Biology subjects

Figeys, D.

Publications and source records attributed to Figeys, D..

At least 19 recordsLinked to original sources

bioRxiv2026

Dissecting functional dynamics of fecal-derived in vitro stable microbial communities with metaproteomics

Fecal-derived in vitro microbial communities represent promising models for investigating gut microbiome function and facilitating translational applications. However, the factors governing community establishment and the underlying mechanisms remain incompletely understood. We employed large-scale quantitative metaproteomics to investigate the functional and community dynamics of human fecal microbiomes cultured in three media over 25 serial passages. Our results demonstrated that both culture medium and donor-specific microbiome characteristics significantly influence community establishment. Taxon-specific functional analyses revealed nutrient availability in the medium and the microbial stress responses as key determinants of in vitro community establishment. Notably, the metabolism of mucin glycans in RapidAIM medium contributed to the maintenance of Bacteroidaceae species that were poorly supported in other media, resulting in an in vitro community that most closely resembled the uncultured fecal inoculum. Overall, these findings provide mechanistic insights into in vitro gut microbial community establishment, supporting further optimization and application of fecal-derived in vitro microbiome models.

microbiology↗

bioRxiv2026

VDAC1 regulates stress-associated matrix localization of DJ-1 to support mitochondrial homeostasis and neuronal survival

DJ-1 is a redox-sensitive protein implicated in early-onset Parkinson's disease, and its mitochondrial localization protects against oxidative stress, but the mechanisms regulating its submitochondrial targeting and functional impact on mitochondrial integrity remain poorly understood. We identify voltage-dependent anion channel 1 (VDAC1) as a regulator of the submitochondrial distribution of DJ-1 during stress. Endogenous DJ-1 interacted with VDAC1, and loss of VDAC1 reduced stress-induced DJ-1 accumulation within the mitochondrial matrix. VDAC1-deficient neurons exhibited mitochondrial fragmentation, impaired oxidative phosphorylation, reduced ATP levels, altered reactive oxygen species (ROS) responses, and increased sensitivity to MPP+;. Matrix-targeted, but not outer-membrane-targeted, DJ-1 rescued basal, ATP-linked, and maximal respiration, improved mitochondrial morphology, and enhanced neuronal survival. ATP synthase inhibition also rapidly increased mitochondrial DJ-1, suggesting bioenergetic stress promotes its mitochondrial accumulation. Our findings identify compartment-specific localization as a key determinant of DJ-1 function and establish VDAC1-dependent matrix targeting as a critical mechanism supporting mitochondrial integrity during stress.

neuroscience↗

bioRxiv2026

MetaPilot: genome-aware adaptive search-space refinement for unified DDA and DIA metaproteomics

Metaproteomics directly measures microbial protein expression in complex communities, but analysis is constrained by large, poorly structured search spaces. Microbial gene catalogues provide broad coverage, but their pooled organization hinders genome-level evidence accumulation and consistent DDA and DIA analysis. Here, we present MetaPilot, a genome-aware workflow that uses conserved marker-protein evidence to guide adaptive genome-resolved search-space refinement. MetaPilot maps identifications to candidate genomes, ranks them by marginal peptide contribution, and constructs refined sample-specific search spaces for final identification, quantification and multi-layer reporting. Across DDA and DIA datasets from defined mixtures and faecal microbiomes, MetaPilot adapted genome selection to sample complexity, preserved substantial overlap with published peptide identifications and expanded the detectable peptide space; iterative refinement further increased DIA identification while controlling search-space expansion. In DDA-independent reanalysis of Orbitrap human gut metaproteomes, MetaPilot identified 24.4% more peptides than the published DDA-derived library and more than twice the number identified by the matched DDA-assisted workflow. In a timsTOF DIA-PASEF mouse intestinal dataset, MetaPilot outperformed uMetaP in identification depth and enabled genome-resolved functional interpretation. These results establish that DDA-independent DIA metaproteomics, guided by genome-resolved marker evidence, can exceed DDA-assisted workflows in identification depth.

bioinformatics↗

bioRxiv2026

Donor Age and Oligosaccharide Structure Jointly Shape the Gut Microbiome Function

Non-digestible oligosaccharides are widely used as prebiotics, yet structurally related glycans can elicit distinct gut microbiome responses. Here, we combined controlled ex vivo fermentation, deep DIA metaproteomics, and targeted metabolomics to determine how oligosaccharide structure and donor age shape microbiome function. Stool microbiomes from 18 healthy donors across three age groups were cultured with seven structurally related oligosaccharides from two glycan families, fructo-oligosaccharides (FOS) and galactosyl-sucrose derivatives (GSD). We found that oligosaccharide structure organized a functional response landscape rather than simply separating substrates into broad prebiotic classes. Structurally related glycans produced more similar response profiles overall, yet closely related FOS substrates remained functionally distinguishable, indicating that subtle structural differences were resolved by the microbiome as graded functional changes. These structure-responsive functions were further associated with producer-level reorganization relative to baseline, while targeted enzyme-level analyses indicated that substrate-specific CAZyme responses could also reflect altered functional investment within shared producer backgrounds. Despite these substrate-specific entry processes, network analysis revealed convergence onto shared downstream physiological states enriched for translation, amino-acid biosynthesis, secretion/export, and chemotaxis-related pathways. Across treatments, major short-chain fatty acids increased while mucin glycan degradation-associated markers decreased, suggesting coordinated shifts toward saccharolytic metabolism and reduced host-glycan foraging. Tryptophan-associated metabolism was also consistently linked to primary fructan processing, accompanied by higher extracellular tryptophan availability. Donor age modified selected microbial functional axes and enzyme-metabolite coupling relationships rather than the overall direction of core fermentation outputs. In particular, oligosaccharides attenuated an Methanobrevibacter smithii (M. smithii) and M00567 methanogenesis-related signature in microbiomes from older adults and altered age-dependent relationships between butyrate-pathway enzymes and extracellular butyrate levels. Together, these findings show that oligosaccharide structure determines how gut microbial communities organize carbohydrate processing and downstream functional states, while donor age reshapes the taxonomic and metabolic context of these responses. This work provides a mechanistic framework for structure-aware and age-aware precision prebiotic design.

microbiology↗

bioRxiv2026

MetaUmbra: Statistically Controlled Genome-Level Presence Inference from Metaproteomic Peptides

Taxonomic interpretation of metaproteomic peptides remains difficult because many peptide sequences are present in proteins from different organisms, reducing taxonomic specificity. Current peptide-centric workflows can report taxonomic summaries or taxon level confidence scores, but they do not provide formal statistical evidence that a taxon is present in the microbiome. Here we present MetaUmbra, a tool that derives genome-level statistical significance values from identified peptides. MetaUmbra builds theoretical peptide lists by in silico digestion of the taxon specific proteins and matches observed peptides against these references. It then combines a conservative significance estimate from unique peptides with a Monte Carlo based p-value for shared peptide evidence estimated under an empirical null model. In the defined community benchmark SIHUMIx, MetaUmbra identified the expected genomes without introducing false-positive genomes after embedding the SIHUMIx genomes in a large gut reference background. In the single strain benchmark Mix24X, all expected genomes were identified with the best statistical significances even after near neighbor and full background expansion. In a hamster gut genome panel, MetaUmbra further preserved an interpretable ranking of candidate genomes in a dense real-data setting. Together, these results show that MetaUmbra can statistically identify the presence of specific microbes in a complex microbiome while maintaining low false-positive calls. MetaUmbra therefore provides a practical framework for converting peptide evidence into genome-level statistical inference in metaproteomics.

bioinformatics↗

bioRxiv2026

MetaTree: an interactive web platform for hierarchical data visualization and multi-group comparison

BackgroundHierarchical quantitative profiles are widely used in microbiome studies and other domains. However, comparing multiple samples and experimental groups while preserving hierarchical structure remains challenging. Many existing workflows require extensive manual figure assembly or do not support aligned comparisons across conditions on a shared hierarchy. ResultsWe developed MetaTree, an open-source platform that runs in a web browser for interactive visualization and comparative analysis of hierarchical quantitative data. MetaTree anchors samples, groups, and contrasts between groups to a shared reference hierarchy, preserving one-to-one node correspondence so that the same clade is compared in the same position across views. In addition to visualization, MetaTree integrates statistical testing for comparisons between two groups with false discovery rate (FDR) control, enabling users to identify clades with consistent differences between conditions and interpret them in hierarchical context. MetaTree also provides user configurable controls for visual encoding, filtering thresholds, label density, and layout, allowing figures to be adapted to different datasets and reporting needs. The interface remains usable for large hierarchies through interactive navigation, adaptive label handling, and branch collapsing. ConclusionsMetaTree is an installation-free web platform (https://byemaxx.github.io/MetaTree) for topology-consistent visualization and comparison of hierarchical profiles, supporting coordinated multi-panel exploration and automated comparison matrices to enable rapid generation of publication-ready figures for microbiome and other hierarchical datasets.

bioinformatics↗

bioRxiv2025

Microbiome-Dependent Functional Responses to Structurally Distinct Oligosaccharides Revealed by Metaproteomics

Dietary oligosaccharides are prebiotics that fuel gut microbes, but individual microbiomes may respond differently depending on oligosaccharide structures as well as microbiome composition and function. The extent to which specific gut microbial communities exhibit personalized functional responses to distinct oligosaccharides remains underexplored. We applied a standardized ex vivo microbiome culture, called RapidAIM, coupled with metaproteomics to examine how six structurally diverse oligosaccharides affect the gut microbiota functional response. Our study shows that while human gut microbiomes share some commonalities in utilizing oligosaccharides (e.g. prioritizing dietary fibers over mucin), the fine-scale metabolic and taxonomic responses are highly individualized. Such findings underscore the importance of considering personal microbiome profiles when predicting the outcome of prebiotic interventions. In a broader context, our metaproteomic approach provides a framework for identifying optimal prebiotic choices tailored to individual microbiomes. Ultimately, understanding these personalized responses could inform precision nutrition strategies.

microbiology↗

bioRxiv2025

Early-life gut microbiome and stress-axis perturbations dysregulate systemic, mucosal, and brain immunity

BackgroundEarly-life disruptions to the gut microbiome and stress-axis significantly influence the development of immune, neuroendocrine, and other physiological systems. However, the precise microbial species and pathways mediating these effects remain poorly characterized. Using a murine model, we investigated the individual and combined effects of early-life antibiotic exposure and chronic stress on gut microbiota composition, short-chain fatty acid (SCFA) production, hypothalamic-pituitary-adrenal (HPA) axis activity, and systemic, mucosal, and neuroimmune responses. ResultsBroad-spectrum antibiotic treatments severely reduced microbial diversity and SCFA concentrations, with changes persisting into adulthood. Chronic early-life stress exerted more modest but notable effects, reducing key SCFA-producing taxa and impacting microbiome metabolic output. Combined disruptions led to altered microglial active phenotype and cytokine profiles, impaired immune cell populations, and suppressed HPA axis activity. Multi-omic correlational analyses revealed strong associations between SCFAs, specific gut microbes, and immune responses, implicating SCFAs as critical mediators of gut-brain communication. Notably, antibiotic exposure exacerbated susceptibility to allergic airway inflammation, highlighting the systemic consequences of early-life microbiome disturbances. ConclusionsThese findings demonstrate that early microbial perturbations impair neuroimmune maturation, HPA axis regulation, and host resilience to inflammatory diseases. Our study underscores the importance of preserving the early-life microbiome to support long-term immune and neurodevelopmental health, offering insights into potential therapeutic interventions for mitigating the impact of early-life microbiota disruptions.

microbiology↗

bioRxiv2025

Peptide abundance correlations in metaproteomics enhance taxonomic and functional analysis of the human gut microbiome

Mass spectrometry (MS)-based proteomics is widely used for quantitative protein profiling and has become a powerful tool for studying protein interactions. However, most current research focuses on single-species proteomics to study protein interactions. Protein interactions within more complex microbiomes, composed of 100s of bacterial species, remain largely unexplored. The human gut microbiome, closely linked to human health and disease, has become a key area of study using metaproteomics. Yet, due to the complexity of the microbiome, the interactions between gut microbes and their host remain largely unknown. In this study, we analyzed peptide abundance correlations within a metaproteomics dataset derived from in vitro cultured human gut microbiomes subjected to various drug treatments. Our analysis revealed that peptides from the same protein or taxon exhibited correlated abundance changes. By using t-SNE for visualization, we generated a peptide correlation map in which peptides from the same taxon formed distinct clusters. Furthermore, peptide abundance correlations enabled genome-level taxonomic assignments for a greater number of peptides. In single-species subsets of the dataset, peptide correlation networks constructed using taxon-based normalized peptide abundance (TNPA) linked peptides from functionally related proteins. These networks also provided insights into the potential functions of previously uncharacterized proteins. Altogether, our study demonstrates that analyzing peptide abundance correlations enhances both taxonomic and functional analyses in human gut metaproteomics research.

microbiology↗

bioRxiv2025

Systematic Metaproteomics Mapping Reveals Functional and Ecological Landscapes of Human Gut Microbiota Responses to Therapeutic Drugs

Therapeutic compounds exert impacts on gut microbiota; however, how they affect the community functional ecology, especially as reflected at the protein level, remains largely unexplored. In this study, we systematically map metaproteomic responses of ex vivo human gut microbiota to 312 compounds, generating 4.6 million microbial protein responses, available as an interactive resource (https://shiny.imetalab.ca/MPR_Viz/). Protein-level analyses identify significant metaproteomic shifts induced by 47 compounds, with neuropharmaceuticals as the sole drug class significantly enriched among these hits. Further analyses on the community level reveal a tri-stability pattern in microbial composition and the emergence of three distinct functional states, based on a functional beta-diversity metric. Notably, neuropharmaceuticals cause particularly strong effects on the microbiomes, lowering the proteome-level functional redundancy and raising the level of antimicrobial resistance proteins, ultimately pushing the microbiome into an alternative functional state. Preliminary validation suggests that enhancing functional redundancy may contribute to maintaining microbiota resilience against neuropharmaceutical-induced antimicrobial resistance. Overall, this work establishes a comprehensive view of how drugs influence gut microbiome function and ecology at the protein level, proposes a landscape-based framework for interpreting community resilience, and highlights the need to consider protein-level and ecological responses in the evaluation of therapeutic interventions.

microbiology↗

bioRxiv2024

Metaproteomics reveals age-specific alterations of gut microbiome in hamsters with SARS-CoV-2 infection

The gut microbiomes pivotal role in health and disease is well-established. SARS-CoV-2 infection often causes gastrointestinal symptoms and is associated with changes of the microbiome in both human and animal studies. While hamsters serve as important animal models for coronavirus research, there exists a notable void in the functional characterization of their microbiomes with metaproteomics. In this study, we present a workflow for analyzing the hamster gut microbiome, including a metagenomics-derived hamster gut microbial protein database and a data-independent acquisition metaproteomics method. Using this workflow, we identified 32419 protein groups from the fecal microbiomes of young and old hamsters infected with SARS-CoV-2. We showed age-specific changes in the expressions of microbiome functions and host proteins associated with microbiomes, providing further functional insight into the dysbiosis and aberrant cross-talks between the microbiome and host in SARS-CoV-2 infection. Altogether this study established and demonstrated the capability of metaproteomics for the study of hamster microbiomes.

microbiology↗

bioRxiv2024

MetaLab Platform Enables Comprehensive DDA and DIA Metaproteomics Analysis

Metaproteomics studies the collective protein composition of complex microbial communities, providing insights into microbial roles in various environments. Despite its importance, metaproteomic data analysis is challenging due to the datas large and heterogeneous nature. While Data-Independent Acquisition (DIA) mode enhances proteomics sensitivity, it traditionally requires Data-Dependent Acquisition (DDA) results to build the library for peptide identification. This paper introduces an updated version of MetaLab, a software solution that streamlines metaproteomic analysis by supporting both DDA and DIA modes across various mass spectrometry (MS) platforms, including Orbitrap and timsTOF. MetaLabs key feature is its ability to perform DIA analysis without DDA results, allowing more experimental flexibility. It incorporates a deep learning strategy to train a neural network model, enhancing the accuracy and coverage of DIA results. Evaluations using diverse datasets demonstrate MetaLabs robust performance in accuracy and sensitivity. Benchmarks from large-scale human gut microbiome studies show that MetaLab increases peptide identification by 2.7 times compared to conventional methods. MetaLab is a versatile tool that facilitates comprehensive and flexible metaproteomic data analysis, aiding researchers in exploring microbial communities functionality and dynamics.

bioinformatics↗

bioRxiv2024

PhyloFunc: Phylogeny-informed Functional Distance as a New Ecological Metric for Metaproteomic Data Analysis

BackgroundBeta-diversity is a fundamental ecological metric for exploring dissimilarities between microbial communities. On the functional dimension, metaproteomics data can be used to quantify beta-diversity to understand how microbial community functional profiles vary under different environmental conditions. Conventional approaches to metaproteomic functional beta-diversity often treat protein functions as independent features, ignoring the evolutionary relationships among microbial taxa from which different proteins originate. A more informative functional distance metric that incorporates evolutionary relatedness is needed to better understand microbiome functional dissimilarities. ResultsHere, we introduce PhyloFunc, a novel functional beta-diversity metric that incorporates microbiome phylogeny to inform on metaproteomic functional distance. Leveraging the phylogenetic framework of weighted UniFrac distance, PhyloFunc innovatively utilizes branch lengths to weigh between-sample functional distances for each taxon, rather than differences in taxonomic abundance as in weighted UniFrac. Proof-of-concept using a simulated toy dataset and a real dataset from mouse inoculated with a synthetic gut microbiome and fed different diets show that PhyloFunc successfully captured functional compensatory effects between phylogenetically related taxa. We further tested a third dataset of complex human gut microbiomes treated with five different drugs to compare PhyloFuncs performance with other traditional distance methods. PCoA and machine learning-based classification algorithms revealed higher sensitivity of PhyloFunc in microbiome responses to paracetamol. We provide PhyloFunc as an open-source Python package (available at https://pydigger.com/pypi/PhyloFunc), enabling efficient calculation of functional beta-diversity distances between a pair of samples or the generation of a distance matrix for all samples within a dataset. ConclusionsUnlike traditional approaches that consider metaproteomics features as independent and unrelated, PhyloFunc acknowledges the role of phylogenetic context in shaping the functional landscape in metaproteomes. In particular, we report that PhyloFunc accounts for the functional compensatory effect of taxonomically related species. Its effectiveness, ecological relevance, and enhanced sensitivity in distinguishing group variations are demonstrated through the specific applications presented in this study.

bioinformatics↗

bioRxiv2024

MetaX: A peptide centric metaproteomic data analysis platform using Operational Taxa-Functions (OTF)

Metaproteomics analyzes the functional dynamics of microbial communities by identifying peptides and mapping them to the most likely proteins and taxa. The challenge in this field lies in seamlessly integrating taxonomic and functional annotations to accurately represent the contributions of individual microbial taxa to functional diversity. We introduce MetaX, a comprehensive tool for analyzing taxa-function relationships in metaproteomics by mapping peptides to their lowest common ancestors and assigning functions based on proportional thresholds, ensuring accurate peptide-level mappings. Importantly, MetaX introduces the Operational Taxa-Functions (OTF), a new conceptual unit for exploring microbial roles and interactions within ecosystems. Additionally, MetaX extends traditional taxonomic classification by adding a genome level below the species level, enhancing the accuracy of function attribution to specific genomes. We demonstrated MetaX by reanalyzing metaproteomic data from gut microbiomes exposed to various sweeteners, achieving results similar to traditional protein analysis. Furthermore, using the peptide-centric approach and OTF, we observed that Parabacteroides distasonis significantly responds to certain sweeteners, highlighting its role in modifying specific metabolic functions. With its intuitive, user-friendly interface, MetaX facilitates detailed study of the complex interactions between microbial taxa and their functions in metaproteomics. It enhances our understanding of microbial roles in ecosystems and health.

bioinformatics↗

bioRxiv2024

MetaDIA: A Novel Database Reduction Strategy for DIA Human Gut Metaproteomics

BackgroundMicrobiomes, especially within the gut, are complex and may comprise hundreds of species. The identification of peptides in metaproteomics presents a significant challenge, as it involves matching peptides to mass spectra within an enormous search space for complex and unknown samples. This poses difficulties for both the accuracy and the speed of identification. Specifically, analysis of data-independent acquisition (DIA) datasets has relied on libraries constructed from prior data-dependent acquisition (DDA) results. This approach requires running the samples in DDA mode to construct a library from the identified results, which can then be used for the DIA data. However, this method is resource-intensive, consumes samples, and limits identification to peptides previously identified by DDA. These limitations restrict the application of DIA in metaproteomics research. ResultsWe introduced a novel strategy to reduce the search space by utilizing species abundance and functional abundance information from the microbiome to score each peptide and prioritize those most likely to be detected. Employing this strategy, we have developed and optimized a workflow called MetaDIA for analysis of microbiome DIA data, which operates independently of DDA assistance. Our method demonstrated strong consistency with the traditional DDA-based library approach at both protein and functional levels. ConclusionOur approach successfully created a smaller, yet sufficient database for DIA data search requirements in metaproteomics, showing high consistency with results from the conventional DDA-based library. We believe this method can facilitate the application of DIA in metaproteomics.

bioinformatics↗

bioRxiv2024

Assessing fecal metaproteomics workflow and small protein recovery using DDA and DIA PASEF mass spectrometry

AimThis study aims to evaluate the impact of experimental workflow on fecal metaproteomic observations, including the recovery of small and antimicrobial proteins often overlooked in metaproteomic studies. The overarching goal is to provide guidance for optimized metaproteomic experimental design, considering the emerging significance of the gut microbiome in human health, disease, and therapeutic interventions. MethodsMouse feces were utilized as the experimental model. Fecal sample pre-processing methods (differential centrifugation and non-differential centrifugation), protein digestion techniques (in-solution and filter-aided), data acquisition modes (data-dependent and data-independent, or DDA and DIA) when combined with parallel accumulation-serial fragmentation (PASEF), and different bioinformatic workflows were assessed. ResultsWe showed that, in DIA-PASEF metaproteomics, the library-free search using protein sequence database generated from DDA-PASEF data achieved better identifications than using the generated spectral library. Compared to DDA, DIA-PASEF identified more microbial peptides, quantified more proteins with less missing values, and recovered more small antimicrobial proteins. We didnt observe obvious impacts of protein digestion methods on both taxonomic and functional profiles. However, differential centrifugation decreased the recovery of small and antimicrobial proteins, biased the taxonomic observation with a marked over-estimation of Muribaculum species, and altered the measured functional compositions of metaproteome. ConclusionThis study underscores the critical impact of experimental choices on metaproteomic outcomes and sheds light on the potential biases introduced at different stages of the workflow. The comprehensive methodological comparisons serve as a valuable guide for researchers aiming to enhance the accuracy and completeness of metaproteomic analyses.

microbiology↗

bioRxiv2024

Multilevel proteomic profiling of colorectal adenocarcinoma cell differentiation to characterize an intestinal epithelial model

Emergent advancements on the intestinal microbiome for human health and disease treatment necessitates well-defined intestinal cellular models to study and rapidly assess host, microbiome, and drug interactions. This study characterized molecular alterations during Caco-2 cell differentiation, an epithelial intestinal model, using quantitative multi-omic approaches. We demonstrated that both spontaneous and medium-induced cellular differentiations displayed similar protein and pathway changes, including the down-regulation of proteins related to translation and proliferation, and up-regulation of proteins related to cell adhesion, molecule binding and metabolic pathways. Acetyl-proteomics revealed decreased histone acetylation and increased acetylation in proteins associated with mitochondria functions in differentiated cells. Butyrate-containing differentiation medium accelerates differentiation, with earlier up-regulation of proteins related to differentiation and host-microbiome interactions. These results emphasize the controlled progression of Caco-2 differentiation toward a specialized intestinal epithelial-like cell. This further enhances their characterization, establishing their suitability for facilitating the effective evaluation of risk and quality in microbiome-directed therapeutics.

systems biology↗

bioRxiv2022

Pairing Metagenomics and Metaproteomics to Pinpoint Ecological Niches and Metabolic Essentiality of Microbial Communities

The genome of a microorganism encodes its potential functions that can be implemented through expressed proteins. It remains elusive how a proteins selective expression depends on its metabolic essentiality to microbial growth or its ability to claim resources as ecological niches. To reveal a proteins metabolic or ecological role, we developed a computational pipeline, which pairs metagenomics and metaproteomics data to quantify each proteins gene-level and protein-level functional redundancy simultaneously. We first illustrated the idea behind the pipeline using simulated data of a consumer-resource model. We then validated it using real data from human and mouse gut microbiome samples. In particular, we analyzed ABC-type transporters and ribosomal proteins, confirming that the metabolic and ecological roles predicted by our pipeline agree well with prior knowledge. Finally, we performed in vitro cultures of a human gut microbiome sample and investigated how oversupplying various sugars involved in ecological niches influences the community structure and protein abundance. The presented results demonstrate the performance of our pipeline in identifying proteins metabolic and ecological roles, as well as its potential to help us design nutrient interventions to modulate the human microbiome.

ecology↗