Shotgun metagenomics reveals environmental instability reduces resistance to shocks by enriching specialist taxa with distinct two component regulatory systems

Shotgun metagenomics reveals environmental instability reduces resistance to shocks by enriching specialist taxa with distinct two component regulatory systems | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Shotgun metagenomics reveals environmental instability reduces resistance to shocks by enriching specialist taxa with distinct two component regulatory systems Simon Mills, Umer Ijaz, Piet Lens This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4382699/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 31 Mar, 2025 Read the published version in npj Biofilms and Microbiomes → Version 1 posted 9 You are reading this latest preprint version Abstract Different microbial communities are impacted disproportionately by environmental disturbances. The degree to which a community can remain stable when faced with a disturbance is referred to as resistance. However, the contributing ecological factors, which infer a community’s resistance are unknown. In this study, we investigate the impact of historical environmental stability on ecological phenomena and in turn, microbial community resistance to shocks. Three separate methanogenic consortia, which were subjected to varying degrees of historical environmental stability, and displayed different levels of resistance to an organic overload were sampled. Their community composition was assessed using high throughput sequencing of 16S rRNA genes and assembly based metagenomics. A suite of ecological analysis were applied to determine the effect of environmental stability on ecological phenomena such as microbial community assembly, microbial niche breadth and the rare biosphere and in turn, the effect of these phenomena on community resistance. Additionally, metagenome assembled genomes were analysed for functional effects of prolonged stability/instability. The system which was subjected to more environmental instability experienced more temporal variation in community beta diversity and a proliferation of specialists, with more abundant two component regulatory systems. This community was more susceptible to deterministic community assembly processes caused by a large environmental disturbance. These results imply that microbial communities experiencing longer term environmental instability (e.g. variations in pH or temperature) are less able to resist a large disturbances. Biological sciences/Microbiology/Microbial communities/Microbial ecology Biological sciences/Microbiology/Applied microbiology 16S rRNA Metagenomics Two component regulatory systems Community Assembly Anaerobic Digestion Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1 Introduction The impact of environmental disturbances on microbial communities has been a central theme of ecological study for decades 1–3 , with implications for global biogeochemical cycling 4 , ecosystem functioning 3 , environmental biotechnology 5,6 and human health 7 . For example, methanogenic microbial communitiesplay a large role in carbon cycling 8 and underpin biotechnologies such as anaerobic digestion 9 . These communities are particularly susceptible to disturbances, as they are highly syntrophic and dysbiosis in one of the necessary trophic groups causes system failure 10,11 . The ability of a microbial community to maintain stability despite disturbances is called resistance, whereas its capacity to recover to its original state is known as resilience 1 . These properties vary among microbial communities, however it is still unclear what causes these variations. One theory is that prior exposure to environmental disturbances increases resistance and resilience 6 . This may be due to changes in underlying ecological properties of the community or the development of functional traits which may give rise to improved resistance and resilience. Ecological properties of a community which may influence resistance and resilience include niche breadth, community assembly, and the extent of the rare biosphere. Niche breadth refers to the extent of a given taxa’s niche, relative to prevailing environmental conditions and is directly influenced by environmental (in)stability 12 . Taxa with wider niches are considered generalists, whereas taxa with narrow niches are considered specialists 13 . It is often stated that generalist taxa will thrive in different environments as they have a wider niche and are not inhibited by variable conditions 13 . In contrast, specialists tend to dominate in stable environments where they can utilise resources more efficiently within a narrow niche 14 . Therefore, historical disturbances are expected to lead to the proliferation of generalist taxa, thus increasing resistance and resilience to further disturbances. The rare biosphere plays a role in fulfilling niches which are altered due to environmental disturbances by acting as a “seed bank”, which supplies new taxa to occupy new or varied niches 15 . In the absence of a disturbance, there are some natural fluctuations in community composition around a stable state of equilibrium, with little opportunities for drastic change in microbial community composition 16 . However, during a disturbance, if total community function is to remain the same, members of the original community must persist (resistance) or be replaced by organisms which can fulfil the same niche, potentially seeded by the rare biosphere 17 . Frequent disturbances can increase the proportion of rare taxa in a community 18 and may therefore strengthen the likelihood of the rare biosphere fulfilling a new or altered niche space. The ecological properties of a microbial community which impart resistance may be developed during microbial community assembly or re-assembly after a disturbance. Therefore, frequent disturbances may compound these effects. Microbial community assembly is broadly categorised as stochastic or deterministic 19 . The natural variations in microbiome stability are often stochastic in nature. Deterministic community assembly often refers to external forces which drive community development such as environmental gradients or disturbances, which may modify niches and force the community to change its composition 20,21 . Functional changes which could occur in response to historical instability may include a more diverse functional capacity or enrichment of stress response mechanisms such as two component regulatory systems (TCRS). Previously TCRSs were enriched during unstable periods of operation in a full scale activated sludge plant 22 . However, the contribution of TCRS to the degree of resistance in an anaerobic microbial community is unknown. Previously, we reported that historical instability led to the development of a significantly more diverse microbial community in high rate anaerobic digesters 23 . However, this increased diversity did not result in improved resistance, as has been reported in several previous studies 24,25 , and was contrary to our original hypothesis that more diverse communities would have improved resistance. Therefore, in the present study, we further investigated the impact of environmental instability on ecological and functional properties of these communities, to identify a potential cause for reduced resistance, despite increased diversity. Three laboratory microcosms of methanogenic microbial consortia were subjected to varying degrees of environmental stability before receiving a massive influx of excess carbon, which typically result in acidosis and community dysbiosis 23 . In depth ecological analysis of the microbial communities prevailing in each of these microcosms revealed that historical environmental instability led to a community with inflated numbers of specialist taxa and reduced resistance to an organic shock load. 2 Methods 2.1 Experimental Design Granular sludge was sampled from three methanogenic bioreactors which were subjected to varying degrees of environmental stability 23 . Associated physiochemical parameters were reported previously 23 and are summarised in Fig. 1 . The bioreactors were supplied with a synthetic dairy wastewater consisting of skimmed milk powder, trace elements and macronutrients. R1 was maintained stable for the entire period of operation. After a period of approximately 40 days R2 and R3 were subjected to various small disturbances (D1-D4) through variations in influent composition (Fig. 1 ). R2 was subjected to repeated disturbances in the form of excess carbon influxes by increasing the concentration of the influent. R3 was subjected to a range of disturbances which are common in methanogenic bioreactors including ammonium and sulfide toxicity as well as a temperature and pH drop(Fig. 1 ). Subsequently, all three bioreactors were subjected to a massive carbon influx by increasing the chemical oxygen demand (COD) concentration of the feed 8-fold. 2.2 16S rRNA Gene Amplicon Sequencing Samples for 16S rRNA gene amplicon sequencing were taken from each microbial community at 9 timepoints (T1-T9) throughout the experiment, before and after a disturbance was applied (Fig. 1 ). DNA extraction, sequencing of the 16S rRNA gene and bioinformatic sequence processing were performed as described previously 23 . In addition to previously reported bioinformatic processing, PICRUSt2 26 within the QIIME2 environment was used to recover KEGG enzymes and MetaCyc pathway predictions (not done in the original publication) for all the samples. For this purpose, we used the parameters --p-hsp-method pic --p-max-nsti 2 in qiime picrust2 full-pipeline [ https://github.com/gavinmdouglas/q2-picrust2 ]. Statistical analysis was performed using R software and the details are provided in the Supplementary Material. Raw amplicon sequencing data used in this study has been deposited in the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) under the BioProject accession number PRJNA1030189. 2.3 Metagenomic analysis A total of 18 metagenomics samples were processed. These samples were taken from all three reactors at timepoint 8 (T8) prior to the large OLR shock (Fig. 1 ). Reads were quality trimmed using Sickle v1.200 27 at an average Phred quality of 50bp were retained after trimming for downstream processing. This gave a total of 641,256,041 reads from all samples. All forward and reverse reads were then collated together together for co-assembly using megahit with the parameters --k-list 27,47,67,87 --kmin-1pass -m 0.95 --min-contig-len 1000 28 . his gave us a total of 653,274 contigs, a total of 213,972,1678 base pairs (bp), maximum of 654,587 bp, average length of 3,275 bp, and an N50 score of 4,774 bp. The MetaWRAP pipeline 29 was used to bin the contigs with three different binning algorithms: metabat2 (610 bins), maxbin2 (520 bins), and CONCOCT (381 bins ). Checkm 30 was applied to the completion and level of contamination of each bin. The bins from the three binners were consolidated together within the MetaWRAP framework, only retaining bins with \(\ge\) 50% completion, and \(\le\) 10% contamination to give a final set of 380 bins or metagenomic assembled genomes (MAGs). We applied CheckM on these bins (Parks et al., 2015) to assess their completion and contamination. Within MetaWRAP framework, the bins from the three binners were consolidated together, retaining bins with >50% completion and <10% contamination to give a final set of 380 bins (MAGs). We obtained a mean genome completion of 78.68% and a mean contamination of 2.29% for bins. To deduce the phylogeny of the Metagenome-Assembled Genomes (MAGs), we employed GToTree (Lee, 2019). The software offers various Single Copy Genes (SCGs) sets based on the resolution of domains and the taxonomic rank of interest. Specifically, we utilized two SCG sets: a 25-gene set for Bacteria and Archaea (resulting in the phylogeny recovery for 275 MAGs). Finally from 380 bins, for downstream statistical analysis, we have used bins with >75% completion, i.e., 219 bins. The METABOLIC 32 pipeline was used to assign functions to each bin using GTDB-TK 33 . METABOLIC was also used for the annotation of proteins using KEGG 34 . The obtained sample read coverages per bin \({C}_{i,j}\) was then multiplied with feature coverages (returned from METABOLIC) per bin \({F}_{j,k}\) to obtain feature coverages per sample \({n}_{i,k}\) as a matrix product \({n}_{i,k}=\sum _{j}{C}_{i,j}{F}_{j,k}\) . Only bins with a genome completeness of \(\ge\) 75% and genome contamination \(\le\) 5% were used, resulting in a final dataset of n = 18 x p=380 bins. Abundance tables for the following databases were then also generated: KEGG Modules (n = 18 samples x p=764 features, i.e., peptidases and inhibitors) and KEGG Submodules (n = 18 samples x p = 2,235 features). 3 Results 3.1 Physiochemical data Physiochemical, bioreactor performance data and basic measures of alpha and beta diversity were reported in detail previously 23 and are summarised in Fig. 1 . R1 demonstrated the most resistance to the shock in terms of performance (soluble COD removal), R2 performed similarly, although slightly worse than R1, whereas R3 performed the worst overall and did not recover to previous levels of performance by the end of the experiment (Fig. 1 ) 23 . In addition, each reactor was sampled at 9 different timepoints throughout the trial (T1-T9) and alpha diversity analysis indicated that the variable environmental conditions of R3 led to significantly higher Shannon diversity at T8, right before the applied shock 23 (Fig. 1 ). 3.2 Diversity The local contribution of each sample to the mean beta diversity (LCBD), across all samples was calculated to identify which reactor had more variation in microbial community composition over time (Fig. 2 ). Beta diversity varied more in R3, when measured in terms of ASV abundance (Bray-Curtis) and phylogeny (unifrac) (Fig. 2 ), particularly at T9. Beta dispersion analysis was applied to identify and compare the degree of inter-sample variability within groups (reactors). Significant differences in phylogenetic (unifrac) beta dispersion were identified between R3 and R1, indicating that the phylogenetic composition of R3 was more variable over time than that of R1. 3.3 Microbial Community Stability A group of core taxa (limited to 30) with stable relative abundance and minimum coefficient of variation in each reactor were identified. These taxa did not vary significantly in composition in response to the applied disturbance regimes (Fig. 3 ). The coefficient of variation for the stable community of R3 was higher (CV: 0.0606) than that of R1 (CV: 0480) and R2 (CV: 0419), indicating that although this community was considered stable in its own right, it was more variable than the communities present in R1 or R2. The core communities of each reactor also differed in composition (Fig. 3 ). The stable community of R1 was dominated by Streptococcus sp., Methanolinea sp., Paludibacter sp., and Desulfovibrio sp., as well as members of the Anaerolinaceae family (Midas_g_667) and the order Cloacimonadales (Midas_g_71685). The stable community of R2 was dominated by Smithella sp., Methanolinea sp., Methanomassiliicoccus sp., as well as members of the family Cloacimonadaeae (Midas_g_63301). The stable community of R3 was heavily dominated by a member of the Spirochaetaceae family (Midas_g_14041), particularly at T9 after the shock load, with lower abundances of Leptolinea sp, genera from the Bacteroidetes_vadinHA17 family (Midas_g_19) and the order Syntrophales (Midas_g_134). In contrast, the taxa that varied significantly with time and had a high correlation with respect to sampling day in each reactor were also identified (Fig. S1 ). The most prominent changes over time in reactor 1 included increases in candidate order Moranbacteria (midas_g_11415) and Methanobacterium sp. In R2 Methanospirillum sp. and a genus in the Syntrophomonadaceae family (midas_g_53834) increased in relative abundance over time, whereas the relative abundance of Syntrophobacter sp. decreased. The most obvious change in the microbial community composition of R3 was an increase in relative abundance of genera from the Eubacteriaceae family (midas_g_2229), the class Thermodesulfovibrionia (midas_g_581) and Anaeromusa − Anaeroarcus sp.. There was also a higher abundance of a genus (midas_g_14041) in the Spirochaetaceae family (Fig. S1 ). 3.4 Community Assembly The relative contributions of stochastic and deterministic influences on community assembly were assessed using two methods; the nearest taxon index (NTI) and normalized stochasticity ratio (NST) 35 , both of which revealed similar patterns. Microbial community assembly is considered entirely deterministic when NST is 0% and entirely stochastic when NST is 100%. A 50% stochasticity ratio separates deterministic ( 50%) assembly processes 35 . A positive NTI value indicates deterministic influence, whereas a negative value indicates stochastic processes dominate. Community assembly in all 3 reactors was largely stochastic in nature (i.e. NST > 0.5) and remained fairly stable in R1 and R2 (Fig. 4 ). However, there was a slightly different temporal pattern in R3, which showed a slight curve over the course of the trial, indicating that stochasticity remained high, but decreased slightly until T5, before increasing again to T8 (Fig. 4 ). The final timepoint in R3 however, experienced a drastic increase in deterministic influences, as evidenced by both NST ( 2) values. 3.5 Rare taxa The relative contribution of abundant taxa, conditionally rare taxa, persistently rare taxa and other rare taxa was calculated according to the methods outlined by Yang et al. 36 . Conditionally rare taxa are defined as taxa which exhibit a maximum relative abundance at least 100 times higher than its minimum value. Persistently rare taxa are defined as taxa which never exhibited a maximum relative abundance 5 times greater than its minimum. Other rare taxa are defined as taxa which exhibited a maximum relative abundance between 5 and 100 times greater than it’s minimum 36 . No conditionally rare taxa were identified in any of the reactors investigated (Fig. 5 ). However, some trends were apparent within the persistently rare taxa and other rare taxa. There was a decreasing trend in the contribution of persistently rare taxa from R1 to R3, which corresponded to a similar trend in the degree of functional resistance that each reactor demonstrated when faced with the applied organic loading rate (OLR) shock load (Fig. 1 ), i.e. R3 had the least persistently rare taxa and responded worst to the large disturbance. However, the inverse was observed for other rare taxa (Fig. 5 ). This indicates that there were no large fluctuations in taxa abundance, resulting in conditionally rare taxa (i.e. maximum relative abundance at least 100 times higher than its minimum value). However, R3 did have more taxonomic fluctuations resulting in the presence of other rare taxa (i.e. maximum relative abundance between 5 and 100 times greater than its minimum) (Fig. 5 ). 3.7 Microbial Generalists and Specialists In order to ascertain how different reactor histories impacted microbial lifestyles (i.e. generalists or specialists), the MicroNiche 37 package was used. To determine how the predominant microbial lifestyles in each reactor impacted their resistance to large environmental shocks, each reactor was considered to be a separate environment and only timepoints 1–8 (prior to the OLR shock) were considered for this analysis. In total, 124 genera were identified which fit the criteria for having a generalist or a specialist lifestyle across all three reactors. R1 contained 82 generalists and 7 specialists, R2 contained 71 generalists and 9 specialists (Fig. 6 ). R3 however, contained only 39 generalists but had 18 specialists (Fig. 6 ). The overlap between these 124 taxa was also considered to identify taxa which coincided with each other. Figs. S5-S7 show that there was much less overlap amongst the taxa in R3 due to the presence of more specialists. A full list of specialist and generalist taxa and their classification in relation to each reactor is provided as supplementary Table S1 . 3.8 Metagenome Assembled Genomes 380 Metagenome assembled genomes with a completeness greater than 75% and contamination of less than 5% were recovered from all samples (n = 18). Phylogenetic marker genes for 183 of these bins were identified and aligned using phylogenetic marker genes for bacteria and archaea to determine the phylogenetic distribution of MAGs. Several bins were more abundant in all three reactors at T8 including bin 57 assigned to the genus Tidjanibacter and bins 115 and 46 assigned to the orders Treponematales and Syntrophomonadia (Fig. 7 ). Three bins (bin. 69, bin.198 and bin.348) within the Patescibacteria phylum (also known as candidate phyla radiation) were also more abundant at T8 than at T1, including one which was included in the 20 most abundant taxa (Fig. 7 ), assigned to order level as Moranbacteriales. Both of these bins became especially abundant in R1 and R2 at T8 but were not as dominant in R3 (Fig. 7 ). 3.9 Functional differences in MAGs The richness of KEGG modules decreased dramatically over time in R1 and R2 but there was no significant change in R3 (Fig. 2 ). However, evenness of KEGG modules in R3 was significantly lower at T8, indicating that the functional profile of the community was dominated by fewer KEGG modules. A similar clustering pattern was observed when detected among KEGG modules when principal coordinate analysis (PCoA) was applied, where samples from each timepoint were distinct (Fig. 2 ). The abundance of two component regulatory systems (TCRS) was variable among samples, with R3 timepoint 8 (R3T8) forming a distinct cluster (Fig. 8 ). 82 two component regulatory systems were more abundant in R3T8 than in R1 timepoint 8 (R1T8). Several distinct clusters were key in differentiating R3T8 from the other samples, particularly R1T8. Two component regulatory systems within these clusters which were more abundant at T8 included LuxQN/CqsS − LuxU − LuxO (quorum sensing), VicK − VicR (cell wall metabolism), AtoS − AtoC (cPHB biosynthesis) and CiaH − CiaR. TCRS which were more abundant in R1T8 included ArcB − ArcA (anoxic redox control) and HupT − HupR (hydrogenase synthesis regulation). TCRS which were more abundant in R1T8 than R3T8 included ArcB − ArcA (anoxic redox control) and HupT − HupR (hydrogenase synthesis regulation) (Fig. 8 ). 4 Discussion This study found that unstable environmental conditions (in R3) resulted in the development of a microbial community rich in specialist taxa (Fig. 6 ), with an abundance of two component regulatory systems (Fig. 8 ). This led to reduced resistance to a shock load, which manifested as a large shift from stochastic community assembly to deterministic community assembly (Fig. 4 ) and ultimately, failure of the system. In contrast, a stable environment (in R1) led to the proliferation of generalist taxa (Fig. 6 ) with MAGs containing less abundant two component regulatory systems (Fig. 8 ), which increased shock resistance and reduced the effect of deterministic community assembly. The failure of R3 was surprising as the microbial community at T8 prior to the application of the final shock load had a significantly higher alpha diversity (Fig. 1 ) than R1 or R2. This is contrary to a lot of research which demonstrates that increased diversity leads to increased stability, resistance and functioning in other anaerobic bioreactors treating different types of wastewaters 24,25 , but also other environments including the human gut 38 and soils 39–41 , thus indicating that it is not diversity itself which imparts resistance, but some underlying ecological property of the community. In addition to increased diversity, there was an increase in the number of KEGG modules detected in MAGs assembled from R3 (Fig. 2 ), but a significant decrease in evenness (Fig. 2 ). The increased abundance of TCRS in R3 also suggests that it should have been better able to respond to a shock (Fig. 8 ). However, this was not the case and therefore, it is important to investigate the properties of the R3 community ecology and functional potential, which were altered by its unstable operational-history and led to reduced resistance to an organic shock load. 4.1 Unstable environment leads to variable community composition The communities of R1 and R2 remained more stable than R3 throughout the course of the trial and community beta diversity fluctuated less in R1 and R2 than in R3 (Fig. 2 ), indicating that the unstable environment of R3 led to more changes in microbial community composition. Interestingly, beta dispersion analysis indicated that there was no significant variation between the reactors in inter-sample variability when only abundance was considered, i.e. bray-curtis (Fig. 2 ). However, when phylogeny (unifrac) was used to assess beta dispersion (Fig. 2 ), significant differences were found between R1 and R3, indicating that the unstable conditions in R3 led to more phylogenetic dispersion in temporal samples in R3 over the course of the trial. A large proportion of the temporally stable community of R3 was made up of the fermentative Spirochaetaceae family (Midas_g_14041) (Fig. 3 ). Since this analysis included all timepoints (including after the shock load), only taxa which were unaffected by the shock were thus identified including Streptococcus sp., Methanolinea sp., Paludibacter sp., and Desulfovibrio sp., as well as members of the Anaerolinaceae family (Midas_g_667) and the order Cloacimonadales (Midas_g_71685) in R1. The stable community of R2 also included Methanolinea sp., and members of the family Cloacimonadaeae (Midas_g_63301) in addition to Smithella sp. and Methanomassiliicoccus sp.,. The stable community of R3 included Leptolinea sp, genera from the Bacteroidetes_vadinHA17 family (Midas_g_19) and the order Syntrophales (Midas_g_134) along with the afformentioned Spirochaetaceae family (Midas_g_14041). The indication that phylogenetic composition (unifrac distance metric) was more variable between sampling timepoints in R3 is interesting. As the Unifrac distance metric does not consider abundance, it is a particularly useful metric for excluding the effect of the rare biosphere in diversity analysis 42 . The greater phylogenetic dispersion between sampling timepoints in R3 therefore were not a result of fluctuations in rare taxa. However, rare taxa have been repeatedly suggested to play a role in resistance to environmental disturbances 43,44 and functional redundancy. Therefore, the composition of the rare biosphere was analysed in further detail, see section 2.5. 4.2 Distribution of Metagenome Assembled Genomes Over the course of the trial, several MAGs increased in relative abundance in all three reactors, especially bin 57 of the genus Tidjanibacter , which was also the 4th most abundant bin overall (Fig. S2). Tidjanibacter species are members of the Rikenellaceae family and were first isolated from the human colon 45 , although there have been suggestions that this species should be classified within the genus Alistipes 46 . Tijanibacter have also been found in the digestive systems of chickens 47 and mice 48 but to the best of the authors knowledge have not been identified in anaerobic digesters. Three members of the Patescibacteria phylum (bins 69, 198 and 348), also increased in abundance in all three reactors (Fig. 7 ), but particularly in R1 and R2. Bin 348 was classified to order level as Moranbacteriales and was especially in R1 and R2, where its relative abundance was 7% and 5% respectively compared to < 1% in R3. The Patescibacteria are an uncultivated superphylum, which seem to be ubiquitous in many environments, but very little is known about their potential functions, other than that they have a small genome size and are thought to often be symbiotic with other microorganisms 49–51 , including methanogens of the Methanothrix genus 52 . 4.3 Functional Changes in Reactor Systems The functional composition, in terms of KEGG modules annotated in recovered MAGs from all reactors clustered based on timepoint, with T1 samples clustering entirely separately from T8 samples in all reactors (Fig. 2 ). Individual reactors were also more distinct at T8, indicating a divergence in the community function of each reactor depending on it’s operational history (Fig. 2 ). There was a significant reduction in the richness of KEGG modules associated with MAGs recovered from R1 and R2, but no change in KEGG module richness in R3. At the same time the evenness of KEGG modules decreased in R3 but there was no significant change in R1 or R2. The decrease in functional evenness and increase in richness in R3 indicates a community which had become dominated by the same functions, which were perhaps more suited to the unstable environment. In terms of functional change in the recovered MAGs, the most striking distinction between the reactors was in TCRS (Fig. 8 ), where R3T8 clustered separately from the other samples (Fig. 8 ). TCRS are the molecular mechanisms which allow microbes to sense and respond to environmental stimuli by (de)activating the transcription of associated genes (Capra and Laub, 2012). Two component systems typically consist of a membrane bound histidine kinase protein which senses environmental stimuli and a response regulator protein, which mediates differential gene expression (Stock et al., 2000). Environmental stimuli could include the presence or absences of toxins and nutrients or physical characteristics such as redox state, pH and osmotic pressure (Jacob-Dubuisson et al., 2018). Therefore it stands to reason that a microbial community which has had a more variable environment (i.e. R3) should have more TCSs. Indeed, 82 two component regulatory systems were more abundant in R3T8 than in R1T8 (Fig. 8 ). Several of the TCRSs which were more abundant in R3T8 have previously been associated with biofilm formation e.g. LuxQN/CqsS − LuxU − LuxO (Murugesan et al., 2023), VicK − VicR (Sun et al., 2023) and CiaH − CiaR (Wu et al., 2010). This indicates that the unstable environment in R3 may have led to increased biofilm forming capacity. Indeed, the biomass dynamics in R3 differed from R1 or R2 23 by producing much more biomass over the course of the trial (Fig. S3). TCRS which were more abundant in R1T8 included ArcB − ArcA (anoxic redox control) and HupT − HupR (hydrogenase synthesis regulation). The Arc two component system is utilised by facultative aerobes to detect and react to changes in respiratory growth conditions 53 and regulates transcription of genes related to anaerobic growth in E.coli 54 . HupT-HupR TCRS has been mostly studied in Rhodobacter species, where it functions in conjunction with HupUV sensor protein in detecting and responding to H 2 concentrations by producing hydrogenases, enabling a switch to autotrophic growth 55,56 . Therefore, R1 may have been more suited to dealing with changes in H 2 concentrations and switching to hydrogenotrophic metabolism. The contribution of these variations in TCRS to the failure of R3 is unclear and in fact their abundance may have had no impact at all on resistance (or the lack of). However, their predominance is indicative of the selection pressures of the applied disturbances, which has implications for further studies. Molecular diagnostics have long been suggested to be the next step in process monitoring of anaerobic digestion 57,58 , but no suitable target genes have so far been identified. The increase in the presence of TCRS in MAGs recovered from R3 indicates that they may be suitable for this purpose, particularly if detected at the transcriptomic level rather than at the genomic level. This would require further, similar studies where expression can be strongly linked to a given environmental disturbance. Once that is achieved, transcriptional screening could be carried out to assess community stress responses prior to process failure. 4.4 Distinct environmental disturbances promotes the establishment of specialist taxa The absence of any conditionally rare taxa in R3 is surprising as environmental disturbances have previously been shown to promote temporal fluctuations in the rare biosphere 17,43 and lead to the development of conditionally rare taxa 59 . In addition, our analysis indicated that persistently rare taxa made up a lower proportion of the total community in R3. Therefore, it may be the case that although these rare taxa did not increase enough in abundance to meet the threshold of conditionally rare taxa, they did increase to some extent, thus resulting in a lower proportion of persistently rare taxa. This also explains the observation that the number of other rare taxa were the highest in R3 (Fig. 5 ). The taxa which shifted from persistently rare to other rare taxa may have been specialised to deal with a given disturbance (e.g. ammonia), but due to the short nature of the applied disturbances did not establish enough to become conditionally rare. Indeed, the unstable operation of R3 did lead to the proliferation of a microbial community with a higher proportion of specialist taxa than the control reactor (R1) (Fig. 6 ). The prevalence of specialist microbes in R3 was likely caused by the variety of applied disturbances, which led to the development of a larger number of narrow niches and less overlap (Fig. S6) throughout the reactor operation. R2 had a similar number of specialist and generalist taxa to R1, indicating that the organic load increases in R2 did not lead to a proliferation of specialist microbes. Generalist taxa have a wider niche breadth, and are able to survive under a wider set of conditions than specialists 60 . In contrast, specialists are more discriminant in the environments in which they thrive 37 . The relative proportion of generalists and specialists in a microbial community is thought to influence its stability in the face of a disturbance 61 . Muller et al (2019) hypothesised that communities with a high proportion of specialist taxa would be more susceptible to functional breakdown in the face of a disturbance as the population performing the dominant function has a narrower niche breadth and will not be able to resist the change in conditions brought on by a disturbance. The present study provides evidence for this hypothesis in that the more specialist community of R3 failed in response to a disturbance, whereas the generalist communities of R1 and R2 were able to resist the applied shock. The observation that different microbial life strategies evolved in the respective reactors and subsequent divergent responses of each reactor to a large disturbance indicates that a community made up of generalist microbes is more resistant to large disturbances. The proliferation of generalists was enhanced in R1 due to the stable environment. This is unexpected as generalist species are often thought to gain an advantage from environmental heterogeneity 13 . However, the distinct nature of each disturbance applied to R3 (i.e. pH, NH 3 , temperature and sulfate) may have led to the establishment of microbes which were specialised to deal with them. Much more taxa overlap was observed in R1 and R2 (Fig. S4, Fig. S5) indicating that more interspecies interactions may have developed, potentially as a result of more consistent environmental conditions. Indeed a recent meta-analysis including pan-genomic data indicated that genomes of generalists are enriched in functions involved with species-species interactions 62 . The extent of interspecies interactions in a microbial community can impact its resistance to a disturbance as key interdependencies, e.g. sharing of public goods 63 are buffered from sudden taxa die-off and ecosystem function is less reliant on a single keystone species, which may be inhibited by a disturbance 64,65 . It has also been suggested that lower numbers of interspecies interactions reduce ecosystem stability 66 and increased interspecies interactions have even been shown to increase robustness in methanogenic communities 67 . Therefore, despite lower diversity, the generalist lifestyle which prevailed in R1 and R2 was crucial in improving their resistance to a major organic carbon influx. 5 Conclusion The environmental disturbances applied to R3 led to a more variable microbial community over time and significantly more phylogenetic dispersion within R3 samples than R1. This made R3 less resistant to the shock load and more susceptible to deterministic influences on community assembly, despite recovered MAGs having more abundant two component regulatory systems. The reduced resistance of R3 to an organic shock load to was concluded to be due to the development of different niche spaces which were occupied by specialist taxa, whereas the stable environment in R1 led to proliferation of generalist taxa. Because specialist taxa were more abundant in R3, less overlap between respective taxa was detected, indicating that there were fewer interspecies interactions, which made the system less robust to changing conditions. These results have implications for microbial communities and indicate that environmental stability promotes the development of a microbial community which is more resistant to shocks. Declarations Ethics approval and consent to participate Not applicable Consent for publication Not applicable Availability of data and material Raw sequencing data can be found in the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) under the BioProject accession number PRJNA1030189. Competing interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding This publication has emanated from research supported by Science Foundation Ireland (SFI) through the SFI Research Professorship Programme entitled Innovative Energy Technologies for Biofuels, Bioenergy and a Sustainable Irish Bioeconomy (IETSBIO 3 ; grant number 15/RP/2763) and the Research Infrastructure grant Platform for Biofuel Analysis (Grant Number 16/RI/3401). UZI is supported by EPSRC (EP/P029329/1 and EP/V030515/1). Authors' contributions Simon Mills: Conceptualization, Data curation, Investigation, Formal analysis, Writing – original draft, Visualization. Umer Z. Ijaz: Data formal analysis, Software, Visualization, Supervision, Writing – review & editing. Piet N.L. Lens: Supervision, Project administration, Writing – review & editing, Funding acquisition. Acknowledgements The authors thank Borja Khatabi Soliman Tamayo, Dr Diana Quispe and Marlee Wasserman (NUIG, Ireland) for their help and support during the laboratory work. References Shade, A. et al. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4382699","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":304403414,"identity":"85743d60-fa95-4ccb-b743-9ebebb9ae21e","order_by":0,"name":"Simon Mills","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-8682-3846","institution":"University of Galway","correspondingAuthor":true,"prefix":"","firstName":"Simon","middleName":"","lastName":"Mills","suffix":""},{"id":304403415,"identity":"c4fc3ebd-aa8c-4ff2-95e8-61e59b095f1f","order_by":1,"name":"Umer Ijaz","email":"","orcid":"https://orcid.org/0000-0001-5780-8551","institution":"University of Glasgow","correspondingAuthor":false,"prefix":"","firstName":"Umer","middleName":"","lastName":"Ijaz","suffix":""},{"id":304403416,"identity":"82a27e69-09ff-4c74-92ae-44e5e1637c10","order_by":2,"name":"Piet Lens","email":"","orcid":"","institution":"University of Galway","correspondingAuthor":false,"prefix":"","firstName":"Piet","middleName":"","lastName":"Lens","suffix":""}],"badges":[],"createdAt":"2024-05-07 11:34:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4382699/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4382699/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41522-025-00679-w","type":"published","date":"2025-03-31T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":57410801,"identity":"c38e6f7a-1040-455f-8066-e35c7a6ea8c0","added_by":"auto","created_at":"2024-05-30 10:28:55","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":782809,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverview \u003c/strong\u003eSummary of reactor performance data, alpha diversity and trial timeline.\u003cstrong\u003e (A) \u003c/strong\u003esCOD removal efficiency (%) throughout the course of the trial in R1, R2 and R3. Timing of applied disturbances and the final shock are indicated by coloured stars. \u003cstrong\u003e(B)\u003c/strong\u003e Alpha Diversity (Shannon Entropy) in R1, R2 and R3. Lines of significance depict significant differences in alpha diversity between the T8 samples, prior to the OLR shock as follows: * (p \u0026lt; 0.05), ** (p \u0026lt; 0.01), or *** (p \u0026lt; 0.001) based on ANOVA). Only T8 was included in significance testing. Coloured stars indicate the timing of a given disturbance. \u003cstrong\u003e(C) \u003c/strong\u003eTimeline of reactor trial indicated the days which disturbances and shocks were applied (in blue) and also the days that samples for microbial community analysis were collected.\u003c/p\u003e","description":"","filename":"Fig.11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4382699/v1/7ca3db95aa4801be653b15f7.jpg"},{"id":57410805,"identity":"9cbc9a08-fa98-4113-9b39-092634e3a3ee","added_by":"auto","created_at":"2024-05-30 10:28:55","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":552530,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBeta Diversity (A)\u003c/strong\u003e Local contribution to beta diversity (LCBD) using the Bray-Curtis distance metric for R1, R2 and R3 where the horizontal grey bar indicates mean beta diversity across all samples. \u003cstrong\u003e(B)\u003c/strong\u003e Pairwise comparisons of beta dispersion among samples from R1, R2 and R3 using the Bray-Curtis distance metric (statistical significance is denoted as follows: * (p \u0026lt; 0.05), ** (p \u0026lt; 0.01), or *** (p \u0026lt; 0.001) based on ANOVA) for pairwise comparisons. \u003cstrong\u003e(C)\u003c/strong\u003e LCBD diversity using the Unifrac distance metric, for R1, R2 and R3. \u003cstrong\u003e(D)\u003c/strong\u003e Pairwise comparisons of beta dispersion among samples from R1, R2 and R3 using the Bray-Curtis distance metric. \u003cstrong\u003e(E)\u003c/strong\u003e Alpha diversity indices Pielous Evenness and Richness for KEGG modules detected in assembled MAGs (Lines of significance depict significant differences as follows: * (p \u0026lt; 0.05), ** (p \u0026lt; 0.01), or *** (p \u0026lt; 0.001) based on ANOVA for all reactors and timepoints. \u003cstrong\u003e(F)\u003c/strong\u003e Principal Component Analysis of detected KEGG modules in assembled MAGs, where samples were grouped by reactor (R1, R2 and R3) and ellipses were drawn using 95% confidence intervals based on standard deviation.\u003c/p\u003e","description":"","filename":"Fig.12.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4382699/v1/3fbc3f5ac857b24fd51aa853.jpg"},{"id":57410800,"identity":"3018ef28-a670-4e85-8dab-b686c9a0ed2d","added_by":"auto","created_at":"2024-05-30 10:28:55","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":358634,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMicrobiome Stability \u003c/strong\u003eStable component of each reactor’s microbial community returned after running the ensemble quotient analysis (EQO) algorithm in uniform phenotypic variable mode showing the relative abundance profiles of stable taxa in R1 \u003cstrong\u003e(A),\u003c/strong\u003e R2 \u003cstrong\u003e(B)\u003c/strong\u003e and R3 \u003cstrong\u003e(C)\u003c/strong\u003e with \u003cem\u003eCoefficient of Variation\u003c/em\u003e (CV) values given on the top of the plot. The lower CV value signifies higher stability.\u003c/p\u003e","description":"","filename":"Fig.13.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4382699/v1/0c9e091b095af4e7dfc41ead.jpg"},{"id":57410803,"identity":"0fdc19e8-f615-4d87-9842-af4dab082876","added_by":"auto","created_at":"2024-05-30 10:28:55","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":266231,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMicrobial Community Assembly\u003c/strong\u003e Normalized stochasticity ratio (NST) using Ružička metric and Taxa-Richness constraints of proportional-fixed (P-F) and proportional-proportional (P-P) abundances for Reactors 1-3. B NTI values for all samples, sorted by reactor and timepoint.\u003c/p\u003e","description":"","filename":"Fig.14.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4382699/v1/93592c93899567df8194aabf.jpg"},{"id":57411449,"identity":"4a599e51-93be-4c95-ac20-5f6028dfe97f","added_by":"auto","created_at":"2024-05-30 10:36:55","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":253415,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRare Taxa \u003c/strong\u003eBoxplots depicting abundant (taxa which exhibit average relative abundance above 1% across all samples), conditionally rare (taxa which exhibit a maximum relative abundance at least 100 times higher than its minimum value), persistently rare (as taxa which never exhibited a maximum relative abundance 5 times greater than its minimum) and other rare (taxa which exhibited a maximum relative abundance between 5 and 100 times greater than its minimum) taxa in Reactors 1-3\u003c/p\u003e","description":"","filename":"Fig.15.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4382699/v1/31a827fda701102b51f168fc.jpg"},{"id":57410804,"identity":"0b5db57e-9315-47da-8f24-2819dd970b3b","added_by":"auto","created_at":"2024-05-30 10:28:55","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":310202,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMicrobial Generalists and Specialists. \u003c/strong\u003eNetwork of relationships (A) recovered after applying Levin’s B\u003csub\u003eN\u003c/sub\u003e to find generalists and specialists. Taxa can be found in Supplementary Table S1.\u003c/p\u003e","description":"","filename":"Fig.16.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4382699/v1/79e57b0f5f047f1a79c51609.jpg"},{"id":57410806,"identity":"c72b05b9-c9bf-4a7b-aea8-94a0843bc62e","added_by":"auto","created_at":"2024-05-30 10:28:55","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":775613,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePhylogenetic Tree. \u003c/strong\u003eKrona Plot of metagenomic bins. Samples are arranged using single copy genes for bacteria and archaea. Abundance of each bin is indicated as a heatmap. Genome completeness is indicated as a bar plot. GC content is indicated as heatmap.\u003c/p\u003e","description":"","filename":"Fig.17.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4382699/v1/98b7cbeec9cbb84b6c8461e6.jpg"},{"id":57410807,"identity":"4b86fb02-2732-405d-92c2-9fb74f91cf83","added_by":"auto","created_at":"2024-05-30 10:28:55","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1378439,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTwo Component Regulatory Systems. \u003c/strong\u003eAbundance of two component regulatory systems across all MAGs recovered from all samples. Samples are ordered on the x axis by hierarchical clustering.\u003c/p\u003e","description":"","filename":"Fig.18.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4382699/v1/3b913d6571e5ea21cee7b96a.jpg"},{"id":79646829,"identity":"61b8ddb4-3fb3-443d-98ff-149530e157e9","added_by":"auto","created_at":"2025-04-01 07:13:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5756906,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4382699/v1/cce80bbc-86ee-4adb-af29-e0427b1f22ad.pdf"},{"id":57410808,"identity":"1fb5ab70-fae9-46db-b340-7f9384f21488","added_by":"auto","created_at":"2024-05-30 10:28:56","extension":"pdf","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":1923072,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4382699/v1/684eb7e446258644fc94066f.pdf"}],"financialInterests":"(Not answered)","formattedTitle":"Shotgun metagenomics reveals environmental instability reduces resistance to shocks by enriching specialist taxa with distinct two component regulatory systems","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eThe impact of environmental disturbances on microbial communities has been a central theme of ecological study for decades \u003csup\u003e1\u0026ndash;3\u003c/sup\u003e, with implications for global biogeochemical cycling \u003csup\u003e4\u003c/sup\u003e, ecosystem functioning \u003csup\u003e3\u003c/sup\u003e, environmental biotechnology \u003csup\u003e5,6\u003c/sup\u003e and human health \u003csup\u003e7\u003c/sup\u003e. For example, methanogenic microbial communitiesplay a large role in carbon cycling \u003csup\u003e8\u003c/sup\u003e and underpin biotechnologies such as anaerobic digestion \u003csup\u003e9\u003c/sup\u003e. These communities are particularly susceptible to disturbances, as they are highly syntrophic and dysbiosis in one of the necessary trophic groups causes system failure \u003csup\u003e10,11\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe ability of a microbial community to maintain stability despite disturbances is called resistance, whereas its capacity to recover to its original state is known as resilience \u003csup\u003e1\u003c/sup\u003e. These properties vary among microbial communities, however it is still unclear what causes these variations. One theory is that prior exposure to environmental disturbances increases resistance and resilience \u003csup\u003e6\u003c/sup\u003e. This may be due to changes in underlying ecological properties of the community or the development of functional traits which may give rise to improved resistance and resilience. Ecological properties of a community which may influence resistance and resilience include niche breadth, community assembly, and the extent of the rare biosphere.\u003c/p\u003e \u003cp\u003eNiche breadth refers to the extent of a given taxa\u0026rsquo;s niche, relative to prevailing environmental conditions and is directly influenced by environmental (in)stability \u003csup\u003e12\u003c/sup\u003e. Taxa with wider niches are considered generalists, whereas taxa with narrow niches are considered specialists \u003csup\u003e13\u003c/sup\u003e. It is often stated that generalist taxa will thrive in different environments as they have a wider niche and are not inhibited by variable conditions \u003csup\u003e13\u003c/sup\u003e. In contrast, specialists tend to dominate in stable environments where they can utilise resources more efficiently within a narrow niche \u003csup\u003e14\u003c/sup\u003e. Therefore, historical disturbances are expected to lead to the proliferation of generalist taxa, thus increasing resistance and resilience to further disturbances.\u003c/p\u003e \u003cp\u003eThe rare biosphere plays a role in fulfilling niches which are altered due to environmental disturbances by acting as a \u0026ldquo;seed bank\u0026rdquo;, which supplies new taxa to occupy new or varied niches \u003csup\u003e15\u003c/sup\u003e. In the absence of a disturbance, there are some natural fluctuations in community composition around a stable state of equilibrium, with little opportunities for drastic change in microbial community composition \u003csup\u003e16\u003c/sup\u003e. However, during a disturbance, if total community function is to remain the same, members of the original community must persist (resistance) or be replaced by organisms which can fulfil the same niche, potentially seeded by the rare biosphere \u003csup\u003e17\u003c/sup\u003e. Frequent disturbances can increase the proportion of rare taxa in a community \u003csup\u003e18\u003c/sup\u003e and may therefore strengthen the likelihood of the rare biosphere fulfilling a new or altered niche space.\u003c/p\u003e \u003cp\u003eThe ecological properties of a microbial community which impart resistance may be developed during microbial community assembly or re-assembly after a disturbance. Therefore, frequent disturbances may compound these effects. Microbial community assembly is broadly categorised as stochastic or deterministic \u003csup\u003e19\u003c/sup\u003e. The natural variations in microbiome stability are often stochastic in nature. Deterministic community assembly often refers to external forces which drive community development such as environmental gradients or disturbances, which may modify niches and force the community to change its composition \u003csup\u003e20,21\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFunctional changes which could occur in response to historical instability may include a more diverse functional capacity or enrichment of stress response mechanisms such as two component regulatory systems (TCRS). Previously TCRSs were enriched during unstable periods of operation in a full scale activated sludge plant \u003csup\u003e22\u003c/sup\u003e. However, the contribution of TCRS to the degree of resistance in an anaerobic microbial community is unknown.\u003c/p\u003e \u003cp\u003ePreviously, we reported that historical instability led to the development of a significantly more diverse microbial community in high rate anaerobic digesters \u003csup\u003e23\u003c/sup\u003e. However, this increased diversity did not result in improved resistance, as has been reported in several previous studies \u003csup\u003e24,25\u003c/sup\u003e, and was contrary to our original hypothesis that more diverse communities would have improved resistance. Therefore, in the present study, we further investigated the impact of environmental instability on ecological and functional properties of these communities, to identify a potential cause for reduced resistance, despite increased diversity. Three laboratory microcosms of methanogenic microbial consortia were subjected to varying degrees of environmental stability before receiving a massive influx of excess carbon, which typically result in acidosis and community dysbiosis\u003csup\u003e23\u003c/sup\u003e. In depth ecological analysis of the microbial communities prevailing in each of these microcosms revealed that historical environmental instability led to a community with inflated numbers of specialist taxa and reduced resistance to an organic shock load.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Experimental Design\u003c/h2\u003e \u003cp\u003eGranular sludge was sampled from three methanogenic bioreactors which were subjected to varying degrees of environmental stability \u003csup\u003e23\u003c/sup\u003e. Associated physiochemical parameters were reported previously \u003csup\u003e23\u003c/sup\u003e and are summarised in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The bioreactors were supplied with a synthetic dairy wastewater consisting of skimmed milk powder, trace elements and macronutrients. R1 was maintained stable for the entire period of operation. After a period of approximately 40 days R2 and R3 were subjected to various small disturbances (D1-D4) through variations in influent composition (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). R2 was subjected to repeated disturbances in the form of excess carbon influxes by increasing the concentration of the influent. R3 was subjected to a range of disturbances which are common in methanogenic bioreactors including ammonium and sulfide toxicity as well as a temperature and pH drop(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Subsequently, all three bioreactors were subjected to a massive carbon influx by increasing the chemical oxygen demand (COD) concentration of the feed 8-fold.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 16S rRNA Gene Amplicon Sequencing\u003c/h2\u003e \u003cp\u003eSamples for 16S rRNA gene amplicon sequencing were taken from each microbial community at 9 timepoints (T1-T9) throughout the experiment, before and after a disturbance was applied (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). DNA extraction, sequencing of the 16S rRNA gene and bioinformatic sequence processing were performed as described previously \u003csup\u003e23\u003c/sup\u003e. In addition to previously reported bioinformatic processing, PICRUSt2 \u003csup\u003e26\u003c/sup\u003e within the QIIME2 environment was used to recover KEGG enzymes and MetaCyc pathway predictions (not done in the original publication) for all the samples. For this purpose, we used the parameters --p-hsp-method pic --p-max-nsti 2 in qiime picrust2 full-pipeline [\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/gavinmdouglas/q2-picrust2\u003c/span\u003e\u003cspan address=\"https://github.com/gavinmdouglas/q2-picrust2\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e]. Statistical analysis was performed using R software and the details are provided in the Supplementary Material. Raw amplicon sequencing data used in this study has been deposited in the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) under the BioProject accession number PRJNA1030189.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Metagenomic analysis\u003c/h2\u003e \u003cp\u003eA total of 18 metagenomics samples were processed. These samples were taken from all three reactors at timepoint 8 (T8) prior to the large OLR shock (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Reads were quality trimmed using Sickle v1.200 \u003csup\u003e27\u003c/sup\u003e at an average Phred quality of \u0026lt;\u0026thinsp;20. Reads which were \u0026gt;\u0026thinsp;50bp were retained after trimming for downstream processing. This gave a total of 641,256,041 reads from all samples. All forward and reverse reads were then collated together together for co-assembly using megahit with the parameters --k-list 27,47,67,87 --kmin-1pass -m 0.95 --min-contig-len 1000 \u003csup\u003e28\u003c/sup\u003e. his gave us a total of 653,274 contigs, a total of 213,972,1678 base pairs (bp), maximum of 654,587 bp, average length of 3,275 bp, and an N50 score of 4,774 bp.\u003c/p\u003e \u003cp\u003eThe MetaWRAP pipeline \u003csup\u003e29\u003c/sup\u003e was used to bin the contigs with three different binning algorithms: metabat2 (610 bins), maxbin2 (520 bins), and CONCOCT (381 bins ). Checkm \u003csup\u003e30\u003c/sup\u003e was applied to the completion and level of contamination of each bin. The bins from the three binners were consolidated together within the MetaWRAP framework, only retaining bins with \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\ge\\)\u003c/span\u003e\u003c/span\u003e50% completion, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\le\\)\u003c/span\u003e\u003c/span\u003e10% contamination to give a final set of 380 bins or metagenomic assembled genomes (MAGs). We applied CheckM on these bins (Parks et al., 2015) to assess their completion and contamination. Within MetaWRAP framework, the bins from the three binners were consolidated together, retaining bins with \u0026gt;50% completion and \u0026lt;10% contamination to give a final set of 380 bins (MAGs). We obtained a mean genome completion of 78.68% and a mean contamination of 2.29% for bins. To deduce the phylogeny of the Metagenome-Assembled Genomes (MAGs), we employed GToTree (Lee, 2019). The software offers various Single Copy Genes (SCGs) sets based on the resolution of domains and the taxonomic rank of interest. Specifically, we utilized two SCG sets: a 25-gene set for Bacteria and Archaea (resulting in the phylogeny recovery for 275 MAGs). Finally from 380 bins, for downstream statistical analysis, we have used bins with \u0026gt;75% completion, i.e., 219 bins. The METABOLIC \u003csup\u003e32\u003c/sup\u003e pipeline was used to assign functions to each bin using GTDB-TK \u003csup\u003e33\u003c/sup\u003e. METABOLIC was also used for the annotation of proteins using KEGG \u003csup\u003e34\u003c/sup\u003e. The obtained sample read coverages per bin \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({C}_{i,j}\\)\u003c/span\u003e\u003c/span\u003e was then multiplied with feature coverages (returned from METABOLIC) per bin \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({F}_{j,k}\\)\u003c/span\u003e\u003c/span\u003e to obtain feature coverages per sample \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({n}_{i,k}\\)\u003c/span\u003e\u003c/span\u003e as a matrix product \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({n}_{i,k}=\\sum _{j}{C}_{i,j}{F}_{j,k}\\)\u003c/span\u003e\u003c/span\u003e. Only bins with a genome completeness of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\ge\\)\u003c/span\u003e\u003c/span\u003e 75% and genome contamination \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\le\\)\u003c/span\u003e\u003c/span\u003e 5% were used, resulting in a final dataset of n\u0026thinsp;=\u0026thinsp;18 x p=380 bins. Abundance tables for the following databases were then also generated: KEGG Modules (n\u0026thinsp;=\u0026thinsp;18 samples x p=764 features, i.e., peptidases and inhibitors) and KEGG Submodules (n\u0026thinsp;=\u0026thinsp;18 samples x p\u0026thinsp;=\u0026thinsp;2,235 features).\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Physiochemical data\u003c/h2\u003e \u003cp\u003ePhysiochemical, bioreactor performance data and basic measures of alpha and beta diversity were reported in detail previously \u003csup\u003e23\u003c/sup\u003e and are summarised in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. R1 demonstrated the most resistance to the shock in terms of performance (soluble COD removal), R2 performed similarly, although slightly worse than R1, whereas R3 performed the worst overall and did not recover to previous levels of performance by the end of the experiment (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) \u003csup\u003e23\u003c/sup\u003e. In addition, each reactor was sampled at 9 different timepoints throughout the trial (T1-T9) and alpha diversity analysis indicated that the variable environmental conditions of R3 led to significantly higher Shannon diversity at T8, right before the applied shock \u003csup\u003e23\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Diversity\u003c/h2\u003e \u003cp\u003eThe local contribution of each sample to the mean beta diversity (LCBD), across all samples was calculated to identify which reactor had more variation in microbial community composition over time (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Beta diversity varied more in R3, when measured in terms of ASV abundance (Bray-Curtis) and phylogeny (unifrac) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), particularly at T9. Beta dispersion analysis was applied to identify and compare the degree of inter-sample variability within groups (reactors). Significant differences in phylogenetic (unifrac) beta dispersion were identified between R3 and R1, indicating that the phylogenetic composition of R3 was more variable over time than that of R1.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Microbial Community Stability\u003c/h2\u003e \u003cp\u003eA group of core taxa (limited to 30) with stable relative abundance and minimum \u003cem\u003ecoefficient of variation\u003c/em\u003e in each reactor were identified. These taxa did not vary significantly in composition in response to the applied disturbance regimes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The coefficient of variation for the stable community of R3 was higher (CV: 0.0606) than that of R1 (CV: 0480) and R2 (CV: 0419), indicating that although this community was considered stable in its own right, it was more variable than the communities present in R1 or R2. The core communities of each reactor also differed in composition (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The stable community of R1 was dominated by \u003cem\u003eStreptococcus\u003c/em\u003e sp., \u003cem\u003eMethanolinea\u003c/em\u003e sp., \u003cem\u003ePaludibacter\u003c/em\u003e sp., and \u003cem\u003eDesulfovibrio\u003c/em\u003e sp., as well as members of the Anaerolinaceae family (Midas_g_667) and the order Cloacimonadales (Midas_g_71685). The stable community of R2 was dominated by \u003cem\u003eSmithella\u003c/em\u003e sp., \u003cem\u003eMethanolinea\u003c/em\u003e sp., \u003cem\u003eMethanomassiliicoccus\u003c/em\u003e sp., as well as members of the family Cloacimonadaeae (Midas_g_63301). The stable community of R3 was heavily dominated by a member of the Spirochaetaceae family (Midas_g_14041), particularly at T9 after the shock load, with lower abundances of \u003cem\u003eLeptolinea\u003c/em\u003e sp, genera from the Bacteroidetes_vadinHA17 family (Midas_g_19) and the order Syntrophales (Midas_g_134).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn contrast, the taxa that varied significantly with time and had a high correlation with respect to sampling day in each reactor were also identified (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The most prominent changes over time in reactor 1 included increases in candidate order Moranbacteria (midas_g_11415) and \u003cem\u003eMethanobacterium\u003c/em\u003e sp. In R2 \u003cem\u003eMethanospirillum\u003c/em\u003e sp. and a genus in the Syntrophomonadaceae family (midas_g_53834) increased in relative abundance over time, whereas the relative abundance of \u003cem\u003eSyntrophobacter\u003c/em\u003e sp. decreased. The most obvious change in the microbial community composition of R3 was an increase in relative abundance of genera from the Eubacteriaceae family (midas_g_2229), the class Thermodesulfovibrionia (midas_g_581) and \u003cem\u003eAnaeromusa\u0026thinsp;\u0026minus;\u0026thinsp;Anaeroarcus\u003c/em\u003e sp.. There was also a higher abundance of a genus (midas_g_14041) in the Spirochaetaceae family (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Community Assembly\u003c/h2\u003e \u003cp\u003eThe relative contributions of stochastic and deterministic influences on community assembly were assessed using two methods; the nearest taxon index (NTI) and normalized stochasticity ratio (NST) \u003csup\u003e35\u003c/sup\u003e, both of which revealed similar patterns. Microbial community assembly is considered entirely deterministic when NST is 0% and entirely stochastic when NST is 100%. A 50% stochasticity ratio separates deterministic (\u0026lt;\u0026thinsp;50%) and stochastic (\u0026gt;\u0026thinsp;50%) assembly processes \u003csup\u003e35\u003c/sup\u003e. A positive NTI value indicates deterministic influence, whereas a negative value indicates stochastic processes dominate.\u003c/p\u003e \u003cp\u003eCommunity assembly in all 3 reactors was largely stochastic in nature (i.e. NST\u0026thinsp;\u0026gt;\u0026thinsp;0.5) and remained fairly stable in R1 and R2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). However, there was a slightly different temporal pattern in R3, which showed a slight curve over the course of the trial, indicating that stochasticity remained high, but decreased slightly until T5, before increasing again to T8 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The final timepoint in R3 however, experienced a drastic increase in deterministic influences, as evidenced by both NST (\u0026lt;\u0026thinsp;0.5) and NTI (\u0026gt;\u0026thinsp;2) values.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Rare taxa\u003c/h2\u003e \u003cp\u003eThe relative contribution of abundant taxa, conditionally rare taxa, persistently rare taxa and other rare taxa was calculated according to the methods outlined by Yang et al.\u003csup\u003e36\u003c/sup\u003e. Conditionally rare taxa are defined as taxa which exhibit a maximum relative abundance at least 100 times higher than its minimum value. Persistently rare taxa are defined as taxa which never exhibited a maximum relative abundance 5 times greater than its minimum. Other rare taxa are defined as taxa which exhibited a maximum relative abundance between 5 and 100 times greater than it\u0026rsquo;s minimum \u003csup\u003e36\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNo conditionally rare taxa were identified in any of the reactors investigated (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). However, some trends were apparent within the persistently rare taxa and other rare taxa. There was a decreasing trend in the contribution of persistently rare taxa from R1 to R3, which corresponded to a similar trend in the degree of functional resistance that each reactor demonstrated when faced with the applied organic loading rate (OLR) shock load (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), i.e. R3 had the least persistently rare taxa and responded worst to the large disturbance. However, the inverse was observed for other rare taxa (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). This indicates that there were no large fluctuations in taxa abundance, resulting in conditionally rare taxa (i.e. maximum relative abundance at least 100 times higher than its minimum value). However, R3 did have more taxonomic fluctuations resulting in the presence of other rare taxa (i.e. maximum relative abundance between 5 and 100 times greater than its minimum) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Microbial Generalists and Specialists\u003c/h2\u003e \u003cp\u003eIn order to ascertain how different reactor histories impacted microbial lifestyles (i.e. generalists or specialists), the MicroNiche \u003csup\u003e37\u003c/sup\u003e package was used. To determine how the predominant microbial lifestyles in each reactor impacted their resistance to large environmental shocks, each reactor was considered to be a separate environment and only timepoints 1\u0026ndash;8 (prior to the OLR shock) were considered for this analysis. In total, 124 genera were identified which fit the criteria for having a generalist or a specialist lifestyle across all three reactors. R1 contained 82 generalists and 7 specialists, R2 contained 71 generalists and 9 specialists (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). R3 however, contained only 39 generalists but had 18 specialists (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The overlap between these 124 taxa was also considered to identify taxa which coincided with each other. Figs. S5-S7 show that there was much less overlap amongst the taxa in R3 due to the presence of more specialists. A full list of specialist and generalist taxa and their classification in relation to each reactor is provided as supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Metagenome Assembled Genomes\u003c/h2\u003e \u003cp\u003e380 Metagenome assembled genomes with a completeness greater than 75% and contamination of less than 5% were recovered from all samples (n\u0026thinsp;=\u0026thinsp;18). Phylogenetic marker genes for 183 of these bins were identified and aligned using phylogenetic marker genes for bacteria and archaea to determine the phylogenetic distribution of MAGs. Several bins were more abundant in all three reactors at T8 including bin 57 assigned to the genus \u003cem\u003eTidjanibacter\u003c/em\u003e and bins 115 and 46 assigned to the orders Treponematales and Syntrophomonadia (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Three bins (bin. 69, bin.198 and bin.348) within the Patescibacteria phylum (also known as candidate phyla radiation) were also more abundant at T8 than at T1, including one which was included in the 20 most abundant taxa (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e), assigned to order level as Moranbacteriales. Both of these bins became especially abundant in R1 and R2 at T8 but were not as dominant in R3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.9 Functional differences in MAGs\u003c/h2\u003e \u003cp\u003eThe richness of KEGG modules decreased dramatically over time in R1 and R2 but there was no significant change in R3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). However, evenness of KEGG modules in R3 was significantly lower at T8, indicating that the functional profile of the community was dominated by fewer KEGG modules. A similar clustering pattern was observed when detected among KEGG modules when principal coordinate analysis (PCoA) was applied, where samples from each timepoint were distinct (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe abundance of two component regulatory systems (TCRS) was variable among samples, with R3 timepoint 8 (R3T8) forming a distinct cluster (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). 82 two component regulatory systems were more abundant in R3T8 than in R1 timepoint 8 (R1T8). Several distinct clusters were key in differentiating R3T8 from the other samples, particularly R1T8. Two component regulatory systems within these clusters which were more abundant at T8 included LuxQN/CqsS\u0026thinsp;\u0026minus;\u0026thinsp;LuxU\u0026thinsp;\u0026minus;\u0026thinsp;LuxO (quorum sensing), VicK\u0026thinsp;\u0026minus;\u0026thinsp;VicR (cell wall metabolism), AtoS\u0026thinsp;\u0026minus;\u0026thinsp;AtoC (cPHB biosynthesis) and CiaH\u0026thinsp;\u0026minus;\u0026thinsp;CiaR. TCRS which were more abundant in R1T8 included ArcB\u0026thinsp;\u0026minus;\u0026thinsp;ArcA (anoxic redox control) and HupT\u0026thinsp;\u0026minus;\u0026thinsp;HupR (hydrogenase synthesis regulation). TCRS which were more abundant in R1T8 than R3T8 included ArcB\u0026thinsp;\u0026minus;\u0026thinsp;ArcA (anoxic redox control) and HupT\u0026thinsp;\u0026minus;\u0026thinsp;HupR (hydrogenase synthesis regulation) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThis study found that unstable environmental conditions (in R3) resulted in the development of a microbial community rich in specialist taxa (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), with an abundance of two component regulatory systems (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). This led to reduced resistance to a shock load, which manifested as a large shift from stochastic community assembly to deterministic community assembly (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) and ultimately, failure of the system. In contrast, a stable environment (in R1) led to the proliferation of generalist taxa (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) with MAGs containing less abundant two component regulatory systems (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e), which increased shock resistance and reduced the effect of deterministic community assembly. The failure of R3 was surprising as the microbial community at T8 prior to the application of the final shock load had a significantly higher alpha diversity (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) than R1 or R2. This is contrary to a lot of research which demonstrates that increased diversity leads to increased stability, resistance and functioning in other anaerobic bioreactors treating different types of wastewaters\u003csup\u003e24,25\u003c/sup\u003e, but also other environments including the human gut \u003csup\u003e38\u003c/sup\u003e and soils \u003csup\u003e39\u0026ndash;41\u003c/sup\u003e, thus indicating that it is not diversity itself which imparts resistance, but some underlying ecological property of the community. In addition to increased diversity, there was an increase in the number of KEGG modules detected in MAGs assembled from R3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), but a significant decrease in evenness (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The increased abundance of TCRS in R3 also suggests that it should have been better able to respond to a shock (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). However, this was not the case and therefore, it is important to investigate the properties of the R3 community ecology and functional potential, which were altered by its unstable operational-history and led to reduced resistance to an organic shock load.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Unstable environment leads to variable community composition\u003c/h2\u003e \u003cp\u003eThe communities of R1 and R2 remained more stable than R3 throughout the course of the trial and community beta diversity fluctuated less in R1 and R2 than in R3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), indicating that the unstable environment of R3 led to more changes in microbial community composition. Interestingly, beta dispersion analysis indicated that there was no significant variation between the reactors in inter-sample variability when only abundance was considered, i.e. bray-curtis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). However, when phylogeny (unifrac) was used to assess beta dispersion (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), significant differences were found between R1 and R3, indicating that the unstable conditions in R3 led to more phylogenetic dispersion in temporal samples in R3 over the course of the trial.\u003c/p\u003e \u003cp\u003eA large proportion of the temporally stable community of R3 was made up of the fermentative Spirochaetaceae family (Midas_g_14041) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Since this analysis included all timepoints (including after the shock load), only taxa which were unaffected by the shock were thus identified including \u003cem\u003eStreptococcus\u003c/em\u003e sp., \u003cem\u003eMethanolinea\u003c/em\u003e sp., \u003cem\u003ePaludibacter\u003c/em\u003e sp., and \u003cem\u003eDesulfovibrio\u003c/em\u003e sp., as well as members of the Anaerolinaceae family (Midas_g_667) and the order Cloacimonadales (Midas_g_71685) in R1. The stable community of R2 also included \u003cem\u003eMethanolinea\u003c/em\u003e sp., and members of the family Cloacimonadaeae (Midas_g_63301) in addition to \u003cem\u003eSmithella\u003c/em\u003e sp. and \u003cem\u003eMethanomassiliicoccus\u003c/em\u003e sp.,. The stable community of R3 included \u003cem\u003eLeptolinea\u003c/em\u003e sp, genera from the Bacteroidetes_vadinHA17 family (Midas_g_19) and the order Syntrophales (Midas_g_134) along with the afformentioned Spirochaetaceae family (Midas_g_14041).\u003c/p\u003e \u003cp\u003eThe indication that phylogenetic composition (unifrac distance metric) was more variable between sampling timepoints in R3 is interesting. As the Unifrac distance metric does not consider abundance, it is a particularly useful metric for excluding the effect of the rare biosphere in diversity analysis \u003csup\u003e42\u003c/sup\u003e. The greater phylogenetic dispersion between sampling timepoints in R3 therefore were not a result of fluctuations in rare taxa. However, rare taxa have been repeatedly suggested to play a role in resistance to environmental disturbances \u003csup\u003e43,44\u003c/sup\u003e and functional redundancy. Therefore, the composition of the rare biosphere was analysed in further detail, see section 2.5.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Distribution of Metagenome Assembled Genomes\u003c/h2\u003e \u003cp\u003eOver the course of the trial, several MAGs increased in relative abundance in all three reactors, especially bin 57 of the genus \u003cem\u003eTidjanibacter\u003c/em\u003e, which was also the 4th most abundant bin overall (Fig. S2). \u003cem\u003eTidjanibacter\u003c/em\u003e species are members of the \u003cem\u003eRikenellaceae\u003c/em\u003e family and were first isolated from the human colon \u003csup\u003e45\u003c/sup\u003e, although there have been suggestions that this species should be classified within the genus \u003cem\u003eAlistipes\u003c/em\u003e \u003csup\u003e46\u003c/sup\u003e. \u003cem\u003eTijanibacter\u003c/em\u003e have also been found in the digestive systems of chickens \u003csup\u003e47\u003c/sup\u003e and mice \u003csup\u003e48\u003c/sup\u003e but to the best of the authors knowledge have not been identified in anaerobic digesters. Three members of the Patescibacteria phylum (bins 69, 198 and 348), also increased in abundance in all three reactors (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e), but particularly in R1 and R2. Bin 348 was classified to order level as Moranbacteriales and was especially in R1 and R2, where its relative abundance was 7% and 5% respectively compared to \u0026lt;\u0026thinsp;1% in R3. The Patescibacteria are an uncultivated superphylum, which seem to be ubiquitous in many environments, but very little is known about their potential functions, other than that they have a small genome size and are thought to often be symbiotic with other microorganisms \u003csup\u003e49\u0026ndash;51\u003c/sup\u003e, including methanogens of the \u003cem\u003eMethanothrix\u003c/em\u003e genus \u003csup\u003e52\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Functional Changes in Reactor Systems\u003c/h2\u003e \u003cp\u003eThe functional composition, in terms of KEGG modules annotated in recovered MAGs from all reactors clustered based on timepoint, with T1 samples clustering entirely separately from T8 samples in all reactors (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Individual reactors were also more distinct at T8, indicating a divergence in the community function of each reactor depending on it\u0026rsquo;s operational history (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). There was a significant reduction in the richness of KEGG modules associated with MAGs recovered from R1 and R2, but no change in KEGG module richness in R3. At the same time the evenness of KEGG modules decreased in R3 but there was no significant change in R1 or R2. The decrease in functional evenness and increase in richness in R3 indicates a community which had become dominated by the same functions, which were perhaps more suited to the unstable environment.\u003c/p\u003e \u003cp\u003eIn terms of functional change in the recovered MAGs, the most striking distinction between the reactors was in TCRS (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e), where R3T8 clustered separately from the other samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). TCRS are the molecular mechanisms which allow microbes to sense and respond to environmental stimuli by (de)activating the transcription of associated genes (Capra and Laub, 2012). Two component systems typically consist of a membrane bound histidine kinase protein which senses environmental stimuli and a response regulator protein, which mediates differential gene expression (Stock et al., 2000). Environmental stimuli could include the presence or absences of toxins and nutrients or physical characteristics such as redox state, pH and osmotic pressure (Jacob-Dubuisson et al., 2018). Therefore it stands to reason that a microbial community which has had a more variable environment (i.e. R3) should have more TCSs. Indeed, 82 two component regulatory systems were more abundant in R3T8 than in R1T8 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Several of the TCRSs which were more abundant in R3T8 have previously been associated with biofilm formation e.g. LuxQN/CqsS\u0026thinsp;\u0026minus;\u0026thinsp;LuxU\u0026thinsp;\u0026minus;\u0026thinsp;LuxO (Murugesan et al., 2023), VicK\u0026thinsp;\u0026minus;\u0026thinsp;VicR (Sun et al., 2023) and CiaH\u0026thinsp;\u0026minus;\u0026thinsp;CiaR (Wu et al., 2010). This indicates that the unstable environment in R3 may have led to increased biofilm forming capacity. Indeed, the biomass dynamics in R3 differed from R1 or R2 \u003csup\u003e23\u003c/sup\u003e by producing much more biomass over the course of the trial (Fig. S3).\u003c/p\u003e \u003cp\u003eTCRS which were more abundant in R1T8 included ArcB\u0026thinsp;\u0026minus;\u0026thinsp;ArcA (anoxic redox control) and HupT\u0026thinsp;\u0026minus;\u0026thinsp;HupR (hydrogenase synthesis regulation). The Arc two component system is utilised by facultative aerobes to detect and react to changes in respiratory growth conditions \u003csup\u003e53\u003c/sup\u003e and regulates transcription of genes related to anaerobic growth in E.coli \u003csup\u003e54\u003c/sup\u003e. HupT-HupR TCRS has been mostly studied in \u003cem\u003eRhodobacter\u003c/em\u003e species, where it functions in conjunction with HupUV sensor protein in detecting and responding to H\u003csub\u003e2\u003c/sub\u003e concentrations by producing hydrogenases, enabling a switch to autotrophic growth \u003csup\u003e55,56\u003c/sup\u003e. Therefore, R1 may have been more suited to dealing with changes in H\u003csub\u003e2\u003c/sub\u003e concentrations and switching to hydrogenotrophic metabolism.\u003c/p\u003e \u003cp\u003eThe contribution of these variations in TCRS to the failure of R3 is unclear and in fact their abundance may have had no impact at all on resistance (or the lack of). However, their predominance is indicative of the selection pressures of the applied disturbances, which has implications for further studies. Molecular diagnostics have long been suggested to be the next step in process monitoring of anaerobic digestion \u003csup\u003e57,58\u003c/sup\u003e, but no suitable target genes have so far been identified. The increase in the presence of TCRS in MAGs recovered from R3 indicates that they may be suitable for this purpose, particularly if detected at the transcriptomic level rather than at the genomic level. This would require further, similar studies where expression can be strongly linked to a given environmental disturbance. Once that is achieved, transcriptional screening could be carried out to assess community stress responses prior to process failure.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Distinct environmental disturbances promotes the establishment of specialist taxa\u003c/h2\u003e \u003cp\u003eThe absence of any conditionally rare taxa in R3 is surprising as environmental disturbances have previously been shown to promote temporal fluctuations in the rare biosphere \u003csup\u003e17,43\u003c/sup\u003e and lead to the development of conditionally rare taxa \u003csup\u003e59\u003c/sup\u003e. In addition, our analysis indicated that persistently rare taxa made up a lower proportion of the total community in R3. Therefore, it may be the case that although these rare taxa did not increase enough in abundance to meet the threshold of conditionally rare taxa, they did increase to some extent, thus resulting in a lower proportion of persistently rare taxa. This also explains the observation that the number of other rare taxa were the highest in R3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe taxa which shifted from persistently rare to other rare taxa may have been specialised to deal with a given disturbance (e.g. ammonia), but due to the short nature of the applied disturbances did not establish enough to become conditionally rare. Indeed, the unstable operation of R3 did lead to the proliferation of a microbial community with a higher proportion of specialist taxa than the control reactor (R1) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The prevalence of specialist microbes in R3 was likely caused by the variety of applied disturbances, which led to the development of a larger number of narrow niches and less overlap (Fig. S6) throughout the reactor operation. R2 had a similar number of specialist and generalist taxa to R1, indicating that the organic load increases in R2 did not lead to a proliferation of specialist microbes.\u003c/p\u003e \u003cp\u003eGeneralist taxa have a wider niche breadth, and are able to survive under a wider set of conditions than specialists \u003csup\u003e60\u003c/sup\u003e. In contrast, specialists are more discriminant in the environments in which they thrive \u003csup\u003e37\u003c/sup\u003e. The relative proportion of generalists and specialists in a microbial community is thought to influence its stability in the face of a disturbance \u003csup\u003e61\u003c/sup\u003e. Muller et al (2019) hypothesised that communities with a high proportion of specialist taxa would be more susceptible to functional breakdown in the face of a disturbance as the population performing the dominant function has a narrower niche breadth and will not be able to resist the change in conditions brought on by a disturbance. The present study provides evidence for this hypothesis in that the more specialist community of R3 failed in response to a disturbance, whereas the generalist communities of R1 and R2 were able to resist the applied shock.\u003c/p\u003e \u003cp\u003eThe observation that different microbial life strategies evolved in the respective reactors and subsequent divergent responses of each reactor to a large disturbance indicates that a community made up of generalist microbes is more resistant to large disturbances. The proliferation of generalists was enhanced in R1 due to the stable environment. This is unexpected as generalist species are often thought to gain an advantage from environmental heterogeneity \u003csup\u003e13\u003c/sup\u003e. However, the distinct nature of each disturbance applied to R3 (i.e. pH, NH\u003csub\u003e3\u003c/sub\u003e, temperature and sulfate) may have led to the establishment of microbes which were specialised to deal with them.\u003c/p\u003e \u003cp\u003eMuch more taxa overlap was observed in R1 and R2 (Fig. S4, Fig. S5) indicating that more interspecies interactions may have developed, potentially as a result of more consistent environmental conditions. Indeed a recent meta-analysis including pan-genomic data indicated that genomes of generalists are enriched in functions involved with species-species interactions \u003csup\u003e62\u003c/sup\u003e. The extent of interspecies interactions in a microbial community can impact its resistance to a disturbance as key interdependencies, e.g. sharing of public goods \u003csup\u003e63\u003c/sup\u003e are buffered from sudden taxa die-off and ecosystem function is less reliant on a single keystone species, which may be inhibited by a disturbance \u003csup\u003e64,65\u003c/sup\u003e. It has also been suggested that lower numbers of interspecies interactions reduce ecosystem stability \u003csup\u003e66\u003c/sup\u003e and increased interspecies interactions have even been shown to increase robustness in methanogenic communities \u003csup\u003e67\u003c/sup\u003e. Therefore, despite lower diversity, the generalist lifestyle which prevailed in R1 and R2 was crucial in improving their resistance to a major organic carbon influx.\u003c/p\u003e \u003c/div\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eThe environmental disturbances applied to R3 led to a more variable microbial community over time and significantly more phylogenetic dispersion within R3 samples than R1. This made R3 less resistant to the shock load and more susceptible to deterministic influences on community assembly, despite recovered MAGs having more abundant two component regulatory systems. The reduced resistance of R3 to an organic shock load to was concluded to be due to the development of different niche spaces which were occupied by specialist taxa, whereas the stable environment in R1 led to proliferation of generalist taxa. Because specialist taxa were more abundant in R3, less overlap between respective taxa was detected, indicating that there were fewer interspecies interactions, which made the system less robust to changing conditions. These results have implications for microbial communities and indicate that environmental stability promotes the development of a microbial community which is more resistant to shocks.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRaw sequencing data can be found in the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) under the BioProject accession number PRJNA1030189.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis publication has emanated from research supported by Science Foundation Ireland (SFI) through the SFI Research Professorship Programme entitled \u003cem\u003eInnovative Energy Technologies for Biofuels, Bioenergy and a Sustainable Irish Bioeconomy\u003c/em\u003e (IETSBIO\u003csup\u003e3\u003c/sup\u003e; grant number 15/RP/2763) and the Research Infrastructure grant \u003cem\u003ePlatform for Biofuel Analysis\u003c/em\u003e (Grant Number 16/RI/3401). UZI is supported by EPSRC (EP/P029329/1 and EP/V030515/1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSimon Mills:\u003c/strong\u003e Conceptualization, Data curation, Investigation, Formal analysis, Writing \u0026ndash; original draft, Visualization. \u003cstrong\u003eUmer Z. Ijaz:\u003c/strong\u003e Data formal analysis, Software, Visualization, Supervision, Writing \u0026ndash; review \u0026amp; editing. \u003cstrong\u003ePiet N.L. Lens:\u003c/strong\u003e Supervision, Project administration, Writing \u0026ndash; review \u0026amp; editing, Funding acquisition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank Borja Khatabi Soliman Tamayo, Dr Diana Quispe and Marlee Wasserman (NUIG, Ireland) for their help and support during the laboratory work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eShade, A. \u003cem\u003eet al.\u003c/em\u003e Fundamentals of Microbial Community Resistance and Resilience. \u003cem\u003eFrontiers in Microbiology\u003c/em\u003e \u003cstrong\u003e3\u003c/strong\u003e, (2012).\u003c/li\u003e\n\u003cli\u003eAllison, S. D. \u0026amp; Martiny, J. B. H. 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Networks of energetic and metabolic interactions define dynamics in microbial communities. \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e \u003cstrong\u003e112\u003c/strong\u003e, 15450 LP \u0026ndash; 15455 (2015).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"npj-biofilms-and-microbiomes","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjbiofilms","sideBox":"Learn more about [npj Biofilms and Microbiomes](http://www.nature.com/npjbiofilms/)","snPcode":"41522","submissionUrl":"https://submission.springernature.com/new-submission/41522/3","title":"npj Biofilms and Microbiomes","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"16S rRNA, Metagenomics, Two component regulatory systems, Community Assembly, Anaerobic Digestion","lastPublishedDoi":"10.21203/rs.3.rs-4382699/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4382699/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDifferent microbial communities are impacted disproportionately by environmental disturbances. The degree to which a community can remain stable when faced with a disturbance is referred to as resistance. However, the contributing ecological factors, which infer a community\u0026rsquo;s resistance are unknown. In this study, we investigate the impact of historical environmental stability on ecological phenomena and in turn, microbial community resistance to shocks. Three separate methanogenic consortia, which were subjected to varying degrees of historical environmental stability, and displayed different levels of resistance to an organic overload were sampled. Their community composition was assessed using high throughput sequencing of 16S rRNA genes and assembly based metagenomics. A suite of ecological analysis were applied to determine the effect of environmental stability on ecological phenomena such as microbial community assembly, microbial niche breadth and the rare biosphere and in turn, the effect of these phenomena on community resistance. Additionally, metagenome assembled genomes were analysed for functional effects of prolonged stability/instability. The system which was subjected to more environmental instability experienced more temporal variation in community beta diversity and a proliferation of specialists, with more abundant two component regulatory systems. This community was more susceptible to deterministic community assembly processes caused by a large environmental disturbance. These results imply that microbial communities experiencing longer term environmental instability (e.g. variations in pH or temperature) are less able to resist a large disturbances.\u003c/p\u003e","manuscriptTitle":"Shotgun metagenomics reveals environmental instability reduces resistance to shocks by enriching specialist taxa with distinct two component regulatory systems","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-30 10:28:50","doi":"10.21203/rs.3.rs-4382699/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2024-06-17T16:27:53+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2024-06-03T16:30:09+00:00","index":2,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2024-05-31T18:39:24+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2024-05-24T08:02:34+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2024-05-21T08:43:07+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2024-05-20T07:24:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-17T07:24:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-08T05:15:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Biofilms and Microbiomes","date":"2024-05-07T11:03:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-biofilms-and-microbiomes","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjbiofilms","sideBox":"Learn more about [npj Biofilms and Microbiomes](http://www.nature.com/npjbiofilms/)","snPcode":"41522","submissionUrl":"https://submission.springernature.com/new-submission/41522/3","title":"npj Biofilms and Microbiomes","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"05a3bd52-a605-47e9-88b7-c3ab2ffcfc10","owner":[],"postedDate":"May 30th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":32133922,"name":"Biological sciences/Microbiology/Microbial communities/Microbial ecology"},{"id":32133923,"name":"Biological sciences/Microbiology/Applied microbiology"}],"tags":[],"updatedAt":"2025-04-01T07:13:11+00:00","versionOfRecord":{"articleIdentity":"rs-4382699","link":"https://doi.org/10.1038/s41522-025-00679-w","journal":{"identity":"npj-biofilms-and-microbiomes","isVorOnly":false,"title":"npj Biofilms and Microbiomes"},"publishedOn":"2025-03-31 04:00:00","publishedOnDateReadable":"March 31st, 2025"},"versionCreatedAt":"2024-05-30 10:28:50","video":"","vorDoi":"10.1038/s41522-025-00679-w","vorDoiUrl":"https://doi.org/10.1038/s41522-025-00679-w","workflowStages":[]},"version":"v1","identity":"rs-4382699","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4382699","identity":"rs-4382699","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}