Metabolic Reconstruction Research Articles

Data associated with publication "The media composition as a crucial element in high-throughput metabolic network reconstruction"

Data associated with publication "The media composition as a crucial element in high-throughput metabolic network reconstruction"

Identifying metabolic shifts in Crohn's disease using 'omics-driven contextualized computational metabolic network models

Crohn's disease (CD) is a chronic inflammatory disease of the gastrointestinal tract. A clear gap in our existing CD diagnostics and current disease management approaches is the lack of highly specific biomarkers that can be used to streamline or personalize disease management. Comprehensive profiling of metabolites holds promise; however, these high-dimensional profiles need to be reduced to have relevance in the context of CD. Machine learning approaches are optimally suited to bridge this gap in knowledge by contextualizing the metabolic alterations in CD using genome-scale metabolic network reconstructions. Our work presents a framework for studying altered metabolic reactions between patients with CD and controls using publicly available transcriptomic data and existing gene-driven metabolic network reconstructions. Additionally, we apply the same methods to patient-derived ileal enteroids to explore the utility of using this experimental in vitro platform for studying CD. Furthermore, we have piloted an untargeted metabolomics approach as a proof-of-concept validation strategy in human ileal mucosal tissue. These findings suggest that in silico metabolic modeling can potentially identify pathways of clinical relevance in CD, paving the way for the future discovery of novel diagnostic biomarkers and therapeutic targets.

Metabolic reconstruction of the human pathogen Candida auris: using a cross-species approach for drug target prediction.

Candida auris is an emerging human pathogen, associated with antifungal drug resistance and hospital candidiasis outbreaks. In this work, we present iRV973, the first reconstructed Genome-scale metabolic model (GSMM) for C. auris. The model was manually curated and experimentally validated, being able to accurately predict the specific growth rate of C. auris and the utilization of several sole carbon and nitrogen sources. The model was compared to GSMMs available for other pathogenic Candida species and exploited as a platform for cross-species comparison, aiming the analysis of their metabolic features and the identification of potential new antifungal targets common to the most prevalent pathogenic Candida species. From a metabolic point of view, we were able to identify unique enzymes in C. auris in comparison with other Candida species, which may represent unique metabolic features. Additionally, 50 enzymes were identified as potential drug targets, given their essentiality in conditions mimicking human serum, common to all four different Candida models analysed. These enzymes represent interesting drug targets for antifungal therapy, some are already known targets of antifungal agents used in clinical practice, but other new potential drug targets also stand out without any human homolog or drug association in Candida species.

Non-Model Bacterium for Metabolic Network Reconstruction: Challenges and Opportunities

Non-Model Bacterium for Metabolic Network Reconstruction: Challenges and Opportunities

Metabolic reconstruction of the near complete microbiome of the model sponge Ianthella basta.

Many marine sponges host highly diverse microbiomes that contribute to various aspects of host health. Although the putative function of individual groups of sponge symbionts has been increasingly described, the extreme diversity has generally precluded in-depth characterization of entire microbiomes, including identification of syntrophic partnerships. The Indo-Pacific sponge Ianthella basta is emerging as a model organism for symbiosis research, hosting only three dominant symbionts: a Thaumarchaeotum, a Gammaproteobacterium, and an Alphaproteobacterium and a range of other low abundance or transitory taxa. Here, we retrieved metagenome assembled genomes (MAGs) representing >90% of I. basta's microbial community, facilitating the metabolic reconstruction of the sponge's near complete microbiome. Through this analysis, we identified metabolic complementarity between microbes, including vitamin sharing, described the importance of low abundance symbionts, and characterized a novel microbe-host attachment mechanism in the Alphaproteobacterium. We further identified putative viral sequences, highlighting the role viruses can play in maintaining symbioses in I. basta through the horizontal transfer of eukaryotic-like proteins, and complemented this data with metaproteomics to identify active metabolic pathways in bacteria, archaea, and viruses. This data provide the framework to adopt I. basta as a model organism for studying host-microbe interactions and provide a basis for in-depth physiological experiments.

Analyzing Prokaryotic Transcriptomics in the Light of Genome Data with the MicroScope Platform.

Large-scale genome sequencing and the increasingly massive use of high-throughput approaches produce a vast amount of new information that completely transforms our understanding of thousands of microbial species occurring in our environment. However, despite the development of powerful bioinformatics approaches, full interpretation of the content of these genomes remains a difficult task. To address this challenge, the MicroScope platform has been developed. It is an integrated Web platform for management, annotation, comparative analysis, and visualization of microbial genomes ( https://mage.genoscope.cns.fr/microscope ). Launched in 2005, the platform has been under continuous development and provides analyzes for complete and ongoing genome projects together with metabolic network reconstruction and transcriptomic experiments allowing users to improve the understanding of gene functions. MicroScope platform is widely used by microbiologists from academia and industry all around the world for collaborative studies and expert annotation. It enables collaborative work in a rich comparative genomic context and improves community-based curation efforts. Here, we describe the protocol to follow for the integration and analysis of transcriptomics data in the Microscope platform. The chapter reviews each key step from the experimental design to the analysis and interpretation of the experiment data and results. The integration of transcriptomics data gives a dynamic view of the genome by allowing the users to improve the understanding of gene functions by interpreting them in the light of regulatory cell processes. Moreover, they can also contribute to the refinement of genome annotation through the discovery of new genes and help to fill metabolic gaps.

Two canonically aerobic foraminifera express distinct peroxisomal and mitochondrial metabolisms

Certain benthic foraminifera thrive in marine sediments with low or undetectable oxygen. Potential survival avenues used by these supposedly aerobic protists include fermentation and anaerobic respiration, although details on their adaptive mechanisms remain elusive. To better understand the metabolic versatility of foraminifera, we studied two benthic species that thrive in oxygen-depleted marine sediments. Here we detail, via transcriptomics and metatranscriptomics, differential gene expression of Nonionella stella and Bolivina argentea, collected from Santa Barbara Basin, California, USA, in response to varied oxygenation and chemical amendments. Organelle-specific metabolic reconstructions revealed these two species utilize adaptable mitochondrial and peroxisomal metabolism. N. stella, most abundant in anoxia and characterized by lack of food vacuoles and abundance of intracellular lipid droplets, was predicted to couple the putative peroxisomal beta-oxidation and glyoxylate cycle with a versatile electron transport system and a partial TCA cycle. In contrast, B. argentea, most abundant in hypoxia and contains food vacuoles, was predicted to utilize the putative peroxisomal gluconeogenesis and a full TCA cycle but lacks the expression of key beta-oxidation and glyoxylate cycle genes. These metabolic adaptations likely confer ecological success while encountering deoxygenation and expand our understanding of metabolic modifications and interactions between mitochondria and peroxisomes in protists.

Bimodal distribution of seafloor microbiota diversity and function are associated with marine aquaculture.

The aim of the current work was to investigate the impact of marine aquaculture on seafloor biogeochemistry and diversity from pristine environments in the northern part of Norway. Our analytical approach included analyses of 182 samples from 16 aquaculture sites using 16S and 18S rRNA, shotgun analyses, visual examination of macro-organisms, in addition to chemical measurements. We observed a clear bimodal distribution of the prokaryote composition and richness, determined by analyses of 16S rRNA gene operational taxonomic units (OTUs). The high OTU richness cluster was associated with non-perturbed environments and farness from the aquaculture sites, while the low OTU richness cluster was associated with perturbed environments and proximity to the aquaculture sites. Similar patterns were also observed for eukaryotes using 18S rRNA gene analyses and visual examination, but without a bimodal distribution of OTU richness. Shotgun sequencing showed the archaeum Nitrosopumilus as dominant for the high OTU richness cluster, and the epsilon protobacterium Sulfurovum as dominant for the low OTU richness cluster. Metabolic reconstruction of Nitrosopumilus indicates nitrification as the main metabolic pathway. Sulfurovum, on the other hand, was associated with sulfur oxidation and denitrification. Changes in nitrogen and sulfur metabolism is proposed as a potential explanation for the difference between the high and low OTU richness clusters. In conclusion, these findings suggest that pollution from elevated loads of organic waste drives the microbiota towards a complete alteration of respiratory routes and species composition, in addition to a collapse in prokaryote OTU richness.

ChiMera: an easy to use pipeline for bacterial genome based metabolic network reconstruction, evaluation and visualization

BackgroundGenome-scale metabolic reconstruction tools have been developed in the last decades. They have helped to reconstruct eukaryotic and prokaryotic metabolic models, which have contributed to fields, e.g., genetic engineering, drug discovery, prediction of phenotypes, and other model-driven discoveries. However, the use of these programs requires a high level of bioinformatic skills. Moreover, the functionalities required to build models are scattered throughout multiple tools, requiring knowledge and experience for utilizing several tools.ResultsHere we present ChiMera, which combines tools used for model reconstruction, prediction, and visualization. ChiMera uses CarveMe in the reconstruction module, generating a gap-filled draft reconstruction able to produce growth predictions using flux balance analysis for gram-positive and gram-negative bacteria. ChiMera also contains two modules for metabolic network visualization. The first module generates maps for the most important pathways, e.g., glycolysis, nucleotides and amino acids biosynthesis, fatty acid oxidation and biosynthesis and core-metabolism. The second module produces a genome-wide metabolic map, which can be used to retrieve KEGG pathway information for each compound in the model. A module to investigate gene essentiality and knockout is also present.ConclusionsOverall, ChiMera uses automation algorithms to combine a variety of tools to automatically perform model creation, gap-filling, flux balance analysis (FBA), and metabolic network visualization. ChiMera models readily provide metabolic insights that can aid genetic engineering projects, prediction of phenotypes, and model-driven discoveries.

SimDFBA: A framework for bioprocess simulation and development

Many efforts have been made to model bio-processes and investigate the main effective parameters using conventional known softwares. The main shortcoming of such models is that the bioreactor is considered a black box. Such simplification causes uncertainty in following different metabolic pathways of microorganisms. This study presents a new method for simulating bio-processes by integrating available conventional simulation softwares such as ASPEN PLUS and MATLAB. The former is used to simulate common process equipment, and the latter is employed to model Bio-reactor. Besides, the Bio-reactor is modeled mathematically with Dynamic Flux Balance Analysis (DFBA) concept using the metabolic network reconstruction model. This framework will simultaneously provide a rigorous basic model of both upstream and downstream equipment. This approach can also be used as a toolbox to implement process optimization and modern control strategies on the bio-processes. A syngas fermentation bioprocess case study was chosen to implement the new framework.

The novel oligopeptide utilizing species Anaeropeptidivorans aminofermentans M3/9T, its role in anaerobic digestion and occurrence as deduced from large-scale fragment recruitment analyses.

Research on biogas-producing microbial communities aims at elucidation of correlations and dependencies between the anaerobic digestion (AD) process and the corresponding microbiome composition in order to optimize the performance of the process and the biogas output. Previously, Lachnospiraceae species were frequently detected in mesophilic to moderately thermophilic biogas reactors. To analyze adaptive genome features of a representative Lachnospiraceae strain, Anaeropeptidivorans aminofermentans M3/9T was isolated from a mesophilic laboratory-scale biogas plant and its genome was sequenced and analyzed in detail. Strain M3/9T possesses a number of genes encoding enzymes for degradation of proteins, oligo- and dipeptides. Moreover, genes encoding enzymes participating in fermentation of amino acids released from peptide hydrolysis were also identified. Based on further findings obtained from metabolic pathway reconstruction, M3/9T was predicted to participate in acidogenesis within the AD process. To understand the genomic diversity between the biogas isolate M3/9T and closely related Anaerotignum type strains, genome sequence comparisons were performed. M3/9T harbors 1,693 strain-specific genes among others encoding different peptidases, a phosphotransferase system (PTS) for sugar uptake, but also proteins involved in extracellular solute binding and import, sporulation and flagellar biosynthesis. In order to determine the occurrence of M3/9T in other environments, large-scale fragment recruitments with the M3/9T genome as a template and publicly available metagenomes representing different environments was performed. The strain was detected in the intestine of mammals, being most abundant in goat feces, occasionally used as a substrate for biogas production.

A workflow for annotating the knowledge gaps in metabolic reconstructions using known and hypothetical reactions

Advances in medicine and biotechnology rely on a deep understanding of biological processes. Despite the increasingly available types and amounts of omics data, significant knowledge gaps remain, with current approaches to identify and curate missing annotations being limited to a set of already known reactions. Here, we introduce Network Integrated Computational Explorer for Gap Annotation of Metabolism (NICEgame), a workflow to identify and curate nonannotated metabolic functions in genomes using the ATLAS of Biochemistry and genome-scale metabolic models (GEMs). To resolve gaps in GEMs, NICEgame provides alternative sets of known and hypothetical reactions, assesses their thermodynamic feasibility, and suggests candidate genes to catalyze these reactions. We identified metabolic gaps and applied NICEgame in the latest GEM of Escherichia coli, iML1515, and enhanced the E. coli genome annotation by resolving 47% of these gaps. NICEgame, applicable to any GEM and functioning from open-source software, should thus enhance all GEM-based predictions and subsequent biotechnological and biomedical applications.

Functional genomics of the lactic acid bacterium Limosilactobacillus fermentum LAB-1: metabolic, probiotic and biotechnological perspectives

A genome-based systematic analysis was conducted to characterize the metabolic, probiotic, fitness, and safety properties of Limosilactobacillus fermentum LAB-1, a lactic acid bacterium demonstrating strong antimicrobial effects against clinical pathogens. Gene functional characterization revealed a large number of genes for carbohydrate metabolism and a heterofermentative system for carbon dissimilation. Genes for intact pyruvate oxidation, pentose phosphate, and PRPP biosynthetic pathways were identified. Substantial carbohydrate-active enzymes and transporters were also predicted. Metabolic reconstruction revealed complete sets of enzymes for arginine, lysine, methionine, threonine, proline, and ornithine biosynthesis. The bacterium harbors a diverse range of peptidases, and a large variety of peptide and amino acid uptake systems. It encodes restriction-modification and CRISPR-Cas systems for protection against phage infections and carries a wide spectrum of stress proteins for adaptation in the gut and industrial conditions. Genes related to the biosynthesis of B-group and K vitamins were identified allowing its application for novel bio-enriched food production. Other beneficial traits of probiotic and industrial importance such as production of flavor compounds, exopolysaccharide, acetoin, and butanediol were identified. Three antimicrobial peptides were predicted which showed >98% sequence-identity to experimentally validated bacteriocins. Negative traits such as transmissible antibiotic resistance, pathogenicity or virulence appeared to be absent suggesting the strain to be considered safe. The genome analysis will allow precisely targeted laboratory research and full exploitation of the probiotic potentials towards functional-food, biotechnology and health-related applications.

Drug transporters OAT1 and OAT3 have specific effects on multiple organs and gut microbiome as revealed by contextualized metabolic network reconstructions

In vitro and in vivo studies have established the organic anion transporters OAT1 (SLC22A6, NKT) and OAT3 (SLC22A8) among the main multi-specific “drug” transporters. They also transport numerous endogenous metabolites, raising the possibility of drug-metabolite interactions (DMI). To help understand the role of these drug transporters on metabolism across scales ranging from organ systems to organelles, a formal multi-scale analysis was performed. Metabolic network reconstructions of the omics-alterations resulting from Oat1 and Oat3 gene knockouts revealed links between the microbiome and human metabolism including reactions involving small organic molecules such as dihydroxyacetone, alanine, xanthine, and p-cresol—key metabolites in independent pathways. Interestingly, pairwise organ-organ interactions were also disrupted in the two Oat knockouts, with altered liver, intestine, microbiome, and skin-related metabolism. Compared to older models focused on the “one transporter-one organ” concept, these more sophisticated reconstructions, combined with integration of a multi-microbial model and more comprehensive metabolomics data for the two transporters, provide a considerably more complex picture of how renal “drug” transporters regulate metabolism across the organelle (e.g. endoplasmic reticulum, Golgi, peroxisome), cellular, organ, inter-organ, and inter-organismal scales. The results suggest that drugs interacting with OAT1 and OAT3 can have far reaching consequences on metabolism in organs (e.g. skin) beyond the kidney. Consistent with the Remote Sensing and Signaling Theory (RSST), the analysis demonstrates how transporter-dependent metabolic signals mediate organ crosstalk (e.g., gut-liver-kidney) and inter-organismal communication (e.g., gut microbiome-host).

Genome-scale metabolic modeling reveals increased reliance on valine catabolism in clinical isolates of Klebsiella pneumoniae

Infections due to carbapenem-resistant Enterobacteriaceae have recently emerged as one of the most urgent threats to hospitalized patients within the United States and Europe. By far the most common etiological agent of these infections is Klebsiella pneumoniae, frequently manifesting in hospital-acquired pneumonia with a mortality rate of ~50% even with antimicrobial intervention. We performed transcriptomic analysis of data collected previously from in vitro characterization of both laboratory and clinical isolates which revealed shifts in expression of multiple master metabolic regulators across isolate types. Metabolism has been previously shown to be an effective target for antibacterial therapy, and genome-scale metabolic network reconstructions (GENREs) have provided a powerful means to accelerate identification of potential targets in silico. Combining these techniques with the transcriptome meta-analysis, we generated context-specific models of metabolism utilizing a well-curated GENRE of K. pneumoniae (iYL1228) to identify novel therapeutic targets. Functional metabolic analyses revealed that both composition and metabolic activity of clinical isolate-associated context-specific models significantly differs from laboratory isolate-associated models of the bacterium. Additionally, we identified increased catabolism of L-valine in clinical isolate-specific growth simulations. These findings warrant future studies for potential efficacy of valine transaminase inhibition as a target against K. pneumoniae infection.

Microbial Community Structure and Metabolic Potential at the Initial Stage of Soil Development of the Glacial Forefields in Svalbard.

Microbial communities have been identified as the primary inhabitants of Arctic forefields. However, the metabolic potential of microbial communities in these newly exposed soils remains underexplored due to limited access. Here, we sampled the very edge of the glacial forefield in Svalbard and performed the 16S rRNA genes and metagenomic analysis to illustrate the ecosystem characteristics. Burkholderiales and Micrococcales were the dominant bacterial groups at the initial stage of soil development of glacial forefields. 214 metagenome-assembled genomes were recovered from glacier forefield microbiome datasets, including only 2 belonging to archaea. Analysis of these metagenome-assembled genomes revealed that 41% of assembled genomes had the genetic potential to use nitrate and nitrite as electron acceptors. Metabolic pathway reconstruction for these microbes suggested versatility for sulfide and thiosulfate oxidation, H2 and CO utilization, and CO2 fixation. Our results indicate the importance of anaerobic processes in elemental cycling in the glacial forefields. Besides, a range of genes related to adaption to low temperature and other stresses were detected, which revealed the presence of diverse mechanisms of adaption to the extreme environment of Svalbard. This research provides ecological insight into the initial stage of the soil developed during the retreating of glaciers.

Deep-branching acetogens in serpentinized subsurface fluids of Oman

Little is known of acetogens in contemporary serpentinizing systems, despite widely supported theories that serpentinite-hosted environments supported the first life on Earth via acetogenesis. To address this knowledge gap, genome-resolved metagenomics was applied to subsurface fracture water communities from an area of active serpentinization in the Samail Ophiolite, Sultanate of Oman. Two deeply branching putative bacterial acetogen types were identified in the communities belonging to the Acetothermia (hereafter, types I and II) that exhibited distinct distributions among waters with lower and higher water-rock reaction (i.e., serpentinization influence), respectively. Metabolic reconstructions revealed contrasting core metabolic pathways of type I and II Acetothermia, including in acetogenic pathway components (e.g., bacterial- vs. archaeal-like carbon monoxide dehydrogenases [CODH], respectively), hydrogen use to drive acetogenesis, and chemiosmotic potential generation via respiratory (type I) or canonical acetogen ferredoxin-based complexes (type II). Notably, type II Acetothermia metabolic pathways allow for use of serpentinization-derived substrates and implicate them as key primary producers in contemporary hyperalkaline serpentinite environments. Phylogenomic analyses indicate that 1) archaeal-like CODH of the type II genomes and those of other serpentinite-associated Bacteria derive from a deeply rooted horizontal transfer or origin among archaeal methanogens and 2) Acetothermia are among the earliest evolving bacterial lineages. The discovery of dominant and early-branching acetogens in subsurface waters of the largest near-surface serpentinite formation provides insight into the physiological traits that likely facilitated rock-supported life to flourish on a primitive Earth and possibly on other rocky planets undergoing serpentinization.

AHR is a master regulator of diverse pathways in endogenous metabolism

The aryl hydrocarbon receptor (AHR) is a transcription factor with roles in detoxification, development, immune response, chronic kidney disease and other syndromes. It regulates the expression of drug transporters and drug metabolizing enzymes in a proposed Remote Sensing and Signaling Network involved in inter-organ communication via metabolites and signaling molecules. Here, we use integrated omics approaches to analyze its contributions to metabolism across multiple scales from the organ to the organelle. Global metabolomics analysis of Ahr−/− mice revealed the role of AHR in the regulation of 290 metabolites involved in many biochemical pathways affecting fatty acids, bile acids, gut microbiome products, antioxidants, choline derivatives, and uremic toxins. Chemoinformatics analysis suggest that AHR plays a role in determining the hydrophobicity of metabolites and perhaps their transporter-mediated movement into and out of tissues. Of known AHR ligands, indolepropionate was the only significantly altered molecule, and it activated AHR in both human and murine cells. To gain a deeper biological understanding of AHR, we employed genome scale metabolic reconstruction to integrate knockout transcriptomics and metabolomics data, which indicated a role for AHR in regulation of organic acids and redox state. Together, the results indicate a central role of AHR in metabolism and signaling between multiple organs and across multiple scales.

Pulmonary hypertension – an explorative metabolomic analysis of the endothelium

Abstract Background Pulmonary hypertension (PH) is classified into five clinical diagnostic groups, including group 1 (idiopathic pulmonary arterial hypertension (IPAH) and connective tissue disease-associated PAH (CTD-aPAH)) and group 4 (chronic thromboembolic pulmonary hypertension (CTEPH)). PH is a progressive, life-threatening, incurable disease. The pathological mechanisms underlying PH remain elusive; recent evidence has revealed that abnormal metabolic activities in the endothelium may play a crucial role. Purpose Introducing a novel approach for studying PH endothelial function, building on genome-scale metabolic (GEM) reconstruction of the endothelial cell (EC) to investigate intracellular metabolism. Methods This prospective observational study includes 35 PH patients with either PAH or CTEPH. We have previously developed a platform to study the endothelium cell (EC) by reconstructing the most comprehensive endothelial genome-scale metabolic model (EC-GEM). Since the EC is in direct contact with the blood flow, we can adjust the EC-GEM by measuring extracellular metabolites from the blood and calculating the intracellular metabolic flux in the EC. We conducted individual EC-GEM models, i.e., in total 35 different models. Additionally, each model was widely analyzed; in total, 190 different intracellular activities were investigated. This enables us to develop a heatmap of the intracellular activities at individual patient levels. Patients with similar intracellular endothelial activities were combined. Results Four distinct intracellular phenotypes were identified; all were independent of the clinical classification of PH. We observed clinically relevant differences in the NT-pro BNP levels and the WHO functional classification between these phenotypes. Also, different important cellular activities were identified separating the four phenotypes. Two phenotypes were characterized by catabolic crisis and extreme citric acid cycle turnover for energy production. However, one of these also suffered from oxidative stress. Conclusion In PH patients metabolic systems analysis identified four metabolic phenotypes that correlated with NT-pro BNP levels and the WHO functional classification but were independent of the clinical diagnosis. This novel finding may enable us to identify metabolic therapeutic targets specific to the respective phenotypes and thus promote personalized medicine in patients with PH. Funding Acknowledgement Type of funding sources: Foundation. Main funding source(s): This study was funded by Novo Nordisk Foundation research grant NNF20OC0064556 and the Heart Center Research Council, Copenhagen University Hospital, Rigshospitalet, provided to Jørn Carlsen

A global phylogenomic and metabolic reconstruction of the large intestine bacterial community of domesticated cattle

BackgroundThe large intestine is a colonization site of beneficial microbes complementing the nutrition of cattle but also of zoonotic and animal pathogens. Here, we present the first global gene catalog of cattle fecal microbiomes, a proxy of the large intestine microbiomes, from 436 metagenomes from six countries.ResultsPhylogenomics suggested that the reconstructed genomes and their close relatives form distinct branches and produced clustering patterns that were reminiscent of the metagenomics sample origin. Bacterial taxa had distinct metabolic profiles, and complete metabolic pathways were mainly linked to carbohydrates and amino acids metabolism. Dietary changes affected the community composition, diversity, and potential virulence. However, predicted enzymes, which were part of complete metabolic pathways, remained present, albeit encoded by different microbes.ConclusionsOur findings provide a global insight into the phylogenetic relationships and the metabolic potential of a rich yet understudied bacterial community and suggest that it provides valuable services to the host. However, we tentatively infer that members of that community are not irreplaceable, because similar to previous findings, symbionts of complex bacterial communities of mammals are expendable if there are substitutes that can perform the same task.5hQ22exGscduocemxiNZpbVideo