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• W3187674939 abstract "•The variation of the community was decoupled from AD performance•The fermentative intermediates affect AD pathway rather than the AD performance•The redundancy of fermentative species induces the independent community variation We investigated the short-term dynamics of microbial composition and function in bioreactors with inocula collected from full-scale and laboratory-based anaerobic digestion (AD) systems. The Bray-Curtis dissimilarity of both inocula was approximately 10% of the predicted Kyoto Encyclopedia of Genes and Genomes pathway and 40% of the taxonomic composition and yet resulted in a similar performance in methane production, implying that the variation of community composition may be decoupled from performance. However, the significant correlation of volatile fatty acids with taxonomic variation suggested that the pathways of AD could be different because of the varying genus. The predicted function of the significantly varying genus was mostly related to fermentation, which strengthened the conclusion that most microbial variation occurred within the fermentative species and led to alternative routes to result in similar methane production in methanogenic bioreactors. This finding sheds some light on the understanding of AD community regulation, which depends on the aims to recover intermediates or methane. We investigated the short-term dynamics of microbial composition and function in bioreactors with inocula collected from full-scale and laboratory-based anaerobic digestion (AD) systems. The Bray-Curtis dissimilarity of both inocula was approximately 10% of the predicted Kyoto Encyclopedia of Genes and Genomes pathway and 40% of the taxonomic composition and yet resulted in a similar performance in methane production, implying that the variation of community composition may be decoupled from performance. However, the significant correlation of volatile fatty acids with taxonomic variation suggested that the pathways of AD could be different because of the varying genus. The predicted function of the significantly varying genus was mostly related to fermentation, which strengthened the conclusion that most microbial variation occurred within the fermentative species and led to alternative routes to result in similar methane production in methanogenic bioreactors. This finding sheds some light on the understanding of AD community regulation, which depends on the aims to recover intermediates or methane. Microorganisms and their surrounding environments are the basis for a range of bioreactors, e.g. aerobic activated sludge, anaerobic digestion (AD), enabling the treatment of carbon or nutrient-enriched wastewaters; recently, increasing research attention has been paid to advancing the understanding of the functional role of microorganisms in bioreactors (Rittmann and McCarty, 2012Rittmann B.E. McCarty P.L. Environmental Biotechnology: Principles and Applications. Tata McGraw-Hill Education, 2012Google Scholar). Culture-dependent characterization was commonly used to understand species functionality and how it influenced reactor performance (Roest, 2007Roest C. Microbial community analysis in sludge of anaerobic wastewater treatment systems: Integrated culture-dependent and culture-independent approaches[M].2007Google Scholar; Vilela et al., 2020Vilela C.L.S. Peixoto R.S. da Rachid C.T.C.C. Bassin J.P. Assessing the impact of synthetic estrogen on the microbiome of aerated submerged fixed-film reactors simulating tertiary sewage treatment and isolation of estrogen-degrading consortium.Sci. Total Environ. 2020; 743: 140428https://doi.org/10.1016/j.scitotenv.2020.140428Crossref PubMed Scopus (3) Google Scholar; Wagner et al., 1993Wagner M. Amann R. Lemmer H. Schleifer K.H. Probing activated sludge with oligonucleotides specific for proteobacteria: inadequacy of culture-dependent methods for describing microbial community structure.Appl. Environ. Microbiol. 1993; 59: 1520-1525Crossref PubMed Scopus (608) Google Scholar). However, the core community of a bioreactor represents an artificial ecosystem consisting of multiple-syntrophic microbial communities, and it remains a significant challenge to understand the hidden mechanisms underlying the microbial ecology of bioreactors. Despite the ongoing controversy of the “1% culturability paradigm,” a majority of microorganisms in bioreactors, including specific functional species, may still be unculturable (Martiny, 2019Martiny A.C. High proportions of bacteria are culturable across major biomes.ISME J. 2019; 13: 2125-2128https://doi.org/10.1038/s41396-019-0410-3Crossref PubMed Scopus (51) Google Scholar, Martiny, 2020Martiny A.C. The ‘1% culturability paradigm’ needs to be carefully defined.ISME J. 2020; 14: 10-11https://doi.org/10.1038/s41396-019-0507-8Crossref PubMed Scopus (8) Google Scholar; Steen et al., 2019Steen A.D. Crits-Christoph A. Carini P. DeAngelis K.M. Fierer N. Lloyd K.G. Cameron Thrash J. High proportions of bacteria and archaea across most biomes remain uncultured.ISME J. 2019; 13: 3126-3130https://doi.org/10.1038/s41396-019-0484-yCrossref PubMed Scopus (105) Google Scholar). The rapid development of sequencing technology in the last 20 years has revealed an enormous microbial diversity; this has emerged as a culture-independent method to explore the ecological mechanisms underlying bioreactor microbial communities, advancing the understanding of hidden microbial ecological systems underpinning reactor performance (Rittmann et al., 2006Rittmann B.E. Hausner M. Loffler F. Love N.G. Muyzer G. Okabe S. Oerther D.B. Peccia J. Raskin L. Wagner M. A vista for microbial ecology and environmental biotechnology.Env. Sci. Technol. 2006; 40: 1096-1103https://doi.org/10.1021/es062631kCrossref PubMed Scopus (106) Google Scholar). The research questions about ecosystem function versus community composition in bioreactors opened up a Pandora's box: how do the microbial communities evolve, and how can the productivity and functional stability of a reactor be achieved or sustained (Fernández et al., 1999Fernández A. Huang S. Seston S. Xing J. Hickey R. Criddle C. Tiedje J. How stable is stable? Function versus community composition.Appl. Environ. Microbiol. 1999; 65: 3697-3704https://doi.org/10.1128/AEM.65.8.3697-3704.1999Crossref PubMed Google Scholar). Next-generation sequencing accelerated the discoveries in this area by generating new knowledge and understating at a molecular biology level. Advanced metagenomic sequencing (targeted 16s rRNA amplicons and/or shotgun sequencing) offers insights into taxonomic classification, microbiome composition, and powerful tools to monitor the AD process performances and inform operators how to optimize AD pathways and performance by regulating the community composition in bioreactors. This could lead to sophisticated bioaugmentation strategies and enhanced performance and stability. Despite the microbial ecology focus on coupling the community composition variance to their function in natural ecosystems (Waldrop et al., 2000Waldrop M.P. Balser T.C. Firestone M.K. Linking microbial community composition to function in a tropical soil.Soil Biol. Biochem. 2000; 32: 1837-1846https://doi.org/10.1016/S0038-0717(00)00157-7Crossref Scopus (459) Google Scholar; Waldrop and Firestone, 2006Waldrop M.P. Firestone M.K. Response of microbial community composition and function to soil climate change.Microb. Ecol. 2006; 52: 716-724https://doi.org/10.1007/s00248-006-9103-3Crossref PubMed Scopus (183) Google Scholar), a “decoupling” phenomenon existed. Notably, a long-term experimental study with amplicon and metagenomic sequencing showed that the microbial assembly relied on functional genes rather than species in accordance with the differences in Bray-Curtis distance (Burke et al., 2011Burke C. Steinberg P. Rusch D. Kjelleberg S. Thomas T. Bacterial community assembly based on functional genes rather than species.Proc. Natl. Acad. Sci. U S A. 2011; 108: 14288-14293https://doi.org/10.1073/pnas.1101591108Crossref PubMed Scopus (463) Google Scholar). This perspective has been enhanced in further studies which considered the variation of community assemblages and associated functions (Louca et al., 2016Louca S. Parfrey L.W. Doebeli M. Decoupling function and taxonomy in the global ocean microbiome.Science. 2016; 353: 1272-1277https://doi.org/10.1126/science.aaf4507Crossref PubMed Scopus (753) Google Scholar, Louca et al., 2018Louca S. Polz M.F. Mazel F. Albright M.B.N. Huber J.A. O’Connor M.I. Ackermann M. Hahn A.S. Srivastava D.S. Crowe S.A. et al.Function and functional redundancy in microbial systems.Nat. Ecol. Evol. 2018; 2: 936-943https://doi.org/10.1038/s41559-018-0519-1Crossref PubMed Scopus (341) Google Scholar). Generally, the community composition and functions are always interlinked because of the presence of functional species, which are often regarded as a performance index, or an indicator to predict the physiological dynamics of sludge, e.g. bulking and foaming (Wagner et al., 2002Wagner M. Loy A. Nogueira R. Purkhold U. Lee N. Daims H. Microbial community composition and function in wastewater treatment plants.Antonie Van Leeuwenhoek. 2002; 81: 665-680https://doi.org/10.1023/A:1020586312170Crossref PubMed Scopus (300) Google Scholar; Wagner and Loy, 2002Wagner M. Loy A. Bacterial community composition and function in sewage treatment systems.Curr. Opin. Biotechnol. 2002; 13: 218-227https://doi.org/10.1016/S0958-1669(02)00315-4Crossref PubMed Scopus (453) Google Scholar; Yang et al., 2011Yang C. Zhang W. Liu R. Li Q. Li B. Wang S. Song C. Qiao C. Mulchandani A. Phylogenetic diversity and metabolic potential of activated sludge microbial communities in full-scale wastewater treatment plants.Environ. Sci. Technol. 2011; 45: 7408-7415https://doi.org/10.1021/es2010545Crossref PubMed Scopus (144) Google Scholar). Several previous studies used the 16S rRNA or metagenomic sequencing technology to show the decoupling relationship between the community composition and the performance stability in artificial bioreactors in the presence of functional redundancy (Fernández et al., 1999Fernández A. Huang S. Seston S. Xing J. Hickey R. Criddle C. Tiedje J. How stable is stable? Function versus community composition.Appl. Environ. Microbiol. 1999; 65: 3697-3704https://doi.org/10.1128/AEM.65.8.3697-3704.1999Crossref PubMed Google Scholar; Fernandez-Gonzalez et al., 2016Fernandez-Gonzalez N. Huber J.A. Vallino J.J. Microbial communities are well Adapted to disturbances in energy input.mSystems. 2016; 1 (e00117–16)https://doi.org/10.1128/mSystems.00117-16Crossref PubMed Scopus (15) Google Scholar; Vanwonterghem et al., 2016Vanwonterghem I. Jensen P.D. Rabaey K. Tyson G.W. Genome-centric resolution of microbial diversity, metabolism and interactions in anaerobic digestion.Environ. Microbiol. 2016; 18: 3144-3158https://doi.org/10.1111/1462-2920.13382Crossref PubMed Scopus (69) Google Scholar; Wang et al., 2011Wang X. Wen X. Yan H. Ding K. Zhao F. Hu M. Bacterial community dynamics in a functionally stable pilot-scale wastewater treatment plant.Bioresour. Technol. 2011; 102: 2352-2357https://doi.org/10.1016/j.biortech.2010.10.095Crossref PubMed Scopus (90) Google Scholar; Wittebolle et al., 2008Wittebolle L. Vervaeren H. Verstraete W. Boon N. Quantifying community dynamics of nitrifiers in functionally stable reactors.Appl. Environ. Microbiol. 2008; 74: 286-293https://doi.org/10.1128/AEM.01006-07Crossref PubMed Scopus (170) Google Scholar). Recent studies showed that the functional redundancy of Fe(II) metabolism impacted the functional stability under a wide range of pH and Fe(II) concentrations (Ayala-Muñoz et al., n.d.,Ayala-Muñoz, D., Simister, R.L., Crowe, S.A., Macalady, J.L., Burgos, W.D., n.d. Functional redundancy imparts process stability to acidic Fe(II)-oxidizing microbial reactors. Environ. Microbiol. n/a. https://doi.org/10.1111/1462-2920.15259Google Scholar). Although functional redundancy was considered as the driver for this decoupling, recent research has provided a different perspective showing that strong links exist between community composition and function, which disagrees with the redundancy widely observed in marine environments (Galand et al., 2018Galand P.E. Pereira O. Hochart C. Auguet J.C. Debroas D. A strong link between marine microbial community composition and function challenges the idea of functional redundancy.ISME J. 2018; 12: 2470-2478https://doi.org/10.1038/s41396-018-0158-1Crossref PubMed Scopus (70) Google Scholar). Interestingly, strong correlations between community composition and function have been also demonstrated in previous research where the microbial community was applied as a training database to predict bioreactor performance (Günther et al., 2012Günther S. Koch C. Hübschmann T. Röske I. Müller R.A. Bley T. Harms H. Müller S. Correlation of community dynamics and process parameters as a tool for the prediction of the stability of wastewater treatment.Environ. Sci. Technol. 2012; 46: 84-92https://doi.org/10.1021/es2010682Crossref PubMed Scopus (45) Google Scholar; Lesnik and Liu, 2017Lesnik K.L. Liu H. Predicting microbial fuel cell biofilm communities and bioreactor performance using artificial neural networks.Environ. Sci. Technol. 2017; 51: 10881-10892https://doi.org/10.1021/acs.est.7b01413Crossref PubMed Scopus (32) Google Scholar). Overall, the relationship between community composition and function in bioreactors remains as a research frontier worthy of more in-depth exploration. Notably, limited studies have been published in this field investigating AD with industrial wastewater, with a notable gap on food-fermentation wastewater. In this study, experiments were performed in continuous stirred-tank reactors (CSTRs) to investigate changing and community composition during reactor start-up with a carbon-rich wastewater generated from the fermentation industry. Quorn Foods was selected to represent the advanced fermentation technology, and wastewater was collected from a mycoprotein production process at Quorn which are currently aerobically treated on-site. We have investigated different inocula originating from a full-scale reactor (inocula-F) and a laboratory-based system (inocula-L). The former was obtained from a centralized full-scale AD plant codigesting wastewater and organic solid waste, while the latter was obtained from a laboratory-scale anaerobic membrane bioreactor described in a previous study (Tao et al., 2020Tao Y. Ersahin M.E. Ghasimi D.S.M. Ozgun H. Wang H. Zhang X. Guo M. Yang Y. Stuckey D.C. van Lier J.B. Biogas productivity of anaerobic digestion process is governed by a core bacterial microbiota.Chem. Eng. J. 2020; 380: 122425https://doi.org/10.1016/j.cej.2019.122425Crossref Scopus (40) Google Scholar). We selected the classical CSTR as the AD reactor in this study; a preadaptation was used to acclimate the inocula to adapt to the unique food-fermentation industrial wastewater from a Quorn mycoprotein production process. The same environmental stressor (ecological factor) was applied on different inocula that represent the distinct microbial sources; the similar performance observed from a CSTR offers evidence to elucidate that the community variation could be decoupled from the reactor performance over a short-term response period. Thus, the parameters for all CSTR were the same, which were also the key to compare the reactor performance with different inocula. Specifically, the two different inocula were preacclimated in batch reactors for 42 days (d) followed by a start-up of CSTRs inoculated with these acclimated sludges. All samples were collected daily during the preacclimation experiment and the start-up period of the CSTRs (23 d). The water samples were characterized by analytical methods to determine chemical oxygen demand (COD), volatile fatty acids (VFAs), total suspended solids (TSS), and volatile suspended solids (VSS). Biosamples were prepared for amplicon sequencing analysis following the DNA extraction and sequencing protocol detailed in the STAR Methods section. Overall, this study tested the hypothesis that the functional stability of AD over a short-term response period in anaerobic bioreactors fed with food-fermentation industry wastewater could be decoupled from the community composition variance because of the presence of functional redundancy, while the community composition variance may have an impact on the intermediate's generation. Two aspects enabled us to investigate the relationship between community variation and reactor performance, i.e. different inocula and the variation of specific community in a short term in anaerobic bioreactors. The former reflected the variance in microbial sources and initial composition, whereas the latter focused on the stability of reactor performance over a short-term community variation. The initial inocula collected from the wastewater treatment systems (inocula-F and inocula-L) were introduced into Automated Methane Potential Test System (AMPTS) batch reactors to characterize the initial activity of these inocula under preacclimation experiments. As shown in Figure S1 and Table S1, similar daily methane production trends were found across samples with varying hydraulic retention times (HRTs). The methane production of each cycle duration (4 d for inocula-F and 6 d for inocula-L) was 241.35 ± 15.23 NmL CH4 for inocula-L and 217.74 ± 40.48 NmL CH4 for inocula-F, indicating significantly higher performance of inocula-L than that of inocula-F (t test, p < 0.05). The inocula-F was originally collected from a wastewater treatment plant with inert and nonbiodegradable organics, which may reduce the activity of microorganisms per mass of culture. Considering the removal of COD each day, the average methane yield of inocula-L was 7.55 ± 2.05 NmL CH4/g COD[removed], which was lower than the inocula-F average (9.41 ± 2.24 NmL CH4/g COD), but these yields were not statistically different (p > 0.05). The preacclimated sludges were inoculated into the CSTRs to obtain stable methane production. As shown in Figure 1, the average methane production was 161.96 ± 76.11 NmL CH4/d and 179.71 ± 80.04 NmL CH4/d for inocula-F and inocula-L, respectively (p > 0.05). The COD removal efficiency was in the range of 30%–50% over the 23 days of operation, averaging 37.64 ± 7.01% for inocula-F, which was close to the performance of inocula-L (36.42 ± 5.85% COD removal, p > 0.05). As a similar amount of organics had been removed during methane generation, there was no significant difference in the observed methane yield (p > 0.05), and the CSTR inoculated with preacclimated inocula-F produced 58.51 ± 31.87 NmL CH4/g of COD daily, whereas inocula-L sludge generated 62.33 ± 30.75 NmL CH4/g COD. Although the original seed sludge was different in the two CSTRs, there was no statistical difference in their performance in terms of methane production and COD removal as the different inocula had evolved and improved over fed batch experiments, especially inocula-F. A further specific methanogenic activity (SMA) test confirmed the similar capacities of inocula-F and inocula-L sludge. As shown in Figure S2, the cultured inocula-F sludge showed a similar performance (0.30 ± 0.08 g CH4/g VSS/d) to inocula-L (0.28 ± 0.11 g CH4/g VSS/d, p > 0.05). A pH increase was observed in both CSTR reactors over time – from 7.5 to 7.88 for inocula-F and from 7.5 to 7.73 for inocula-L (Figure S3), although the decrease of pH in the first days could be caused by the accumulation of VFAs (Figure S4). The fluctuation in pH was significantly different (p < 0.05), where inocula-F and inocula-L averaged 7.66 ± 0.25 and 7.56 ± 0.19, respectively. The variation in VFAs exhibited visible differences in Figure 2. The propionate concentration in inocula-L was significantly higher than that of inocula-F (p < 0.05); in contrast, significantly lower iso-butyrate concentrations were found in inocula-L CSTR than in inocula-F (p < 0.05). As shown in Figure S4, the change of VFAs in inocula-F and inocula-L CSTR was clearly different. In AD, VFA production from complex organics would eventually be converted into acetate and hydrogen which are the main substrates for methanogenesis, although the dynamics of VFA production are influenced by thermodynamic limitations caused by high-hydrogen partial pressure; therefore, the conversion pathways of VFAs could be diverse (Łukajtis et al., 2018Łukajtis R. Hołowacz I. Kucharska K. Glinka M. Rybarczyk P. Przyjazny A. Kamiński M. Hydrogen production from biomass using dark fermentation.Renew. Sustain. Energy Rev. 2018; 91: 665-694https://doi.org/10.1016/j.rser.2018.04.043Crossref Scopus (171) Google Scholar). Despite the similarity of performance in methane production, the variation in VFAs suggested that the fermentative pathway of the microbial community could be different for inocula-F and inocula-L CSTRs.Figure 3A clustering results of all samplesShow full captionMDS plots of KEGG (A) and OTU (B) based on Bray-Curtis distance.View Large Image Figure ViewerDownload Hi-res image Download (PPT)Figure 4Microbial taxonomy for CSTR stageShow full captionVariation of microbial composition at genus level (A) and KEGG pathway 2 level (B).View Large Image Figure ViewerDownload Hi-res image Download (PPT)Figure 5The variation of relative abundance of fermentative microorganisms over the operational period of the CSTRView Large Image Figure ViewerDownload Hi-res image Download (PPT) MDS plots of KEGG (A) and OTU (B) based on Bray-Curtis distance. Variation of microbial composition at genus level (A) and KEGG pathway 2 level (B). The microbial taxonomy initially present in inocula-F and inocula-L sludge differed; Mesotoga accounts for nearly 80.70% and 31.20% in inocula-L and inocula-F sludge (Figure S5), respectively, resulting in a clear difference of diversity (Shannon index, 1.40 vs 3.34, shown in Figure S6). The subsequent preacclimation eliminated the dominant advantage of Mesotoga, which declined to 53.80% ± 17.60% and 17.30 ± 6.60% at the end of the batch cycle. As shown in Figure S6, the Shannon index increased in the batch mode of both inoculated reactors, considering the microbial evolution, while the phylogenetic diversity (PD) was steady over the batch operational period (Figure S7). The PD of inocula-F and inocula-L was 9.84 ± 0.23 and 8.55 ± 0.44, respectively (p < 0.05). The PD value of inocula-F was still higher than that of inocula-L, which accorded with the alpha diversity index. There is a slight change of PD value in both inocula over the adaptation period, implying the change of microbial community was insignificant. In the continuous mode (shown in Figure S8), the phylogenetic diversity decreased over time in both the CSTRs inoculated with full-scale and laboratory sludge. The microbial community of inocula-F evolved in the CSTR with a significantly higher PD compared to inocula-L (p < 0.05). The PD value has been previously shown to be relevant to the stability of reactors, as the diversity provided a broader metabolic potential to adapt to environmental shocks in wastewater treatment bioreactors (Yang et al., 2011Yang C. Zhang W. Liu R. Li Q. Li B. Wang S. Song C. Qiao C. Mulchandani A. Phylogenetic diversity and metabolic potential of activated sludge microbial communities in full-scale wastewater treatment plants.Environ. Sci. Technol. 2011; 45: 7408-7415https://doi.org/10.1021/es2010545Crossref PubMed Scopus (144) Google Scholar; Zhang et al., 2019Zhang Z. Deng Y. Feng K. Cai W. Li S. Yin H. Xu M. Ning D. Qu Y. Deterministic assembly and diversity gradient altered the biofilm community performances of bioreactors.Environ. Sci. Technol. 2019; 53: 1315-1324https://doi.org/10.1021/acs.est.8b06044Crossref PubMed Scopus (56) Google Scholar). From the viewpoint of microbial ecology, the species as phylogenetic relatives could compete for resources with a similar metabolism, and a lower PD value indicates that most species evolved from the same ancestor with a similar metabolism; however, in contrast, a higher PD may reflect complex metabolic patterns (Cordero et al., 2012Cordero O.X. Ventouras L.-A. DeLong E.F. Polz M.F. Public good dynamics drive evolution of iron acquisition strategies in natural bacterioplankton populations.Proc. Natl. Acad. Sci. U S A. 2012; 109: 20059-20064https://doi.org/10.1073/pnas.1213344109Crossref PubMed Scopus (195) Google Scholar; David et al., 2014David L.A. Materna A.C. Friedman J. Campos-Baptista M.I. Blackburn M.C. Perrotta A. Erdman S.E. Alm E.J. Host lifestyle affects human microbiota on daily timescales.Genome Biol. 2014; 15: R89https://doi.org/10.1186/gb-2014-15-7-r89Crossref PubMed Scopus (459) Google Scholar). The preacclimation enabled the cultures to maintain the PD values which would be beneficial for a community adapting to the environment. However, the washout and enrichment in the CSTRs indicated that a specifically functional unit would be enhanced with decreasing PD, which commonly occurred in our previous studies (Cai et al., 2016Cai W. Liu W. Yang C. Wang L. Liang B. Thangavel S. Guo Z. Wang A. Biocathodic methanogenic community in an integrated anaerobic digestion and microbial electrolysis system for enhancement of methane production from waste sludge.ACS Sustain. Chem. Eng. 2016; 4: 4913-4921https://doi.org/10.1021/acssuschemeng.6b01221Crossref Scopus (68) Google Scholar, Cai et al., 2019Cai W. Liu W. Zhang Z. Feng K. Ren G. Pu C. Li J. Deng Y. Wang A. Electro-driven methanogenic microbial community diversity and variability in the electron abundant niche.Sci. Total Environ. 2019; 661: 178-186https://doi.org/10.1016/j.scitotenv.2019.01.131Crossref PubMed Scopus (16) Google Scholar, Cai et al., 2020Cai W. Liu W. Wang B. Yao H. Guadie A. Wang A. Semiquantitative detection of hydrogen-associated or hydrogen-free electron transfer within methanogenic biofilm of microbial electrosynthesis.Appl. Environ. Microbiol. 2020; 86 (e01056–20)https://doi.org/10.1128/AEM.01056-20Crossref Scopus (11) Google Scholar). A variation in community composition was revealed by the change in PD; a clustering result is depicted in Figure 3A where the nonmetric multidimensional scaling (NMDS) based on the Bray-Curtis distance demonstrated that the inocula-F batch was far away from the cluster of inocula-L batch, and the difference of operational taxonomic units (OTU) composition contributes to this distance. The corresponding Bray-Curtis dissimilarity between inocula-F and inocula-L batches was 75.96% ± 4.72%. The scatters representing inocula-F CSTR and inocula-L CSTR were clustered together; if considering time variation, the position of the time-dependent plots distributed from left to right on the main axis and from bottom to top in the second axis. The averaged OTU-based dissimilarity between the inocula-F and inocula-L CSTRs was 41.60% ± 5.40%, which was nearly half that in the batch mode. In addition, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways (level 3) predicted by Tax4fun2 exhibited different clustering results of NMDS and Bray-Curtis dissimilarity in contrast to OTU-based analyses; as shown in Figure 3, inocula-F and inocula-L batches were clustered together with lower dissimilarity (18.39 ± 4.57%). However, an average dissimilarity of 14.23 ± 3.69% was found throughout the operation of the CSTRs. The difference in distance between the OTU and KEGG matrices suggests that the function dynamic was different from OTU variation, i.e., a stable function might be obtained with varying composition; this research finding is in accordance with a previous study by Burke et al. Considering the time effect, as shown in Figure S9, there is no observed trend in CSTR dissimilarity over time, but the stationary check augmented Dicky-Fuller test (ADF) verified that the change in OTU-based and KEGG-based Bray-Curtis dissimilarity is nonstationary, i.e., time relevant (both p > 0.05). The positive coefficient of linear regression indicated an invisible increased trend of dissimilarity for OTU-based and KEGG-based dissimilarity (0.0027 for OTU and 0.0003 for KEGG). As shown in Figure 4, the variation of genus was clear, whereas the fluctuation in the KEGG pathway level 2 was invisible. These results were in accordance with the Bray-Curtis dissimilarity as the function profile was stable over the operational period. The contribution of environmental factors to community assembly was summarized as a deterministic theory, which is in contrast to stochastic or neutral processes (Zhou et al., 2013Zhou J. Liu W. Deng Y. Jiang Y.-H. Xue K. He Z. Van Nostrand J.D. Wu L. Yang Y. Wang A. Stochastic assembly leads to alternative communities with distinct functions in a bioreactor microbial community.MBio. 2013; 4 (e00584–12)https://doi.org/10.1128/mBio.00584-12Crossref Scopus (187) Google Scholar). The null model can quantify the dominance of each process by adopting the index of normalized stochasticity ratio (NST). Clearly, the community assembly was more stochastic for inocula-F (72.17%) than the stochastic assembly of inocula-L CSTR (87.67%), indicating the shift in community could be mostly explained by stochastic processes rather than environmental determinism; in addition, a higher proportion of neutral processes was commonly found in previous studies (Zhou et al., 2013Zhou J. Liu W. Deng" @default.
• W3187674939 created "2021-08-16" @default.
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• W3187674939 date "2021-09-01" @default.
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• W3187674939 title "Linkage of community composition and function over short response time in anaerobic digestion systems with food fermentation wastewater" @default.
• W3187674939 cites W1986358771 @default.
• W3187674939 cites W1990935806 @default.
• W3187674939 cites W1999964316 @default.
• W3187674939 cites W2011646401 @default.
• W3187674939 cites W2034285706 @default.
• W3187674939 cites W2038168681 @default.
• W3187674939 cites W2049655406 @default.
• W3187674939 cites W2053555532 @default.
• W3187674939 cites W2064079625 @default.
• W3187674939 cites W2069776901 @default.
• W3187674939 cites W2087885930 @default.
• W3187674939 cites W2097557545 @default.
• W3187674939 cites W2102720408 @default.
• W3187674939 cites W2103088074 @default.
• W3187674939 cites W2109225133 @default.
• W3187674939 cites W2110082875 @default.
• W3187674939 cites W2112757906 @default.
• W3187674939 cites W2116041602 @default.
• W3187674939 cites W2117457769 @default.
• W3187674939 cites W2121957100 @default.
• W3187674939 cites W2123000746 @default.
• W3187674939 cites W2135293770 @default.
• W3187674939 cites W2154693275 @default.
• W3187674939 cites W2159918517 @default.
• W3187674939 cites W2165437036 @default.
• W3187674939 cites W2166563879 @default.
• W3187674939 cites W2166655850 @default.
• W3187674939 cites W2319571072 @default.
• W3187674939 cites W2329781616 @default.
• W3187674939 cites W2334320120 @default.
• W3187674939 cites W2336330109 @default.
• W3187674939 cites W2464271760 @default.
• W3187674939 cites W2483670055 @default.
• W3187674939 cites W2509238054 @default.
• W3187674939 cites W2520355709 @default.
• W3187674939 cites W2596081734 @default.
• W3187674939 cites W2604335996 @default.
• W3187674939 cites W2748086219 @default.
• W3187674939 cites W2755594975 @default.
• W3187674939 cites W2797101767 @default.
• W3187674939 cites W2806575427 @default.
• W3187674939 cites W2809204052 @default.
• W3187674939 cites W2908912112 @default.
• W3187674939 cites W2909740554 @default.
• W3187674939 cites W2932736878 @default.
• W3187674939 cites W2950158987 @default.
• W3187674939 cites W2964771029 @default.
• W3187674939 cites W2965859353 @default.
• W3187674939 cites W2966514282 @default.
• W3187674939 cites W2976969251 @default.
• W3187674939 cites W3036160038 @default.
• W3187674939 cites W3037891450 @default.
• W3187674939 cites W3091137023 @default.
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