively (Fig. 5 A). Nineteen bacterial isolates from five classes were candidates for new species, which had a 16S rRNA gene sequence with ≤ 97% identity to their closest references (Additional file 14 : Table S14) [ 61 ].\n Fig. 5 Bacterial and fungal isolates from the Challenger Deep sediment habitat. A Phylogeny and percentage of bacterial and fungal isolates (Additional file 1 : Table S12 and S13). B Visualization of representative bacterial isolates from four different phyla. Cells were cultured under 0.1 and 100 MPa, respectively. Scale bars, 10 μM; DIC, images taken with a DIC microscope; DAPI, cells stained with DAPI and observed with an epifluorescence microscope. C Visualization of representative fungal isolates from nine genera. Scale bars, 30 μM; CW, cells stained with Calcofluor White and observed with an epifluorescence microscope. The phylogeny of fungal isolates is shown to the right. Red arrows indicate swollen hypha (MT19_19) or swollen conidia (MT20_3) We characterized representative isolates from each of the four phyla, i.e., MTFD_053 ( Zunongwangia sp.), MTFD_039 ( Microbacterium sp.), MTFD_075 ( Halomonas sp . ), MTFD_0511 ( Salipiger sp . ), MTFD_0323 ( Pseudoalteromonas sp), and MTFD_035 ( Paenibacillus sp.), which were visualized after incubation at elevated pressure (100 MPa) for 7 days. Some morphologic changes were observed with MTFD_039, MTFD_075, MTFD_0323, and MTFD_0511, when compared between their cells cultured at 0.1 and 100 MPa (Fig. 5 B). Cases were found that sister cells were connected after cell divisions under culture at elevated pressure. These isolates exemplified the small fraction of culturable microbes in contrast to the community background in the Challenger Deep sediment illustrated by the MAG data, which complemented the lack of taxonomic details from the metagenomic analysis. The results suggested that they were piezotolerant, which are likely derived from the microbes that descended from the water column, contributing to the diversity and metabolic functions of the hadal sediment microbiome. Microeukaryotic isolates Nineteen microbial eukaryotes (fungi) were isolated from the Challenger Deep sediment (Additional file 13 : Table S13 and Additional file 17 : Fig. S4). Their taxonomy was assigned based on their ITS sequences using the NCBI ITS database. They belonged to nine fungal genera, namely Aspergillus, Penicillium, Acremonium, Microascus, Exophiala, Cladosporium, Gymnoascus, Purpureocillium, and Aureobasidium. They matched the eukaryotic microbes represented by sequences in the metagenome. Representative isolates from each genus were further characterized after incubation at 0.1 and 100 MPa for 14 days. MT19_18 ( Aureobasidium sp.) tended to form long pseudomycelia under 100 MPa (Fig. 5 C). Some elongated cells contained multiple nuclei but did not have a diaphragm separating them. The filamentous fungi, i.e., MT20_2 ( Exophiala sp.), MT20_5 ( Aspergillus sp.), MT20_3 ( Penicillium sp.), MT19-20 ( Onygenaceae sp.), MT19_19 ( Gymnoascus sp.), MT19_17 ( Cladosporium sp.), MT20_1 ( Purpureocillium sp.), and MT20_8 ( Microascus sp.), have some morphologic variations induced by elevated pressure, such as hyphal swelling (MT19_19) and swollen conidia (MT20_3) (Fig. 5 C). Conidiophore appeared normal for MT19_17 under 100 MPa compared to that under 0.1 MPa. MT20_1 ( Purpureocillium sp.), MT19_19 ( Gymnoascus sp.), and MT20_8 ( Microascus sp.) may represent new species specialized in the Challenger Deep habitat, whose ITS sequences diverged from their closest known species (Additional file 13 : Table S13)."
} | 11,825 |
35076467 | PMC8788473 | pmc | 6,543 | {
"abstract": "The granular media friction pad (GMFP) inspired by the biological smooth attachment pads of cockroaches and grasshoppers employs passive jamming, to create high friction forces on a large variety of substrates. The granular medium inside the pad is encased by a flexible membrane which at contact formation greatly adapts to the substrate profile. Upon applying load, the granular medium undergoes the jamming transition and changes from fluid-like to solid-like properties. The jammed granular medium, in combination with the deformation of the encasing elastic membrane, results in high friction forces on a multitude of substrate topographies. Here we explore the effect of elasticity variation on the generation of friction by varying granular media filling quantity as well as membrane modulus and thickness. We systematically investigate contact area and robustness against substrate contamination, and we also determine friction coefficients for various loading forces and substrates. Depending on the substrate topography and loading forces, a low filling quantity and a thin, elastic membrane can be favorable, in order to generate the highest friction forces.",
"conclusion": "4. Conclusions To summarize, we investigated the stiffness dependence of the bioinspired granular media friction pad on the friction coefficient by varying filling quantity, membrane modulus, or membrane thickness. We could show that the amount of granular media inside the GMFP is extremely important for contact formation since an excessive filling quantity, hindering unjammed motion without load, greatly reduces adaptability to substrate asperities. However, the filling quantity has to be sufficient to be able to compensate for the highest substrate asperities of the substrate. At higher normal loads, the difference in filling quantity becomes less apparent and only facilitates friction on strongly contaminated substrates. Higher pressure inside the granular media due to high filling quantity and the resulting stretching of the membrane does not outweigh the loss in contact formation and resulting adhesion-mediated friction at all loading conditions. Membrane elasticity is shown to be an equally important criterion for maximizing friction. While the effect of changing the membrane modulus is less pronounced on both contact area and friction coefficient, although stiffer and more durable material is to be favored, a change in membrane thickness greatly modifies the properties of the GMFP. By using a thicker membrane, more energy is needed for the deformation during sliding. However, at low normal forces, on the structure and especially on the contaminated substrate, a soft and thin membrane results in much higher friction forces. Thus, an ideal GMFP’s filling quantity should be as low as possible, and its membrane should be the thinnest and most elastic while still retaining enough robustness for all conceivable loading conditions.",
"introduction": "1. Introduction For the maximization of friction forces on an unknown substrate, we recently introduced the granular media friction pad (GMFP) [ 1 ] that combines the advantages of an extremely soft, liquid-like material when coming into contact with the substrate with a rigidified material exhibiting high internal friction when pressed onto the substrate and sliding along the substrate. This is achieved employing a very thin elastic membrane that encases a loosely filled granular media which undergoes the jamming transition [ 2 , 3 ] when a load is applied by pressing the GMFP against the substrate. When coming into contact with a surface, the granular media behaves like a fluid [ 4 , 5 ] and flows around the substrate asperities (see Figure 1 a). After applying normal load, the granular media undergoes the jamming transition [ 2 , 3 ], thus creating high friction forces by mechanical interlocking as well as high internal friction of the granular media [ 6 , 7 , 8 , 9 , 10 , 11 ] (see Figure 1 b). Upon removing the normal load from the GMFP, the granular material returns to its fluid-like state and the GMFP can be removed from the substrate without requiring high pull-off forces. The thin flexible membrane encasing the granular media contributes to maximizing contact area for adhesion-mediated friction [ 12 , 13 , 14 ], as well as to energy dissipation during sliding due to the stretching of the membrane. The combination of normal force pressing the GMFP onto the substrate and the encasing membrane is sufficient for the GMFP to undergo the jamming transition. No active control mechanism, such as applying a vacuum to induce and control the jamming of the granular material [ 4 , 15 , 16 , 17 , 18 , 19 , 20 ] is needed. When not in contact with a substrate or under a static load, the function of the membrane is mostly to encase the granular media. During sliding, however, the membrane plays a vital role. The extent, to which the granular material can dissipate energy by jamming, and the dilatation, due to shear and normal load, are partly determined by the elasticity of the membrane resulting from membrane thickness and modulus. To characterize the friction properties of a system, often the static and the dynamic friction coefficients are used. These two properties describe the relationship between the normal force acting on the system and the resulting friction force from a shearing motion of two contacting bodies. However, it is equally important, (1) how the two bodies come into contact and (2) how the GMFP adapts to the substrate topography since this greatly influences the resulting contact area between the two bodies, the basic requirement for the friction generation in soft materials. What is more relevant for the generation of friction on any substrate and any loading condition: the sample being very soft, to increase adaptability and contact formation, or the sample, being very stiff to increase energy dissipation by the granular medium, as well as by the deformation of the membrane? The stiffness of the GMFP is determined by two factors, the granular media, and the encasing membrane. For granular media, detailed analyses on the jamming transition and interparticle friction depending on particle type and shape have been conducted [ 7 , 17 , 21 , 22 , 23 , 24 ], and knowledge about particle selection for maximizing friction exists. For membrane materials used in granular jamming systems, studies investigating the material’s influence on the behavior of the system have been conducted [ 25 ]. However, regardless of the individual components’ properties, frictional systems always have to be examined as a whole. The interplay of components and the resulting properties are as important as the components’ properties themselves. How does the filling quantity of the granular media influence the stiffness of the GMFP and the friction coefficient through an earlier onset of granular jamming when densely packed? How does the thickness or the elastic modulus of the membrane change the adaptability to substrate asperities or the amount of energy dissipation during stretching? In this paper, we will investigate the effect that changing the bioinspired GMFP’s stiffness has on its friction properties by examining the resulting contact area and the dynamic friction coefficients on three different types of substrates. We systematically varied the filling quantity and the stiffness D of the membrane, being D \n ~ \n Et 3 with the membrane’s elastic modulus E and its thickness t. The interplay between the elastic behavior of the membrane and the deformation of the granular medium was analyzed experimentally and numerically in the first publication, where details of the functional mechanism were studied [ 1 ]. In the present paper, we focus more on the applied aspects of the GMFP. The idea was to test, how this system behaves in a real-world situation and how good is the chance for using this system in real applications. That is why three extreme scenarios of substrates were chosen: smooth, rough, and covered with loosened particles. The main goal of this paper is a further exploration of functional mechanisms and adaptation of previous research to the case of real application, for example as a foot for walking robot that should function well on the wide range of challenging substrates.",
"discussion": "3. Results and Discussion To investigate the effect of stiffness on the bioinspired granular media friction pad for contact formation, as well as for friction coefficient, we varied filling quantity, membrane modulus, and membrane thickness individually. To be able to both see the maximum contact area on a smooth substrate as well as demonstrate the GMFP’s ability to adapt to large substrate asperities, we visualized the contact area of the GMFP in static contact with a glass substrate. A 2.5 mm diameter glass sphere, which resembles the contamination of the gravel particles of 1–2 mm particle size for the dynamic friction experiments, was put between GMFP and substrate, and the pad was loaded with eight different normal loads. The contact area lights up by total internal reflection. In the resulting pictures, the glass sphere is marked by a red dot. The dynamic friction coefficient was measured by pulling the sample over all three substrates (smooth, structured, and contaminated) at four different normal loads. 3.1. Filling Quantity By varying the filling quantity, we can change the elasticity of the GMFP. With less granular media inside, the GMFP becomes floppy and can easily adapt to the substrate. When the GMFP is filled more densely, an increase in pressure inside the granular media due to the stretching of the encasing membrane could result in higher energy dissipation from an earlier jamming transition at lower normal loads. While previously [ 1 ] the granular pad was filled with 1.7 g of ground coffee, we now examine four different capacities ranging from 1 g up to 3.7 g of granular material. The resulting static contact areas for the different filling capacities can be seen in Figure 2 a. For higher filling capacities, higher normal loads are required for the GMFP to form around the glass sphere and to get into contact with the glass substrate. At high normal loads, all filling capacities achieve large contact areas with the substrate despite the glass sphere. However, the largest contact area with the substrate was still achieved on the less filled GMFPs with 1 g and 1.7 g granular media inside. The GMFP with the lowest filling quantity is only just about as high as the 2.5 mm glass sphere between sample and substrate. Thus, under all loading conditions, the glass sphere rests directly onto the sample holder, transferring most of the normal load. However, because of the low filling quantity, the nearly unstressed membrane can still achieve large contact areas with the glass substrate at low normal loads. For the 1.7 g filling quantity, the sample is large enough that normal loads are mostly transferred through the granular media, resulting in the largest contact areas overall. The dynamic friction coefficients on the three different substrates can be seen in Figure 2 b–d. For all filling capacities, the highest friction coefficient was measured on the smooth clean substrate (see Figure 2 b). Due to the smooth substrate, contact area maximizes and adhesion-mediated friction strongly contributes to the occurring friction forces, resulting in very large friction coefficients at low normal loads [ 13 , 26 ]. Especially at the lower normal load, a trend towards higher friction forces at lower filling capacities is observed owing to a larger contact area. On the structured substrate (see Figure 2 c), friction forces are lower compared to the smooth clean substrate. The lower filling capacities result in higher friction forces for all loading conditions. A clear difference in dynamic friction coefficients can be observed on the contaminated substrate (see Figure 2 d). The GMFP filled with only 1 g of ground coffee is so flat that it sometimes collides with the contamination particles. Especially when the granular material is compacted even more at higher normal forces, the sample holder gets in direct contact with some of the larger gravel particles, resulting in lower friction force. The highly filled GMFP also achieves relatively low friction forces, since a high normal load is needed to press the pre-stretched membrane around the gravel particles on the substrate. The strange trend was visible for some filling capacities, especially for 1.0 g and 1.7 g, on the surfaces contaminated by particles. The reason for this behavior is the random distribution of differently sized particles on the substrate. This randomness leads to some specific behavior of the entire system in different individual experiments, depending on how many and which concrete size of particles are in contact. 3.2. Membrane Modulus Previously [ 1 ], it has been shown that while the granular material contributes to the friction forces at the beginning of the pulling, the deformation of the membrane significantly contributes to the friction forces during the rest of the sliding motion. A stiffer membrane compresses the granular media more strongly than a softer membrane. Thus, the GMFP with the stiffer membrane will not adapt to surface asperities as easily, which results in a reduction of contact area [ 27 ]. However, the stiffer membrane will require more energy when deformed during sliding. Here we use silicone rubber with three different Young’s moduli of 0.15 MPa, 0.33 MPa, and 0.6 MPa. While the different elastic moduli strongly influence how the materials feel when manually handling it, the difference in static contact area for the different membrane moduli is very small (see Figure 3 a). Only the softest 0.15 MPa sample shows a slightly larger contact area at lower normal loads due to its higher flexibility. Figure 2 Variation of the filling quantity of the granular friction pad. ( a ) Visualization of the contact area on a glass sphere and smooth glass substrate. ( b – d ) Friction coefficient of the differently filled granular friction pad at different normal loads F n : ( b ) on a smooth substrate, ( c ) on a rough/structured substrate, ( d ) on a smooth substrate contaminated by 1–2 mm large particles. Figure 3 Variation of the membrane modulus of the granular friction pad. ( a ) Visualization of the contact area on a glass sphere and smooth glass substrate. ( b – d ) Friction coefficient of the samples at different normal loads F n : ( b ) on a smooth substrate, ( c ) on a rough/structured substrate, ( d ) on a smooth substrate contaminated by 1–2 mm large particles. In the dynamic friction experiments, the highest friction forces were always observed on the smooth clean substrate (see Figure 3 b). Interestingly, the stiffer the membrane, i.e., the higher the elastic modulus is, the higher the friction coefficient. Since the contact areas for all moduli are very similar, the difference in friction coefficient results from higher energy dissipation in stiffer membranes during their deformation when sliding over the substrate. At higher normal loads, the softest membrane ruptures and completely fails. Thus, a stiffer membrane is preferable, since contact formation and friction coefficient are similar, but the stiffer membranes are more resistant to failure. Under low normal loads, the stiffest membrane performed best on the smooth clean substrate. However, having even softer membrane results in higher adaptability to substrate asperities, which can not only be seen in Figure 3 a for the low normal forces but also on the structured and the contaminated substrates (see Figure 3 c,d). Here, the softer samples reach higher friction forces by coming into contact with the substrate despite the particle contamination. 3.3. Membrane Thickness The stiffness of a GMFP’s membrane can not only be modified by changing its elastic modulus, but also by changing its thickness. Since the membrane thickness increases the membrane’s stiffness by the power of three instead of linearly like the modulus, a much more noticeable change in friction properties is expected. While the membrane thickness for the other tests was always 0.45 mm, here we also investigated thicknesses of 0.15 mm, 1 mm, and 2 mm. The change in membrane stiffness results in a big difference for the static contact area, as can be seen in Figure 4 a. For the low loading conditions, the thinnest membrane achieves the largest contact area with the substrate. The thicker membranes need much higher loading forces to be pushed around the glass bead. Thus, for high robustness against contamination at low normal forces, a more flexible membrane is beneficial to facilitate the flowing of the granular particles. A similar effect can be seen for the dynamic friction experiments (see Figure 4 b–d). On the clean smooth substrate, the granular friction pad does not have to deform much to achieve large contact areas (see Figure 4 b). Friction coefficients of the different membrane thicknesses are very close, with the thicker membranes achieving slightly higher friction coefficients at higher loading forces F n . On the clean structured substrate (see Figure 4 c), a difference in friction coefficients can be seen at low normal loads. The thicker membranes do not adapt easily enough to create high friction forces at these low normal loads. Only at higher normal loads, similar friction forces as with a thinner membrane can be achieved. The largest differences between the four membrane thicknesses can be seen on the smooth substrate contaminated with particles (see Figure 4 d). Due to the random distribution of the contaminating particles, even at low normal loads, the thinner membranes can sometimes conform around the particles and still achieve contact with the substrate during sliding, resulting in high friction forces. The thicker membranes are not flexible enough and only achieve contact at higher normal forces. The concept of the granular friction pad even works with the thickest 2 mm membrane, which is able to adapt around the particles and come into contact with the substrate but only at high normal forces. 3.4. Technological Implications During the jamming transition of a granular medium, the dynamics become increasingly spatially heterogeneous and strongly reminiscent of the behavior of glass-forming liquids [ 28 ]. Such amorphous fluids become solid-like if either the temperature is lowered, or the density is increased (glass transition or jamming transition). The jamming transition was previously numerically modeled considering external forces and the orientations of contacts between particles, to compute all the interparticle forces [ 1 , 29 ]. In principle, this phenomenon is well understood; however, there is a number of other interesting effects related to this phenomenon, such as phase separation in the heterogeneous medium [ 30 ] or friction/adhesion effects at the interface between granular medium and non-smooth substrate separated by a flexible membrane [ 1 , 31 ]. Both these types of effects have interesting technological implications. Small-amplitude high-frequency vibrations affect the size separation of particles of a granular material, which may open new ways not only for the separation of fluids from solids and solids from solids but also for controlled pattern formations during the synthesis of composite materials [ 30 ]. Figure 4 Variation of the membrane thickness of the granular friction pad. ( a ) Visualization of the contact area on a glass sphere and smooth glass substrate. ( b – d ) Friction coefficient of the samples at different normal loads F n : ( b ) on a smooth substrate, ( c ) on a rough/structured substrate, ( d ) on a smooth substrate contaminated by 1–2 mm large particles. For maximizing friction forces of robotic legs on an unknown/unpredictable substrate, the granular media friction pad was recently introduced [ 1 ]. It consists of a thin elastic membrane encasing loosely filled granular material. On coming into contact with a substrate, the fluid-like granular material flows around the substrate asperities and achieves large contact areas with the substrate. Upon applying load, the granular material undergoes the jamming transition, rigidifies, and becomes solid-like. High friction forces are generated by mechanical interlocking on rough substrates, internal friction of the granular media, and the enhanced contact area caused by the deformation of the membrane. The crucial difference of this system from any other previously studied tribological system is that it can adapt to a large variety of dry substrate topologies. To further increase its performance on moist or wet substrates, the granular media friction pad was recently enhanced by structuring the outside of the membrane with a 3D hexagonal pattern [ 31 ]. In the present study, we obtained the most detailed data on the bioinspired artificial GMFP depending on different parameters of the system, which might open the ways for the technological implication of the knowledge from the biology of smooth adhesive pads and from physics of granular media to artificial robotic systems with enhanced ability to contact formation and generation of grip even on rather challenging substrates. 3.5. Biological Implications The elastic modulus of the pad is controlled here by the filling quantity of the granular medium, membrane stiffness, and membrane thickness. In biological systems, the use of these three mechanisms for tuning the mechanical properties of the pad was previously reported. The study on morphology, ultrastructure, effective elastic modulus, and attachment properties of two different smooth-type insect pads has been done for two orthopteran species: Tettigonia viridissima (Ensifera) and Locusta migratoria (Caelifera) [ 32 ]. Similar to the GMFP studied here, both insect species have in principle a similar structural organization of their attachment pads. However, the details of pad material structure were found to be different in these two species. L. migratoria pads bear a thick sub-superficial layer of tanned (stiffer) exocuticle, as well as a higher density of rods filling inside the pad, whereas T. viridissima pads possess a thin flexible superficial exocuticle containing rubber-like protein resilin. The obtained experimental results demonstrated a clear correlation between the density of the fibers, the thickness of the superficial layer, compliance of the pad, and its adhesive properties [ 32 ]. The material structures and properties in organisms are related to the preferred ecological niches of species. The data obtained in the present study on the bioinspired artificial GMFP is the most detailed implication of the knowledge from the biology of smooth adhesive pads to the artificial system with enhanced ability to contact formation and generation of grip even on rather challenging substrates."
} | 5,756 |
33717679 | PMC7937340 | pmc | 6,544 | {
"abstract": "The rhizosphere soil microbiome (RSM) plays an important role in the nutritional metabolism of the exotic weed Ageratina adenophora . However, our understanding of the composition and metabolic activity of this microbiome is limited. We used high-throughput sequencing of bacterial 16S rRNA genes and fungal internal transcribed spacer fragments in combination with transcriptome analysis to compare the composition and metabolic features of the RSMs of A. adenophora and the native plant species Artemisia indica and Imperata cylindrica . A. indica cohabitates with the weed and I. cylindrica grows in uninvaded soil areas. We found fungi belonging to the phyla Ascomycota and Basidiomycota and bacteria belonging to the phyla Proteobacteria, Acidobacteria and Bacteroidetes were highly abundant in the RSMs of A. adenophora and both native plant species. The RSM of A. adenophora differed to varying degrees in the relative abundances of bacterial and fungal phyla and genera, and in levels of expression of functional genes from those of both the native species. The RSM of A. adenophora was more metabolically active than both of these, as indicated by marked increases in the expression levels of genes associated with cell wall, membrane, and envelope biogenesis, energy production and conversion, and the transport and metabolism of carbohydrates, amino acids, coenzymes, nucleotides, and secondary metabolites. Ascomycota and Basidiomycota contributed most significantly to these differences. The composition and metabolic activities of A. adenophora RSM differed less to the RSM of A. indica than to the RSM of I. cylindrica . Fungal communities contributed most to the metabolic genes in the RSM of A. adenophora. These included the arbuscular mycorrhizal fungi Glomeromycota. The different relative abundances in the RSMs of these three plant populations may explain why A. adenophora is more successful in colonizing soils than the two native populations.",
"conclusion": "Conclusions In conclusion, the data obtained in this study suggests that the RSM of the weed, A. Adenophora , differs substantially in its bacterial and fungal communities to those of the two native plant species. The RSM of A. adenophora contained higher percentage abundances of Proteobacteria and Bacteroidetes than those of the native plant species. Increased expression levels of genes associated with metabolic activities of transport and metabolism of amino acids, coenzymes, nucleotides and carbohydrates; energy production and conversion; transcription; and eventual cell growth in the A. adenophora RSM play an important enhancing role in the nutritional metabolism of its rhizosphere soils, allowing it to invade a habitat already occupied by two native species, A. indica and I. cylindrica. The data also indicate that an increased level of gene expression in members of the fungal communities, especially the saprotrophic Ascomycota and Basidiomycota and the AM fungi Glomeromycota, played a role in its successful invasion.",
"introduction": "Introduction Ageratina adenophora (Sprengel, also known as Eupatorium adenophorum Sprengel) is a weed originating in Mexico and Costa Rica that has invaded more than 40 tropical and subtropical countries in Asia, Oceania, Africa , and Europe ( Poudel et al., 2019 ). Its dominance can interfere with nutritional cycles, hydrological conditions, and the energy budgets of the plant-soil ecosystem, causing severe economic losses to the agricultural, forestry, and livestock industries ( Xu et al., 2006 ; Kong et al., 2017 ). Thus, A. adenophora is often used as a model organism to study the possible mechanisms of plant invasion ( Datta et al., 2017 ; Chen et al., 2019 ; Fang et al., 2019 ; Poudel et al., 2019 ; Zhao et al., 2019 ). The impact of plant soil feedback is of interest to explain the successful exotic invasion of A. adenophora ( Niu et al., 2007 ; Poudel et al., 2019 ; Zhao et al., 2019 ). The presence of A. adenophora may alter soil nutrient availability by modifying the composition and function of its soil microbiome, thus facilitating its growth and competitiveness ( Niu et al., 2007 ; Zhao et al., 2019 ). Most soil microbiological studies associated with the invasion of A. adenophora have examined the bulk soil beneath the plant. A. adenophora tends to have higher levels of available C, N, and P ( Niu et al., 2007 ; Kong et al., 2017 ; Poudel et al., 2019 ; Zhao et al., 2019 ), higher nitrogen-cycling rates ( Zhao et al., 2019 ), higher enzymatic (urase, phosphatase, and invertase) activities ( Li et al., 2009 ), and a higher abundance of vesicular-arbuscular mycorrhizal fungi and fungi/bacteria ratio ( Niu et al., 2007 ) than soils inhabited by native plant species. In previous studies, the composition of the bulk soil microbiomes beneath A. adenophora differed from those beneath native plant species ( Niu et al., 2007 ; Kong et al., 2017 ; Zhao et al., 2019 ). However, there have been few studies conducted on the structure and function of the rhizosphere soil microbiome (RSM) of this weed. The soil immediately around the plant roots, known as the rhizosphere, is an area where plant soil feedback is important because it harbors an active and diverse microbiota consisting of both bacteria and fungi ( Inderjit & Van der Putten, 2010 ; Van der Putten et al., 2013 ). These microorganisms receive 20%–50% of the plant host’s photosynthetically generated carbon ( Drigo et al., 2010 ). However, the extent to which the composition and metabolism of the bacterial and fungal communities in the RSM of A. adenophora differ from those of native plant species is still unclear. In particular, the root leachate of A. adenophora contains structurally diverse phytochemicals including terpenoids, phenylpropanoids, flavonoids, coumarins, and alkaloids ( He et al., 2008 ; Dong et al., 2017 ), and some of these have been shown to possess allelopathic, phytotoxic, and antifeedant activities, and have the potential to substantially alter the microbiota of invaded soils ( Liu et al., 2010 ). We investigated the composition and metabolic activities of the bacterial and fungal communities in the RSMs of A. adenophora and compared them to those of two native plant species, Artemisia indica and Imperata cylindrica, using 16S rRNA gene and internal transcribed spacer (ITS) fragment high-throughput sequencing combined with transcriptome analysis. The results of this study improve our understanding of the role of RSM in the growth and invasiveness of A. adenophora .",
"discussion": "Discussion We compared the compositional and metabolic characteristics of the RSM of the highly invasive weed A. adenophora against those of two native species to elucidate the effects of A. adenophora root exudates on the composition and metabolism of its RSM and assessed the role of bacterial and fungal communities present in its nutrient cycles. We found that the composition of the bacterial and fungal communities of the A. adenophora RSM varied in differing degrees to those of the two native species ( Tables S1 & S2 , Figs. 2C & 2D ). More compositional differences were observed in the RSM of I. cylindrica in the uninvaded area than in that of A. indica co-existing with A. adenophora . The RSM of A. adenophora had a higher ( P ≤ 0.05) percentage of bacterial Norank 480-2, Rhodoplanes , uncultured Xanthomonadales , Variibacter, and fungal Penicillium than those of the native plant species. In an earlier study, Zhao et al. (2019) found members of the genera Gp6 ( Acidobacteria ), together with Sphingomonas , and Spartobacteria contributed most to the dissimilarities between rhizosphere soils of the weed A. adenophora and other different native plant species. Such a striking discrepancy probably reflects differences in the native plant species and soil types used in the two studies. 10.7717/peerj.10844/fig-4 Figure 4 Taxonomic classification and heatmap of unigenes identified in the rhizosphere soils of weed Ageratina adenophora , and native plant species Artemisia indica and Imperata cylindrica . (A & B) Taxonomic classification at kingdom (A) and phylum (B) levels and relative abundances of the classifiable unigenes. (C) Heatmap of unigenes showing composition and relative abundances of the unigenes. The symbol labels in (A) apply to (B). 10.7717/peerj.10844/fig-5 Figure 5 Expression of nitrogen metabolism genes in the rhizosphere soils of invasive weed Ageratina adenophora , and native plant species Artemisia indica and Imperata cylindrica . Our 16S rRNA gene and ITS fragment sequencing ( Figs. 2C & 2D ) and transcriptome data ( Fig. 4B ) are in agreement in showing fungi belonging to phyla Ascomycota and Basidiomycota, and bacteria belonging to phyla Proteobacteria, Acidobacteria, and Bacteroidetes are important players in the RSMs of A. adenophora and the two native plant species. The RSM fungal community of A. adenophora is thought to play a major role in its nutrition. The fungal community contributed 89.3% of the expressed genes ( Fig. 4A ) and most (71.2%) were from members of Ascomycota and Basidiomycota ( Fig. 4B ). Of these, Ascomycota constituted more than half of ITS total reads (55%) and expressed genes (51%) in the A. adenophora RSM. Saprotrophic fungi are known to be highly abundant active organisms in the rhizosphere, where they metabolize root exudates ( Denef et al., 2007 ; Bueé et al., 2009 ) even more successfully than the rhizosphere soil bacteria under certain conditions ( Rudnick, Van Veen & De Boer, 2015 ). Glomeromycota contributed 10.1% of the genes expressed in the A. adenophora RSM, which was substantially higher than that expressed in the RSMs of both A. indica (5.7%) and I. cylindrica (0.1%) ( Fig. 4B ), and so the RSM of A. adenophora had more ( P = 0.05) of these symbiotic arbuscular mycorrhizal (AM) fungi than did that of I. cylindrica ( Fig. 2D ). Therefore, it is likely they play a positive role in successful A. adenophora invasion by enhancing the provision of N and P, especially if there are limited amounts in the soil, and in protecting it from parasite attack at the roots ( Johnson et al., 2010 ). The supply of nitrogen is known to play an important role in exotic plant invasion ( Bajpai & Inderjit, 2013 ; Perkin & Nowak, 2013 ) especially since the gene expression profiles of enzymes associated with nitrogen fixation and transformation metabolism in the A. adenophora RSM were similar to those of both A. indica and I. cylindrica ( Fig. 5 ). These data suggest that attracting and stimulating the metabolic activity of Glomeromycota enhances the invasive capabilities of A. adenophora , and is the first metabolic evidence to support an important role for AM fungi in A. adenophora invasion. We showed that there were greater similarities in the composition and expression levels of functional genes in the A. adenophora and A. indica RSMs than in I. cylindrica ( Figs. 3 and 4A , 4B , & 4C ). Such compositional and metabolic similarities in the RSMs may explain why A. indica can withstand invasion attempts by A. adenophora, as shown by their coexistence at one site for at least 8 years. Higher levels of expression of genes associated with cell wall, membrane, and envelope biogenesis; amino acid transport and metabolism; energy production and conversion; carbohydrate transport and metabolism; secondary metabolite biosynthesis, transport, and catabolism; transcription; translation; ribosomal structure and biogenesis; replication, recombination, and repair; coenzyme transport and metabolism; and nucleotide transport and metabolism were seen in the RSM of A. adenophora than in either the RSMs of A. indica and especially I. cylindrica ( Fig. 3 ). Such enhancement of expression of these genes would suggest that the A. adenophora RSM is more metabolically active than those of either native species. Invasion by A. adenophora did not affect gene expression levels associated with microbial nitrogen fixation, ammonia oxidation, and nitrite and nitrate reduction in the invaded soil. There was no evidence for the increased expression of N metabolic genes, which may also reflect high amounts of organic C (litter) present in the RSM of A. adenophora ( Table 1 ), where its decomposition may release organic N in higher amounts than from either native plants, as reported for other invasive plant species ( Ehrenfeld, Ravit & Elgersma, 2005 ; Hata, Kato & Kachi, 2012 ; Kaur et al., 2012 ; Meisner et al., 2012 ; Li et al., 2017 ). This is supported by evidence that more genes are associated with carbohydrate metabolism in the RSM of A. adenophora than in those of the native plant species. Interestingly, in their GeoChip study, Zhao et al. (2019) showed that the A. adenophora RSM had higher nitrogen metabolism gene expression levels than those found in the native plant species examined. Such differences may arise from the measurement techniques used. GeoChip assays quantify gene expression in terms of the total DNA, transcriptomic analysis measures gene expression levels as mRNA synthesis. Zhao et al. (2019) compared the rhizosphere soil of A. adenophora to that of a mixture of soil samples from seven native plant species, and we compared the rhizosphere soil of A. adenophora individually with those of two native plant species."
} | 3,365 |
27812750 | PMC5206262 | pmc | 6,545 | {
"abstract": "Key message \n Transcriptomes of two switchgrass genotypes representing the upland and lowland ecotypes will be key tools in switchgrass genome annotation and biotic and abiotic stress functional genomics. \n Abstract Switchgrass ( Panicum \n virgatum L.) is an important bioenergy feedstock for cellulosic ethanol production. We report genome-wide transcriptome profiling of two contrasting tetraploid switchgrass genotypes, VS16 and AP13, representing the upland and lowland ecotypes, respectively. A total of 268 million Illumina short reads (50 nt) were generated, of which, 133 million were obtained in AP13 and the rest 135 million in VS16. More than 90% of these reads were mapped to the switchgrass reference genome (V1.1). We identified 6619 and 5369 differentially expressed genes in VS16 and AP13, respectively. Gene ontology and KEGG pathway analysis identified key genes that regulate important pathways including C4 photosynthesis, photorespiration and phenylpropanoid metabolism. A series of genes (33) involved in photosynthetic pathway were up-regulated in AP13 but only two genes showed higher expression in VS16. We identified three dicarboxylate transporter homologs that were highly expressed in AP13. Additionally, genes that mediate drought, heat, and salinity tolerance were also identified. Vesicular transport proteins, syntaxin and signal recognition particles were seen to be up-regulated in VS16. Analyses of selected genes involved in biosynthesis of secondary metabolites, plant–pathogen interaction, membrane transporters, heat, drought and salinity stress responses confirmed significant variation in the relative expression reflected in RNA-Seq data between VS16 and AP13 genotypes. The phenylpropanoid pathway genes identified here are potential targets for biofuel conversion. Electronic supplementary material The online version of this article (doi:10.1007/s00299-016-2065-0) contains supplementary material, which is available to authorized users.",
"conclusion": "Conclusion Comprehensive transcriptome profiling in both lowland and upland switchgrass ecotypes (AP13 and VS16), as analyzed here provided the identification of differentially expressed genes and transcription factors that are associated with biomass yield, disease resistance, and abiotic stresses such as, heat, drought, flood, and salinity. The lowland genotype, AP13, showed higher expression of biomass-related genes while the upland genotype, VS16, showed upregulation of some stress-related genes. This study also identified biomass production and quality associated key enzymes in phenylpropanoid, C4-photosynthesis, and photorespiratory pathways. The other major group of genes identified here belonged to plant stress and disease resistance. We validated selected genes from transcriptome analysis using RT-PCR and qRT-PCR and identified similar trends in expressions. Further studies taking into account a broader array of ecotypes and different plant tissues sampled at vegetative and reproductive stages of plant development will be useful, as these will broaden the datasets of this current work to match phenotypic variations that reflect this prolific native species of North America. Author contribution statement VA helped design and plan the study, conducted the experiments, and wrote the manuscript; MCS helped with design of the study, and helped edit and write the manuscript; JT helped analyze the data and edited the manuscript, VRS helped analyze the data, and write the manuscript, KPB helped analyze the data, and edited the manuscript, EF helped analyze the data, and write the manuscript, RKH organized and edited the manuscript, and VK designed and planned the study, and helped edit and write the manuscript.",
"introduction": "Introduction Switchgrass ( Panicum \n virgatum L.) is an important, warm season, C4 perennial grass. Switchgrass was selected as a dedicated feedstock for the production of biofuels by the US Department of Energy (DOE). Although it is native to North America, it is grown in South America, Europe and Asia (Parrish et al. 2012 ). Based on plant morphology and adaptation area, switchgrass has been classified into two distinct ecotypes, lowland and upland (Moser and Vogel 1995 ; Porter 1966). The lowland ecotypes are mainly adapted to flood plains of the southern USA and characterized by tall and coarse stems, long-wide leaves, high biomass potential, and relatively tolerant to pests and disease (Sanderson et al. 1996 ). The upland ecotypes are mainly adapted in the dry and cool habitats in the northern USA. Plants of this ecotype have short and narrow stems and leaves, usually less productive, and are more susceptible to damage by pests and disease (Sanderson et al. 1996 ). Conversely, upland ecotypes are more drought and cold tolerant than lowland ecotypes. Improvement of both ecotypes is very important to meet the one billion dry ton biomass production target of the DOE by 2030 (US DOE 2011 ). Identification of ecotype-specific genes associated with inherent biotic and abiotic tolerance, and understanding their role and expression pattern can greatly aid in switchgrass crop improvement. Earlier gene expression studies in switchgrass mostly relied on expressed sequence tag (EST)-based sequencing (Tobias et al. 2008 ) and microarrays (Sharma et al. 2012 ). RNA sequencing (RNA-Seq) has been widely used in providing genome-wide transcript profiles in model and non-model organisms including complex polyploid plant species such as switchgrass. So far limited reports are available on switchgrass transcriptome analysis. The first transcriptomic study using the Roche 454 sequencing platform generated ~40,000 transcripts from 980,000 reads and 243,600 contigs in switchgrass (Wang et al. 2012 ). Transcriptomic analysis of nodes and buds from high and low tillering switchgrass identified several transcription factors involved in the regulation of genes that determine axillary bud initiation and development (Wang et al. 2013a ). Comparative transcriptome analysis of an upland cultivar (“Summer”) and a lowland cultivar (“Kanlow”) revealed early on-set of dormancy in crowns and rhizomes of Summer plants when compared to Kanlow (Palmer et al. 2014 ). Screening of switchgrass flag leaf transcriptomes helped in identification of molecular patterns in leaf development, senescence, and mineral utilization (Palmer et al. 2015 ). RNA-Seq data has been used to understand genes associated with biomass production in switchgrass (Meyer et al. 2014 ) and P. hallii (Hall’s Panicgrass) (Meyer et al. 2012 ). Recently, role of plant transcription factors (TFs) in the development of lignocellulosic feedstocks for biofuels has also been suggested (Wuddineh et al. 2015 ). In switchgrass, transcriptome profiling for rust resistance (biotic stress) identified 84,209 transcripts from 98,007 gene loci from eight samples (Serba et al. 2015 ). Two abiotic stress-(drought- and heat-) responsive transcriptomes have been developed in switchgrass (Meyer et al. 2014 ; Li et al. 2013 ). Using comparative transcriptome analyses of four monocot species, 16 common genes have been identified as heat-responsive that were implicated in protein refolding (Li et al. 2013 ). The role of microRNAs in drought and salinity stress in switchgrass has also been reported (Xie et al. 2014 ). AP13 is the lowland switchgrass genotype used of genome sequencing. VS16 is the upland genotype deeply sequenced at the JGI to aid in genome assembly. These two genotypes are the parents of a mapping population for which genetic linkage maps are available (Serba et al. 2013 ), used for comparative mapping, and QTL analyses. These two genotypes are very important to the switchgrass research community. Moreover, a comprehensive comparative transcriptome profiling of both upland and lowland ecotypes targeting genes associated with biomass production, biotic and abiotic tolerance in unstressed plants is not available. Identification of ecotype-specific genes associated with inherent biotic and abiotic stress tolerance, and understanding their role and expression pattern can greatly aid in switchgrass crop improvement. This study specifically aimed at: (1) developing reference transcriptomes of VS16 and AP13 genotypes in switchgrass; and (2) conducting comparative and targeted transcriptome analysis of VS16 and AP13 genotypes to identify differentially expressed biomass-related (including phenylpropanoid synthesis genes), biotic and abiotic tolerant genes that are inherent to upland and lowland ecotypes of unstressed switchgrass plants.",
"discussion": "Discussion RNA sequencing followed by bioinformatics analysis and experimental validation as presented here provide comprehensive transcriptome profiles for two contrasting genotypes, AP13 and VS16, belonging to two switchgrass ecotypes, lowland and upland, respectively. The transcriptome data generated in this experiment can also be used as references in future genomics and transcriptomic studies. The majority of the transcripts annotated here belonged to cellular and metabolic processes based on Agrigo analysis (Fig. 1 ). Data mining and assigning gene ontology terms to different gene classes helped in the identification of several important transcript homologs that belonged to pathway-related genes, transcription factors, transporters and genes involved in both biotic and abiotic stresses. Genes associated with photosynthesis Though several pathways with differentially expressed genes were identified, we highlight the pathways that are directly or indirectly linked with photosynthetic efficiency. Our analysis focused on genes associated with C4 photosynthesis. The majority of the genes in this pathway were abundantly expressed in AP13 when compared to VS16 supporting the fact that specifically AP13 is photosynthetically more active and could have impact on higher biomass yield than VS16 (Serba et al. 2013 ). Our finding is in accordance with previous reports that lowland ecotypes of switchgrass are more productive especially in biomass yield (Alexopoulou et al. 2008 ). Interestingly, carbonic anhydrase involved in carbon-fixing metabolism in C4 plants was abundantly expressed in AP13 (twofold increase) when compared to VS16 supporting reports on potential use of lowland ecotypes for biofuel production. The stomatal conductance was seen to be reduced in carbonic anhydrase-deficient mutants that were treated with carbonyl sulfide (COS) in grasses (Stimler et al. 2012 ). The second important enzyme in the C4 pathway is PEPC, which was also highly expressed in AP13 (fourfold higher when compared to VS16). Another important enzyme identified here was pyruvate orthophosphate dikinase (PPDK) and it was only expressed significantly in VS16 but not in AP13. An increase in PPDK has been reported in cold tolerant species of sugar cane indicating the ability of PPDK content as a factor for cold tolerance (Halther et al. 2015 ). VS16 is able to tolerate cold temperatures better than AP13 and this may be the reason for the significant increase of PPDK in VS16 compared to AP13. In our study, Pyruvate orthophosphate dikinase regulatory protein (PDRP) was significantly expressed only in AP13, supporting its role in higher biomass production (Chastain et al. 2011 ). PDRP activates PPDK by reversible phosphorylation of an active threonine (Thr) residue. Genes associated with photorespiration Photorespiration is a physiological process, which has significant influence in biomass accumulation. In plants, eight key enzymes regulate photorespiration and they are primarily located in chloroplast, mitochondrion, and peroxisome (Chen et al. 2014 ). All eight of these enzymes were identified in this study. The role of dicarboxylate transporters in photorespiration was elaborately discussed in several plant species in ammonia assimilation (Buchner et al. 2015 ). Here we identified three dicarboxylate transporter homologs that were highly expressed in AP13. This finding may imply that the process of nitrogen recycling occurs more in AP13 than VS16 (Rao et al. 2016 ). Further, we found that AP13 has more glutamate synthase (GS) compared to VS16. The number of significantly enriched GS genes in VS16 and AP13 was one and four, respectively. GS are the enzymes responsible for conversion of inorganic nitrogen to glutamine and glutamate during ammonium assimilation and they donate nitrogen during biosynthesis of amino acids and to compounds including chlorophyll, hormones and secondary metabolite products. GS is also involved in the transport of toxic metabolites and these attributes of AP13, may indicate that it undergoes more plant metabolic processes compared to VS16 (Oliveira et al. 2001 ). Another important enzyme involved in photorespiration is hydroxypyruvate reductase (HPR). In a recent study, using over expression and RNAi lines of HPR1, it was found that HPR1 gene activity is important for photorespiratory metabolite flux in rice (Bauwe et al. 2010 ). In this study, we identified one significantly enriched HPR gene in AP13 which means it can assimilate more CO 2 and thus produce more biomass. Further, RuBisCO is a predominant CO 2 fixing enzyme in plants. This study identified 11 significantly enriched RuBisCO genes in AP13. Furthermore, serine hydroxyl methyltransferases (SHM) not only play an important role in carbon metabolism and photorespiration but also in controlling the cell damage/death induced by biotic and abiotic stresses (Dellero et al. 2015 ). The level of expression of SHM was two folds more in VS16 when compared to AP13. Genes associated with phenylpropanoid pathway Previous studies showed relationship between lignin biosynthesis and saccharification/ethanol yield (Chen and Dixon 2007 ; Gressel 2008 ; Leplé et al. 2007 ; Dien et al. 2006 ). We identified phenylpropanoid pathway genes that play a role in lignin biosynthesis. Though there are more than 25 genes associated with phenylpropanoid metabolism, tyrosine is assumed to be the starting point in plants such as grasses (Moreno et al. 2005 ). More upstream genes in phenylpropanoid pathway are expressed in VS16 than in AP13 but the last gene (CAD) are equally expressed in both. Down-regulation of upstream enzymes, HCT, C3H and COMT in the lignin biosynthesis pathway resulted in lower lignin content in switchgrass (Escamilla-Trevino et al. 2014 ; Rao et al. 2013 ). In transgenic switchgrass cv. Alamo, suppression of 4CL and CAD genes showed reduction in lignin content and increased saccharification efficiency (Xu et al. 2011 ; Fu et al. 2011 ). Previous report suggested that phenylpropanoid pathway genes, PAL, 4-CL, CAD, COMT and CCoAOMT were abundantly expressed in lowland cultivar Kanlow than upland cultivar Summer (Palmer et al. 2014 ). Contrarily, this study identified ferulic acid, CCR, PAL, 4CL, CCoAMOT, HCT, and C4H genes uniquely expressed in the upland cultivar, VS16. This supports that VS16 (upland) can produce more lignin than the lowland ecotypes which affects downstream processes such as digestion of cellulose and hemicellulose and thus reducing the use of upland ecotypes as biofuel stock compared to lowland ecotypes (Bhandari et al. 2014 ). Transcription factors in switchgrass We evaluated both the ecotypes for differentially expressed transcription factors that regulate various physiological processes including plant growth and development (Ramachandran et al. 1994 ). However, their level of expression varied between the ecotypes. The differentially expressed transcription factors were more in VS16 (428) when compared with AP13 (197), suggesting ecotype-specific variation in gene expression. The TFs that were commonly identified in the two ecotypes of switchgrass include DREB, AP2 domain-containing, C2H2, MYB, NAC, NAM, Integument, zip, and MADS box families of TFs. Several TFs identified here were in common with the TFs reported in other grasses such as Oryza sativa , Zeal mays , Sorghum bicolor , Saccharin ophidiarium and Brachypodium distachyon (Yilmaz et al. 2009 ). The significant TFs identified here overlapped with recent studies (Li et al. 2013 ; Bhatia and Bosch 2014 ) that include: AUX/IAA transcriptional regulator family (8), FAR1-related sequence (6), beta-8 tubulin (5), beta-6 tubulin (5), and TRAF-like family protein (4). In addition to these, we also identified TFs, homeodomain-like superfamily protein (22), basic leucine zipper (33) transcription factor family protein (19), C2H2-type zinc finger family protein (17), mitochondrial transcription termination factor family protein (15), integrase-type DNA-binding superfamily protein (13), RING/FYVE/PHD zinc finger superfamily protein (13), GRAS family transcription factor (12), and BTB-POZ and MATH domain (12). Previous reports suggested that several TFs play a vital role in regulating the gene expression in plant stress responses and some are discussed here. The down-regulation of WRKY family transcription factors resulted in increased lignin content in cell walls that enhanced the biomass content in Medicago sativa (Gallego-Giraldo et al. 2015 ). WRKY-mediated transcriptional regulation in flooding tolerance has been reported in switchgrass (Barney et al. 2009 ). Transgenic switchgrass lines that overexpress the MYB4 TF showed higher lignocellulosic content (Shen et al. 2013 ). Here, we identified bHLH, MYB, WRKY, NAM, AP2 domain-containing, BWDNA-binding domain-containing, heat shock factor (HSF), NAC, MADS box, and Aintegumenta (Ant) families of TFs that had suggested roles in regulating lingocellulosic content in lateral meristems of switchgrass (Li et al. 2013 ). Recently, higher expression of WRKY and NAC genes independently involved in pathogen responses, and senescing of flag leaves of switchgrass have been reported (Serba et al. 2015 ). Majority of the TFs identified in this study overlapped with the abiotic stress-responsive TFs that have been reported in rice (Todaka et al. 2012 ). Genes associated with biotic and abiotic stress responses We identified a suit of biotic and abiotic stress-responsive gene transcripts in the two switchgrass genotypes. Interestingly, none of the drought-responsive transcripts identified here overlapped with previous studies in switchgrass but overlapped with the transcripts from other grasses such as rice and sorghum (Pandey and Shukla 2015 ; Sharma et al. 2006 ). Drought-responsive genes uniquely identified here include plasma membrane intrinsic protein (9), NOD26-like intrinsic protein (8), tonoplast intrinsic protein (3), and delta tonoplast integral protein (2). The majority of the drought-responsive genes identified here were classified as aquaporins and NOD26-like intrinsic proteins (Fetter et al. 2004 ). Plant aquaporins play an important role in drought tolerance by facilitating water and small solute transport across the cell membrane and thus regulate plant growth and development. The expression of aquaporins varies with spatial and temporal expression and with environmental conditions (Gomez et al. 2009 ). An increase in expression of aquaporin-dependent plasma membrane intrinsic proteins (PIP) was evident in abiotic stress responses including drought, high-salt, low-temperature, and heavy-metal stress (Jang et al. 2004 ). Heat stress adversely affects membrane and cytoskeleton structures by modulating gene expression; to overcome this, plants develop a considerable amount of tolerance by reprograming their transcriptomes. However, relative heat tolerance varies between the upland (VS16) and lowland (AP13) switchgrass ecotypes and their transcriptomes were analyzed for heat tolerant genes. Upland ecotypes are more adapted to colder climate and lowland ecotypes in hot and humid regions. In our study, we identified more than 20 DnaJ and DnaK transcripts that were associated with heat tolerance by directly or indirectly binding to hsp70, which is in concurrence with a previous study (Mayer and Bukau 2005 ). The majority of the heat tolerant genes such as DnaJ-domain superfamily proteins (20), DnaJ heat shock N-terminal domain-containing proteins (10), DnaJ heat shock family protein (9), heat shock cognate protein 70-1 (3), and hsp70 (3) identified here overlapped with a previous study in switchgrass (Li et al. 2013 ). Conversely, three heat tolerant genes uniquely identified in this dataset include DnaJ/Hsp40 cysteine-rich domain superfamily proteins (9), chloroplast hsp70-1 (2), and DnaJ domain-containing protein (1). Comparative transcriptome profiling showed differential gene expression related to heat shock proteins, including hsp90 in Pyropia \n yezoensis (Sun et al. 2015 ). In wheat and barley, the genome-wide identification revealed 27 and 13 newly identified hsps (Pandey et al. 2015 ). The majority of the flooding related genes identified here belonged to cell division and cell wall loosening that include cyclin, expansin, replicon protein A2, xyloglucan endotransglucosylase family protein, and growth-regulatory factor 5. In an earlier report, flooding induced shoot elongation by regulating apoplastic acidification, cell wall loosening, cell division, and starch breakdown by employing at least three hormones viz., ethylene, ABA and GA in Rumex \n palustris (Voesenek et al. 2003 ). AP13 (lowland) can grow well in flood plains. Three days of flooding did not impact switchgrass survival in Oklahoma (Personal communication, M.C.Saha). The relative salinity tolerance varies among the ecotypes; lowland has 26X and 15X more sodium than upland at maturity and after senescence, respectively (Yang et al. 2009 ) and here we compared the transcript profiles of VS16 and AP13 to determine the relative salinity tolerance between the two genotypes of switchgrass. Salinity stress-responsive genes identified here were in concurrence with an earlier study in switchgrass (Liu et al. 2014 ) and included: ABC transporter family proteins, multidrug resistance-associated proteins, general control non-repressible, non-intrinsic ABC proteins, NSP-interacting kinases, and P-glycoproteins. Importantly, the unique salt stress-responsive genes identified in this study were aldehyde dehydrogenase, PHE ammonia-lyase, plasma membrane intrinsic protein, NOD26-like intrinsic protein, and tonoplast intrinsic proteins, which have been reported in rice and sorghum (Liu et al. 2009 ). In this study, we identified 17 xyloglucan endotransglucosylase/hydrolase genes. Here we validated expression of three salinity-responsive genes using RT-PCR. The five most important plant transporter families that have been implicated in biotic and abiotic stress responses included: ATP-binding cassette (ABC), multidrug and toxic compound exporters (MATE), major facilitator superfamily (MFS), small multidrug resistance (SMR), and resistance-nodulation-division proteins (RND) (Peng et al. 2011 ). However, ABC is the largest transporter family protein found in all living organisms. Here, we identified 33 and 27 ABC transporter family proteins in VS16 and AP13, respectively (Table 8 ; Supplementary Table S9). We found two subfamilies of ABC transporters, ABCG (19) and ABCC (1) in AP13. Whole transcriptome analysis in rice revealed several differentially expressed genes involved in drought signaling pathways under Cd stress (Oono et al. 2014 ). We identified 18 AAA-type ATPase family proteins in AP13. There were significantly expressed transporters identified uniquely in VS16 which are; ammonium transporter (AMT), cation/H (+) antiporter (CHX), copper transporter (COPT), sodium transporter (HKT), K (+) efflux antiporter (KEA), magnesium/proton exchanger (MHX), molybdate transporter (MOT), metal tolerance protein (MTP), nicotianamine synthase 1 (NAS), nitrate excretion transporter (NAXT), sodium/hydrogen exchanger (NHX), metal transporter (NRAMP), and zinc transporter (ZIP). Genes associated with disease resistance Several NBS domain-containing R proteins were identified in AP13 and VS16. Additionally, disease resistance genes, bi-functional inhibitor/lipid transfer protein/seed storage 2S albumin superfamily proteins and azelaic acid-induced transcripts were also identified (Serba et al. 2015 ). However, plant syntaxin proteins were abundantly and uniquely identified in this study between the two ecotypes. The lipid transfer proteins (LTPs) primarily transfer lipids between the monolayers, and their role in biotic and abiotic stresses has been reported in Arabidopsis (Safi et al. 2015 ). In this study, 33 significantly enriched LTPs were identified and overlapped with the LTPs that have been reported in the high-tillering genotype (VS16) of switchgrass (Li et al. 2013 ). Vesicular transport proteins, such as syntaxins and signal recognition particles, play an important role in auxin signaling, cytokinesis, and disease resistance (Wang et al. 2013b ). Vesicular transport proteins, syntaxin and signal recognition particles have been up-regulated in high-tillering genotype, VS16 (Li et al. 2013 ). Similarly, we identified 6 syntaxin proteins and 2 signal recognition particles in VS16. Pathogenesis-related genes including chitinase have been shown to be important for defense against plant pathogens. We identified 10 chitinase genes, among which, chitinase 16, chitinase A, and basic chitinase were highly expressed (Log2FC > 2) in both AP13 and VS16 (Supplementary Table S11). About fourfold higher expression of these chitinases were observed in VS16 than in AP13. In switchgrass, cytochrome P450 has been implicated in heat stress and eleven different variants of cytochrome P450 have been reported (Li et al. 2013 ). Here we identified 85 and 46 cytochrome P450 transcript homologs in VS16 and AP13, respectively (Table 9 ; Supplementary Table S11) and found that 43 thioredoxins were induced in heat stress in AP13. Both genotypes showed elevated levels of thioredoxin in response to oxidative stress that resulted from heat stress (Li et al. 2013 ). We also identified two calcineurin b genes in the two ecotypes that were up-regulated in heat stress. In Arabidopsis , calcineurin B-like gene was preferentially expressed in stems and roots, and its expression was up-regulated in response to drought, cold and wounding stresses (Kudla et al. 1999 ). At least 75 up-regulated heat stress-responsive calmodulin superfamily proteins were expressed in VS16 (Table 9 ; Supplementary Table S11). In a recent study, the genome-wide transcriptome analysis of soybean identified various Hsfs in drought, low temperature, and ABA stress responses (Li et al. 2014 ). The genotype AP13 exhibited 11 up-regulated Hsfs and also identified two calcium Atpase. Genes encoding various protective proteins such as late embryogenesis abundant proteins, GSTs, Mn SOD, glutathione gamma-glutamyl cysteinyl transferase important for GSH biosynthesis, peroxiredoxin, thioredoxin, and PMSR were also identified in this study. Table 9 Significant up- and down-regulated genes involved in disease resistance as well as other stress-related genes in VS16 compared to AP13 Gene ID Log2FC Description Genes in disease resistance Pavir.Ga01060 8.02562 Extensin-like protein; LTPL121—Protease inhibitor/seed storage/LTP family protein precursor, putative, expressed Pavir.Ga01059 7.94306 Bifunctional inhibitor/lipid transfer protein/seed storage 2S albumin superfamily protein; LTPL122 Protease inhibitor/seed storage/LTP family protein precursor, expressed Pavir.J14567 7.88782 Bifunctional inhibitor/lipid transfer protein/seed storage 2S albumin superfamily protein; LTPL128 Protease inhibitor/seed storage/LTP family protein precursor, expressed Pavir.Ba03729 7.46549 Bifunctional inhibitor/lipid transfer protein/seed storage 2S albumin superfamily protein; LTPL100 Protease inhibitor/seed storage/LTP family protein precursor, expressed Pavir.Aa01606 −3.28972 Azelaic acid-induced 1; LTPL114—Protease inhibitor/seed storage/LTP family protein precursor, expressed Pavir.J12674 −3.95316 RING/U-box superfamily protein; LTPL9—Protease inhibitor/seed storage/LTP family protein precursor, expressed Other important genes Pavir.J06588 8.42896 Cytochrome P450, family 71, subfamily B, polypeptide 37; cytochrome P450, putative, expressed Pavir.Ib03890 7.81 Cytochrome P450, family 71, subfamily B, polypeptide 2; cytochrome P450, putative, expressed Pavir.Db00359 7.77268 Cytochrome P450, family 77, subfamily B, polypeptide 1; cytochrome P450, putative, expressed Pavir.Ea02184 7.01089 Tetratricopeptide-repeat thioredoxin-like 1; TTL1, putative, expressed Pavir.Ga01037 6.96059 Cytochrome P450, family 86, subfamily A, polypeptide 4; cytochrome P450, putative, expressed Pavir.Eb01893 6.4813 Tetratricopeptide-repeat thioredoxin-like 1; TTL1, putative, expressed Pavir.J23416 6.38279 Tetratricopeptide-repeat thioredoxin-like 1; TTL1, putative, expressed Pavir.Ab01476 6.34185 Cytochrome P450, family 76, subfamily C, polypeptide 4; cytochrome P450, putative, expressed Pavir.J09753 6.33701 Cytochrome P450, family 72, subfamily A, polypeptide 15; cytochrome P450 72A1, putative, expressed Pavir.Ga01931 6.1496 Cytochrome P450, family 77, subfamily A, polypeptide 4; cytochrome P450, putative, expressed Pavir.Ib03534 6.07324 Cytochrome P450, family 86, subfamily B, polypeptide 1; cytochrome P450, putative, expressed Pavir.Ab01784 6.07018 Plant calmodulin-binding protein-related; expressed protein Pavir.Bb03711 6.04474 CBL-interacting protein kinase 3; CAMK_KIN1/SNF1/Nim1_like.32—CAMK includes calcium/calmodulin dependent protein kinases, expressed Pavir.J30784 4.62782 Calcium-binding EF-hand family protein; calcineurin B, putative, expressed Pavir.Eb00202 4.60844 Tetratricopeptide-repeat thioredoxin-like 3; TTL1, putative, expressed Pavir.Ea00158 4.5998 Tetratricopeptide-repeat thioredoxin-like 3; TTL1, putative, expressed Pavir.Ea03756 4.422 Chitinase A Pavir.Ib00193 4.36714 Chitinase 16 Pavir.J34564 4.25235 Basic Chitinase Pavir.Ea02316 −3.4228 Chitinase A Pavir.Ab00798 −4.0587 Chitinase 4 Pavir.Ab00798 −4.0587 Basic Chitinase Pavir.J24864 −4.16364 Chitinase 2 Pavir.Ib00871 −4.30914 Cytochrome P450, family 76, subfamily C, polypeptide 1; cytochrome P450, putative, expressed Pavir.J10796 −4.59569 Cytochrome P450, family 709, subfamily B, polypeptide 2; cytochrome P450 72A1, putative, expressed Pavir.Ia00714 −4.69108 Thioredoxin superfamily protein; expressed protein Pavir.Ha00715 −4.81133 Cytochrome P450, family 716, subfamily A, polypeptide 1; cytochrome P450, putative, expressed Pavir.Da01689 −4.90659 Cytochrome P450 superfamily protein; cytochrome P450, putative, expressed Pavir.Ia00715 −5.18689 Thioredoxin superfamily protein; expressed protein Pavir.Hb00116 −5.18783 Calmodulin-binding protein; calmodulin-binding protein, putative, expressed Pavir.Aa02817 −5.32122 NADPH-dependent thioredoxin reductase C; bifunctional thioredoxin reductase/thioredoxin, putative, expressed Pavir.Ib03354 −5.35319 Cytochrome P450 superfamily protein; cytochrome P450, putative, expressed Pavir.Ia03284 −5.41344 Cytochrome P450, family 709, subfamily B, polypeptide 3; cytochrome P450 72A1, putative, expressed Pavir.Cb01800 −5.42569 Thioredoxin superfamily protein; thioredoxin, putative, expressed Pavir.J01458 −5.81263 Ataurora3; CAMK_CAMK_like_Aur_like.2—CAMK includes calcium/calmodulin dependent protein kinases, expressed Pavir.J40555 −7.06753 Cytochrome P450, family 71, subfamily B, polypeptide 23; cytochrome P450, putative, expressed Pavir.Ia01888 −7.24124 Cytochrome P450 superfamily protein; cytochrome P450, putative, expressed Pavir.J36245 −9.84065 Thioredoxin superfamily protein; thioredoxin, putative, expressed \n RT-PCR and qRT-PCR validation of RNA-Seq The reasons for selecting stress-responsive genes were: (1) Switchgrass ecotypes have distinct geographic niches, thus their morphology is largely influenced by environmental conditions; (2) lowland ecotypes are relatively more tolerant to pests and disease (biotic stress) than upland ecotypes; (3) upland ecotypes are comparatively more cold tolerant (abiotic stress) than lowland ecotypes (Sanderson et al. 1996 ). Lowland genotypes have high sodium content than their upland counterparts (Yang et al. 2009 ). Three highly and significantly enriched heat-responsive genes including hsp20, hsp90, and chaperone DnaJ-domain superfamily proteins were qualitatively validated their expression by amplifying cDNA from both genotypes. RNA-Seq analysis revealed that hsp20 was uniformly expressed between AP13 and VS16 as confirmed by using RT-PCR. We observed changes in hsp90 and chaperone DnaJ-domain superfamily protein expression between AP13 and VS16. Both genes were highly expressed in AP13 compared to VS16 (Fig. 3 a). Concurrently, we validated three drought-responsive proteins including delta tonoplast integral protein (TIP), plasma membrane intrinsic protein 3 (PIP3), and NOD-26 intrinsic protein 5 (NIP). PIP 3 and NOD-26 intrinsic protein 5 were highly expressed in VS16 when compared to AP13 (Fig. 3 b). The expression of delta tonoplast integral protein was slightly higher in AP13 than in VS16. Also, we validated three salinity-responsive genes that include: vacuolar H + -ATPase subunit E isoform, leucine-rich repeat protein kinase family protein BRASSINOSTEROID INSENSITIVE 1 precursor, and aldehyde dehydrogenase 2C4. All three genes showed higher expression in AP13 when compared to VS16 (Fig. 3 c). Furthermore, five differentially expressed genes from RNA-Seq analyses, secondary metabolite biosynthesis, plant pathogen interaction, plant transposon protein, membrane transporter protein and putative methyltransferase were selected and validated by qRT-PCR. The expression of monolignol biosynthesis enzymes that produce secondary metabolites which have diverse functional roles upon modification have been validated through qRT-PCR in Populus \n trichocarpa (Shi et al. 2009 ). Similarly, we validated the expression of cytochrome P450 monooxygenease homolog in switchgrass (pavir.Ia03311.1). The expression of this gene was higher in AP13 when compared to VS16 as predicted from RNA-Seq analyses. The expression of pathogenesis-related gene (Pavir.Ia00685.1) was higher in AP13 (lowland ecotype) when compared to VS16 (upland ecotype) as identified from RNA-Seq analyses. In another study, the relative expression of four transposable proteins was evaluated in leaf and sheath samples of rice (Zheng et al. 2013 ). Here, we compared the relative expression of transposable proteins between AP13 and VS16 using qRT-PCR and identified similar trend in expression. We validated the expression of an aquaporin transporter protein (Pavir.J37677) and its expression was higher in AP13 when compared to VS16. The qRT-PCR analysis revealed that the expression of putative methyltransferase was higher in AP13 than VS16 but its function was still unclear. However, the single nucleotide resolution genome-wide methylation maps specific for AP13 and VS16 may aid in understanding the epigenetic landscape of these two contrasting ecotypes."
} | 8,794 |
26968016 | null | s2 | 6,548 | {
"abstract": "The first step in the development of a bacterial biofilm is contact with the surface on which the microbe will form this community. We review recent progress on 'surface sensing', and engage the question of 'how does a microbe know it is on a surface?'"
} | 63 |
34712401 | PMC8529026 | pmc | 6,549 | {
"abstract": "Single-celled yeasts form spatially structured populations - colonies and biofilms, either alone (single-species biofilms) or in cooperation with other microorganisms (mixed-species biofilms). Within populations, yeast cells develop in a coordinated manner, interact with each other and differentiate into specialized cell subpopulations that can better adapt to changing conditions (e.g. by reprogramming metabolism during nutrient deficiency) or protect the overall population from external influences (e.g. via extracellular matrix). Various omics tools together with specialized techniques for separating differentiated cells and in situ microscopy have revealed important processes and cell interactions in these structures, which are summarized here. Nevertheless, current knowledge is still only a small part of the mosaic of complexity and diversity of the multicellular structures that yeasts form in different environments. Future challenges include the use of integrated multi-omics approaches and a greater emphasis on the analysis of differentiated cell subpopulations with specific functions.",
"conclusion": "4 Conclusions and perspectives The development of omics techniques that provide comprehensive information on cellular expression at multiple levels (transcriptomics & proteomics), localize target genes of specific regulators (ChIP seq), and identify extracellular metabolites, along with the widespread use of deletion strains, has dramatically expanded knowledge of molecular mechanisms and regulation in structured yeast populations. Further development of integration tools that combine multi-omics data to better understand the interrelationships of the biomolecules involved and their functions at different levels will undoubtedly accelerate this progress. At the same time, however, genome-wide analyses have clearly demonstrated that the formation of multicellular structures, whether colonies or biofilms, is a highly complex process involving a number of parallel regulations that can vary depending on the conditions under which the structure develops (e.g., different nutrient sources) and the genetic makeup of the microorganism. Divergences in the processes and regulations involved in the formation of a given structure are evident not only between individual yeast species (e.g. biofilms of different Candida spp., [28] ), but also between individual strains of the same species (e.g., differences in the ability to form biofilms in S. cerevisiae , [30] ). Also, the question of which external signals contribute to or block biofilm development, which signaling pathways respond to them, and how the transition between the different developmental stages of multicellular structures is controlled is still almost completely open. Moreover, most omics analyses to date have examined whole structures, often in comparison to structures that have been altered, either by changing growth conditions or by the absence of a gene, important for structure development. Thus, little information is available on the properties, regulation, and interactions of specific differentiated cellular subpopulations of multicellular structures. The development of microscopic techniques and fluorescent markers to visualize the presence of a specific cell subpopulation within the structure, as well as techniques to separate differentiated cells for subsequent omics analyses, is essential for development in this area. However, the development of these methods in biofilms is limited in part by the fact that biofilms are a complex structured population of tightly interconnected cells. Single cell sequencing methods, the development of which is also advanced in yeast [77] , in combination with micromanipulation techniques, could partially circumvent this limitation.",
"introduction": "1 Introduction In nature, microbes occur predominantly in multicellular communities that positively or negatively affect the lives of other organisms, including humans. Yeast communities have long been used in the food industry for the production of bread and alcoholic beverages, and more recently for the production of various enzymes and chemicals. On the other hand, many yeasts are opportunistic pathogens that pose a risk, especially to immunocompromised patients, causing both skin and systemic infections. The formation of multicellular biofilms is an important risk factor for these infections. Understanding the mechanisms involved in the formation, characteristics and development of communities formed by different yeast species is a prerequisite for improving the beneficial and reducing the harmful effects of such communities. Complex spatial organization and cell specialization are key features of structured microbial communities with attributes of multicellularity, including intercellular communication, coordinated development, and cell differentiation [1] , [2] . Microorganisms, including yeasts, in such communities cooperate by secreting extracellular components such as the extracellular matrix (ECM), adhesins, enzymes, siderophores, and signaling molecules [2] that can be used or sensed by other cells in the population. Community organization thus depends on interactions among microbial cells and with the environment. Cells within structured populations integrate information in the form of gradients of nutrients, metabolites, and signaling molecules, each of which contributes to cell differentiation and specialization. Spatial patterns are formed with specific differentiated cell types located in specific regions of the community [3] , [4] . Research on structured communities relies on a number of methodological approaches that allow direct study of cells in a population context or rapid separation of cells from the structure for further analysis [5] , [6] , [7] , [8] , [9] , [10] , [11] . Rapid manipulation is critical to minimize cellular changes when manipulation alters the “multicellular context” in which cells reside. Omics analyses play a vital role in providing initial insight into the processes that occur in differentiated cells of structured populations, whether single or multi-species structures. These methods are also key to identifying processes regulated by specific regulators and signaling cascades (Table 1 and S1). Structured microbial communities can arise in different ways, depending on the properties of their cellular constituents. During community construction, motile cells may come together and then specialize, as in fruiting bodies formed by certain bacteria (e.g., Myxobacteria ) or amoebae (e.g., Dictyostelium ), and in multispecies bacterial biofilms. An alternative, typical of non-motile cells (e.g. yeast), is to form structures by “staying together” after cell division [1] . However, non-motile cells can also aggregate through a combination of passive movement and interaction via their surface adhesins [12] . Therefore, both strategies are often combined. Complex structures of colonies formed by the division of non-motile yeasts typify “staying together”, whereas biofilms formed after yeasts attach to biotic or abiotic surfaces use a combination strategy, “coming together & staying together”. Whether a colony or biofilm forms depends primarily on the properties of the yeast strain, with the ability to adhere to solid/semi-solid surfaces being one of the properties essential for biofilm formation (see Part 3). A comparison of the distribution of biofilm- and colony-forming S. cerevisiae cells shows that the decision is made at an early stage of structure development [12] . Here we summarize current knowledge on structure formation, cell differentiation and coordination in yeast colonies and biofilms, and also address changes and relationships that occur when yeast interact with bacteria in mixed species biofilms ( Fig. 1 ). Each structure has advantages and disadvantages for studying cell interaction and differentiation. Biofilms are more complex structures that protect their cells from environmental influences, but some biofilm properties, such as the embedding of cells in extracellular matrix (ECM) and their connection by extracellular fibers, hinder some experimental manipulations. Biofilm research has become increasingly important in recent years, mainly because of biofilm key role in fungal infections [13] , [14] , [15] . Colonies are less structured and consist of precisely localized cell subpopulations that evolve synchronously over time, making specific cell types amenable to separation, relocalization, and other manipulation techniques. For these reasons, colonies have become an essential model for identifying mechanisms of multicellular structure formation, development, and regulation [3] , [4] . In this minireview, emphasis is placed on the use of omics methods (Table 1 and S1) in the study of different types of biofilms and colonies and their differentiated cell subpopulations, and on their combination with other techniques. Fig. 1 Various types of yeast multicellular structures. A, schematic representation of the internal structures of colonies and different types of biofilms (side view of the middle part of the structure). Colored arrows indicate interactions between two cell types. B, cross sections of the structures visualized by 2-photon confocal microscopy (colony and colony biofilm) or wide-field microscopy (biofilm). Colonies: green, U-layer of cells expressing Cit3p-GFP; red, L-cells visualized as cell autofluorescence (adapted from [22] ); colony biofilm: Rpa19p-GFP level differs in aerial and root parts (adapted from [7] ); vertical structure of SLI biofilm (adapted from [30] ). Dashed lines, surface of agar (white), polystyrene surface (black). Bars, 100 μm. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)"
} | 2,464 |
36913588 | PMC10041095 | pmc | 6,550 | {
"abstract": "Significance It is challenging to construct a microbial cell factory for chemical overproduction from methanol due to the toxicity of methanol and the complex cellular metabolism. We here observed a compromised fatty alcohol production when constructing the cytosolic biosynthesis pathway in the methylotrophic yeast Ogataea polymorpha . We showed that peroxisomal compartmentalization significantly improved fatty alcohol production by coupling the cellular metabolism and product biosynthesis. Further enhancing the supply of precursor and cofactor in peroxisome improved the cellular fitness and fatty alcohol biosynthesis. This study provides a feasible engineering strategy to improve methanol biotransformation and shows some insights of methanol metabolism and peroxisome biogenesis.",
"discussion": "Discussion Engineering microbial cell factories for methanol biotransformation will expand the feedstocks for sustainable bio-manufacturing with a zero carbon footprint ( 38 , 39 ); however, the rigidity of methanol metabolism and the toxicity of methanol limit the biosynthetic efficiency of the target chemicals ( 3 , 40 ). Here, we compartmentalized the biosynthetic pathway into peroxisomes and rewired the cellular metabolism to couple fatty alcohol biosynthesis and methanol utilization, which enabled the highest production of fatty alcohols at 3.6 g/L from sole methanol ( Table 1 ). Peroxisomes are ideal microfactories for producing chemical and exhibit several advantages, including a high concentration of enzymes and precursors, and a low loss of intermediates. In particular, peroxisome compartmentalization relieved the competition of cytosolic enzymes and improved the selective production of monoterpenoids ( 41 ), alkanes ( 7 ), and prodeoxyviolacein ( 42 ). Furthermore, peroxisome compartmentalization relieved the cytotoxicity of an alkaloid biosynthetic enzyme and improved its production ( 43 ). We and others found that targeting fatty acyl-CoA reductases to the peroxisomes significantly improved the medium- or long-chain length of fatty alcohols in S. cerevisiae with a high-level supply of the precursors ( 7 – 9 ). In these studies, peroxisome engineering resulted in a marginal effect on cell growth when cultivated in glucose media, which suggested that peroxisomes were relatively flexible for metabolic rewiring since peroxisomes were nonessential under glucose metabolism. In contrast, we observed that peroxisomal compartmentalization of fatty alcohol biosynthetic pathways reduced cell growth when cultivated in methanol media. Furthermore, fatty alcohol biosynthesis in peroxisomes induced cellular stresses under methanol media, resulting in the perturbation of peroxisome homeostasis and the consequent accumulation of formaldehyde. These observations suggested that peroxisomes were relatively rigid for methanol-cultivated methylotrophic yeasts. To relieve the metabolic stress of peroxisome engineering, we deleted LPL1 and IZH3 to prevent phospholipid degradation and enhanced formaldehyde assimilation, which significantly improved cell growth and fatty alcohol production in methanol media. Previous studies have mainly focused on reconstructing and optimizing the biosynthetic pathways in suborganelles ( 41 , 44 – 47 ). However, the enhanced metabolic flux results in a shortage of precursors and cofactors. In particular, some cofactors and intermediates that are involved in cellular metabolism are unevenly distributed in different suborganelles ( 48 , 49 ), which might compromise the endeavor of pathway optimization. Here, we comprehensively engineered peroxisome metabolism by enhancing the supply of precursor fatty acyl-CoA and cofactor NADPH, which significantly boosted fatty alcohol production. In summary, we extensively harnessed O. polymorpha peroxisomes to improve fatty alcohol production from sole methanol by coupling methanol utilization and fatty alcohol biosynthesis. The strategies described here to overcome methanol toxicity will be helpful for peroxisome compartmentalization toward high-level production of chemicals from methanol."
} | 1,026 |
27528764 | PMC4984444 | pmc | 6,552 | {
"abstract": "Human exploration off planet is severely limited by the cost of launching materials into space and by re-supply. Thus materials brought from Earth must be light, stable and reliable at destination. Using traditional approaches, a lunar or Mars base would require either transporting a hefty store of metals or heavy manufacturing equipment and construction materials for in situ extraction; both would severely limit any other mission objectives. Long-term human space presence requires periodic replenishment, adding a massive cost overhead. Even robotic missions often sacrifice science goals for heavy radiation and thermal protection. Biology has the potential to solve these problems because life can replicate and repair itself, and perform a wide variety of chemical reactions including making food, fuel and materials. Synthetic biology enhances and expands life's evolved repertoire. Using organisms as feedstock, additive manufacturing through bioprinting will make possible the dream of producing bespoke tools, food, smart fabrics and even replacement organs on demand. This new approach and the resulting novel products will enable human exploration and settlement on Mars, while providing new manufacturing approaches for life on Earth.",
"introduction": "Introducing a new enabling technology for space: life Imagine a technology that has the following properties. It is programmable like a computer, and modular in design. The technology is self-replicating so multiple units are available for the cost of one. Damage is not an issue as it is self-repairing. It can perform advanced chemical transformations in a tiny form factor and in a non-toxic manner at room temperature and near neutral pH. Its abilities in the field of nanotechnology are unparalleled, with atomic scale precision assembly a nearly constant activity. It can sense as little as a single molecule. Its energy requirements are modest, and never involve petrochemical or electrical sources. In fact, some have built in solar converters whereas others use inorganic energy sources such as hydrogen sulfide (H 2 S), elemental sulfur, ferrous iron (iron II), molecular hydrogen (H 2 ), manganese (Mn 2+ ) and ammonia (NH 3 ). However, some can go for periods of time in excess of a century without any energy input. There are probably over 9 million variants available today [ 1 , 2 ]. This technology is, of course, life. Once we begin to think of biology as a technology, the potential applications are stunning. For example, the materials that are produced by life have an array of mechanical properties unrivalled by either natural or man-made products. For example, spider silk is reputed to have a tensile strength greater than steel. Although it does fall in the range of steel (0.2–2 GPa), its stiffness is less than steel but its density is almost six times less. As a result, the strength to density ration of spider silk exceeds that of steel [ http://phys.org/news/2013-06-spider-silk-nature-stronger-steel.html#jCp ]. And then think of other structural marvels of nature, from wood to bone, from fibres to shells. We are now in an era where we can engineer microbes to make materials that were previously the exclusive domain of larger creatures. This allows us to control the content of the materials through synthetic biology, and position the deposition of the materials through additive manufacturing and cell activation. Bone is wonderful, but production is limited by evolution and ethical sensibilities on the Earth and absent on Mars. But what if bone could be made without animals and laced with spider silk? Spider silk itself has been laced with carbon nanotubes and graphene flakes to produce an enriched spider silk that has a tensile strength greater than that of synthetic fibres such as Kevlar, making it the strongest fibre known [ 4 ]. Although such ‘not-as-futuristic-as-one-might-think’ ideas would no doubt benefit terrestrial industries, they may well be the key to enabling long-term human space exploration and colonization."
} | 1,006 |
36103620 | null | s2 | 6,554 | {
"abstract": "The DNA origami technique allows the precise synthesis of complex, biocompatible nanomaterials containing small molecules, biomolecules, and inorganic nanoparticles. The negatively charged phosphates in the backbone make DNA highly water-soluble and require salts to shield its electrostatic repulsion. DNA origamis are therefore not soluble in most organic solvents. While this is not problematic for applications in biochemistry, biophysics, or nanomedicine, other potential applications, processes, and substrates are incompatible with saline solutions, which include the synthesis of many nanomaterials, and reactions in templated synthesis, the operation of nanoelectronic devices, or semiconductor fabrication. To overcome this limitation, we coated DNA origami with amphiphilic poly(ethylene glycol) polylysine block copolymers and transferred them into various organic solvents including chloroform, dichloromethane, acetone, or 1-propanol. Our approach maintains the shape of the nanostructures and protects functional elements bound to the structure, such as fluorophores, gold nanoparticles, or proteins. The DNA origami polyplex micellization (DOPM) strategy hence enables solubilization or a phase transfer of complex structures into various organic solvents, which significantly expands the use of DNA origami for a range of potential applications and technical processes."
} | 346 |
35953496 | PMC9372150 | pmc | 6,556 | {
"abstract": "Acidic and chemical inhibitor stresses undermine efficient lactic acid bioproduction from lignocellulosic feedstock. Requisite coping treatments, such as detoxification and neutralizing agent supplementation, can be eliminated if a strong microbial host is employed in the process. Here, we exploited an originally robust yeast, Saccharomyces cerevisiae BTCC3, as a production platform for lactic acid. This wild-type strain exhibited a rapid cell growth in the presence of various chemical inhibitors compared to laboratory and industrial strains, namely BY4741 and Ethanol-red. Pathway engineering was performed on the strain by introducing an exogenous LDH gene after disrupting the PDC1 and PDC5 genes. Facilitated by this engineered strain, high cell density cultivation could generate lactic acid with productivity at 4.80 and 3.68 g L −1 h −1 under semi-neutralized and non-neutralized conditions, respectively. Those values were relatively higher compared to other studies. Cultivation using real lignocellulosic hydrolysate was conducted to assess the performance of this engineered strain. Non-neutralized fermentation using non-detoxified hydrolysate from sugarcane bagasse as a medium could produce lactic acid at 1.69 g L −1 h −1 , which was competitive to the results from other reports that still included detoxification and neutralization steps in their experiments. This strategy could make the overall lactic acid bioproduction process simpler, greener, and more cost-efficient.",
"introduction": "Introduction Lactic acid is currently one of the most important chemical commodities due to widespread commercial applications in the pharmaceutical, cosmetics, chemical, and food industries 1 . With 400,000 tons of global production per year, lactic acid is considered a top-value platform chemical 2 , 3 . Moreover, lactic acid is the key precursor of poly-lactic acid—a popular biodegradable plastic with physicochemical, thermal, and mechanical properties comparable to typical petroleum-based polymers, such as polypropylene (PP) and low-density polypropylene (LDPE) 4 , 5 . Almost 90% of industrial lactic acid has been manufactured via fermentation rather than chemical synthesis 6 as the former strategy is more environmentally friendly, less energy-intensive, and yields an optically pure product. Production of lactic acid is even more cost-effective when a low-cost feedstock is employed in the process. Due to its abundant availability, lignocellulosic biomass has been widely used as a substrate for producing various bio-based chemicals 7 – 9 . Besides, sugarcane bagasse (SCB) generated by the sugar and alcohol industry is ideal for this objective. Data show that more than 1.8 billion tons of sugarcane were produced around the world in 2017 10 , with bagasse accounting for 31.8% of the sugarcane composition 11 . Considering its availability, the utility of SCB has become the subject of numerous studies related to establishing a circular bio-economy and sustainable industries. Despite the compelling benefits, utilizing lignocellulose as a feedstock for bioprocessing has several bottlenecks. The generation of various by-products during the pretreatment process is one of the challenging issues. These by-products, which include furan derivatives (furfural, 5-HMF, etc.), weak organic acids (formic acid, acetic acid, etc.), and phenolic compounds (vanillin, ferulic acid, etc.), inhibit microbial metabolism 12 , which renders fermentation and diminishes productivity. Biological, physical, and chemical methods have been explored in a quest to detoxify these chemicals 13 . However, those methods necessitate additional equipment, which drives up cost, and reduce the quantity of fermentable sugar in the hydrolysate 14 . Therefore, employing a stress-tolerant microorganism in the fermentation step would undoubtedly be more desirable than performing additional detoxification steps. During lactic acid bioproduction, acidic products, including the target product itself, may cause significant stress for a microbial host. Many microorganisms, particularly bacteria, suffer growth-rate inhibition under highly acidic conditions. Commonly, a neutralizer, such as calcium carbonate, is added to maintain the pH of the medium. However, in addition to increasing cost, some of these neutralizing agents are toxic for microorganisms and react with the fermentation products to form insoluble calcium salts that can easily mix with biomass and complicate the subsequent downstream process. This process generates the target product as calcium lactate instead of its (free) acid form. Consequently, a recovery step by acidification, which increases the total operating cost of lignocellulosic lactic acid production 15 , is needed to obtain lactic acid. Most importantly, this acidification process generates a by-product, gypsum, in a large quantity (1 ton per ton of lactic acid production 6 ), which must be disposed of in landfills 16 and, therefore, magnifies the environmental burden. Based on a life-cycle assessment (LCA), operational systems comprising the neutralization-acidification steps exhibited higher environmental impacts related to climate change, freshwater eutrophication, terrestrial acidification, etc., compared to process scenarios catalyzed by an acid-tolerant microorganism 15 . Hence, from both economic and ecological perspectives, eliminating neutralizing agents by applying a strong microbial host would be advantageous. Various approaches to obtaining a robust microorganism have been proposed. Tolerance engineering by genetic modification is an example of common tools to enhance strain robustness. For instance, the co-expression of TAL1 and ADH1 in Saccharomyces cerevisiae enhances ethanol production in a medium containing furfural 17 . Co-overexpression of HAA1 and PRS3 or disruption of FPS1 could also improve acetic acid tolerance 18 , 19 . Nevertheless, due to the complexity of biomass chemical composition, a large number of tolerance-related genes must be simultaneously introduced to the microorganism of interest 20 – 22 , making this approach cumbersome. On the contrary, the strategy proposed here focuses on increasing the lactic acid production of a naturally robust microorganism engineered rather than performing extensive genetic engineering. In the present study, we selected newly isolated yeast, identified as S. cerevisiae BTCC3, obtained from screening Ascomycota yeasts deposited in the Indonesian Culture Collection (InaCC). This strain can survive at low pH and in the presence of lignocellulose-derived chemical inhibitors, such as furfural, formic acid, acetic acid, and other inhibitors. However, similar to other yeasts, this strain lacks the metabolic pathway for lactic acid generation. Therefore, we introduced an exogenous L- LDH gene to enable the lactic acid fermentation from glucose as an example. This experiment intended to construct a microbial strain with phenotypes suitable for utilizing lignocellulosic biomass as a low-cost carbon feedstock, such as SCB, with high tolerance to acidic and chemical inhibitor stresses. Also, we considered the potential of this recombinant strain to ferment glucose to lactic acid without detoxifying and neutralizing treatments.",
"discussion": "Discussion Establishing a strong microbial host can simplify the overall process of lactic acid bioproduction because additional treatments, such as detoxification and neutralization, can be eliminated. Rather than employing extensive tolerance engineering, our approach attempted to exploit a microbial platform that has a natural tolerance to acid and numerous lignocellulose-derived inhibitors. S. cerevisiae BTCC3, an originally robust strain, was utilized as a fermentation host that was expected to be suitable for lactic acid production from lignocellulosic biomass. Pathway adjustment was conducted to reduce the metabolic flux to ethanol—a major product of fermentation by yeast—through the disruption of pyruvate decarboxylation gene, including PDC1 and PDC5 . However, although our strain could still maintain its rapid growth after several pathway adjustments, these genetic modifications also exerted several undesired effects. For instance, in all mutants, the accumulations of by-products, such as ethanol and glycerol, were higher than in wild-type strain, which could have been because of the response of the microbial host to cope with higher acid accumulations. In fact, the accumulations of ethanol and glycerol are known to induce the generation of NAD + , which has an essential role in countering the negative impact of various stressors in cells 24 , 42 , 43 . Also, our results revealed that inserting an additional copy of the LDH gene into the same locus does not necessarily improve lactic acid production. However, this could be the consequence of employing an identical promoter, i.e., TDH3p , in two different plasmids, namely pAUR101-TDH3pro-LcLLDH-dPDC1 and pAUR101-TDH3pro-LcLLDH-dPDC5. Promoter rivalry might have resulted in a conflict in the use of transcription factors during the expression of the two LDH genes. Moreover, we observed that the engineered strains grew slower when more genes were disrupted. For those reasons, keeping the genetic modifications at a modest level is much preferable rather than conducting immense gene modifications to the microbial host. In essence, with only a few pathway adjustments, we managed to enhance the accumulation of lactic acid from 0 to 43.23 g L −1 while still maintaining the natural feature of the strain to rapidly consume glucose. As one of the most concerning bottlenecks in industrial lactic acid production, removing the neutralizing step during fermentation to enable the generation of free-form lactic acid that requires no subsequent acidification is important. In fact, according to the life-cycle assessment and techno-economic analysis of SCB valorization to lactic acid, the removal of neutralizing agents by employing an acid-tolerant host could reduce the environmental burden and total capital investment because the process would no longer require an acidification reactor unit set after the fermentation stage 44 . Also, the production of second-generation (2G) lactic acid from SCB, to date, still requisites the neutralizing step, either at the beginning or during the fermentation. Our results proved that BTCC3 strain could still grow without any neutralizing treatment both in low and high cell density cultivation. However, increasing the initial cell concentration could optimize the amount of glucose consumed for lactic acid accumulation. Further, LA2 strain could also facilitate a rapid neutralizing-agent-free lactic acid generation using undetoxified hydrolysate of SCB with a level of productivity that was competitive to other studies that still included additional treatment, such as neutralization, detoxification, and cell adaptation, in their experiment 38 – 41 . Based on our best knowledge, no previous reports discuss the SCB valorization to lactic acid that utilizes metabolically engineered strain as a host in this simplified setting. For upcoming experiments, constructing a switchable metabolic disruption strategy using an optogenetic tool 45 or advanced CRISPR strategies may help knocking-out other gene candidates without severely reducing the rate of cell growth. Utilizing xylose as an additional carbon source by introducing a xylose-assimilating pathway is also essential as this sugar is the second most abundant monosaccharide present in various biomass. Also, BTCC3 strain can also be harnessed as a microbial factory to produce other important chemicals from various renewable sources. In conclusion, sustainable lactic acid production is more efficient when several treatment stages can be omitted from the process. In this work, we demonstrated a rapid fermentation of lactic acid from hydrolysate of sugarcane bagasse without performing detoxification and neutralization steps. This process was catalyzed by an originally robust microorganism with only a few metabolic adjustments. This engineered host might be suitable for cost-effective and greener lactic acid production for industry."
} | 3,057 |
37885595 | PMC10598558 | pmc | 6,557 | {
"abstract": "Hydrogels are compelling\nmaterials for emerging applications including\nsoft robotics and autonomous sensing. Mechanical stability over an\nextensive range of environmental conditions and considerations of\nsustainability, both environmentally benign processing and end-of-life\nuse, are enduring challenges. To make progress on these challenges,\nwe designed a dehydration–hydration approach to transform soft\nand weak hydrogels into tough and recyclable supramolecular phase-separated\ngels (PSGs) using water as the only solvent. The dehydration–hydration\napproach led to phase separation and the formation of domains consisting\nof strong polymer–polymer interactions that are critical for\nforming PSGs. The phase-separated segments acted as robust, physical\ncross-links to strengthen PSGs, which exhibited enhanced toughness\nand stretchability in its fully swollen state. PSGs are not prone\nto overswelling or severe shrinkage in wet conditions and show environmental\ntolerance in harsh conditions, e.g., solutions with pH between 1 and\n14. Finally, we demonstrate the use of PSGs as strain sensors in air\nand aqueous environments.",
"conclusion": "Conclusion In this study, we developed a tough and recyclable PSG through\na dehydration–hydration method using water as the only solvent.\nThe approach is facile and environmentally benign and could be applied\nto various hydrogels. In general, traditional hydrogels are usually\nweakened upon hydration, shrink severely upon solvent exchange, and\ncannot be recycled. In contrast, the PSG’s hydrophilicity favors\nwater absorption, and its phase-separated domains inhibited overswelling\nin aqueous solution and stiffen the polymer network. The phase-separated\nstructure also enabled the PSG to achieve enhanced toughness and excellent\nstability in various liquid environments such as seawater, organic\nsolvents, and extremely acidic/alkaline solutions. Additionally, the\nPSG exhibited antifouling and self-cleaning properties. These characteristics\nmake the PSG highly adaptable for applications in challenging environments.\nAs demonstrated, the tough and recyclable PSG has great potential\nfor use as a strain sensor in air and underwater, thus furthering\nthe\npotential for applying hydrogel materials in smart sensors and electronic\ndevices. Overall, this work introduces a highly effective dehydration–hydration\nmethod for developing phase-separation-induced tough gels that are\nrecyclable.",
"introduction": "Introduction Hydrogels are soft and elastic materials\nconsisting of flexible\npolymer chains solvated in water. The unique combination of softness\nand hydration enables their impact on fields including, personal hygiene\n(e.g., diapers, personal healthcare products, etc.), 1 , 2 agriculture (e.g., carriers of agrochemicals), 3 , 4 environmental\nremediation (e.g., adsorbents and evaporators for water purification), 5 , 6 and food packaging systems. 7 Hydrogels\ncan be functionalized and are also biocompatible, which are attractive\nfeatures for applications at the interface of medicine and electronics,\nincluding drug delivery, 8 , 9 soft actuators, 10 − 12 strain sensors, 13 − 15 touch panels, 16 and artificial\nmuscles. 17 , 18 Despite their tremendous potential and societal\nbenefit, the growing consumption of hydrogels could contribute to\nenvironmental pollution as they are generally non-biodegradable under\nnatural conditions. 19 − 21 Moreover, conventional hydrogels are covalently cross-linked,\nthus challenging to recycle and reprocess after use or damage. 22 , 23 One of the most effective approaches to resolve these shortcomings\nis to replace conventional covalent cross-linked gels with supramolecular\nhydrogels (SGs) cross-linked by dynamic hydrogen bonds. 24 Supramolecular hydrogels, however, are prone\nto overswelling and disintegration in aqueous environments because\nthe presence of water molecules disrupts hydrogen bonds, 25 , 26 thus further weakening their mechanical strength. Such instability\nand poor mechanical properties inhibit the long-term use and implementation\nof SGs. Therefore, constructing mechanically robust, yet recyclable\ngels that resist overswelling in a wide variety of aqueous environments\nis particularly attractive, but remains a significant materials challenge. In another approach, phase separation has been exploited to improve\nthe stiffness and restrict the overswelling of hydrogels by increasing\nthe physical cross-linking density and reducing the flexibility of\nchain segments. 27 , 28 Recently, researchers have embarked\non efforts to design phase-separated hydrogels that include a mechanism\nfor energy dissipation, such as hydrogen bonding, 29 ionic bonding, 30 , 31 hydrophobic association, 31 − 33 or crystallization, 34 to increase strength\nand toughness. The most widely employed approaches to induce phase\nseparation in hydrogels include the use of solvent exchange 35 − 37 or the addition of salt, i.e., the salting out effect. 33 , 38 However, the use of organic solvents and other chemicals is not\nenvironmentally benign nor are the gels reprocessable, which may further\nnegatively impact the environment at the end of service life. Finally,\nthe structures of gels formed by these approaches are unstable when\nre-exposed to a wet environment. With these considerations in\nmind, we introduce a dehydration–rehydration\nmethod to create tough and recyclable, phase-separated supramolecular\nhydrogels (PSGs). The PSG was prepared by copolymerization of sodium\nstyrenesulfonate (NaSS) and sulfobetaine methacrylate (SBMA) in an\naqueous PVA solution, followed by dehydration via air drying and rehydration\nvia swelling in water ( Figure 1 a). The approach is facile, sustainable, and low-energy: all\nsynthesis occurred in water, and the dehydration–rehydration\nprocess required no energy input. Upon dehydration, strong polymer–polymer\ninteractions between PVA and poly(NaSS- co -SMBA) copolymer\nwere activated, as illustrated in Figure S1 . Subsequent, rehydration weakened hydrogen-bonding interactions\nbetween polymer chains while simultaneously promoting the aggregation\nof the hydrophobic polymer segments, leading to a PSG composed of\nwater-rich and polymer-rich domains ( Figure 1 a). The polymer-rich domains acted as robust\nphysical cross-links, which endowed the PSG with an unusual combination\nof material properties, including toughness, restricted swelling,\nand recyclability. The PSG exhibited stability in various environments,\nincluding mixed solvents containing ethanol, isopropyl alcohol (IPA),\ndimethyl sulfoxide (DMSO), and even extremely acidic/alkaline conditions\nwith pH 1 and 14. We demonstrate PSG’s performance as a flexible\nand recyclable strain sensor in both air and underwater. Figure 1 Fabrication\nand porous structures of the hydrogel. (a) Schematic\nof the fabrication route for PSGs. Lifting performance and microstructures\nof (b) SGs and (c) PSGs after dehydration–hydration treatment.",
"discussion": "Results and Discussion The synthetic\nroute for tough and recyclable PSGs is illustrated\nin Figure 1 a. In the\nfirst step, NaSS and SBMA monomers and Irgacure 2959, the initiator,\nwere dissolved in an aqueous PVA (10 wt %) solution (see chemical\nstructures in Figure S1 ). The aqueous solution\ntransformed into a soft and light-yellow SG after being irradiated\nby UV light at 365 nm for 2 h. The failure to form a gel using solely\nPVA or NaSS in the presence of SBMA highlights the essential role\nof the polymer interactions between PVA and poly(NaSS- co -SMBA) in the gelation process ( Figure S2 ). The SG was weak and translucent because of the weak hydrogen-bonding\ninteractions between the −OH groups of PVA and the −SO 3 – groups of the poly(NaSS- co -SMBA) copolymer ( Figure 1 b). In the second step, the SG was dehydrated via air-drying\nat room temperature and subsequently incubated in water for 12 h to\nform tough and recyclable PSG. Once immersed in water, the hydrophilic\ngroups facilitated water diffusion into the dehydrated SG network.\nAs the polymer backbone is hydrophobic, hydrogen bonding (between\n−SO 3 – and −OH) and electrostatic\ninteractions (between SO 3 – and −N + (CH 3 ) 3 ) favor the aggregation of polymer\nchains. These competing effects lead to phase separation and the resulting\nvisual color transformation to opaque. The morphological difference\nbetween SG and PSG was illustrated\nby scanning electron microscopy (SEM). The SG exhibited a rough and\nhomogeneous structure ( Figure 1 b). In contrast, the PSG exhibited a continuous porous structure\nwith uniformly distributed pores ( Figure 1 c). Another stark difference between SGs\nand PSGs is reflected in their mechanical performance. As illustrated\nin Figure 1 b,c, we\nattempted to lift a 2 lb weight in water using an SG and PSG with\na cylindrical structure (45 mm × 5 mm, Figure S3 ). The SG stretched but failed before lifting the weight.\nIn contrast, the PSG was capable of lifting the weight. Notably, lifting\ntests were performed in water, implying PSG’s viability for\napplications in aqueous environments. The difference in mechanical\nresponse is due to the difference in morphology. The phase-separated\nhard domains of PSG act as physical cross-links that strengthen the\npolymer network. Remarkably, the PSG achieves a desirable balance\nbetween mechanical robustness and water content (∼90% water\nby mass), which distinguishes it from conventionally cross-linked\ngels or SGs without molecular interactions. The dehydration–hydration\napproach and design strategy has been applied to additional chemistries\nto illustrate the generality of the approach ( Figure S4 ). To examine the quantitative effect of phase\nseparation on the strength\nand toughness of the gels, tensile and compression tests were performed\nfor SG and PSG samples ( Figure 2 ). The SG exhibited an elongation at break of ∼820%\nand a fracture stress of ∼130 kPa ( Figures 2 a and S5 ). In\ncontrast, a maximum stress up to 340 kPa and a high stretchability\nof 433% were observed for the PSG ( Figures 2 a and S5 ). Both\nSG and PSG exhibited substantial extensibility, as they can dissipate\nstress by dissociation of noncovalant physical bonds. The enhanced\nstiffness of PSG can be further improved by an order-of-magnitude\nby incubation in a salt solution due to the Hofmeister effect ( Figure S7 ). This is a consequence of the complexes\nformed between introduced ions and water, leading to significant\naggregation and crystallization of the polymer chains. As shown in Figure 2 b, the compressive\nstrength of PSG (∼1.8 MPa at 80% compression) is ∼6\ntimes greater than SG (∼0.3 MPa at 80% compression). After\nremoval of the stress, the PSG rapidly recovered its original shape,\nwhile the SG exhibited only a slight recovery ( Figure S6 ). Even after 50 continual loading–unloading\ncycles at a strain of 100% and a compression of 50%, the PSG maintained\nfatigue resistance, as revealed by the nearly constant hysteresis\nloops in Figure 2 c,d.\nThe PSG’s remarkable elasticity was also demonstrated through\nan extreme compressibility test that was performed using a car, as\nseen in Figure 2 e.\nThe PSG, initially measuring 120 mm in diameter and 3 mm in thickness,\nwas enclosed in a plastic bag and positioned between two plastic sheets\nto prevent slippage and surface contamination during the test. A four-wheel\ncar with a total weight of ∼2200 kg was driven over the PSG\nrepeatedly and held atop the PSG for 1 min ( Video S1 ). After the car-compression test, no fracture or irreversible\ndeformation was observed for the PSG. In addition, even in an aqueous\nenvironment, the PSG possessed excellent stretchability of 420% ( Figure 2 f), which ensured\nits viability for applications in different environmental conditions. Figure 2 Mechanical\ncharacterization of the hydrogels. (a) Typical tensile\nstress−strain curves of SG and PSG. (b) Compressive stress–strain\ncurves of SG and PSG. (c) Consecutive cyclic tensile stress−strain\ncurves of the PSG for 50 successive loading–unloading cycles\nat a strain of 100%. (d) Consecutive cyclic compressive stress−strain\ncurves of the PSG. (e) Photographs of a car-compression test. (f)\nPhotographs of underwater stretchability of PSG at a strain of 50%. The network formation of PSGs is enabled by noncovalent\ninteractions,\nwhich enable recyclability and reprocessability of the gel. Reprocessing\nis accomplished in a two step process: (1) heating the PSG and (2)\nperforming the dehydration–hydration process ( Figure S8 ). Heating to ∼80 °C disrupts the hydrogen\nbonds and hydrophobic interactions within the PSG and enhances polymer\nmobility. The first step results in a translucent liquid, which upon\ncooling can be reformed into various shapes via the second step, i.e.,\nthe dehydration–hydration process ( Figure 3 a). The morphology of the reprocessed gel\nafter dehydration–hydration was examined by AFM and SEM. The\nstructure is consistent with that of the original material ( Figures 3 b and S9 ). Figure 3 (a) Photos demonstrating the recycling process\nfor the PSG gel.\n(b) AFM and SEM images of the recycled PSG gel. SEM image shows homogeneous,\nevenly distributed pores within the recycled PSG gel. The stability of PGSs in an aqueous environment is a major\ncriterion\nfor their practical use. Visualization of PSGs after swelling in water\nwas compared to a covalently cross-linked polyNaSS hydrogel and noncovalent\nSGs without the dehydration–rehydration process ( Figures 4 and S10 ). The polyNaSS hydrogel (5 mol % cross-linking density) expanded\nsignificantly in water with a swelling ratio of ∼20 g/g and\nbroke into pieces when gently agitated ( Figure 4 a). The SG softened when submerged in water\nand eventually fully dissolved ( Figure S10 ). In contrast, the PSG absorbed water and reached an equilibrium\nswollen state within 24 h and remained unchanged in water for a minimum\nof 10 days, i.e., the length of the experiment ( Figure 4 c). No overswelling or dissolution was observed.\nIn pure water, the abundance of −SO 3 – groups within PSG caused a higher osmotic pressure and strong electrostatic\nrepulsion, which facilitated water molecule penetration and network\nexpansion. However, the phase-separated hard domains act against expansion\nby limiting excess water adsorption. On balance, the two competing\neffects resulted in a material with an anti-overswelling behavior.\nThe anti-overswelling of PSG is distinctive and indicates that the\nphase-separated domains can stabilize the supramolecular bonds in\na wet environment and maintain a stress ( Figure 4 b). Figure 4 Swelling properties in various liquids. (a)\nOverswelling behavior\nof conventional PolyNaSS hydrogels. (b) Anti-overswelling of PSG and\ndemonstration of toughness and shape recovery upon loading of a 2\nkg weight. (c–e) Swelling kinetics of PSG in water, pH 1, 10,\nand 14, seawater, and organic solvents. SGs are typically unstable in water, 23 salt solutions, 39 organic solvents, 40 and acidic/alkaline conditions. 25 , 41 For instance, Wang et al. developed a tough SG of poly(methacrylamide- co -methacrylic acid) by utilizing hydrophobic methyl groups\nto stabilize hydrogen bonds. 25 The gel\nwas stable in neutral or weakly alkaline conditions (pH ≤ 9.6)\nbut dissolved in strongly alkaline conditions. Creating hydrogels\nthat can remain stable in harsh environments remains a challenge.\nPSGs are structurally stable under various environmental conditions,\nincluding extreme acidic/alkaline solutions, seawater, concentrated\nsalt solutions, and mixed solvents ( Figures 4 d,e and S11–S13 ). For instance, PSGs exhibited similar swelling behavior in pure\nwater and pH = 1, 10, and 14 solutions. In seawater, the PSG’s\nswelling ratio was slightly lower than that in pure water and decreased\nfurther in more saline solutions ( Figure 4 d). Finally, upon exposure to various mixed\nsolvents, the PSGs maintained a similar swelling ratio and shape ( Figures 4 e and S13 ). The remarkable stability in diverse conditions\ncan be attributed to the combination of osmotic pressure, anti-polyelectrolyte, 42 − 44 and Hofmeister effects 45 − 48 (further explanation can be found in the Supporting Information ). The integration\nof antifouling properties into mechanically strong\nhydrogels is crucial for their use in many applications, including\nas flexible strain sensors. 49 , 50 Here, we demonstrate\nthe incorporation of antifouling within the suite of synergistic properties\nof PSG. The underwater–oil contact angles (OCA) of PSG were\nmeasured after being immersed in water ( Figure 5 a). An OCA of ∼163° for a high\nviscosity olive oil was observed, indicating PSG’s underwater\nsuperoleophobicity. When the PSG was tilted at a small-angle underwater,\nthe oil droplets rolled quickly over the PSG surface ( Figures 5 b,c and S14 ). This effect was attributed to the zwitterionic components’\nability to form a hydration layer, which acted as a lubricant to decrease\nthe oil adhesion force and penetration. 51 − 53 In a water environment,\na jet of 1,2-dichloroethane spontaneously bounces off the surface\nof the PSG without any noticeable oil residue remaining ( Figure S15 ). When exposed to a pure oil, the\nPSG surface was initially partially wetted with olive oil but was\nrestored to a pristine state by washing with water ( Figure 5 d). This property is vital\nfor the use of hydrogels in strain sensors and biomedical applications. 49 , 54 − 56 Figure 5 Antifouling and self-cleaning property. (a) Underwater–oil\ncontact angle (OCA) of olive oil on the PSG. (b) Underwater sliding\nbehavior of (b) olive oil and (c) 1,2-dichloroethane droplets on the\nPSG. (d) Photos of the PSG contaminated by Nile red-labeled olive\noil before and after being washed with water. (e) Demonstration of\nconductivity of PSG. (f) Relative resistance changes of PSG in air\nunder different strains. (g) Real-time resistance variations of the\nPSG upon stretching to 50% strain for 70 cycles. The good ionic conductivity of the PSG suggests its use as a sensor\n( Figure S7b ). Ionic conduction characteristics\nwere observed for the PSG by integration into a circuit with a light-emitting\ndiode (LED) indicator. As shown in Figure 5 e, the LED indicator lit under a 3 V power\nsupply by the PSG. As a strain sensor, the relative resistance changes\nof PSG in air were investigated under stepped cyclic strains of 10%,\n20%, 40%, and 60% ( Figure 5 f). The relative resistance change rate at different strains\nrecovered the initial value, indicating the strain-sensing reversibility.\nMore importantly, the PSG exhibited durability and fatigue resistance:\nthe resistance change remained consistent during continuous stretching\n(∼70 cycles) at a fixed strain of 50% ( Figure 5 g). The PSG’s stability was also revealed\nby the consistent current obtained from chronoamperometry experiments\nunder prolonged cathodic potential sweeps ( Figure S16 ). Remarkably, the PSG is amenable for use in an underwater\nenvironment owing to its stability in wet environments. As shown in Figure S17 , significant differences in electrical\nsignal were clearly observed upon stretching the PSG to strains of\n∼20% and ∼50% in distilled water. The PSG sensor did\nnot suffer any lag or attenuation upon continuous stretching, which\nindicates a stable cycle performance and short response–recovery\ntime. Therefore, incorporating phase separated segments into a hydrogel\nshows great potential for addressing the primary issue of long-term\nunderwater sensing stability in hydrogel-based sensors."
} | 4,875 |
24292439 | PMC4070498 | pmc | 6,558 | {
"abstract": "An NAD-dependent d -lactate dehydrogenase ( d -nLDH) of Lactobacillus bulgaricus ATCC 11842 was rationally re-designed for asymmetric reduction of a homologous series of α-keto carboxylic acids such as phenylpyruvic acid (PPA), α-ketobutyric acid, α-ketovaleric acid, β-hydroxypyruvate. Compared with wild-type d -nLDH, the Y52L mutant d -nLDH showed elevated activities toward unnatural substrates especially with large substitutes at C-3. By the biocatalysis combined with a formate dehydrogenase for in situ generation of NADH, the corresponding ( R )-α-hydroxy carboxylic acids could be produced at high yields and highly optical purities. Taking the production of chiral ( R )-phenyllactic acid (PLA) from PPA for example, 50 mM PPA was completely reduced to ( R )-PLA in 90 min with a high yield of 99.0% and a highly optical purity (>99.9% e.e.) by the coupling system. The results presented in this work suggest a promising alternative for the production of chiral α-hydroxy carboxylic acids.",
"discussion": "Discussion Enzymes have been widely accepted as useful biocatalysts for synthesis of a series of valuable organic compounds, especially their capacities for asymmetric catalysis. However, despite the widespread uses of enzymes in biosynthesis, the candidate enzymes with desirable characteristics are scarce. Many methods for exploitation and screening of the target enzymes have been reported in previous reports. Here, we demonstrated the utility of rational re-design to modify d -nLDH by using bioinformatics and gene cloning techniques. The resultant biocatalysts, d -nLDH mutants, possess good catalytic activities capable of transforming targeted substrates. The approach adopted in this study is efficient and the preconditioning is based on the structure and related catalytic mechanism of the objective enzyme. d -nLDH has been widely researched, including its property, characteristic, and structure. Based on these preconditions, Tyr52 and Phe299 were considered as pivotal residues for substrate spectrum and chosen for site-directed mutagenesis. Enantiomerically pure α-hydroxy carboxylic acids are important synthons for fine chemicals. Stereospecific nLDHs responsible for enantiomerically pure lactic acid production are of interest, as optically pure lactic acid is the most common α-hydroxycarboxylic acid. In fact, in addition to catalyzing the reduction of pyruvate, d -nLDH has been reported to reduce other α-keto carboxylic acids. For example, the d -nLDH in B. coagulans has proved to be responsible for transformation 8a into 8b 3 . d -nLDH of L. pentosus has showed high activities toward hydroxypyruvate (2a), α-ketobutyrate (3a) and 8a 29 . However, the catalytic efficiencies of these d -nLDHs were poor on non-natural substrates. Therefore, the mutants obtained in this study are attractive for production of α-hydroxy carboxylic acids. Because conversion of α-keto carboxylic acids to α-hydroxy carboxylic acids is accompanied by the oxidation of NADH to NAD, a cosubstrate is necessary to supply NADH. Therefore, the E. coli transformant coexpressing both ldhD mutant and fdh was constructed. In the absence of FDH, the reduction of 8a was slow and then stopped after the exhaustion of intracellular NADH ( Fig. 4B ). Thus, this process provides excellent bioreduction efficiency and high enantioselectivity for the production of α-hydroxy carboxylic acids. In summary, the substrate selectivity of d -nLDH was successfully altered, and its activity toward substrates with large aliphatic or aromatic groups at C-3 was drastically improved. This study expands its range of application in the production of ( R )-α-hydroxy carboxylic acids. More importantly, because of the high yield and high stereoselectivity of d -nLDH Y52L mutant, the whole-cell catalysis system containing d -nLDH Y52L mutant and FDH was successfully applied to the direct synthesis of ( R )-phenyllactic acid. The method developed in this study could be used as a promising alternative for the production of highly optically pure α-hydroxy carboxylic acids. Our results for d -nLDH mutants might open up a way to reconstruct other enzymes, such as NAD-dependent l -lactate dehydrogenases and NAD-independent lactate dehydrogenases, based on their structures in order to modify the substrate spectra and improve the catalytic efficiency for the synthesis of valuable chiral compounds."
} | 1,103 |
33972618 | PMC8110780 | pmc | 6,559 | {
"abstract": "The Southwestern Atlantic rocky reef ecosystems are undergoing significant changes due to sun-corals ( Tubastraea tagusensis and T. coccinea ) invasion. At Búzios Island, on the northern coast of São Paulo State, where the abundance of T. tagusensis is particularly high, some colonies are displaying tissue necrosis, a phenomenon never reported for this invasive nor any other azooxanthellate coral species. Using next-generation sequencing, we sought to understand the relationship between T. tagusensis tissue necrosis and its microbiota. Thus, through amplicon sequencing, we studied both healthy and diseased coral colonies. Results indicate a wide variety of bacteria associated with healthy colonies and an even higher diversity associated with those corals presenting tissue necrosis, which displayed nearly 25% more microorganisms. Also, as the microbial community associated with the seven healthy colonies did not alter composition significantly, it was possible to verify the microbial succession during different stages of tissue necrosis (i.e., initial, intermediate, and advanced). Comparing the microbiome from healthy corals to those in early tissue necrosis suggests 21 potential pathogens, which might act as the promoters of such disease.",
"introduction": "Introduction Due to the ever-increasing anthropogenic challenges, coral environments are being impacted by pollution, overfishing 1 , bleaching 2 – 4 , and fluctuations and changes associated with the corals’ symbiont community 5 – 11 . Such fluctuations and changes are disrupting the long-established species-specific relationships between corals and microorganisms like Bacteria , Archaea , eukaryotes, and viruses, ultimately resulting in several diseases in their coral hosts 5 , 7 , 9 , 11 – 15 . Our understanding of the factors that influence coral's health and, consequently, its symbiont community, has progressed mainly through studies that determined the microbiota associated with healthy 16 – 19 and diseased corals 7 , 11 , 14 , 15 . In brief, these studies suggested that there are fundamental microorganisms to the host and that the balance of the microbial community is the result of long (co-evolutionary) and short-term (ecological) processes acting simultaneously. Consequently, biotic and abiotic conditions that trigger coral stress unbalance such relationship, which, in turn, may lead to several host diseases 20 . Nowadays, there are over 20 diseases known to affect scleractinian corals 21 , but only a few had its pathogens identified 20 . Other studies have described the changes in the coral microbiota, indicating that the rise in the abundance of microorganisms from the genus Ruegeria leads to several coral diseases 10 , 11 , 14 , 22 – 24 . In Brazilian waters, analyses of the microbial community have been performed on the endemic corals Mussismilia braziliensis 7 , 25 and Mussismilia hispida 26 , 27 , as well as in other more widely spread species, such as Madracis decactis 26 , 28 , Siderastrea stellata 29 , and also on the invasive corals Tubastraea coccinea 26 and T. tagusensis 30 . Some of these studies focused on the microbiota associated with healthy corals, but the microbial community associated with bleached and/or diseased colonies of M. braziliensis, S. stellata, and M. decactis were also determined. Nonetheless, besides diseases 7 and acute bleaching events 31 , the Brazilian coral- and rocky-reef environments are being severely impacted by the invasion of sun-corals, T. tagusensis, and T. coccinea 32 – 34 . Currently, T. tagusensis is widespread in Brazil, particularly in São Paulo, Rio de Janeiro, Espírito Santo, Bahia, and Ceará states 35 – 40 . At Búzios Island, São Paulo State, a place that was known to harbor mainly M. decactis , M. hispida , Palythoa caribaeorum , turf, and sponges 41 , several rocky shores are saturated (100%) with invasive corals. Although several biological and ecological aspects are known to be key to the invasion success of T. tagusensis, it seems like they may be related to its reproductive characteristics, such as high production of planula 42 ; early reproductive age 43 ; clonality 44 , 45 ; regeneration capacity 46 ; and quick incubation—all hallmarks of opportunistic species 47 . As a result of asexual planulae production, Southwestern Atlantic invasive T. tagusensis displays a high clonal rate 44 , and therefore a low genetic diversity, a phenomenon previously observed in other invasive species populations 48 . Such a decrease in diversity is caused by the founding effect—few specimens colonizing a new environment, which can reduce the adaptive potential of the species over time 48 . This condition of T. tagusensis may be the downside of this invasive species because, in addition to having a high rate of clonality, it also showed the absence of significant differences in the microbial community along a depth gradient 30 . In 2014, when several rocky shores of the Búzios Island were already saturated with invasive corals, colonies of T. tagusensis displaying tissue necrosis were observed. Initially, affected colonies were seen in only one small location, but since then, affected colonies have become widespread. Such tissue necrosis is the first report of disease in azooxanthellate scleractinian corals. To better understand this tissue necrosis, here we characterize and compared its bacterial composition during different necrosis stages. Thus, the T. tagusensis microbiome was quali-quantitatively determined during the tissue necrosis progression, but a better understanding of the cause of such lesions requires further studies.",
"discussion": "Discussion Changes in the microorganism community associated with the colonies of the invasive coral Tubastraea tagusensis are significant from the first signs of tissue necrosis. Such changes lead to increased microbial diversity, richness, and the effective number of genera (e.g. bacteria), which is compatible with previous studies on coral diseases 54 – 57 . However, most of the microbiota associated with diseased corals (~ 83.6%; 213 OTUs) is present in healthy colonies. Thus, despite the detection of nearly 750 OTUs exclusive in diseased colonies, they did not replace the microbiota associated with healthy hosts but instead reduced it as they grew. Despite the community shift, the relative frequency of bacterial OTUs found exclusively in diseased colonies is low, with the highest disease-specific genus composing 5.1% of the total microbiota in the intermediate stage (unclassified Cyclobacteriaceae ). Such low frequency may indicate that the majority of the microbiota in diseased corals is composed of opportunistic species and/or secondary colonizers 55 , 56 , which survived in the coral due to the imbalance of the microbial community caused by the primary infection. However, the opposite has also been detected (e.g., 63 OTUs present only in healthy colonies, all with low relative frequencies [an average of ~ 0.097%, representing 3.19 of the total abundance]). We also observed that the necrotic affected area is not directly related to the diversity of associated microorganisms. In the initial stage, in which it has less than 0.25 cm 2 , we identified a greater diversity of microorganisms. Also, this is a stage of tissue necrosis that has the most exclusive diversity, suggesting candidate triggers of the disease. In the intermediate stage, which displayed an enlargement of the area affected by the necrosis (Fig. 1 C), the number of identified OTUs is lower compared to that from the onset of the disease, possibly indicating a holobiont response. At the advanced stage, the number of identified genera is higher than that in the previous stage, but such increment is not significant compared to the beginning of the infection. Such a variation in the microbiome indicates a rapid destabilization of the symbiont community during the initial stages of T. tagusensis infection, and subsequent colonization by opportunistic microorganisms, as observed in other diseases from zooxanthellate counterparts 55 , 58 . In parallel to the microbiome related to necrosis, attention was given to the microbial core. Among the eight genera considered to be part of the microbial core of T. tagusensis (Zanotti et al. 2020), four were not found in colonies presenting tissue necrosis (Table 3 ). Such a difference might be a result of the microbiome imbalance caused by the disease. Nonetheless, the relative frequency of four microbial core genera was significantly lower in polyps presenting necrosis. However, Ruegeria had different patterns once it was the most abundant genera in diseased colonies. This genus is known to be associated with several healthy 14 , 59 – 61 and diseased zooxanthellate corals 10 , 11 , 14 , 22 – 24 . Previous studies suggested that Ruegeria inhibits/controls the growth of other bacteria genera through tropodithietic acid 62 . Also, Ruegeria inhibited a widely known opportunistic coral pathogen, Vibrio coralliilyticus 63 . Statistical analyses of the microbiome also indicate that within the bacterial OTUs identified, 21 (Table 4 ) might represent pathogenic ones. Of these, only five were classified to genus level, one of them being Ruegeria , which as mentioned has no pathogenic profile. Within the remainder, only Arenicella has been associated with coral disease (skeletal growth anomalies in Platygyra carnosa 23 ), although the herein detected Sphingomonadaceae has also been described as a putative pathogen associated with Acropora cervicornis and A. palmata disease 64 . Besides that, although there is no previous report of Epibacterium in relationship to scleractinian corals (a group described associated with seaweed surfaces 65 ), its detection and increased abundance during T. tagusensis tissue necrosis may be related to the exposure of the coral skeleton to the environment. As the necrosis advances, the exposed skeleton becomes a potential substrate for several organisms, like filamentous algae. Apart from the aforementioned OTUs, several remained unclassified at the genus level. Among them, the Flavobacteriaceae and Rhodobacteriaceae were found in high abundance in T. tagusensis tissue necrosis and were previously associated with the following scleractinian diseases: White band 55 ; White plague 7 , 66 ; Yellow-band 14 ; White syndrome 67 ; White-spot syndrome in Porites 11 ; White Plague 66 ; stony coral tissue loss disease 68 ; and Black band 10 . Additionally, the skeleton fragility in those colonies affected by the tissue necrosis may be related to a higher frequency of Mastigocoleus , a bacterial genus known to have bioerosion capabilities 69 . Although concerning as another disease is reported to a scleractinian coral, the described tissue necrosis brings a glimpse of hope in the face of T. tagusensis unprecedented bioinvasion and spreading in the Southwestern Atlantic. Thus, despite its invasion capacity, the founding effect 44 , 70 , and the associated microbial community without major significant differences might be a disadvantage for this species, making it highly susceptible to diseases as the tissue necrosis reported herein."
} | 2,811 |
23448147 | null | s2 | 6,563 | {
"abstract": "All methane-producing Archaea (methanogens) are strict anaerobes, but the majority of species are tolerant to oxidants. Methanosarcina species are important environmental and industrial methanogens as they are one of only two genera capable of producing methane with acetate. Importantly, Methanosarcina species appear to be the most oxidant-tolerant; however, the mechanisms underlying this tolerance are poorly understood. We report herein two similar methods (spot-plating and microtiter plate) developed to examine the oxidant tolerance of Methanosarcina acetivorans by viability assessment. Both methods revealed that M. acetivorans can tolerate exposure to millimolar levels of hydrogen peroxide (H2O2 ) without a complete loss of viability. The exogenous addition of catalase was also shown to protect M. acetivorans from H2O2 toxicity, indicating catalase can serve as an antioxidant enzyme in methanogens even though oxygen is a byproduct. Of the two methods, the microtiter plate method provided a simple, reliable, and inexpensive method to assess viability of M. acetivorans. Combined with recent advances in the genetic manipulation of methanogens, methods in assessment of methanogen oxidant tolerance will aid in the identification of components of the antioxidant defense systems."
} | 324 |
36335212 | PMC9860064 | pmc | 6,565 | {
"abstract": "Due to their potential impact on ecosystems and biogeochemistry, microbial interactions, such as those between phytoplankton and bacteria, have been studied intensively using specific model organisms. Yet, to what extent interactions differ between closely related organisms, or how these interactions change over time, or culture conditions, remains unclear. Here, we characterize the interactions between five strains each of two globally abundant marine microorganisms, Prochlorococcus (phototroph) and Alteromonas (heterotroph), from the first encounter between individual strains and over more than a year of repeated cycles of exponential growth and long-term nitrogen starvation. Prochlorococcus - Alteromonas interactions had little effect on traditional growth parameters such as Prochlorococcus growth rate, maximal fluorescence, or lag phase, affecting primarily the dynamics of culture decline, which we interpret as representing cell mortality and lysis. The shape of the Prochlorococcus decline curve and the carrying capacity of the co-cultures were determined by the phototroph and not the heterotroph strains involved. Comparing various mathematical models of culture mortality suggests that Prochlorococcus death rate increases over time in mono-cultures but decreases in co-cultures, with cells potentially becoming more resistant to stress. Our results demonstrate intra-species differences in ecologically relevant co-culture outcomes. These include the recycling efficiency of N and whether the interactions are mutually synergistic or competitive. They also highlight the information-rich growth and death curves as a useful readout of the interaction phenotype.",
"conclusion": "Conclusions and future prospects Elucidating the mechanisms of microbial interactions requires well-characterized model systems. However, extending the insights from such models across the diversity of organisms and environmental conditions remains challenging. Our results from the highly simplified system of multiple Prochlorococcus and Alteromonas strains provide an important step towards this goal. Using the rich information on interaction phenotypes present in the growth and decline curves, we identify conserved and strain-specific facets of these interactions. Despite the genetic diversity across the Alteromonas strains studied [ 30 ], it was primarily the identity of the Prochlorococcus strain that determined the interaction phenotype. This manifests in the growth and decline rates, in the shape of the curve (primarily the decline phase), in the amount of N retained in biomass, and in whether the co-cultures are mutually synergistic or, potentially, competitive. Under our laboratory conditions, it is likely that the combined response of both interacting partners to nitrogen starvation underlies the dynamics of the long-term co-cultures, although other stressors such as the increase in osmolarity/salinity or the accumulation of waste products cannot be ruled out [ 18 , 34 , 60 ]. This response is dynamic, as illustrated by the reproducible deviations of the fluorescence curves from the monotonic decline predicted by all models tested (“second growth” stages; Fig. 4 ). Three different (non-mutually-exclusive) processes may underlie these dynamics. Firstly, it is likely that one or both organisms modify their physiology or metabolism over time, for example through the activation of stringent responses, utilization of N or C storage pools, rewiring of metabolism to utilize available N sources, or activation of mechanisms such as extracellular enzymes allowing the cells to access previously unusable substrates (e.g. [ 61 , 62 ]). Secondly, it is possible that there are “invisible” ecological dynamics underlying the observed fluorescence curves, for example cyclic changes in the abundance of Alteromonas cells. Under such a scenario, rapid Prochlorococcus mortality could produce an increase in Alteromonas abundance, resulting in degradation and remineralization of dead Prochlorococcus biomass and the release of resources that can drive subsequent Prochlorococcus growth. Thirdly, both Prochlorococcus and Alteromonas populations may be evolving, for example through emergence of genetically distinct populations better adapted to nutrient starvation (reminiscent of the GASP phenotype described in E. coli and other bacteria [ 63 ]). Why is it the identity of the primary producer ( Prochlorococcus ) rather than the heterotrophic “recycler” ( Alteromonas ) that determines the outcome of the co-culture? A-priori, it was reasonable to assume that the co-culture phenotype would be affected by the differences between the Alteromonas strains in their ability to degrade and utilize polysaccharides and a variety of other organic molecules [ 30 , 64 ]. We speculate that the increased growth of Alteromonas in the co-cultures compared to the axenic ones is fueled primarily by the availability of major biomass components released by Prochlorococcus as they die, such as proteins, amino acids, and nucleotides. Such common macromolecules do not require highly specialized metabolic processes to degrade and utilize, and hence can be utilized by all of the Alteromonas strains [ 65 ]. It is possible that the differences between Alteromonas strains may manifest when more complex macromolecules are available, e.g. from plant material, or when all of the “easy to digest” (labile) organic matter has been utilized and only complex macromolecules remain [ 66 ]. These conditions may not have been met in our experiments. It is also possible that co-culture with a more diverse range of heterotrophic bacteria, including additional Alteromonas species, would reveal more pronounced differences in the effect of the heterotroph of the primary producer. Similarly, we currently do not know why some Prochlorococcus strains support a mutually synergistic interaction with Alteromonas relatively early during the long-term N starvation (day 60) whereas other strains do not, and why at a later stage (day 100) almost all interactions are mutually beneficial. We could not identify any metabolic traits [ 11 ] clearly differentiating MIT9313 and MIT9312 (the “competitive” strains) from the others, suggesting more subtle differences exist between the Prochlorococcus strains in the organic matter they produce or in their response to N starvation (e.g. [ 67 , 68 ]). Our results identify patterns in the interactions between clades of abundant marine phototrophs and heterotrophs, under conditions where nutrients are scarce, and their availability likely depends on recycling between phototrophs and heterotrophs. Whether or not such mechanisms may be physiologically relevant in the oligotrophic ocean, much of which is N-stressed [ 40 ], remains to be tested. For example, in the oceans, rapid turnover of Prochlorococcus cells due to grazing and viral lysis likely means that cells are, on average, younger than those in laboratory cultures, which may affect their mortality rates [ 69 ]. Furthermore, stressors such as phage infection and grazing are missing in laboratory cultures. It is, however, noteworthy that the high heterotroph/phototroph biomass ratio observed during long-term N starvation here and in other studies [ 18 ] is similar to that of much of the open oligotrophic ocean (e.g. [ 70 ] and references therein). Additionally, Alteromonas may allow Prochlorococcus to adapt to light starvation [ 43 ] and to the presence of ROS (e.g. [ 71 ]), as well as other stressors that can be encountered in the open ocean. The supportive role of Alteromonas cannot be taken for granted, as it also depends on culture conditions, for example CO 2 concentrations [ 27 ]. The co-cultures did not reach a steady state, and did not represent a closed system. Thus, processes not represented in these simplified laboratory co-cultures, are necessary to explain the long-term stability over decades of Prochlorococcus in the oceans [ 72 ]. Such processes could include multi-organism interactions, as natural communities are much more complex than the laboratory co-cultures, as well as oceanographic processes such as nutrient injection through deep mixing. More generally, cell mortality is intimately linked with the amount and type of recycled organic matter, yet the rate of mortality in natural communities is highly unconstrained [ 73 ]. Hence, better representation of mortality in mathematical models (e.g. the use of appropriate mortality formulations) is likely important for understanding biogeochemical cycles [ 73 ]. This may entail using one of the “off the shelf” models presented here, with their limitations (e.g. the Weibull model requires an estimate of the time of decline, whereas quadratic expressions for mortality are already used in such models [ 74 – 76 ]), or the development of new models that better link cell physiology, ecology, perhaps genome structure, and mortality.",
"introduction": "Introduction Interactions among microorganisms occur in every known ecosystem (recently reviewed by [ 1 , 2 ]). Detailed studies of the interactions between selected model organisms (often in laboratory co-cultures) have begun to reveal the diversity of molecular mechanisms whereby organisms interact with each other [ 2 – 4 ]. However, it is currently unknown to what extent the studied interactions differ between organism pairs, growth stages, or environmental conditions. For example, while broad-scale phylogenetic patterns are often observed in microbial interactions, closely related bacteria may differ in the way they interact with other organisms, likely as a result of the significant genetic diversity observed in many microbial clades (e.g. [ 5 , 6 ]). Additionally, the same pair of interacting organisms can synergize or compete depending on the composition of the culture media and the growth stage of (co)-culture (e.g. [ 7 – 9 ]). Finally, both the coarse-grained ecological classification of microbial interactions (e.g. positive/negative) and the high-resolution mechanistic view obtained using advanced physiology and ‘omics approaches are difficult to translate into quantitative, predictive models of organismal growth and decline [ 1 , 10 , 11 ]. Here we explore to what extent intra-clade diversity affects the outcome of microbial interactions, using growth curves as an information-rich view of microbial growth and mortality. Growth curves can be divided into discrete phases (lag, exponential, stationary, decline, and long-term stationary phases), and can be used to extract quantitative parameters such as growth rates and lag times [ 12 , 13 ]. An extra layer of more subtle information may exist in the shapes of the growth curves, providing hints of important shifts in the physiology of the growing organisms, as classically demonstrated by Jacques Monod for diauxic growth in Escherichia coli [ 14 ]. While many studies of bacterial interactions focus on the exponential growth stage or on culture yield at a specific time-point (e.g. [ 15 – 17 ]), fewer studies look at the shape and dynamics of the decline phases, which can provide important hints regarding the effect of interactions on the process of microbial mortality (e.g. [ 18 – 20 ]). Our model organisms are two globally abundant clades of marine bacteria: a cyanobacterial primary producer ( Prochlorococcus ) and a heterotrophic γ-proteobacterium ( Alteromonas ). Interactions between marine phototrophs (phytoplankton, including cyanobacteria) and heterotrophic bacteria have been studied intensively, as phytoplankton are responsible for about one-half of the photosynthesis on Earth (e.g. [ 21 – 25 ]). Thus, phytoplankton-bacteria interactions may strongly affect community structure and function on scales from microns to thousands of kilometers [ 26 , 27 ]. Our model primary producer, Prochlorococcus , is found throughout the euphotic zone, the sunlit upper portion, of the oligotrophic (nutrient-poor) ocean. There are multiple Prochlorococcus clades, broadly partitioned into high-light (HL) and low-light (LL) adapted ecotypes, which differ in their photosynthetic parameters and occupy different niches in the ocean (e.g. surface verses deep water, reviewed by [ 28 ]). Strains differ also in traits such as the capacity to utilize different forms of inorganic nutrients and organic matter, as well as in their interactions with heterotrophic bacteria and phage. Alteromonas is a clade of free-living marine bacteria, which are also partitioned into surface and deep groups ( A. macleodii and A. mediterranea , respectively) [ 29 ]. Alteromonas strains also exhibit diverse capabilities to utilize carbohydrates, to acquire iron, and in motility [ 30 ]. Interactions between individual strains of Prochlorococcus and Alteromonas have been characterized in some detail [ 12 , 27 , 31 – 35 ]. While the phenotype and gene expression patterns during interactions vary between strains, this variability has not been explored systematically ([ 12 , 32 , 36 ] and Supplementary Text S1 ). Strain- and condition-dependent phytoplankton-heterotroph interactions are observed also in other systems, including Synechococcus , a close relative of Prochlorococcus [ 18 , 37 , 38 ], as well as eukaryotic microalgae (e.g. coccolithophores and diatoms, [ 7 – 9 , 39 ]). We characterized the interactions between five strains each of Prochlorococcus and Alteromonas , from the first encounter between previously axenic strains (i.e., grown in mono-culture) and across ~1.2 years of co-culture (25 phototroph-heterotroph combinations). The culturing period spanned multiple cycles of exponential growth, culture decline and long-term nitrogen starvation [ 33 ]. Nitrogen limitation occurs across wide swaths of the global ocean, and affects a substantial proportion of Prochlorococcus diversity [ 40 , 41 ]. We focused our analysis on Prochlorococcus growth and decline. Using this dataset of 429 growth curves, as well as associated cell counts, we ask: (i) How do the interactions between Prochlorococcus and Alteromonas vary across the diversity of each organisms? (ii) Do the interactions change over time (i.e. do the organisms adapt to “living together”)? (iii) When, during the life-cycle of a Prochlorococcus batch culture, do microbial interactions have the largest impact on growth, death, and overall culture carrying capacity, and can this impact be quantified?",
"discussion": "Results and discussion All Alteromonas strains support long-term survival of Prochlorococcus under N starvation Previous research showed that Prochlorococcus , and to some extent Synechococcus depend on co-occurring heterotrophic bacteria to survive various types of stress, including nitrogen starvation [ 33 , 34 , 42 , 43 ]. At the first encounter between previously axenic Prochlorococcus and Alteromonas (E1), all co-cultures and axenic controls grew exponentially (Fig. 1B, C ). However, all axenic cultures showed a rapid and mostly monotonic decrease in fluorescence starting shortly after the cultures stopped growing, reaching levels below the limit of detection after ~20–30 days. None of the axenic Prochlorococcus cultures were able to re-grow when transferred into fresh media after 60 days (Fig. 1C ). In contrast, the decline of co-cultures rapidly slowed, and in some cases was interrupted by an extended “plateau” or second growth stage (Fig. 1B ). Across multiple experiments, 92% of the co-cultures contained living Prochlorococcus cells for at least 140 days, meaning that they could be revived by transfer into fresh media. Thus, the ability of Alteromonas to support long-term N starvation in Prochlorococcus was conserved in all analyzed strains. Fig. 1 Experimental designs and overview of the dynamics of Prochlorococcus - Alteromonas co-cultures from first encounter and over multiple transfers. A Schematic illustration of the experimental design. One ml from Experiment E1 was transferred into 20 ml fresh media after 100 days, starting experiment E2. Experiment E2 was similarly transferred into fresh media after 140 days, starting experiment E3. Additional experiments replicating these transfers are described in Supplementary Fig. S1 . B Overview of the growth curves of the 25 Prochlorococcus-Alteromonas co-cultures over three transfers spanning ~1.2 years (E1, E2 and E3). Results show mean + standard error from biological triplicates, colored by Prochlorococcus strain as in panel D . C Axenic Prochlorococcus grew exponentially in E1 but failed to grow when transferred into fresh media after 60, 100, or 140 days. Axenic Alteromonas cultures were counted after 60 and 100 days, as their growth cannot be monitored sensitively and non-invasively using fluorescence (optical density is low at these cell numbers). D High reproducibility and strain-specific dynamics of the initial contact between Prochlorococcus and Alteromonas strains (E1). Three biological replicates for each mono-culture and co-culture are shown. Note that the Y axis is linear in panels B , C and logarithmic in panel D . Au: arbitrary units. It has previously been shown that Prochlorococcus MIT9313 is initially inhibited by co-culture with Alteromonas HOT1A3, while Prochlorococcus MED4 is not [ 12 , 32 ]. This “delayed growth” phenotype was observed here too, was specific to MIT9313 (not observed in other Prochlorococcus strains) and occurred with all Alteromonas strains tested (Fig. 1D ). MIT9313 belongs to the low-light adapted clade IV (LLIV), which are relatively distant from other Prochlorococcus strains and differ from them in multiple physiological aspects including the structure of their cell wall [ 44 ], the use of different (and nitrogen-containing) compatible solutes [ 45 ], and the production of multiple peptide secondary metabolites (lanthipeptides, [ 46 , 47 ]). LLIV cells also have larger genomes, and are predicted to take up a higher diversity of organic compounds such as sugars and amino acids [ 48 ]. It is intriguing that specifically this strain, which has higher predicted metabolic and regulatory flexibilities [ 49 ], is the only one initially inhibited in co-culture with Alteromonas . Differences in co-culture phenotype are related to Prochlorococcus and not Alteromonas strains and occur primarily during the decline stage While co-culture with all Alteromonas strains had a major effect on Prochlorococcus viability after long-term starvation, there was no significant effect of co-culture on traditional metrics of growth such as maximal growth rate, maximal fluorescence, and lag phase (with the exception of the previously described inhibition of MIT9313; Fig. 2A–C ). However, a visual inspection of the growth curves suggested subtle yet consistent differences in the shape of the growth curve, and especially the decline phase, between the different Prochlorococcus strains in the co-cultures (Fig. 1D ). To test this, we used the growth curves as input for a principal component analysis (PCA), revealing that the growth curves from each Prochlorococcus strain clustered together, regardless of which Alteromonas strain they were co-cultured with (Fig. 2D ). The growth curves of all high-light adapted strains (MED4, MIT9312, and MIT0604) were relatively similar, the low-light I strain NATL2A was somewhat distinct, and the low-light IV strain MIT9313 was a clear outlier (Fig. 2D ), consistent with this strain being the only one initially inhibited in all co-cultures. Random forest classification supported the observation that the growth curve shapes were affected more by the Prochlorococcus rather than Alteromonas strains, and also confirmed the visual observation that most of the features differentiating between Prochlorococcus strains occurred during culture decline (random forest is a supervised machine learning algorithm explained in more detail in Supplementary Text S2 ; see also Supplementary Fig. S2 ). Thus, co-culture with Alteromonas affects the decline stage of Prochlorococcus in co-culture in a way that differs between Prochlorococcus but not Alteromonas strains. Fig. 2 Growth analysis and principal component analysis (PCA) of the growth curves from all co-cultures during 140 days of E1. A Growth rate, B Maximum fluorescence, and C duration of lag phase during experiment E1. Box-plots represent mean and 75th percentile of co-cultures, circles represent measurements of individual cultures of the axenic controls. The only significant difference between axenic and co-cultures is in the length of the lag phase for MIT9313 (Bonferroni corrected ANOVA, p < 0.001). D PCA ordination of the growth curves colored by Prochlorococcus (left) and by Alteromonas (right) strains. The growth curves cluster by Prochlorococcus strain (Adonis test, F (4,68) = 42.3, R 2 = 0.71, p = 0.001) and only marginally by Alteromonas strain (Adonis test, F (4,68) = 2.29, R 2 = 0.11, p = 0.017). Au arbitrary units. We next asked whether the phenotypes of interaction, which were observed when high cell densities of Prochlorococcus and Alteromonas interacted for the first time (E1), were maintained after the cells had lived together in co-culture for extended periods. We therefore continued to transfer the co-cultures into fresh media over multiple additional transfers, performed 40–200 days after the initial inoculations. In total, fluorescence measurements are available for a cumulative period of 380 days, which the cells spent in co-culture (Fig. 1A ; Supplementary Fig. S1 ). The ability of Prochlorococcus to survive long-term N starvation, the clustering of the growth curves by Prochlorococcus but not Alteromonas strains, and the results of the random forest classification, were all reproduced in subsequent transfers (Fig. 1B ; Supplementary Figs. S2 , S3 ; Supplementary Text S3 ). These observations are thus robust to the cumulative time the organisms have been interacting and the cell densities of both organisms when transferred to new media (see below). Differences in the carrying capacity suggest different modes of interaction While Alteromonas clearly support Prochlorococcus , by enabling it to survive long-term N starvation, is the reciprocal interaction also synergistic? Do Prochlorococcus enhance the growth of Alteromonas , and does the interaction affect the overall carrying capacity of the system, defined here as the ability to efficiently utilize the limiting resource (nitrogen)? To answer these questions, we used the flow cytometry cell counts of Prochlorococcus and Alteromonas on days 60, 100, and 140 of experiment E1 to infer the nitrogen (N) biomass of each population grown alone or in co-culture (Fig. 3 ; Supplementary Table S1 ; see Methods and Supplementary Text S4 for the calculations and caveats). Fig. 3 Carrying capacity and type of interactions. A Growth curves of experiment E1, arrows showing the days where cell numbers were counted by flow cytometry (see also Supplementary Table S1 ). Axenic curves shown are from all Prochlorococcus strains. Thick lines represent mean fluorescence. B Total calculated N biomass of the cultures in E1 on days 60 and 100 in μmol N/L. Dashed line indicates total available N in co-cultures. C Log2 Fold Change (log2FC) of Alteromonas N biomass relative to axenic Alteromonas controls on days 60 and 100. Asterisks indicate statistically significant FC (Bonferroni corrected ANOVA p < 0.05). ALT Alteromonas , PRO Prochlorococcus , Au arbitrary units. The overall carrying capacity of the co-cultures was higher than the axenic Alteromonas cultures, and much higher than the axenic Prochlorococcus (Fig. 3B ). On day 60, the mean carrying capacity of the co-cultures was 2–3 times higher than that of the Axenic Alteromonas (69 ± 35 compared with 32 ± 22 μmol N/L), suggesting that the heterotroph benefited from carbon fixed by the phototrophic Prochlorococcus partner. Indeed, most of this cellular N was found in the Alteromonas cells (76 ± 13%, see Supplementary Text S4 for a sensitivity analysis). The ability of axenic Alteromonas to survive in the absence of organic matter from Prochlorococcus is not surprising, as an Alteromonas strain distantly related to the ones studied here, AltSIO, can utilize a large fraction of the labile organic material found in natural seawater used to make the growth media [ 50 ]. In contrast, in axenic Prochlorococcus cultures only a small fraction of N in the system was found in cell biomass (~0.01 μmol N/L). This likely reflects the inability of all Prochlorococcus strains to recycle organic nitrogen lost due to exudation or cell lysis (Fig. 3B ). Mutual synergism was not observed across all strain combinations. While some Prochlorococcus strains (MED4, MIT0604, and NATL2A) supported significantly higher Alteromonas N biomass compared to the axenic control (log2FC 1.3 ± 0.6), co-cultures with MIT9312 and MIT9313 resulted in similar or lower Alteromonas biomass (log2FC −0.2 ± 0.9) (Fig. 3C ). Therefore, on day 60, some of the interactions were mutually synergistic whereas in other cases Prochlorococcus do not support Alteromonas and may even compete with it. The two “non-mutually-synergistic” Prochlorococcus strains belong to different ecotypes but were isolated from the same drop of water from the Gulf Stream [ 51 ]. In all co-cultures the Prochlorococcus population benefited from the presence of Alteromonas (log2FC 10 ± 4 in N biomass compared to axenic controls). In contrast to day 60, after 100 days essentially all of the interactions were mutually synergistic, with Alteromonas supporting the growth of all Prochlorococcus strains (log2FC 10 ± 1) and Prochlorococcus increasing the Alteromonas biomass in all strains with the exception of BS11 (log2FC 3 ± 1.5) (Fig. 3C ). This suggests that the mode of interaction (synergist verses competition) may change temporally during the extended period of N starvation. On day 140 the carrying capacity of the co-cultures declined further (only 1% of the N was in biomass). This suggests that the system is not in steady state, with a slow yet constant reduction in carrying capacity. We speculate that this is driven by the loss of bioavailable N from the system (i.e., most of the nitrogen is in a recalcitrant form that cannot be utilized by either partner). This is further supported by the observation that while the co-cultures after 140 days were still alive and could be transferred to new media, in a subsequent experiment, only 16/30 cultures could be transferred after 195 days, and only 3/75 cultures could be transferred after 250 days (Supplementary Fig. S5 ). MIT0604 (HLII) was the strain most likely to survive transfer after these extended periods and was also the most abundant Prochlorococcus strain after 140 days (1.46 ± 1 μmol N/L MIT0604 biomass verses 0.26 ± 0.56 μmol N/L for all other strains). While we do not currently have an explanation for the higher survival of this strain, it is noteworthy that it is the only strain to utilize nitrate [ 52 ]. Modeling the effect of co-culture on Prochlorococcus mortality Given that the clearest effect of co-culture was on the decline phase of the co-cultures, we asked whether we could quantify and model the effect of Alteromonas on Prochlorococcus mortality. While the growth of bacteria has been extensively studied and modelled, the decline of bacterial cultures is much less studied, and mortality is rarely represented in ecological or biogeochemical models of microbial dynamics [ 53 ]. Bacterial mortality has, however, often been modelled in the context of food safety and genome evolution, using either mechanistic or descriptive approaches [ 53 – 57 ]. We chose to focus on four of these previously described models which are relatively simple and have a clear biological interpretation (Table 1 ). The exponential model is the simplest and most commonly used one, where a constant portion of the population dies over time [ 58 ]. The bi-exponential model is slightly more complex, representing two separate subpopulations in the community, each with its own death rate [ 55 ]. The Weibull model is probabilistic, modeling a heterogeneous population with a diverse stress tolerance [ 53 , 59 ], finally, the harmonic model employs a quadratic rate of decline which is often associated with predator-prey interactions or cellular encounter rates [ 58 ]. When fitting each of these models to the decline phase of the growth curves, the Weibull model stands out as it has a low error for both axenic and co-cultures (Table 1 ) as well as in consequent transfers (Supplementary Table S2 ), the bi-exponential model is a better fit for the co-cultures but does not represent well the axenic ones. Based on the Weibull model, and assuming that culture fluorescence is related to the number of non-lysed cells in the media (Fig. S7 ), axenic Prochlorococcus cells die more than ten-fold faster than cells in co-culture (2-decimal reduction time, td 2 , is 12.58 ± 3.85 days for axenic cultures and 316 ± 337 days for co-cultures). Similar results were obtained with the bi-exponential model (Supplementary Text S5 ). Table 1 Mathematical description and biological interpretation of four models used to describe bacterial mortality. Mathematical model a Derivative b Free Parameters Physiological interpretation Axenic RMSE c ( n = 13) Co-culture RMSE ( n = 343) Reference Exponential \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$X = X_0\\;e^{ - a\\;t}$$\\end{document} X = X 0 e − a t \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\frac{{dX}}{{dt}} = - aX$$\\end{document} d X d t = − a X 1 Constant ratio of the population dies in each time point 0.26 ± 0.11 0.29 ± 0.21 [ 58 ] Bi-exponential \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$X = X_0\\left( {f\\;e^{ - a_1\\;t} + \\left( {1 - f} \\right)e^{ - a_2\\;t}} \\right)$$\\end{document} X = X 0 f e − a 1 t + 1 − f e − a 2 t \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\begin{array}{l}\\frac{{dX}}{{dt}} = - fa_1X_1\\\\ - (1 - f)a_2X_2\\end{array}$$\\end{document} d X d t = − f a 1 X 1 − ( 1 − f ) a 2 X 2 3 Two subpopulations with different persistence under stress and death rates 0.26 ± 0.11 0.16 ± 0.12 [ 55 ] Harmonic \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$X = X_0\\frac{1}{{1\\; +\\; a\\;t}}$$\\end{document} X = X 0 1 1 + a t \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\frac{{dX}}{{dt}} = - a\\;X^2$$\\end{document} d X d t = − a X 2 1 Quadratic mortality rate – dependence on cell-cell encounters 0.50 ± 0.19 0.19 ± 0.13 [ 58 ] Weibull \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$X = X_0\\;e^{ - \\frac{t}{a}^n}$$\\end{document} X = X 0 e − t a n \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\frac{{dX}}{{dt}} = - c\\;t^{n - 1}X$$\\end{document} d X d t = − c t n − 1 X * 2 Probabilistic model with a heterogeneous distribution of stress tolerance 0.14 ± 0.08 0.18 ± 0.11 [ 59 ] a In the mathematical models, t stands for time since the beginning of culture decline, X and X 0 are the current and initial cell numbers respectively, and the rest of the variables are model parameters ( a , f , a 1 , a 2 , n ) b In the derivatives, X 1 , and X 2 stand for current cell numbers in 2 sub-populations, c stands for a constant value. c RMSE - Root Mean Square Error. Supplementary Table S2 shows also the Bayesian Information Criterion (BIC), which is less intuitive but takes into account also the number of model parameters. In the Weibull model, the “shape parameter” (n) represents the change over time (as the cultures decline) in the susceptibility of the bacterial community to stress. A shape parameter above one represents an increasing probability that cells will die as time increases (e.g. due to the accumulation of damage), whereas a shape parameter below one suggests that, as the culture declines, the cells become more resistant to damage. Axenic cultures have high mean shape value of 2.1 ± 0.9, suggesting an accumulation of cell damage leading to increasing death rate (Fig. 4A ). In contrast, the mean shape value of co-cultures is significantly lower and below 1 (0.4 ± 0.2, student t -test, p < 0.001), suggesting that during N starvation in co-culture the Prochlorococcus cells are acclimating over time to the nutrient stress conditions. Fig. 4 Weibull modeling of long-term starvation. A Weibull shape ( n ) in Axenic Prochlorococcus (PRO) and in Co-cultures. In all axenic samples n > 1, in most co-cultures n < 1 (stude n ts t-test, p < 0.001). B Scatter p lot showing the reverse correlation between the total N biomass of the co-cultures on day 60 and Weibull shape ( n ). Pearson r = −0.33, p = 5e-3. Circles rep r esent co-cultures with mutual synergistic interactions (i.e. Prochlorococcus strains MED4, MIT0604, and NATL2A), X represent potential competitive interactions (strains MIT9312, MIT9313). C Scatter plot showing the correlation between the total N biomass of the co-cultures on day 60 and Weibull root mean square error (RMSE). Pearson r = 0.64, p < 0.001. For the cor r elations with total N biomass on day 100 see Supplementary Fig. S6 . D – F Weibull model fit for selected decline curves (FL). D Mutually synergistic co-culture of Prochlorococcus NATL2A and Alteromonas DE1. E Potentially competitive co-culture of Prochlorococcus MIT9312 and Alteromonas DE1. F Axenic Prochlorococcus NATL2A. FL fluorescence (arbitrary units). RMSE root mean square error. While the molecular and physiological mechanisms of Prochlorococcus adaptation are currently unclear, the Weibull shape parameter decreases as the total N in cellular biomass increases, suggesting that the Prochlorococcus acclimation process is related to the ability to recycle N between the specific Prochlorococcus and Alteromonas strains in co-culture (Fig. 4B ). Thus, the rate of acclimation is higher in the co-cultures supporting high N biomass and mutually synergistic interaction (NATL2A, MED4, and MIT0604) compared to MIT9312 and MIT9313, where Alteromonas do not gain from the interaction (0.36 ± 0.14 verses 0.52 ± 0.16, student t -test p < 0.001). While the Weibull model is useful in quantifying mortality rates and raising the hypothesis that Prochlorococcus cells are acclimating to starvation over time, none of the tested models was able to fully capture the intricate dynamics of culture decline (Fig. 4C, D ). In most mutually synergistic co-cultures involving Prochlorococcus strains NATL2A, MED4, and MIT0604, culture decline was not monotonic, and was interrupted by additional growth phases about 40–50 and 100 days after the cultures started declining (Fig. 4D ). These latter growth phases were mostly absent in co-cultures with MIT9312 and MIT9313 (RMSE 0.36 ± 0.1 verses 0.2 ± 0.1 in the other strains, student t -test p < 0.001). The correlation between N biomass and the secondary growth phases (i.e., higher deviation from simple Weibull model; Fig. 4C ) suggest that these phases may also be related to the ability of the interacting partners to recycle N through mutually beneficial metabolic interactions. Conclusions and future prospects Elucidating the mechanisms of microbial interactions requires well-characterized model systems. However, extending the insights from such models across the diversity of organisms and environmental conditions remains challenging. Our results from the highly simplified system of multiple Prochlorococcus and Alteromonas strains provide an important step towards this goal. Using the rich information on interaction phenotypes present in the growth and decline curves, we identify conserved and strain-specific facets of these interactions. Despite the genetic diversity across the Alteromonas strains studied [ 30 ], it was primarily the identity of the Prochlorococcus strain that determined the interaction phenotype. This manifests in the growth and decline rates, in the shape of the curve (primarily the decline phase), in the amount of N retained in biomass, and in whether the co-cultures are mutually synergistic or, potentially, competitive. Under our laboratory conditions, it is likely that the combined response of both interacting partners to nitrogen starvation underlies the dynamics of the long-term co-cultures, although other stressors such as the increase in osmolarity/salinity or the accumulation of waste products cannot be ruled out [ 18 , 34 , 60 ]. This response is dynamic, as illustrated by the reproducible deviations of the fluorescence curves from the monotonic decline predicted by all models tested (“second growth” stages; Fig. 4 ). Three different (non-mutually-exclusive) processes may underlie these dynamics. Firstly, it is likely that one or both organisms modify their physiology or metabolism over time, for example through the activation of stringent responses, utilization of N or C storage pools, rewiring of metabolism to utilize available N sources, or activation of mechanisms such as extracellular enzymes allowing the cells to access previously unusable substrates (e.g. [ 61 , 62 ]). Secondly, it is possible that there are “invisible” ecological dynamics underlying the observed fluorescence curves, for example cyclic changes in the abundance of Alteromonas cells. Under such a scenario, rapid Prochlorococcus mortality could produce an increase in Alteromonas abundance, resulting in degradation and remineralization of dead Prochlorococcus biomass and the release of resources that can drive subsequent Prochlorococcus growth. Thirdly, both Prochlorococcus and Alteromonas populations may be evolving, for example through emergence of genetically distinct populations better adapted to nutrient starvation (reminiscent of the GASP phenotype described in E. coli and other bacteria [ 63 ]). Why is it the identity of the primary producer ( Prochlorococcus ) rather than the heterotrophic “recycler” ( Alteromonas ) that determines the outcome of the co-culture? A-priori, it was reasonable to assume that the co-culture phenotype would be affected by the differences between the Alteromonas strains in their ability to degrade and utilize polysaccharides and a variety of other organic molecules [ 30 , 64 ]. We speculate that the increased growth of Alteromonas in the co-cultures compared to the axenic ones is fueled primarily by the availability of major biomass components released by Prochlorococcus as they die, such as proteins, amino acids, and nucleotides. Such common macromolecules do not require highly specialized metabolic processes to degrade and utilize, and hence can be utilized by all of the Alteromonas strains [ 65 ]. It is possible that the differences between Alteromonas strains may manifest when more complex macromolecules are available, e.g. from plant material, or when all of the “easy to digest” (labile) organic matter has been utilized and only complex macromolecules remain [ 66 ]. These conditions may not have been met in our experiments. It is also possible that co-culture with a more diverse range of heterotrophic bacteria, including additional Alteromonas species, would reveal more pronounced differences in the effect of the heterotroph of the primary producer. Similarly, we currently do not know why some Prochlorococcus strains support a mutually synergistic interaction with Alteromonas relatively early during the long-term N starvation (day 60) whereas other strains do not, and why at a later stage (day 100) almost all interactions are mutually beneficial. We could not identify any metabolic traits [ 11 ] clearly differentiating MIT9313 and MIT9312 (the “competitive” strains) from the others, suggesting more subtle differences exist between the Prochlorococcus strains in the organic matter they produce or in their response to N starvation (e.g. [ 67 , 68 ]). Our results identify patterns in the interactions between clades of abundant marine phototrophs and heterotrophs, under conditions where nutrients are scarce, and their availability likely depends on recycling between phototrophs and heterotrophs. Whether or not such mechanisms may be physiologically relevant in the oligotrophic ocean, much of which is N-stressed [ 40 ], remains to be tested. For example, in the oceans, rapid turnover of Prochlorococcus cells due to grazing and viral lysis likely means that cells are, on average, younger than those in laboratory cultures, which may affect their mortality rates [ 69 ]. Furthermore, stressors such as phage infection and grazing are missing in laboratory cultures. It is, however, noteworthy that the high heterotroph/phototroph biomass ratio observed during long-term N starvation here and in other studies [ 18 ] is similar to that of much of the open oligotrophic ocean (e.g. [ 70 ] and references therein). Additionally, Alteromonas may allow Prochlorococcus to adapt to light starvation [ 43 ] and to the presence of ROS (e.g. [ 71 ]), as well as other stressors that can be encountered in the open ocean. The supportive role of Alteromonas cannot be taken for granted, as it also depends on culture conditions, for example CO 2 concentrations [ 27 ]. The co-cultures did not reach a steady state, and did not represent a closed system. Thus, processes not represented in these simplified laboratory co-cultures, are necessary to explain the long-term stability over decades of Prochlorococcus in the oceans [ 72 ]. Such processes could include multi-organism interactions, as natural communities are much more complex than the laboratory co-cultures, as well as oceanographic processes such as nutrient injection through deep mixing. More generally, cell mortality is intimately linked with the amount and type of recycled organic matter, yet the rate of mortality in natural communities is highly unconstrained [ 73 ]. Hence, better representation of mortality in mathematical models (e.g. the use of appropriate mortality formulations) is likely important for understanding biogeochemical cycles [ 73 ]. This may entail using one of the “off the shelf” models presented here, with their limitations (e.g. the Weibull model requires an estimate of the time of decline, whereas quadratic expressions for mortality are already used in such models [ 74 – 76 ]), or the development of new models that better link cell physiology, ecology, perhaps genome structure, and mortality."
} | 11,122 |
30354092 | null | s2 | 6,566 | {
"abstract": "Recent studies have suggested the potential for release of iron (hydr)oxide-bound organic carbon (OC) during dissimilatory iron oxide reduction (DIR). However, the stability of iron (hydr)oxide-bound OC in the presence of a natural microbial consortium capable of driving both OC metabolism and DIR has not been resolved. Pure ferrihydrite (Fhy) and Fhy-humic acid coprecipitates (Fhy-HA) were inoculated with a small quantity of freshwater sediment and incubated under anoxic conditions in the presence and absence of H"
} | 130 |
30354092 | null | s2 | 6,567 | {
"abstract": "Recent studies have suggested the potential for release of iron (hydr)oxide-bound organic carbon (OC) during dissimilatory iron oxide reduction (DIR). However, the stability of iron (hydr)oxide-bound OC in the presence of a natural microbial consortium capable of driving both OC metabolism and DIR has not been resolved. Pure ferrihydrite (Fhy) and Fhy-humic acid coprecipitates (Fhy-HA) were inoculated with a small quantity of freshwater sediment and incubated under anoxic conditions in the presence and absence of H"
} | 130 |
39630938 | PMC11775550 | pmc | 6,568 | {
"abstract": "Abstract The polymeric structures of synthetic gels are typically static, which makes them damage‐prone and nonrecyclable. Inspired by the dynamic reconfigurability of biological tissues, which eliminate old/damaged cells and regenerate new ones via biological triggers/signals, a reconfigurable biopolymer gel is presented based on a glycerol‐mediated supramolecular gelation strategy. In response to an eco‐friendly triggering agent water, this gel undergoes on‐demand molecular‐level reconfiguration. The versatility of the approach enables the development of reconfigurable gels with modulated functionality. As a proof‐of‐concept, a reconfigurable glycerogel electrode and electrolyte are developed and used to prototype an all‐gel supercapacitor that exhibits exceptional self‐healing, degradation, and rebuilding abilities. Furthermore, it can tolerate extreme mechanical deformations (e.g., stretching, bending, and twisting) and temperatures (−20 to 80 °C). The device exhibits excellent energy storage performance, with a maximum areal capacitance of 450 mF cm −2 (at 0.035 mA cm −2 ) and remarkable capacitance retention of 89% following 20 000 charge/discharge cycles (at 0.35 mA cm −2 ). Moreover, following self‐healing and rebuilding, the capacitance remains at 91% and 110% (at 0.35 mA cm −2 ) of the original value, respectively. This generalized strategy for preparing multifunctional reconfigurable gels will facilitate the development of high‐performance flexible and wearable devices with improved durability and recyclability.",
"conclusion": "3 Conclusion We devised a facile, bioinspired strategy to develop reconfigurable extremotolerant gels for sustainable application in next‐generation flexible and wearable devices. The approach involves a bottom‐up structure‐building process via the air‐drying of an aqueous biopolymer/glycerol‐based system. This results in the formation of biopolymer gels with a glycerol‐mediated supramolecular crosslinking structure. Similar to the structural remodeling process of biological tissues, these gels exhibit on‐demand structural switchability simply by water‐triggering owing to the facile mass exchange between the gel and sol phases. Furthermore, the inherent ability of glycerol to tolerate extreme temperatures and its molecular‐level involvement in forming a 3D crosslinked structure enables the resulting glycerogel to function effectively across a wide temperature range. This method can be scaled up and adapted to create various reconfigurable composite gels with diverse functions and applications. We successfully utilized our bioinspired strategy to fabricate a reconfigurable glycerogel electrode and electrolyte for a high‐performance, sustainable AGSC. This supercapacitor possesses numerous functions, including self‐healing capacity, degradability, reconfigurability, wide temperature stability (−20 to 80 °C), and tolerance to mechanical deformation. Furthermore, the intercalated polymeric structure and robust mechanical and interfacial integrity result in remarkable capacitance (≈450 mF cm −2 at a current density of 0.035 mA cm −2 ) and impressive capacitance retention (≈89%) after 20 000 charge/discharge cycles (at a current density of 0.35 mA cm −2 ). Moreover, the device preserved ≈100% of its capacitance when subjected to extreme mechanical deformations such as bending, twisting, and stretching. Importantly, the device performed well at high and low temperatures of −20 and 80 °C, with minimal changes in capacitance (100% and 96% of the initial values, respectively) after incubation at these temperatures for 24 h. This extreme temperature tolerance was ascribed to the robust structure and inherent resilience of glycerol, which ensures reliable performance across a broad temperature range. The molecular‐level structural reconfigurability of the device ensured that it maintained excellent capacitance (91% and 110% of the original value at a current density of 0.35 mA cm −2 ) after self‐healing and rebuilding, respectively. This study provides a basis for the development of recyclable, self‐healing, and long‐lasting wearable and flexible energy storage devices with superior performance and sustainable usability.",
"introduction": "1 Introduction Biological tissues constantly change and remodel to achieve adaptability and longevity. [ \n \n 1 \n , \n 2 \n , \n 3 \n \n ] Conversely, synthetic gels typically lack the capacity for tissue‐like reconstruction despite their biomimetic structures and functionalities. [ \n \n 1 \n , \n 2 \n , \n 3 \n , \n 4 \n \n ] This is because their polymeric structures are inherently static, preventing them from undergoing molecular‐level reconfiguration. [ \n \n 1 \n , \n 2 \n \n ] In recent years, gel utilization has increased considerably in fields such as soft robotics and stretchable and wearable devices. [ \n \n 4 \n , \n 5 \n , \n 6 \n , \n 7 \n , \n 8 \n \n ] More than a billion individuals across the globe already use wearable devices, [ \n \n 9 \n \n ] and the global market for smart wearables, valued at USD 60 billion in 2023, is forecast to increase to USD 375 billion by 2033. [ \n \n 10 \n \n ] This is likely to result in considerable environmental waste if synthetic gels that lack adaptive and reconstructive abilities are used, necessitating immediate, innovative, and sustainable management solutions. [ \n \n 11 \n , \n 12 \n , \n 13 \n \n ] \n Compared with conventional solid or solid/liquid supercapacitors, all‐gel supercapacitors (AGSCs) offer unique advantages for stretchable and wearable devices. [ \n \n 14 \n , \n 15 \n \n ] However, AGSCs are still in the early stages of development and face several notable challenges that hinder their practical application. [ \n \n 14 \n , \n 15 \n , \n 16 \n , \n 17 \n , \n 18 \n , \n 19 \n , \n 20 \n , \n 21 \n , \n 22 \n , \n 23 \n \n ] First, most reported AGSCs are constructed from non‐degradable, environmentally unfriendly, and non‐biocompatible materials. [ \n \n 14 \n , \n 15 \n , \n 16 \n , \n 17 \n , \n 18 \n , \n 19 \n \n ] Second, AGSC fabrication strategies typically involve the self‐healing of predesigned gel components (electrodes and electrolytes) with electrical and ionic conductivity. [ \n \n 14 \n , \n 15 \n , \n 16 \n \n ] Nevertheless, the structural and mechanical incompatibility between the predesigned components often leads to weak interfaces and poor electrochemical performance, which is exacerbated by deformation. An all‐in‐one fabrication strategy has also been developed, whereby electroactive layers are created in situ on the surface of electrolyte gels to form electrodes. [ \n \n 17 \n , \n 18 \n , \n 19 \n , \n 20 \n , \n 21 \n \n ] However, the electrode layers are typically brittle with low electrical conductivity, thereby yielding poor mechanical stability and energy loss. Third, reported AGSCs are primarily composed of water‐borne hydrogels or organohydrogels, which are environmentally unstable; [ \n \n 12 \n , \n 13 \n , \n 14 \n , \n 15 \n , \n 16 \n , \n 17 \n , \n 18 \n , \n 19 \n , \n 20 \n , \n 21 \n , \n 22 \n , \n 23 \n \n ] the in‐air performance is significantly hindered by the natural evaporation of water. Finally, most reported AGSCs cannot undergo structural reconfiguration, although some self‐healable AGSC components do exhibit reconfigurability. [ \n \n 11 \n , \n 12 \n , \n 13 \n , \n 14 \n , \n 15 \n , \n 16 \n , \n 17 \n , \n 18 \n , \n 19 \n , \n 20 \n , \n 21 \n , \n 22 \n , \n 23 \n \n ] Single‐use AGSCs will result in a significant amount of electronic waste. AGSCs show considerable promise in advancing flexible and wearable devices, health‐monitoring sensors, and point‐of‐care diagnostics. However, no specific strategy has been reported for developing fully reconfigurable, high‐performance AGSCs. To ensure the sustainable long‐term use of AGSCs in wearable and stretchable devices, the aforementioned problems need to be addressed. In this study, we present a bioinspired and environmentally friendly strategy for developing multifunctional glycerogels and glycerogel‐based devices that can be reconfigured at the molecular level. This strategy was inspired by the remodeling ability of biological tissues, which undergo mass exchange between their solid (e.g., biopolymers and cells) and liquid (e.g., water, small ions, and molecules) phases to destruct old cells and reconstruct new ones. [ \n \n 1 \n , \n 24 \n , \n 25 \n \n ] This process is often triggered by biosynthesized molecules. For instance, in bone remodeling, osteoclasts secrete acid and collagenase to dissolve bone, whereas osteoblasts secrete collagen that is physically crosslinked with bone minerals for reconstruction. [ \n \n 25 \n , \n 26 \n \n ] In a similar way, our biopolymer‐based glycerogel system exhibits facile mass exchange between its solid and liquid phases, which enables supramolecular reconfiguration in response to water ( Figure \n 1 a ). These water‐triggered glycerogels are structurally reconfigurable and adaptable to different environments. Moreover, they can be easily modulated to achieve a wide range of functionalities. Figure 1 The design strategy of reconfigurable glycerogels. a) Illustration of a reconfigurable gel undergoing facile and reversible mass exchange between the sol and gel states by water‐triggering. In the presence of water, the polymer and glycerol are in a hydrated solution state. Upon the removal of water by evaporation, the system transitions to glycerogel. Glycerol acts as a supramolecular gelling agent owing to its strong hydrogen‐bonding ability with polysaccharides, providing the glycerogels with strong yet switchable supramolecular architectures. b) Fabrication strategy of reconfigurable glycerogels. Precursor solutions are air‐dried to form reconfigurable glycerogel electrodes and electrolytes. Annealing of the glycerogel electrode facilitates PEDOT:PSS crystallization, which enhances performance without losing reconfigurability. c) A reconfigurable, high‐performance AGSC is prepared from glycerogel electrodes and electrolyte. The AGSC exhibits self‐healing and rebuilding abilities and can tolerate extreme temperatures (−20 to 80 °C). We prepared electrically and ionically conductive reconfigurable glycerogels to prototype a sustainable, high‐performance AGSC (Figure 1b,c ). The fabricated AGSC demonstrated a maximum areal capacitance of ≈450 mF cm −2 (at a current density of 0.035 mA cm −2 ) and a capacitance retention of ≈89% after 20 000 charge/discharge cycles (at a current density of 0.35 mA cm −2 ). Furthermore, the device tolerated extreme temperatures (ranging from −20 to 80 °C) and mechanical deformations (stretching, bending, and twisting). Owing to its unique water‐triggered molecular reconfiguration capability, the developed AGSC demonstrated excellent self‐healing, degradation, and rebuilding properties. The concept presented in this study is anticipated to pave the way for developing diverse, high‐performance, and reconfigurable gels and gel‐based devices by utilizing different combinations of polymers and functional materials. They could be sustainably used in next‐generation flexible and wearable devices.",
"discussion": "2 Results and Discussion 2.1 Design Strategy of Reconfigurable Glycerogels Na‐alginate, a widely used biocompatible and biodegradable polysaccharide, [ \n \n 4 \n , \n 27 \n \n ] was employed as the model system to demonstrate our reconfigurable gels. Most polysaccharides form stable gels through supramolecular bonding. Because of the rigid nature of linear polysaccharides, they generate internal stress during supramolecular interactions, thereby facilitating the formation of microscopic domains with an aggregated structure. However, when water is used as the solvent, the typical supramolecular structures in alginate‐based hydrogels are formed through metal‐ion (Ca 2+ , Cu 2+ , Zn 2+ , Al 3+ , etc.)‐mediated ionic/coordination‐bonding and hydrogen‐bonding. [ \n \n 27 \n , \n 28 \n \n ] These structures are not fully retrievable to the original state (sol) because of the formation of some irreversible aggregates. Therefore, developing fully reconfigurable and stretchable gels from alginate/water systems is challenging. Glycerol, an inexpensive and environmentally friendly bioderived compound, has been used as a solvent to prepare alginate gels with reconfigurable structures (Figure 1a ). Moreover, the unique properties of glycerol, such as its exceptional supercoolability (as low as −83 °C), extremely low vapor pressure (133.3 Pa at 125 °C), and extremely high boiling temperature (290 °C) make the resultant gels tolerant to extreme environments. [ \n \n 5 \n , \n 14 \n , \n 29 \n \n ] The additional hydroxyl groups and the bulkier structure of glycerol compared to that of pure water contribute to a more robust and stable hydrogen‐bonded structure with polysaccharides. Thus, glycerol functions as both a solvent and crosslinker in alginate/glycerol systems. Furthermore, glycerol has a lower polarity than water. Because of these features, the gel point of Na‐alginate is lower in glycerol than in water. For instance, a 10 wt.% Na‐alginate solution in glycerol exists as a stretchable gel (Figure S1a,b and Movie S1 , Supporting Information), whereas the solution in water exists as a viscous slurry (Figure S1c,d and Movie S2 , Supporting Information). As such, glycerol is readily miscible with water via facile hydrogen bond formation. Therefore, the glycerol‐mediated supramolecular aggregates in alginate glycerogels, which are water‐soluble but glycerol‐insoluble, are expected to be switchable through water‐triggering (Figure 1a ). That is, the addition of water would revert the gel state to a sol, while the gel state would form upon water evaporation. Notably, mass exchange between the gel and sol phases would enable self‐healing and reconfiguration capabilities. By controlling the polymer‐to‐solvent ratio, we can create reconfigurable supramolecular aggregates in an alginate‐based glycerogel system. 2.2 Fabrication of Reconfigurable Glycerogel Electrode and Electrolyte Based on the rational design strategy described above, we developed reconfigurable alginate‐based glycerogels using a straightforward drying‐induced sol/gel process, as depicted in Figure 1 . Electrically and ionically conductive reconfigurable glycerogels were fabricated to serve as the electrode and electrolyte, respectively, for a sustainable AGSC (Figure 1b ). The electrically conductive glycerogel electrode was prepared from a solution of Na‐alginate, poly(3,4‐ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS), glycerol, and water. The incorporation of PEDOT:PSS was based on its biocompatibility, exceptional capacitance and electrical conductivity. [ \n \n 6 \n , \n 14 \n , \n 30 \n , \n 31 \n \n ] Furthermore, PEDOT:PSS is water‐dispersible and can form various aggregated structures depending on its processing. [ \n \n 6 \n , \n 14 \n , \n 30 \n , \n 31 \n \n ] The solution was cast into a rectangular mold and then air‐dried. As the water gradually evaporated, the polymer chains moved closer together, and once the water had completely evaporated, the polymer concentration in glycerol increased to its gel point. At this stage, glycerol facilitated the formation of aggregated, crosslinked alginate domains through hydrogen bonding, thereby providing mechanical integrity. Concurrently, the PEDOT:PSS formed an interpenetrating percolated structure with PEDOT crystals in the gel network. This structure provided electrical conductivity. The reconfiguration ability of the glycerogel was not affected by the incorporation of PEDOT:PSS because of its water‐dispersible nature. The ionically conductive glycerogel electrolyte was fabricated from a solution of Na‐alginate, lithium perchlorate (LiClO 4 ), glycerol, and water using a similar strategy (Figure 1b ). The selection of LiClO 4 was based on its high solubility in glycerol and extensive usage as an ion‐conducting material in energy storage devices. The hydrogen‐bonded alginate structure provided mechanical integrity, while the dissolved Li + and ClO 4 \n − ions in the gel phase provided ionic conductivity. The most interesting feature of our fabrication strategy is the capacity to finely adjust the bonding strength. Glycerol acts as a triggerable molecular switch that can facilitate the formation and disruption of inter‐ and intra‐polymer interactions, giving it the capacity to self‐heal and rebuild. By precisely controlling the polymer density, we fabricated soft, stretchable, and reconfigurable glycerogel electrodes and electrolytes. These components could be readily assembled and reconfigured into any form, facilitating the development of a sustainable AGSC with a wide operating temperature range (Figure 1c ). 2.3 Mechanical Properties and Environmental Tolerance of Reconfigurable Glycerogels The mechanical properties of the glycerogel electrolytes and electrodes were assessed under tensile testing ( Figure \n 2 a,b ). A typical glycerogel electrolyte denoted as alginate/LiClO 4 ‐glycerol(3/3‐9), where the values in parentheses correspond to the concentrations of Na‐alginate (3 wt.%), LiClO 4 (3 wt.%), and glycerol (9 wt.%) in the aqueous precursor solution, respectively. It exhibited an excellent tensile strength of 2.99 ± 0.41 MPa, elastic modulus of 2.31 ± 0.45 MPa, and stretchability (tensile strain) of 94% ± 8% (Figure 2a ). In addition, it exhibited an excellent ionic conductivity of 0.41 ± 0.026 mS cm −1 (Figure S2 , Supporting Information), which is of the same order as that of our recently reported LiClO 4 ‐containing glycerogels. [ \n \n 14 \n , \n 29 \n \n ] \n Figure 2 Mechanical performance of glycerogel electrolyte and electrode. Representative tensile stress–strain curves of the glycerogel a) electrolyte and b) electrode (insets: schematic of the tensile testing setup) and c,d) photographs of pristine glycerogel electrolyte and electrode. e,f) Demonstration of extreme temperature tolerance of the glycerogel electrolyte (left) and electrode (right) by stretching the gels after 24 h incubation at e) −20 °C and f) 80 °C. To identify a mechanically compatible electrode, we designed glycerogels with different concentrations of alginate and PEDOT:PSS. As the alginate‐to‐PEDOT:PSS ratio increased, the strength and stretchability of the electrode increased, whereas its modulus decreased (Figure 2b ; Figure S3 , Supporting Information). Increasing the alginate concentration increased the ratio of hydrogen‐bonded alginate aggregates to PEDOT crystals. This increased the toughness and flexibility; however, it also reduced the electrical conductivity owing to the effective decrease in the concentration of PEDOT crystals (Figure S4 , Supporting Information). At a lower alginate‐to‐PEDOT:PSS ratio, the higher effective concentration of PEDOT crystals and improved intercalated PEDOT structure afforded higher electrical conductivity. Based on these observations, the alginate/PEDOT:PSS‐glycerol(2.5/2‐9) glycerogel electrode was considered optimal. It exhibited good mechanical properties, including a tensile strength of 1.61 ± 0.08 MPa, elastic modulus of 5.51 ± 0.47 MPa, and stretchability of 64% ± 10%. In addition, it exhibited excellent electrical conductivity of 6.5 ± 0.2 S cm −1 . It should be noted that the air‐equilibrated final glycerogel electrode (air‐dried and annealed) and electrolyte (air‐dried) retained small amounts of water (≈6% and 9% of their final weight, respectively) due to the hygroscopic nature of glycerol. The slightly higher amount of water in the glycerogel electrolyte compared to the electrode is due to the presence of high‐density salt ions in the former gel. Nevertheless, major portion of both the glycerogel electrode and electrolyte was composed of polymer and glycerol. The excellent mechanical properties of the glycerogel electrode and electrolyte are attributed to the presence of strong yet sacrificial supramolecular aggregates in the gel network, which are formed by facile, strong, and reversible hydrogen bonding between glycerol and alginate (Figure 1a ). To confirm the microstructure of the gels, scanning electron microscopy (SEM) analysis was performed, which revealed the formation of a high‐density interconnected aggregated domain structure in both the electrode and electrolyte (Figure S5 , Supporting Information). The relatively higher density and finer aggregates in the electrode compared to the electrolyte is attributed to the presence of PEDOT crystals in the former gel system. The glycerogel electrode exhibited a slightly decreased mechanical performance compared to its electrolyte counterpart. This was caused by the presence of a PEDOT:PSS network in the glycerogel electrode. PEDOT:PSS can enhance crystallinity but it does not possess any hydroxyl groups that may form an extensive hydrogen‐bonding network with alginate/glycerol, resulting in a slight decrease in stretchability and strength (Figure 2a,b ). To confirm these findings, we also fabricated a control glycerogel electrode composed of pure PEDOT:PSS and glycerol without alginate for analysis (Figure S6 , Supporting Information). Due to the complete absence of alginate, the gel exhibited a highly brittle nature with limited stretchability (24.2% ± 0.1%) and strength (0.96 ± 0.01 MPa). To design our sustainable AGSC, the optically transparent alginate/LiClO 4 ‐glycerol(3/3‐9) glycerogel (Figure 2c ) was used as an electrolyte and separator, whereas the optically opaque alginate/PEDOT:PSS‐glycerol(2.5/2‐9) glycerogel (Figure 2d ) was used to form active electrodes and current collectors. The uniform appearance of these glycerogels indicated that the components inside each material did not exhibit macro‐phase separation. The molecular distribution of glycerol and its unique properties enabled the resulting gels to tolerate extreme temperatures. This phenomenon was demonstrated by exposing them to low (−20 °C) and high temperatures (80 °C) for 24 h, and immediately stretching them at respective temperatures (Figure 2e,f ). 2.4 Self‐Healing, Degradation, and Rebuilding Capabilities of Reconfigurable Glycerogels The water‐triggered self‐healing, degradation, and rebuilding capabilities of the glycerogel electrolyte and electrode are illustrated in Figure \n 3 a,b , respectively. When a small droplet of water was applied to the cut interface of the glycerogels, it was readily absorbed owing to the facile glycerol/water interactions. The absorbed water molecules interacted with the hydrogen‐bonding network, resulting in partial breakage of the polymer aggregates that enhanced the polymer dynamicity at the cut interfaces. Consequently, the cut surfaces coalesced via structural reconfiguration. Finally, the contacted gel specimen was air‐dried, allowing the glycerol molecules to regenerate the hydrogen‐bonding crosslinks within the polymer, thereby reconfiguring the internal structure to complete self‐healing. Figure 3 Self‐healing and rebuilding ability of the glycerogel electrolyte and electrode. Demonstration of the cutting/self‐healing and dissolution/rebuilding processes of the glycerogel a) electrolyte and b) electrode. Water acts as an environmentally friendly triggering agent that disrupts the supramolecular crosslinking structure of glycerogel, which is subsequently restored by air drying. Representative tensile stress–strain curves of the glycerogel c) electrolyte and d) electrode and e) their tensile strengths before and after self‐healing and rebuilding. Data in (e) are presented as the mean and mean absolute deviation ( n = 3). The water‐triggered internal bonding tunability of our glycerogels also provided the capacity for structural degradation and rebuilding. Upon immersing the gels in a water bath, water molecules infiltrated the gel network. The glycerogels initially underwent swelling, followed by breakage, and eventually dissolved entirely in water by complete degradation of the aggregated bonding structures. The glycerogels could be restored to their original form by casting the degraded solution and air‐drying. Upon the completion of drying, the polymers and glycerol had rebuilt the original hydrogen‐bonded aggregate structure. This water‐mediated self‐healing, degradation, and rebuilding approach was applicable to both the glycerogel electrolyte and electrode (Figure 3a,b ). For the glycerogel electrolyte, the alginate hydrogen‐bonding structure was degraded upon the addition of water and rebuilt as it evaporated. Meanwhile, for the glycerogel electrode, both the alginate hydrogen‐bonding structure and the PEDOT:PSS crystalline structure were modulated by water. The overall dissolution speed of the glycerogel electrode was slower than that of the glycerogel electrolyte. For example, a glycerogel electrode (1 × 1 cm) required ≈6 h to completely dissolve in water, whereas a glycerogel electrolyte of similar size dissolved in ≈30 min. We quantitatively assessed the self‐healing and rebuilding capacities of the reconfigurable glycerogels using tensile testing (Figure 3c–e ). Both self‐healed and rebuilt glycerogel electrolytes exhibited similar or slightly higher tensile performance than the original electrolyte. Specifically, the tensile strengths of the healed and rebuilt glycerogel electrolytes were ≈105% and 142% of the original value, respectively (Figure 3e ), and the elastic moduli were ≈99% and 189% of the original value, respectively (Figure S7 , Supporting Information). The unexpected increase in these mechanical properties after rebuilding could be attributed to a slight reduction in the specimen thickness (≈80% of the original gel), likely due to the loss of some viscous precursor solution during transfer from the glass vial to the mold. This loss can be minimized when fabricating gels on a larger scale and such gels could exhibit a consistent performance after remodeling. The mechanical behavior of our thinner gel is consistent with the findings of our previous study, [ \n \n 32 \n \n ] where we revealed that physically crosslinked polysaccharide gels tend to exhibit much improved mechanical behavior when their thickness is reduced. Conversely, the tensile strengths of the self‐healed and rebuilt glycerogel electrodes were slightly lower than that of the original electrode, at ≈82% and 83% of the original value, respectively (Figure 3e ). The elastic moduli were ≈113% and 73% of the original value, respectively (Figure S7 , Supporting Information). The slight reduction in self‐healing and rebuilding capacities for the glycerogel electrode, as compared to the glycerol electrolyte, were attributed to the crystalline PEDOT structure within the electrode, which is comparatively less sensitive to water. The presence of PEDOT crystals enabled a higher degree of crystallinity and aggregated structures in the glycerogel electrode compared to the electrolyte, and the SEM images (Figure S5 , Supporting Information) show that denser and finer aggregates appeared in the former gel. The X‐ray diffraction profiles also demonstrated the presence of a higher degree of crystallinity in the glycerogel electrode compared to the electrolyte, as the former exhibited distinct crystalline peaks (2θ ≈ 21° and 26°) [ \n \n 6 \n , \n 14 \n \n ] with a lower full width at half maximum value (Figure S8 , Supporting Information). Further, the inherent hydrophobic nature of PEDOT crystals and absence of hydroxyl groups on the PEDOT polymer makes the PEDOT:PSS aggregates less sensitive to water. These factors cause a slower dissolution of the electrode compared to the electrolyte. They also cause the slight reduction in the self‐healing and rebuilding capacities of the electrode compared to the electrolyte (Figure 3c–e ). Nevertheless, the electrical conductivities of the self‐healed and rebuilt glycerogel electrodes were 6.5 ± 0.3 and 6.9 ± 0.4 S cm −1 (≈100% and ≈106% relative to that of the original gel), respectively (Figure S9 , Supporting Information). This suggests the PEDOT percolation network was perfectly restored in the reconfigured glycerogel electrode. Because the glycerogel electrode and electrolyte had similar structures, components, and compositions, they readily attached to one another via the water‐triggered reconfiguration mechanism. The electrode/electrolyte attachment efficiency was evaluated by fabricating a lap‐joint sample ( Figure \n 4 a ). Electrode and electrolyte strips (width: 3 mm) were placed in a longitudinal overlap configuration (overlap length: 5 mm). The interface between the samples was then healed by introducing a few droplets of water and allowing it to dry. Following this, the shear adhesive strength of the electrode/electrolyte interface was evaluated using a standard lap‐shear test (Figure 4b and Movie S3 , Supporting Information). An excellent adhesive force of 1.82 ± 0.02 N was obtained, corresponding to an adhesive strength of 0.12 ± 0.01 MPa. Notably, the electrode/electrolyte lap‐joint sample could stretch by ≈60% of its initial length before experiencing failure, which closely matches the fracture strain of the individual components. This indicates that the toughness of the interface is similar to that of the bulk material. Thus, the excellent reconfiguration and self‐healing capacities of the glycerogel electrode and electrolyte facilitated the fabrication of a stretchable and reconfigurable AGSC. Figure 4 Interfacial and electrochemical characterization of the AGSCs. a) Photograph of the lap‐joint sample for analyzing the bonding strength of the self‐healed electrode/electrolyte interface and schematic of the lap‐shear test. b) Lap‐shear test results, demonstrating the shear adhesive force and strength of the self‐healed electrode/electrolyte interface. c) Photograph of a prototype AGSC with a healed electrode/electrolyte interface and schematic of the setup for electrochemical characterization. d) Cyclic voltammetry (CV) curves of AGSCs at different scan rates. e) Galvanostatic charge/discharge (GCD) curves of AGSCs at different current densities. f) Photographs of mechanically deformed AGSCs in different states (90° bend, 90° twist, and 30% stretch). g) GCD curves of an AGSC under different mechanical deformations at a current density of 0.350 mA cm −2 . h) Photograph of an AGSC with an unhealed electrode/electrolyte interface. i) GCD curves at a current density of 0.350 mA cm −2 and j) Nyquist curves of AGSCs with unhealed and healed electrode/electrolyte interfaces. 2.5 Prototype AGSC Using Reconfigurable Glycerogels 2.5.1 Electrochemical Performance Figure 4c demonstrates a prototype AGSC with a sandwich structure (electrode/electrolyte/electrode) fabricated by a water‐triggered attachment method. This method involved attaching both surfaces of a glycerogel electrolyte (thickness: ≈0.45 mm) between two glycerogel electrodes (thickness: ≈0.8 mm). The active cross‐sectional area was 15 × 10 mm. Subsequently, CV and GCD tests were performed in a two‐electrode system to evaluate the electrochemical performance of our AGSC (Figure 4c–e ). CV was performed at various scan rates within the voltage range of 0–1 V (Figure 4d ). The CV curves exhibited a quasi‐rectangular shape at lower scan rates and lacked redox peaks, signifying that the charge‐storage mechanism was governed by electric double layer capacitance. The GCD curves at different current densities exhibited a symmetrical triangular shape within a potential window of 0–1 V, demonstrating the capacitive behavior of the supercapacitor (Figure 4e ). The device exhibited an excellent areal capacitance of 126.51 mF cm −2 (mass‐specific capacitance: 9385.01 mF g −1 ) at a current density of 0.035 mA cm −2 . Despite a ten‐fold increase in current density (0.35 mA cm −2 ), the device maintained a high areal capacitance of 63.66% (80.54 mF cm −2 ) (Figure S10 , Supporting Information). Thereafter, the AGSC was subjected to various complex mechanical deformations (e.g., bending, twisting, and stretching) to assess its electrochemical stability (Figure 4f ). Even under extreme mechanical deformations, the device retained ≈100% of its original capacitance (Figure 4g ). The CV and GCD curves in the deformed state were similar to those in the initial state, demonstrating the stable charge‐storage behavior of the AGSC under mechanical deformation (Figure 4g ; Figure S11 , Supporting Information). The exceptional performance of the supercapacitor during mechanical deformation results from the strong electrode/electrolyte interfacial connection and compatibility between the components in terms of mechanical properties. These factors ensure the structural integrity of the device, thereby enabling the applied force to be distributed uniformly across the polymer network and thus preventing interfacial failure. The electrochemical properties of a supercapacitor with unhealed interfaces were also tested to emphasize the significance of interfacial healing (Figure 4h ). In stark contrast to its healed counterpart, the unhealed device exhibited a reduction of 40.90% (47.6 mF cm −2 ) in its areal capacitance and a larger ohmic drop ( iR drop) (≈40%) (Figure 4i ). Further, while the Nyquist plot (Figure 4j ) of the healed device demonstrated negligible charge‐transfer resistance at high frequencies and a short slope at mid‐frequencies, indicative of low electrolyte resistance, [ \n \n 33 \n \n ] that of the unhealed device indicated higher internal resistance, resulting in substantial energy loss (Figure 4i ). This is because the robustly healed electrode/electrolyte interface, facilitated by structural reconfiguration, provided a percolated pathway for ion diffusion. This reduced the internal resistance and enhanced the energy efficiency. The electrochemical stability of the AGSC was characterized by subjecting it to 20 000 charge/discharge cycles at a current density of 0.35 mA cm −2 within the potential range of 0–1 V ( Figure \n 5 a ). After 10 000 and 20 000 cycles, the device exhibited capacitance retentions of ≈100% and 88.95%, respectively, along with an impressive Coulombic efficiency of >99.5%. This outstanding cycling stability is attributed to the well‐integrated internal structure of the glycerogel components, which comprise rigid polymers such as alginate and PEDOT:PSS. This structure forms a 3D hydrogen‐bonding network through facile polymer/solvent interactions. In addition, the AGSC has robust electrode/electrolyte interfaces. Consequently, its cycling performance surpasses that of several state‐of‐the‐art gel supercapacitors (Figure 5b and Table S1 , Supporting Information). [ \n \n 16 \n , \n 17 \n , \n 18 \n , \n 19 \n , \n 20 \n , \n 21 \n , \n 22 \n \n ] \n Figure 5 Electrochemical stability and reconfiguration ability of the AGSCs. a) Capacitance retention and Coulombic efficiency following 20 000 GCD cycles at a current density of 0.35 mA cm −2 . b) Capacitance retention of the prepared AGSC and state‐of‐the‐art reported AGSCs. Demonstration of c) cutting/self‐healing, d) detaching/reattaching, and e) dissolution/rebuilding processes and f) GCD curves of the reconfigured AGSCs at a current density of 0.35 mA cm −2 demonstrating the electrochemical performance. 2.5.2 Self‐Healing, Reattaching, and Rebuilding Capabilities The sustainable usability of the designed AGSC was further demonstrated by its self‐healing, reattaching, and rebuilding (regeneration) capacities, which are derived from the water‐triggered switching ability of its physical bonds. This reconfiguration ability enabled the entire device to repair itself following cutting, simply by applying a few drops of water to the cut interface followed by air‐drying (Figure 5c ). The reversible physical bonding at the electrode/electrolyte interface facilitated straightforward detachment when exposed to water and the reattachment of the components (Figure 5d ). Furthermore, it enabled easy rebuilding (regeneration) of the device by detaching the components and dissolving them separately in water, regenerating them, and then reconstructing the device using the interfacial healing process (Figure 5e ). The reconfigured AGSCs maintained excellent electrochemical performance. The capacitances of the self‐healed and reattached devices were 91.01% and 87.50% of the original value, respectively (Figure 5f ). Notably, the CV curves exhibited no apparent deviations from their original characteristics (Figure S12 , Supporting Information). Despite undergoing molecular‐level rebuilding, the AGSC maintained an exceptional capacitance of 110.19% of the original value. The unexpected increase in capacitance, accompanied by a slight deviation in the CV characteristics post‐rebuilding, was attributed to the potential decrease in device thickness due to some loss of the precursor solution during the rebuilding process. The change in the thickness resulted in a slight variation of the charge‐storage kinetics of the rebuilt AGSC compared to the original AGSC, resulting in higher capacitance at a higher current density (0.35 mA cm −2 ). To adequately compare these systems, we also verified their capacitance at lower current densities (Figure S13 , Supporting Information). The capacitance of the rebuilt AGSC at lower current densities (0.035−0.175 mA cm −2 ) was very close (91%–98%) to that of the original AGSC. Nevertheless, the reconfigured AGSC maintained high capacitance, indicating that the glycerogel electrode and electrolyte maintained their electrical and ionic conductivity after reconfiguration. This indicates that the device has the ability to seamlessly self‐heal, reattach, and reconstruct itself through molecular‐level reconfiguration via water‐triggering. Consequently, the AGSCs can be used sustainably for extended periods. 2.5.3 Environmental Tolerance An additional benefit of our AGSC is its ability to operate reliably across a wide temperature range. This benefit was assessed by conducting electrochemical tests on the device at different temperatures (−20, 25, and 80 °C) ( Figure \n \n 6 a–d ; Figure S14 , Supporting Information). In contrast to conventional gel supercapacitors, which frequently experience operational issues at extremely low or high temperatures owing to solvent freezing or drying, [ \n \n 12 \n , \n 13 \n , \n 14 \n , \n 15 \n , \n 16 \n , \n 17 \n , \n 18 \n , \n 19 \n , \n 20 \n , \n 21 \n , \n 22 \n , \n 23 \n , \n 29 \n \n ] our AGSC exhibited robust performance at temperatures as low as −20 °C and as high as 80 °C. Indeed, the GCD curves revealed impressive capacitance values of 65.73 and 125.26 mF cm −2 at −20 and 80 °C, respectively (Figure 6a,c ). The extreme temperature tolerance of the AGSC was attributed to the inherent temperature stability of glycerol. In addition, the glycerol‐mediated hydrogen‐bonding network enables the polymer network to remain intact and function efficiently within a broad temperature range. The reduction in capacitance at low temperatures is caused by the enhanced viscosity of glycerol, which decreases the ionic conductivity. [ \n \n 14 \n , \n 29 \n \n ] Nevertheless, after incubating the AGSCs for 24 h at −20, 25, and 80 °C, the devices maintained excellent capacitances at 100.48%, 96.50%, and 95.64%, respectively, of the values prior to incubation (Figure 6a–d ). Because the device was tested in an ambient environment, we also investigated its mass retention capabilities under extreme temperatures. After incubating the device at −20, 25, and 80 °C for 24 h, it maintained ±8 wt.% of its initial weight (Figure S15 , Supporting Information), suggesting high‐level stability similar to our previously reported glycerogels. [ \n \n 14 \n , \n 29 \n \n ] A slight increase or decrease in weight could be due to the absorption of moisture from the air or a loss of the water retained in the gel. The aforementioned results indicate that the device remains operationally stable at extreme temperatures. Figure 6 Electrochemical performance of AGSCs at extreme temperatures and their scalability. GCD curves at a current density of 0.035 mA cm −2 at a) −20, b) 25, and c) 80 °C, before and after 24 h incubation at the respective temperatures, and d) corresponding capacitance values. (e) Photographs of AGSCs fabricated from electrodes with varying thicknesses (0.8–3.2 mm) and f) their GCD curves at a current density of 0.035 mA cm −2 . g) Relationship between capacitance (calculated from GCD curves) and electrode thickness. h) Performance of prepared and reported AGSCs in terms of capacitance, capacitance retention after self‐healing and rebuilding, and working temperature range. Next, we investigated the effect of humidity on device performance considering the hygroscopic nature of glycerol. When the device was maintained at a high humidity of 70%–80% for 24 h, a slight weight increase of ≈10% was observed due to moisture absorption. This moisture absorption led to a reduction in the equivalent series resistance in the higher frequency region of the Nyquist plot, indicating the increased ionic conductivity of the device (Figure S16a , Supporting Information). The absorbed moisture enhanced the ion mobility within the device, resulting in improved capacitance retention at higher current densities (Figure S16b , Supporting Information). Even after prolonged exposure to high humidity (70%–80%), the device maintained 100% of its areal capacitance at a current density of 0.035 mA cm −2 , demonstrating a stable performance under humid conditions. However, when exposed to abnormally high humidity (>90%), the interface of the device weakened due to excessive moisture absorption, leading to the delamination of its components. However, the delaminated device can be reassembled by our water‐triggered self‐healing process (Figure 5d ), which can provide stable performance in humid air (≤80%). 2.5.4 Scalability and Generalizability Another interesting feature of our AGSC is its scalability. Devices were fabricated with varying electrode thicknesses, and their electrochemical performance was evaluated (Figure 6e–g ). For AGSCs with electrode thicknesses of 0.8, 1.5, 2.6, and 3.2 mm, the areal capacitances of the corresponding devices (measured through GCD) were 126.51, 204.49, 408.27, and 449.85 mF cm −2 , respectively. This increase in areal capacitance with electrode thickness was attributed to the increased amount of active material in the thicker electrodes. Conventional supercapacitors exhibit a negligible or minimal increase in capacitance with an increase in electrode thickness. [ \n \n 34 \n \n ] In contrast, our AGSC displayed a sharp and linear increase in capacitance with increasing electrode thickness (Figure 6g ) up to an electrode thickness of 3.2 mm; therefore, this thickness range (0.8–3.2 mm) can be considered as optimal for the electrode. This thickness range is well within that of commonly used electrodes for AGSCs. The optimal thickness may be selected depending on the energy requirements and packing limitations of specific applications. The 3D crosslinked alginate structure facilitated the incorporation of active materials, forming a percolated network even when the thickness was increased. Notably, the 3D structure enables facile ion diffusion, efficient charge storage at the molecular level, and high electrical conductivity. Therefore, our AGSC retains high electrochemical efficiency even at high thicknesses. The facile fabrication process, along with its scalability, demonstrates the adaptability of the AGSC for industrial applications that demand high energy densities. The excellent multifunctional performance of the AGSC significantly surpasses that of many state‐of‐the‐art gel supercapacitors, as evident from a comparison of their capacitance, capacitance retention after self‐healing and rebuilding, and working temperature range (Figure 6h ). [ \n \n 16 \n , \n 17 \n , \n 18 \n , \n 19 \n , \n 20 \n , \n 21 \n , \n 22 \n , \n 23 \n \n ] \n In this study, we used an alginate‐based system to establish the fundamental strategy and fabrication process for water‐triggered reconfigurable glycerogels with tailored functionalities. However, the resulting gels exhibited limited stretchability (≤100%) because of the inherent rigidity of alginate. This limitation could restrict the range of potential applications. The selection of polymers and solvents is crucial in developing reconfigurable gels for diverse applications. The choice of polymer is limited to those that possess adequate physical crosslinking sites. Equally important is the choice of solvent, as different solvents exhibit varying crosslinking capabilities and might require different polymer densities to reach the gel point. This would ultimately affect the mechanical properties of the designed gels. Future studies should identify suitable polymers with a flexible backbone and abundant reconfigurable crosslinking capabilities. These polymers would offer enhanced stretchability and elasticity, making them suitable for use in various stretchable and wearable devices."
} | 11,261 |
38005689 | PMC10674191 | pmc | 6,569 | {
"abstract": "Zirconium (Zr) is one of the toxic metals that are heavily incorporated into the ecosystem due to intensive human activities. Their accumulation in the ecosystem disrupts the food chain, causing undesired alterations. Despite Zr’s phytotoxicity, its impact on plant growth and redox status remains unclear, particularly if combined with elevated CO 2 (eCO 2 ). Therefore, a greenhouse pot experiment was conducted to test the hypothesis that eCO 2 can alleviate the phytotoxic impact of Zr upon oat ( Avena sativa ) plants by enhancing their growth and redox homeostasis. A complete randomized block experimental design (CRBD) was applied to test our hypothesis. Generally, contamination with Zr strikingly diminished the biomass and photosynthetic efficiency of oat plants. Accordingly, contamination with Zr triggered remarkable oxidative damage in oat plants, with concomitant alteration in the antioxidant defense system of oat plants. Contrarily, elevated levels of CO 2 (eCO 2 ) significantly mitigated the adverse effect of Zr upon both fresh and dry weights as well as the photosynthesis of oat plants. The improved photosynthesis consequently quenched the oxidative damage caused by Zr by reducing the levels of both H 2 O 2 and MDA. Moreover, eCO 2 augmented the total antioxidant capacity with the concomitant accumulation of molecular antioxidants (e.g., polyphenols, flavonoids). In addition, eCO 2 not only improved the activities of antioxidant enzymes such as peroxidase (POX), superoxide dismutase (SOD) and catalase (CAT) but also boosted the ASC/GSH metabolic pool that plays a pivotal role in regulating redox homeostasis in plant cells. In this regard, our research offers a novel perspective by delving into the previously unexplored realm of the alleviative effects of eCO 2 . It sheds light on how eCO 2 distinctively mitigates oxidative stress induced by Zr, achieving this by orchestrating adjustments to the redox balance within oat plants.",
"conclusion": "5. Conclusions This investigation improves our knowledge about how eCO 2 differentially alleviates the phytotoxic effect of Zr on the growth, photosynthesis, and redox status of Avena sativa plants. Contamination with Zr greatly reduced the photosynthesis of oat plants with concomitant reduction in their biomass. Moreover, contamination with Zr triggered remarkable oxidative stress in oats. On the other hand, eCO 2 significantly recovered the adverse effect of Zr on the biomass and redox status of oats. Furthermore, eCO 2 modulated the redox homeostasis of oat plants by enhancing their photosynthetic efficiency. Accordingly, enhanced photosynthesis can improve the plant biomass under both clean and contaminated conditions. This beneficial effect of eCO 2 extends beyond photosynthesis and biomass; it also mitigates the severe oxidative damage in oats by decreasing the levels of H 2 O 2 and MDA induced by Zr pollution. Moreover, eCO 2 ameliorated the negative impact of Zr upon the non-enzymatic antioxidants by improving the total antioxidant capacity (FRAP) and then the levels of the non-enzymatic antioxidants (polyphenols and flavonoids). Moreover, eCO 2 caused a noticeable activation in all measured antioxidant enzymes that were inhibited in oats contaminated with Zr. In conclusion, our study, for the first time, introduces a new insight on the ability of eCO 2 in harnessing the redox homeostasis of oat plants to cope with the phytotoxic effect imposed by toxic metals, particularly Zr.",
"introduction": "1. Introduction Growing environmental and health apprehensions have been raised regarding toxic metals, primarily due to their extensive dissemination throughout the ecosystem. The accumulation of such toxic metals in the ecosystem imposes rigorous imperilment on both human health and agricultural productivity. There are several reasons for the introduction of such toxic metals to the environment. Anthropogenic endeavors such as mining activities, the application of pesticides and industrial wastes are the common sources of such toxic metals [ 1 ]. Once they have been introduced to the ecosystem, they are incorporated into the food chain, causing undesired alterations [ 2 ]. Although plants need some of these metals for their growth and development, the existence of these metals in high levels causes a tremendous disruption in plant physiology and metabolism [ 3 ]. Like other toxic metals, Zirconium (Zr) is widely distributed in the Earth’s crust [ 4 ]. It is considered a reactive metal but is more resistant to corrosion than many other metals. This is due to the formation of a thin layer of zirconium dioxide (ZrO 2 , also known as zirconia) that acts as a protective layer for the underlying metal against further oxidation [ 5 ]. Zr and its alloys are widely used in different human activities. It is commonly applied in the nuclear industry due to its high resistance to corrosion, in addition to being used as cladding for nuclear fuel rods in reactors [ 5 ]. Furthermore, Zr compounds can be utilized in various sectors of industries such as ceramics, refractories, and electronics. More interestingly, ZrO 2 is a material that can be utilized in various applications, including dental implants, catalysts, and solid oxide fuel cells. The widespread occurrence of Zr in various activities has led to its extensive presence in our surroundings. This situation has raised alarm within the scientific community due to the potential detrimental effects it poses on human health and agricultural productivity. Zr and its compounds are toxic to human health, so proper handling and safety precautions should be taken to prevent its dispersion [ 6 ]. Concerning plants, although Zr is not an essential element for plant growth, excessive exposure to Zr and its compounds may slow plant growth and development and change the soil structure [ 7 ]. Nonetheless, when it comes to understanding the effects of Zr on plants, there has been relatively limited research compared to more extensively studied elements. The excessive buildup of Zr within plant tissues has the potential to interfere with nutrient absorption and crucial metabolic processes, ultimately resulting in a significant decline in plant growth [ 6 ]. Shaid et al. [ 6 ] also declared that Zr can interfere with the availability of some essential nutrients, such as Ca, Mg, and K which are vital for most plant metabolic processes, something that can hinder overall plant growth and development. Moreover, excessive exposure to Zr has been reported to alter the photosynthetic pigments of Chlorella pyrenoidosa , leading to a negative impact on culture mass and yield [ 8 ]. Both root function and development can also be affected by higher exposure to Zr. The disturbance in root function and growth, in turn, can impact the plant’s capacity to absorb nutrients and water, diminishing its ability to acquire vital resources, leading to stunted growth and decreased vitality [ 9 ]. Furthermore, Fodor et al. [ 9 ] declared that plant exposure to Zr may trigger plant oxidative stress that causes an imbalance between the production of ROS and the ability to detoxify them. This oxidative damage can disrupt the cellular components and slow different metabolic pathways in plants [ 10 ]. It is crucial to understand that the detrimental impacts of Zr on plants can be influenced by various factors, including the type and the concentration of Zr in the soil, soil conditions and the specific plant species involved. Nonetheless, the precise mechanisms through which Zr influences plant metabolism remain incompletely elucidated, underscoring the need for further research to comprehensively assess the impact on plant growth and metabolic processes. To this end, researchers should explore environmentally friendly and economically viable agricultural approaches to mitigate the potential harmful effects of excessive Zr accumulation. One of these tools is by enriching the atmosphere of plants with carbon dioxide. The intensive increase in atmospheric CO 2 levels is a major environmental challenge. This elevation is expected to further modify soil properties, altering the growth and development of economically important crops. Progressive industrial activities have caused a rapid increase in CO 2 concentration from 280 ppm to 400 ppm [ 11 ]. Prognostications have shown that this increment will continue to increase as a consequence of immense human industrial impact [ 12 ]. In point of fact, elevation in CO 2 within a physiological range has been proven to enrich plant growth, enhancing photosynthetic carbon metabolism with consequent boosting in assimilation [ 10 ]. The enhancement in carbon metabolism will accordingly be reflected in the partitioning of plant carbon and nitrogen [ 11 ]. Furthermore, several studies showed that eCO 2 could alleviate the detrimental effects of a variety of environmental constraints upon plant growth and metabolism [ 10 , 13 , 14 ]. This potential of eCO 2 is attributed to its effective changes in stomatal conductance, which play a crucial role in enhancing plant water uptake [ 15 ]. More interestingly, eCO 2 could mitigate the imposed stress by modulating the redox homeostasis through regulating ROS production and scavenging [ 16 ]. Therefore, the scientific community should pay attention to the impact of eCO 2 in boosting plant tolerance under different environmental challenges, especially for economically important crops such as oat plants. In this regard, eCO 2 raised the tolerance level of two barley cultivars grown under salinity stress [ 17 ]. Furthermore, Mhamdi and Noctor [ 18 ] suggested that Arabidopsis subjected to eCO 2 treatment exhibits increased resistance against bacterial and fungal invasions. Likewise, it was found that treatment with eCO 2 enhanced the defense system of Arabidopsis against different leaf and root pathogenic fungi [ 19 ]. Furthermore, tomato plants treated with eCO 2 can cope with heat stress by enhancing photosynthesis and redox homeostasis [ 20 ]. Overall, these investigations and more were directed to study the combined effect of both eCO 2 and different biotic and abiotic stresses. Nevertheless, the effect of raising atmospheric CO 2 upon a toxic metal like Zr has not yet been considered. Therefore, our study was conducted to closely investigate the impact of two levels of CO 2 (ambient, 420 ppm, and elevated, 710 ppm) on the growth and redox status of oat ( Avena sativa ) grown under the curb of Zr pollution. We further aim to verify the hypothesis that eCO 2 amplifies carbohydrate portioning by bolstering photosynthetic efficiency. This, in turn, fortifies the antioxidant defense system, enabling it to counteract the oxidative damage caused by Zr.",
"discussion": "4. Discussion Upon being incorporated into the food chain, Zr becomes a threat to human health as it can be easily taken in by the human body through food or water. Concerning plants, Zr was found to inhibit the germination of seeds and the growth of plant shoots and roots [ 35 ]. Nevertheless, there has been a lack of information about its impact upon plant physiology and metabolism. Therefore, our study aims to investigate the phytotoxic impact of Zr upon the growth, photosynthesis and ROS homeostasis of oat plants ( Avena sativa ) and how elevated CO 2 could ameliorate this phytotoxic effect. 4.1. eCO 2 Ameliorated the Growth Reduction and Oxidative Burst in Oat That Was Initiated by Zr Our results showed a remarkable reduction in the growth as well as photosynthesis of oat plants in response to contamination with Zr. These findings highlight the phytotoxic impact of Zr upon plant growth and development. Comparable findings were noted in the case of algae by Simon et al. [ 8 ], who reported an adverse impact of Zr on the growth and photosynthesis of Chlorella pyrenoidosa . Moreover, the dry matter of maize shoots and roots were dramatically decreased by increasing the concentration of Zr [ 35 ]. It is likely that treatment with Zr not only inhibits the germination of Triticum aestivum but also adversely inhibits the growth of their shoots and roots [ 9 ]. Generally, soil contamination with toxic metals adversely delays the growth and development of plants. In this context, the growth of Oryza sativa was highly delayed when grown in soils contaminated with Indium [ 36 ]. A similar observation was opined by Kopittke et al. [ 37 ], who found that contamination with trace metals such as Ga, In, Hg and Ru significantly reduced the growth of cowpea roots and caused cell rupture. This phytotoxic effect could be explained by the high binding tendency of these elements to the cell wall, increasing the cell rigidity, delaying the cell growth and finally causing the rupturing of cells [ 33 ]. It is important to recognize that the biogeochemical behavior of toxic elements like Zr in the environment and their phytotoxic impact on plants are primarily contingent on their speciation. Speciation refers to the existence of metals in diverse chemical forms, which is greatly influenced by environmental factors such as soil pH and interactions with soil organic matter [ 6 ]. Regarding Zr, it exists in the soil in different chemical forms that have a wide variety of solubility and bioavailability [ 38 ]. This speciation could explain the adverse effect of Zr upon plant growth and biomass. On the other hand, our study revealed that the phytotoxic effect of Zr was obviously mitigated by the application of eCO 2 . This ameliorative effect was embodied in the recovery of the plant biomass and photosynthetic efficiency relative to contaminated plants. In line with our findings, the growth of both maize and barley contaminated with As 2 O 3 and HgO, respectively, exhibited a noticeable recovery in both growth and photosynthetic efficiency in response to treatment with eCO 2 [ 39 ]. This advantageous effect might be ascribed to the ability of eCO 2 , within the physiological range, to enhance plant biomass by stimulating its photosynthetic machinery [ 33 ]. This, in turn, will modulate the photosynthetic carbon metabolism and so improve carbohydrate partitioning [ 10 ]. Furthermore, the enhancement in photosynthesis due to eCO 2 can also be attributed to the elevated levels of CO 2 at the RuBisco site, which reduces the availability of NADPH and ATP for photorespiration, thus facilitating CO 2 assimilation in plants [ 40 ]. Kaiser et al. [ 41 ] reported that eCO 2 increased the relative carbon gain with concomitant enhancements in the photosynthesis of tomato by initiating carboxylation reaction and slowing oxygenation reaction at RuBisco. Likewise, the photosynthesis in lettuce was improved in response to eCO 2 , the thing that reflected on its growth due to the availability of the carbon skeleton, resulting in higher levels of carbohydrates [ 42 ]. In conclusion, it is hypothesized that eCO 2 could provide protection to significant crops against various environmental threats, including metal pollution like Zr, by enhancing their growth and photosynthesis. 4.2. eCO 2 Highly Quenched the Oxidative Damage Caused by Zr in Oat Plants Improvement in plant growth and photosynthesis is associated with modulating the plant redox homeostasis, including the regulation of reactive oxygen species under various environmental cues [ 33 ]. Our results declared that Zr triggered oxidative stress in oat plants by increasing the levels of H 2 O 2 and MDA. In this regard, the treatment of wheat seedlings with Zr caused an oxidative damage by altering the activities of the antioxidant enzymes [ 9 ]. Similarly, Urtica dioica grown in heavy-metal-contaminated soils experienced remarkable oxidative stress with concomitant DNA damage [ 43 ]. Likewise, soil contamination with Zn and Pd provoked noticeable oxidative damage in sorghum plants [ 44 ]. Oxidative stress was triggered in both wheat and soybean plants treated with As [ 45 ]. Moreover, cereal crops treated with tungsten nanoparticles exhibited a remarkable accumulation in both H 2 O 2 and MDA, the main cause of oxidative damage in plants [ 46 ]. Plants respond to metal stress by triggering diverse signaling pathways, including those related to ROS metabolism, as tools to cope with the different environmental challenges [ 47 ]. The adverse effects of toxic metals, one of the environmental challenges, on the growth and oxidative status of plants could be due to direct or indirect reasons. The direct toxic impact lies in the ability of heavy metals to inhibit the cytoplasmic enzymes and the destruction of cell structures in response to oxidative damage [ 48 ]. Conversely, toxic metals can indirectly influence plant growth and oxidative balance by inhibiting the functions of soil microorganisms. This, in turn, impacts the decomposition of organic matter, leading to adverse effects on soil nutrient levels [ 49 ]. Furthermore, essential metabolic enzymes may be hampered due to the interference of the toxic metals with the soil microorganisms [ 50 ]. Toxic metals may also accumulate H 2 O 2 by impairing photorespiration, one of most important H 2 O 2 -generating pools in plants [ 51 ]. Additionally, toxic metals caused ABA accumulation, leading to the hyperaccumulation of H 2 O 2 and the slowing of cell division [ 52 ]. These detrimental effects, when combined, can impede plant growth and initiate plant oxidative damage. On the other hand, the co-application of eCO 2 with Zr obviously reduced the oxidative damage caused by Zr alone by reducing the levels of H 2 O 2 and MDA in oat plants. Similar results were obtained by AbdElgawad et al. [ 53 ], who proclaimed that the growth of both wheat and soybean in an atmosphere enriched with CO 2 reduced the oxidative damage caused by As stress. The authors also reported that eCO 2 enhanced the antioxidative defense system in cereals grown in As-contaminated soils [ 45 ]. Moreover, eCO 2 obviously quenched the oxidative damage triggered in both wheat and maize treated with both NiO and HgO by diminishing the accumulation of H 2 O 2 and MDA [ 13 , 54 ]. This mitigative impact of eCO 2 could be attributed to its potency to reduce the chance of oxygenation reaction on RuBisco [ 55 ]. This will increase the rate of carboxylation reaction that, in turn, will increase the carbon assimilation [ 11 ]. Moreover, high levels of CO 2 can reduce the activity of the enzymes of photorespiration, leading to a reduction in the accumulated H 2 O 2 , accordingly [ 56 ]. 4.3. eCO 2 Raised Oat Tolerance to Zr Contamination by Regulating the Antioxidant Defense System In plants, the antioxidant defense system includes low molecular weight non-enzymatic antioxidants (ascorbate, glutathione, polyphenols, flavonoids, etc.) and antioxidant enzymes that function in a coordinated manner to restrain the over-production of ROS in plants [ 57 ]. Superoxide dismutase (SOD) is the key enzyme in the conversion of superoxide ions into H 2 O 2 , which is further turned into H 2 O by CAT, GPX, or APX [ 34 ]. Therefore, the elevation in the activities of such enzymes in response to Zr treatments is an attempt by the plant to rein in the overproduction of H 2 O 2 , one of the main causes of oxidative damage. We also opined an elevation in the ascorbate/glutathione pathway that functions mainly in scavenging H 2 O 2 in oat plants under contamination conditions [ 58 ]. Pollution with Zr, like other environmental stresses, causes oxidative damage in oat plants. Likewise, we previously found that tungsten nanoparticles caused an increment in the components of both enzymatic and non-enzymatic antioxidants [ 46 ]. Moreover, the growth of Camellia sinensis in media containing Cd enhanced the accumulation of phenolics including flavans [ 59 ]. Similarly, oilseed rape treated with Cd showed a noticeable increase in the levels of non-enzymatic antioxidants [ 60 ]. Also, the application of Zn caused a noticeable increase in lipid peroxidation in Brassica juncea due to the hyperaccumulation of ROS [ 61 ]. Analogous findings were documented in the moss Taxithelium nepalense , wherein exposure to Cr and Pd led to the generation of reactive oxygen species (ROS), accompanied by a simultaneous reduction in the antioxidant defense system [ 62 ]. In rice, the application of Cu caused an over-accumulation of ROS and an observable decline in the antioxidant defense arsenal [ 63 ]. In this context, ascorbic acid was induced in wheat shoots subjected to nickel stress, indicating its pivotal role in ROS manipulation [ 64 ]. Moreover, the redox statuses of ASC (ASA/DHA) and GSH (GSH/GSSG) in oat plants were reduced in response to Zr contamination, the thing that delayed the antioxidant defense system in oat plants. Overall, plants activate the production of secondary metabolites such as phenylpropanoids in response to different environmental cues [ 65 ]. This process provides the cell with a wide array of phenolic metabolites by activating the phenylpropanoid pathway [ 66 ]. By activating the phenylpropanoid pathway, non-enzymatic antioxidants were synthesized to enhance plant tolerance against environmental stresses such as toxic metals [ 67 ]. On the other hand, eCO 2 not only augmented the detoxification of ROS, but also kept the balance of both GSH/GSSG and ASC/DHA in Zr-polluted oat plants. In line with our findings, AbdElgawad et al. [ 68 ] declared that eCO 2 caused a remarkable improvement in the growth of cereals grown in soils contaminated with As via augmenting the ascorbate/glutathione metabolism. It is worth noting that the ASC/DHA metabolic pool keeps the redox homeostasis in the photosystem by maintaining high ratios of GSH/GSSG and ASC/DHA, which is important for plant tolerance against different environmental challenges [ 69 ]. Therefore, the concurrent suppression of photorespiration and improvement of photosynthesis highlights the crucial contribution of eCO 2 in mitigating the phytotoxic effect of Zr-contaminated oats. In other words, the mitigative role of eCO 2 could be mainly due to its ability to increase carboxylation at the expense of the oxygenation reaction in RuBisco [ 10 ]. More interestingly, eCO 2 can provide the cells with the C skeleton and energy needed for the growth of stressed plants [ 70 ]. This increment in carbon availability will supply the cells with antioxidant molecules, so raising the plant tolerance against the stress-induced oxidative damage [ 40 ]. In our study, the observed elevation in total antioxidant capacity (FRAP) due to treatment with eCO 2 indicates an awakening within the plant to tolerate the ROS generated by Zr. In accordance, we opined a noticeable increment in the levels of polyphenols and flavonoids in response to eCO 2 in plants contaminated with Zr ( Figure 4 ). Indeed, this increment could quench the toxicity induced by Zr pollution as an adaptive response to environmental stresses [ 71 ]. Another perspective is that the sugars accumulated in response to enhanced photosynthesis can scavenge free radicals generated by ROS leading to alleviation of the oxidative damage caused by environmental stresses [ 72 ]. To this end, our study, for the first time, shed light on the positive role of increasing atmospheric CO 2 in modulating the ROS homeostasis in oat plants to cope with pollution with Zr."
} | 5,847 |
39165409 | PMC11334944 | pmc | 6,572 | {
"abstract": "Highlights • Impact of biofertilizers on soil microbial diversity and functionality. • Role of microbial-based bio-inoculum in improving plant growth and soil health. • Growth-promoting attributes of biofertilizer and its effectiveness over inorganic fertilizers for sustainable agriculture. • Prospects of commercialization, bioeconomy and production of biofertilizers for the development of agricultural productivity and socioeconomic status.",
"conclusion": "14 Conclusion Bioinoculants offer a sustainable solution for modern agriculture by harnessing beneficial microorganisms, such as bacteria, fungi, and algae. They naturally enhance soil fertility, boost plant growth, and increase crop yield by fixing nitrogen, solubilizing phosphorus, and mobilizing nutrients. Biofertilizers mitigate the environmental issues caused by chemical fertilizers, such as soil degradation and water pollution. As agriculture moves towards sustainability, the widespread use of biofertilizers promotes agroecological principles, improves food security, and helps tocombat climate change. This study underscores promising prospects for the commercialization of biofertilizers, driven by the growing demand for sustainable agricultural practices. Effective market policies and government support can further catalyze their adoption, creating opportunities for job growth in the production, research, and distribution sectors within the burgeoning biofertilizer industry.However, further research, education, and infrastructure development are needed to realize the full potential of biofertilizers and to effectively integrate them into agricultural systems worldwide. Through collaborative efforts among scientists, policymakers, farmers, and consumers, biofertilisers can play a crucial role in shaping a sustainable and resilient future for global agriculture.",
"introduction": "1 Introduction The Green Revolution, characterized by advancements in crop productivity through high-yielding varieties, modern farming techniques, and chemical fertilizers, has raised concerns about environmental degradation and soil health ( Swaminathan, 2006 ; Pramanik et al., 2023 ). In response, biotechnological approaches utilizing beneficial bacteria have gained traction ( Biswas et al., 2021 , 2022 ; Chattaraj et al., 2023a , 2023b ; Ganguly et al., 2024 ). The integration of biofertilizers into agricultural practices has emerged as a promising strategy, offering eco-friendly alternatives to chemical fertilizers ( Nad et al., 2024 ). Derived from natural sources like bacteria, fungi, and algae, biofertilizers enhance soil fertility, nutrient cycling, and plant growth, aligning with sustainable agriculture principles ( Chakraborty and Akhtar, 2021 ; Mitra et al., 2021a ; Nosheen et al., 2021 ; Kumar et al., 2022 ). Additionally, biofertilizers can remediate heavy metals present in the soil which are hazardous to the environment ( Haroun et al., 2023 ; Sen et al., 2023 ; Chattaraj et al., 2024a ). Biofertilizers play important role in nitrogen fixation, phosphorus solubilization and potassium mobilizationwhich complement the goals of the Green Revolution while promoting sustainable agriculture and resilience to climate change ( Khoshru et al., 2023b ; Kaur and Purewal, 2019 ). Biofertilizers, originating with \"Nitragin\" in 1895, address economic challenges of conventional methods, providing a cost-effective and sustainable solution ( Riaz et al., 2020 ). In 2022, the global biofertilizers market reached a value of approximately USD 2.15 billion, and it is expected to reach approximately USD 6.83 billion by 2032, with a compound annual growth rate (CAGR) of 12.3 %, driven by the increasing demand for organically produced plants and vegetables ( Precedence Research, 2023 ).The Indian biofertilizer market is projected to be valued at approximately USD 10.63 million in 2024 and is anticipated to achieve a valuation of about USD 16.5 million by 2029, with a CAGR of around 9.19 % over the forecast period from 2024 to 2029 ( Gii research, 2024 ). The perspectives on the production, distribution, and access to biofertilizers can be portrayed with enhancing agricultural productivity, soil health, and socioeconomic development while aligning with climate change mitigation efforts ( Ajmal et al., 2018 ). Moreover, from an economic perspective, biofertilizers are lauded for their cost-effectiveness compared to chemical fertilizers ( Tiwari et al., 2004 ; Hassanpour et al., 2021 ). The viewpoints collectively underscore the potential of biofertilizers to contribute to a more sustainable and prosperous agricultural system. Biofertilizers can mitigate soil erosion, conserve water resources, and enhance biodiversity by promoting healthy and resilient agroecosystems ( Riaz et al., 2020 ). This ecological approach to farming not only benefits the environment but also safeguards the long-term viability of agricultural production systems, ensuring food security for future generations ( Garrity et al., 2010 ). In many developing countries, where agriculture remains the backbone of the economy and the primary source of livelihood for millions of people, the adoption of biofertilizers can have transformative effects on rural communities ( Sarkar et al., 2022 ; Angom and Viswanathan, 2023 ). Biofertilizers, as sustainable agricultural inputs, empower farmers to enhance productivity and income while improving overall well-being ( Mohanand Reddy, 2020 ). Additionally, decentralized production and distribution of biofertilizers create rural employment opportunities, stimulate local economies, and contribute to poverty reduction ( Cong and Thomsen, 2021 ). Agricultural enterprises require improved marketing strategies to adapt to market conditions and enhance competitiveness, while farmer training in biofertilizer applications is essential for successful adoption ( Lohosha et al., 2023 ). The inclusive adoption of biofertilizers promotes social cohesion and economic prosperity, representing a paradigm shift in agricultural practices with holistic and sustainable solutions ( Barragán-Ocañaand and del Carmen del-Valle-Rivera, 2016 ; Ray et al., 2020 ). Realizing the full potential of biofertilizers necessitates collaborative efforts among policymakers, researchers, and farmers to overcome adoption barriers, raise awareness, and invest in research and development ( Parida, 2016 ). Embracing sustainability and innovation can address biofertilizers to create a resilient, inclusive, and environmentally sustainable agricultural future ( Sultan and El–Qassem, 2021 ; Prabhu et al., 2022 ). Hence, this review aims to assess the efficacy of biofertilizers in enhancing soil health and crop productivity and to provide recommendations for sustainable adoption and commercialization of biofertilizer."
} | 1,700 |
39927512 | PMC12021080 | pmc | 6,573 | {
"abstract": "Abstract Multicellular organisms have hierarchical structures where multiple cells collectively form tissues with complex 3D architectures and exhibit higher‐order functions. Inspired by this, to date, multiple protocell models have been assembled to form tissue‐like structures termed prototissues. Despite recent advances in this research area, the programmed assembly of protocells into prototissue fibers with emergent functions still represents a significant challenge. The possibility of assembling prototissue fibers will open up a way to a novel type of prototissue subunit capable of hierarchical assembly into unprecedented soft functional materials with tunable architectures, modular and distributed functionalities. Herein, the first method to fabricate freestanding vesicle‐based prototissue fibers with controlled lengths and diameters is devised. Importantly, it is also shown that the fibers can be composed of different specialized modules that, for example, can endow the fiber with magnetotaxis capabilities, or that can work synergistically to take an input diffusible chemical signals and transduce it into a readable fluorescent output through a hosted enzyme cascade reaction. Overall, this research addresses an important challenge of prototissue engineering and will find important applications in 3D bio‐printing, tissue engineering, and soft robotics as next‐generation bioinspired materials.",
"conclusion": "3 Conclusion In conclusion, we have reported a novel technique for the extrusion of robust and freestanding prototissue fibers composed of multiple vesicles directly interconnected via salt bridges. Our technique allows for the extrusion of prototissue fibers of controlled lengths, diameters, and shapes, and for the fabrication of modular fibers with transversal sections that are constituted of protocell populations of different phenotypes and specialized functions. The utility and versatility of our technique was then showcased by developing magnetic fiber modules that allowed the fiber to be manipulated using magnetic fields. The magnetic manipulation and the adhesive properties of the fibers could then be synergistically exploited to magnetically drive the attachment of different modules either tip‐to‐tip or side by side. Finally, we showcased the possibility of engineering the fibers with specialized modules for guiding diffusible chemical signals through the fiber itself. This opens up a way to the engineering of reaction diffusion fronts that can propagate directly within tissue‐like materials, while thus far it has only been demonstrated that they can propagate through the bulk aqueous medium using spatially separated periodic arrays of individual protocells or prototissues. [ \n \n 16 \n , \n 31 \n \n ] \n Since our technique for the fabrication of prototissue fibers is based on the extrusion of prototissues through a pipette tip, this should be compatible with applications in 3D printing. We are already exploring this possibility. Thus far only the works of H. Bayley et al. demonstrated the possibility of 3D printing prototissues. [ \n \n 17 \n \n ] This would open up the possibility to 3D print prototissues that could be conjugated to failing living tissues, applied to the skin for the release of drugs, or that could be used to fill congenital fistulas, for example. [ \n \n 4 \n , \n 32 \n \n ] In fact, the prototissue fibers extruded in this work were made from POPC vesicles. Since liposomes comprising phospholipids such as POPC are known to be biocompatible, [ \n \n 33 \n \n ] the fibers are also expected to be biocompatible. Our prototissue fibers could also find applications as soft robots because they are composed of multiple vesicles with softness similar to bio‐organisms and can carry out mechanical and chemical tasks. [ \n \n 4 \n , \n 34 \n \n ] For example, we could build actuators [ \n \n 35 \n \n ] or swimmers [ \n \n 36 \n \n ] for soft robotics by combining movement modules made of magnetic protocells or of protocells capable of chemical propulsion to manipulation modules that could bend or roll‐up with external physical (light or temperature) or chemical stimuli (pH, chemical gradients). Furthermore, spatiotemporal signal transduction, in which chemical information was transmitted from one side to the opposite side of the fiber, was achieved by combining sensing and transmission modules. This highlights potential for application of the prototissue fibers as soft robots that can sense external stimuli and transmit the chemical signal to other areas of the same tissue‐like material or to another material to trigger a programmed chemical or mechanical response. From a general perspective, our results pave the way for development of vesicle‐based prototissue fibers as next‐generation bioinspired materials, addressing an important challenge of prototissue engineering. These new materials will find important applications in soft robotics, 3D bio‐printing, microbioreactor technologies, and flow chemistry.",
"introduction": "1 Introduction Multicellular organisms have hierarchical structures where multiple cells assemble into living tissues to form complex 3D architectures. [ \n \n 1 \n \n ] Living tissues exhibit higher‐order behaviors (e.g., signal transduction, contractility, phototropism, etc.), meaning that the cell units are able to interact with one another and generate a novel property of the ensemble, which is on a higher level. [ \n \n 2 \n \n ] Creating such hierarchical structures of living tissues artificially can be one of the most significant breakthroughs in bioinspired material science. [ \n \n 3 \n \n ] This could lead to the development of a next generation of soft materials for diverse applications ranging from soft robotics to microbioreactor technology and tissue engineering. [ \n \n 4 \n \n ] Moreover, modularity is one of the fundamental aspects of biological organization where different building blocks cooperate to form complex biological systems. [ \n \n 5 \n \n ] In terms of modularity, multicellular organisms are considered to be composed of multiple specialized modules such as organs, tissues, and cells that are fully integrated and continuously interact to provide higher‐order functions. Thus, the development of tissue‐like structures comprising different specialized modules could lead to the integration of other building blocks into one hierarchical structure with synergistic functionalities. In recent years, research efforts in this direction have led to the concept of protocells in the field of bottom‐up synthetic biology where cell functions can emerge from inanimate molecules and materials. [ \n \n 6 \n \n ] Protocells are synthetic microcompartmentalized systems such as vesicles, [ \n \n 7 \n \n ] coacervates, [ \n \n 8 \n \n ] DNA droplets, [ \n \n 9 \n \n ] lipid‐coated aqueous droplets having droplet interface bilayers (DIB), [ \n \n 10 \n \n ] and proteinosomes, [ \n \n 11 \n \n ] which are chemically programmed to mimic at least one fundamental aspect of a living cell. Based on the concept of the protocells, prototissues have been created as 3D assemblies of multiple protocells and can be designed and chemically programmed to mimic basic aspects of living tissues, such as chemo‐mechanical transduction, [ \n \n 12 \n \n ] signal transduction, [ \n \n 13 \n \n ] conversion of external signals into changes in phenotype properties, [ \n \n 14 \n \n ] and enhanced survivability against predators. [ \n \n 15 \n \n ] \n One of the most significant challenges of prototissue engineering is the fabrication of robust freestanding prototissues from the direct adhesion of protocells units. In this regard, the Gobbo group has succeeded in developing a programmed assembly of proteinosomes. Micrometer‐sized prototissue spheroids comprising proteinosomes were first reported using the Pickering emulsion procedure via an interfacial strain‐promoted alkyne‐azide cycloaddition (I‐SPAAC). [ \n \n 12 \n \n ] Later, they devised a floating mold technique to make protocellular materials where millimeter‐sized proteinosome‐based prototissues can be made in any shape. [ \n \n 16 \n \n ] Another example of technique that allowed for the assembly of prototissues with desired shapes is the aqueous droplets 3D‐printing developed by Bayley and co‐workers. [ \n \n 17 \n \n ] \n Among the many protocell models, vesicles are the most biomimetic protocells because, similarly to the membranes of biological cells, their membrane is composed of a lipid bilayer. [ \n \n 7 \n \n ] Thus, the controlled assembly of multiple vesicles could lead to important advancements toward the bottom‐up chemical construction of fully functioning forms of prototissues. Some methods to promote the aggregation of multiple vesicles using light, [ \n \n 18 \n \n ] metal ions, [ \n \n 19 \n \n ] cadherins, [ \n \n 20 \n \n ] and squeezed sponges, [ \n \n 21 \n \n ] or by the formation of streptavidin–biotin pair, [ \n \n 22 \n \n ] lectin–glycan pair, [ \n \n 23 \n \n ] and DNA complementary strands [ \n \n 24 \n \n ] have been reported in the literature. However, these methods could not provide freestanding prototissues with controlled shapes. Recent research reported programmable capillary‐induced assemblies of vesicles, which however had to be immersed in oil with consequent limited technological applications. [ \n \n 25 \n \n ] Optical tweezers have also been used to construct vesicle networks, [ \n \n 26 \n \n ] but with this technique it is technically challenging to achieve millimeter‐sized assemblies. External fields such as magnetic fields [ \n \n 27 \n \n ] and acoustic fields [ \n \n 28 \n \n ] have been applied as well to assemble vesicles, which resulted in the structure dissipation when the external field was turned off. Another approach to the bottom‐up chemical construction of prototissues relies on the embedment of protocells within a hydrogel matrix. [ \n \n 29 \n \n ] However, this method does not allow for direct protocell–protocell adhesions. To date, the fabrication of robust, large (millimeter to centimeter size) and freestanding vesicle‐based prototissues remains a considerable challenge. The programmed assemblies of multiple vesicles into prototissues with controlled shapes will pave the way toward the application of prototissues not only in the field of bioengineering, regenerative medicine and tissue engineering, but also in soft robotics, filtration technologies, and photocatalysis. Recently, our group reported the possibility of using salt bridges to assemble vesicle consortia with higher‐order cooperative functionalities; however, their controlled assembly into freestanding prototissues with controlled shapes was not achieved. [ \n \n 30 \n \n ] \n To complete this challenging scenario, the possibility of producing prototissues in the form of freestanding fibers of directly interconnected vesicles with controlled lengths, diameters, and shapes still remains unexplored. The possibility of producing prototissue fibers not only will lead to an unprecedented type of prototissue subunit capable of hierarchical assembly into soft functional materials with tunable architectures, modular and distributed functionalities, but they could also open up a way to the 3D printing of much more complex and larger prototissue architectures. Herein, we describe the first method to fabricate robust freestanding millimeter‐sized modular prototissue fibers with controlled lengths and diameters. Importantly, we showed that modular prototissue fibers with distributed functionalities could be assembled from the controlled adhesion of fiber subunits composed of specialized protocells. We demonstrated this by assembling prototissue fibers composed of a module for magnetotaxis capable of dragging the rest of the fiber, or comprising modules capable of working synergistically to take an input diffusible chemical signal and transduce it into a readable fluorescent output through a hosted enzyme cascade reaction. From a general perspective, our results address an important challenge of prototissue engineering and will find important applications in soft robotics, microbioreactor technologies, and flow chemistry.",
"discussion": "2 Results and Discussion 2.1 Fabrication of Vesicle‐Based Prototissue Fibers Based on thin‐film hydration methods, cationic vesicles were prepared by mixing 1‐palmitoyl‐2‐oleoyl‐ sn ‐glycero‐3‐phosphocholine (POPC, 2 × 10 −3 \n m ) and 4 mol% amphiphilic amines or guanidium chloride (80 × 10 −6 \n m ) in HEPES buffer (10 × 10 −3 \n m , pH 7.2). Likewise, anionic vesicles were prepared by mixing POPC (2 × 10 −3 \n m ) and 4 mol% amphiphilic carboxylic acids (80 × 10 −6 \n m ) in HEPES buffer (10 × 10 −3 \n m , pH 7.2). POPC was chosen as the major components of lipids because POPC membranes have moderate membrane fluidities which both enhances the attachment of different vesicles and suppresses their fusion. Size distribution and ζ ‐potential of the vesicles were characterized (Figures S1 and S2 , Supporting Information). The obtained dispersions of cationic and anionic vesicles were mixed at a ratio of 1:1 ( Figure \n \n 1 a and S3 , Supporting Information). Salt bridge‐mediated adhesion of vesicles was used to make assemblies of vesicles. [ \n \n 30 \n \n ] Salt bridges are intermolecular interactions comprising ionic and hydrogen bonds, which selectively interact between ammonium/guanidium and carboxylate ions. IR measurement clearly showed the intermolecular interactions (Figure S4 , Supporting Information). The mixed 1:1 dispersion of cationic and anionic vesicles was centrifuged at 16 000 g for 10 min to obtain a concentrated vesicle phase. The concentrated vesicle phase was loaded into a pipette tip, and the tip was set to a device made of a microcentrifuge tube, a polystyrene pipette tip holder, and an adhesive pad used to plug the pipette tip (Figure 1b ). The device enabled us to load and pack the vesicles inside the pipette tip using mild centrifugation (400 g for 5 min). The pipette tip was then carefully removed from the microcentrifuge tube, attached to a mechanical pipette, and the packed vesicles were extruded in HEPES buffer (pH 7.2) to obtain a stable and freestanding prototissue fiber (Figure 1c,d , Figure S5 and Video S1 , Supporting Information). The details of the prototissue fiber were captured using a two‐photon excitation fluorescence microscope. Figure 1e,f shows that the fiber was composed of Texas Red‐tagged (red fluorescence) cationic and NBD‐tagged (green fluorescence) anionic vesicles which adhered to one another via salt bridge interactions, and that the vesicles retained their structures even after the centrifugation processes. Prototissue fibers were found to be robust as they retained their structures over one week (Figure S6 , Supporting Information). Importantly, control experiments carried out using only cationic or anionic vesicles to try to assemble the prototissue fibers showed prompt dispersion of the packed vesicles extruded from the tip, indicating that salt bridges are key to achieve vesicle‐vesicle adhesions and their fiber assembly (Figure S7 , Supporting Information). Figure 1 Fabrication of prototissue fibers. a) Scheme describing the formation of vesicle–vesicle adhesions via salt bridges by mixing cationic and anionic vesicles in 1:1 ratio. b) Scheme describing our method to assemble prototissue fibers supported by photos of the different steps involved in the experimental procedure. Scale bar: 1 cm. c) Photo and d) digital microscopy images of a prototissue fiber immersed in HEPES buffer (10 × 10 −3 \n m , pH 7.2). Scale bar: 1 cm. e,f) Two‐photon excitation microscopic images of the prototissue fiber in (c) and (d). Texas Red (red fluorescence) and NBD (green fluorescence) were used to tag the cationic and anionic vesicles, respectively. Scale bar: (e) 200 µm, (f) 50 µm. Subsequently, we explored the versatility of our new method for the generation of prototissue fibers, and showed that it can be used to produce fibers of different lengths and diameters. To control the length of the prototissue fibers, the volume of the concentrated vesicle phase loaded into the pipette tips was progressively varied from 5 to 25 µL, and the resulting length of the fibers could be changed from 8 ± 1 to 49 ± 5 mm ( Figures \n \n 2 a and S8 , Supporting Information). The length of the prototissue fiber was found to increase linearly with the volume of the concentrated vesicle phase (coefficient of determination: 0.98). To control the diameter of the prototissue fibers, the inner diameter of the pipette tip was instead systematically changed from 370 to 910 µm. Using this strategy, the diameter of the prototissue fibers could be progressively varied from a minimum of 444 ± 7 µm to a maximum of 1240 ± 60 µm (Figures 2b and S9 , Supporting Information). The diameter of the fibers was also found to increase linearly with the inner diameter of the tips (coefficient of determination: 0.99). However, through this set of systematic experiments we noticed that the width of the obtained fibers was in average ≈25% larger than the inner diameter of the pipette tip because the prototissues tended to swell slightly when extruded from the pipette tip. Figure 2 Controllability of prototissue fiber lengths and diameters. a) Control of the length of prototissue fibers when changing the volume of the concentrated vesicle phase. b) Control of the diameter of prototissue fibers when changing inner diameter of the used pipette tips. c) Bright‐field and d) fluorescent images of our group's logo “BANNO GROUP” made of prototissue fibers containing Nile red as fluorescent dyes. λ \n ex = 530 nm, λ \n em > 570 nm. Scale bar: 1 cm. Overall, these results show that our new method enables the controlled assembly of a very high number of vesicle units into robust and freestanding prototissue fibers with controlled lengths and diameters. Our methodology is simple, effective, and highly reproducible. Significantly, since the prototissue fibers could be extruded using a pipette, it was also possible to achieve a spatial control over the assembly of the fibers. Figure 2c,d shows multiple prototissue fibers extruded and spatially organized to display the “BANNO GROUP” text. From a general perspective, these experimental results show that there could be the potential for utilizing our new prototissue fabrication methodology with 3D printing technologies to achieve an even higher spatial resolution and assemble prototissue fibers into complex 3D objects. 2.2 Fabrication of Multimodular Fiber Assemblies Modularity is ubiquitous in living tissues. The development of prototissue fabrication techniques that allow for a precise spatial segregation of different protocell phenotypes with specific properties and functions is key for the development of more advanced tissue‐like materials, and still remains a technological challenge. Next, we therefore showed that our vesicle extrusion technique allows for the sequential attachment of different fibers composed of different types of protocells. We started by sequentially loading and centrifuging inside the same pipette tip populations of differently tagged vesicles ( Figures \n \n 3 \n and S10 , Supporting Information). The highly packed, layered vesicle populations were then extruded using a mechanical pipette, resulting in modular fiber assemblies. Figure 3a shows a bi‐modular prototissue fiber produced by extruding Texas Red‐tagged (red fluorescence) population of cationic and anionic vesicles, and NBD‐tagged (yellow fluorescence) population of cationic and anionic vesicles. Figure 3b shows instead a tri‐modular prototissue fiber comprising three different vesicle populations, the first population comprised vesicles tagged with Texas Red (red fluorescence), the second population tagged with Marina Blue (blue fluorescence), and the third population tagged with NBD (yellow fluorescence). Another tri‐modular prototissue fiber comprising alternate populations containing Texas Red‐tagged (red fluorescence) vesicles and NBD‐tagged (yellow fluorescence) vesicles was prepared (Figure S11 , Supporting Information). Finally, Figure 3c shows a tetra‐modular prototissue fiber composed of alternate populations containing Texas Red‐tagged (red fluorescence) vesicles and NBD‐tagged (yellow fluorescence) vesicles. Importantly, in the prototissue fibers produced, all modules remained well localized, and the differently tagged protocell populations did not mix over time. Figure 3 Fabrication of multimodular prototissue fibers. a) A bi‐modular fiber where a Texas Red‐tagged (red fluorescence) fiber and an NBD‐tagged (yellow fluorescence) fiber were connected. b) A tri‐modular fiber where a Texas Red‐tagged (red fluorescence) fiber, a Marina Blue‐tagged (blue fluorescence) fiber, and an NBD‐tagged (yellow fluorescence) fiber were connected. c) A tetra‐modular fiber where Texas Red‐tagged (red fluorescence) fibers and NBD‐tagged (yellow fluorescence) fibers were connected alternately. The excitation and emission wavelength are λ \n ex = 450 nm, λ \n em > 530 nm for Texas Red and NBD; λ \n ex = 365 nm, λ \n em > 420 nm for Marina Blue. Scale bar: 5 mm. Next, we explored the possibility of increasing the chemical complexity of the prototissue modules. We thus started by generating magnetic protocells and used them to fabricate a prototissue fiber capable of magnetotaxis. Magnetic nanoparticles were incorporated in cationic and anionic NBD‐tagged vesicles using thin‐film hydration methods (Section S1.4 , Supporting Information). Subsequently, the magnetic cationic and anionic vesicles were mixed and concentrated using centrifugation, loaded in a pipette tip, and extruded to generate a prototissue fiber ( Figure \n \n 4 a ). Notably, the fiber could move in response to a locally applied magnetic field (Figure 4b and Video S2 , Supporting Information), which could also be used to move the fiber through a U‐shaped path (Figure S12 and Video S3 , Supporting Information). During magnetotaxis, the fiber remained stable and retained its length and diameter. Figure 4 Magnetic manipulation of prototissue fibers. a) Bright‐field and fluorescent images of a magnetic prototissue fiber composed of NBD‐tagged vesicles enclosing magnetic nanoparticles. λ \n ex = 450 nm, λ \n em > 530 nm. Scale bar: 5 mm. b) Magnetic manipulation of the prototissue fiber described in (a). Scale bar: 5 mm. c) A bi‐modular prototissue fiber comprising a driving module capable of magnetotaxis fabricated using NBD‐tagged (yellow fluorescence) vesicles enclosing magnetic nanoparticles, and a cargo module fabricated from Texas Red‐tagged (red fluorescence) vesicles. λ \n ex = 450 nm, λ \n em > 530 nm for Texas Red and NBD; λ \n ex = 365 nm, λ \n em > 420 nm for Marina Blue. Scale bar: 5 mm. d) Magnetic manipulation of the prototissue fiber described in (c), highlighting the possibility of rolling up and unrolling the bi‐modular prototissue fiber by exploiting the magnetotaxis of the driving module (yellow color). Scale bar: 5 mm. e) Connection of an NBD‐tagged magnetic fiber (yellow color, left) to a Texas Red‐tagged normal fiber (red color, right) by their length. Scale bar: 5 mm. f) Magnetic manipulation of the bi‐modular fiber resulting from the attachment of the NBD‐tagged magnetic fiber (yellow color, left) to the Texas Red‐tagged normal fiber (red color, right). Scale bar: 5 mm. g) Connection of an NBD‐tagged magnetic tissue (yellow color, left) and a Texas Red‐tagged normal tissue (red color, right) by their extremities. Scale bar: 5 mm. h) Magnetic manipulation of the bi‐modular fiber resulting from the attachment of the NBD‐tagged magnetic fiber (yellow color, bottom) to the Texas Red‐tagged normal fiber (red color, top). Scale bar: 5 mm. Subsequently, we explored the possibility of fabricating modular prototissue fibers capable of magnetotaxis. This could be accomplished through two different strategies. The first strategy involved the possibility of directly extruding a bi‐modular prototissue comprising a driving module for magnetotaxis and a cargo module. The second strategy instead relied on generating two separated modules and joining them by exploiting the interfacial reactivity of the protocell membranes that were capable of salt bridge adhesions. Figure 4c shows a bi‐modular prototissue fiber comprising NBD‐tagged magnetic nanoparticle‐containing vesicles and Texas Red‐tagged vesicles without magnetic nanoparticles that were directly extruded from a pipette tip. The magnetic module of the fiber could be used to drive the entire prototissue fiber via an applied magnetic field. The fiber remained intact during magnetotaxis and could also perform complex twists, rolling ups, and unrolls without breaking (Figure 4d and Video S4 , Supporting Information). Importantly, the same type of modular prototissue fiber could be assembled by joining the driving module for magnetotaxis and the cargo module using salt bridge adhesions (second strategy). Figure 4e shows that the two fiber modules can be joined by their length, and then the entire prototissue can be moved by applying a magnetic field (Figure 4f and Video S5 , Supporting Information). Figure 4g shows instead that the two fiber modules can be joined by their tips and then the resulting bi‐modular fiber can undergo magnetotaxis (Figure 4h and Video S6 , Supporting Information). In both cases, salt bridges generated prompt and robust adhesions between the two prototissue fibers that could withstand the dragging pull exerted by the driving module. Overall, these results showcase the versatility of our technique for generation of modular prototissue fibers, and highlight the potential of our new biomimetic materials for applications not only in bottom‐up synthetic biology but also in soft robotics and biomedical engineering. 2.3 Combination of Input and Output Modules to Induce Signal Transduction In the last part of this work, we built on the breakthroughs described above and further advanced the biochemical complexity of the prototissue fibers. We started by fabricating vesicles containing glucose oxidase (GOx) or horseradish peroxidase (HRP). In general, to obtain the enzyme‐containing vesicles, aqueous solutions of the enzymes were used during the thin‐film hydration methods (Section S1.4 , Supporting Information). Subsequently, we tested the functionality of individual prototissue fibers formed from GOx‐containing vesicles or HRP‐containing vesicles. For this, a fiber made from GOx‐ and melittin‐containing vesicles was placed in an aqueous solution of HRP (≈0.4 U mL −1 ) and Amplex Red (≈0.1 × 10 −3 \n m ). After adding 100 µL of an aqueous solution of glucose (100 × 10 −3 \n m ), red fluorescence gradually generated in the bulk aqueous solution (Figure S13 , Supporting Information). This indicated permeation of glucose from the bulk aqueous solution into the vesicle through pores made by melittin, with subsequent production and diffusion of H 2 O 2 from the lumen of the vesicles into the bulk aqueous solution. In the bulk aqueous solution, the dissolved HRP could use H 2 O 2 to catalyze the oxidation of Amplex Red into red fluorescent Resorufin first and subsequently into nonfluorescent Resazurin ( Figure \n \n 5 a ). Similarly, a prototissue fiber made from HRP‐ and Amplex Red‐containing vesicles was placed in an aqueous solution, and 100 µL of an aqueous solution H 2 O 2 (1 m ) was injected on the left end side of the fiber. Red fluorescence generated from the left end of the fiber and gradually moved toward the other end following the diffusion of H 2 O 2 . This indicated permeation of H 2 O 2 from the bulk aqueous solution into the vesicles that compose the prototissue fiber, with subsequent HRP‐catalyzed oxidation of Amplex Red into Resorufin inside the vesicle lumen (Figure S14 and Video S7 , Supporting Information). Figure 5 Signal transduction through prototissue fibers connecting input and output modules. a) Mechanism of transient red fluorescence as a consequence of the oxidation of Amplex Red to Resorufin, followed by the oxidation of Resorufin to Resazurin. b) Scheme illustrating the mechanism of the enzyme cascade reaction hosted within the protocells that compose the materials. c) Time‐lapse acquired using digital microscopy upon addition of 100 µL of a solution of glucose (100 × 10 −3 \n m ) to the prototissue fiber described in (b). λ \n ex = 530 nm, λ \n em > 570 nm. Scale bar: 5 mm. d) Changes in the gray value of (c) obtained by image analysis during 0–20 min. e) Changes in the gray value of (c) obtained by image analysis during 0–120 min. The front of the red fluorescence gradually moved from left to right. f) Time‐dependent changes in the gray value of time‐lapse in (c) obtained by image analysis with ImageJ at the length of 5 mm, where the transient red fluorescence was observed. Following on these promising results, next, we exploited salt bridge adhesions to assemble a prototissue fiber comprising an input and an output module (Figure 5b ). For the input module, we intended a module capable of generating a diffusible chemical signal, whereas for the output module, we intended a module capable of sensing the signal from the input module and generating an output fluorescence signal in response. Specifically, a GOx‐containing fiber was used as the input module because, in the presence of glucose, it is capable of producing the signal H 2 O 2 . An HRP‐containing fiber was used instead as the output module since it is capable of sensing the H 2 O 2 produced by the input module and catalyzing the oxidation of Amplex Red into Resorufin with consequent red fluorescence. The GOx/HRP bi‐modular prototissue fiber was then placed in an aqueous solution, followed by addition of 100 µL of an aqueous solution of glucose (100 × 10 −3 \n m ) at the left side of the fiber (Figure S15 , Supporting Information). A red fluorescent signal readily generated from the left end side of the HRP containing the output module, which was connected to the GOx containing the input module. Figure 5c–f , Figure S16 and Video S8 (Supporting Information) show that the optical signal progressively moved through the prototissue fiber, highlighting a reaction diffusion front due to a localized formation of the signaling molecule H 2 O 2 in the input module. From a general perspective, these results showed that our methodology can be utilized to generate, for the first time, freestanding and reconfigurable prototissue fibers of tightly interconnected protocells with a programmable endogenous reactivity for potential applications in bioengineering and flow chemistry."
} | 7,673 |
37399405 | PMC10334754 | pmc | 6,574 | {
"abstract": "Significance Energy transport over long distances with high efficiency is a critical design parameter of photosynthetic light harvesting, where photonic energy travels across multiple antenna proteins to reach the reaction center. Understanding transport requires that energy transfer between proteins be resolved, but this crucial step has been experimentally inaccessible. Here, we used near-native membrane nanodiscs to isolate two antenna proteins from purple bacteria, one of the most ancient and most efficient photosynthetic organisms. By measuring the structural organization and dynamics, we characterized interprotein energy transfer across the physiological range of distances. Simulations revealed that the presence of fast energy transfer steps enhances long-distance transport. These results point to pairwise interactions as a key mediator of energy transport.",
"conclusion": "Conclusion In summary, we report the reconstitution of nanoconfined pairs of LH2 and the low-light variant LH3 from Ph. molischianum and their structural and spectroscopic characterization. Our results establish a method to reconstitute heterogeneous antennae, a long-standing challenge in the field. Overall, nanodiscs, ultrafast TA spectroscopy, and cryoEM together enable investigations of interprotein energy transfer in photosynthetic light harvesting. Through this combination, we measured distance-dependent interprotein energy transfer timescales from ∼5 to 15 ps between protein pairs. Simulations and comparison to previous measurements suggest that interprotein energy transfer is dominated by these closely spaced protein pairs, and their presence may be required for long-distance energy transport. The ability of these protein pairs to dominate interprotein energy transfer may also be a mechanism by which similar timescales are maintained despite heterogeneous protein organizations, allowing the light-harvesting dynamics to be robust to the variations in antenna protein expression and organization by which the light-harvesting machinery responds to the fluctuating conditions of natural environments.",
"discussion": "Results and Discussion LH2 Variants in Doubly Loaded Membrane Discs. To isolate energy transfer between LH2 proteins, we constructed doubly loaded nanodiscs (DLDs) with both LH2 and LH3 incorporated as illustrated in Fig. 1 A . Nanodiscs self-assemble into a discoidal membrane encircled by a stabilizing belting protein. The membrane composition and size depend on the ratios of the belting proteins, constituent lipids, and target membrane proteins in the initial mixture. By varying the stoichiometry of the mixture, the nanodiscs were optimized for two LH2 per disc with different diameters. Negative-stain transmission electron microscopy (nsTEM) and cryoEM analysis of the DLDs revealed discoidal structures, confirming nanodisc formation. The structures exhibited apparent diameters of 17.8 ± 0.1 nm and 20.4 ± 0.2 nm for the small and large nanodiscs, respectively ( Fig. 1 B and C ). Successful incorporation of LH2 into nanodiscs was suggested by SDS-PAGE of the purified DLDs samples showing both LH monomer subunits and the belting protein, ApoE422K ( Fig. 1 D ). The integrity of the LH2 variants within the nanodiscs was established with steady-state linear absorption and fluorescence spectra ( Fig. 1 E and F ) for three DLDs samples: LH2 only (LH2–LH2 DLDs), LH3 only (LH3–LH3 DLDs), and the LH2–LH3 mixture (LH2–LH3 DLDs). The overall peak profiles are similar to the proteins in detergent. For LH2–LH2 DLDs, the maximum of the B850 band red-shifted from 846 to 849 nm upon incorporation into the lipid bilayer, consistent with previous reports ( 13 , 14 ). Similarly, for LH3–LH3 DLDs, the maximum of the B820 band red-shifted from 817 nm to 819 nm upon incorporation into the bilayer. The red tail of the B820 band for LH3–LH3 DLDs is due to a contribution of 5 to 10% LH2 ( SI Appendix , Fig. S6 ), as full conversion has not been achieved in this species ( 14 , 40 ). For the LH2–LH3 DLDs, incorporation of both LH2 and LH3 into the nanodiscs was confirmed through the linear absorption spectrum of the purified sample, which was decomposed into a ∼50%/50% combination of the spectra of the two variants ( Fig. 1 E and SI Appendix , Fig. S8 ). Finally, commixture of LH2 and LH3 within nanodiscs was established through the fluorescence spectrum of the LH2–LH3 DLDs sample ( Fig. 1 F ). The LH2–LH3 DLDs sample contained a mixture of LH2–LH2 DLDs, LH3–LH3 DLDs, and LH2–LH3 DLDs owing to the stochastic nature of the nanodisc formation reaction. Decomposition of the fluorescence spectrum into those of the two individual variants showed a ∼35 to 40% increase in LH2 content ( SI Appendix , Fig. S8 ), consistent with a contribution from LH3-to-LH2 energy transfer at the level expected for stochastic insertion. At the 0.1 μM concentration of the fluorescence measurement, the presence of interprotein energy transfer strongly implies incorporation of LH2 and LH3 into the same nanodisc. Cryogenic Electron Microscopy of Doubly Loaded Nanodiscs. To determine the architecture of the proteins within the DLDs, we used cryoEM. Single-particle reconstruction of the LH2–LH3 DLDs revealed how the proteins arrange in the nanodisc by resolving the structures to a resolution of up to 6.5 Å ( SI Appendix , Figs. S3 and S4 ). As shown in Fig. 2 , the formation of the DLDs was clearly observed in the electron density, with the encircling belting protein and two embedded membrane proteins all distinctly visible. Each of the embedded proteins had an apparent diameter of 7.1 nm and an apparent octameric symmetry, consistent with both the crystal structure of LH2 (PDB Code 1LGH) and the homology model of LH3 from Ph. molischianum . These features were used to dock one LH2 and one LH3 into the densities. Fig. 2. Structural organization of LH2 variants in nanodiscs. CryoEM analysis of small ( A and B ) and large ( C and D ) LH2–LH3 DLDs showing density maps (dark blue for small nanodiscs and light blue for large nanodiscs) with docked crystal structures (LH3 helices, orange; LH2 helices, red; B800 BChla, light blue; B820/B850 BChla, dark blue). In both small and large DLDs, the antiparallel and the parallel orientations are 85% and 15% of the total population, respectively. The composition of LH2 and LH3 in the DLDs was determined through analysis of the steady-state spectra ( SI Appendix , section 1.7 and Table S4 ). Top views of all nanodisc types illustrate the DLDs assembly with the electron densities of belting proteins encircling the two LH proteins. Side views illustrate that the two LH proteins are at the same approximate vertical position within the lipid bilayer for the parallel orientation ( B and D ) and are displaced vertically for the antiparallel orientation ( A and C ). For the small nanodisc, both orientations are closely packed with ∼25 Å between the BChla of the B820 and B850 rings ( A and B ; black squares). For the large nanodisc, the orientations are less packed, and the antiparallel shows association with ∼28.5 Å between the BChla of the B820 and B850 rings, while in the parallel case, the association brings the two bands ∼31.4 Å apart ( C and D ; black squares). Two types of structures emerged from the analysis. One is similar to the native membrane arrangement with both LH complexes in a parallel orientation (with respect to facing the cytosol/periplasm) within their surrounding nanodisc ( Fig. 2 B and D ). The other showed the two LH proteins in an antiparallel orientation (with respect to facing the cytosol/periplasm) within their surrounding nanodisc ( Fig. 2 A and C ). Reconstituted systems tend to adopt this antiparallel orientation ( 30 , 31 ). Despite containing some nonphysiological orientations, these systems have consistently exhibited spectroscopic signatures similar to those observed in the native system and provided many insights into protein–protein interactions ( 66 – 68 ). The antiparallel orientation dominates our samples, accounting for ∼85% of total particles analyzed. The antiparallel orientation also shows a vertical displacement between neighboring proteins of ∼12 Å along the perpendicular axis to the membrane plane ( Fig. 2 A and C ). As a result of this displacement, the two low-energy chlorophyll rings in the antiparallel orientation remain at nearly identical vertical height as in the parallel orientation. The geometry of the B850 band also means that the relative angles of the transition dipole moments are similar in the two orientations ( SI Appendix , Fig. S21 A ). For the small DLDs, the antiparallel and parallel orientations have similar distance values of 24.8 and 25.3 Å, respectively. The similar values are due to the confinement of the nanodisc, which positions the proteins in van der Waals contact as illustrated by the protein density in Fig. 2 A and B , Inset . For the large DLDs, the antiparallel and parallel orientations have distance values of 28.5 and 31.4 Å, respectively ( Fig. 2 C and D ). All distance values are consistent with typical LH2–LH2 distances as measured by atomic force microscopy (AFM) ( 28 , 65 , 69 ). The tendency to adopt the antiparallel orientation suggests that it is energetically favored. Perhaps, the same surface charge distribution of the two proteins disfavors parallel orientation in the membrane due to symmetry-derived repulsion. Consistently, the proteins adopted an angled interaction in the parallel orientation with a tilt of 5° and 15° between the proteins for the small and large DLDs, respectively, both within the range of tilt angles observed for these proteins through other methods ( 64 , 70 ). The repulsion in the native orientation may be required in vivo for the disassembly and reorganization of LH2 into less densely packed organizations under high-light conditions or to comply with the curvature of the membrane in chromatophores from purple bacteria ( 70 ). Interprotein Energy Transfer between LH2 Variants. To probe the dynamics of LH3-to-LH2 energy transfer, spectrally resolved TA spectroscopy was performed on the DLDs as shown in Fig. 3 A and SI Appendix , Fig. S15 . In order to accurately extract the interprotein energy transfer timescale, LH2–LH2 DLDs and LH3–LH3 DLDs were also measured separately ( SI Appendix , Figs. S13 and S14 ). By analysis of the spectral and temporal evolution of the TA data in a self-consistent manner, we resolved the dynamics of each sample and isolated the LH3-to-LH2 energy transfer timescales. Fig. 3. LH3-to-LH2 energy transfer dynamics. ( A ) 2D representation of TA spectra of small LH2–LH3 DLDs. The color bar describes signal intensity ( Δ mOD). ( B ) Absorption transients of small (teal) and large (purple) LH2–LH3 DLDs at 840 nm. Experimental values are shown as open symbols, and the fitted kinetics are shown as solid lines. The large DLDs transient was deconvoluted into antiparallel (light purple dotted line) and parallel (purple dashed line) LH2–LH3 and combined LH2–LH2 and LH3–LH3 (dark gray line) contributions. The transient of small DLDs was deconvoluted into antiparallel (turquoise dotted line) and parallel (dark cyan dashed line) LH2–LH3 and combined LH2–LH2 and LH3–LH3 (dark gray line) contributions. The large DLDs transient is offset by 2 mOD. ( C ) Normalized SADS of B820 (orange) and B850 (red) components from the TA data of small LH3–LH3 DLDs and LH2–LH2 DLDs, respectively. Results are shown from both simultaneous fitting of the single-wavelength kinetics (dots) and global analysis (lines). ( D ) Transient absorption spectra of small LH2–LH3 DLDs (teal), LH3–LH3 DLDs (orange), and LH2–LH2 DLDs (red) at 5-ps time delay. The turquoise dashed line is the LH2–LH3 DLDs spectrum constructed from the linear combination of the spectra from LH3 and LH2 DLDs. ( E ) The population dynamics of excited B820 from the model fitting (solid lines) and the results of fitting the TA spectra (open symbols). The relative contributions of the LH3–LH3 and LH2–LH2 DLDs spectra to the small (teal) and large (purple) LH2–LH3 DLDs spectra are plotted as a function of time. The correspondence between the two indicates that the fitting successfully extracted the kinetics of LH3-to-LH2 energy transfer. ( F ) Schematic of the model used to fit the LH2–LH3 DLDs with corresponding timescales. The values held fixed from LH2–LH2 DLDs and LH3–LH3 DLDs are shown as gray arrows, and the extracted LH3-to-LH2 energy transfer timescale is shown as a black arrow. Both individual protein samples, the LH2–LH2 DLDs and LH3–LH3 DLDs, were fit using global analysis, which identifies the characteristic time constants and their associated spectral profiles, known as species-associated-decay-spectra (SADS) ( 71 ). The data were best fit with a sequential model in which the system evolves through three components ( SI Appendix , Fig. S11 A ). The first component decayed on a subpicosecond timescale (850 fs for LH2–LH2 DLDs; 680 fs for LH3–LH3 DLDs), and the SADS was dominated by a negative peak at ∼805 nm from the ground state bleach (GSB)/stimulated emission (SE) of the B800 band. Therefore, this component was assigned to B800 to B820/B850 energy transfer. Consistent with this assignment, the SADS associated with the second component were dominated by a negative peak at ∼856 nm for LH2 and ∼836 nm for LH3 from the GSB/SE of the B850 and B820 bands, respectively, as shown in Fig. 3 C . A broad positive feature at lower wavelengths (< 842 nm for LH2 and < 818 nm for LH3) was also present due to excited-state absorption (ESA) primarily from the B850/B820 bands. The timescale of the second component was 6.6 to 14 ps for LH2–LH2 DLDs and 8.1 ps for LH3–LH3 DLDs and was assigned to vibrational relaxation. The final component exhibited a similar, but slightly red-shifted, spectral profile and decayed on a ∼1.3-ns timescale, which is the approximate fluorescence lifetime of the LH2 variants. The time constants and their associated spectra are consistent with previous work ( 13 , 14 , 23 , 44 ). The spectra were also fit using a three-step kinetic model in which, after photoexcitation of B800, i) energy transfers from B800 to B850, ii) B850 undergoes vibrational relaxation, and iii) B850 relaxes to the ground state ( SI Appendix , Fig. S11 ). Single wavelength transients were taken at 2.5-nm intervals from 790 to 880 nm and globally fit to the kinetic model. The amplitudes associated with step (ii) of the kinetic model are overlaid on the SADS from global analysis in Fig. 3 C . As illustrated, the extracted time constants and spectral profiles were consistent with the components from the global analysis. For the LH2–LH3 DLDs, LH3-to-LH2 energy transfer is present, and so energy transfer can also be observed in the spectra. As shown in Fig. 3 A , the GSB/SE signal centered at ∼835 nm from the B820 band decays with a corresponding rise in the GSB/SE signal centered at ∼855 nm from the B850 band. In order to extract the energy transfer time, the TA data were fit through the single-wavelength kinetics, as the complexity of the data precluded direct fitting via global analysis. Owing to the stochastic nature of the nanodisc self-assembly process, the LH2–LH3 DLDs samples contain a mixture of LH2–LH2 DLDs, LH3–LH3 DLDs, and LH2–LH3 DLDs. Therefore, the signal was fit to a sum of the evolution of the three samples described by kinetic models ( SI Appendix , Fig. S11 ). The intra-LH2 and intra-LH3 time constants and the SADS for all species were held fixed to the values determined through global fitting of the LH2–LH2 DLDs and the LH3–LH3 DLDs, leaving only the LH3-to-LH2 energy transfer time and the relative amplitudes for the samples as free parameters. LH3-to-LH2 energy transfer is most prominent within the spectral regions from 825 to 850 nm where the SADS of B820 and B850 have opposite signs ( Fig. 3 C ). In this region, the negative signal becomes positive due to the decay of the negative B820 and the rise of the positive B850 as illustrated in Fig. 3 B . Therefore, transients within this spectral range were simultaneously fit in order to determine the LH3-to-LH2 energy transfer timescales. For the small LH2–LH3 DLDs, a timescale of 5.7 ps was extracted. Applying two LH3-to-LH2 timescales to capture the two orientations gave comparable fitting quality and nearly coincident values, consistent with the similar distances in the cryoEM structures ( SI Appendix , Fig. S16 ). For the large LH2–LH3 DLDs, the two orientations give rise to different distances ( Fig. 2 C and D ). Consistent with this model, two timescales of 9.8 ps and 14.7 ps were extracted, likely corresponding to the two orientations. The deconvoluted amplitudes of the components are 70%, 13%, and 17% for the antiparallel LH2–LH3, the parallel LH2–LH3, and the LH2–LH2 and LH3–LH3 combined signal in the key wavelength for our analysis (840 nm). To confirm the LH3-to-LH2 energy transfer timescale was successfully described by fitting to the kinetic model, the TA spectra from the LH2–LH3 DLDs were also decomposed into a linear combination of the TA spectra from the LH2–LH2 and LH3–LH3 DLDs as illustrated in Fig. 3 D . The ratios of the contributions of LH3 to the contributions of LH2 were calculated as shown as teal dots in Fig. 3 E . The ratio decreased with delay time in the LH2–LH3 DLDs, consistent with the presence of LH3-to-LH2 energy transfer. In contrast, the ratio was nearly constant with delay time in a control sample of a simple mixture of LH2 and LH3 ( SI Appendix , Figs. S17–S19 ). The ratio from the LH2–LH3 DLDs was overlaid with the population dynamics of B820 from the fitting results for the TA spectra of LH2–LH3 DLDs. The evolution from both approaches showed good agreement, supporting the extracted timescales for energy transfer within the LH2–LH3 DLDs. Calculations of Energy Transfer Timescale. The timescales of LH3-to-LH2 energy transfer were also calculated using generalized Förster theory as shown in Fig. 4 A . Typically, B850-to-B850 energy transfer dominates in vivo, and so we focus on energy transfer between the low-energy rings (B820 to B850). For the small DLDs, the ∼25 Å distance extracted from the cryoEM structures had a theoretical timescale of 6.5 ps ( SI Appendix ), similar to the experimental value of 5.7 ps. For the large DLDs, the ∼28.5 and ∼31.4 Å distances extracted from the cryoEM structures had theoretical timescales of 11.3 and 18.1 ps, respectively ( SI Appendix , Fig. S24 ). These were again similar to the experimental values of 9.8 and 14.7 ps, respectively. Generalized Förster theory describes the intermediate regime appropriate for photosynthetic light-harvesting apparatuses, i.e., the interbacteriochlorophyll distances give rise to interactions between the far-field and near-field limits ( SI Appendix ) ( 72 – 75 ). The good agreement between experiment and theory also illustrates that, even for interprotein energy transfer, the far-field regime breaks down due to the nanoscale distances involved. Fig. 4. LH3-to-LH2 energy transfer within the photosynthetic membrane. ( A ) The rate (left axis) and timescale (right axis) for interprotein energy transfer across the biologically relevant range as a function of the LH3-to-LH2 distance, which was defined as the separation between the nearest B820 and B850 BChla. Energy transfer rates in the far-field (dashed gray line, Top ) and near-field (dashed gray line, Bottom ) regimes are also plotted. The experimentally measured LH3-to-LH2 distances and energy transfer timescales for the small and large DLDs are indicated in teal and two shades of purple dots, respectively. The theoretical generalized FRET rate is indicated by the solid line ( 65 , 69 ). ( B ) Schematic representation of the Ph. molischianum photosynthetic membrane ( 26 ). Dotted ovals highlight LH2 pairs expected to exhibit faster and slower energy transfer (teal and purple, respectively). ( C ) The energy transport distance was simulated using an LH2 network with hexagonal packing for 156 interprotein distances. Three organizations with the same average spacings ( Top ) were compared: uniform spacing ( Left ), tightly interacting cluster ( Center ), and random tightly interacting pairs ( Right ). Total transport distances ( Bottom ) were calculated for all three organizations as the distance traveled after 1 ns, which is approximately the lifetime of LH2. The theoretical energy transfer timescales were also calculated for LH3-to-LH3 and LH2-to-LH2 energy transfer ( SI Appendix , Fig. S24 ). Overall, similar timescales and distance scaling were observed for the three pairs of variants, confirming that LH3-to-LH2 energy transfer captures the behaviors associated with energy transfer among LH2 variants in the membrane. The similar timescales also suggest that LH3-to-LH2 conversion does not interfere with robust energy transfer, enabling spectral broadening without a corresponding cost in energy transport efficiency. That is, these timescales prevent LH2 traps for transport between LH3 or LH3 hills for transport between LH2. In fact, the introduction of LH3 actually leads to slightly faster interprotein energy transfer in vivo. At thermal equilibrium, the B800 population is larger for LH3 than for LH2 due to the smaller energy gap between the two bands, increasing the interprotein energy transfer via the B800 band from 10% to 14% ( SI Appendix , Fig. S21 ). Overall, for all protein pairs, the predominant energy transfer pathway is via the lower energy bands, leading to similar behavior ( SI Appendix , Table S8 ). Thus, the conversion to LH3 under low light not only helps with light harvesting through spectral broadening but also maintains efficient energy transport. Energy Transfer in the Heterogeneous Membrane. AFM images of native membranes from purple bacteria revealed a range of distances within the heterogeneous protein network ( 26 , 51 – 54 ). The most common, and closest possible, distance between LH2 was 25 Å, which was replicated in the small DLDs and gave rise to a 5.7-ps timescale of LH3-to-LH2 energy transfer. Distances up to 50 Å were also present with decreasing probability. Other fairly common distances were ∼30 Å, which were replicated in the large DLDs and gave rise to 10- to 14-ps timescales of LH3-to-LH2 energy transfer. Previous indirect measurements of LH2-to-LH2 energy transfer in these native membranes found 3–5-ps timescales. Consistently, measurements of LH2-to-LH1 energy transfer, which has a similar energy gap as LH3-to-LH2 transfer, found 5- to 20-ps timescales ( 15 , 56 – 59 , 76 ). Remarkably, the measurements on native membranes recovered values similar to the closely spaced protein pairs despite averaging over the full distributions of distances. The similarity may in part arise from the ability of the fastest component to dictate the rise of a spectroscopic signal, obfuscating slower dynamics. Furthermore, energy transfer will occur preferentially to the closest of multiple neighboring LH2, and so energy transfer between these proteins can also dominate the signal. These energy transfer steps between closely spaced proteins, however, describe local equilibration, and such local effects do not capture the dynamics of long-distance transport ( 77 ). Instead, energy transfer timescales between proteins separated by multiple distances are needed for such descriptions. To investigate the role of the different energy transfer timescales in long-distance energy transport, total energy transport distances were simulated. The LH2 variants were positioned in a hexagonal organization, which is a well-established model for the arrangement of LH2 in the bacterial membrane ( Fig. 4 B and C ) ( 78 – 80 ). The total transport distance within the one nanosecond lifetime of LH2 was calculated for three different membrane arrangements, as shown in Fig. 4 C : i) uniform organization with equivalent interprotein distances; ii) protein cluster with 12 short distances and 144 long distances within and without the cluster, respectively; and iii) random short distances within a lattice of long distances. Of note, 25 and 35 Å were selected as the short and long distances, respectively, because both correspond to the distances found experimentally in the DLDs and because they are both observed in native membranes, with 25 Å the most common and closest possible distance and 35 Å a longer distance ( 65 , 69 ). The distance in the uniform organization is the weighted average of the short and long distances in the other two organizations. As shown in Fig. 4 C and SI Appendix , Table S9 , the simulations revealed a ∼15% increase in transport distances for the heterogeneous arrangements (ii, iii) over the uniform arrangement (i). The overall similarity in transport distance between the cluster arrangement (ii) and the random arrangement (iii) suggests that enhancement of energy migration occurs without ordered positioning of membrane complexes. Fundamentally, the increase in transport distance comes from the nonlinear relationship between energy transfer time and distance. That is, a small decrease in spacing can lead to a many-fold speed-up, and so it is better to have a few short distances than a uniform spread of longer distances. Based on this enhancement, the closely spaced protein pairs facilitate long-distance migration by extending the transport distances through the creation of a “superhighway” for photonic energy through the protein network."
} | 6,403 |
26315440 | null | s2 | 6,575 | {
"abstract": "A challenge of synthetic biology is the creation of cooperative microbial systems that exhibit population-level behaviors. Such systems use cellular signaling mechanisms to regulate gene expression across multiple cell types. We describe the construction of a synthetic microbial consortium consisting of two distinct cell types—an \"activator\" strain and a \"repressor\" strain. These strains produced two orthogonal cell-signaling molecules that regulate gene expression within a synthetic circuit spanning both strains. The two strains generated emergent, population-level oscillations only when cultured together. Certain network topologies of the two-strain circuit were better at maintaining robust oscillations than others. The ability to program population-level dynamics through the genetic engineering of multiple cooperative strains points the way toward engineering complex synthetic tissues and organs with multiple cell types."
} | 234 |
26989618 | PMC4793327 | pmc | 6,576 | {
"abstract": "Much research has focused on growing microalgae for biofuel feedstock, yet there remain concerns about the feasibility of freshwater feedstock systems. To reduce cost and improve environmental sustainability, an ideal microalgal feedstock system would be fed by municipal, agricultural or industrial wastewater as a main source of water and nutrients. Nonetheless, the microalgae must also be tolerant of fluctuating wastewater quality, while still producing adequate biomass and lipid yields. To address this problem, our study focused on isolating and characterizing microalgal strains from three municipal wastewater treatment systems (two activated sludge and one aerated-stabilization basin systems) for their potential use in biofuel feedstock production. Most of the 19 isolates from wastewater grew faster than two culture collection strains under mixotrophic conditions, particularly with glucose. The fastest growing wastewater strains included the genera Chlorella and Dictyochloris . The fastest growing microalgal strains were not necessarily the best lipid producers. Under photoautotrophic and mixotrophic growth conditions, single strains of Chlorella and Scenedesmus each produced the highest lipid yields, including those most relevant to biodiesel production. A comparison of axenic and non-axenic versions of wastewater strains showed a notable effect of commensal bacteria on fatty acid composition. Strains grown with bacteria tended to produce relatively equal proportions of saturated and unsaturated fatty acids, which is an ideal lipid blend for biodiesel production. These results not only show the potential for using microalgae isolated from wastewater for growth in wastewater-fed feedstock systems, but also the important role that commensal bacteria may have in impacting the fatty acid profiles of microalgal feedstock.",
"introduction": "Introduction World energy consumption of petroleum is currently estimated to be 4.4 billion tonnes per year to fuel electricity, automobiles, and industrial processes ( Roddy, 2013 ). To meet the growing global energy demand, nations worldwide have considered renewable sources of fuel to offset the dependence on non-renewable sources like fossil fuels. Algal biofuels are a promising source of renewable energy since production is non-seasonal, and yields are not limited to one or two harvests per year ( Da Silva et al., 2009 ). When compared to other biofuel feedstocks like corn or switchgrass, algal biofuel crops possess 6–12 times greater yearly energy production ( Sandefur, Matlock & Costello, 2011 ). An additional benefit of algal feedstock relates to the variety of possible conversion processes such as direct combustion, pyrolysis, or chemical conversion. Algal biomass can be converted to fuels like hydrogen, oil, or even raw electricity ( Brennan & Owende, 2010 ; Tsukahara & Sawayama, 2005 ). Research in algal biofuels has been primarily directed towards improving algal biomass yield or increasing lipid content. Researchers have determined optimal temperatures, light conditions, and nutrients to obtain high growth rates for many strains ( Chinnasamy et al., 2010 ; Price, Yin & Harrison, 1998 ). These parameters are not the same for each strain of microalgae, and as a result, they need to be optimized individually. One challenging aspect of growing microalgae compared to terrestrial plant crops is the sizeable amount of water required to sustain yields. To grow algal biofuel-feedstock at large-scale cultivation would require approximately 1.5 million liters of water per hectare ( Cho et al., 2007 ). If grown in open ponds, the evaporation loss would be 7–11 million liters of water per hectare per year ( Cho et al., 2007 ). This requirement is juxtaposed against the prediction that 66% of the world’s population will be residing in regions without access to drinking water by 2025 ( Mehanna et al., 2010 ). Additionally, Sandstrom (1995) showed that a 15% reduction in precipitation due to climate change may result in a 40–50% reduction in aquifer water recharge. As such, a major stumbling block for the economic and environmental feasibility of algal-based biofuel production is the potentially unsustainable requirement for freshwater. In recent years, research has started to focus on exploiting wastewater rather than freshwater as a growth medium for algal feedstock production. Wastewater, particularly municipal and agricultural, typically contain elevated levels of essential nutrients such as nitrogen and phosphorous. For example, nitrogen and phosphorus concentrations in municipal wastewaters can range from 10–100 mg L −1 and >1,000 mgL −1 in agricultural wastewater ( De la Noue, Laliberte & Proulx, 1992 ). Not only does wastewater serve as a good growth medium for microalgae, but conversely, algal growth in wastewater can serve as a tertiary treatment option ( De la Noue, Laliberte & Proulx, 1992 ; Lau, Tam & Wong, 1995 ; Woertz et al., 2009 ). Sandefur, Matlock & Costello (2011) have shown that algal strains grown in treated wastewater were able to significantly decrease both phosphorus and nitrogen concentrations. When microalgae are grown in piggery wastewater, An et al. (2003) showed that the green alga Botryococcus braunii could remove 80% of the nitrate content. Another significant component of municipal wastewater is the organic load ( Rogers, 1996 ; Fadini, Jardim & Guimarães, 2004 ). Sewage sourced from human populations is replete with biodegradable compounds such as sugars, amino acids and other breakdown products of digestion ( Painter & Viney, 1959 ; García, Hernández & Costa, 1991 ). Although readily mineralized by bacteria during secondary treatment, these organic compounds can also be utilized by mixotrophic algae. Even though wastewater has great potential as a growth medium for algal-feedstock production, a major caveat is the broad variation in wastewater quality and levels of toxic constituents. Most studies assessing algal growth in wastewater use isolates from lakes, rivers, or other naturally occurring water bodies ( Park et al., 2012 ; Zhou et al., 2011 ). Zhou et al. (2011) reported that only a few strains from the genera Chlorella and Scenedesmus have been analyzed for their ability to grow in municipal wastewater. Some work ( Liu et al., 2011 ) has been done to isolate and culture microalgae from wastewater treatment systems, but assessment of their potential use as a biofuel feedstock remains largely unknown. Also, most studies characterizing microalgae for biofuel production typically use axenic strains and sterile media ( Price, Yin & Harrison, 1998 ; Liu et al., 2011 ; Zhou et al., 2011 ), which eliminates the potential role of bacteria in influencing algal growth and lipid production. In order to move forward the prospects of large-scale cultivation of microalgae in wastewater, we set out to assess the potential of microalgae isolated from municipal wastewater treatment systems to serve as candidates for biofuel-feedstock production. The rationale for this is that microalgae isolated from municipal wastewater would have inherently higher tolerance to wastewater compared to lab strains or isolates from natural systems, as well as an increased capacity to grow mixotrophically to exploit organic compounds in wastewater. The first phase of our assessment is presented in this paper, which focuses on: (1) Isolating algal strains from municipal waste-treatment systems; (2) Characterizing the metabolic growth characteristics of wastewater isolates (i.e., photoautotrophy, mixotrophy, and heterotrophy), including their fatty acid composition; and (3) Assessing the effects of commensal bacteria on algal growth and lipid production compared to axenic strains.",
"discussion": "Discussion The chlorophyte genera isolated from municipal wastewater treatment plants in this study: Chlorella, Botrydiopsis, Dictyochloris, Ellipsoidon and Scenedesmus reflect a variety of algal strains that are viable in wastewater, but also easy to grow under laboratory conditions. Although found to be among the fastest growing strains, the cyanobacterium Microcystis was not a notable producer of fatty acids in this study. Chlorella strains were prevalent across the three wastewater treatment systems. This may be due in part to Chlorella ’s high tolerance of low oxygen conditions, such as in wastewater treatment systems ( Shanthala, Hosmani & Hosetti, 2009 ). Park et al. (2012) commented that both Scenedesmus and Chlorella are among the most commonly isolated species from wastewater treatment effluent, which explains their high prevalence in all sampling locations in this study. Mixotrophic growth, with either glucose or acetate, resulted in higher growth rates compared to photoautotrophic growth for the large majority of strains. This was not unexpected as others have demonstrated higher growth yields for algae under mixotrophic growth conditions ( Kirkwood, Nalewajko & Fulthorpe, 2003 ; Das, Aziz & Obbard, 2011 ; Yan et al., 2012 ). Yan et al. (2012) also demonstrated that the addition of either glucose or acetate actually increased the energy conversion efficiencies over photoautrophic growth. In most cases under heterotrophic growth, the initial algal inoculation either resulted in cell death or showed minimal to no growth. Price, Yin & Harrison (1998) found that many microalgal taxa derived from naturally occurring water bodies have generally adapted their cellular processes to daily light fluctuations and therefore are unable to grow strictly heterotrophically. As such, mixotrophy rather than photoautotrophy or heterotrophy appears to be the ideal strategy for maximizing growth rate and biomass yield. Since municipal wastewater and some industrial wastewaters (e.g., pulp and paper effluent) are replete with organic compounds, including sugars, amino acids, and other degradation products, it would be an ideal medium for supplementing algal growth. Similar to other studies (e.g., Nascimento et al., 2013 ), the fastest growing strains were not necessarily the best lipid producers (compare Fig. 1 and Table 3 ). Also, the relative amount of fatty acids produced in each strain varied by axenic status and growth condition and in general, the highest amount of biofuel-targeted fatty acids, such as oleic and linoleic FAMEs, were produced by non-axenic algal strains, particularly slower growing Scenedesmus isolates grown mixotrophically with glucose ( Fig. 3C ). Under photoautotrophic conditions, any bacterial effect on fatty acid profiles of non-axenic microalgal strains were not discernable. However, the effect became evident under mixotrophic growth, particularly with glucose. A possible explanation is that the bacteria consumed not only the organic compounds in the microalgal cultures, but inorganic nutrients as well. This would create nutrient-deprived conditions for the host microalgal strain, which in turn could induce lipid accumulation as a stress response. Increased lipid production in algae is a common response to stressful or rapidly changing conditions ( Saha et al., 2013 ). Since the relative amount and type of fatty acids increased most notably in non-axenic strains of microalgae in our study, this may be the result of a nutrient-stress response via competition with bacteria. Liu et al. (2012) discovered that when glucose was added to growth media, it stimulated bacterial growth in algal cultures, which in turn reduced the availability of inorganic nutrients to the algae. A similar result was found when Scenedesmus obliquus was grown with a natural bacterial community, where both phosphorus and nitrogen became limited at the plateau of the alga’s growth cycle ( Daufresne et al., 2008 ). When grown in co-culture, bacteria can also produce allelopathic exudates that either inhibit algal growth or lyse cells ( Mayali & Azam, 2004 ). However, it is unclear if bacterial allelopathy can also induce hyperaccumulation of lipids in algae. Although for the majority of strains, the total amount of neutral lipids was comparable between axenic and non-axenic strains, there were two striking exceptions ( Table 3 ). Strains C3N and S7H accumulated more neutral lipids particularly under axenic and photoautotrophic conditions. If hyperaccumulation of lipids tends to occur under stressful growth conditions, the results for these two strains may indicate that photoautotrophic growth without bacteria is a stressful growth condition. Of course, this is a preliminary finding, but does highlight the unique differences that can exist among algal taxa isolated from the same environment. Regardless of mechanism, these findings also highlight that, depending on the strain you are working with, the presence of bacteria can have significant impacts on not only the type of fatty acids being produced, but the total amount. Comparing the microalgal strains under the three different growth conditions showed that axenic strains had a small but detectable difference in fatty acid composition compared to their non-axenic counterparts. A similar result was noted with C. vulgaris and C. sorokiniana when grown with the bacterium A. brasilene , which caused the variation in fatty acids to change from five to eight different fatty acids with increasing amounts of unsaturation ( De Bashan et al., 2002 ). Adding glucose to media can change the lipid composition in photoautotrophically grown microalgae by significantly increasing oleic acid concentration ( Sunja et al., 2011 ). This was also found in our experiments, where an increased number of strains produced oleic acid under mixotrophic growth with glucose compared to photoautotrophic growth. Additionally, Cherisilp & Torpee (2012) have confirmed that microalgae grown under mixotrophic conditions using glucose showed an overall increase in lipid content over photoautotrophic growth and heterotrophic growth, which we also found. Overall, our assessment of wastewater microalgae has not only shown that mixotrophy can significantly increase growth rate, but that most wastewater Chorella and Scenedesmus strains exceed the mixotrophic growth-rates of representative culture collection strains. This infers that wastewater strains may be ideal candidates for growth in organic-substrate rich wastewater for biomass and/or biofuel feedstock production. Most of the Chlorella wastewater strains were the fastest growers, particularly under mixotrophic and heterotrophic growth conditions. This may increase their potential as biofuel feedstock due to their growth-condition versatility. Additionally, the Chlorella wastewater isolates from this study produced total-lipid concentrations on par with wastewater-grown strains (as total lipid in culture-medium) in a study by Woertz et al. (2009) . Although fast growth is an ideal characteristic for any algal strain used in biofuel feedstock production, it must be coupled with comparatively high lipid yields. As previously discussed, the fastest growing microalgal strains were typically not the highest lipid producers. Thus acceptable trade-offs in growth rate vs. lipid yield must be established. To date, the majority of research in algal biofuels involves the maintenance of axenic or low bacterial contamination in microalgal cultures. Yet, our study has demonstrated that the fatty acid amount and composition can change with bacterial presence. Additionally, the even blend of saturated and unsaturated fatty acids tended to be found in non-axenic microalgal cultures. These findings bode well for growing microalgae in bacteria-laden wastewater. However, it remains to be determined if microalgal growth in wastewater with bacteria results in higher lipid yields and desirable fatty acid composition. As such, the next phase of our research is focusing on the efficacy of growing microalgal wastewater-isolates in municipal wastewater, and assessing the conditions under which growth and lipid production are optimized."
} | 4,008 |
22275502 | PMC3262234 | pmc | 6,578 | {
"abstract": "ABSTRACT Hydrothermal chimneys are a globally dispersed habitat on the seafloor associated with mid-ocean ridge (MOR) spreading centers. Active, hot, venting sulfide structures from MORs have been examined for microbial diversity and ecology since their discovery in the mid-1970s, and recent work has also begun to explore the microbiology of inactive sulfides—structures that persist for decades to millennia and form moderate to massive deposits at and below the seafloor. Here we used tag pyrosequencing of the V6 region of the 16S rRNA and full-length 16S rRNA sequencing on inactive hydrothermal sulfide chimney samples from 9°N on the East Pacific Rise to learn their bacterial composition, metabolic potential, and succession from venting to nonventing (inactive) regimes. Alpha-, beta-, delta-, and gammaproteobacteria and members of the phylum Bacteroidetes dominate all inactive sulfides. Greater than 26% of the V6 tags obtained are closely related to lineages involved in sulfur, nitrogen, iron, and methane cycling. Epsilonproteobacteria represent <4% of the V6 tags recovered from inactive sulfides and 15% of the full-length clones, despite their high abundance in active chimneys. Members of the phylum Aquificae , which are common in active vents, were absent from both the V6 tags and full-length 16S rRNA data sets. In both analyses, the proportions of alphaproteobacteria, betaproteobacteria, and members of the phylum Bacteroidetes were greater than those found on active hydrothermal sulfides. These shifts in bacterial population structure on inactive chimneys reveal ecological succession following cessation of venting and also imply a potential shift in microbial activity and metabolic guilds on hydrothermal sulfides, the dominant biome that results from seafloor venting.",
"discussion": "DISCUSSION V6 tag sequencing versus full-length 16S rRNA clones. Prior work has shown a good correlation between full-length rRNA clone libraries and hypervariable region tag sequencing through the generation of extensive full-length data sets for comparison to tag sequencing ( 25 , 26 ). Our full-length data set was smaller per sample but comparable to those of other environmental diversity studies. We also found agreement between the two sequencing methods. Differences were more apparent with individual samples ( Fig. 2 and 3 ) than when the entire data sets from V6 tags and full-length clones were compared ( Fig. 5 ). The largest noted differences between the two methods were higher percentages of Epsilonproteobacteria and members of the phylum Bacteroidetes recovered by the full-length clones than by the V6 tags. Data sets from V6 tag sequencing appear to be comparable to those from full-length sequencing. However, both of the PCR methods used here introduce inherent biases and future studies could use a quantitative method such as fluorescence in situ hybridization to further constrain the patterns observed here. FIG 5 Bacterial distribution in composite inactive chimneys and a composite active black smoker chimney. “Inactive Chimneys Pyrotags” ( n = 206,647) is the sum of all of the tags in this study. “Inactive Chimneys Full-Length” ( n = 452) is the sum of all of the full-length clones in this study. The composite active chimney (“Active Chimneys Full-Length,” n = 834) was generated by compiling data from previously published studies of full-length 16S rRNA clones on active black smoker chimneys and represents the sum of all of the clones in these studies ( 2–9 , 36 , 37 ). Only studies where clone frequency was reported were used. The color code is same as that in Fig. 1 , and the category “other” is shown in detail in Fig. S4 in the supplemental material. Inactive sulfide chimneys represent a biogeochemically active microbial ecosystem. This is the first deep sequencing investigation into the bacterial diversity of inactive chimneys, permitting the evaluation of both major and minor taxa within inactive sulfide ecosystems. Following the cessation of active venting, hydrothermal sulfides are transformed from an ecosystem that is supported through energy from hot, reduced hydrothermal fluids to one that is supported through the chemical energy that can be derived from oxidative weathering of the sulfide structures. Gases in hydrothermal fluids such as ammonium, methane, and hydrogen are no longer available to support chemolithoautotrophic production but are replaced by the chemical energy present in reduced minerals within the sulfide structure. Here we discuss the likely biogeochemical roles on inactive sulfides based on the sequences recovered. More than one-quarter of the V6 tags and more than half of the full-length clones recovered represent bacterial taxa for which defined ecological roles can be hypothesized, based on high sequence similarity to cultivated representatives ( Table 2 ; see Table S2 in the supplemental material). Many of these taxa are closely related to known autotrophs, suggesting that they might represent a base of the food web to the inactive sulfide ecosystem. The exceptions to this observation are among the sulfate-reducing bacterial tags detected; most are related to bacteria that cannot fix carbon or display a variable ability to fix carbon (e.g., Desulfobulbaceae ). TABLE 2 Potential ecological roles of tags for which obvious metabolisms can be inferred a Potential ecological role Taxa No. of tags % of tags in data set S oxidation \n Gammaproteobacteria : Chromatiales 157 0.0760 \n Gammaproteobacteria : Chromatiales : Ectothiorhodospiraceae 20,121 9.7369 \n Gammaproteobacteria : Chromatiales : Chromatiaceae 39 0.0189 \n Gammaproteobacteria : Thiotrichales : Piscirickettsiaceae : Thiomicrospira 1,546 0.7481 \n Gammaproteobacteria : Thiotrichales : Francisellaceae : Francisella 217 0.1050 \n Epsilonproteobacteria \n 2,015 0.9751 SO 4 2− reduction \n Deltaproteobacteria : Desulfobacterales : Desulfobacteraceae 20,709 10.0214 \n Deltaproteobacteria : Desulfovibrionales : Desulfovibrionaceae : Desulfovibrio 1,074 0.5198 \n Deltaproteobacteria : Desulfuromonadales : Desulfuromonadaceae 621 0.3005 \n Deltaproteobacteria : Syntrophobacterales 49 0.0237 \n Thermodesulfobacteria \n 23 0.0111 Sum of S oxidation and SO 4 2− reduction \n 22.5365 \n Nitrite oxidation \n Nitrospira \n 1,392 0.6736 \n Deltaproteobacteria : Desulfobacterales : Nitrospinaceae : Nitrospina 766 0.3707 Nitrification \n Betaproteobacteria : Nitrosomonadales : Nitrosomonadaceae 51 0.0247 N fixation \n Alphaproteobacteria : Rhizobiales 2,404 1.1633 Sum of nitrite oxidation, nitrification, and N fixation \n 2.5798 \n Fe oxidation \n Betaproteobacteria : Nitrosomonadales : Gallionellaceae : Gallionella 2,087 1.0099 H oxidation \n Gammaproteobacteria : Thiotrichales : Piscirickettsiaceae : Hydrogenovibrio 58 0.0281 CH 4 oxidation \n Gammaproteobacteria : Methylococcales : Methylococcaceae 1,602 0.7752 Autotrophy \n Chlorobi \n 72 0.0348 Total sum 26.6168 \n a Data are pooled from all samples; therefore, multiple tags are represented per lineage listed. The percentage of tags in the data set is for the entire data set (the sum of all tags sequenced on all samples). Taxa are designated by class (phylum for Nitrospira and Chlorobi), order, family, and genus. The highest level at which an ecological role can be assigned per group of tags is shown. Tags affiliated with taxa known to be capable of sulfur oxidation or sulfate reduction each comprised greater than 10% each of the total V6 tags from all of the samples and 30% (S oxidation) and 13% (sulfate reduction) of the full-length clones. Many of these lineages are shared between the V6 tags and full-length sequences, including Chromatiales , Epsilonproteobacteria , and Deltaproteobacteria . Close to 5% of the V6 tags and full-length clones were associated with N redox cycling capabilities (N fixation, nitrification, and nitrite oxidation). Other ecological functions that are suggested based on the sequences recovered include Fe, H 2 , and methane oxidation. The bacterial communities on these seven samples support our hypothesis that active biogeochemical cycling of S, N, and Fe is supported within and on inactive hydrothermal sulfide minerals. Previous work has shown that total organic carbon on the exterior of inactive sulfides is up to five times higher than on the exterior of actively venting sulfides ( 16 ), indicating that the members of the community observed here are contributing to substantial production of organic matter on sulfides once venting ceases. The ecological role of the cosmopolitan V6 tags (see Table S1 in the supplemental material) is rarely possible to define, but many of the V6 tags that are most abundant can be linked to N, Fe, and S redox cycling. For example, the second most abundant V6 tag in the entire data set is most closely related to members of the family Ectothiorhodospiraceae , chemolithoautotrophic S-oxidizing gammaproteobacteria. This tag occurs on all samples from chimney 9M4 and accounts for 7.2% of the V6 tags sequenced in this study. Tags related to known nitrite oxidizers in the order Nitrospirales and the family Nitrospinaceae are common on chimney 9M4 as well. Tags with sequence similarity to sulfate reducers in the bacterial family Desulfobulbaceae are also among the most abundant detected in this study. A Fe-oxidizing member of the family Gallionellaceae was one of the most common tags on sample 7M24, a massive sulfide deposit with extensive alteration and Fe oxyhydroxide deposition ( 12 ). Similar to the analysis with V6 tags, many of the most abundant clones can be attributed to an ecological function based on their lineage. Many clones belong to the Chromatiales order within the class Gammaproteobacteria , which are all capable of sulfide oxidation ( 27 ). Recovered clones related to Sulfurospirillum deleyianum may be active in sulfur reduction, while the other epsilonproteobacterial clones in this tree are more closely related to organisms active in sulfur oxidation. Other cosmopolitan clones represented in the full-length libraries that can be assigned potential ecological roles are related to genera of known sulfur reducers ( Desulfobulbus ) and iron oxidizers ( Gallionella ). The bacterial communities on the inactive sulfides sampled here harbor communities with an estimated 872 to 2,372 OTUs, as measured with ACE ( Table 1 ). This is similar to the number of OTUs measured on the carbonate chimneys at the Lost City hydrothermal field (715 to 2,499 OTUs [ 22 ]) but much lower than that measured in samples from diffuse-flow hydrothermal fluids at Axial Volcano on the Juan de Fuca Ridge (5,971 to 8,434 OTUs [ 28 ]), along the Mariana Trench (713 to 8,416 OTUs [ 29 ]), and in deep water from the North Atlantic (3,008 to 10,586 OTUs [ 19 ]). The Chao1 estimator indicates trends in diversity between these habitats similar to those indicated by ACE, but here, as discussed earlier, there are samples with values of Simpson’s reciprocal index that differ from the trends observed with ACE and the Chao1 estimator. These small differences result because ACE and the Chao1 estimator are estimates of the total species richness in a sample and both account for singletons ( 30 ), whereas Simpson’s reciprocal index is an overall measure of diversity and is not as heavily influenced by singletons. Although rarefaction curves did not plateau for any samples, indicating that the full diversity of these samples has not yet been revealed, these analyses suggest that inactive sulfides represent low-diversity environments in comparison to other deep-sea sites examined to date ( 19 , 28 , 29 ). While diversity indices indicate that these inactive sulfides are not as diverse as some other deep ocean habitats, the sequences recovered here by both methods reveal that a diverse set of geochemical transformations are likely on these structures. In particular, there was co-occurrence of tags and clones associated with both oxidation and reduction of S and N compounds on the same samples ( Table 2 ; see Table S2 in the supplemental material). Inactive sulfide chimneys appear to represent an active ecosystem with elevated metal concentrations that is slowly transitioning from a reduced environment, created by formerly present hydrothermal fluids and precipitates, to an oxidized environment. It is notable that the alphaproteobacterial family Rhodobacteraceae comprised ~14% of the tags recovered. While a definitive ecological function cannot be assigned to this family, a recent isolate from the TAG hydrothermal field on the Mid-Atlantic Ridge is a chemolithoautotrophic S and H 2 oxidizer ( 31 ), indicating that at least some of the Rhodobacteraceae V6 tags in our samples may represent organisms that can oxidize S and/or H 2 . The ubiquitous Roseobacter clade of marine bacteria falls within the Rhodobacteraceae family, and 72% of the sequenced genomes from this group contain the sox cluster of genes responsible for S oxidation ( 32 ), lending further support to potential participation in S cycling among the members of the family Rhodobacteraceae detected here. Variation in community composition is evident among our inactive sulfide chimney samples ( Fig. 2 and 3 ). It has been shown previously that variation among taxa identified at some active hydrothermal vents are related to variation in subseafloor fluid chemistry ( 33 ). For microbial communities inhabiting chimneys that no longer vent fluids, we hypothesize that geochemical diversity of the substrate exerts an influence on microbial ecology. Overall, we observe with both V6 tags and full-length clones that Alpha -, Delta - and Gammaproteobacteria and members of the phylum Bacteroidetes dominate inactive sulfide chimneys. With the exception of 3M33, Betaproteobacteria were either absent or not more than a few percent of the total V6 tags on the inactive chimneys. Two extremely abundant V6 tags accounted for the high proportion of Betaproteobacteria in sulfide 3M33. While neither could be classified beyond the class level by the Global Alignment for Sequence Taxonomy (GAST) method ( 26 ), one of these tags (1,855 copies) is identical to the betaproteobacterial species “ Sideroxydans paludicola ,” a chemolithoautotrophic Fe oxidizer ( 34 ), Zoogloea ramigera , an Mn oxidizer, and “ Candidatus Nitrotoga arctica,” a nitrite oxidizer ( 35 ), while the other tag (6,251 copies) is 96% similar to the same betaproteobacterial species. These data suggest the likely presence and activity of metal-oxidizing bacteria associated with this sample. Full-length clones also revealed low incidences of Betaproteobacteria , and those that were observed potentially play a role in N (9M32_81) and Fe (7M24_50) transformations. A previous study found a high percentage of clones related to “ Candidatus Magnetobacterium” in inactive sulfides from both the western Pacific and Indian Oceans ( 17 ). We recovered 125 V6 tags classified as “ Candidatus Magnetobacterium” from 9M4I and suggest that it may be a common resident of inactive sulfides. The clones represented by OTU 9M4O_80 are identical to a clone in the NCBI database recovered from another inactive sulfide structure, but these clones are not closely related (>85% similarity) to any known organisms. These may also be common on inactive sulfide structures. Bacterial succession on hydrothermal chimneys. Initial studies of bacterial diversity on inactive sulfides have suggested that communities on inactive sulfides are different from those on active chimneys ( 16 , 17 ), most notably the lower proportion of Epsilonproteobacteria , which dominate bacterial communities on active sulfides ( 2 – 9 , 36 , 37 ). An active sulfide chimney from 9°N on the EPR investigated previously was found to be dominated almost exclusively by Epsilonproteobacteria of the genera Sulfurimonas and Sulfurospirillum and epsilonproteobacterial group F ( 3 ), which includes the genus Sulfurovum ( 38 ). These are the genera within which our full-length clones belonging to the Epsilonproteobacteria belong. In contrast, a prior study of bacterial populations on inactive chimneys in the Okinawa Trough and the Central Indian Ridge found these structures to be dominated by sequences from Alpha - and Gammaproteobacteria and also sequences related to Delta - and Epsilonproteobacteria , Actinobacteria , Nitrospira , Bacteroidetes , Planctomycetes , and Verrucomicrobia ( 17 )—results that are consistent with the data presented here in comparison to prior active-chimney studies. Another study of the Juan de Fuca Ridge detected a predominance of Gammaproteobacteria Marinobacter , S-oxidizing Thiomicrospira , mesophilic Fe and S oxidizers, and an uncultured epsilonproteobacterium most closely related to Caminibacter mediatlanticus , which is capable of both nitrate ammonification and sulfur reduction ( 18 ). In our study, V6 tags classified as Epsilonproteobacteria are nearly all within the thermophilic genera Sulfurimonas or Sulfurovum , indicating that these tags may be relict populations, residual from when the sulfides were active and warm. We constructed two composite inactive chimneys using all of the sequences recovered from our samples for both V6 tag sequencing and full-length clone libraries and a composite active chimney based on full-length sequences from previously published papers ( Fig. 5 ). Conceptually, by binning these samples, the resulting composites “average” taxa from these two end-member sulfide biomes, minimizing sample-to-sample variation that may arise from fine-scale variation in geochemistry discussed above, in order to reveal variation that may be more specifically related to major differences between active and inactive sulfides. We chose only basalt-hosted hydrothermal sulfide studies (i.e., excluding ultramafic and sediment-hosted systems) that used universal bacterial primers for PCR and reported clone frequencies ( 2 – 9 , 36 , 37 ). The difference between the bacterial populations on active chimneys and those on inactive chimneys is apparent for both of the sequencing methods used here. The most obvious difference is the general lack of Epsilonproteobacteria in the inactive chimneys analyzed here versus an overwhelming predominance on active chimneys. Even for the full-length clones, which recovered proportionately more Epsilonproteobacteria than V6 tag sequencing did, there are much fewer Epsilonproteobacteria than on active chimneys. Similarly, members of the phylum Aquificae are absent from inactive sulfides, while they are common on active chimneys. We infer that Epsilonproteobacteria and members of the phylum Aquificae , which can comprise up to 60% of the active-chimney communities, are succeeded in active sulfides by members of the Alpha -, Beta -, Delta - and Gammaproteobacteria and members of the phylum Bacteroidetes on inactive sulfide chimneys. All of these phyla are more prevalent on the inactive chimneys than on the active chimneys. Succession on sulfide chimneys was recently observed in the Southern Mariana Trough, where active and inactive sulfides were shown to host statistically significantly different microbial communities ( 16 ). Thermophilic Archaea and Epsilonproteobacteria were found to dominate active chimneys, while both groups were nearly absent from inactive sulfides. A recent study using V6 tag pyrosequencing also observed microbial succession on the carbonate chimneys at the Lost City hydrothermal field, an ultramafic system on the Mid-Atlantic Ridge flank ( 22 ). Young, active chimneys at Lost City are dominated by Archaea from a clade known as the Lost City Methanosarcinales , while an inactive-chimney sample, dated at 1,245 years, is dominated by a group of ANME-1, anaerobic methanotrophic archaea ( 22 ). Interestingly, the ANME-1 tag that dominates the older sample is detected in very low numbers on the younger samples via tag sequencing, supporting the hypothesis that members of the rare biosphere can become dominant under favorable conditions. This observation at Lost City, in addition to the bacterial succession observed here, prompts additional examination of sulfide systems to examine the role and dispersal of rarely occurring tags in acting as seed populations in hydrothermal systems. Future studies should endeavor to examine archaeal succession as well. Certainly, there should be a succession from thermophilic Archaea to mesophiles or psychrophiles, but given the presence of organisms derived from the water column in some of the samples studied here, it may be likely to see an increase in marine group I Archaea on inactive sulfides or even the association of methanogens with the bacterial sulfate reducers seen here ( 39 ). This study described the bacterial community that is present on inactive hydrothermal sulfides. Compared to the bacterial community present on active sulfides, there is a clear shift from communities dominated by Epsilonproteobacteria and members of the phylum Aquificae to bacterial communities dominated by Alpha -, Beta -, Delta -, and Gammaproteobacteria and members of the phylum Bacteroidetes . Many of these are likely to participate in redox transformations of S, N, and Fe, and despite the loss of chemical energy derived from hydrothermal fluids, the mineralogy of inactive sulfides appears to support a community with chemolithoautotrophs that provide the organic carbon needed to maintain a microbial ecosystem, likely without a need for carbon input from the surface ocean. It is likely that once the heat from hydrothermal fluids disappears, microbes are able to colonize the internal microniches of inactive sulfides and thrive on both reduced and oxidized minerals present in the structures."
} | 5,480 |
39278981 | PMC11402984 | pmc | 6,579 | {
"abstract": "Kelps are vital for marine ecosystems, yet the genetic diversity underlying their capacity to adapt to climate change remains unknown. In this study, we focused on the kelp Macrocystis pyrifera a species critical to coastal habitats. We developed a protocol to evaluate heat stress response in 204 Macrocystis pyrifera genotypes subjected to heat stress treatments ranging from 21 °C to 27 °C. Here we show that haploid gametophytes exhibiting a heat-stress tolerant (HST) phenotype also produced greater biomass as genetically similar diploid sporophytes in a warm-water ocean farm. HST was measured as chlorophyll autofluorescence per genotype, presented here as fluorescent intensity values. This correlation suggests a predictive relationship between the growth performance of the early microscopic gametophyte stage HST and the later macroscopic sporophyte stage, indicating the potential for selecting resilient kelp strains under warmer ocean temperatures. However, HST kelps showed reduced genetic variation, underscoring the importance of integrating heat tolerance genes into a broader genetic pool to maintain the adaptability of kelp populations in the face of climate change.",
"introduction": "Introduction The increasing frequency and duration of marine heat waves, likely exacerbated by climate change, present a growing threat to kelp forests, an ecologically important habitat formed by large brown algae (i.e., kelps) in shallow coastal waters that provide a myriad of goods and services to society (as reviewed in refs. 1 , 2 ). There is a pressing need to understand how kelps respond to heat stress and to explore their potential for heat-stress tolerance (HST) or adaptation 3 , 4 . Extreme climatic events like marine heatwaves often exceed the physiological limits of individual organisms within a population, leading to selective mortality that can drive evolutionary change 5 . Studies show many species of kelp are highly susceptible to local extinctions and range contractions caused by marine heatwaves 6 – 8 . Consequently, decreases in genetic biodiversity caused by temperature extremes may hinder the capacity of kelp populations to adapt to future climate change and other challenges 9 , 10 . However, recent findings by Klingbeil et al. 11 suggest that kelp populations in southern California have maintained stable genetic diversity despite prolonged warming events, indicating potential HST. Additionally, Mohring et al. 12 emphasize the importance of research on the under-explored microscopic gametophyte stage of kelps, particularly their response to abiotic stressors, to fully understand kelps resilience. Building upon this, we hypothesized a predictive relationship between HST traits across the biphasic life stages of kelps 13 . The biphasic haplodiplontic life history of kelps (Order Laminariales), characterized by a microscopic haploid gametophyte and macroscopic diploid sporophyte, likely plays a key role in their HST. Veenhof et al. 14 underscore that traits associated with the microscopic stages of kelp’s complex life history are central to its adaptive capacity, yet research at this life-cycle stage in this area is sparse. Research by Wernberg et al. 8 shed light on the need to monitor the response of marine algae to warming anomalies with the understanding that extreme climate events will shift and damage critical kelp ecosystems. Further, Veenhof et al. 14 , found that survival, relative growth rate (RGR) and sex ratio of the gametophytes the kelp Ecklonia radiata from different latitudes (high, mid, and low) tended toward adaptation to their local temperatures, with a heat stress maximum of 2–3 °C above in situ temperatures 14 . In this study, we examined how genetic differences in HST of the gametophyte stage of Macrocystis pyrifera ( M. pyrifera ) are associated with the growth of the sporophyte stage. Using microscopy-derived chlorophyll fluorescent intensity (FI) values from a 3D tomography system, we determined the HST of 204 M. pyrifera genotypes obtained from a germplasm collection derived from southern California, USA 15 , 16 and compared this to the biomass yield of related sporophytes from an independent experiment conducted in 2019, in which sporophytes were cultivated in an in situ ocean setting during the warm summer season. Finally, macroalgal germplasm banking has been introduced in recent years to preserve the biodiversity of marine algal species with the potential to aid in kelp restoration initiatives and regenerative ocean farming efforts 16 . Here we search for HST tolerant gametophyte strains in a large giant kelp germplasm collection that can assist future restoration efforts. Our findings suggest a predictive relationship between gametophyte-stage HST and sporophyte-stage growth performance, underscoring the potential for selecting resilient kelp strains under warmer ocean temperatures.",
"discussion": "Discussion Our results support that gametophyte genotypes exhibiting greater heat-stress tolerance at 25 °C in ex situ laboratory environments also correspond to genetically similar adult sporophytes grown in situ warmer (18 °C–20 °C) summer months exhibiting higher biomass phenotypes. Research by Hollarsmith et al. 17 treated M. pyrifera gametophytes from higher-latitude populations in California and found significant reproductive failure at elevated temperatures, whereas lower latitude strains from San Diego populations exhibited greater reproduced success under the same treatment conditions. Such predictive relationships suggest that even at the earliest stages of development, the genetic tolerance or susceptibility to heat stress can be gauged. This finding may have significant ramifications for kelp forest management aimed at mitigating the escalating effects of climate change. For example, Buschmann et al. 18 , demonstrated that M. pyrifera gametophyte cultivation strategies focused on optimized cultivation practices through selective breeding could help to “future-proof” kelp in regenerative ocean farming conditions under threat of warming ocean conditions. Similarly, the use of HST genotypes in kelp forest restoration has been posited to improve the success and long-term resilience of these initiatives 19 , 20 . It is important to note that we cannot definitively conclude HST strains will produce HST sporophytes without further investigation. Building upon the HST screening data, future studies should examine the heat tolerance of sporophytes derived from both non-HST and HST strains. As suggest by Umanzor et al. 21 , functional validation steps in this field could provide valuable insights into the potential for transgenerational inheritance of HST traits in M. pyrifera . Other environmental factors including light and nutrient availability may contribute to the observed differences in HST between genotypes in both the heat stress screen and in situ sporophyte experiment. Umanzor et al. 21 revealed complex interactions between the combined effects of temperature and nitrate availability on juvenile sporophytes. Further studies utilizing the gametophyte data set should include a more comprehensive understanding of interactions of light, temperature, and nutrient availability as our focus solely on temperature manipulations lead to a more conservative estimate of HST differences between genotypes. Finally, in vascular plant systems tissue and organ-dependent HST is variable 22 ; a consideration of future M. pyrifera HST research should seek to understand the translation of HST across life stages as well as tissue types. In the ex situ gametophyte screening panel for HST, the use of kelp genotypes originating from populations with an approximately 1–2 °C average annual temperature difference and resultant pattern of HST genotypes ranging across populations suggests that HST in M. pyrifera may be influence by other factors such as phenotypic plasticity and genetic diversity. Populations of M. pyrifera along the Chilean coast were determined to have significant variation in their genetic and phenotypic diversity, underscoring the importance of considering genetic and phenotypic diversity with developing breeding programs for this species 23 . Because our findings shed light on HST from populations exhibiting slight differences in temperature climes, it’s important to consider that heat tolerance is potentially influenced by both genetic and environmental factors. Our identification of HST genotypes provides insight into the genetic structure and diversity of heat-stress adaptability in M. pyrifera populations in southern California. However, we also conclude here that there is a lower, yet not significant, genetic variation among HST strains. This may be confounded by sample size, however, the need to determine genetic variation rests on the supposition that lower genetic variation in HST genotypes underscores the potential for certain alleles to confer heat tolerance. Therefore, this trait is likely to be selected under the increasing threat of climate change. Management strategies aimed at using resilient genotypes to mitigate the effects of climate change should consider integrating thermal tolerance genes into a wider range of genetic backgrounds. Such introgression would likely help sustain a broad genetic base, which is essential for the long-adaptability and resilience of kelp populations. This approach could help balance the benefits of HST genotypes with the need to preserve genetic variation that might be critical for other aspects of kelp survival and adaptability. These results may have significant implications for conservation strategies regarding vulnerability to climate change, breeding and restoration programs and monitoring genetic health. Lower genetic diversity in HST strains used in restoration initiatives would likely lack the variability necessary to adapt to changing conditions and may prompt conservationists to preserve a broader genetic base. Finally, regular monitoring of genetic health and diversity for natural and restored populations may be necessitated when incorporating a mix of genotypes that include HST strains and those with higher genetic variability."
} | 2,550 |
39278981 | PMC11402984 | pmc | 6,579 | {
"abstract": "Kelps are vital for marine ecosystems, yet the genetic diversity underlying their capacity to adapt to climate change remains unknown. In this study, we focused on the kelp Macrocystis pyrifera a species critical to coastal habitats. We developed a protocol to evaluate heat stress response in 204 Macrocystis pyrifera genotypes subjected to heat stress treatments ranging from 21 °C to 27 °C. Here we show that haploid gametophytes exhibiting a heat-stress tolerant (HST) phenotype also produced greater biomass as genetically similar diploid sporophytes in a warm-water ocean farm. HST was measured as chlorophyll autofluorescence per genotype, presented here as fluorescent intensity values. This correlation suggests a predictive relationship between the growth performance of the early microscopic gametophyte stage HST and the later macroscopic sporophyte stage, indicating the potential for selecting resilient kelp strains under warmer ocean temperatures. However, HST kelps showed reduced genetic variation, underscoring the importance of integrating heat tolerance genes into a broader genetic pool to maintain the adaptability of kelp populations in the face of climate change.",
"introduction": "Introduction The increasing frequency and duration of marine heat waves, likely exacerbated by climate change, present a growing threat to kelp forests, an ecologically important habitat formed by large brown algae (i.e., kelps) in shallow coastal waters that provide a myriad of goods and services to society (as reviewed in refs. 1 , 2 ). There is a pressing need to understand how kelps respond to heat stress and to explore their potential for heat-stress tolerance (HST) or adaptation 3 , 4 . Extreme climatic events like marine heatwaves often exceed the physiological limits of individual organisms within a population, leading to selective mortality that can drive evolutionary change 5 . Studies show many species of kelp are highly susceptible to local extinctions and range contractions caused by marine heatwaves 6 – 8 . Consequently, decreases in genetic biodiversity caused by temperature extremes may hinder the capacity of kelp populations to adapt to future climate change and other challenges 9 , 10 . However, recent findings by Klingbeil et al. 11 suggest that kelp populations in southern California have maintained stable genetic diversity despite prolonged warming events, indicating potential HST. Additionally, Mohring et al. 12 emphasize the importance of research on the under-explored microscopic gametophyte stage of kelps, particularly their response to abiotic stressors, to fully understand kelps resilience. Building upon this, we hypothesized a predictive relationship between HST traits across the biphasic life stages of kelps 13 . The biphasic haplodiplontic life history of kelps (Order Laminariales), characterized by a microscopic haploid gametophyte and macroscopic diploid sporophyte, likely plays a key role in their HST. Veenhof et al. 14 underscore that traits associated with the microscopic stages of kelp’s complex life history are central to its adaptive capacity, yet research at this life-cycle stage in this area is sparse. Research by Wernberg et al. 8 shed light on the need to monitor the response of marine algae to warming anomalies with the understanding that extreme climate events will shift and damage critical kelp ecosystems. Further, Veenhof et al. 14 , found that survival, relative growth rate (RGR) and sex ratio of the gametophytes the kelp Ecklonia radiata from different latitudes (high, mid, and low) tended toward adaptation to their local temperatures, with a heat stress maximum of 2–3 °C above in situ temperatures 14 . In this study, we examined how genetic differences in HST of the gametophyte stage of Macrocystis pyrifera ( M. pyrifera ) are associated with the growth of the sporophyte stage. Using microscopy-derived chlorophyll fluorescent intensity (FI) values from a 3D tomography system, we determined the HST of 204 M. pyrifera genotypes obtained from a germplasm collection derived from southern California, USA 15 , 16 and compared this to the biomass yield of related sporophytes from an independent experiment conducted in 2019, in which sporophytes were cultivated in an in situ ocean setting during the warm summer season. Finally, macroalgal germplasm banking has been introduced in recent years to preserve the biodiversity of marine algal species with the potential to aid in kelp restoration initiatives and regenerative ocean farming efforts 16 . Here we search for HST tolerant gametophyte strains in a large giant kelp germplasm collection that can assist future restoration efforts. Our findings suggest a predictive relationship between gametophyte-stage HST and sporophyte-stage growth performance, underscoring the potential for selecting resilient kelp strains under warmer ocean temperatures.",
"discussion": "Discussion Our results support that gametophyte genotypes exhibiting greater heat-stress tolerance at 25 °C in ex situ laboratory environments also correspond to genetically similar adult sporophytes grown in situ warmer (18 °C–20 °C) summer months exhibiting higher biomass phenotypes. Research by Hollarsmith et al. 17 treated M. pyrifera gametophytes from higher-latitude populations in California and found significant reproductive failure at elevated temperatures, whereas lower latitude strains from San Diego populations exhibited greater reproduced success under the same treatment conditions. Such predictive relationships suggest that even at the earliest stages of development, the genetic tolerance or susceptibility to heat stress can be gauged. This finding may have significant ramifications for kelp forest management aimed at mitigating the escalating effects of climate change. For example, Buschmann et al. 18 , demonstrated that M. pyrifera gametophyte cultivation strategies focused on optimized cultivation practices through selective breeding could help to “future-proof” kelp in regenerative ocean farming conditions under threat of warming ocean conditions. Similarly, the use of HST genotypes in kelp forest restoration has been posited to improve the success and long-term resilience of these initiatives 19 , 20 . It is important to note that we cannot definitively conclude HST strains will produce HST sporophytes without further investigation. Building upon the HST screening data, future studies should examine the heat tolerance of sporophytes derived from both non-HST and HST strains. As suggest by Umanzor et al. 21 , functional validation steps in this field could provide valuable insights into the potential for transgenerational inheritance of HST traits in M. pyrifera . Other environmental factors including light and nutrient availability may contribute to the observed differences in HST between genotypes in both the heat stress screen and in situ sporophyte experiment. Umanzor et al. 21 revealed complex interactions between the combined effects of temperature and nitrate availability on juvenile sporophytes. Further studies utilizing the gametophyte data set should include a more comprehensive understanding of interactions of light, temperature, and nutrient availability as our focus solely on temperature manipulations lead to a more conservative estimate of HST differences between genotypes. Finally, in vascular plant systems tissue and organ-dependent HST is variable 22 ; a consideration of future M. pyrifera HST research should seek to understand the translation of HST across life stages as well as tissue types. In the ex situ gametophyte screening panel for HST, the use of kelp genotypes originating from populations with an approximately 1–2 °C average annual temperature difference and resultant pattern of HST genotypes ranging across populations suggests that HST in M. pyrifera may be influence by other factors such as phenotypic plasticity and genetic diversity. Populations of M. pyrifera along the Chilean coast were determined to have significant variation in their genetic and phenotypic diversity, underscoring the importance of considering genetic and phenotypic diversity with developing breeding programs for this species 23 . Because our findings shed light on HST from populations exhibiting slight differences in temperature climes, it’s important to consider that heat tolerance is potentially influenced by both genetic and environmental factors. Our identification of HST genotypes provides insight into the genetic structure and diversity of heat-stress adaptability in M. pyrifera populations in southern California. However, we also conclude here that there is a lower, yet not significant, genetic variation among HST strains. This may be confounded by sample size, however, the need to determine genetic variation rests on the supposition that lower genetic variation in HST genotypes underscores the potential for certain alleles to confer heat tolerance. Therefore, this trait is likely to be selected under the increasing threat of climate change. Management strategies aimed at using resilient genotypes to mitigate the effects of climate change should consider integrating thermal tolerance genes into a wider range of genetic backgrounds. Such introgression would likely help sustain a broad genetic base, which is essential for the long-adaptability and resilience of kelp populations. This approach could help balance the benefits of HST genotypes with the need to preserve genetic variation that might be critical for other aspects of kelp survival and adaptability. These results may have significant implications for conservation strategies regarding vulnerability to climate change, breeding and restoration programs and monitoring genetic health. Lower genetic diversity in HST strains used in restoration initiatives would likely lack the variability necessary to adapt to changing conditions and may prompt conservationists to preserve a broader genetic base. Finally, regular monitoring of genetic health and diversity for natural and restored populations may be necessitated when incorporating a mix of genotypes that include HST strains and those with higher genetic variability."
} | 2,550 |
39278981 | PMC11402984 | pmc | 6,580 | {
"abstract": "Kelps are vital for marine ecosystems, yet the genetic diversity underlying their capacity to adapt to climate change remains unknown. In this study, we focused on the kelp Macrocystis pyrifera a species critical to coastal habitats. We developed a protocol to evaluate heat stress response in 204 Macrocystis pyrifera genotypes subjected to heat stress treatments ranging from 21 °C to 27 °C. Here we show that haploid gametophytes exhibiting a heat-stress tolerant (HST) phenotype also produced greater biomass as genetically similar diploid sporophytes in a warm-water ocean farm. HST was measured as chlorophyll autofluorescence per genotype, presented here as fluorescent intensity values. This correlation suggests a predictive relationship between the growth performance of the early microscopic gametophyte stage HST and the later macroscopic sporophyte stage, indicating the potential for selecting resilient kelp strains under warmer ocean temperatures. However, HST kelps showed reduced genetic variation, underscoring the importance of integrating heat tolerance genes into a broader genetic pool to maintain the adaptability of kelp populations in the face of climate change.",
"introduction": "Introduction The increasing frequency and duration of marine heat waves, likely exacerbated by climate change, present a growing threat to kelp forests, an ecologically important habitat formed by large brown algae (i.e., kelps) in shallow coastal waters that provide a myriad of goods and services to society (as reviewed in refs. 1 , 2 ). There is a pressing need to understand how kelps respond to heat stress and to explore their potential for heat-stress tolerance (HST) or adaptation 3 , 4 . Extreme climatic events like marine heatwaves often exceed the physiological limits of individual organisms within a population, leading to selective mortality that can drive evolutionary change 5 . Studies show many species of kelp are highly susceptible to local extinctions and range contractions caused by marine heatwaves 6 – 8 . Consequently, decreases in genetic biodiversity caused by temperature extremes may hinder the capacity of kelp populations to adapt to future climate change and other challenges 9 , 10 . However, recent findings by Klingbeil et al. 11 suggest that kelp populations in southern California have maintained stable genetic diversity despite prolonged warming events, indicating potential HST. Additionally, Mohring et al. 12 emphasize the importance of research on the under-explored microscopic gametophyte stage of kelps, particularly their response to abiotic stressors, to fully understand kelps resilience. Building upon this, we hypothesized a predictive relationship between HST traits across the biphasic life stages of kelps 13 . The biphasic haplodiplontic life history of kelps (Order Laminariales), characterized by a microscopic haploid gametophyte and macroscopic diploid sporophyte, likely plays a key role in their HST. Veenhof et al. 14 underscore that traits associated with the microscopic stages of kelp’s complex life history are central to its adaptive capacity, yet research at this life-cycle stage in this area is sparse. Research by Wernberg et al. 8 shed light on the need to monitor the response of marine algae to warming anomalies with the understanding that extreme climate events will shift and damage critical kelp ecosystems. Further, Veenhof et al. 14 , found that survival, relative growth rate (RGR) and sex ratio of the gametophytes the kelp Ecklonia radiata from different latitudes (high, mid, and low) tended toward adaptation to their local temperatures, with a heat stress maximum of 2–3 °C above in situ temperatures 14 . In this study, we examined how genetic differences in HST of the gametophyte stage of Macrocystis pyrifera ( M. pyrifera ) are associated with the growth of the sporophyte stage. Using microscopy-derived chlorophyll fluorescent intensity (FI) values from a 3D tomography system, we determined the HST of 204 M. pyrifera genotypes obtained from a germplasm collection derived from southern California, USA 15 , 16 and compared this to the biomass yield of related sporophytes from an independent experiment conducted in 2019, in which sporophytes were cultivated in an in situ ocean setting during the warm summer season. Finally, macroalgal germplasm banking has been introduced in recent years to preserve the biodiversity of marine algal species with the potential to aid in kelp restoration initiatives and regenerative ocean farming efforts 16 . Here we search for HST tolerant gametophyte strains in a large giant kelp germplasm collection that can assist future restoration efforts. Our findings suggest a predictive relationship between gametophyte-stage HST and sporophyte-stage growth performance, underscoring the potential for selecting resilient kelp strains under warmer ocean temperatures.",
"discussion": "Discussion Our results support that gametophyte genotypes exhibiting greater heat-stress tolerance at 25 °C in ex situ laboratory environments also correspond to genetically similar adult sporophytes grown in situ warmer (18 °C–20 °C) summer months exhibiting higher biomass phenotypes. Research by Hollarsmith et al. 17 treated M. pyrifera gametophytes from higher-latitude populations in California and found significant reproductive failure at elevated temperatures, whereas lower latitude strains from San Diego populations exhibited greater reproduced success under the same treatment conditions. Such predictive relationships suggest that even at the earliest stages of development, the genetic tolerance or susceptibility to heat stress can be gauged. This finding may have significant ramifications for kelp forest management aimed at mitigating the escalating effects of climate change. For example, Buschmann et al. 18 , demonstrated that M. pyrifera gametophyte cultivation strategies focused on optimized cultivation practices through selective breeding could help to “future-proof” kelp in regenerative ocean farming conditions under threat of warming ocean conditions. Similarly, the use of HST genotypes in kelp forest restoration has been posited to improve the success and long-term resilience of these initiatives 19 , 20 . It is important to note that we cannot definitively conclude HST strains will produce HST sporophytes without further investigation. Building upon the HST screening data, future studies should examine the heat tolerance of sporophytes derived from both non-HST and HST strains. As suggest by Umanzor et al. 21 , functional validation steps in this field could provide valuable insights into the potential for transgenerational inheritance of HST traits in M. pyrifera . Other environmental factors including light and nutrient availability may contribute to the observed differences in HST between genotypes in both the heat stress screen and in situ sporophyte experiment. Umanzor et al. 21 revealed complex interactions between the combined effects of temperature and nitrate availability on juvenile sporophytes. Further studies utilizing the gametophyte data set should include a more comprehensive understanding of interactions of light, temperature, and nutrient availability as our focus solely on temperature manipulations lead to a more conservative estimate of HST differences between genotypes. Finally, in vascular plant systems tissue and organ-dependent HST is variable 22 ; a consideration of future M. pyrifera HST research should seek to understand the translation of HST across life stages as well as tissue types. In the ex situ gametophyte screening panel for HST, the use of kelp genotypes originating from populations with an approximately 1–2 °C average annual temperature difference and resultant pattern of HST genotypes ranging across populations suggests that HST in M. pyrifera may be influence by other factors such as phenotypic plasticity and genetic diversity. Populations of M. pyrifera along the Chilean coast were determined to have significant variation in their genetic and phenotypic diversity, underscoring the importance of considering genetic and phenotypic diversity with developing breeding programs for this species 23 . Because our findings shed light on HST from populations exhibiting slight differences in temperature climes, it’s important to consider that heat tolerance is potentially influenced by both genetic and environmental factors. Our identification of HST genotypes provides insight into the genetic structure and diversity of heat-stress adaptability in M. pyrifera populations in southern California. However, we also conclude here that there is a lower, yet not significant, genetic variation among HST strains. This may be confounded by sample size, however, the need to determine genetic variation rests on the supposition that lower genetic variation in HST genotypes underscores the potential for certain alleles to confer heat tolerance. Therefore, this trait is likely to be selected under the increasing threat of climate change. Management strategies aimed at using resilient genotypes to mitigate the effects of climate change should consider integrating thermal tolerance genes into a wider range of genetic backgrounds. Such introgression would likely help sustain a broad genetic base, which is essential for the long-adaptability and resilience of kelp populations. This approach could help balance the benefits of HST genotypes with the need to preserve genetic variation that might be critical for other aspects of kelp survival and adaptability. These results may have significant implications for conservation strategies regarding vulnerability to climate change, breeding and restoration programs and monitoring genetic health. Lower genetic diversity in HST strains used in restoration initiatives would likely lack the variability necessary to adapt to changing conditions and may prompt conservationists to preserve a broader genetic base. Finally, regular monitoring of genetic health and diversity for natural and restored populations may be necessitated when incorporating a mix of genotypes that include HST strains and those with higher genetic variability."
} | 2,550 |
39278981 | PMC11402984 | pmc | 6,580 | {
"abstract": "Kelps are vital for marine ecosystems, yet the genetic diversity underlying their capacity to adapt to climate change remains unknown. In this study, we focused on the kelp Macrocystis pyrifera a species critical to coastal habitats. We developed a protocol to evaluate heat stress response in 204 Macrocystis pyrifera genotypes subjected to heat stress treatments ranging from 21 °C to 27 °C. Here we show that haploid gametophytes exhibiting a heat-stress tolerant (HST) phenotype also produced greater biomass as genetically similar diploid sporophytes in a warm-water ocean farm. HST was measured as chlorophyll autofluorescence per genotype, presented here as fluorescent intensity values. This correlation suggests a predictive relationship between the growth performance of the early microscopic gametophyte stage HST and the later macroscopic sporophyte stage, indicating the potential for selecting resilient kelp strains under warmer ocean temperatures. However, HST kelps showed reduced genetic variation, underscoring the importance of integrating heat tolerance genes into a broader genetic pool to maintain the adaptability of kelp populations in the face of climate change.",
"introduction": "Introduction The increasing frequency and duration of marine heat waves, likely exacerbated by climate change, present a growing threat to kelp forests, an ecologically important habitat formed by large brown algae (i.e., kelps) in shallow coastal waters that provide a myriad of goods and services to society (as reviewed in refs. 1 , 2 ). There is a pressing need to understand how kelps respond to heat stress and to explore their potential for heat-stress tolerance (HST) or adaptation 3 , 4 . Extreme climatic events like marine heatwaves often exceed the physiological limits of individual organisms within a population, leading to selective mortality that can drive evolutionary change 5 . Studies show many species of kelp are highly susceptible to local extinctions and range contractions caused by marine heatwaves 6 – 8 . Consequently, decreases in genetic biodiversity caused by temperature extremes may hinder the capacity of kelp populations to adapt to future climate change and other challenges 9 , 10 . However, recent findings by Klingbeil et al. 11 suggest that kelp populations in southern California have maintained stable genetic diversity despite prolonged warming events, indicating potential HST. Additionally, Mohring et al. 12 emphasize the importance of research on the under-explored microscopic gametophyte stage of kelps, particularly their response to abiotic stressors, to fully understand kelps resilience. Building upon this, we hypothesized a predictive relationship between HST traits across the biphasic life stages of kelps 13 . The biphasic haplodiplontic life history of kelps (Order Laminariales), characterized by a microscopic haploid gametophyte and macroscopic diploid sporophyte, likely plays a key role in their HST. Veenhof et al. 14 underscore that traits associated with the microscopic stages of kelp’s complex life history are central to its adaptive capacity, yet research at this life-cycle stage in this area is sparse. Research by Wernberg et al. 8 shed light on the need to monitor the response of marine algae to warming anomalies with the understanding that extreme climate events will shift and damage critical kelp ecosystems. Further, Veenhof et al. 14 , found that survival, relative growth rate (RGR) and sex ratio of the gametophytes the kelp Ecklonia radiata from different latitudes (high, mid, and low) tended toward adaptation to their local temperatures, with a heat stress maximum of 2–3 °C above in situ temperatures 14 . In this study, we examined how genetic differences in HST of the gametophyte stage of Macrocystis pyrifera ( M. pyrifera ) are associated with the growth of the sporophyte stage. Using microscopy-derived chlorophyll fluorescent intensity (FI) values from a 3D tomography system, we determined the HST of 204 M. pyrifera genotypes obtained from a germplasm collection derived from southern California, USA 15 , 16 and compared this to the biomass yield of related sporophytes from an independent experiment conducted in 2019, in which sporophytes were cultivated in an in situ ocean setting during the warm summer season. Finally, macroalgal germplasm banking has been introduced in recent years to preserve the biodiversity of marine algal species with the potential to aid in kelp restoration initiatives and regenerative ocean farming efforts 16 . Here we search for HST tolerant gametophyte strains in a large giant kelp germplasm collection that can assist future restoration efforts. Our findings suggest a predictive relationship between gametophyte-stage HST and sporophyte-stage growth performance, underscoring the potential for selecting resilient kelp strains under warmer ocean temperatures.",
"discussion": "Discussion Our results support that gametophyte genotypes exhibiting greater heat-stress tolerance at 25 °C in ex situ laboratory environments also correspond to genetically similar adult sporophytes grown in situ warmer (18 °C–20 °C) summer months exhibiting higher biomass phenotypes. Research by Hollarsmith et al. 17 treated M. pyrifera gametophytes from higher-latitude populations in California and found significant reproductive failure at elevated temperatures, whereas lower latitude strains from San Diego populations exhibited greater reproduced success under the same treatment conditions. Such predictive relationships suggest that even at the earliest stages of development, the genetic tolerance or susceptibility to heat stress can be gauged. This finding may have significant ramifications for kelp forest management aimed at mitigating the escalating effects of climate change. For example, Buschmann et al. 18 , demonstrated that M. pyrifera gametophyte cultivation strategies focused on optimized cultivation practices through selective breeding could help to “future-proof” kelp in regenerative ocean farming conditions under threat of warming ocean conditions. Similarly, the use of HST genotypes in kelp forest restoration has been posited to improve the success and long-term resilience of these initiatives 19 , 20 . It is important to note that we cannot definitively conclude HST strains will produce HST sporophytes without further investigation. Building upon the HST screening data, future studies should examine the heat tolerance of sporophytes derived from both non-HST and HST strains. As suggest by Umanzor et al. 21 , functional validation steps in this field could provide valuable insights into the potential for transgenerational inheritance of HST traits in M. pyrifera . Other environmental factors including light and nutrient availability may contribute to the observed differences in HST between genotypes in both the heat stress screen and in situ sporophyte experiment. Umanzor et al. 21 revealed complex interactions between the combined effects of temperature and nitrate availability on juvenile sporophytes. Further studies utilizing the gametophyte data set should include a more comprehensive understanding of interactions of light, temperature, and nutrient availability as our focus solely on temperature manipulations lead to a more conservative estimate of HST differences between genotypes. Finally, in vascular plant systems tissue and organ-dependent HST is variable 22 ; a consideration of future M. pyrifera HST research should seek to understand the translation of HST across life stages as well as tissue types. In the ex situ gametophyte screening panel for HST, the use of kelp genotypes originating from populations with an approximately 1–2 °C average annual temperature difference and resultant pattern of HST genotypes ranging across populations suggests that HST in M. pyrifera may be influence by other factors such as phenotypic plasticity and genetic diversity. Populations of M. pyrifera along the Chilean coast were determined to have significant variation in their genetic and phenotypic diversity, underscoring the importance of considering genetic and phenotypic diversity with developing breeding programs for this species 23 . Because our findings shed light on HST from populations exhibiting slight differences in temperature climes, it’s important to consider that heat tolerance is potentially influenced by both genetic and environmental factors. Our identification of HST genotypes provides insight into the genetic structure and diversity of heat-stress adaptability in M. pyrifera populations in southern California. However, we also conclude here that there is a lower, yet not significant, genetic variation among HST strains. This may be confounded by sample size, however, the need to determine genetic variation rests on the supposition that lower genetic variation in HST genotypes underscores the potential for certain alleles to confer heat tolerance. Therefore, this trait is likely to be selected under the increasing threat of climate change. Management strategies aimed at using resilient genotypes to mitigate the effects of climate change should consider integrating thermal tolerance genes into a wider range of genetic backgrounds. Such introgression would likely help sustain a broad genetic base, which is essential for the long-adaptability and resilience of kelp populations. This approach could help balance the benefits of HST genotypes with the need to preserve genetic variation that might be critical for other aspects of kelp survival and adaptability. These results may have significant implications for conservation strategies regarding vulnerability to climate change, breeding and restoration programs and monitoring genetic health. Lower genetic diversity in HST strains used in restoration initiatives would likely lack the variability necessary to adapt to changing conditions and may prompt conservationists to preserve a broader genetic base. Finally, regular monitoring of genetic health and diversity for natural and restored populations may be necessitated when incorporating a mix of genotypes that include HST strains and those with higher genetic variability."
} | 2,550 |
36187961 | PMC9515657 | pmc | 6,581 | {
"abstract": "Due to global change, increasing nutrient input to ecosystems dramatically affects the nitrogen cycle, especially the nitrification process. Nitrifiers including ammonia-oxidizing archaea (AOAs), ammonia-oxidizing bacteria (AOBs), nitrite-oxidizing bacteria (NOBs), and recently discovered complete ammonia oxidizers (comammoxs) perform nitrification individually or in a community. However, much remains to be learned about their niche differentiation, coexistence, and interactions among those metabolically distinct nitrifiers. Here, we used synthetic microbial ecology approaches to construct synthetic nitrifying communities (SNCs) with different combinations of Nitrospira inopinata as comammox, Nitrososphaera gargensis as AOA, Nitrosomonas communis as AOB, and Nitrospira moscoviensis as NOB. Our results showed that niche differentiation and potential interactions among those metabolically distinct nitrifiers were determined by their kinetic characteristics. The dominant species shifted from N. inopinata to N. communis in the N4 community (with all four types of nitrifiers) as ammonium concentrations increased, which could be well explained by the kinetic difference in ammonia affinity, specific growth rate, and substrate tolerance of nitrifiers in the SNCs. In addition, a conceptual model was developed to infer niche differentiation and possible interactions among the four types of nitrifiers. This study advances our understanding of niche differentiation and provides new strategies to further study their interactions among the four types of nitrifiers.",
"conclusion": "Conclusion In summary, we constructed SNCs with four types of nitrifiers to understand niche differentiation in response to ammonium concentrations and found that niche differentiation was driven by ammonia affinity, specific growth rate, and ammonium/nitrite tolerance under a gradient of ammonium concentrations among those nitrifiers. In the N4 community, the dominant species was shifted from comammoxs to AOBs as ammonium concentrations increased. Comammox predominated at low ammonium concentrations due to its high ammonia affinity and high maximum specific growth rates. AOB predominated at high ammonium concentrations due to its high maximum specific growth rate, high ammonium oxidation rate and no obvious inhibition by ammonium. AOA had constantly low growth rates due to its weak competitiveness with comammox (0.2 to 1 mM) and AOB (2 mM), and inhibition by high ammonium concentrations (≥10 mM). Niche differentiation of NOBs could be compromised by comammox at 0.2 to 2 mM ammonium and inhibited by high ammonium/nitrite concentrations. Although the mechanism of nitrifier interactions and trade-offs between ammonium concentration and abundance detection sensitivity needs to be further explored, this study advances our understanding of niche differentiation of nitrifiers in response to ammonium concentrations in the environment, and it has important implications for exploring interactions among metabolically distinct nitrifiers using the established SNCs and multi-omics technologies.",
"introduction": "Introduction Nitrification is an important process in the nitrogen cycle, and it was widely believed as a two-step process for more than one century with ammonia oxidized to nitrite by ammonia-oxidizing bacteria (AOBs) or ammonia-oxidizing archaea (AOAs) (Könneke et al., 2005 ), and subsequently nitrite oxidization to nitrate by nitrite-oxidizing bacteria (NOBs). Complete ammonia oxidizers (comammoxs) were initially proposed according to optimal pathway length analysis (Costa et al., 2006 ), functionally enriched and finally isolated in recent efforts (Daims et al., 2015 ; van Kessel et al., 2015 ; Kits et al., 2017 ; Sakoula et al., 2021 ). The identification of comammox raises questions on their diversity and distribution in the environment. Metagenome and marker gene sequencing data analyses have demonstrated that newly discovered comammox populations are widely distributed in natural and engineered systems (Palomo et al., 2016 ; Pinto et al., 2016 ; Wang et al., 2017 ; Annavajhala et al., 2018 ; Orellana et al., 2018 ; Xia et al., 2018 ; Yang et al., 2020 ), which are highly overlapped with the habitat of AOAs, AOBs, and NOBs (Hatzenpichler, 2012 ; Prosser and Nicol, 2012 ; Stahl and de la Torre, 2012 ; Daims et al., 2016 ; Lawson and Lucker, 2018 ). These results indicate comammox is a common nitrifier in the environment, raising further questions about their niche differentiation and interactions in the environment (Daims et al., 2016 ; Santoro, 2016 ). Niches of nitrifiers are highly overlapped as they could share similar habitats and substrates in the environment (Martens-Habbena et al., 2009 ). Cultivation studies of niche differentiation could reveal mechanisms about the origin and maintenance of species biodiversity, community assembly (HilleRisLambers et al., 2012 ), coexistence (File et al., 2012 ), species physiology (Hatzenpichler et al., 2008 ; Hatzenpichler, 2012 ), and habitat preferences (Bauer et al., 2018 ). For example, the high ammonia affinity of comammox indicated their prevalence at oligotrophic environments (Kits et al., 2017 ) and the cooperation possibility between anammox and AOAs determined by their ammonia affinity according to a biofilm model (Straka et al., 2019 ). As ammonia and nitrite are key substrates of nitrification (Falkowski et al., 2008 ; Canfield et al., 2010 ; Stein, 2015 ), of particular interest is niche differentiation among nitrifiers in response to ammonium concentrations, which may provide new insights into the biodiversity, ecophysiology, and evolution of metabolically distinct nitrifiers in the environment. To identify patterns of niche differentiation and interactions of nitrifiers driven by ammonium, synthetic microbial ecology theories and approaches may provide a new strategy. Synthetic microbial communities are designed and built by microorganisms with known genome information, physiology, and metabolic characteristics in a well-defined medium (Johns et al., 2016 ). Natural environmental communities are generally complex and subjected to the influence of a multitude of environmental factors, making them difficult, if not impossible, to identify drivers of niche differentiation. While a major drawback of laboratory studies on pure cultures is the inherited inability to study population interactions and underlying mechanisms, which is critical to the manifestation of niche differentiation. To bridge such a gap, synthetic microbial ecology has recently been developed (Dolinsek et al., 2016 ; Lindemann et al., 2016 ; Zomorrodi and Segre, 2016 ; Lawson et al., 2019 ), and synthetic microbial communities have been used as a powerful tool to simplify natural microbial communities with reduced complexity and a controlled environment (Shou et al., 2007 ; Momeni et al., 2011 ; De Roy et al., 2014 ; Grosskopf and Soyer, 2014 ), which is promising to address niche differentiation of nitrifiers and their interactions in the environment. In this study, we aimed to study niche differentiation among four types of nitrifiers and their underlying mechanisms in response to ammonium concentrations using synthetic nitrifying communities (SNCs). We used a bottom-up approach to construct SNCs with different combinations of Nitrospira inopinata (comammox), Nitrososphaera gargensis (AOA), Nitrosomonas communis (AOB), and Nitrospira moscoviensis (NOB) and examined their responses to comparable or beyond environmentally ammonium concentrations (0.2 to 20 mM). Our results showed that the niche differentiation of nitrifiers was largely driven by their ammonia affinity, specific growth rates, and ammonium/nitrite tolerance. This study advances our understanding of niche differentiation, coexistence, and interactions driven by ammonium among metabolically distinct nitrifiers in the environment.",
"discussion": "Discussion Understanding the niche differentiation, interaction, and coexistence of nitrifiers and their associated mechanisms is a central issue in microbial ecology. Our current knowledge of nitrifiers is generally based on limited monocultures and environmental studies, and their coexistence, niche differentiation, and interactions in the environment are extremely difficult or impossible to address. As various differences in physiology of monocultures exist, it is well inferred that substrate (ammonium and nitrite), temperature, pH, and H 2 O 2 detoxification are considered the main drivers of niche differentiation among metabolically distinct nitrifiers (Prosser and Nicol, 2012 ; Hu and He, 2017 ; Kits et al., 2017 ). The physiology and diversity of cultivated AOAs indicated that they had a wider range of temperature and pH adaption than most AOBs (Lehtovirta-Morley et al., 2011 ; Zhang et al., 2017 ; Prosser et al., 2020 ; Picone et al., 2021 ); α-keto acids (e.g., pyruvate) which were reported to enhance the growth of some AOAs (Tourna et al., 2011 ; Qin et al., 2014 ) were further confirmed as H 2 O 2 scavengers (Kim et al., 2016 ). Recently, several studies further indicated that nitrifiers were metabolically versatile beyond the N cycle and involved in hydrogen and sulfide oxidation (Lehtovirta-Morley et al., 2011 ; Tourna et al., 2011 ; Stahl and de la Torre, 2012 ; Daims et al., 2016 ). Among such many drivers, we gave priority to answering niche differentiation to ammonium as it is the energy source for nitrifiers. Thus, in this study, we applied synthetic microbial ecology theories and approaches to construct SNCs under unified medium and temperature and tested niche differentiation with comammox and other three types of metabolically distinct nitrifiers under a gradient of environmentally comparable ammonium concentrations to reflect the performance of nitrifiers in the environment. We found that those nitrifiers could coexist at low ammonium concentrations, and their niches differentiated under different ammonium concentrations with possible mechanisms, including ammonia/nitrite affinity, specific growth rate, and ammonium/nitrite tolerance. In addition, the results allowed us to explore possible interactions among those nitrifiers. Specifically, N. inopinata was the most competitive population at low ammonium concentrations, and N. communis was dominant at high ammonium concentrations. While N. gargensis and N. moscoviensis were less competitive than N. inopinata and N. communis for ammonium and N. inopinata for nitrite, respectively, at our tested ammonium ranges, the growth of N. inopinata, N. gargensis , and N. moscoviensis were inhibited at high ammonium concentrations. First, ammonia/nitrite affinity of nitrifiers is the most important theoretical basis of niche differentiation at low ammonium environments (Hatzenpichler, 2012 ; Prosser and Nicol, 2012 ; Kits et al., 2017 ). For ammonia affinity, how AOAs and AOBs establish their niches and function in the environment has been discussed for decades, and comammox has recently joined in this debate. Previous laboratory studies showed that N. inopinata had the highest ammonia affinity, followed by non-marine AOAs and AOBs (Kits et al., 2017 ), which was reflected in the apparent half-saturation constant value [K m(app) ] of N. inopinata, N. gargensis , and Nitrosomonas AOBs at 0.65, 5.6, and ~1,000 μM total ammonium, respectively (Martens-Habbena et al., 2009 ; Kits et al., 2017 ). Comammox populations have been considered as oligotrophs and could be prevalent at low ammonium environments, which is evidenced by many studies with microbial enrichments or environmental samples from oligotrophic environments, such as drinking water systems, rapid gravity sand filter, and groundwater-fed rapid sand filter (Palomo et al., 2016 ; Kits et al., 2017 ; Pjevac et al., 2017 ; Fowler et al., 2018 ; Xia et al., 2018 ). Also, AOAs were reported to have an advantage over AOBs in oligotrophic environments like open ocean (Könneke et al., 2005 ; Prosser and Nicol, 2008 ; Santoro et al., 2008 ; Walker et al., 2010 ), while eutrophic environments (e.g., fertilized agricultural soils) favored AOBs (Jia and Conrad, 2009 ; Prosser and Nicol, 2012 ; Hink et al., 2018 ). In this study, we found that the dominant species in N4 was N. inopinata , followed by N. gargensis and N. communis at low ammonium concentrations. N. gargensis exhibited the lowest K m(app) , followed by N. inopinata and N. communis (282 and 2,553 μM total ammonium, respectively). Such relatively high numerical values of K m(app) observed may be due to higher ammonium concentrations used in this study than in previous physiological studies, and the contrast of K m(app) between N. inopinata and N. gargensis could also result from the low activity and growth of N. gargensis in our experiments, thus affecting the degree of model fitting. These results generally support the predominance of comammox at low ammonium concentrations due to its high ammonia affinity, which is consistent with previous studies that comammox preferred oligotrophic lifestyle (Palomo et al., 2016 ; Pinto et al., 2016 ; Kits et al., 2017 ; Fowler et al., 2018 ; Xia et al., 2018 ). For nitrite affinity, known NOBs are affiliated with seven genera (Daims et al., 2016 ), of which Nitrospira NOBs are considered as K strategists and Nitrobacter NOBs as r- strategists (Nowka et al., 2015 ), and coincidently, all known comammoxs belong to Nitrospira . Nevertheless, the K m (app) of N. inopinata to nitrite was 372 μM, close to that of Nitrobacter NOB (Kits et al., 2017 ), which was higher than Nitrospira NOB. In this study, we found that the K m (app) of N. moscoviensis was 228 μM nitrite, which was lower than that of N. inopinata , indicating a niche of N. inopinata and N. moscoviensis in nitrite oxidation; thus, N. inopinata may prefer higher nitrite concentrations than N. moscoviensis . Second, the ammonium/nitrite inhibition of nitrifiers may play important roles in their niche differentiation (Prosser and Nicol, 2012 ) as the growth and activity of nitrifiers were inhibited by different concentrations of FA and FNA (Anthonisen et al., 1976 ; Liu et al., 2019 ). A previous study showed that FA inhibition concentrations of AOB and NOB were 10–150 mg NH 3 -N/L and 0.1–1.0 mg NH 3 -N/L, respectively (Anthonisen et al., 1976 ). Also, the oxidation rate of Nitrosomonas and Nitrobacter decreased when FNA concentrations reached 0.10 (Vadivelu et al., 2006 ) and 0.011 mg HNO 2 -N/L, respectively, and was completely inhibited at the FNA concentration of 0.40 mg and 0.023 mg HNO 2 -N/L, respectively (Vadivelu et al., 2007 ), which are consistent with the results of this study, showing that high ammonium concentrations inhibited the growth of N. inopinata and N. moscoviensis , but no obvious inhibition was observed for N. communis . In addition, we found 2 mM ammonium would inhibit both ammonium oxidation and growth of AOA, which agrees with a previous study that shows an inhibition concentration of 3.08 mM ammonium (Hatzenpichler et al., 2008 ). Therefore, our results indicated that the ammonium/nitrite tolerance was different among these nitrifiers, which could explain the dominance of AOBs at high ammonium concentrations. Third, the specific growth rate of nitrifiers under different ammonium concentrations may provide new insights into our understanding of niche differentiation. Generally, the growth rate of nitrifiers against substrate concentrations is described by the Monod equation (Prosser and Nicol, 2012 ). Previous studies showed that AOBs had higher specific growth rates than AOAs (Prosser and Nicol, 2012 ; Terada et al., 2013 ), and comammox nitrifiers were predicted to have a lower specific growth rate than canonical ammonia oxidizers (Costa et al., 2006 ). Kits et al. ( 2017 ) reported that the maximum specific ammonia oxidation activity of N. inopinata and N. gargensis was 0.032 h −1 (37°C) and 0.028 h −1 (46°C), respectively, and the specific ammonia oxidation activity of N. gargensis was 0.014 h −1 at 37°C. In this study, we found that N. communis had the highest maximum specific growth rate, followed by N. inopinata, N. moscoviensis , and N. gargensis from N1, N2A, and N2B, respectively, which was different from the prediction of comammox (Costa et al., 2006 ). However, the maximum specific growth rates of nitrifiers were different in N4, with the highest maximum specific growth rate for N. inopinata , followed by N. communis, N. gargensis , and N. moscoviensis , indicating complex interactions among metabolically distinct nitrifiers. Such interactions and underlying mechanisms need to be further explored in future. Based on the aforementioned results and current knowledge, we developed a conceptual model to understand kinetics-driven niche differentiation among those nitrifiers. It may be generally assumed that comammox had the highest ammonia affinity, followed by non-marine AOAs and AOBs, and NOBs had higher nitrite affinity than comammox when ammonium was used as the substrate. AOBs had the highest maximum specific growth rate or activity, followed by comammoxs, NOBs, and AOAs. The growth or activity of nitrifiers was inhibited by ammonium for AOAs, comammoxs, and AOBs sequentially, and NOBs appeared to tolerate higher nitrite than comammoxs. It is also noted that nitrite produced by N. inopinata (comammox) may be converted in the periplasm (Daims et al., 2015 ), and nitrite may leak out of comammox cells for NOB growth, as observed in this study, which may relieve its ammonium/nitrite inhibition and entail NOB abundance, which were higher in N4 at high ammonium concentrations ( Figure 5B ). Also, we used the aforementioned kinetics parameters (e.g., ammonia affinity, maximum specific growth rate, and ammonium oxidation rate) to construct the niche differentiation model of nitrifiers ( Figure 6A ), predicting that comammox might be dominant at low and moderate ammonium concentrations, and AOBs at high ammonium concentrations, which was different from our hypothesis. However, the chosen temperature was not optimal for AOAs, and the tested ammonium concentrations appeared not to be sufficiently low, preventing us from observing the predominance of AOAs at the lowest ammonium concentration tested in this study ( Figure 6B ). Our results showed that NOBs would utilize nitrite leaked by comammox when ammonium was high (e.g., 2 mM). The niche differentiation of comammoxs and NOBs for nitrite oxidation with ammonium as substrate was predicted ( Figure 6C ), and NOBs would also take leaked nitrite from comammox for growth, indicating their complex interactions. These results indicated that ammonia affinity, substrate inhibition, and specific growth rates could drive niche differentiation and potential interactions among metabolically distinct nitrifiers in response to ammonium concentrations in the environment. Figure 6 Conceptual framework for kinetics-driven niche differentiation of four nitrifiers ( N. inopinata, N. gargensis, N. communis , and N. moscoviensis ) in response to ammonium or nitrite. (A) Kinetics of nitrifiers including ammonia affinity, maximum specific growth rate or activity, and substrate inhibition under a wide gradient of ammonium concentrations. Words in red represent extracellular or environmental substances, and words in yellow represent intracellular metabolic pathways for nitrification processes with ammonium as substrate. The solid lines with arrows show the direction of pathways, and the dotted line with arrows and a question mark means uncertain. The red solid lines represent the growth or activity of nitrifiers in response to substrate concentrations, and the dotted part of line of AOB is a theoretical extension for potential inhibition at high ammonium concentrations as no obvious inhibition was observed in this study. (B) Niche differentiation of N. inopinata, N. gargensis , and N. communis in response to a wide gradient of ammonium concentrations based on our study. (C) Niche differentiation of N. inopinata and N. moscoviensis in response to nitrite with ammonium as substrate. K S was represented by K m(app) in Table 2 (282, 42, 2,553, and 228 μM total ammonium or nitrite, respectively); K I of N. inopinata and N. communis was from Table 2 , and K I of N. gargensis and N. moscoviensis was obtained from the literature and modified (Hatzenpichler et al., 2008 ; Nowka et al., 2015 ). The specific growth rates of nitrifiers from N1, N2A, and N2B communities (0.064, 0.039, 0.078, and 0.048 h −1 ) were used for the Haldane model."
} | 5,201 |
37303518 | PMC10248044 | pmc | 6,582 | {
"abstract": "Large productions of plastics worldwide are greater concern to the environment because of their non degradability and thus, damaging the ecosystem. Recent advancements in biobased plastics are growing exponentially because of their promise of a sustainable environment. Biobased polycoumarates plastics have a wood-like appearance with liquid crystalline grains, light brown color, and cinnamon-like aroma, but have very low toughness. The polycoumarates were hybridized via main-chain transesterification with poly (butylene succinate) (PBS). PBS itself being a biobased material has added more value to the final product due to biodegradability. The mechanical flexibility and toughness of the bio-based copolymers were controlled by varying the PBS content. As a result, well-processable and in-soil degradable artificial woods with a high strain energy density of approximately 76 MJ/m 3 were developed while maintaining the wood-like appearance.",
"conclusion": "6 Conclusions Flexible and tough bio-based copolymers were prepared via polycondensation of biobased monomers 4HCA and DHCA in the presence of a biodegradable polyester, PBS. Spectroscopy results confirmed the formation of the aromatic copolymer poly (4HCA- co -DHCA) and copolymers with the PBS block via the acidolysis of 4HCA and DHCA with PBS esters. These copolymers may act as compatibilizers of poly (4HCA- co -DHCA) and PBS to produce homogeneous copolymers. An increase in the PBS content enhanced the thermal degradation temperature and mechanical toughness. In particular, the copolymers of poly (4HCA- co -DHCA) with 40 wt% PBS (PB4) had a high strain energy density of 76 MJ/m 3 and exhibited enzymatic and in-soil degradation. Over the course of 5 weeks, M w for sample PB4 decreased from 24,300 to 26400 g mol −1 to 14,600–16500 g mol −1 . After in-soil deterioration for 10 months, up to 10% weight loss had been observed for sample PB4, while for PLA, the control sample, there was no weight change. This durable biodegradable resin has a brown wood-like appearance and can be applied as an artificial wood material in a future sustainable society.",
"introduction": "1 Introduction Wood has attracted enormous attention because of its biodegradability, light weight, high mechanical toughness, and nontoxic chemical composition [ [1] , [2] , [3] , [4] ]. However, the requirement for material reproducibility has widened artificial forest areas with low diversity of tree species, damaging the environment. Thus, bottom-up strategies have been employed to develop wood-like materials also known as “artificially engineered wood” [ [5] , [6] , [7] , [8] , [9] ]. The wood drawbacks such as instability and limited processability have activated the field [ 10 , 11 ]. In order to produce hierarchical structures modeled after balsa wood, Brett et al. reported epoxy-based inks that enable 3D printing of cellular composites with the controlled alignment of multiscale, high-aspect-ratio fiber reinforcement. The Young's moduli of commercially accessible 3D-printed polymers, which are made from thermoplastics and photocurable resins created specifically for industrial 3D printing processes, are greater than those materials [ 12 ]. Pan et al. have developed xylem-like monoliths (XMs) with honeycomb-like penetrating microchannels using unidirectional freeze-drying approach [ 13 ]. However, the use of toxic chemicals in the production of artificial wood and its non-degradability in the environment are still problematic and thus invite alternative designs that utilize biobased bottom-up strategies to develop the artificially engineered wood [ [14] , [15] , [16] ]. We previously developed environmentally-degradable aromatic biobased polyesters using phenolic bio-monomers such as p -coumaric acid (4-hydroxycinnamic acid; 4HCA), m -coumaric acid (3-hydroxycinnamic acid), 3-methoxy-4-hydroxycinnamic acid (ferulic acid), and caffeic acid (3,4-dihydroxycinnamic acid; DHCA), showing better thermal and mechanical performances than conventional biobased polyesters [ [17] , [18] , [19] ]. In addition, the co-polyester resin had light-brown color and thermotropic liquid crystalline to show banded texture on the surface and then the appearance was more woody than real wood [ [20] , [21] , [22] ]. However, low mechanic flexibility and toughness prevented commercial application of these materials in polymer electronics with high deformation ability. At the same time, there are flexible polyesters, such as polybutylene succinate (PBS), which has an aliphatic chain derived from 1,4-butanediol and succinic acid [ [23] , [24] , [25] ]. PBS shows good biodegradability in the environment, and some of its derivatives have been registered as biodegradable polymers in marine environments [ 24 , 26 ]. PBS finds its place in various flexible polymer applications, such as foamed sheets, bottles, films, and many other disposable products [ 27 , 28 ]. Zhang combined PBS with poly (lactic acid) (PLA) to enhance the mechanical properties. Obtained polymer copolymerss showed greater elongation at break and impact strength than short block chains [ 29 ]. Platnieks et al. utilized PBS with cellulose to prepare wood-like materials to produce stabilized wood-like materials [ 30 ]. This study presents a novel approach to fabricating flexible and durable bioplastics. By incorporating PBS into the aromatic matrix of poly (4HCA- co -DHCA)s using a one-step fabrication strategy, we were able to create a final product that resembles wood in appearance. This innovation holds significant potential as a sustainable alternative to traditional wood materials and could be utilized across a wide range of industries. We believe that our development of artificially engineered woods will pave the way for exciting advancements in material science and contribute to a more sustainable future.",
"discussion": "3 Results and discussion 3.1 Preparation of copolymers Copolymerization of 4HCA and DHCA (equimolar feed composition) was performed in the presence of acetic anhydride and PBS to prepare copolymers of poly (4HCA- co -DHCA)s with PBS. One sample without PBS (PB 0) and six samples with different weight% of the PBS (PB 1, PB 2, PB 2.5, PB 3, PB 4, PB 5) were prepared, as shown in Scheme 1 . The phenolic groups of 4HCA and DHCA should react with acetic anhydride at 150 °C to produce acetylated monomers, although only a trace amount of PBS hydroxyl ends could also be acetylated to avoid affecting the reaction system. Acetylated monomers were polymerized in the presence of NaOAc as an acidolysis catalyst at 200 °C in vacuo, where the carboxylate group of the monomers attacked aliphatic esters to eliminate aliphatic carboxylates. The monomer carboxyls attacked PBS esters in the acidolysis reaction mechanism to produce two main types of block copolymers composed of two blocks: PBS and poly (4HCA- co -DHCA)s, diblock, and triblock copolymers, as shown in Scheme 1 . Although multiblock copolymers might also be formed by transesterification of the acetyl ester monomer with PBS esters, the probability is negligibly low. The excess acetic anhydride and eliminated acetic acid were evaporated under vacuum, and the viscosity of the reaction mixture gradually increased until solidification, which would not allow further stirring. Aromatic polyesters Poly (4HCA- co -DHCA)s were intrinsically immiscible with aliphatic polyester PBS, but these polyesters did not appear phase-separated during polymerization, presumably because of the role of block copolymers acting as compatibilizers. As a result, a homogeneous mesoscopic copolymers was formed, as confirmed by the SEM image ( Fig. 2 ) of the copolymers. In the reported work, a polymer blend compatibilizer introduces an interfacial active polymer that improves miscibility with the polymer blend component. The blocks can be chemically or structurally identical to a blend component, or they can include functionalities that allow chemical interactions with particular blend components, all of which promote miscibility of one block into each polymer mix component [ 29 ]. The final product was mechanically milled into powder, and the remaining acetic acid derivatives were washed with acetone two or three times before further characterization. The resulting dried powders were then cast into rectangular shapes using a hot press machine. Actual pictures of the copolymers before and after hot pressing are shown in Fig. 1 , which confirms the wood-like appearance of the copolymers. Fig. 1 Actual picture of the copolymers before and after hot press depicting the woody appearance of the copolymers. Copolymer sample a) PB 4 before hot press and copolymer samples (b–h) PB 0, PB 1, PB 2, PB 2.5, PB 3, PB 4, PB 5 after hot pressing. Fig. 1 Fig. 2 SEM image of copolymers PB 4 showing homogeneity. a) and b) are the SEM mages of the copolymer PB 4 showing the homogeneity. Fig. 2 3.2 Structural analysis of copolymers The successful syntheses of PBS and seven copolymers: PB 0, PB 1, PB 2, PB 2.5, PB 3, PB 4, and PB 5 were confirmed using 1 H NMR spectroscopy, as shown in Fig. 3 . 1 H NMR for poly (4HCA- co -DHCA) ( Fig. S1a ) showed proton signals at ranges of δ = 2.20–2.43 ppm (acetyl protons), δ = 6.60–6.82 ppm ( α -CH), δ = 7.13–7.82 ppm (aromatic protons), and δ = 7.82–8.18 ppm ( β -CH). This assignment confirmed the synthesis of poly (4HCA- co -DHCA). The detailed assignment of the branching of PBS with DHCA and 4HCA units is shown in Figs. S1(b–d) , and the 1 H NMR spectra of PB 1, PB 2, PB 3, PB 4, PB 5, and PBS (copolymers of poly (4HCA- co -DHCA) with different wt.% of PBS). The molar amount of acetyl end groups, C acetyl , should be equal to C DHCA + 1, where C DHCA is the moles of DHCA, in this hyperbranching system from the AB 2 monomer. If the polymerization degree is sufficiently high, C acetyl is almost equal to C DHCA . Representative 1 H NMR and calculations of the molar fraction of PBS incorporated into the copolymers material for sample PB 5 are shown in Fig. S2 . The molar fractions of PBS ( W PBS NMR ) incorporated into the copolymers material of HCA and DHCA were calculated from the integral signal intensities ( I ) in the 1 H NMR of the respective copolymers materials using the following formula, and the actual values are summarized in Table 1 . W P B S = I P B S I H C A + D H C A + I P B S × 100 Fig. 3 1 H NMR spectra of poly (4HCA- co -DHCA) with PBS. Fig. 3 Table 1 Molar fractions of PBS in copolymers materials with HCA and DHCA. Table 1 Samples W HCA (mol %) W DHCA (mol %) W PBS (mol %) W PBS a (mol %) PB 0 50.0 50.0 0 0 PB 1 45.0 45.0 10.0 9.0 PB 2 40.0 40.0 20.0 11.4 PB 2.5 37.5 37.5 25.0 16.3 PB 3 35.0 35.0 30.0 26.5 PB 4 30.0 30.0 40.0 37.3 PB 5 25.0 25.0 50.0 46.1 PBS 0 0 100.0 100.0 a WPBS is calculated using NMR using the integral strength ratio of PBS and poly (HCA- co -DHCA). The calculated mole fractions of PBS are slightly lower than the theoretical values, which is possibly caused by the formation of oligomeric degrades that were removed by reprecipitation. The functional groups of the resulting copolymers were further analyzed using FTIR, as shown in Fig. 4 . Aromatic and aliphatic ester linkages (C \n \n\n<svg xmlns=\"http://www.w3.org/2000/svg\" version=\"1.0\" width=\"20.666667pt\" height=\"16.000000pt\" viewBox=\"0 0 20.666667 16.000000\" preserveAspectRatio=\"xMidYMid meet\"><metadata>\nCreated by potrace 1.16, written by Peter Selinger 2001-2019\n</metadata><g transform=\"translate(1.000000,15.000000) scale(0.019444,-0.019444)\" fill=\"currentColor\" stroke=\"none\"><path d=\"M0 440 l0 -40 480 0 480 0 0 40 0 40 -480 0 -480 0 0 -40z M0 280 l0 -40 480 0 480 0 0 40 0 40 -480 0 -480 0 0 -40z\"/></g></svg>\n\n O) were detected in the wavenumber range of 1700–1765 cm −1 for all the copolymers. Moreover, C C vibrations for the aromatic and vinylene groups were observed in the wavenumber range of 1530–1600 cm −1 for all the copolymers except the aliphatic PBS. Aromatic/aliphatic esters were not detected. Fig. 4 FTIR spectrograms for copolymers of poly (4HCA- co -DHCA) with PBS. Fig. 4 3.3 Crystallinity of copolymers The X-ray diffraction patterns of PB 0, PB 1, PB 2, PB 2.5, PB 3, PB 4, PB 5, and PBS are shown in Fig. 5 a. PB 0 shows a broad peak around a diffraction angle 2θ = 22.0°, indicating an amorphous phase. At the same time, PBS showed two intense diffraction peaks at 22.9° and 19.9°, indicating crystalline polymers ( Fig. 5 b). For the copolymers, it was clearly observed that the crystalline peaks became sharper and more intense as the PBS content increased. The relative crystallinity was estimated quantitatively from XRD measurements using the approach used by Nara and Komiya et al. [ 31 ] The crystallization degrees of the copolymers increased from 5 to 30% with the PBS content and were almost linear ( Fig. 6 ). Fig. 5 X-Ray diffractograms (a) for all the copolymers samples, and (b) enlarged view for all the samples in the 2θ: 16–28°. Fig. 5 Fig. 6 Plot representing crystallinity with respect to change in the weight % of PBS. Fig. 6 This tendency indicates the homogeneity of all copolymers irrespective of the composition. The liquid crystalline behavior was also studied for all the copolymers samples. It was found that the copolymers sample without PBS, that is, PB 0, showed liquid crystallinity, which decreased with an increase in the PBS wt.% in the polyester copolymers, as shown in Figure S4 . In Figure S4 , Cross-polarised images of different bio copolymers were observed at 220 °C using crossed-polarizing microscopy. Cross-polarised microscopy revealed that the copolymer samples melted at particular temperatures to demonstrate a thermotropic liquid crystalline phase. The schlieren texture indicative of a nematic state was observed for samples PB0, PB1, PB2, PB3, PB4 but PBS showed spherulitic morphology. 3.4 Thermal properties The thermal properties of poly (4HCA- co -DHCA), PBS, and their copolymers were analyzed using TGA and DSC. Fig. 7 clearly shows that PBS has a higher thermal degradation temperature than poly (4HCA- co -DHCA) because of the relatively low stability of the vinylene group in 4HCA and DHCA [ [32] , [33] , [34] ].However, the char yield of poly (4HCA- co -DHCA) was much higher than that of PBS owing to its aromaticity. For the copolymers, the thermal degradation temperatures, T d1 , T d5 , and T d10 , increased with the weight percentage of PBS in the copolymers. The T g values of the copolymers were lower than that of poly (4HCA- co -DHCA) and higher than that of PBS, as listed in Table 2 and DSC curves shown in Figure S3 . Figure S3 describes the second heating cycle of DSC run. It can be clearly seen that on increasing the PBS wt. % in copolymer matrix, the T g value (first shoulder downwards) is shifting to lower temperatures. It means that the material becomes softer and more flexible. Thus, a decrease in T g can lead to increased flexibility, and elongation at break. The T g of the copolymers ranged from 66 to 124 °C, which is higher than those of previously reported biodegradable polymers [ 35 ]. Fig. 7 Thermal degradation properties of poly (4HCA- co -DHCA- co -PBS). Fig. 7 Table 2 Thermal degradation and mechanical properties of poly (4HCA- co -DHCA- co -PBS). Table 2 Samples T g b (°C) T d0 b (°C) T d5 b (°C) T d10 b (°C) X c c (%) M n × 10 5 M w × 10 5 PDI σ a (MPa) E a (GPa) ε a Φ (MJ/m 3 ) Ref. PB 0 124 164 254 270 5 3.1 6.7 2.1 50 ± 0.3 116 ± 1.2 0.6 2 ± 0.6 * PB 1 78 186 221 270 7 3.2 6.6 2.0 19 ± 0.7 10 ± 0.3 0.5 2 ± 0.6 * PB 2 66 201 234 270 9 3.4 6.4 1.9 22 ± 0.5 12 ± 0.3 0.3 7 ± 1.0 * PB 2.5 66 204 240 272 12 4.1 6.4 1.9 41 ± 0.9 28 ± 0.8 0.2 18 ± 3.0 * PB 3 66 238 279 300 17 4.0 7.0 1.7 158 ± 3.3 90 ± 1.8 1.5 69 ± 11.5 * PB 4 67 238 285 300 20 4.5 6.9 1.5 125 ± 2.1 81 ± 0.9 1.3 76 ± 16.0 * PB 5 67 240 290 310 24 4.4 7.0 1.6 199 ± 1.1 111 ± 1.4 – – * PBS −47 317 356 368 30 9.1 2.3 3.9 – – – – * PHB 5 – – – – – – – 35.0 4.0 – – [ 36 ] PLA 52.5 – – – 1.4 – – – 42.0 2.0 – – [ 37 ] PC 157 – – – – – – – 55 2.0 – – [ 38 ] PCL −54 – – – 43.7 – – – 24 0.3 – – [ 39 ] Silicon rubber −123 – – 441 – – – – 8 – – 200 [ 40 ] Raw bamboo – – – – – – – – 135 9 – – [ 41 ] Sitka spruce – – – – – – – – 67 8 – – [ 42 ] Douglas-fir LVL – – – – – – – – 68 13 – – [ 43 , 44 ] Bleached bamboo – – – – – – – – 77 10.3 – – [ 41 ] *This work. a The mechanical properties were measured by a stress–strain bending test. ‘ σ ’ ‘ E ’ ‘ ε ’ and ‘ Φ ’ refer to flexural strength, flexural modulus, maximal strain and strain energy density respectively. b T g , and T d10 values are measured using DSC and TGA, respectively. c X c (%) values are measured using XRD. The copolymers were not soluble in most of the solvents used here as shown in Table S1 . Pentaflurophenol and TFA/DCM (1/5 v/v%) were identified as the good solvents for all the copolymers with different PBS composition. Solubility data of all the monomers, and copolymers samples in various solvents were summarized in Table S1 . 3.5 Mechanical properties Three-point bending analysis was performed on poly (4HCA- co -DHCA), PBS, and their copolymers. PB 5 and PBS test specimens were too soft to show the relevant data in the bending mode. Sample PB 0 is a very rigid material, as can be inferred from the mechanical properties ( Table 2 ) but lacks flexibility. To induce flexibility, PBS was combined as a flexibility inducer at different wt.% with 4HCA and DHCA. The flexural strength ( σ ), modulus ( E ), and maximal strain ( ε ) of the polyester copolymers materials are shown in Fig. 8 a. As shown in Fig. 8 b, the mechanical strength of the resulting copolymers materials increased with the PBS wt.%, which in turn resulted in high flexural modulus ( Fig. 8 c) of these copolymers materials. As a result, it was found that the flexibility and toughness of the copolymers were controlled by PBS composition ( Fig. 8 d). Here, we can emphasize that an appropriate composition of PBS drastically increased the toughness of the copolymers resins to 76 MJ/m 3 , approaching that of the silicone rubber. Such a high level of toughness was attributed to the appropriate structural balance of rigid polyphenols and flexible PBS, possibly compatibilized by the block copolymers. The flexural strength and modulus increased as the PBS unit ratio increased, demonstrating that the crystalline phase formed from the PBS unit increases the hardness of the PB 5 copolymer. This finding suggests that the poly (4HCA- co -DHCA) unit disrupts the crystallinity of the PBS unit and provides great flexibility to PBS, a hard plastic [ 26 ].Strain energy density data was calculated based on the mechanical behavior of all the copolymers samples, as shown in Fig. 8 and Table 2 . Fig. 8 (a) Schematic of the rectangular samples used for the 3-point bending analysis, (b) Stress-Strain curve for the copolymers. (c) Flexural strength ( σ ) and flexural modulus ( E ) of the copolymers with respect to PBS compositions. (d) Strain energy density ( Φ ) of the copolymers with respect to PBS composition. Fig. 8"
} | 4,782 |
39990417 | PMC11844431 | pmc | 6,583 | {
"abstract": "2-mercaptoethanesulfonate (Coenzyme M, CoM) is an organic sulfur-containing cofactor used for hydrocarbon metabolism in Archaea and Bacteria. In Archaea, CoM serves as an alkyl group carrier for enzymes belonging to the alkyl-CoM reductase family, including methyl-CoM reductase, which catalyzes methane formation in methanogens. Two pathways for the biosynthesis of CoM are present in methanogenic archaea. The initial steps of these pathways are distinct but the last two reactions, leading up to CoM formation, are universally conserved. The final step is proposed to be mediated by methanogenesis marker metalloprotein 16 (MMP16), a putative sulfurtransferase, that replaces the aldehyde group of sulfoacetaldehyde with a thiol to generate CoM. The assignment of MMP16 as CoM synthase (ComF) is not widely accepted as deletion mutants have been shown to grow without any CoM dependence. Here, we investigate the role of MMP16 in the model methanogen, Methanosarcina acetivorans . We show that a mutant lacking MMP16 has a CoM-dependent growth phenotype and a global transcriptomic profile reflective of CoM-starvation. Additionally, the ΔMMP16 mutant is a CoM auxotroph in sulfide-free medium. These data reinforce prior claims that MMP16 is a bona fide ComF but point to backup pathway(s) that can conditionally compensate for its absence. We found that L-aspartate semialdehyde sulfurtransferase (L-ASST), catalyzing a sulfurtransferase reaction during homocysteine biosynthesis in methanogens, is potentially involved in genetic compensation of the MMP16 deletion. Even though, both, L-ASST and MMP16 are members of the COG1900 family, site-directed mutagenesis of conserved cysteine residues implicated in catalysis reveal that the underlying reaction mechanisms may be distinct. Altogether, we have provided concrete evidence that MMP16 is the primary ComF in methanogenic archaea.",
"conclusion": "Conclusions Here we have demonstrated that MMP16 is the primary Coenzyme M synthase (ComF), both throughout the full diversity of alkane-metabolizing archaea through comparative genomics, as well as through in vivo physiologic and transcriptional investigations in the model methanogen M. acetivorans . This work, in combination with a prior report that MMP16 expressed in E. coli resulted the formation of CoM ( 8 ), strongly supports the notion that MMP16 homologs are the main source of CoM produced in methanogenic archaea. The non-essentiality of MMP16, particularly at high sulfide concentrations, remains incompletely resolved. The participation of L-ASST as an alternate coenzyme M synthase seems likely, but we cannot rule out a significant contribution from a non-orthologous enzyme or an uncatalyzed reaction between sulfoacetaldehyde and sulfide in the strongly reducing environment of the methanogen cytoplasm. Future studies on the structure and catalytic activity of MMP16 enzymes in vitro will be required to develop a more complete understanding of enzymology of the COG1900 family.",
"introduction": "Introduction 2-mercaptoethanesulfonate (Coenzyme M, CoM) is the smallest organic cofactor, consisting of just two carbons joining thiol and sulfonate functional groups. CoM is used in the metabolism of alkenes and alkanes, in bacteria and archaea, respectively. The most broadly distributed and ecologically relevant function of CoM is to act as a methyl-carrier for the final step of methanogenesis in archaea ( 1 ). In this role, the thiol moiety of CoM receives a methyl group either from methyl-tetrahydrosarcinopterin, or directly from a methylated growth substrate via substrate-specific methyl-transferase enzymes. Methyl-Coenzyme M Reductase (MCR), an enzyme unique to methane-metabolizing archaea, reduces Methyl-CoM using Coenzyme B (CoB) to generate methane and the heterodisulfide of CoM and CoB (CoM-S-S-CoB). In anaerobic methanotrophic and alkanotrophic archaea, the net flux of substrates through MCR homologs is in the reverse direction, leading to the consumption of methane or other short chain alkanes. There are presently three known biosynthetic pathways for CoM, two in methanogenic archaea and one in bacteria ( Fig. 1 ). The bacterial pathway for CoM biosynthesis has been completely determined in Xanthobacter autotrophicus Py2 and is distinct from the two versions found in methanogenic archaea ( 2 ). The initial steps of the two archaeal pathways vary and use either phosphoenolpyruvate ( 3 ) or L-phosphoserine ( 4 ) as the starting substrate ( Fig. 1 ). Both archaeal pathways converge on sulfopyruvate as a common biosynthetic intermediate, which is decarboxylated by ComDE to sulfoacetaldehyde. In the final step, the aldehyde group of sulfoacetaldehyde is likely replaced by a thiol to produce CoM ( 3 ). Almost all enzymes involved in CoM biosynthesis have been characterized, with the notable exception of the last step in methanogenic archaea i.e. the enzymatic conversion of sulfoacetaldehyde to CoM. This reaction is chemically analogous to the conversion of aspartate semialdehyde to homocysteine by L-aspartate semialdehyde sulfurtransferase (L-ASST) during methionine biosynthesis ( Fig. 1 ). In the model methanogen, Methanosarcina acetivorans , L-ASST comprises two subunits encoded by MA1821 and MA1822 ( 5 ). MA1821 contains a COG1900 domain thought to be responsible for the aldehyde sulfurtransferase reaction, while MA1822 is a small ferredoxin-containing protein likely involved in electron supply for the reaction ( 6 ). Like L-ASST, methanogenesis marker 16 metalloprotein (MMP16)—a protein family broadly distributed in methanogenic archaea—contains both a COG1900 domain and a ferredoxin domain. This observation led to the initial hypothesis that MMP16 homologs mediates the final step of Coenzyme M biosynthesis in methanogenic archaea ( 7 ). In support of this hypothesis, it was recently reported that Escherichia coli can convert sulfoacetaldehyde to CoM when the MMP16 homolog from Methanocaldococcus jannaschii (MJ1681) is introduced on a plasmid ( 8 ). Based on this evidence, MMP16 was assigned as the putative Coenzyme M synthase and designated ComF. However, two independently derived pieces of genetic evidence complicate what would otherwise appear to be a straightforward functional assignment of MMP16 homologs to the final step of CoM biosynthesis. First, a comprehensive transposon mutagenesis screen in Methanococcus maripaludis recovered mutants with a disruption in the MMP16 homolog (Mmp1603) on minimal media lacking exogenous CoM ( 5 , 9 ). Second, it was reported (via personal communication in ( 8 )), that a clean Mmp1603 knockout strain can grow without any CoM-dependence. Since CoM is an integral component of MCR, which is vital for energy metabolism in methanogens, one would expect the enzymes involved in CoM biosynthesis to also be essential. One possible explanation for this conundrum is genetic compensation by L-ASST i.e. this enzyme can compensate for ComF in its absence ( 8 ). Another possibility is that sulfoacetaldehyde may react slowly with sulfide to produce CoM in an uncatalyzed reaction as has been reported in vitro ( 10 ). That said, given the abundance and conservation of MMP16 homologs across the extant diversity of methanogens, it is unlikely that its function is completely redundant to that of another biosynthetic enzyme. Taken together, the role of MMP16 in Coenzyme M biosynthesis, if at all, and its evolutionary connection to the rest of methane metabolism warrants further investigation. Here we revisit the role of MMP16 in CoM biosynthesis and methanogen physiology. First, through a comparative genomics approach we show that MMP16 co-occurs with other genes involved in CoM biosynthesis across all MCR-containing genomes, supporting its association with CoM biosynthesis through vast evolutionary time. We then show that a mutant of M. acetivorans lacking MMP16 can grow, but with a substantial fitness cost, under standard laboratory conditions and is a CoM auxotroph only when exogenous sulfide is eliminated from the growth media. We explored transcriptional profile of this mutant under various conditions and observed substantial genetic compensation occurs upon loss of MMP16 and is alleviated with the addition of exogenous CoM. Finally, complementation experiments with various MMP16 mutants improved our understanding of the biochemical function of the COG1900 family. Taken together, our results provide strong evidence that MMP16 (or ComF) is a bona fide Coenzyme M synthase."
} | 2,129 |
38793405 | PMC11123197 | pmc | 6,584 | {
"abstract": "A thermoelectric generator (TEG) is one of the important energy harvesting sources for wearable electronic devices, which converts waste heat into electrical energy without any external stimuli, such as light or mechanical motion. However, the poor flexibility of traditional TEGs (e.g., Si-based TE devices) causes the limitations in practical applications. Flexible paper substrates are becoming increasingly attractive in wearable electronic technology owing to their usability, environmental friendliness (disposable, biodegradable, and renewable materials), and foldability. The high water-absorbing quality of paper restricts its scope of application due to water failure. Therefore, we propose a high-performance flexible waterproof paper-based thermoelectric generator (WPTEG). A modification method that infiltrates TE materials into cellulose paper through vacuum filtration is used to prepare the TE modules. By connecting the TE-modified paper with Al tape, as well as a superhydrophobic layer encapsulation, the WPTEG is fabricated. The WPTEG with three P–N modules can generate an output voltage of up to 235 mV at a temperature difference of 50 K, which can provide power to portable electronic devices such as diodes, clocks, and calculators in hot water. With the waterproof property, the WPTEG paves the way for achieving multi-scenario applications in humid environments on human skin.",
"conclusion": "4. Conclusions In summary, a waterproof paper-based wearable thermoelectric generator (WPTEG) for collecting low-grade thermal energy from the human body and serving as a power supply for portable devices has been demonstrated. The paper-based generator is composed of TE-modified paper which was prepared by using a vacuum filtration process and Al electrode connection. Such a device has a high Seebeck coefficient of 5.14 mV·K −1 , and a WPTEG with three units of N–P modules could obtain a maximum output power of ~3.32 nW at a ΔT of 40 K. After the superhydrophobic layer encapsulation, the formed WPTEG can be used in high humidity and underwater environments, and the performance is not infected basically. Moreover, the WPTEG presents excellent high-temperature resistance and good stability. Eventually, the device can provide stable power for various portable electronics in water environments, revealing huge potential in practical applications, such as underwater use.",
"introduction": "1. Introduction Wearable electronics are growing rapidly because of emerging applications in different scenarios, especially in human health monitoring, intelligent robots, and human–machine interaction [ 1 , 2 , 3 ]. As an energy source, conventional power modules have serious defects such as frequent charging, replacement, and maintenance. For example, micro batteries with limited power cannot provide long-lasting energy for wearable electronic devices, and the environmental pollution and unexpected explosions also need to be addressed [ 4 , 5 ]. Flexible generators with wearability, sustainability, and eco-friendliness can effectively convert various ambient energies to electricity. At present, the common power supply devices used for energy collection and conversion include flexible solar cells [ 6 , 7 ], piezoelectric generators [ 8 , 9 ], triboelectric nanogenerators, and thermoelectric generators (TEGs) [ 10 , 11 , 12 , 13 ]. Solar energy is the most abundant renewable resource, but solar cells are subjected to weather conditions in practical applications. Piezoelectric generators and triboelectric nanogenerators often require continuous motions of the human body. As a constant temperature heat source, the human body can continuously provide a constant temperature for TEGs. TEGs can collect low-grade heat through the temperature difference between the human body and the surrounding environment [ 14 , 15 ]. In order to develop flexible TEGs, various types of substrates (e.g., textiles, PDMS, and hydrogels) have been proposed [ 16 , 17 , 18 ]. Paper-based flexible thermoelectric generators (PTEGs) have attracted great interest due to their flexibility, low cost, abundant resources, biocompatibility, and environmental friendliness. For example, Li et al. designed a paper-based TEG based on multi-walled carbon nanotubes/carboxylated nanocellulose, which has excellent mechanical flexibility and thermoelectric performance [ 19 ]. Kim et al. developed a foldable TEG based on the solution-processed carbon nanotube buckypapers with high power generation efficiency and high-level integration [ 20 ]. These paper-based TEGs have high flexibility and efficient energy collection. However, once applied to the skin for a long time, paper-based TEGs often experience power generation performance degradation owing to the inevitable sweaty penetration. Therefore, there is an urgent need to address the issue of unavoidable liquid effects. Many additional materials (e.g., PDMS, PI, and PTFE) are proposed to package paper devices [ 21 , 22 , 23 ]. Nevertheless, this packaging can also restrain the thermoelectric performance of the devices to a certain extent. Consequently, it is necessary to develop more efficient preparation techniques to solve the problems of sweaty penetration and water failure for paper-based flexible thermoelectric generators. Here, we proposed a waterproof paper-based thermoelectric generator (WPTEG) with high power generation performance, excellent stability, and water resistance. Such a device is fabricated by infiltrating thermoelectric materials (Bi 2 Te 3 doped with Se and Sb 2 Te 3 doped with Bi) into a cellulose paper matrix through vacuum filtration. After a superhydrophobic layer encapsulation, it possesses excellent resistance to water permeability as well as environmental disturbances, as shown in Figure 1 a. In addition, the water resistance property enables the device to operate in various operational environments, such as a sweating body or wet weather, as depicted in Figure 1 b,c. Moreover, the device composed of strip-shaped TE papers and an Al electrode can form a woven structure to apply to the human body ( Figure 1 d) [ 24 ]. The obtained WPTEG with three P–N modules can generate 235.76 mV at a temperature difference of 50 K. Furthermore, owing to the superhydrophobic layer encapsulation, the device can provide stable power for various portable electronics in a water environment. Constructing a waterproofing thermoelectric generator based on cellulose paper substrate provides a facile and practical approach to solving the failure problem of thermoelectric generators when in contact with water and sweat.",
"discussion": "3. Results and Discussion 3.1. Preparation of PTEGs and WPTEGs Figure 2 a illustrates a simple strategy for preparing the N–type- and P–type-modified cellulose papers and the PTEG. The TE dispersions of the N–type and P–type are prepared by dispersing each TE powder and carboxymethylcellulose sodium (CMC-Na) in deionized water. The N–type material is Bi 2 Te 3 doped with Se, while the P–type material is Sb 2 Te 3 doped with Bi. This is achieved by infiltrating TE materials into a cellulose paper matrix to fabricate N–type- and P–type-modified cellulose papers. By heating and drying at 60 °C for 2 h, the two modified papers are cut into the same strips (4 mm × 30 mm), serving as TE legs. Subsequently, these legs are alternately connected to the Al foil to form a conductive path. By now, the paper-based thermoelectric generator (PTEG) is successfully prepared. After the superhydrophobic layer encapsulation, the flexible WPTEG is finally fabricated. Figure 2 b shows the pictures of N–type- and P–type-modified cellulose papers after drying. In addition, the modified papers have excellent flexibility and can get really close contact with curved surfaces, as shown in Figure 2 c. The fabricated WPTEG is revealed in Figure 2 d, which comprised three pairs of N–P modules. Moreover, due to the flexibility and tenacity of cellulose paper, a complex woven structure can be formed, as shown in Figure 2 e. 3.2. Characterization of Modified Cellulose Papers Cellulose paper composed of adjacent and interlaced cellulose fibers exhibits lightweight, flexibility, hydrophily, and porosity and can be used for wearable devices. The surface morphology of cellulose paper is characterized by scanning electron microscopy (SEM), as shown in Figure S1 . Figure 3 a,b show the SEM images of N–type-modified paper. During the vacuum filtration, part of the N–type TE particles is deposited on the paper’s surface and adhered to the cellulose fibers, and the others penetrated deep into the gaps between fibers. The N–type TE particles adhered to the surface of the paper are adjacent to each other to form conductive pathways, and the particles in the fiber gaps serve as conductive supplements. As illustrated in Figure 3 c–f, Energy Dispersive Spectrometer (EDS) elemental mappings are performed to further analyze the distributions of N–type TE particles on cellulose paper. Since the N–type material is Bi 2 Te 3 doped with Se, the distributions of Te and Bi are highly overlapped, as shown in Figure 3 d,e, while Se is distributed discretely in gaps of Bi 2 Te 3 . It could be seen that the N–type TE particles are spread roughly evenly across the cellulose paper. SEM images of P–type-modified paper are shown in Figure 3 g,h. Similarly, P–type TE particles are deposited on the surface and gaps of cellulose fibers, forming the conductive pathways. Moreover, the EDS elemental mappings, as shown in Figure 3 i–l, illustrate the overlapped Te and Sb, as well as relatively small amounts of Bi. The EDS results of N–type and P–type TE papers are depicted in Figure S2 , implying that the atomic percentage of Bi:Te:Se is approximately 8.34:9.85:31.12 and Sb:Te:Bi is approximately 18.63:26.86:3.04. Figure 4 a–c show the XPS peak-differentiation-imitating results for the Te 3d, Bi 4f, and Se 3d peaks of N–type-modified paper before superhydrophobic treatment. The binding energies for Te 3d 5/2 and Te 3d 3/2 are 575 eV and 585.3 eV, and the binding energies for Bi 4f 7/2 and Bi 4f 5/2 are 158.2 eV and 163.5 eV, respectively. The experimental values for the binding energies are close to the reported value [ 25 , 26 ]. The binding energy of Se 3d at 54.1 eV is consistent with the data reported in the literature [ 27 ]. Figure 4 d–f show the XPS peak-differentiation-imitating results for the Te 3d, Sb 3d, and Bi 4f peaks in P–type-modified paper before superhydrophobic treatment. The binding energies for Te 3d 5/2 and Te 3d 3/2 are 576.3 eV and 586.6 eV, and the binding energies for Bi 4f 7/2 and Bi 4f 5/2 are 158.7 eV and 164.4 eV, respectively. The experimental values for the binding energies are close to the reported values [ 25 , 26 ]. The binding energies for Sb3d 5/2 and Sb3d 3/2 are 530.5 eV and 539.7 eV with a separation of 9.2 eV, and the position and separation of these two peaks are close to the reported value [ 28 ]. Figure S3 shows the XRD images of N–type and P–type-modified paper, respectively. The XRD peaks of the samples match well with the peaks of previously published single crystal samples [ 29 , 30 , 31 ]. These results confirmed the existence of the Te, Bi, and Se elements in N–type-modified paper, as well as the Te, Sb, and Bi elements in P–type-modified paper. 3.3. Performance of the PTEGs In order to achieve a PTEG with a high voltage output, we investigated the weight percentages of TE materials in cellulose paper and the numbers of N–P modules, respectively. As shown in Figure 5 a–c, for one unit of the N–P module, the open circuit voltage of PTEGs constantly increases with a rise in the weight percentages of TE materials (45% to 67%) and temperature differences (ΔT). When the weight percentages are the same, the open circuit voltage of PTEGs increases with the number of N–P modules. Thus, the performance of a PTEG is determined by the weight percentages of TE materials and the number of N–P modules, and the open circuit voltage of a PTEG reaches ~235.76 mV with a weight percentage of 67% and ΔT of 50 K. A TEG using similar materials can also achieve an output voltage of 200 mV [ 32 ]. We have attached a video showing a voltage above 200 mV. The open circuit voltages of PTEGs with different units and weight percentages at a ΔT of 30 K are extracted, as shown in Figure 5 d. When the weight percentages are 45%, 56%, and 67%, PTEGs with three N–P modules generate open circuit voltages of 125.79 mV, 152.12 mV, and 166.83 mV, respectively. The large weight percentage allows more active materials to participate in power generation, thereby increasing the generated voltage. The Seebeck coefficient ( S ), which is defined as the change rate of the thermoelectric potential with temperature variation, is the key parameter that influences the thermoelectric performance. The S can be defined by Equation (1): (1) S = d V d T \nwhere V is the open circuit voltage and T is the temperature. The S of PTEGs with different weight percentages and units can be calculated using the slope of a dV versus dT plot by linear fitting [ 33 ]. The Seebeck coefficients of the flexible PTEGs are summarized in Figure 5 e. When the weight percentages of modified paper are 45%, 56%, and 67%, the S value is 4.03 mV·K −1 , 4.9 mV·K −1 , and 5.14 mV·K −1 , respectively. In addition, we also tested the Seebeck coefficient values of individual N–type and P–type thermoelectric materials using a self-made heating and signal acquisition system, as shown in Figure S4 . As shown in Figure S5 , the maximum Seebeck coefficient for a single N–type TE leg and a single P–type TE are −789 μV·K −1 and 798 μV·K −1 , respectively. In order to observe the thickness of the deposited thermoelectric materials, we measured the height of the different content of thermoelectric materials deposited on cellulose paper using a step gauge. The thickness variation of the TE materials with different weight percentages is shown in Figure S6 . The thickness of the deposited layer of modified paper also increases with the weight of the thermoelectric materials. As shown in Figure 5 d and Figure S12 , as the thickness of the thermoelectric materials increases, more active materials participate in power generation, resulting in a higher voltage output and an increase in the Seebeck coefficient. Figure S7 shows SEM images of the cross-section of N–type- and P–type-modified cellulose paper. We use a microtome to cut the modified paper to observe the cross-section, and the cutting process causes the modified paper to deform and thin. However, it can still be observed from the cross-sectional SEM images that the mass percentage of the TE material increases, and the thickness of the modified paper also increases. As it turns out, the obtained S value is impressive by comparing with the previous reports [ 34 ]. Figure 5 f shows the variations of the external circuit voltage and output power of a PTEG with three modules as the current changes. The value of output power is the product of an external circuit voltage and current, and can be defined by Equation (2): (2) P = ( U 0 R i + R l ) 2 · R l \nwhere U 0 is the open-circuit voltage of the TE device, R i is the internal resistance of the PTEG, and R l is the load resistance of the circuit. The current in the circuit decreases and the voltage of the resistance increases as the load increases. The maximum output power is 1.03 nW, 2.12 nW, and 3.32 nW, when the ΔT is 20 K, 30 K, and 40 K, respectively. Also, the TE performance of a PTEG is much higher than that of previously reported flexible TEGs [ 35 , 36 , 37 , 38 , 39 , 40 , 41 ] ( Figure S8 ). Therefore, the PTEG at the nanowatt level is a promising candidate power supply device to be applied to low-power wearable chips in the future [ 42 ]. The prepared PTEG can be applied to human skin for power generation by utilizing the temperature difference between the ambient air and the body. As shown in Figure S9a , the prepared PTEG attached to the human arm can generate an open-circuit voltage of ~13.85 mV. When encountering rainy days or sweating, the performance of PTEGs is badly affected due to the super water absorption of cellulose paper, as shown in Figure S9b . 3.4. Characterization and Performance of the WPTEGs Figure 6 a shows the SEM images of P–type-modified paper after superhydrophobic treatment. The inset suggests that the superhydrophobic coating is compounded from micrometer-size clusters and aggregates. Figure 6 b shows the EDS diagrams of superhydrophobic P–type-modified paper. Aside from the Te, Bi, and Sb elements, a small amount of F element is detected. The content of each element is shown in Figure S10 . It is widely known that cellulose paper is superhydrophilic and has a contact angle of <5° ( Figure S11 ). The wettability of N– and P–type-modified papers is similar to that of cellulose paper, as shown in Figure S12a . Meanwhile, the contact angle of the modified paper turns into ~152.4° after superhydrophobic treatment, indicating the water resistance of the modified paper. Figure S13 shows that the modified paper, before superhydrophobic treatment, rapidly absorbs water and curls up in contact with the solution and reaches saturation, while the modified paper, after superhydrophobic treatment, isolated the water from the device and maintains the morphology and stiffness after 10 min, reflecting the waterproof properties and adaptability to humidity or water. Figure S14 shows that the WPTEG has good flexibility. In Figure S12b , the XPS peak-differentiation-imitating result for F 1s on superhydrophobic P–type-modified paper also demonstrated the presence of fluorine groups. To verify the water resistance, the voltage variation rates of the PTEG and WPTEG in different humidities are measured, as illustrated in Figure 6 c. With the increase in humidity, the voltage variation of the PTEG is very obvious. When the relative humidity was close to 100%, the voltage change rate reaches over 50% at ΔT of 20 K, implying that the PTEG is highly affected by humidity. Instead, the WPTEG exhibited a relatively stable voltage output with a change rate of less than 5%, illustrating its excellent water-resisting property. The inset of Figure 6 c shows the humidity chamber. Also, after 50 wet–dry cycles, the changes in the Seebeck coefficient and internal resistance are less than 5% and 4% when the humidity reached ~100%, as shown in Figure S15 . The slight changes in the internal resistance and Seebeck coefficient indicate that the modified paper has high stability, waterproofing, and durability. To illustrate the stability of the device, the WPTEG is baked in an oven for 30 min at different temperatures (100 °C, 150 °C, 200 °C, 250 °C). With the increase in the baking temperature, the voltage variation rate remained basically stable (within 2%), as depicted in Figure 6 d. Meanwhile, the contact angle still maintains over 150°, resulting in excellent high-temperature resistance (red line in Figure 6 d). Figure 6 e shows the open-circuit voltage of the PTEG and WPTEG with different units at different ΔT. The results illustrate that the superhydrophobic coating rarely affects the performance of the WPTEG. Additionally, the mechanical stability of the WPTEG is explored. As shown in Figure 6 f, after 500 bending cycles, the changes in the Seebeck coefficient and internal resistance are less than 10% and 6% with a bending angle of ~120°. This small change in the internal resistance and Seebeck coefficient indicate that the modified paper has good stability and durability, and has promising application prospects in various heat source surfaces. Subsequently, the output voltages of the PTEG and WPTEG attached to dry and wet arms are acquired. As shown in Figure S12c , the output voltage of the PTEG on a dry arm is 13.85 mV (PTEG-1), while the voltage of the PTEG on a wet arm is 2.27 mV (PTEG-2). This means that PTEGs are unable to maintain a stable and good power supply capability under wet conditions. For the WPTEG on a dry arm, an output voltage of 12.23 mV is achieved (WPTEG-1). When the WPTEG is on a wet arm, the output voltage is 11.85 mV (WPTEG-2). The output voltage of the WPTEG on a wet arm is slightly smaller than that on a dry arm since a small amount of water seeped into the paper. Nevertheless, the performance of the WPTEG is demonstrated to be excellent. The results from what have been discussed above suggest that the WPTEG has potential applications in energy collection on the human body, particularly on sweat or humid skin. 3.5. Applications of the WPTEGs The WPTEGs can not only convert human heat into electrical energy, but also collect waste heat from hygrothermal or water environments. As shown in Figure 7 c, two hot water droplets placed on one side of the WPTEG generate an output voltage of 12.3 mV. Moreover, droplets always keep a spherical shape due to the water resistance of the WPTEG, as depicted in Figure 7 b. Figure 7 a shows the infrared image of the two droplets with a maximum temperature difference of ~28 K between the droplet and air. We drip the left droplet and the right droplet in sequence, resulting in a lower temperature of the left droplet compared with the right one. Figure 7 d illustrates a WPTEG pasted onto the surface of a beaker filled with hot water, and an output voltage of 39.18 mV is achieved. It indicated that the WPTEG has excellent mechanical flexibility and is suitable for the energy collection of various complex curved heat sources. Figure 7 e shows the WPTEG and PTEG are immersed in water at ~100 °C, respectively. The output voltage of the WPTEG is 33.31 mV, while that of the PTEG is 0 mV. Many devices lose performance in an underwater environment. To verify that the WPTEG can harvest thermal energy from underwater environments, we simulate a scenario where thermal energy is harvested in an underwater environment. As shown in Figure S16a , we stick the WPTEG to our wrist and insert it into cold water (25 degrees, 40 s). The output voltage of the WPTEG is 4.94 mV. Also, we stick the WPTEG onto a hose filled with hot water (100 °C) and immerse the hose deep into cold water. The output voltage of the WPTEG is 10.33 mV, as shown in Figure S16b . The results show that the WPTEG is still able to harvest thermal energy from the human body, water pipes, and other heat sources in underwater environments. Prior to application, the interface is tightly encapsulated so that the WPTEG is not affected by short circuits in the water. Accordingly, the WPTEG as a wearable power source has huge potential in practical applications, such as underwater use. Furthermore, a woven WPTEG can be obtained owing to the pliability of the paper. Figure 7 f shows an intersectant woven structure with three N–P modules that are set on the arm on a rainy day. Meanwhile, an output voltage of 5.97 mV is obtained, illustrating the applicability of the WPTEG in various weather environments. In recent years, flexible generators have been considered a promising power source for wearable electronic devices, overcoming traditional batteries’ shortcomings, such as frequent charging and environmental pollution. A reliable method is to use a power management circuit to regulate the voltage, and then directly integrate the generator with functional electronic devices to form a self-powered microsystem. In this work, the fabricated WPTEG is used to power portable electronic devices (diodes, clocks, and calculators) to verify their feasibility as wearable power sources ( Figure 7 g–i). Due to its output performance at the millivolt level, a PTEG cannot directly drive ordinary electronic devices. Therefore, we use a power management circuit to boost the output voltage. In practical applications, the WPTEG is placed in hot water as the power source, and the output voltage from the WPTEG is amplified to light up the diode and power the clock and calculator, illustrating the reliability and availability of the power generation of the WPTEG in an underwater environment."
} | 6,043 |
22708076 | null | s2 | 6,585 | {
"abstract": "A device containing a 3D microchannel network (fabricated using sacrificial melt-spun microfibers) sandwiched between lithographically patterned microfluidic channels offers improved delivery of soluble compounds to a large volume compared to a simple stack of two microfluidic channel layers. With this improved delivery ability comes an increased fluidic resistance due to the tortuous network of small-diameter channels."
} | 105 |
24855324 | null | s2 | 6,586 | {
"abstract": "Recent work in ecology suggests that the diversity of responses to environmental change among species contributing to the same ecosystem function can strongly influence ecosystem resilience. To render this important realization more useful for understanding coupled human-natural systems, we broaden the concept of "
} | 78 |
37560665 | PMC10407216 | pmc | 6,587 | {
"abstract": "The chemical structure of lignite plays a fundamental role in microbial degradation, which can be altered to increase gas production. In this study, the structural changes in lignite were analyzed by conducting pretreatment and biomethane gas production experiments using crushing and ball milling processes, respectively. The results revealed that different particle size ranges of lignite considerably influence gas production. The maximum methane yield under both treatments corresponded to a particle size range of 400–500 mesh. The gas production after ball milling was higher than that after crushing, irrespective of particle size. Compared with lignite subjected to crushing, that subjected to ball milling exhibited more oxygen-containing functional groups, less coalification, more disordered structures, and small aromatic ring structures, demonstrating more unstable properties, which are typically favorable to microbial flora for the utilization and degradation of lignite. Additionally, a symbiotic microbial community comprising multiple species was established during the microbial degradation of lignite into biogas. This study provides new insights and a strong scientific foundation for further research on microbial lignite methanation.",
"conclusion": "4 Conclusions For the lignite treated by crushing and ball milling, the gas production after microbial degradation was found to be related to the particle size. The gas production reaches the highest at 400−500 mesh, and the biogas (methane) reaches 21.00, 28.87 mL/2 g lignite respectively. The structure of lignite after the two treatments was different. The increase in oxygen-containing functional groups and small aromatic ring structures in the lignite after the ball milling treatment leads to instability of the lignite structure and decrease in the coalification degree, which was conducive to the degradation and utilization of lignite by microorganisms. Therefore, the biogas production of lignite was improved under these pretreatment methods. The pretreatment of lignite can promote its degradation by microbial flora via destroying the structure of lignite. Some basic analyses conducted in this study provides reference data for future research of the degradation mechanism and to promote new ideas of microbial degradation of lignite.",
"introduction": "1 Introduction Coal biogasification is a crucial step towards achieving the goals of clean use, energy savings, and emission reductions from coal [ 1 ]. It is the focus of microbial enhanced coal bed methane (MECBM) mining. With the development and utilization of coal bed methane (CBM) resources, a deeper understanding of biogenic CBM has been gained. Many scholars have successfully simulated biogas generation in coal seam under laboratory conditions [ [2] , [3] , [4] ]. Scott proposed the concept of bio-enhanced CBM production for biogasification [ 5 ]. The microbial flora obtained from abandoned coal mines can be introduced into a refractory coal-containing matrix, which can then be degraded into methane and carbon dioxide gases under appropriate conditions [ 6 , 7 ]. The methane produced during coal biogasification provides a safe, efficient, and inexpensive source of energy. Research on anaerobic microbial degradation of subbituminous coal [ 8 ] demonstrates that organic intermediates related to gas production mainly include long-chain fatty acids, alkanes, and various low-molecular weight aromatic hydrocarbons (including phenols) [ 9 ]. Methanogenic flora obtained from subsurface reservoirs can produce methane from more than 30 types of methoxyl aromatic compounds [ 10 ] through oxygen demethylation, carbon dioxide reduction, and acetyl-CoA metabolism. Physical, chemical, and biological measures can be used to increase biological methane production by promoting the hydrolysis of organic materials in coal [ 11 , 12 ]. The small molecular organic matter in coal, particularly in low-rank coal, is readily degraded by bacteria [ [13] , [14] , [15] ]. The hydrolysis of coal to soluble organic intermediates is the limiting step for the conversion of coal to biomethane [ 1 , 16 ]. To date, efforts have been made in the biodegradation of lignite, although mostly from the perspective of microbial flora [ 17 , 18 ]; however, the impact of the structure of lignite on biodegradation has rarely been studied. There have been studies of the biological gas production after simulated lignite pretreatment, revealing subsequent increase in gas production following pretreatment [ 12 , 19 , 20 ]. However, there are still some gaps in the research, such as insufficient in-depth analysis on the mechanism controlling the increase in gas production. Therefore, in this study, the pretreatment methods of crushing and ball milling were used to treat lignite to analyze the mechanism that stimulates gas production. After the treatment, the lignite was subjected to microbial degradation and gas production experiment. The primary contributions of this research were an examination of structural differences between lignite processed using the two methods, a discussion of the factors both external and internal to the coal-to-methane conversion, and the provision of reference results for the development of coal biogasification pretreatment technology. Our research has substantial theoretical and practical implications for the exploration and development of coalbed methane resources because it can provide the groundwork for a better understanding of the biogas generation mechanism of coal seams and subsurface microbial gasification mining of coal.",
"discussion": "3 Results and discussion 3.1 Characterization of gas production The gas products were sampled after the lignite microbial degradation experiment ran for 15 days and analyzed subjectively qualitatively and quantitatively. Fig. 1 displays the outcomes of microbial breakdown of lignite over particle size ranges following the two treatments, which shows that the gas production contains two gases: methane and carbon dioxide, and the degradation gas production results of lignite with different particle sizes are different. Initially, the gas production increases with increasing particle size range to a maximum value and then decreased with increasing particle size range. The coal samples after the two treatment methods show the same gas production law in different particle size range. As the particle size range decreased, the degree of surface fragmentation of lignite increases; the smaller the particles of lignite, the higher the degradation rate and gas production of methanogens. On the other hand, lignite particles in the medium with smaller particle size ranges may agglomerate, reducing the contact surface between bacteria and lignite particles and, consequently, gas generation. In conclusion, the degradation of lignite under different treatment methods induced the highest gas production within the same particle size range (400−500 mesh). Fig. 1 Total gas production of the lignite biodegradation at different particle size range. (a) Crushing pretreatment; (b) Ball milling pretreatment; (c) Comparison of two treatments with same particle size range. Fig. 1 After pretreatment with crushing and ball milling, the highest gas production after microbial degradation of lignite reached 39.90 mL and 50.90 mL, respectively, in which the methane content was 21.00 mL and 28.87 mL ( Fig. 1 (a and b)). The comparison results of two pretreatments in the five particle size ranges are presented in Fig. 1 (c), in which the gas production after ball milling was higher than that after crushing, which had nothing to do with particle size. Raw coal and microbial flora were both held constant throughout all trials to ensure consistency. Each experiment had three parallel samples to reduce the influence of error. However, the biogas results of lignite after ball milling treatment were better after microbial degradation, which was more conducive to the utilization of microorganisms. It has been hypothesized that the structure of lignite had been changed. Therefore, the coal samples after different pretreatments were characterized to determine if the treatments caused structural changes to the lignite. 3.2 X-ray diffraction analysis The XRD spectra of the lignite samples that were crushed and ball milled are displayed in Fig. 2 (a). The characteristic peaks of the treated samples were essentially the same, among which, the presence of extremely disordered amorphous carbon elements in the coal samples was evidenced by the evident and highly intense diffraction peaks of kaolinite and quartz. There were two pronounced peaks at the 002 and 100 diffraction peak band, with diffraction angles of 26° and 47°, respectively. Diffraction peaks for aromatic microcrystalline structure (002 band) and fatty carbon microcrystalline structure (γ band) were observed. Comparatively, there were minimal difference in the peak type, area and height between the two pretreated coal samples. The crystalline structure of the coal samples was barely affected by the pretreatment procedures, while the coal samples after ball milling are more easily degraded with microorganisms, and the biogas yield was higher than that of the other one. Therefore, it was speculated that there were differences in functional groups or some structures between the two coal samples. Fig. 2 XRD spectra and FTIR spectra of samples. (a) XRD spectra; (b) FTIR spectra. Fig. 2 3.3 Fourier transform infrared spectroscopy analysis 3.3.1 Peak fitting of infrared spectra of coal Generation of spectral peak superposition at certain positions in the infrared spectrum is relatively easy; determining the location and boundary of absorption peaks is challenging due to the presence of absorption bands from numerous functional groups in coal. Sample FTIR spectra are displayed in Fig. 2 (b). Spectral lines between 3600 and 3000 cm −1 have been attributed to coal’s primary functional groups—hydroxyl, aliphatic, and oxygen-containing—in prior research [ 25 ]. The analysis of the abovementioned functional groups is discussed below. 3.3.1.1 Spectral peak fitting of hydroxyl groups The hydroxyl group on coal molecules was the most important for forming hydrogen bonds in the absorption vibration region, with wavenumber 3000–3600 cm −1 . The hydroxyl group may establish several different types of hydrogen bonds with various hydrogen bond receptors. The baseline corrected FTIR diagram revealed a significant spike near the wavenumber at 3620 cm −1 , which typically occurs when the steric hindrance cannot form hydrogen bonds or the hydrogen bonds formed are very weak; most scholars ascribe this peak to the vibration of free hydroxyl in coal [ 26 ], while others ascribe it to water (water of crystallization) in clay minerals (kaolin) [ 27 ]. Considering lignite’s high ash content, the researchers hypothesized that the peak around the wavenumber at 3620 cm −1 was due to water in the silicate minerals. Coal molecules' primary functional group for forming hydrogen bonds was the hydroxyl group, which may form several hydrogen bond types with various hydrogen bond receptors. According to the classification of hydrogen bonds formed by hydroxyl groups in coal [ 26 ], the spectrum of this region includes six types: free hydroxyl hydrogen bonds (3620 cm −1 ); hydrogen bonds (3560 cm −1 ), with π electrons on an aromatic ring (OH-π); hydrogen bonds formed by self-associating hydroxyl groups (near 3428 cm −1 ); oxygen-hydrogen bonds occurring in OH-ether formed from the interaction with ether (near 3300 cm −1 ); hydrogen bonds forming ring structure (near 3218 cm −1 ); and OH–N bonds (near 3098 cm −1 ) [ 28 ]. Six Gaussian peaks were selected for fitting according to the above classification. The results of the peak fitting were displayed in Fig. 3 (a,b). As a result of its low degree of coalification and high concentration of oxygen-containing functional groups, lignite is particularly susceptible to the influence of hydrogen bonding of various types. These properties enable methanogenic bacteria to more effectively use lignite to produce methane. The peak fitting results indicated that the self-associating hydroxyl group hydrogen bond contributed the most to the total hydrogen bond concentration in coal. Fig. 3 FTIR spectra fitting curve of samples in different wavenumber ranges. (a, b) 3600−3000 cm −1 ; (c, d) 3000−2800 cm −1 ; (e, f) 1800−1000 cm −1 . Fig. 3 Fig. 4 TG and DTG curves of different coal samples. (a) crushing pretreatment; (b) ball milling pretreatment. Fig. 4 3.3.1.2 Spectral peak fitting of aliphatic hydrocarbons Coal’s infrared spectrum was observed to lie in the region of 3000–2800 cm −1 , which corresponds to the aliphatic C–H bond absorption vibration zone. Baseline correction of the FTIR figure ( Fig. 2 (b)) reveals two major peaks at around 2855 and 2925 cm −1 , which were attributed to the symmetric and antisymmetric stretching vibration of the saturated hydrocarbon methylene, respectively [ 27 ]. The symmetric stretching vibration of methyl groups attached directly to oxygen atoms was shown to be responsible for the peaks at 2871 and 2955 cm −1 wavenumbers [ 29 ], whereas the antisymmetric stretching vibration of methyl groups was found to be responsible for the shoulder peak at 2825 cm −1 . Additionally, the stretching vibration of methylidyne was linked to a peak location about 2890 cm −1 within the spectrum of symmetric and antisymmetric stretching vibration of methylene. As a result, six Gaussian peaks were chosen for fitting from this area, and the outcomes are depicted in Fig. (c) and (d) . According to the data, aliphatic hydrocarbons predominated as long chains with few side chains in the treated lignite, which had more methylene and less methyl and methylidyne. 3.3.1.3 Spectral peak fitting of oxygen-containing functional groups Coal’s oxygen-containing functional groups mostly consist of hydroxyl, carboxyl, carbonyl, and ether oxygen, with hydroxyl groups having already been studied in Section 3.3.1.1 . The other three types were distributed in the wavenumber region of 1800–1000 cm −1 ; this regional spectrum is relatively complex as it also includes deformation vibration of methyl and methylene and stretching vibration of aromatic carbon-carbon double bond. For this purpose, peak fitting was performed on a set of 18 Gaussian peaks [ 22 ], and the results are displayed in Fig. 3 (e) and (f). The C–O–C stretching vibration in Ar-O-C and Ar-O-Ar was shown to be responsible for the peak at 1034 cm −1 in the spectral distribution. It was determined that the 1200 cm −1 peak in the carbon oxygen bond stretching vibration was due to the stretching vibration of the phenolic hydroxyl group (C 6 H 5 –OH). The fundamental characteristic of lignite is the stretching vibration peak at 1700 cm −1 , which is due to the carboxylic acid functional group (-COOH). The carbonyl group stretching vibration peak occurred at 1340 cm −1 . Using acetic acid as substrate, methanogenic bacteria can convert it to methane by deoxymethylation. The existence of oxygen-containing functional groups in lignite is more conducive to the occurrence of microbial fermentation degradation process. The following parameter calculation proves this point. 3.3.2 Analysis of infrared structural parameters Compared with the crushed lignite, the I 1 and I 2 values of lignite treated by ball milling increased significantly ( Table 2 ), which could be attributed to three potential cases: an increase in the fatty chain or content of oxygen-containing functional groups, or a decreased in the aromatic hydrocarbons; the low carbonization of coal is more conducive to microbial degradation [ 30 , 31 ]; the I 3 was almost unchanged, indicating that there was an elevated levels of oxygen-containing functional groups when lignite was treated by ball milling; this provided a more conducive condition for biodegradation, and thus promoted gas production [ 32 ]. Table 2 FTIR parameters of coal samples. Table 2 Samples A 2924 A 2964 A 1703 A 1745 A 1618 I 1 I 2 I 3 Crushing pretreatment 34.825 17.242 3.768 2.626 8.661 2.020 0.738 0.697 Ball milling pretreatment 42.703 16.339 3.604 2.661 7.661 2.614 0.818 0.680 3.4 Flammability testing Coal samples after both processes are treated and their TG and DTG curves are showed in Fig. (a,b) and combustion response characteristics are listed in Table 3 . The ignition temperature was the most affected of the four stages of coal sample combustion, however there was no statistically significant difference between the two treatment procedures. The lignite ignition point after ball milling treatment was 315.89 °C, which decreased by 4.92% compared that after crushing. The R w of lignite after crushing was 3.117, while that of lignite after ball milling increased by 8.53%, indicating that ball milling was not conducive to the combustion stability of coal. As the ignition point is strongly related to the degree of coalification, the decreased ignition point after ball milling indicates that the degree of coalification was lower. Infrared study shows that when the amount of oxygen-containing functional groups in coal increases during oxidation, its ignition point drops. Having them on hand aids in the biodegradation of lignite by microorganisms and boosts biogas output. Table 3 Combustion performance parameters of coal samples. Table 3 Samples T i (°C) T p (°C) (dw/dt) max (S −1 ) R w Crushing pretreatment 332.22 352.28 0.04248 3.117 Ball milling pretreatment 315.89 346.57 0.04313 3.383 3.5 Raman analysis First-order Raman spectra for highly soluble carbon compounds like low-rank coal or coal coke were fitted with a 10-band Gaussian distribution [ 33 ]. Fig. 5 (a,b) shows that the majority of the 10 fitting peaks occurred at 1540 cm −1 (GR), 1465 cm −1 (V), and 1380 cm −1 (VR), all of which are characteristic of methylene or methyl structures, in particular those containing a small aromatic ring. Furthermore, aromatic ring vibration was found to be more responsible for the G-peak band [ 34 ]. C–C bonds inside aromatic rings, especially those with more than six rings but fewer than the number of carbon atoms in graphite, were found to be primarily responsible for the D-peak band. In addition, the coal structure was responsible for the appearance of spectral lines at 1700 (GL), 1230 (SL), 1185 (S), 1060 (SR), and 960 (R) cm −1 . Fig. 5 Raman spectra fitting curve of samples. (a) Crushing pretreatment; (b) Ball milling pretreatment. Fig. 5 The Raman spectra of carboniferous materials had two noticeable peaks: the D-peak (1327−1360 cm −1 ) and G-peak (1550−1598 cm −1 ). D-peak refers to planar defects or disordered structures between infrastructural units [ 35 ]. The G-peak arises from the aromatic ring of aromatic carbonaceous materials, which forms the ordered structure. As shown in Table 4 , the D-peak and G-peak intensities of ball-milled coal samples were lower than those of crushed coal samples. This happened because the ball milling operation used up some of the carbon sources. A reduction in the lignite ratio ( I D / I G ) after ball milling generally leads to an increase in the ordered structure. In the process of ball milling, more refers to the disordered structure represented by the D-peak is stripped. Although the ordered structure represented by G-peak consumed, it consumed to a lesser degree, which was indicated by the slight increase in the peak height, indicating that it is difficult to alter the ordered structure of lignite. Corresponding to biogas production from microbial degradation of the two types of lignite, the increase in the disordered structure promoted the biodegradation and subsequent biogas production. Table 4 Raman fitting data of coal samples. Table 4 Samples D-peak G-peak I D /I G (A) Max height Area intg Max height Area intg Crushing pretreatment 6288.13 816502.82 8382.90 1088504.54 0.75 Ball milling pretreatment 5246.74 598388.43 8463.04 965206.30 0.62 The usual structure of amorphous carbon materials is represented by the peaks labelled GR, VL, and VR between the D-peak and the G-peak. Specifically, it alludes to the compact aromatic ring complexes that have between three and five condensed rings [ 33 ]. An easy way to quantify the huge aromatic ring system and aromatic rings often present in amorphous carbon is to compare the ratio of the highest peak of the D-peak to the sum of the peak heights of the three peaks. Table 5 reveals shows that after ball milling, the structure of small aromatic rings in lignite increased, while the structure of large aromatic rings decreased. The ball milling treatment caused the disordered structure to become increasingly unstable, which was conducive to the degradation of lignite by microbial flora, thus promoting gas production. It also corresponds to the gas production after ball milling was higher than that after crushing treatment in the previous test. Table 5 Raman fitting data of coal samples. Table 5 Max height D G R V L V R specific value Crushing pretreatment 6288.13 3562.07 2508.53 1008.17 0.89 Ball milling pretreatment 5246.74 3281.22 2400.34 1318.84 0.75 3.6 Microbial community analysis The results of diversity analysis of the microbial community are shown in Fig. 6 , showing the structure of microbial communities from kingdom, phylum, order, and genus, respectively. Fig. 6 (a) shows the microbial diversity and distribution in sample at the kingdom level. The microbial community composition was dominated by bacteria, whose relative abundance was 95%, much higher than that of archaea (4%). The flora was taxonomically attributed to 13 phyla in addition to unclassified and other phyla (<0.4%). The core bacteria were Firmicutes , Bacteroidetes , and Proteobacteria , the core of archaea was Euryarchaeota ; the sum of the abundance of core bacteria accounted for more than 80%. Fig. 6 Microbial diversity and distribution in sample at the kingdom, phylum, order, and genus level. (a) kingdom; (b) phylum; (c) order; (d) genus. Fig. 6 Fig. 6 (c) shows the microflora at the order classification level in the degradation process. Clostridiales and Bacteroidales accounted for more than 45% and had an important position. Methanosarcinales were methanogens detected at the level. Methanosarcinales produced methane by decomposing acetic acid and consuming hydrogen as the main metabolic pathways. Methane was mainly produced by metabolizing formate, alcohol, and carbon dioxide under the action of Methanosarcinales [ 36 ]. Twenty-three genera were identified in addition to unclassified and other genera (<0.5%) ( Fig. 6 (d)). The core genera were acidogenic bacteria that could metabolize organic matter, among which Bacteroides could degrade nitrogenous heterocyclic macromolecules such as pyridine and indoles to form low weight molecule organic acids and alcohols. Formate dehydrogenase produced in cells of some genera decomposed formic acid into carbon dioxide and hydrogen [ [37] , [38] , [39] ]. Some strains of Clostridium used carbon dioxide to produce acid [ 40 ] and their existence confirmed the source of carbon dioxide in the gas production. Sedimentibacter fermented amino acids to produce acetic acid and butyric acid, and other bacteria could convert 4-hydroxybenzoic acid and 3, 4-dicarboxylic acid into phenol and catechol by reversible dihydroxylation [ 41 ]. The archaea were primarily Methanocorpusculum and Methanosarcina , which played an important role and were the main functional bacteria that produce methane. The former accounted for approximately 97% of the archaea and was the dominant flora, it could form methane in three ways [ 42 , 43 ]. After several passages, the biogas degradation of lignite by microbial flora is relatively stable, and the number of methanogens in the flora is always changing [ 44 ]. In this study, Methanosarcina is the dominant bacterium, which may be because it has a self-protection mechanism through exopolysaccharide, thus possessing a certain tolerance to adversity and less affected [ 45 , 46 ]. In the biodegradation process of lignite, there was a synergistic effect among microbial communities, and some microbiota can utilize metabolites from each other. The biodegradation of lignite is a progressive process. Therefore, the structure destruction pretreatment of lignite is conducive to its utilization by microorganisms."
} | 6,127 |
40000663 | PMC11861253 | pmc | 6,589 | {
"abstract": "Coalbeds have the potential as geobioreactors for producing renewable natural gas from biomass derived from photosynthesis. This brings about a number of benefits, including support for sustainable energy and the sequestration of carbon dioxide in coal. In this study, freshwater bloom algae were employed as the substrate to examine the influence of hydrothermal and hydrothermal-alkaline pretreatment on methane production using an inoculum from an anaerobic digester. The morphology and chemical structures of the biomass, as well as the volatile fatty acids (VFAs) in the liquid fraction of the post-treatment and gas production, were analyzed to understand their relationship with the efficacy of methane yields and changes in microorganisms. The results revealed that both hydrothermal and hydrothermal-alkaline pretreatment, under the right conditions, can lead to an increase in methane production. Particularly, a pretreatment condition of 0.2 mol/L NaOH at 150 °C for 30 min resulted in a significant increase in methane yield by up to 303.9%. The addition of NaOH facilitated the hydrothermal-alkaline pretreatment, effectively destroying the cell structure of the bloom algae, promoting the dissolution of intracellular sugars and other substances, and reducing the loss of VFAs caused by heating. Moreover, hydrothermal-alkaline pretreatment was found to support the growth of acetoclastic methanogens and enhance methane production by mitigating pH drops. Overall, the results of this study suggest that hydrothermal-alkaline pretreatment offers significant advantages in methane production compared to hydrothermal pretreatment. These findings have important implications for harnessing bloom algae as a viable source for generating renewable natural gas.",
"conclusion": "Conclusions The present study evaluated the effectiveness of hydrothermal pretreatment and hydrothermal-alkaline pretreatment in enhancing methane production from bloom algae. In a 69-day gas production experiment, the effects of pretreatment conditions on methane production by anaerobic fermentation of bloom algae were investigated, and the changes of liquid components as well as structural changes in solids were analyzed. The results indicate that both pretreatments can increase methane yields, but to various degrees. The most effective conditions were found to be the use of 0.2 mol/L NaOH and heating at 150 °C for 30 min, resulting in a 303% increase in methane production. Analyses have shown that both hydrothermal pretreatment and hydrothermal-alkaline pretreatment can effectively alter the cell structure of the bloom algae and promote the dissolution of intracellular carbohydrates and other substances. Additionally, an appropriate increase in the concentration of NaOH in the hydrothermal-alkaline pretreatment can reduce the loss of VFAs during the pretreatment process and slow down the decrease of pH, which is beneficial to methane production. These results indicate that hydrothermal-alkaline pretreatment offers significant advantages in methane production compared to hydrothermal pretreatment. The findings in this study have implications for utilizing bloom algae as a source for the production of renewable natural gas.",
"introduction": "Introduction Although approximately 95% of the vast 21 trillion-ton coal resources are not mineable, they can be explored for many other applications including the production of renewable natural gas with carbon sequestration 1 , 2 . Photosynthesis-derived biomass including plants and algae has been reported for such endeavors 3 – 5 . Eutrophication in freshwater can result in excessive growth of algae and the formation of algal bloom, causing harm to aquatic organisms due to the reduction in dissolved oxygen and the increase of turbidity 6 . Despite various physical, chemical, and biological methods being deployed to control the occurrence of algal bloom, their efficacy has been inconsistent 7 . Conventional methods are typically associated with intensive labor and high costs 7 , 8 . On the other hand, algae are widely regarded as a promising source for biofuels because of their highly efficient biological machinery in the conversion of solar energy to biomass 9 . Algae are readily available and not restricted by geographical and seasonal factors 10 . They can fix atmospheric CO 2 into carbohydrates, lipids, and proteins with versatile food sources 11 . Therefore, the utilization of algal resources particularly bloom algae for renewable natural gas production has multifaceted environmental and socio-economic benefits. A range of microalgae have been evaluated for their potential in methane production 12 . Among them, bloom algae have been found to be particularly promising substrates in terms of methane conversion rate. However, the cell walls of algae can impede the dissolution and hydrolysis of intracellular organic matter, such as carbohydrates and proteins, leading to low conversion rates by microorganisms. This has greatly limited the prospects of industrial application 13 . A pretreatment is usually needed in this regard. Previous studies have reported that hydrothermal and chemical methods (acid, alkali) can be used to pretreat microalgae biomass for the production of value-added products 14 . Hydrothermal pretreatment can greatly improve the solubility of microalgae cells, but high temperature can lead to the formation of melanoidin, an inhibitor to anaerobes 15 . Acid and alkaline pretreatment can disrupt cell walls and intracellular components, reducing the polymerization and crystallinity of polymers. However, the effectiveness of these methods depends on cell wall composition and intracellular components of the microalgae species 16 , 17 . Despite extensive investigation into the anaerobic fermentation of algal biomass in recent years, most studies have focused on a single-factor variable, with multi-factor pretreatment conditions for biogas production being poorly reported. In addition, the effect of pretreatment methods on the subsequent anaerobic processes is also not well understood, limiting the potential for using bloom algae for biomethane production. The hydrothermal treatment and anaerobic digestion of algal biomass have been extensively studied, with numerous reports highlighting the significant impact of single-factor variables on process efficiency and product yields. Temperature is one of the most influential factors, as it affects the hydrolysis, depolymerization, and solubilization of organic matter. Previous studies have demonstrated that increasing hydrothermal treatment temperatures can enhance the solubilization of algal biomass, leading to higher biogas yields during subsequent anaerobic digestion 18 – 20 . Similarly, the concentration of biomass and the pH conditions during hydrothermal treatment have been shown to affect the composition and properties of the hydrolysate, which in turn influences the methane production potential 21 , 22 . In the context of anaerobic digestion, the substrate-to-inoculum ratio is a critical parameter that dictates the microbial activity and stability of the digestion process 23 . Research indicates that an optimal substrate-to-inoculum ratio is essential for maximizing methane production while preventing process inhibition 24 . Additionally, the hydraulic retention time (HRT) and organic loading rate (OLR) are crucial operational parameters that need to be optimized to ensure efficient digestion and high biogas yields 25 , 26 . The interplay between these single-factor variables underscores the complexity of optimizing hydrothermal treatment and anaerobic digestion processes for algal biomass. This study aimed to evaluate the potential of using bloom algae as a substrate for methane production with an inoculum sourced from a food brewing facility. The experiments were conducted with various hydrothermal and hydrothermal-alkaline pretreatments, taking into consideration the effects of treatment temperature, duration, and alkaline concentrations. The characteristics of the treated biomass and the liquid fractions were analyzed both before and after gas production of the optimal pretreatment conditions. In addition, the study aimed to shed light on the main treatment parameters responsible for the differences in methane production potential and the reduction in microbial diversity. By exploring the impact of different pretreatment conditions on the bloom algae, the goal of this study was to provide insights that could help improve the production of methane from this renewable source.",
"discussion": "Discussion The observed darkening color of the algal solution during pretreatment can be related to the Maillard reaction in the pretreatment system 57 . This reaction, which consumes dissolved proteins and carbohydrates, is thermodynamically favorable at high reaction temperatures (70 ~ 180 °C) and strong alkaline conditions 58 . This could well explain why the COD values were lower for hydrothermal-alkaline than hydrothermal pretreatment because of the presence of NaOH (Fig. 1 ). In addition, the hydrothermal-alkaline condition could significantly promote the Maillard reaction, producing by-products such as melanoidins from reducing sugars and proteins 59 . Overall, hydrothermal pretreatment was more effective than hydrothermal-alkaline pretreatment with respect to COD. The surface morphology and structure of bloom algae biomass can undergo changes after pretreatment and microbial attacks (Fig. 5 ). Hydrothermal pretreatment tends to break weak chemical bonds such as hydrogen bonds on the surface of the algal cells. The dissolution of the extracellular polymeric substances can cause the lysis of cells that results in partial dissolution and exhibits in shattered fragments 60 . In hydrothermal-alkaline pretreatment, in addition to cell lysis, saponification reactions also occur, breaking down the components of cell walls and membranes (including cellulose and hemicellulose) and reducing the extent of fiber crystallinity 17 . After biodegradation, the roughness of algae increased further, forming honeycomb-like structures with numerous holes on the surface. This implies intensive microbial attacks during methane production. The increases in porous structure and roughness after pretreatment may increase the specific surface area of the biomass, especially in the hydrothermal-alkaline pretreatment, highlighting the importance of alkali in this process. As a result, the accessibility of algal biomass to microbial attachment and attacks can be greatly increased 61 . Concomitantly, the biodegradation further changes the structure of the biomass and enhances the dissolution of the inclusions in the algae cells and hence the increase in gas production. Klebsiella is a crucial microbe in fermentation-based industries, known for its ability to effectively hydrolyze a variety of monosaccharides and disaccharides to produce H 2 and succinic acid, with glucose being the most efficient substrate 51 , 62 , 63 . This microbe can also utilize acetic acid, furfural, and 5-hydroxymethylfurfural as substrates 64 , 65 . The Maillard reaction of proteins and sugars at high temperatures can produce fermentation inhibitors, including 5-hydroxymethylfurfural, which is a favorable product in alkaline environments 59 . The FT-IR analysis (Fig. 6 ) has shown a C = O structure at 1651 cm − 1 , which can be consolidated to 5-hydroxymethylfurfural C = O 38 , 39 . The intensity of the peaks of pretreatments was higher than the control, implying the formation of 5-hydroxymethylfurfural, which Klebsiella can convert to furoic acid and eventually to 2-oxy-glutaric acid, participating in the tricarboxylic acid cycle 66 . The intensity of the peaks in the treated samples decreased after incubation, corresponding to the increased abundance of Klebsiella . This observation may explain why methane production with bloom algae biomass treated with high temperature was not inhibited significantly in the microcosms. After pretreatment, the pH of controls, hydrothermal pretreatment, and hydrothermal-alkaline pretreatment were 5.7, 5.2, and 8.7. The production of VFAs has contributed to the acidity of the samples while in the hydrothermal-alkaline pretreatment, the excess NaOH served as a neutralizing agent to increase the pH to above 7. Although the decomposition temperatures of formic acid, oxalic acid, and malic acid are high (306.8, 190, and 180 °C), the acid condition may facilitate the decomposition of some VFAs 63 , 67 . However, VFAs with low boiling temperatures may be lost due to volatilization in high-treatment temperatures (100 and 150 °C). On the other hand, the condition of hydrothermal-alkaline pretreatment was not conducive to the decomposition of chemically unstable VFAs due to its elevated pH. Both treated samples also contained formic and acetic acids, which are direct substrates for methanogenesis (Fig. 7 ). This may have facilitated the reduction in lag time in gas production. After 20 days of incubation, all eight VFAs were detected in microcosms of control and hydrothermal pretreatment. This was due to the degradation of carbohydrates and other algal biomass 68 . Whereas, in the microcosms with hydrothermal-alkaline pretreatment, some VFAs including lactic, tartaric, and succinic acids were not detected throughout the incubation. In general, VFAs with high carbon numbers were not detected or present in low concentration upon the termination of incubation while low-carbon-number intermediates such as formic and acetic acids were found accumulated. This was not anticipated and the reason for this accumulation is unclear. Pearson correlation analysis revealed that there was a strong negative correlation between methane production and the total concentration of formic, acetic, and oxalic acids (ρ=-0.925). The concentrations of these acids were lower in the hydrothermal-alkaline pretreatment sample, suggesting the treatment has advantages over hydrothermal-only treatment in methane production. Another factor that could account for the high methane yield in hydrothermal-alkaline pretreatment is the concentration of Na + . Previous studies have reported that an increase in Na + concentration within a certain range can promote the growth of acetoclastic methanogens and increase methane production 69 , 70 . The carryover Na + from hydrothermal-alkaline pretreatment was found to be beneficial for the growth of Methanosarcina using acetic acid for methane production 71 , as inferred by its high abundance in the microcosms (Fig. 9 d). Its continuous consumption of acetic acid has mitigated the accumulation of acids and slowed down the pH drop which can reduce methane production. This agrees well with the VFAs analysis and gas production. Studies have shown that algae could be used to produce methane reliably 72 . However, there are challenges associated with production. For instance, pH levels can affect the gas production from algae, but the adverse effects can be reduced by adjusting the pH with a buffering agent. In addition, the process of utilizing bloom algae for methane production involves energy-intensive heat treatment. Studies have shown that algae can be treated at 100 °C for 1 h for cost-saving purposes on an industrial-scale 73 . In this study, the pretreatment condition of 0.1 mol/L NaOH, 30 min, 100 °C is within the range and has also been shown to improve methane yields by as much as 237.5%. In terms of the acquisition of bloom algae, there are two main types of cultivation methods 72 . The first involves utilizing open ponds for bloom algae cultivation, benefitting from their rapid growth rate and higher economic returns. The second method employs photobioreactors for the cultivation. Considering the significant biomass requirements for production and application, the primary focus can be on using bloom algae from lakes, supplemented with open-air pond cultures. This approach not only fulfills our gas production needs but also addresses the pressing issue of water bloom pollution, maintaining a balanced water environment and preserving biodiversity 74 ."
} | 4,038 |
38079538 | PMC10805106 | pmc | 6,590 | {
"abstract": "A universal biochemical\nsignal for bacterial cell–cell\ncommunication\ncould facilitate programming dynamic responses in diverse bacterial\nconsortia. However, the classical quorum sensing paradigm is that\nGram-negative and Gram-positive bacteria generally communicate via\nhomoserine lactones (HSLs) or oligopeptide molecular signals, respectively,\nto elicit population responses. Here, we create synthetic HSL sensors\nfor Gram-positive Bacillus subtilis 168 using allosteric LuxR-type regulators (RpaR, LuxR, RhlR, and\nCinR) and synthetic promoters. Promoters were combinatorially designed\nfrom different sequence elements (−35, −16, −10,\nand transcriptional start regions). We quantified the effects of these\ncombinatorial promoters on sensor activity and determined how regulator\nexpression affects its activation, achieving up to 293-fold activation.\nUsing the statistical design of experiments, we identified significant\neffects of promoter regions and pairwise interactions on sensor activity,\nwhich helped to understand the sequence–function relationships\nfor synthetic promoter design. We present the first known set of functional\nHSL sensors (≥20-fold dynamic range) in B. subtilis for four different HSL chemical signals: p -coumaroyl-HSL,\n3-oxohexanoyl-HSL, n -butyryl-HSL, and n -(3-hydroxytetradecanoyl)-HSL. This set of synthetic HSL sensors\nfor a Gram-positive bacterium can pave the way for designable interspecies\ncommunication within microbial consortia.",
"introduction": "Introduction Synthetic microbial consortia have emerged\nas a powerful platform\nto utilize living systems for a variety of applications, ranging from\nbiomanufacturing to bioremediation. 1 − 6 These collections of microbial cells can effectively distribute\nand specialize tasks among individual members, thereby reducing metabolic\nburden of each constituent microorganism. 7 , 8 Increasingly\ncomplex biological processes can benefit from the specialty of different\nspecies. 9 − 14 Interaction between cells is required to elicit coordinated multicellular\nresponses and can be achieved by cell−cell communication. 5 , 15 Bacterial intercellular communication can be implemented using direct\nDNA exchange by conjugation 16 − 18 or molecular signals, 19 − 25 though each approach is typically limited to a subset of bacteria.\nExpanding the toolset for programmable intercellular communication\ncould allow for a greater diversity of bacteria to be harnessed in\nengineered consortia. Chemical signals from bacterial quorum\nsensing have been widely\nused by synthetic biologists to engineer bacterial multicellular behaviors,\nand among them, diffusible homoserine lactone (HSL) signals have been\nused prevalently. 19 , 23 , 26 − 37 HSL signals are commonly found in natural quorum sensing of Gram-negative\nbacteria using LuxR-family transcriptional regulators, 24 , 38 , 39 and most known examples have\nacylated HSL signal molecules. 24 , 40 HSL quorum sensing\nhas been well-studied and offers tremendous natural diversity of regulators\nand ligands, 41 − 43 making it a promising mode of bacterial intercellular\ncommunication for engineering consortia. 44 , 45 However, HSL sensors have been limited to use in Gram-negative bacteria\nwithin engineered consortia and, therefore, are not suitable for interspecies\ncell–cell communication between diverse bacteria. While arguably\nmore complicated to implement, peptide-based interspecies communication\nwas engineered between Gram-negative Escherichia coli and Gram-positive Bacillus megaterium . 46 Gram-positive bacteria in nature are\nbelieved to commonly use quorum sensing via oligopeptides, 24 , 38 , 39 which require exporters for secretion\nand are sensed by two-component membrane-bound sensors. 47 We speculated that it may be possible to engineer\nHSL sensing in a Gram-positive bacterium through synthetic DNA design,\nwhich could offer an alternative approach for engineering bacterial\ninterspecies communication. Supporting this idea, a Gram-positive Exiguobacterium species was reported to produce and sense\nan HSL, 48 and recently, a sensor using\nLuxR was engineered in Gram-positive Bacillus subtilis for autoinduction. 49 , 50 Additionally, analyses of sequence\nconservation have shown that LuxR homologues are well-conserved among\nGram-positive bacteria. 51 These studies\ninspired us to attempt to engineer HSL sensors for B. subtilis and investigate the sequence–function\nrelationships that govern their activity. HSL transcription\nfactor-based sensors typically contain a LuxR-type\nquorum sensing regulator (QSR) and a cognate quorum sensing promoter\n(P QS ). 24 , 40 , 52 For sensing, the regulator binds the HSL signal molecule and dimerizes\nto form the activated complex that then binds the DNA binding site\nin the P QS to activate transcription by RNA polymerase. 53 , 54 To design a functional synthetic P QS promoter, it must\ncontain the DNA binding site for the regulator and other necessary\npromoter motifs. While the housekeeping sigma factors of E. coli (σ 70 ) and B. subtilis (σ A ) are homologous 55 and share identical\nconsensus hexamer −35 and −10 sequences, 56 , 57 promoters from Gram-negative bacteria are usually not transferable\nto Gram-positive bacteria. 58 − 63 The σ A -dependent promoters in B. subtilis are known to differ from E. coli σ 70 -dependent promoters\nby commonly requiring an additional 4-bp motif known as the −16\nregion (TRTG, where R = A or G), sometimes also referred to as the\nextended −10 region. 60 , 64 − 66 In comparison, few E. coli promoters\nhave a consensus sequence upstream of the −10 region (only\n∼20% of natural promoters) and those that do have a shorter\n2-bp motif (NNTG). 67 , 68 The promoter’s sequence\ndownstream of the −10 region, called the transcription start\nregion (TSR), has also been shown to significantly affect promoter\nactivity in B. subtilis . 69 , 70 For a two-component light sensor in B. subtilis , its activity was improved by substituting a TSR from a strong promoter\nfor B. subtilis and the consensus −10\nmotif into the original E. coli promoter\nsequence. 71 Very few studies have explored\nthe generality of this strategy for redesigning sensor promoters from E. coli for B. subtilis . Here, we develop HSL sensors in B. subtilis for four different HSL chemical signals using the combinatorial\ndesign of synthetic promoters and tuning of regulator expression ( Figure 1 A). Sensors containing\nthe quorum sensing promoters from E. coli had little or no activity in B. subtilis , yet by screening of sensor designs containing promoters comprised\nof different DNA sequences (−35, −16, −10, and\nTSR regions), we identified highly functional sensors for each HSL\nsignal. In total, 99 sensor designs and 86 synthetic promoters were\nassayed in these experiments. All sensors were characterized in standard\nrelative promoter units (RPU). This work resulted in the creation\nof a set of characterized HSL sensors for B. subtilis with at least a 20-fold dynamic range and up to 293-fold induction.\nWe systematically quantified the effects of the regulator’s\nexpression level on sensor activity and identified that it is crucial\nfor high activation. We also assayed a complete library of combinatorial\npromoters generated by the statistical design of experiments (DOE)\nto quantify the effects of each promoter region and their interactions\nfor one sensor ( Figure 1 B). Through this work, we demonstrate the feasibility of creating\nHSL sensors and synthetic inducible promoters regulated by LuxR-type\nregulators in B. subtilis , which could\nbe used to expand the use of HSL-mediated intercellular communication\nin bacteria. Figure 1 Homoserine lactone (HSL) sensor engineering in B.\nsubtilis . (A) Engineered HSL sensors for Gram-positive B. subtilis 168. The HSL sensors consist of a LuxR-type\nquorum sensing regulator (QSR) that binds the HSL ligand and a cognate\nquorum sensing promoter (P QS ) that it regulates. Four LuxR-type\nregulators were selected from different Gram-negative bacteria. (B)\nSynthetic P QS promoters for B. subtilis are combinatorially designed using different DNA sequences for each\npromoter region: −35 region, −16 region, −10\nregion, and transcription start region (TSR). A sensor containing\neach promoter is constructed and screened. Design of experiments (DOE)\nfurther quantitatively studies the effect of each promoter region\nand their interactions on the activity of an HSL sensor.",
"discussion": "Discussion In this study, we created and assayed 86\nsynthetic P QS promoters and 99 HSL sensor designs in total\nto develop the first\nset of sensors for four different HSLs in B. subtilis . Combinatorial design of promoters with different −10, −16,\n−35, and TSR sequences allowed us to assess the effects of\nthese promoter regions on the HSL sensor activity. We found that the\neffects of each region and combinations of sequences differ for the\nsensor OFF state (basal promoter activity) and sensor ON state (activated\nP QS promoter output). Through quantitative analysis and\nstatistical design, we determined the varying contribution of individual\npromoter regions and their pairwise interactions to HSL sensor activity,\nwhich could provide valuable insight for future sensor engineering\nin B. subtilis . This set of synthetic\nHSL sensors developed in this study has many potential applications\nfor engineering interspecies bacterial communication and could enable\nthe construction of tailored multispecies microbial communities with\nmulticellular functions. While the Gram-positive bacterium B. subtilis is generally not known to naturally utilize\nHSL-mediated quorum sensing, 40 , 116 , 117 we demonstrated that LuxR-type regulators from Gram-negative bacteria\ncan be used to create transcription factor-based biosensors for HSL\nmolecules in B. subtilis via careful\npromoter engineering and tuning of regulator expression. By studying\nHSL sensors using four different LuxR-type regulators, we showed that\nthis strategy is not limited to only LuxR itself in B. subtilis , 50 and this\nsuggests that the approach may be extended further and generalizable\namong the large family of LuxR-type regulators. 52 , 118 , 119 The addition of HSL sensors\nand HSL-inducible promoters expands\nthe genetic toolbox for tunable gene expression in B. subtilis , which is notable given the relative\nscarcity of inducible promoters in B. subtilis compared to E. coli . 120 The available inducible promoters for B. subtilis mostly utilize native regulators with\nsugar ligands 121 , 122 or antibiotic-based inducible\npromoters, 121 , 123 along with a few other transcription\nfactor-based 124 , 125 or two-component system 71 sensors. To complement strategies that have\nbeen developed to engineer inducible promoters in B.\nsubtilis , 124 , 126 designing sensors\nbased on the rules learned from this study could serve as another\napproach to translate transcription factor-based sensors from E. coli to B. subtilis . Combinatorial promoter engineering has proven to be a useful\nstrategy\nfor designing new inducible promoters for different microrganisms, 92 , 113 , 120 , 127 − 129 and inspired by those studies, here we showed\nthat similar approaches were effective to create functional HSL sensors\nin B. subtilis . The limits of these\napproaches in B. subtilis and Gram-positive\nbacteria still need to be determined. We offer our experience with\none other LuxR-type regulator as an example. While we constructed\nsynthetic 3-oxododecanoyl-HSL sensors in B. subtilis using the LasR regulator from P. aeruginosa and applying similar design strategies and promoter region sequences,\nonly up to 3-fold induction was achieved by any sensor design ( Figure S12 ). Therefore, there may be other factors\nand interactions that must be uncovered to extend this approach universally.\nIn this work, we demonstrated the activity of the synthetic HSL sensors\nin B. subtilis 168 with exogenous HSLs.\nHowever, it is important to recognize that B. subtilis strains, along with other Bacilli, are known to commonly have enzymes\nthat can degrade HSL molecules (e.g., lactonases and HSL oxidoreductases). 130 − 136 These so-called quorum quenching enzymes are believed to provide\na mechanism of inhibiting or interfering with cell–cell signaling\nof other bacteria in their environment 39 , 137 , 138 and may present challenges for use of these sensors\nin different bacterial systems. While we did not knock out any native\nenzymes in this work, this could be necessary in other strains. Our linear regression models for the P Rpa sensor output\nconfirmed that the −35 and −10 sequences alone are inadequate\nto explain the observed B. subtilis promoter activity 58 and identified significant\neffects of the −16 region and TSR. Here, we identified the\nsignificant role of interactions between promoter regions affecting\nsensor activity using statistical design of experiments, and this\nsuggests that statistical design could be a valuable approach to efficiently\nscreen the combinatorial design space of similar promoters in B. subtilis . Other strategies, such as large libraries\ncontaining diverse promoter region sequences and regulator binding\nsites, could also aid to systematically elucidate factors impacting\nsensor activity and establish the sequence–function relationship\nfor promoters in Gram-positive bacteria. As more design rules for\nsynthetic inducible promoters are learned, it may become possible\nto utilize de novo promoter design strategies as have been demonstrated\nin E. coli . 65 , 139 Nevertheless, the combinatorial design strategies used in this work\neffectively developed highly functional sensors for four different\nHSL ligands in Gram-positive B. subtilis . We believe these novel HSL sensors could have broad utility to\nallow diverse bacteria to speak the same biochemical language and\nestablish new channels for interspecies communication in bacterial\nconsortia."
} | 3,492 |
27685371 | PMC5215560 | pmc | 6,591 | {
"abstract": "Abstract The transfer of photoenergized electrons from extracellular photosensitizers across a bacterial cell envelope to drive intracellular chemical transformations represents an attractive way to harness nature's catalytic machinery for solar‐assisted chemical synthesis. In Shewanella oneidensis MR‐1 (MR‐1), trans‐outer‐membrane electron transfer is performed by the extracellular cytochromes MtrC and OmcA acting together with the outer‐membrane‐spanning porin ⋅ cytochrome complex (MtrAB). Here we demonstrate photoreduction of solutions of MtrC, OmcA, and the MtrCAB complex by soluble photosensitizers: namely, eosin Y, fluorescein, proflavine, flavin, and adenine dinucleotide, as well as by riboflavin and flavin mononucleotide, two compounds secreted by MR‐1. We show photoreduction of MtrC and OmcA adsorbed on Ru II ‐dye‐sensitized TiO 2 nanoparticles and that these protein‐coated particles perform photocatalytic reduction of solutions of MtrC, OmcA, and MtrCAB. These findings provide a framework for informed development of strategies for using the outer‐membrane‐associated cytochromes of MR‐1 for solar‐driven microbial synthesis in natural and engineered bacteria.",
"conclusion": "Conclusion The extracellular and outer‐membrane‐spanning cytochromes MtrC, OmcA, and MtrCAB of MR‐1 can be reduced in photocatalytic processes by water‐compatible photosensitizers. Complete reduction of the cytochromes was achieved with abiotic eosin Y, proflavine, and fluorescein with TEOA as SED, whereas cytochrome photoreduction plateaued at 60 % for biotic RF, FMN, and FAD with TEOA as SED. Under comparable conditions, solutions of abiotic Ru[bpy] 3 \n 2+ and RuP sustained very slow cytochrome reduction. This kinetic limitation was overcome by adsorption of MtrC(OmcA) on RuP‐sensitized P25 TiO 2 nanoparticles that were able to perform photocatalytic reduction of solutions of MtrC, OmcA, and MtrCAB. These findings provide a framework for informed development of strategies to use the outer‐membrane‐associated cytochromes of MR‐1 for solar microbial synthesis in natural and engineered bacteria.",
"introduction": "Introduction Certain species of bacteria conduct electrons across the cell envelope in a manner that couples intra‐ and extracellular redox reactions. In nature, this extracellular electron transfer (EET) offers a competitive advantage in anaerobic habitats because electrons released by intracellular energy‐conserving pathways cross the cell membrane to reduce extracellular terminal electron acceptors that include particles containing Fe III and Mn IV . 1 However, EET can also exchange electrons between electrodes and bacteria known as electrotrophs. Electricity is generated when respiratory electrons from the oxidation of waste‐water‐derived electron donors are delivered to the anode of a microbial fuel cell. 2 Microbial electrosynthesis is performed when the pathway of this respiratory electron transfer is reversed such that cathode‐derived electrons are delivered to intracellular enzymes. 2b , 2c , 3 This approach affords strategies for tapping into the catalytic diversity and selectivity of enzymes for sustainable molecular syntheses that extend beyond H 2 production and CO 2 reduction while at the same time negating the need for time‐consuming and costly enzyme purification. Indeed, the prospect of using EET to couple robust and efficient extracellular light‐harvesting systems to intracellular catalysis represents a particularly attractive approach to sustainable solar‐assisted production of chemicals. 3a , 4 \n \n Shewanella oneidensis MR‐1 (MR‐1) provides a model for the biochemistry and biophysics of EET 5 and as a consequence a platform for the rational design of strategies for microbial electro‐ and photosynthesis. EET in this Gram‐negative electrotroph is underpinned by arrays of closely packed, protein‐bound heme cofactors that conduct electrons within and between proteins. The MR‐1 outer membrane is spanned by porin ⋅ cytochrome complexes that conduct electrons between the periplasm and external materials. Foremost amongst these is a tight 1:1 complex of two proteins: MtrA and MtrB (MtrAB, Figure 1 A). It is proposed that the decaheme cytochrome MtrA conducts electrons across the outer membrane by virtue of its insertion within a porin formed by MtrB. 6 MtrAB forms a tight complex with the extracellular cytochrome MtrC, with which it exchanges electrons (Figure 1 A). During EET a second extracellular cytochrome, OmcA, can bind to, and exchange electrons with, MtrC. 7 X‐ray diffraction has shown that MtrC and OmcA are structural homologues 8 with ten heme groups bound in a staggered cross constellation (Figure 1 B). In addition, MtrC and OmcA were shown to possess one and two disulfide bonds, respectively. In vitro reduction of these bonds triggered tight binding of flavin mononucleotide (FMN) or riboflavin (RF) to both proteins; 8b this might be significant because cellular studies have suggested that MtrC and OmcA act as flavocytochromes during anaerobic respiration. 9 \n Figure 1 Proteins from S. oneidensis MR‐1 with key roles in extracellular electron transfer (EET). A) Schematic representation of the MR‐1 outer membrane illustrating the proposed locations and interactions of porin MtrB and the decaheme cytochromes MtrA, MtrC, and OmcA. The arrows indicate proposed interactions leading to electron exchange between MtrC and OmcA; see text for details. B) Structure of MtrC. Domains II (blue) and IV (cyan) contain heme groups shown as spheres with C in black, O in red, N in blue, and Fe in orange. Cysteine residues linked by disulfide bonds are shown as spheres in Domain III (purple). Structure rendered in PyMol from PDB ID: 4LM8. 8b \n Several mechanisms by which MtrC and OmcA might facilitate electron exchange between the MR‐1 cell surface and extracellular materials have been proposed. Direct electron exchange might occur between these materials and the cofactors of MtrC and OmcA. 9 , 10 Electrons might be shuttled between these materials and the cell‐surface cytochromes through the diffusion of extracellular, redox‐active mediators that include flavins and low‐molecular‐weight Fe complexes. 11 In addition, outer‐membrane extensions, coated with MtrC and OmcA and sometimes termed nanowires, have been implicated in mechanisms for electron exchange with remote materials across distances exceeding the cell dimensions. 12 \n We have explored strategies to effect the photoreduction of MR‐1 extracellular cytochromes with the aid of water‐compatible light‐harvesting systems, because we envisage this as a route by which to facilitate solar‐assisted microbial production of chemicals by delivering photoexcited electrons to intracellular enzymes. We recently described how a monolayer of MtrC supported light‐driven charge transport to an underlying ultraflat gold electrode when coated with 3,4‐dihydroxybenzoic‐acid‐capped TiO 2 nanocrystals (diameter ≈7 nm) that had been photosensitized with a phosphonated Ru II ‐tris(bipyridine) dye. 13 Here we report the photoreduction of solutions of MtrC, OmcA, and the MtrCAB complex by both biotic and abiotic photosensitizers that include organic dyes and transition‐metal complexes (Figure 2 A). We demonstrate photoreduction of MtrC and OmcA adsorbed on widely available TiO 2 nanoparticles sensitized with a Ru dye (Figure 2 B). In addition, we show that MtrC or OmcA adsorbed on TiO 2 particles serve as electron relays in the photoreduction of solutions of MtrC, OmcA, and the MtrCAB complex (Figure 2 C). These results extend the framework from which informed approaches to artificial microbial photosynthesis can be developed for strains of native and engineered 14 bacteria that support EET through the action of multiheme cytochromes from, or homologous to those of, MR‐1.\n Figure 2 Schematic representation of strategies for photoreduction of MR‐1 cytochromes investigated in this work, illustrated for MtrC and MtrCAB. A) Photoreduction of MtrC by a soluble photosensitizer (PS), which may be an organic dye or an inorganic complex. B) Photoreduction of MtrC adsorbed on TiO 2 nanoparticles sensitized with a phosphonated Ru II dye (RuP). C) Photocatalytic reduction of solutions of MtrCAB by MtrC‐coated, dye‐sensitized TiO 2 particles. SED: sacrificial electron donor. CB: conduction band. VB: valence band.",
"discussion": "Results and Discussion Photoreduction of MtrC, OmcA, and MtrCAB by soluble photosensitizers Evidence for visible‐light‐driven reduction of MtrC by soluble photosensitizers (Figure 2 A) was sought through electronic absorbance spectroscopy. Oxidized (ferric) heme groups contribute a single broad feature to spectra between 500 and 600 nm whereas two sharper and more intense features with maxima at 523 and 552 nm are indicative of reduced (ferrous) heme groups. 15 Experiments were performed with eight photosensitizers, the structures and key photochemical properties of which are provided in Table S1 in the Supporting Information. FMN, RF, and flavin adenine dinucleotide (FAD) are naturally occurring flavins. The first two are secreted by MR‐1 and participate in EET. 11a – 11d Proflavine, fluorescein, and eosin Y are well‐studied light‐harvesting analogues of redox‐active molecules that serve as electron shuttles to enhance the performance of microbial fuel cells. 16 [Ru(2,2′‐bpy) 3 ]Cl 2 (bpy=2,2′‐bipyridine) and [Ru(bpy) 2 {4,4′‐(PO 3 H 2 ) 2 bpy}]Br 2 (RuP) are robust light‐harvesting analogues of Fe III chelates used as extracellular terminal electron acceptors by MR‐1. 1a , 1b , 5a Triethanolamine (TEOA) and HEPES were included unless stated otherwise because these tertiary amines can serve as pH buffers and sacrificial electron donors during photoreduction. 17 \n Anaerobic solutions of 32 μ m MtrC displayed spectral features that are typical of the oxidized protein and unchanged by the addition of 10 μ m FMN (e.g., Figure 3 A, black line). Illumination of this sample ( λ >390 nm, power ≈400 W m −2 ; see the Experimental Section) resulted in the appearance of peaks with maxima at 523 and 552 nm indicative of ferrous heme (Figure 3 A, gray lines). The magnitudes of these peaks, which represent the extent of MtrC reduction, increased to a maximum during 90 min illumination and were unchanged by 30 min further illumination (Figure 3 A, blue line). A similar experiment in the absence of FMN provided no evidence for heme reduction (Figure S2 A). Thus, FMN was revealed to be an effective photosensitizer for the visible‐light‐driven reduction of heme groups within MtrC.\n Figure 3 Photoreduction of MtrC by A) FMN, and B) eosin Y, visualized by electronic absorbance spectroscopy. A) Spectra of an MtrC (32 μ m )/FMN (10 μ m ) solution as prepared (black line) and after illumination for 5, 15, 30, 60, 90 (gray lines), and 120 min (blue line) prior to the addition of excess dithionite (dashed red line). B) Spectra of an MtrC (26 μ m )/eosin Y (14 μ m ) solution as prepared (black line) and after illumination for 5, 15, 30, 60 (gray lines), and 90 min (blue line) prior to the addition of excess dithionite (dashed red line). Arrows indicate the direction of spectral change during illumination ( λ >390 nm, power ≈400 W m −2 ). Anaerobic samples in 50 m m TEOA, 50 m m HEPES, 2 m m CaCl 2 , 10 m m KCl, pH 7 at 20 °C. Path length: 1 mm. The extent of heme photoreduction was quantified by addition of excess sodium dithionite (e.g., Figure 3 A, dashed red line). Dithionite (S 2 O 4 \n 2− ) has a reduction potential of about −500 mV (all potentials quoted vs. SHE) under the conditions of our study, 18 and when present in excess it reduces all ten MtrC heme groups. 6 , 15 , 19 As a consequence, the electronic absorbance of fully reduced MtrC at 552 nm was compared with that of the MtrC generated by FMN‐dependent photoreduction, and for the example shown in Figure 3 A photoreduction was found to proceed to 56 %. Similar results were obtained in repeat experiments and for ratios of FMN to MtrC that ranged from approximately 1:0.005 to 1:1.5 with 10 or 100 μ m MtrC (Table 1 ). Thus, the extent of MtrC photoreduction was independent of whether the concentration of FMN exceeded that of MtrC or was catalytic (sub‐stoichiometric) with respect to the protein.\n Table 1 Photoreduction of MR‐1 cytochromes by the indicated photosensitizers. Extent of heme reduction [%] Photosensitizer MtrC OmcA MtrCAB RF [a] \n 60±6 [b] ( n =2) 63±5 [b] ( n =5) 62±6 [c] ( n =2) FMN [a] \n 61±5 [b] ( n =4) 59±5 [c] ( n =2) 58±5 [c] ( n =2) FAD [a] \n 62±5 [b] ( n =2) 66±8 [b] ( n =3) 61±5 [c] ( n =2) fluorescein [a] \n 100±2 [b] ( n =3) 100±2 [b] ( n =4) 100±2 [c] ( n =4) proflavine [a] \n 100±2 [b] ( n =3) 100±2 [b] ( n =4) 100±2 [c] ( n =2) eosin Y [a] \n 100±2 [b] ( n =4) 100±2 [b] ( n =4) 100±2 [c] ( n =4) [Ru(bpy) 3 ] 2+[a] \n ≈2 [d] ( n =2) 14±6 [b] ( n =2) ≈2 [e] ( n =2) RuP [a] \n ≈2 [d] ( n =2) ≈1 [d] ( n =2) ≈3 [e] ( n =2) RuP ⋅ TiO 2 \n ⋅ MtrC [f] \n 76±10 ( n =3) 84±4 ( n =3) 78±13 ( n =3) RuP ⋅ TiO 2 \n ⋅ OmcA [f] \n 82±12 ( n =2) 75±15 ( n =2) 49±25 ( n =2) none n.d. [g] ( n =2) n.d. [g] ( n =2) 3±2 ( n =2) [a] Experiments performed in anaerobic 50 m m TEOA, 50 m m HEPES, 2 m m CaCl 2 , 10 m m KCl, pH 7 at 20 °C and with Triton X‐100 (0.06 %, v / v ) included for MtrCAB. Extent of reduction during 90 min illumination ( λ >390 nm, power 400 W m −2 ) is the average of n experiments where the error is the difference between the average and the maximum and minimum values. Heme reduction calculated from absorbance at 552 nm except for eosin Y when present in excess because the photosensitizer absorbance meant that the heme oxidation state was more clearly assessed at 420 nm. [b] Photosensitizer/protein ratios from 1:0.005 to 1:1.5 with 10 μ m and 100 μ m photosensitizer. [c] Photosensitizer/protein ratios from 0.5:0.005 to 0.5:1.5 with 10 μ m and 100 μ m photosensitizer. With 20 and 10 heme groups per MtrCAB and MtrC(OmcA), respectively, this ensured comparable optical densities for the experiments with each protein. [d] 100 μ m photosensitizer, 0.5 μ m protein. [e] 100 μ m photosensitizer, 0.25 μ m MtrCAB. [f] Experiments performed in anaerobic 150 m m , pH 6 at 20 °C with Triton X‐100 (0.2 %, v / v ) included with MtrCAB. Particles (0.037 mg mL −1 ) with 0.65 μ m MtrC, 0.61 μ m OmcA, or 0.31 μ m MtrCAB. Extent of reduction during 60 min illumination ( λ >390 nm, power 400 W m −2 ) is the average of n experiments for which the error is the difference between the average and the maximum and minimum values. Heme reduction calculated from absorbance at 552 nm. [g] n.d.: none detected. Wiley‐VCH Verlag GmbH & Co. KGaA Results very similar to those described above were obtained in parallel experiments with MtrC replaced by OmcA, and also when FMN was replaced by RF or FAD with either protein (Table 1 ). However, 100 % photoreduction of MtrC and OmcA was triggered by 90 min illumination of samples that contained eosin Y, proflavine, or fluorescein (e.g., Figure 3 B and Table 1 ). As in the cases of FMN, RF, and FAD, during the 90 min a steady‐state level of cytochrome photoreduction was reached; this was independent of whether eosin Y, proflavine, or fluorescein were present either in catalytic quantities or in excess. In contrast, less than 15 % photoreduction of the cytochromes was observed during 90 min illumination with either [Ru(bpy) 3 ] 2+ or RuP present even at ≈200‐fold excess over protein, and the photoreduction failed to reach steady‐state conditions (Table 1 ). To gain insight into the extent of photoreduction observed after 90 min illumination with each photosensitizer (PS) it was of interest to identify the corresponding photocatalytic cycle(s). Photoreduction of a protein can in principle be associated with oxidative and reductive quenching 20 of a photoexcited state (PS*, Figure 4 A). The protein substrate would be directly reduced when PS* is oxidatively quenched. The product (PS + ) would then be regenerated to the ground state (PS 0 ) through oxidation of a sacrificial electron donor (SED). During reductive quenching, PS* would first oxidize the SED and form PS − . This would then reduce the protein to recover the ground state (PS 0 ) and complete the photocatalytic cycle (Figure 4 A). The feasibility of these pathways is determined by the (photo)reduction potentials of the photosensitizer relative to those of the SED and protein (e.g., Figure 4 B and Table S1). In practice the rates of these reactions and those of competing processes, productive or not with respect to reduction of the protein, will define the extent of photoreduction under any given conditions. 20 Oxidative and reductive quenching of organic photosensitizers typically produce radical species, and as a consequence their photochemical behavior often extends beyond that illustrated in Figure 4 A.\n Figure 4 Overview of photochemistry relevant to the photosensitizers employed in this study. A) Pathways of oxidative and reductive quenching after light absorption by a photosensitizer (PS). The ground (PS 0 ), photoexcited (PS*), one‐electron‐reduced (PS − ), and one‐electron‐oxidized states (PS + ) are indicated. SED is the sacrificial electron donor. For simplicity only those steps that contribute directly to photoreduction are shown. B) (Photo)reduction potentials relevant to photoreduction of the MR‐1 cytochromes by eosin Y (EY), fluorescein (Fl), proflavine (PF), RF, FMN, FAD, [Ru(bpy) 3 ] 2+ , RuP at pH 7, and the conduction band (CB) of TiO 2 nanoparticles at pH 6 (see text and Table S1 for details). Thick (thin) arrows relate to processes of reductive (oxidative) quenching. Potentials relevant to oxidation of HEPES and TEOA are indicated, together with the window spanned by the reduction potentials of the heme groups (Heme ox/red ) for each of MtrC, OmcA, and MtrCAB. [a] PS 0/2− for RF, FMN, and FAD denotes the quinone/hydroquinone couple. In order to assess the contributions made by TEOA and HEPES as SEDs during photoreduction of the extracellular cytochromes, OmcA (0.5 μ m ) was illuminated for 90 min with a 20‐fold excess of a given photosensitizer in a 50 m m phosphate solution and, in separate experiments, with TEOA or HEPES. Phosphate is inactive as a SED, and we chose to restrict these studies to OmcA, because MtrC and OmcA have very similar structures, thermodynamic properties, 8b , 19 and, as we have shown here, photochemical behavior (Table 1 ). Eosin Y with TEOA (or HEPES) supported 100 % photoreduction of OmcA (Figure 5 ). However, no photoreduction was detected in the absence of the tertiary amines, which suggests that OmcA reduction is coupled to reductive quenching of the eosin Y photoexcited state by the SED. Under conditions comparable to those used here the corresponding PS 0/− couple has a reduction potential ( E \n m ) of ≈−580 mV. 21 \n Figure 5 Impact of SED on the extent of OmcA photoreduction triggered by 90 min illumination. Extent of heme reduction (%) after 90 min illumination ( λ >390 nm, power ≈400 W m −2 ) of 0.5 μ m OmcA at pH 7, 20 °C with 10 μ m of each indicated photosensitizer in anaerobic 50 m m phosphate (inactive as SED) or with 50 m m TEOA, or 50 m m HEPES, as indicated. Heme reduction was quantified by change in electronic absorbance at 552 nm except in the case of eosin Y, in which spectral overlap of contributions from the photosensitizer and protein at this wavelength necessitated an equivalent analysis for the Soret peak at 420 nm (see text for details). Extents of reduction are the averages of two experiments and the errors are the differences between the average and the maximum and minimum values. The E \n m values for the OmcA heme groups span from approximately +50 to −450 mV at the neutral pH of these experiments and they are relatively evenly distributed across this potential window. 19 The same is true of the corresponding values for the MtrC heme groups. 6 , 13 Thus, the observed 100 % photoreduction of OmcA, and of MtrC, by eosin Y supported by TEOA and/or HEPES is consistent with the relevant (photo)reduction potentials (Figure 4 B). Similarly, the pattern of fluorescein‐dependent OmcA photoreduction indicates that reductive, but not oxidative, quenching operates (Figure 5 ). Reductive quenching of fluorescein under conditions comparable to those in our studies produces a reductant 21 with sufficient driving force to reduce OmcA and MtrC completely (Figure 4 B). That this occurs with TEOA but not with HEPES indicates additional complexity in the latter system. Previous studies have revealed complex photochemistry of proflavine under conditions comparable to those employed here. 20a , 20b In anaerobic aqueous solutions reductive quenching predominates when a SED is present. However, in the absence of a SED the corresponding PS* decays by triplet–triplet annihilation and photoionization to produce a solvated electron. The proflavine‐dependent photoreduction of OmcA proceeded more effectively when TEOA or HEPES were present (Figure 5 ) and presumably occurred through reductive quenching. The PS 0/− couple relevant to this pathway has a sufficiently negative reduction potential 20c to account for the complete photoreduction of OmcA and MtrC observed when the SEDs are present (Figure 4 B). For RF, FMN, and FAD it is generally accepted that the corresponding PS* is reductively quenched to yield a semiquinone that undergoes rapid disproportionation to generate a hydroquinone, this being the active photoreductant. 20d , 20e Both inter‐ and intramolecular electron transfer reactions lead to reductive quenching 20d , 20e and this is consistent with the cytochrome photoreduction that was observed in the absence of TEOA or HEPES as SED in our experiments (Figure 5 ). The hydroquinone forms of RF, FMN, and FAD have E \n m values in the middle of the range spanned by the heme groups of OmcA and MtrC (Figure 4 B). Thus, in the presence of TEOA and HEPES the steady‐state levels of cytochrome photoreduction (≈60 %, Table 1 ) produced by these photosensitizers in comparison with the complete reduction produced by eosin Y, fluorescein, or proflavine were consistent with consideration of the available driving forces. However, this was not the case for [Ru(bpy) 3 ] 2+ and RuP, for which significantly less photoreduction was observed. Oxidative and reductive quenching of the corresponding photoexcited states would generate stronger reductants 20f , 22 than the photocatalytic cycles operative with any of the organic photosensitizers studied here (Figure 4 B). Because negligible photoreduction of the extracellular cytochromes was induced by the Ru II dyes their effectiveness as photosensitizers relative to the organic dyes must be compromised, by, for example, slow net electron exchange with the cytochromes and SED and/or nonproductive side reactions. Indeed, greater levels of OmcA and MtrC photoreduction were observed over 5 h illumination (e.g., Figure S2 B and C). Two additional series of experiments were performed to quantify the behavior of MtrC under conditions that might be relevant to those on the surface of MR‐1. The first series of experiments quantified photoreduction of the heme groups in detergent‐solubilized MtrCAB complex purified from the MR‐1 outer membrane (Figure 1 A). MtrCAB is a stable 1:1:1 complex of the MtrC, MtrA, and MtrB proteins that contains 20 heme groups with E \n m values spanning a potential window similar to those of MtrC and OmcA. 6 , 19 Suspensions of MtrCAB illuminated with each of the photosensitizers discussed above, TEOA, and HEPES produced behavior similar to that displayed by MtrC and OmcA alone (Table 1 ). As a consequence, the response of MtrCAB to these photosensitizers is most likely determined by the same factors as for the extracellular cytochromes alone. Finally, in view of recent reports that MtrC might exist as a flavocytochrome on the surface of MR‐1, 9 it was of interest to characterize the response of such a protein to illumination with visible light. Flavocytochrome, composed of FMN tightly bound to MtrC, was prepared as described previously, by anaerobic incubation of MtrC with glutathione and FMN, followed by gel filtration to separate the flavocytochrome from free FMN. 8b Electronic absorbance spectroscopy established that the heme groups and FMN remained oxidized throughout these processes, which were performed in the dark, and also after 90 min illumination (Figure S3). Previous studies have established that the fluorescence of FMN is partially quenched on binding to the glutathione‐reduced MtrC and that this fluorescence is recovered when the FMN is released by oxidation of the flavocytochrome. 8b This quenching most likely occurs through FRET due to the proximity of the bound FMN to heme. We suggest that bound FMN does not support photoreduction of the flavocytochrome because the corresponding excited state is quenched more rapidly by energy transfer than by electron transfer. It is proposed that the observed photoreduction of MtrC by solutions of FMN, and presumably also RF or FAD (Table 1 ), occurs through electron transfer at a site other than that occupied by the flavin in the flavocytochrome. Photoreduction of MtrC and OmcA adsorbed on dyesensitized TiO 2 nanoparticles A previous study reported negligible intermolecular transfer of photoenergized electrons from RuP to a molecular cobalt catalyst in a pH‐neutral aqueous solution in the presence of a SED. 23 However, co‐adsorption of this dye and catalyst on TiO 2 nanoparticles promoted efficient light‐driven H 2 evolution. Charge separation from photoexcited RuP was enhanced through rapid oxidative quenching by TiO 2 , and photoenergized electrons in the conduction band were readily transferred to the catalyst. The TiO 2 conduction band has a reductive potential of approximately −620 mV at pH 6 24 such that complete photoreduction of MtrC and OmcA by RuP‐coated TiO 2 particles (RuP ⋅ TiO 2 ), is thermodynamically feasible (Figure 4 B). As a consequence it was attractive to establish whether MtrC or OmcA would adsorb on RuP ⋅ TiO 2 in an electroactive configuration (Figure 2 B). RuP forms a stable linkage with TiO 2 at slightly acidic pH by virtue of its phosphonic acid groups. 25 The ability of OmcA or MtrC to adsorb on P25 TiO 2 nanoparticles under these conditions was assessed when each protein (1 μ m ) in 200 μL of 150 m m SED MES at pH 6 was incubated with 0.5 mg mL −1 particles. After 30 min incubation with occasional inversion the samples were centrifuged to pellet the particles and any adsorbed cytochrome. The white particles were found to have taken on a pink‐red color after incubation with the proteins (Figure S4). The amount of cytochrome that remained in the supernatant was quantified by electronic absorbance spectroscopy and found to be significantly less than that in the protein solution prior to incubation with the particles (Figure 6 A, B). Both observations were consistent with protein adsorption on the TiO 2 particles.\n Figure 6 Adsorption and photoreduction of MtrC and OmcA on (RuP ⋅ )TiO 2 particles. Electronic absorbance of A) MtrC and B) OmcA solutions (1 μ m , 200 μL) before (black) 30 min incubation with TiO 2 particles (0.5 mg mL −1 ) in 150 m m MES (pH 6) followed by centrifugation to pellet the particles and retrieval of the supernatant (red). The protein‐coated particles were resuspended in 200 μL of 25 m m phosphate, 150 m m MES (pH 6) and incubated for 30 min to release the bound protein. The particles were pelleted by centrifugation, and spectroscopy of the supernatant (blue) quantified the released protein. C) Electronic absorbance recorded with an integrating‐sphere spectrophotometer for suspensions (0.055 mg mL −1 ) of (RuP ⋅ )TiO 2 \n ⋅ MtrC (OmcA) particles as indicated. Spectra are presented after subtraction of the response from an equivalent solution of unmodified particles and offset on the y‐axis for clarity; TiO 2 \n ⋅ MtrC particles before (black) and after (red) addition of excess sodium dithionite (top), RuP ⋅ TiO 2 \n ⋅ MtrC particles before (black) and after (red) 20 min illumination (middle), RuP ⋅ TiO 2 \n ⋅ OmcA particles before (black) and after (red) 10 min illumination (bottom). Experiments performed in anaerobic 150 m m MES (pH 6), 20 °C. Illumination λ >390 nm, power≈ 400 W m −2 . The integrity of the adsorbed proteins was confirmed by electronic absorbance of the protein‐coated (TiO 2 \n ⋅ MtrC(OmcA)) particles measured with an integrating‐sphere spectrophotometer (e.g., Figure 6 C, top). Use of the integrating sphere mitigated against loss of incident light due to scattering by the particles and facilitated the resolution of spectral features from the adsorbed proteins. The corresponding spectra displayed features typical of the oxidized proteins: namely, a strong absorbance in the Soret region with a maximum at 420 nm and a broader, less intense feature in the αβ region between 500 and 600 nm. These features disappeared on addition of excess dithionite, when peaks with maxima at 420 and 552 nm characteristic of the fully reduced proteins were revealed (e.g., Figure 6 C top). Thus, the adsorbed proteins retained the spectral properties and redox activities of their soluble counterparts. To assess the longevity of protein adsorption, TiO 2 \n ⋅ MtrC(OmcA) particles were suspended in a fresh 200 μL solution of 150 m m MES (pH 6) and incubated with occasional inversion for 20 h at 4 °C. The particles were then pelleted by centrifugation, and electronic absorbance spectroscopy of the supernatant provided no evidence for protein desorption. In contrast, protein was clearly present in supernatant recovered from centrifugation of TiO 2 \n ⋅ MtrC(OmcA) particles incubated for 30 min at 4 °C in 200 μL of 25 m m phosphate, 150 m m MES, pH 6. Recovered particles were white. Electronic absorbance spectra of the desorbed proteins provided no evidence for perturbation of protein structure by adsorption (Figure 6 A, B, blue). It was concluded that adsorption of MtrC and OmcA in their native states was tight and essentially irreversible in 150 m m MES at pH 6 but that the binding was reversed under conditions of competitive phosphate binding. The maximum extents of protein adsorption were estimated from the differences in electronic absorbance of protein solutions before and after incubation with TiO 2 particles, and from the electronic absorbance of the protein released from the particles on exposure to the phosphate‐containing buffer (Figure 6 A, B). Both methods quantified the amount of MtrC and OmcA that had been adsorbed as approximately 1.8 and 1.4 nmol, respectively, per mg of TiO 2 nanoparticles. Taking the surface area of the particles as 50 m 2 g −1 and the dimensions of MtrC and OmcA to be approximately 4×6×8 nm and 5×6×10 nm, respectively, 8 indicated that both proteins adsorbed at close to monolayer coverage. The stable adsorption of MtrC(OmcA) on TiO 2 having been established, the possibility of visible‐light‐driven reduction of the adsorbed cytochromes by RuP‐sensitization of the TiO 2 (Figure 2 B) was assessed. In the desired configuration, adsorbed RuP should pass photoexcited electrons to adsorbed protein through the conduction band of the TiO 2 . RuP was consequently first adsorbed on the P25 TiO 2 particles to 20 % of its maximal coverage as described in the Experimental Section. The RuP ⋅ TiO 2 particles were then exposed to sufficient MtrC(OmcA) to saturate the sites that were available for protein adsorption. Washed RuP ⋅ TiO 2 \n ⋅ MtrC(OmcA) particles were then resuspended in anaerobic 150 m m MES (pH 6) and illuminated ( λ >390 nm, 400 mW m −2 ). After this illumination the electronic absorbance spectra showed a clear red shift of the Soret maximum and peaks with maxima at 420 and 552 nm (e.g., Figure 6 C, middle, bottom). These spectral changes revealed visible‐light‐driven reduction of MtrC(OmcA) adsorbed on the TiO 2 particles sensitized with RuP. Photocatalytic reduction of solutions of MtrC, OmcA and MtrCAB by RuP⋅TiO 2 ⋅MtrC(OmcA) particles A motivation for this work was to inform strategies that might allow extracellular photosensitizers to generate photoenergized electrons that can enter MR‐1 with the aid of extracellular and outer‐membrane‐associated cytochromes (Figure 1 A), in order to drive reductive catalysis by intracellular enzymes. For the RuP ⋅ TiO 2 \n ⋅ MtrC(OmcA) particles this requires that the adsorbed proteins be able to pass electrons to redox partner proteins in a photocatalytic process (Figure 2 C). Given that the coverage of the TiO 2 by MtrC(OmcA) approached that predicted for a monolayer, and that no desorption was detected over 20 h in 150 m m MES (pH 6) (see above), we reasoned that the protein‐coated particles offered very little opportunity for direct electron exchange to occur from the surface of the RuP ⋅ TiO 2 particles to proteins in solution. As a consequence, stirred, anaerobic suspensions of RuP ⋅ TiO 2 \n ⋅ MtrC particles (0.037 mg mL −1 ) were added to solutions of MtrC, OmcA, or MtrCAB (≈6.2 μ m heme) and illuminated to seek evidence for electron transfer through the adsorbed proteins to protein molecules in solution. Electronic absorbance spectroscopy revealed 75–85 % heme reduction in each solution over 60 min illumination (Figure 7 , Table 1 , and Figure S5). Particles recovered by centrifugation at the end of the experiment had a red color, and no reduction was observed when the experiment was repeated without illumination. It was concluded that the RuP ⋅ TiO 2 \n ⋅ MtrC particles performed photocatalytic reduction of the cytochrome solutions (ca. one RuP particle per 21 heme groups). Parallel experiments established that RuP ⋅ TiO 2 \n ⋅ OmcA particles performed photocatalytic reduction of MtrC, OmcA, and MtrCAB (Table 1 and Figure S7).\n Figure 7 Photoreduction of MtrC, OmcA, and MtrCAB solutions by RuP ⋅ TiO 2 \n ⋅ MtrC particles. Electronic absorbance of RuP ⋅ TiO 2 \n ⋅ MtrC suspensions (0.037 mg mL −1 ) with 0.65 μ m MtrC (top), 0.61 μ m OmcA (middle), and 0.31 μ m MtrCAB (bottom) before (black) and after (blue) 30 min illumination ( λ >390 nm, power ≈400 W m −2 ), followed by the addition of excess sodium dithionite (red). Experiments performed at 20 °C in anaerobic 150 m m MES (pH 6) with Triton X‐100 (0.2 %, v / v ) included for MtrCAB. Spectra offset on the y ‐axis for clarity. In principle, electron transfer from RuP to MtrC(OmcA) on the particles could occur by two pathways: 20f through the conduction band of the TiO 2 or by direct RuP‐to‐protein electron transfer. Results from a final series of experiments were consistent with electron transfer through the TiO 2 conduction band. No evidence for photoreduction was observed when solutions of RuP and protein were illuminated in the absence of TiO 2 particles [RuP in 175‐fold excess of MtrC, RuP with MtrC (OmcA or MtrCAB) at concentrations equivalent to those in the particle‐containing suspensions, Figure S6]. Similarly, no photoreduction was detected when 25 m m phosphate was included in suspensions that contained RuP, TiO 2 , and MtrC because phosphate at these concentrations adsorbed on the TiO 2 in preference to RuP and MtrC (see above). Direct TiO 2 ‐to‐protein electron transfer was demonstrated when TiO 2 \n ⋅ MtrC particles were seen to catalyze photoreduction of MtrC solutions when illuminated by UV light that excited electrons across the TiO 2 band gap (Figure S8). These particles failed to catalyze photoreduction when illuminated by visible light (Figure S8), due to the absence of RuP that can be photoexcited by d‐to‐π* metal‐to‐ligand charge‐transfer transition. 23 , 24 , 25 , 26 Taken together these results are consistent with facile visible‐light‐driven reduction of MtrC(OmcA) after charge injection into the conduction band of TiO 2 initiated by photoexcitation of the RuP. In agreement with this conclusion, use of the integrating‐sphere spectrophotometer showed no evidence for photoreduction of the MtrC adsorbed on TiO 2 particles in the absence of RuP during 60 min illumination with λ >390 nm (Figure S9). This experiment also revealed a time‐dependent decrease in the apparent absorbance through the Soret region (<450 nm) that is most likely to arise from changes in light scattering by the particles. This behavior, rather than damage to the protein, can account for the spectral differences displayed by suspensions of (RuP ⋅ )TiO 2 \n ⋅ MtrC particles in relation to solutions of MtrC, Figures 6 C and 7 . Prospects for light‐driven microbial synthesis Enzymes are excellent catalysts for processes very relevant to developing the production of solar fuels and chemicals. 4 , 26 , 27 However, time‐consuming and costly purification procedures, together with limited stability of the isolated proteins, often present bottlenecks to their effective utilization. By performing catalysis inside bacteria the need for enzyme purification is removed and catalyst self‐repair and regeneration might be possible. 4 , 27 Furthermore, there are opportunities to improve on the efficiencies of natural photosynthetic processes 28 by employing extracellular photosensitizers designed, for example, to absorb light across the solar spectrum and to deliver photoenergized electrons to intracellular enzymes through EET pathways. A framework for informed development of light‐driven microbial synthesis in MR‐1 and heterologous hosts 14 is provided by the photoreduction of the MR‐1 extracellular and outer‐membrane‐spanning cytochromes demonstrated here. MtrCAB, MtrC, and OmcA are redox‐active over similar windows of electrochemical potential, and their complete photoreduction is triggered by eosin Y, fluorescein, and proflavine when combined with an appropriate SED. Comparable reduction of MtrCAB is achieved by dithionite, 6 and when MtrCAB spans the bilayer of a lipid vesicle it couples oxidation of external dithionite to reduction of internalized methyl viologen. 10 As a consequence it is thermodynamically feasible that the eosin‐Y‐, proflavine‐, and fluorescein‐dependent photoreduction of MR‐1 extracellular cytochromes would support strongly endoergic intracellular reactions, because reduced methyl viologen drives the reduction of CO 2 and water in well‐established biochemical assays. The same thermodynamic predictions are made for the RuP ⋅ TiO 2 ‐dependent cytochrome reductions because these particles catalyze light‐driven proton and CO 2 reduction by molecular catalysts and metalloenzymes. 26 Facile intracellular electrosynthesis of succinate by MR‐1 29 is driven by electron transfer from MtrCAB to a periplasmic fumarate reductase ( E \n fumarate/succinate ≈20 mV at pH 7). If the effective light‐driven microbial synthesis of additional products is to be achieved similarly, facile electron transfer to appropriate enzymes through natural or engineered 14 pathways will be required. Clearly, the effective translation of information gained in these studies into solar microbial synthesis by MR‐1, or by bacteria engineered to contain the MR‐1 outer‐membrane‐associated cytochromes, requires a consideration of many factors, not least the viability of the bacteria in the presence of photocatalytic concentrations of the photosensitizers. In this regard it is significant that FMN and RF are secreted by MR‐1 and enhance EET. 11 It is also of note that MR‐1 retains the ability to grow and to secrete RF in the presence of P25 TiO 2 particles. 30 As a consequence, experiments that explore the possibilities of employing the photosensitizers described here for visible‐light‐driven synthesis by MR‐1 are ongoing in our laboratories. Given that the outer‐membrane cytochromes of MR‐1 evolved to deliver electrons to extracellular Fe III ‐containing mineral particles, an alternative strategy for their photoreduction could employ nanocrystalline, semiconductive Fe III oxide particles 31 as photosensitizers. It will be of interest to establish whether such particles with appropriate conduction band energies and optical properties can transfer electrons to MR‐1 outer‐membrane cytochromes."
} | 10,108 |
39289346 | PMC11408693 | pmc | 6,593 | {
"abstract": "This study introduces a synthetic biology approach that reprograms the yeast mating-type switching mechanism for tunable cell differentiation, facilitating synthetic microbial consortia formation and cooperativity. The underlying mechanism was engineered into a genetic logic gate capable of inducing asymmetric sexual differentiation within a haploid yeast population, resulting in a consortium characterized by mating-type heterogeneity and tunable population composition. The utility of this approach in microbial consortia cooperativity was demonstrated through the sequential conversion of xylan into xylose, employing haploids of opposite mating types each expressing a different enzyme of the xylanolytic pathway. This strategy provides a versatile framework for producing and fine-tuning functionally heterogeneous yet isogenic yeast consortia, furthering the advancement of microbial consortia cooperativity and offering additional avenues for biotechnological applications.",
"introduction": "Introduction Microbial biotechnology has predominantly been centered on the engineering of single-cellular organisms, a focus that inherently constrains the complexity of achievable functionalities 1 . Contrasting with this, evolutionary processes have favored the emergence of microbial consortia as a means to undertake complex biological functions. These consortia are characterized by cooperative interactions among specialized individuals within a collective, highlighting the evolutionary advantage of such complexity 2 – 4 . This pattern of synergistic and mutualistic interactions is not isolated but widespread across nature, indicating their critical role in enhancing the physiological and ecological complexity of microbial communities 5 . Leveraging nature’s designs, researchers have begun harnessing microbial consortia for cooperative applications, such as distributing biosynthetic pathways across different cell types to enhance productivity while minimizing metabolic burden and separating incompatible processes 1 , 6 , 7 . However, the reliance on manual mixing of different cellular platforms introduces challenges in the formation of microbial consortia, limiting the broader application of this strategy, especially in scenarios where direct human intervention is not feasible. Bioengineering strategies have sought to overcome these limitations by engineering genetic circuits that enable the formation of microbial consortia via asymmetric cell division and differentiation 8 , 9 . Despite this progress, inducible cell differentiation in eukaryotic models, such as Saccharomyces cerevisiae , remains largely unexplored. Nonetheless, haploid S. cerevisiae naturally divides and differentiates asymmetrically, presenting an opportunity to harness these underlying mechanisms for tunable cell differentiation to facilitate synthetic microbial consortium formation and cooperativity. The processes of asymmetric cell division and differentiation in haploid S. cerevisiae are highly coordinated events. While the asymmetric budding of a smaller haploid daughter cell from a larger haploid mother cell is common across yeast strains, the asymmetric differentiation process enabling a haploid mother cell to switch mating type is unique to homothallic yeast strains with a dominant HO gene (Fig. 1a ). The HO gene encodes a site-specific endonuclease essential for the mating-type switching process, facilitating sexual differentiation by initiating a directional DNA repair mechanism that interconverts the mating-type allele at the MAT locus 10 – 12 (Fig. 1b ). The MAT locus houses either the α- or a-mating-type allele but only the transcriptional factors (α1 and α2) encoded by the former are involved in the regulation of mating-type-specific gene expression. A switch in the allele at the MAT locus thus changes the mating type of haploids 10 , 11 , 13 , 14 . Naturally, the asymmetry in mating-type switching is crucial in ensuring the concurrent presence of haploids of opposite mating types, facilitating self-fertilization to convert haploids to the advantageous diploid form 10 , 15 . Specifically, mating-type switching is programmed to occur in the older haploid mother cell only after it has divided, producing a smaller haploid daughter cell that retains the original mating type 10 , 16 . This process is achieved mechanistically by restricting the translation of the Ash1 transcriptional repressor and its subsequent inhibition of HO expression, to the haploid daughter cell through the asymmetric transport of its mRNA to the distal bud tip 17 – 20 . In contrast, the depletion of Ash1 in the haploid mother cell enables HO expression, following sequential activation by the Swi5 and SBF (Swi5/Swi6) transcriptional activators 21 , 22 . The intricate process of mating-type switching led us to hypothesize its potential to be rewired into a genetic circuit for tunable cell differentiation to facilitate synthetic microbial consortium formation and cooperativity. Fig. 1 Mating-type switching and mating-type-specific gene expression. a A typical homothallic life cycle of S. cerevisiae . b The mating type of a haploid is governed by the active MAT locus, which houses either the α- or a-mating-type alleles. The α-mating-type allele encodes the transcriptional factors α1 and α2, which are respectively involved in the activation of α-mating-type-specific genes (α’sg) and the repression of a-mating-type-specific genes (a’sg) in α-mating type haploids. In a-mating type haploids, the expression of a’sg occurs in the absence of α2 and does not involve gene products (a1 and a2) of the a-mating-type allele. Mating-type switching is initiated upon the introduction of a double-stranded break (DSB) by the HO endonuclease, which triggers a directional DNA repair mechanism at the MAT locus mediated by the recombinational enhancer (RE). By using the opposite mating-type allele at the silent HMLα or HMRa locus as the donor template for homologous recombination (HR), this process interconverts the mating-type allele at the MAT locus. HO expression is activated by the sequential actions of the Swi5 and SBF (Swi5/Swi6) transcriptional activators and repressed by the Ash1 transcriptional repressor. The haploid yeast strain BY4742 used in this study possesses a T-to-A “stuck mutation” at the silent HMRa locus. c Generation of sterile α-mating type haploid BY4742 strains (Lys − , Met + ) by deleting the MFα1 - 2 and STE3 genes. Sterility was examined by mating with the a-mating type haploid BY4741 (Lys + , Met − ) and inferred as a growth defect on the lysine and methionine drop-out plate that selected only diploids (Lys + , Met + ). Diploid BY4743 was included as a control. d Development of a fluorescent reporter system by integrating the GFP and mCHERRY genes into the mfα1∆ and MFa1 loci, respectively. The expression of GFP and mCHERRY was mutually exclusive, with the former specific to the α-mating type haploids (green; represented by MTS008α strain) and the latter to the a-mating type haploids (red; represented by MTS008a strain). Scale bars on the micrographs represent 10 µm. The micrographs shown are representative of n = 3 biological replicates. Here, we present a synthetic biology approach that repurposes the yeast mating-type switching mechanism into a genetic logic gate capable of asymmetric cell differentiation, generating tunable microbial consortia comprising two isogenic but phenotypically and functionally distinct haploids of opposite mating types. As a proof-of-concept, we demonstrated the utility of this approach in facilitating microbial consortia cooperativity, by distributing the expression of xylanolytic enzymes across two haploids of opposite mating types for the sequential conversion of xylan into xylose. This study provides a versatile tool for producing and fine-tuning functionally heterogeneous, yet isogenic yeast consortia characterized by mating type heterogeneity, contributing significantly to the advancement of microbial consortia cooperativity.",
"discussion": "Discussion In this study, we present a synthetic biology approach that repurposes the yeast mating-type switching mechanism into a genetic logic gate capable of inducing cell differentiation, generating consortia with mating-type heterogeneity from an initially homogeneous population. By modulating the concentrations of the inducers, we established a method for controlling population composition by differentially regulating the asymmetric HO expression and mating-type switching processes. Additionally, we demonstrated the utility of the consortia formed in facilitating cooperativity, particularly between two isogenic haploids of opposite mating types, each expressing a unique xylanolytic enzyme, working sequentially to convert xylan into xylose. We posit that the approach delineated in this study represents a significant advancement in the development of synthetic microbial consortia. Our approach offers the advantage of utilizing isogenic consortium members derived from the same strain, thereby enhancing stability as the members can be readily differentiated from one another. Indeed, instability arising from differential cell growth, incompatibility, and competition has been a major issue hampering synthetic microbial consortia. A recent study addressed this challenge by fostering interdependency among consortium members 53 . Specifically, the authors engineered and deployed cross-feeding auxotrophic and overexpression yeast strains to construct two- and three-member consortia and demonstrated their stability and effectiveness in enhancing resveratrol production through division of labor. While our present study focused on constructing a two-member consortium, our approach holds potential for further adaptation. We envisage the incorporation of additional homothallic yeast strains and their combinations to expand the genetic diversity, complexity, and functionality of the microbial consortia formed. Moreover, the inducibility of our approach eliminates the need for manual mixing of various consortium members, enabling microbial consortia formation and cooperativity in environments where direct human intervention is impractical. Future iterations could explore using alternative inducible promoters to regulate SHE3 expression to facilitate the formation of microbial consortia and cooperativity under specific environmental conditions. The ability to generate microbial consortia with diverse population compositions holds significant promise in synthetic biology, particularly for optimizing bioproduction. By distributing biosynthetic enzymes into two parts expressed under haploids of opposite mating types, the optimal enzyme ratio can be inferred by observing the population composition needed for maximum productivity. Additionally, apart from modulating the population composition, the ratios of these enzymes could be adjusted using the seven mating-type-specific promoters characterized in this study. While their varying expression strengths offer advantages in fine-tuning enzyme expression, the moderate expression strengths of these mating-type specific promoters may pose limitations in certain contexts. Further investigation is therefore warranted to explore engineering strategies that can enhance the expression strengths of these promoters while preserving their mating-type specificity. In summary, our study offers a versatile toolkit for the generation and precision-tuning of functionally heterogeneous yet isogenic yeast consortia. This contributes to the advancement of microbial consortia cooperativity and opens additional avenues for biotechnological applications."
} | 2,931 |
39211383 | PMC11351707 | pmc | 6,595 | {
"abstract": "The large-scale production of platform chemicals from\nbiomass requires\nefficient, cost-effective, and sustainable methods. Here, we present\nthree one-pot synthesis routes for producing diformylxylose (DFX),\na sugar-based solvent and platform chemical, using d -xylose\nor corncobs as feedstocks. With yields of approximately 80%, these\nroutes were seamlessly scaled from lab to kilogram-scale in a 15 L\nbatch reactor. Techno-economic assessment demonstrates the competitiveness\nof the proposed methods against fossil- and biobased analogues. Life-cycle\nanalysis shows the potential of these processes to reduce environmental\nand societal impacts from cradle to gate. At the “end of life”,\nDFX is demonstrated to be inherently biodegradable. Overall, we present\na compelling case study of scaling a novel platform chemical guided\nby techno-economic and environmental concerns leading to balanced\ncost-competitiveness and life-cycle sustainability.",
"conclusion": "3 Conclusions We present scalable methods\nto produce the new platform chemical\ndiformylxylose from both d -xylose and agricultural waste\nbiomass while balancing cost and sustainability. The developed one-pot\nprocesses were successfully scaled up to a multikilogram scale and\nproven to be economically competitive at different production scales.\nThe cradle-to-gate life-cycle assessment of different production routes\nindicated the potential for reduced environmental impacts compared\nto traditional petroleum- and some biobased solvents. The inherent\nbiodegradability of DFX makes it less likely to cause issues in case\nof environmental leakage. The relative ease of batch scalability\ndemonstrated here along\nwith the projected low cost, limited environmental burden, and low-risk\ntoxicological profile of DFX makes it an attractive candidate for\nbroader applications. These promising results can be explained in\npart by the minimal alternation of the xylose structure when forming\nDFX. This proof-of-concept further demonstrates that retaining natural\nstructures in biobased products leads to inherent advantages when\ndeveloping and scaling the production of sustainable chemicals.",
"introduction": "1 Introduction According to a recent\nassessment, over 99% of the most common chemicals\nare still not produced sustainably as they transgress at least one\nof the planetary boundaries. 1 Meanwhile,\nthere are growing concerns over the sustainability and safety of chemical\nprocesses, and an increasing demand to shift away from the reliance\non fossil-based products. Biomass can play an important role in this\ntransition since it is the largest source of fixed renewable carbon\non earth. Key to furthering the industrial implementation of\nbiomass conversion\nis the large-scale production of biobased platform chemicals. Diformylxylose\n(DFX), a xylose-based diacetal isolated from lignocellulosic biomass\nvia the aldehyde-assisted fractionation technology, 2 , 3 demonstrated\nsuperior performance as a versatile platform chemical in various applications. 4 − 7 For example, DFX was used as a starting material to produce xylitol\nat much higher yields compared to unmodified xylose. 5 The use of DFX as an alternative to xylose also doubled\nthe furfural yield in a biphasic water-methyl isobutyl ketone (MIBK)\nsystem in the presence of an acid catalyst. 6 Finally, DFX proved to be a highly effective and nontoxic polar\naprotic solvent with comparable performance to toxic and environmentally\nharmful solvents such as N -methyl-2-pyrrolidone (NMP),\ndimethylacetamide (DMAc), and dimethylformamide (DMF), 4 whose use in industry has become restricted. 8 To exploit these and other opportunities, a larger-scale\nproduction of DFX is necessary. A primary objective of DFX scale-up\nwas to identify an abundant\nand inexpensive source of xylose-rich biomass. Corncobs are a promising\nfeedstock, owing to their high availability and low cost as a major\nagricultural waste. 9 An especially attractive\nfeature of corncobs in the context of DFX production is that they\ncontain an important fraction of xylan (20–40 wt %), in addition\nto the cellulose (30–40 wt %), and a lower amount of lignin\n(8–15 wt %), especially compared to woody biomass that usually\ncontains 20–30 wt % of lignin and less xylan (typically 5–20\nwt %). 3 , 10 , 11 This composition\nand availability have made corncobs a prime feedstock to produce carbohydrates\nand especially xylose-based platform chemicals, such as xylitol. 12 , 13 A recent study from our group has also demonstrated the potential\nfor corncobs as the feedstock of xylose-based sustainable polyesters. 14 More generally, notable examples of carbohydrate-based\nsolvents\nand platform chemicals include 2-methyltetrahydrofuran (2-MeTHF), 15 γ-valerolactone (GVL), 16 dihydrolevoglucosenone (Cyrene) 17 and its ketal derivatives, 18 tetrahydropyran\n(THP), 19 levulinic acid (LA) and its derivatives, 20 as well as diols and polyols. 21 The synthesis of these compounds mostly revolves around\nrepeated dehydration and hydrogenation reactions to remove the abundant\nhydroxyl groups naturally present in carbohydrates. These multiple\nsteps often entail high energy consumption, high cost, and complicated\nsynthetic routes. For instance, GVL and 2-MeTHF can be produced by\nhydrogenation of sugar dehydration products, furfural or LA, at elevated\ntemperatures (200–300 °C) and pressures (>10 bar). 22 , 23 Cyrene can be produced at a high yield (>90%) from the hydrogenation\nof levoglucosenone, a major product of acid-catalyzed pyrolysis of\ncellulose in the Furacell process. 17 A\nrecently introduced biobased ether, THP, is produced via hydrogenation\nof furfural-derived 3,4-dihydropyran (DHP) at high yield (>98%),\nbut\nthe production of DHP still involves dehydration and hydrogenation\nof furfural via tetrahydrofurfuryl alcohol. 19 Production of LA derivatives also suffers from difficult product\nseparation from mineral acids and byproducts, and high reaction temperatures\n(>200 °C). To reduce the complexity of multistep synthetic\nroutes,\none-pot approaches have been proposed recently though they tend to\nsuffer from lower product selectivity compared to the conventional\nroutes. 24 , 25 These limitations highlight the need for\nmore cost-effective and sustainable biomass conversion methods. Here, we report the one-pot production of DFX from d -xylose\nand corncobs in high yields, while adhering to the principles of green\nchemistry and OECD guidelines. 26 New processes\nwere successfully scaled up from lab to multikilogram scale in a 15\nL reactor. To assess the viability of DFX production at different\nscales and cost scenarios, these processes were simulated for techno-economic\nanalysis. We also conducted a preliminary life-cycle analysis (LCA)\nto evaluate the cradle-to-gate footprint of DFX production. We further\nperformed a biodegradation test to explore the end-of-life of DFX.\nOverall, this study provides a practical example of how to sustainably\ntransform waste biomass into a valuable platform chemical at low cost\nand with high efficacy.",
"discussion": "2 Results and Discussion 2.1 DFX from d -Xylose: A Greener Synthesis\nDesign Diformylxylose (or 1,2;3,5- O -dimethylene-α- d -xylofuranose) was first reported in 1949 by German chemists\nwho reacted d -xylose with polyoxymethylene (POM) in the presence\nof phosphoric acid, achieving 54% DFX yield ( Figure 1 , route a). 27 Our\ngroup reported an alternative synthesis procedure using an aqueous\nsolution of formaldehyde (FA) (i.e., formalin) and HCl aqueous solution\nin 1,4-dioxane, followed by hexane extraction, distillation, and recrystallization\nto obtain pure DFX in 74% yield ( Figure 1 , route b). 7 The\nuse of water-containing reagents (HCl and FA) required a relatively\nlarge volume of 1,4-dioxane (1 L per 22 g of d -xylose) to\nmaintain a water content below 10 wt %, thereby preventing the reaction\nequilibrium from shifting toward reactants. In a recent publication,\nwe replaced the HCl 37 wt % aqueous solution with concentrated H 2 SO 4 and the formalin solution with paraformaldehyde\n(PFA). 4 These modifications allowed us\nnot only to reduce solvent usage 3-fold but also to replace the carcinogenic\n1,4-dioxane with a biobased alternative, 2-Me-THF, which is not highly\nmiscible with water. Although PFA, as a formaldehyde-based substance,\nis classified as a suspected carcinogen under various regulations, 28 it is widely used in industrial processes, 29 , 30 and its associated risks can be mitigated. For example, in our process,\nwe employed a scrubber with 10 wt % sodium bisulfite solution to capture\npotential formaldehyde emissions, converting them into the nontoxic\nand biodegradable sodium formaldehyde bisulfite salt. To further minimize\nrisks, we used PFA in the form of beads/pellets to limit dust formation.\nIn addition, PFA is much less toxic by inhalation with a lethal concentration\n(LC 50 ) of 1070 mg/L compared to formalin (LC 50 = 0.578 mg/L). Also, the oral median lethal dose (LD 50 ) for PFA is slightly higher than that for FA (800 vs 500 mg/kg). 31 , 32 In this work, we further simplified this synthesis procedure in\n2-MeTHF by eliminating the extraction and distillation steps to orient\nthe process toward larger-scale production ( Figure 1 , route c). Here, DFX crystallizes directly\nfrom the concentrated reaction mixture after evaporating the reaction\nsolvent. The resulting crystals have over 98 wt % of DFX after ethanol\nwash and drying, with an isolated yield of 82%. This simplification\nsaves materials, labor, and energy, which are critical considerations\nfor scale-up. Notably, the direct crystallization of DFX from the\nconcentrated reaction mixture does not occur with the previous methodology\nemploying HCl and formalin even at the same concentration of DFX in\nthe final liquor. Figure 1 Synthesis routes of DFX using purified d -xylose,\nacid,\nand a formaldehyde source. (a) Route for the first reported synthesis\nof DFX, 27 (b) route developed by Questell-Santiago\net al., 7 and (c) the route developed in\nthis work. 2.2 Reaction Sensitivity Analysis Improves Process\nScalability To improve productivity and to reduce the usage\nof chemicals and process costs, the reaction had to be concentrated.\nA reaction time of 3 h and acid loading of 60 g/L were fixed to assess\nthe combined effect of xylose loading and formaldehyde-to-xylose molar\nfraction on gross cost of production (COP) while aiming for a yield\nof DFX > 80% ( Figure 2 c). The reaction time and acid loading were selected based on sensitivity\nanalyses ( Figure 2 a,b).\nWhen using PFA work, we calculated the molar equivalence of FA in\nthe mass loading assuming full hydrolysis. Our aggregated objective\nfunction was the gross cost of DFX production, which was calculated\nas the total cost of all raw materials in this experiment divided\nby the mass of DFX produced (see detailed calculations in Section S2.1 , Supporting Information (SI)). Only\n1% of the solvent cost was considered in the calculation as we assumed,\nas a first approximation, that 99% of the solvent could be recycled\nin an industrial process. The cost of each chemical is summarized\nin Table S13 . Above an FA-to-xylose molar\nratio of 2.2, the DFX yield fluctuated around 80% (see Table S1 , SI), which agreed with our previous\nwork. 6 Further increasing the formaldehyde\nloading did not improve productivity but instead wasted excess formaldehyde,\nleading to a higher gross cost of production. In contrast, decreasing\nthe formaldehyde-to-xylose molar fraction below ca. 2.2 resulted in\ngreater xylose degradation to humins, as evidenced by the formation\nof dark insoluble solid during the reaction and consequently, lower\nyield of DFX (see Table S1 , SI). Figure 2 Effect of (a)\nreaction time (115 g/L xylose and 75 g/L H 2 SO 4 in 2-MeTHF, FA/xylose mol ratio = 5:1, 80 °C)\nand (b) H 2 SO 4 concentrations (50 g/L xylose\nin 2-MeTHF, FA/xylose mol ratio = 5:1, 80 °C, 3 h) on xylose\nconversion and DFX yield. (c) Gross cost of production of DFX as a\nfunction of xylose loading and FA-to-xylose mole fraction (60 g/L\nH 2 SO 4 in 2-MeTHF, 80 °C, 3 h). Data fitted\nusing least-squares regression shown in the color map. The yellow\nstar indicates the lowest cost of production predicted by the regression. An empirical model was constructed using a least-squares\nregression\n( R 2 = 0.95) to predict the conditions\nfor the lowest gross production cost (indicated by a yellow star in Figure 2 c). The regression\nequation is detailed in Section S2.1 , SI,\nand the optimized conditions were 0.544 kg of xylose and 0.06 kg of\nH 2 SO 4 per 1 L of 2-MeTHF, FA/xylose mol ratio\n= 2.3, at 80 °C for 3 h ( Table S1 ,\nentry 36). The predicted reaction condition was verified experimentally\nat laboratory scale (see detailed experimental method in Section 4.2 ) with a DFX\nyield of 81% and a gross cost of production of $0.92/kg, which was\nwithin 2.2% from the model predictions (see Section S2.1 , SI). This condition was then used in the 15 L reactor\nfor the second (final) scale-up test (see Table S1 , SI). The xylose loading did not significantly affect\nthe DFX yield within\nthe tested range. However, at high xylose loading, the reaction mixture\nbecame a dense slurry due to the low solubility of xylose in 2-MeTHF\nat the beginning of the reaction, which made efficient stirring difficult.\nAs the reaction proceeded, xylose was gradually converted to soluble\nintermediates and eventually to DFX, fully soluble in 2-MeTHF. The\nformation of water during acetalization further improved xylose solubility.\nWe hypothesize that the reaction initiates at local water enrichments\nformed around xylose molecules as a similar effect has been observed\nin systems containing 99 wt % organic solvent and 1 wt % water for\nfructose dehydration to 5-hydroxymethylfurfural. 33 Meanwhile, the inorganic acid catalyst is attracted to\nthese water-rich zones, accelerating the reaction. As the reaction\nproceeds, the produced DFX preferentially moves to the organic bulk\nwhile more water is formed, overall ensuring full xylose solubilization\nand homogenization of the reaction over time. Importantly, introducing\nwater at the beginning of the reaction to solubilize all xylose has\nlimited effectiveness since additional water shifts the equilibrium\ntoward reactants rather than DFX (see Figure S1 ). 2.3 Pilot kg-Scale Production of DFX from d -Xylose and Techno-Economic Considerations Before\nthe first kg-scale trial in a 15 L reactor, we conducted reaction\ncalorimetry in a 0.5 L-scale (see Section S2.2 , SI) to assess possible thermal risks during scale-up (conditions\nin Table S1 , SI, entry 37). We identified\ntwo exothermic steps: the addition of sulfuric acid to the reaction\nmixture (Δ H = −60 kJ/mol) and the neutralization\nof sulfuric acid with aqueous sodium hydroxide (Δ H = −126 kJ/mol). At a laboratory scale, the generated heat\ncan be dissipated safely to the surroundings, while at a larger scale,\ndue to the reduced surface-to-volume ratio, precise temperature control\nis necessary. To ensure process safety, we calculated the adiabatic\ntemperature rise (Δ T ad ) for each\nexothermic step, which was found to be 52 °C for the acid addition\nand 39 °C for the neutralization. Additionally, DFX formation\nwas calculated to be exothermic as well (Δ H ° rxn = −36 kJ/mol, see Section S2.2 , SI, for detailed calculation). However, the\ndepolymerization of PFA is a well-known endothermic process, that\nmight compensate for the exothermicity of the former reaction. Overall,\nwe found that the exothermicity during DFX production can be easily\ncontrolled with common cooling systems and further mitigated by gradual\nreagent addition, as no heat accumulation was observed in the calorimetric\nmeasurements. In the first kg-scale batch, the reaction yield\nof DFX remained comparable to the lab-scale value (see Table S3 , SI). However, the isolated yield of\nthe product was only 52% mainly due to mass loss during the workup\n( Table 1 ) where a NaOH\naqueous solution was used to neutralize the acid, followed by adding\nextra water to dissolve the resulting Na 2 SO 4 salt. The large resulting aqueous layer was separated ( Figure 3 c), leading to DFX\nloss because of its solubility in water (13 g/100 g at 24 °C). Figure 3 Stages\nof DFX production in 15 L reactor during the first kg-scale\nbatch. (a) End of the reaction, (b) acid neutralization, (c) phase\nseparation (top: 2-MeTHF, bottom: water and formed Na 2 SO 4 salt), (d) concentrated organic layer before crystallization,\nand (e) crystallized pure DFX. Table 1 Results of kg-Scale Batches Performed\nin a 15 L Reactor to Isolate DFX from d -Xylose and Corncobs xylose route corncobs route batch 1 batch 2 neutralized non-neutralized DFX yields DFX reaction yield, mol % on xylose/xylan 73 74 90 a 90 a DFX isolated\nyield, mol % on xylose/xylan 52 71 76 a 78 a DFX mass losses during workup, wt % 28 3 15 13 DFX isolated\nyield, wt % of raw biomass N/A N/A 19.8 b 20.3 b process green metrics E -factor 17 1 6 2 process mass intensity (PMI), kg/kg 47 9 13 17 mass productivity, % 2 11 8 6 reaction mass efficiency (RME), % 38 57 49 46 atom economy, % 83 83 N/A N/A biomass utilization efficiency stoichiometric (BUE S ), % N/A N/A 97 97 biomass utilization efficiency based on highest\nmolar yield (BUE H ), % N/A N/A 74 76 reaction productivity for DFX, kg/L/h 0.019 0.076 0.031 0.031 process\nproductivity (all products), kg/L/h N/A N/A 0.079 0.074 process\nproductivity for DFX, kg/L/h 0.014 0.074 0.026 0.027 a The yields are presented based on\nthe amount of xylose monomer in corncob xylan (see Table S4 , SI, for compositional analysis). b The mass yield is corrected for\nthe mass of added water and formaldehyde to represent the fraction\nof original biomass that ends up in the final product. In the second kg-scale trial, we addressed the workup\ndrawbacks\nto achieve a more than 5-fold increase in productivity compared to\nthe first batch (73 vs 13 g/L/h). Guided by the sensitivity analysis\ndescribed in the previous section, we increased the xylose loading\nfrom 1 to 4 kg and correspondingly increased the PFA loading from\n0.6 to 1.8 kg, leading to a higher amount of produced DFX in the same\nreactor volume at the same reaction yield (73–74%, Table 1 ). We also reduced\nthe amount of H 2 SO 4 over 2-fold (0.4 vs 1 kg)\ncompared to the first batch, resulting in a lesser addition of NaOH\nsolution during neutralization (see Table S2 for mass balance). As a result, the mixture remained single-phase\nafter neutralization, and the precipitated Na 2 SO 4 was removed by filtration, without adding extra water. DFX crystallized\ndirectly from a concentrated reaction mixture after evaporation of\n2-MeTHF and water. With this workup procedure, the product losses\nwere reduced from 28% for the first batch to only 3% for the second\nbatch ( Table 1 ). Notably,\nwaste Na 2 SO 4 was reduced over 8-fold and the\noverall waste generated decreased by 69% compared to the first batch\n(see Table S2 , SI). This improvement was\nfurther evidenced by the 17-fold reduction in the E -factor, calculated as the ratio of the total mass of waste to the\nmass of isolated DFX ( Table 1 ). Importantly, the E -factor for the second\nbatch reached a value of 1, aligning with the typical range for commodity\nchemical production. 34 Overall, we propose\nthat this workup, implying filtration of the salt and its recovery\nas solid waste, would be easier to implement at an industrial scale\nthan the extraction and distillation steps that would have been needed\nto minimize product loss in the first batch. A preliminary techno-economic assessment\nof the described process\n(batch 2 conditions) simulated with Aspen Plus (see Section S4 , SI) revealed that over half of the production\ncost in this scenario is attributed to the cost of raw materials with\nxylose being the greatest contributor regardless of production scales\n( Figure 4 a). The market\nprice of xylose contains large uncertainties due to a small market\nsize, which could heavily impact DFX production cost ( Figure 4 b). To accommodate the price\ndisparity of xylose reported in various sources, 35 , 36 the DFX production cost was estimated using xylose selling price\nrange of $0.5 to $2/kg. As a result, the DFX minimum selling price\nvaried between $1.5 and $3.4/kg at a production scale of over 100\nktonne DFX/year. Heat integration (HI) was performed to minimize the\ncost of production (COP) of DFX by varying the minimum approach temperature\n(see Figures S8 and S9 , SI). However, HI\ndid not substantially reduce the production cost due to the process\nsimplicity and low heating and cooling demands (see Figure S10 , SI). However, HI reduced utility needs and thus\nimproved the production’s carbon footprint. Overall, the production\ncost is competitive with other solvents produced at comparable scales\n(see Table S15 , SI). Nevertheless, the\nstrong dependence on one raw material with uncertain pricing motivated\nswitching to a lower-cost agricultural waste biomass as a starting\nmaterial. Figure 4 (a) DFX production cost distribution at 150 ktonne DFX/year based\non a xylose cost of $0.5/kg, with heat integration. (b) DFX production\ncost as a function of the production scale at various xylose prices,\nwith and without heat integration (HI). 2.4 One-Pot Production of DFX from Corncobs Corncobs as a feedstock for DFX production is particularly interesting\nsince these cobs have a high xylan content of 26 wt % (see Table S4 , SI, for compositional analysis). Our\ngroup had previously reported the first example of direct synthesis\nof DFX from woody biomass via FA-assisted fractionation of hardwood\nat the lab scale, resulting in yields of 76–78 mol % based\non xylan content, while isolating FA-stabilized lignin and cellulose\npulp. 3 However, the method presented challenges\nin terms of scalability due to multiple processing steps, resulting\nin over 10 h of procedure required to isolate pure DFX from biomass.\nAdditionally, the process involved the use of carcinogenic solvent\n1,4-dioxane during pretreatment and the neurotoxic n -hexane during the workup. Furthermore, directly applying this method\noriginally developed for hardwood to another species like corncobs\nled to undesired side reactions, insufficient yields, and chemical\nwaste (see Section S3.1 , SI). Here,\nwe have developed a new and scalable method for processing corncobs,\naiming to produce DFX in high yield while retaining other valuable\nfractions. Similar to the xylose route ( Section 2.1 ), we used solid PFA and the biobased solvent\n2-MeTHF for corncob pretreatment. We replaced H 2 SO 4 with HCl 37 wt % as a catalyst, which allowed for 90 mol\n% reaction yield of DFX (on a xylan basis) in less than 1 h. In contrast,\nsulfuric acid required >6 h to achieve the same yield at identical\nconditions while keeping the same water content of 12 wt % and concentration\nof [H + ] = 0.002 mol/g (see Figure S3 , SI). The superior performance of HCl could be attributed to chlorine\nincorporation in the β–O–4 motif of lignin that\nimproves lignin solubility and accelerates biomass deconstruction, 37 and/or the highly exothermic H 2 SO 4 and its interactions with water which may lead to undesired\nlocal sugar degradation, thereby lowering the DFX yield. After\nthe pretreatment, the cellulose-rich pulp was filtered off\nand enzymatically hydrolyzed to produce glucose ( Figure 5 ). Lignin was then separated\nfrom the pulp-free reaction liquor. Following the original procedure\ndeveloped for hardwood (the neutralized route), the reaction liquor\nwas neutralized and lignin precipitated simultaneously. The lignin\nprecipitate was separated by filtration and the DFX-containing filtrate\nunderwent phase separation. Further distillation of the organic layer\nresulted in the isolation of DFX with an overall yield of 76 mol %\non a xylan basis. However, neutralization posed several disadvantages.\n(1) The presence of a separate aqueous phase caused DFX losses due\nto its partial solubility in water. Further extraction could be implemented\nto combat this, but this adds extra steps to the process. (2) NaCl\nas the neutralization product must be removed and discarded as additional\nwaste, increasing the environmental burden. (3) Neutralization is\nan exothermic process that poses thermal safety risks and requires\nprecise cooling control. (4) Neutralization with concentrated NaOH\nrequires extra safety precautions. Figure 5 Sankey diagram of the fractionation of\ncorncobs with PFA and HCl\ndepicting the two developed routes and isolated fractions: cellulose,\nformaldehyde-protected lignin, and formaldehyde-protected xylose (DFX).\nAll of the numbers are provided as weight per weight of nondried and\nnonextracted biomass percentages with the arrow thickness being proportional\nto the fraction’s weight. The weight percentages of DFX and\nFA-lignin have been corrected for the mass of incorporated formaldehyde\nto match the mass of the precursors as native biomass constituents.\nThe yields represent the results of the kg-scale process. To overcome these drawbacks, we developed an alternative\nstrategy\n(the non-neutralized route), where, instead of neutralization, we\nadded di- n -butyl ether as an antisolvent to the reaction\nliquor to induce lignin precipitation. Di- n -butyl\nether changed the overall chemical environment of the mixture (e.g.,\npolarity, dispersion forces, hydrogen bonding), destabilizing the\nlignin–MeTHF interactions and leading to lignin precipitation\nwhile keeping DFX, which is soluble in both solvents, in solution.\nThe remaining HCl in the mixture could be removed by evaporation using\ncorrosion-resistant equipment. During this process, the low-boiling\nazeotrope of water and 2-MeTHF can be distilled first (bp 71 °C).\nWithout the presence of the antisolvent, the acid concentration in\nthe reaction mixture would have continually increased, causing DFX\ndegradation. However, the high-boiling di- n -butyl\nether (bp 141 °C) remained with the DFX until all HCl was removed,\npreserving the quality of the product and ensuring a stable and controlled\nenvironment during distillation. The yields of DFX and other\nfractions isolated in pilot kg-scale\nprocess via both routes were consistent with lab-scale results (see Table S6 , SI). Overall, 90 mol % of the xylan\nin corncobs was converted into DFX, resulting in an isolated yield\nof ca. 20 wt % DFX based on the raw biomass in both routes. The total\nprocess productivity for all isolated fractions including cellulose\npulp, FA-stabilized lignin, and DFX was comparable to that of the\noptimized xylose route ( Table 1 ). The individual DFX productivity from corncobs was of course\nlower than that of the optimized xylose route due to the low xylan\ndensity in corncobs by volume compared to the use of isolated sugar. Table 1 compares\nother performance metrics for the developed processes (for calculations\nsee Section S1.3 , SI). Waste was significantly\nreduced when switching from the neutralized to the non-neutralized\nroute, as evidenced by the E -factor. Reaction mass\nefficiency (RME) shows the percentage of corncob and PFA mass that\nremains in the three isolated fractions, indicating atom economy,\nyield, and reactant stoichiometry. RME remained close to 50% primarily\nin our processes due to the use of excess PFA, the inclusion of other\ncomponents in corncobs (26 wt %), and less than 100% yield of isolated\nproducts. Biomass utilization efficiency (BUE), is a specialized metric\ndeveloped for biobased products. 38 BUE\nis defined as “the percentage of initial biomass ending up\nin the end product based on the molar mass of the monomer of the corresponding\nbiopolymer (e.g., xylose from xylan) and target biobased product”.\nThe theoretical stoichiometric BUE (or BUE S ) (i.e., assuming\n100% isolated yield of DFX from xylan) is as high as 97% as only four\nhydrogens in xylose are substituted to form the final product. Considering\nthe isolated yield of DFX on a xylan basis, the real biomass utilization\nefficiency (BUE H ) is 74–76% ( Table 1 ), demonstrating the process’ ability\nto use natural chemical structures very efficiently. 2.5 Techno-Economic Assessment of Corncob Routes The economic feasibility of the two corncob-based production routes\nwas assessed with process simulations using the experimental results\n( Figure 6 ). Due to\nthe coproduction of lignin and cellulose alongside DFX in these processes,\nthe DFX cost of production (COP) can be calculated by either prescribing\nselling prices of the other two products (product-specific) or averaging\nthe COP over the total mass of the three products (weight-specific).\nWe focus on the former, which is expected to be more realistic since\ndifferent products would have prices specific to their market reality\n(nevertheless, we report the weight-specific prices in the Figures S11 and S12 , SI). The non-neutralized\nroute is far more cost-effective over the entire range of production\nscales. The COP stabilizes below $0.94/kg at a scale above 150 ktonne\nDFX/year regardless of HI, assuming a selling price of $0.94/kg for\nKraft lignin 39 and $0.86/kg for cellulose. 40 This price range is much lower than via the\nxylose route even with the lowest xylose price estimations. In contrast\nto the other routes requiring constant replenishment of acid and base,\nthe non-neutralized route embodies less material cost (17.9% of the\ntotal COP), compared to 46.2% in the xylose route and 33.0% in the\nneutralized route. The costs associated with wastewater disposal are\nnotedly reduced by eliminating the water loading through acid neutralization\nand lignin washing. With heat integration, the DFX production cost\nwas reduced in the neutralized route at a production scale below 50\nktonne DFX/year, whereas the price stayed virtually unchanged for\na higher production scale. Similar to the xylose route, heat integration\nslightly increased the COP in the non-neutralized route. This can\nbe attributed to the lesser utility savings compared to the costly\nheat exchanger network needed for heat integration (see Figure S10 , SI). The production of 50–150\nktonne DFX/year would require a corncob supply of 163–490 ktonne/year.\nThough similar feedstock requirements have been met in the bioethanol\nproduction at multiple industrial plants around the United States, 41 this significant input could also be supplemented\nby other xylan-containing biomass sources. With an average cob yield\nof about 200 tonnes/km 2 in the U.S., 42 this translates to 815–2450 km 2 of farmland,\nrepresenting 0.2–0.6% of the total U.S. cornfields in 2021. 43 Constructing the plant close to the corn producing\nregions would likely be an important factor in managing supply chain\nuncertainties. Figure 6 (a) Product-specific cost of production of DFX at various\nproduction\nscales compared between the neutralized and non-neutralized routes\nwith and without heat integration (HI), assuming a selling price of\n$0.94/kg for lignin and $0.86/kg for cellulose. (b) Cost distribution\nof the neutralized and non-neutralized route at 150 ktonne DFX/year\nwith heat integration. The minimum selling price of DFX as a function\nof the lignin and cellulose prices for (c) neutralized and (d) non-neutralized\nroutes with heat integration and at a production scale of 150 ktonne\nDFX/year. Despite the insensitivity of the DFX production\ncost to the corncob\nprice (see Figure S8 , SI), the process\neconomy heavily depends on the selling prices of cellulose and formaldehyde-protected\nlignin. Due to the price uncertainty of these two fractions that are\nfrom a new process and have unique characteristics, we studied the\neffect of said prices on the minimum selling price of DFX ( Figure 6 c,d). For the lignin,\nwe explored a range that varied from the average Kraft lignin price\nof $0.6/kg 39 (which is considered to be\nvery low-quality lignin) all the way to $2/kg. For cellulose, we considered\na range between $0.86/kg 40 based on high-quality\nbleached pulp and $0.1/kg based on a quarter of the minimum glucose\nselling price to account for additional processing steps. 44 The selling prices of lignin and cellulose were\ncorrected to the 2021 price level from the prices in the reporting\nyear using the U.S. Consumer Price Index (CPI). 45 The DFX minimum selling price is $1.87/kg using the non-neutralized\nroute at the lowest lignin and cellulose prices we considered ($0.6/kg\nfor lignin and $0.1/kg for cellulose), which is still reasonable for\na biobased solvent (for comparison prices of other common aprotic\nsolvents are given in the Table S15 , SI).\nSuch price can be much reduced if the aldehyde-protected lignin is\ntraded at a higher price than low-quality Kraft lignin and cellulose\ncan be used to make more valuable products than glucose. 2.6 Comparative Cradle-to-Gate Life-Cycle Analysis We performed cradle-to-gate life-cycle analyses (LCA) of the three\nDFX production routes to compare their overall sustainability and\nenvironmental impact to that of existing petrochemical solvents (see Section S6 for a detailed description of the\nmethodology). The LCA revealed a global warming potential (GWP)\nimpact of 0.16 kg CO 2 equivalents (kg CO 2 -equiv)\nper kg of DFX when using xylose as feedstock, which is 93% lower than\nfor dimethyl sulfoxide (DMSO) and Cyrene ( Figure 7 a), despite the use of fossil-based paraformaldehyde\nand steam produced from natural gas, which are the major carriers\nof the environmental impact in this scenario. Production of DFX from\ncorncobs, either through the neutralization or non-neutralized route\nresulted in a net negative GWP impact of −0.12 and −0.39\nkg CO 2 -equiv/kg of products, respectively. In the case\nof the neutralized route, the main burden comes from the makeup of\nacid and base which cannot be recycled, while for the non-neutralized\nroute, the treatment of spent organic solvents is the main source\nof emissions. The carbon emission calculation is limited to the end\nof the production phase and additional carbon emissions would occur\nat DFX use and disposal, which would be identical for all three production\nroutes. Overall, all routes to produce DFX offer a substantial reduction\nin CO 2 emissions compared to other solvents (see Figure S14 , SI). Figure 7 (a) Global warming potential (GWP) impact\nof DFX for the three\nprocess routes. The dashed red and orange lines represent the biogenic\ncarbon of corncobs that is taken up during growth. The top of each\nbar represents the total GWP value for the corresponding route. Utilities\ninclude steam produced using natural gas and cooling water; wastes\ninclude the treatment of spent solvents, wastewater, and salts; other\nreagents include solvents (2-MeTHF, ethanol, and di- n -butyl ether), homogeneous acids (HCl or H 2 SO 4 ), and the neutralizing agent. (b) Heatmap comparing different DFX\nproduction scenarios for 10 midpoint impact categories of the ReCiPe\nmethodology. Data were standardized within their respective categories,\nand the standard scores were color-mapped (see calculations details\nin Section 6.4 , SI): dark green indicates\na significantly lower-than-average impact (more than twice the standard\ndeviation); yellow, average impacts; and dark red, a significantly\nhigher-than-average impact (more than twice the standard deviation).\nThe nonstandardized data are given in the Tables S16 and S19 , SI. To provide a more comprehensive environmental assessment,\nwe extended\nour analysis beyond climate change and included nine additional indicators\nfrom the ReCiPe framework ( Figure 7 b). The production of DFX from xylose can be economically\nadvantageous at a small industrial scale and low xylose price (e.g.,\n30 ktonne/year, $0.5/kg xylose) among the three routes. However, this\nroute suffers from a high impact on natural land transformation due\nto the inefficient washing step with biomethanol during xylose purification.\nThe extensive use of NaOH in the neutralized route explains its high\nimpact on acidification and freshwater eutrophication. The non-neutralized\nroute provides a minimal environmental footprint in all impact categories.\nThis route makes the life-cycle impacts of DFX comparable to those\nof GVL while reducing water usage and production cost (see Table S19 , SI). These results demonstrate how\nthe optimization of downstream processing in this case reduces energy\nand material needs, thereby minimizing the environmental footprint\nof the process. Once the impact of the process is minimized, the environmental\nbenefits of DFX production are closely tied to the sustainability\nof the corncob supply chain. 2.7 Biodegradability of DFX The cradle-to-gate\nLCA of DFX covered its life-cycle stages up to where the product is\nready for distribution, and it does not account for disposal. To explore\npossible end-of-life scenarios for DFX, we performed a biodegradability\nassessment under aqueous aerobic conditions by manometric respirometry\ntest following OECD 301F guidelines (see Section S7 , SI). 46 DFX showed an overall\n47% biodegradation after 28 days of incubation with microorganisms,\nwhile d -xylose exhibited near-complete biodegradation, which\nis consistent with the literature ( Figure S15 ). In comparison, 2-MeTHF provided only 5% biodegradation, 47 GVL—70–93%, 48 and dimethyl isosorbide was not biodegradable at all (0%)\nin the same test. 49 DFX fit into the category\ndefined by degradation between 20 and 60% within a 28-day window is\nreferred to as “inherently biodegradable” (see Table S20 , SI). The presence of a stable acetal\ngroup in DFX can possibly make the molecule less accessible to bacteria\ncompared to native sugars. However, the lag phase (time necessary\nto achieve 10% biodegradation) for DFX was less than 3 days meaning\nthat the typical bacteria from wastewater could adapt to the chemical\nquite rapidly. The final biodegradation extent was confirmed for both\nxylose and DFX but using alternative methods: gas chromatography-mass\nspectrometry (GC-MS) with Soft Ionization by Chemical Reaction Interface\nTechnology (SICRIT) showed 46% degradation of DFX, and high-performance\nliquid chromatography (HPLC) showed 99.7% degradation of d -xylose in the samples on the 28th incubation day (see Section S7.3 , SI). The results of this test show\nthe potential of DFX to be biodegraded in natural and technical conditions\n(such as wastewater treatment plants) on a relatively short time scale,\nalthough higher tiers of tests such as OECD 302 or 303 should be conducted\nto further investigate environmental effects."
} | 9,646 |
35476443 | PMC9045720 | pmc | 6,596 | {
"abstract": "At the Rowley Shoals in Western Australia, the prominent reef flat becomes exposed on low tide and the stagnant water in the shallow atoll lagoons heats up, creating a natural laboratory for characterizing the mechanisms of coral resilience to climate change. To explore these mechanisms in the reef coral Acropora tenuis , we collected samples from lagoon and reef slope habitats and combined whole-genome sequencing, ITS2 metabarcoding, experimental heat stress, and transcriptomics. Despite high gene flow across the atoll, we identified clear shifts in allele frequencies between habitats at relatively small linked genomic islands. Common garden heat stress assays showed corals from the lagoon to be more resistant to bleaching, and RNA sequencing revealed marked differences in baseline levels of gene expression between habitats. Our results provide new insight into the complex mechanisms of coral resilience to climate change and highlight the potential for spatially varying selection across complex coral reef seascapes to drive pronounced ecological divergence in climate-related traits.",
"introduction": "INTRODUCTION The impacts of anthropogenic climate change and associated extreme weather events are devastating natural ecosystems around the world. Coral reefs are especially threatened, and it is uncertain whether reef-building corals can keep pace with this unprecedented rate of environmental change ( 1 ). Fortunately, most coral species have large effective population sizes ( 2 – 4 ) with broad geographic ranges that span strong environmental gradients over which their phenotypes vary. As a result, they tend to harbor an abundance of phenotypic and genetic variation in fitness-related traits that are facing intensified selection with climate change, such as thermal tolerance ( 5 – 9 ). The resulting ecological divergence among populations and associated levels of connectivity will underlie the capacity of species to adapt to rapid environmental change over the coming decades ( 10 – 12 ). Thermal tolerance in corals is a highly heritable and polygenic trait ( 9 , 13 , 14 ), governed by a complex interplay between host genetics and symbiont community composition ( 15 – 17 ). Studies documenting transcriptome-wide changes in gene expression regularly report hundreds of genes that respond to thermal stress and that are associated with a diverse range of cellular pathways ( 18 – 21 ). Consistent with these patterns, tens to hundreds of loci are often reported in F ST -based outlier analyses among corals across large environmental gradients ( 14 , 22 , 23 ). When gene flow is high, as in most broadcast-spawning corals, adaptation favors the tight linkage of small effect loci into regions of reduced genetic distance and recombination ( 24 ). In this case, specific genomic regions may play a particularly important role in driving evolutionary change ( 13 , 25 ). Identifying genomic regions that control complex climate-related traits is central to developing a mechanistic and predictive understanding of climate change resilience in corals. In turn, this information can be used to help design effective spatial management strategies aimed at protecting areas of reef that harbor high levels of stress-tolerant alleles, as well as improve more proactive management interventions such as reef restoration and assisted gene flow initiatives ( 26 – 28 ). Variation in thermal tolerance occurs across large spatial scales ( 29 , 30 ), where populations are exposed to contrasting environmental conditions, as well as across fine-spatial scales on the same reef ( 31 – 33 ). A combination of complex bathymetry, hydrodynamics, and water quality can expose some coral communities to more variable or extreme environmental conditions than other areas of reef. Such environments can select for heat resistance and offer important insight into the mechanisms of climate change resilience in corals. Populations from these variable or more extreme habitats are a natural asset for reef management, yet we have a limited understanding of the genetic mechanisms at play. The lagoon at each of the three reef atolls at Western Australia’s Rowley Shoals is a highly variable habitat that supports extensive coral growth. These atolls have a prominent reef flat that greatly restricts any flushing or exchange of water with the open ocean. As a result, coral populations in the lagoon are regularly exposed to warmer temperature and more stagnant flow dynamics than those on the reef slope ( Fig. 1 and figs. S1 to S3). Despite these contrasting environmental conditions, some coral species, such as the widespread and ecologically important Acropora tenuis , are common in both habitats. Here, we combined low-coverage whole-genome sequencing (WGS), Symbiodiniaceae internal transcribed spacer 2 (ITS2) metabarcoding, common garden acute experimental heat stress, and RNA sequencing (RNA-seq) to explore the mechanisms that confer resilience to the periodic environmental extremes of the lagoon habitat at Clerke Reef in the Rowley Shoals. To place levels of divergence among habitats into a broader ecological and evolutionary context, we included samples from the neighboring Scott Reef system, located approximately 400 km to the northeast along the continental shelf ( Fig. 1A ). Fig. 1. Sample sites, experimental design, and sequencing methods. ( A ) Map of sampling locations in lagoon and slope habitats at Clerke Reef, Rowley Shoals. ( B ) Time-series temperature plot from the lagoon (L1) and slope (S1) habitats across the 2017/2018 summer. ( C ) Tissue samples for whole-genome sequencing (WGS) and symbiont ITS2 metabarcoding were collected from A. tenuis at two lagoon ( n = 20) and two slope ( n = 20) sites ( n = 80 total). Samples were also collected from the reef slope habitat at South Scott ( n = 10) in the Scott Reef system. ( D ) Experimental setup for common garden acute heat stress assays. Colony fragments used in experimental heat stress and RNA-seq ( n = 10 per habitat) were collected from slope 1 and lagoon 1.",
"discussion": "RESULTS AND DISCUSSION Isolated in space and time We used a low-coverage WGS approach and mapped 573,937,253 paired-end (250 cycles) shotgun sequence reads from 85 colonies of the spring spawning lineage of A. tenuis ( 34 ) to our pseudo-chromosome assembly ( 13 , 35 ), achieving a mean genome sequencing coverage of 4.2× (± 0.10 SE) per sample (fig. S4 and table S2). Multiple clustering methods based on the nuclear data indicated strong genetic subdivision between Scott Reef and Rowley Shoals (global F ST = 0.25; Fig. 2, C and D ). We did not, however, observe any clear geographic clustering of samples based on the complete mitochondrial genomes (fig. S6). Considering the strong divergence across the nuclear genome, a lack of clear structure across the mitochondrion likely reflects historical introgression between reef systems and/or the low mutation rates characteristic of cnidarians ( 36 ). Estimates of Tajima’s D at the Rowley Shoals showed a strong positive shift relative to Scott Reef, indicative of a recent bottleneck ( Fig. 2A ). Demographic changes from 50 thousand years (ka) to 5 million years (Ma) ago inferred from the Multiple Sequentially Markovian Coalescent (MSMC) based on a single high-coverage (46×) individual showed a steady decline in N e at the Rowley Shoals following a broad peak between ~100 ka and 1 Ma ago ( Fig. 2B and fig. S7). This peak in effective population size approximately 150,000 years before present is similar to other estimates for this species from the Indo-Pacific ( 35 , 37 ) and coincides with the Last Interglacial, a period of global warming and rapid sea-level change ( 38 ). Fig. 2. Demographic and evolutionary history of A. tenuis from the Rowley Shoals. ( A ) Density plot of Tajima’s D in 1-kb windows across the genome for Rowley Shoals (black) and neighboring Scott Reef (blue). For Rowley Shoals, individual black lines ( n = 10) represent bootstrap replicates. ( B ) Changes in population size through time at the Rowley Shoals using partially sequentially Markovian coalescent from deep sequencing (45×) of a single colony. Individual black lines show bootstrap replicates, and bold gray line represents the mean. ( C ) Scatterplot of the first two principal components (PC) and ( D ) admixture proportions for samples collected from Scott Reef (blue) and Rowley Shoals (gray) based on 5,493,423 loci (MAF > 0.05). High gene flow between habitats We detected low levels of nuclear divergence among lagoon and slope samples at Clerke Reef (weighted F ST = 0.007), with no clear spatial clustering of colonies by habitat ( Fig. 3B ). Principal components analysis (PCA) based on 5,493,423 single-nucleotide polymorphisms (SNPs) [minor allele frequency (MAF) > 0.05] showed all samples to be admixed across habitats, indicating high gene flow. This lack of differentiation between habitats was not surprising considering the spatial proximity of sample sites and the broadcast spawning life history strategy of A. tenuis . It is also consistent with patterns of gene flow between habitats in the congener Acropora digitifera from the Rowley Shoals ( 39 ). A lack of any clear genetic structure between habitats at Clerke Reef suggests that local recruitment comes from a mixed larval pool. In such cases, environmental heterogeneity gives rise to patterns of spatially varying selection where different habitats select for genotypes from a common gene pool that match the local conditions ( 40 ). In turn, this results in an overall increase in adaptive genetic variation in the broader metapopulation ( 41 ). Unlike directional selection, which reduces genetic variation by fixing beneficial alleles, balancing selection maintains genetic variation within a species and plays a crucial role in adaptation in species with high gene flow and that span strong environmental gradients ( 42 ). Fig. 3. Genome-wide patterns of differentiation in A. tenuis from lagoon and slope habitats. ( A ) Manhattan plot of F ST across the genome using 100-kb windows (10-kb step) and based on the unfiltered SFS. Alternating colors indicate different chromosomes. Each point is the average F ST for all SNPs in that window. Red dashed line denotes top 0.01% windows. ( B ) Scatterplot of the first two principal components for colonies from lagoon (red) and slope (blue) habitats. ( C and D ) Zoomed-in plots of F ST (red line, 100-kb windows) and population-independent selection coefficients (PCAngsd) for SNPs (black points) surrounding outlier regions calculated in PCAngsd ( 44 ). The program uses posterior expectations of the genotypes to identify SNPs with a distribution that exceeds expectations under neutrality along the first principal component ( 44 ). We calculated this selection statistic for each SNP (MAF > 0.05) along each chromosome separately and calculated outlier probabilities using the pchisq function (two-tailed mode) in R. Signatures of selection amidst high gene flow When gene flow is high, selection favors the tight linkage of small effect loci into regions of reduced genetic distance and recombination ( 24 , 43 ). To this end, we searched for signs of adaptation to the warmer lagoon habitat at Clerke Reef by calculating F ST between habitats using 100-kb windows with 10-kb steps. Using this sliding window approach, we identified several large regions of the genome that showed high levels of differentiation and were therefore strong candidates for selection ( Fig. 3A ). The most extreme values genome-wide overlapped six islands of divergence spread across four different chromosomes (chromosomes 1, 2, 4, and 11). Mean F ST across these windows was 0.047, compared to a genome-wide average of 0.007 across all windows. To provide further support to these findings, we used a habitat-blind approach and tested whether any of the outlier regions also included SNPs with a distribution that exceeded expectations under neutrality along the first principal component ( 44 ), indicating that they were likely candidates for selection. We calculated this selection statistic based on genotype likelihoods for each SNP (MAF > 0.05) along each chromosome separately and then converted this value to an outlier probability using the pchisq function in R (two-tailed mode). This population-blind approach identified two of the six outlier loci identified using the sliding window approach, locus 1 and locus 3, to include a large number of SNPs showing signs of selection, with distributions that exceeded what was expected under neutrality along the first principal component ( Fig. 3, C and D , and fig. S8). Estimates of linkage disequilibrium (LD) across locus 1 and locus 3 showed F ST window peaks to span strong linkage blocks across hundreds of loci ( Fig. 4, A and B ). Across these regions, we observed a significant increase in MAFs in lagoon colonies relative to those from the reef slope (Welch two-sample t test, P < 0.001; Fig. 4, C and D ). Allele frequencies seemed to be concentrated at a particular value in each habitat, a further sign of reduced recombination and supporting the observed patterns of LD. When we carried out a PCA across locus 1 and locus 3, samples clustered strongly into three distinct groups along the first principal component, likely representing the three possible genotypes for each haplotype block ( Fig. 4, E and F ). Notably, the two dominant genotypes showed different proportions in lagoon and slope samples. Fig. 4. Linkage disequilibrium in A. tenuis across F ST outlier regions. ( A ) Heatmap of linkage disequilibrium ( D ′) across locus 1 on chromosome 1 and ( B ) locus 3 on chromosome 2. Warmer colors indicate higher linkage among SNPs. ( C ) Histogram of MAFs in lagoon (red) and slope (blue) samples from Rowley Shoals across outlier locus 1 and ( D ) locus 3. ( E ) Sample loadings onto the first principal component across outlier locus 1 and ( F ) locus 3 showing different frequencies of the three possible haplotypes in each habitat. Locus 1 not only directly overlapped genic regions with homologs to two Tolloid-like proteins involved in zinc ion binding and development but also included a number of genes within the broader linkage group that had homology to classic stress response proteins, including E3 ubiquitin protein ligases (Lin41/Cop1), tumor necrosis factor receptors (TFIP8, TRAF2, and TRAF3), and heat shock proteins (HSP70) and chaperones (DnaJ homologs) (table S3), but this gene list was not enriched for any specific molecular function ( P adj < 0.05). Locus 3 F ST window peak directly overlapped genic regions with homology to phosphatidylinositol-glycan biosynthesis class A proteins. Several other notable genes fell within the broader linkage block upstream of locus 3, including a death-associated protein kinase 3 (DAP kinase 3), a serine/threonine kinase (Kalirin), myosin light chain kinase (MLCK), a heat shock protein (HSP75), and a NACHT domain-containing protein (NLRP5) (table S4). Genes in this broader linkage group upstream of locus 3 were highly enriched for adenosine triphosphate (ATP) binding [Gene Ontology (GO) 0005524, P adj = 0.00026], suggesting a role in enzyme regulation and environmental response pathways. Areas of high linkage and reduced recombination are often associated with chromosomal rearrangements, such as inversions, which play a key role in driving phenotypic variation and local adaptation ( 24 , 45 ). The role of structural rearrangements driving adaptation in the marine environment is well documented in teleost fishes ( 46 ), but less so for other marine organisms. For example, an approximate 5-Mb chromosomal rearrangement in Atlantic cod ( Gadus morhua ) enables adaptation of fjord populations to hyposaline conditions despite high gene flow with nearby oceanic populations ( 47 ). Whether the patterns observed here are driven by actual inversions remains unclear, and future research that uses long-read sequencing will help elucidate the role of structural rearrangements in the adaptation of reef coral to climate change. Gene flow in A. tenuis between habitats is high across most of the genome, but barriers to gene flow exist. A number of genomic regions showed strong habitat-specific shifts in allele frequencies across hundreds of linked SNPs. This pattern is consistent with adaptation amidst gene flow, where beneficial genetic variants are consolidated into regions of reduced genetic distance and recombination ( 43 , 48 ). Our data show that no single locus confers resilience to the warm lagoon, but coordinated changes in allele frequencies at hundreds of loci contribute to survival. However, if those allele frequency shifts confer resilience to certain environment stressors, then that shift must come at a cost; otherwise, the alternate allele would reach fixation in the broader metapopulation, and there would be no variation in this trait. A better understanding of the fitness-related trade-offs is essential future research but points to growth, calcification, and reproduction as primary physiological costs of survival in stressful environments ( 6 , 49 , 50 ). Symbiont associations are stable across habitats Complex interactions between the coral host and the endosymbiotic algae (family Symbiodiniaceae) are superimposed on the host genetics that drive variation in environmental tolerance ( 17 , 51 , 52 ). Despite the pronounced environmental differences between habitats, metabarcoding of the Symbiodiniaceae ITS2 region of ribosomal DNA (rDNA) for a subset of colonies ( n = 65) used for WGS and that were collected across the four sample sites at Clerke Reef revealed no major differences in community composition between habitats ( Fig. 5A ). All colonies associated exclusively with Cladocopium spp. and were dominated by ITS2 type C40, with various background levels of other Cladocopium ITS2 types ( Fig. 5B ). An average of 106,213 reads (± 5990 SE) per sample were clustered into 60 distinct ITS2 sequence variants (table S9). Analyses in SymPortal indicated that these ITS2 variants formed two distinct ITS2 type profiles in the data ( Fig. 5B ), one of which occurred at high frequency in one of the slope sites. This ITS2 type profile was driven by the background ITS2 variant 1373_C, which occurred at moderate frequencies (~7%) in most S1 colonies but was mostly absent from other sites. However, on the basis of the defining intragenomic variant (DIV) profiles, it is possible that more high-resolution loci may reveal ecologically relevant differences in symbiont communities between habitats. Fig. 5. Symbiont community composition in A. tenuis from lagoon and slope habitats. ( A ) Scatterplot of the first two principal components of the symbiont communities (family Symbiodiniaceae) in coral colonies from the lagoon (red) and slope (blue) habitats. ( B ) Barplot of relative abundance of the different ITS2 types [amplicon sequence variants (ASVs)] at each sample site. Each vertical bar represents an individual colony. Only the most abundant ITS2 variants are displayed in the legend. Boxes below the plot show the distribution of the two symbiont “ITS2 type profiles” identified in the data using SymPortal ( 77 ). Lagoon corals are primed for heat stress To explore the physiological differences between coral from the two habitats, we combined common garden acute heat stress experiments with RNA-seq. We returned to Clerke Reef a year later and exposed fragments of 15 colonies of A. tenuis (unknown spawning lineage) from the lagoon and slope habitats to acute experimental heat stress using a portable temperature-controlled flow-through seawater system aboard the Australian Institute of Marine Science’s research vessel RV Solander . After 24 hours of acclimation in the tanks, nubbins were exposed to acute heat stress, which consisted of a 3-hour temperature ramp to 34°C starting at 10:00 a.m., followed by a 3-hour hold at 34°C and then a 2-hour ramp back down to ambient temperatures ( 15 , 53 , 54 ). The next morning, 20 hours after the acute heat stress assay began, replicate nubbins from control and heated treatments were photographed to quantify pigment loss using the photographic method ( 55 , 56 ) and then flash-frozen in liquid nitrogen for gene expression analyses. Samples were first screened using the RNA-seq dataset to ensure that only colonies from the spring cohort were included in any downstream analyses. Results from the experiment showed that coral colonies from the reef slope were far more pigmented than lagoon corals at the onset of the experiment but suffered greater pigment loss following heat stress ( Fig. 6A ). These patterns were consistent with our visual scoring method with the CoralWatch Coral Health Chart (fig. S9). Although we did not measure cell density or chlorophyll directly, our photographic method has been shown to correlate strongly with chlorophyll levels ( 56 ). Reduced symbiont cell densities and chlorophyll levels have been directly linked to bleaching susceptibility in coral ( 57 , 58 ), and maintaining lower levels appears to be one mechanism through which lagoon corals minimize photooxidative damage of a shallow high-temperature environment. Fig. 6. Physiological differences in A. tenuis from lagoon and slope habitats. ( A ) Changes in pigmentation (red channel intensity) of coral nubbins from lagoon (red) and slope (blue) habitats exposed to control (28°C) and heated (34°C) conditions. The darker colors indicate higher density of that pigment value. Values range from 0 (black) to 255 (white), and a positive shift to the right indicates a decline in pigment. ( B ) Scatterplot of the first two principal components of normalized gene expression in A. tenuis in control (blue) and heated (red) treatments across 21,230 genes with a minimum mean depth per sample of 5. Circles denote samples from lagoon, and triangles denote samples from reef slope. ( C ) Density distribution of colonies along the first principal component shows control samples from lagoon to cluster closer with the heated treatments than with the other slope controls. ( D ) Overall transcriptomic shift measured as the distance in multivariate space between heated and control samples for each genotype. ( E ) Regression of differentially expressed genes between habitats after 2 days of acclimation at ambient temperatures. Each point represents a gene that showed significant differences in baseline expression between habitats. Blue points are down-regulated in the lagoon, and orange points are up-regulated in the lagoon. Red points represent genes that form part of the heat stress response and were also differentially expressed between control and heated treatments across all samples. ( F ) Linear regression of levels of log fold change in heat-responsive genes between habitats, illustrating that patterns of gene expression under acute heat stress mirror expected changes in expression between habitats. ( G ) Functional enrichment analyses indicating enriched GO annotations for genes that showed differences in baseline levels of expression between habitats. Blue is up-regulated and red is down-regulated in lagoon samples. RNA-seq of samples used in the common garden heat stress experiments revealed pronounced differences between corals from the two habitats. Normalized gene expression profiles across 21,230 genes (table S5) showed that lagoon corals held at ambient conditions clustered closely with samples from the heated treatments and experienced a smaller overall transcriptomic shift in response to acute heat stress than corals from the slope ( Fig. 6, B to D ). In total, we identified 274 genes that differed in baseline levels of expression ( P adj < 0.05) between colonies from the two habitats ( Fig. 6E and table S6). This gene list was enriched for GO terms related predominantly to transport, metabolism, and signaling ( Fig. 6G ). Data from our acute heat stress assay showed that approximately 12% ( n = 34) of the genes that differed in baseline expression between habitats were heat-responsive genes that were differentially expressed between heated and control treatments across all samples ( Fig. 6E and table S7). This gene set followed intuitive expression profiles based on habitat: Genes that were up-regulated under acute heat stress had higher baseline expression in lagoon corals, and genes that were down-regulated in response to acute heat stress had higher baseline expression in slope corals ( Fig. 6F ). This gene set included classic stress response genes that repeatedly appear in genomic studies of thermal tolerance in corals ( 19 , 59 – 61 ), such as ubiquitin and zinc finger proteins and serine/threonine protein kinases (table S8). Levels of congruence were relatively low between differentially expressed genes from RNA-seq dataset (habitats or heat stress) and the genomic islands of divergence identified using our low-coverage WGS approach; few heat- or habitat-responsive genes occurred in regions of high genetic differentiation in the WGS datasets. The only exception to this was locus 1 (fig. S11), with an F ST window peak that directly overlapped till1 , a Tolloid-like proteinase involved in zinc-finger binding and that showed strong changes in regulation under experimental acute heat stress (log 2 fold change = 2.62, P adj = 5.8 × 10 −14 ) but that was not differentially expressed between habitats under control or heated conditions (log 2 fold change = 0.46, P adj = 0.78). It was also noteworthy that a number of other heat-responsive ( n = 7) or habitat-responsive ( n = 3) genes occurred up- or downstream of the window peak on locus 1 (fig. S11) in the broader linkage block. However, these genes occurred well outside the F ST window peak and were spread among dozens of other genes that did not show habitat- or temperature-driven differences in gene expression. Corals are capable of large physiological adjustments in response to heat stress, and individuals thriving in environments that are regularly exposed to environmental extremes often show pronounced differences in baseline levels of gene expression than adjacent colonies from more benign environments ( 15 , 62 , 63 ). Higher baseline expression of stress response genes can promote stress tolerance and maintenance of cell homogeneity, while reduced expression can act to limit the negative physiological responses to stress ( 64 ). Throughout the daily tidal cycle, corals in the lagoon at Clerke Reef experience warmer water and less current flow than their reef slope counterparts. Our data show that survival in such an environment requires a physiological shift across hundreds of genes. Through this physiological shift, lagoon corals are primed for stressors associated with the daily tidal cycle. Ecological divergence among populations of coral will be a key driver in their capacity to adapt to a rapidly changing environment. This is particularly true for isolated reef systems, which are largely reliant on local standing genetic variation to adapt to climate-related stressors. In Western Australia, the offshore oceanic atolls of the Rowley Shoals are isolated in space and time, characterized by strong genetic differentiation with neighboring reef systems and a skew in the allele frequency spectrum indicative of a recent bottleneck. Across the Clerke Reef atoll, we identified high gene flow between slope and lagoon habitats but identified a number of genomic islands of divergence that exhibited signs of restricted gene flow and strong LD, suggesting that they may play an important role in adaptation to the marginal lagoon environment. Acute heat stress experiments showed lagoon corals to be more resistant to heat stress than slope corals, and accompanying RNA-seq data showed that this was, in part, achieved through a habitat-specific transcriptomic shift that involved hundreds of genes, a large fraction of which were part of a larger heat stress response. Identifying the genomic regions that confer resilience to different stressors, and understanding the physiological trade-offs associated with those shifts, will allow the development of targeted genetic assays to rapidly screen corals for the complex traits needed to effectively survive climate change."
} | 7,111 |
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