Hierarchical 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. We 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. MetaTree 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.

The Metaproteomics Initiative was officially launched in 2021 to strengthen collaboration, promote knowledge exchange, and support and lead standardization efforts within the growing metaproteomics community. Over the past 5 years, the Initiative has developed into a structured, global network of researchers. It has launched community-driven benchmark studies, helped shape emerging metadata and reporting standards, developed practical guidance and training materials, organized international symposia, and fostered connections across the microbiome research landscape ( https://metaproteomics.org/ ). We outline the Initiative’s organization, activities, achievements, and ongoing efforts, and reflect on how sustained, community-led coordination has shaped the development of metaproteomics as a field. We further position the Grand Metaproteome Challenges as a next step toward coordinated, community-scale biological research, aimed at advancing functional microbiome studies across clinical, industrial, and environmental application domains, and invite engagement from the wider microbiome and omics communities.

Metaproteomics enables functional insight into microbial communities by identifying and quantifying proteins in complex samples. Yet, heterogeneous analytical workflows and the lack of standardization across experimental and bioinformatics stages hinder reproducibility and comparability, limiting integration with other omics data. We here present a community-developed reporting checklist tailored to the specific needs of metaproteomics. We also outline current efforts to enable structured and interoperable metadata capture, drawing on standards from proteomics and microbiome research wherever possible. By promoting transparent reporting and advancing metadata practices, our recommendations aim to align metaproteomics more closely with FAIR principles and support reproducible and interoperable research practices.

Abstract 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.

Microbiomes, especially within the gut, are complex and may comprise hundreds of species. The identification of peptides in metaproteomics presents a substantial 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. However, this method is resource-intensive, consumes samples, and limits identification to peptides previously identified. These limitations restrict the application of DIA in metaproteomics research. We 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. Using this strategy, we have developed and optimized a workflow called MetaDIA for the analysis of microbiome data generated by DIA, which operates independently of DDA assistance. Our approach successfully created a smaller, yet sufficient database for DIA data search in metaproteomics. The results demonstrated strong consistency with the traditional DDA-based library approach at both protein and functional levels. MetaDIA is readily accessible as an open-source project hosted on GitHub (https://github.com/northomics/MetaDIA).

Abstract 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.

Abstract 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.

Among the various posttranslational modifications (PTMs) found in microbiome samples, lysine acetylation is known to be abundant and plays an important role in regulating microbial short-chain fatty acid (SCFA) metabolism. The latter is a crucial microbiome function that significantly impacts human intestinal health. This chapter describes a detailed protocol for lysine acetylomic profiling of microbial proteins in human fecal microbiome samples. The protocol consists of stool sample preprocessing, microbiome protein extraction and digestion, immunoaffinity enrichment of lysine acetylated peptides, and high-resolution mass spectrometry analysis for the identification and quantification of lysine-acetylated proteins.

Summary 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.

Fiber-based therapies focus on butyrate production, a process often dysregulated in inflammatory bowel disease (IBD), but seldomly examine other metabolites or functional pathways. Here, we systematically profiled ex vivo responses of 66 pediatric IBD microbiomes to nine resistant starches (RS), with extensive multi-omic characterization in a subset. Our study demonstrates that inter-individual variability dominates over RS-specific effects, yielding consistent yet highly personalized fermentation phenotypes, microbial compositional shifts, and metabolite outputs. Beyond butyrate, we identify previously unreported RS fermentation metabolites, revealing hidden functional pathways and cross-feeding interactions not captured by conventional short chain fatty acid-focused analyses. Metaproteomic profiling further revealed a coordinated shift from host mucin-degrading activity toward RS utilization. Together, these findings show that RS fermentation is shaped by both RS type and participant microbiome composition, and establish the RapidAIM ex vivo platform as a fiber personalization pipeline fit for interventions aimed at restoring microbial functions disrupted in human diseases.

Multi-omics approaches have revolutionized our understanding of microbial communities by enabling simultaneous interrogation of genomic, transcriptomic, proteomic, and metabolomic data. The systematic integration and analysis of these deep datasets help decipher the functional roles of microbiomes, providing critical insights into microbial activities, interactions, and dynamics across diverse environments. Biological complexity makes multi-omics analysis of a single, isolated organism demanding but highly informative, yet this complexity increases further when samples comprise hundreds to thousands of individual species. As microbiome research continues to expand into clinical, environmental, and engineered systems, standardized workflows, benchmarked datasets, and community-driven initiatives are essential to ensure reproducibility, standardization and interpretability. Establishing and disseminating best practices for experimental design, data processing, and integrative analyses will be critical for maximizing comparability and scientific rigor across studies. This perspective highlights recent advances in multi-omics microbiome research, outlines key obstacles in data integration and metadata harmonization, and proposes a collaborative roadmap for scalable, FAIR-compliant multi-omics investigations and potentially disruptive Artificial Intelligence (AI) advances comparable to those of AlphaFold in the field of microbiome science.

Beta-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. Here, 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 PhyloFunc’s 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://pypi.org/project/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. Unlike 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.

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.

Mass spectrometry (MS)-based proteomics is widely used for quantitative protein profiling and protein interaction studies. However, most current research focuses on single-species proteomics, while protein interactions within complex microbiomes, composed of hundreds of bacterial species, remain largely unexplored. 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. For instance, 1880 (48.9%) of the 3845 peptides initially assigned only to the family Bacteroidaceae could now be assigned to a specific genome. In species representative genome subsets, peptide correlation networks based on taxon-normalized peptide abundance (TNPA) linked functionally related peptides and provided insights into uncharacterized proteins. Altogether, our study demonstrates that analyzing peptide abundance correlations enhances both taxonomic and functional analyses in human gut metaproteomics research.

Grape polyphenols (GPs) are rich in B-type proanthocyanidins, which promote metabolic resilience. Longitudinal metabolomic, metagenomic, and metaproteomic changes were measured in 27 healthy subjects supplemented with soy protein isolate (SPI, 40 g per day) for 5 days followed by GPs complexed to SPI (GP-SPI standardized to 5% GPs, 40 g per day) for 10 days. Fecal, urine, and/or fasting blood samples were collected before supplementation (day –5), after 5 days of SPI (day 0), and after 2, 4 and 10 days of GP-SPI. Most multi-omic changes observed after 2 and/or 4 days of GP-SPI intake were temporary, returning to pre-supplementation profiles by day 10. Shotgun metagenomics sequencing provided insights that could not be captured with 16S rRNA amplicon sequencing. Notably, 10 days of GP-SPI decreased fasting blood glucose and increased serum hyocholic acid (HCA), a glucoregulatory bile acid, which negatively correlated with one gut bacterial guild. In conclusion, GP-induced suppression of a bacterial guild may lead to higher HCA and lower fasting blood glucose.

Metaproteomics analyzes the functional dynamics of microbial communities by identifying peptides and mapping them to the most likely proteins and taxa. One 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 taxon-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 Taxon-Function (OTF), a new conceptual unit for exploring microbial roles and interactions within ecosystems. Additionally, MetaX includes extensive statistical and visualization tools, establishing it as a robust platform for metaproteomics analysis. We validated MetaX by reanalyzing ex vivo gut microbiome metaproteomic data exposed to various sweeteners, yielding more detailed results than 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 a 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.

Metabolic syndrome (MetS) constitutes a spectrum of interconnected conditions comprising obesity, dyslipidemia, hypertension, and insulin resistance (IR). While a singular, all-encompassing treatment for MetS remains elusive, an integrative approach involving tailored lifestyle modifications and emerging functional food therapies holds promise in preventing its multifaceted manifestations. Our main objective was to scrutinize the efficacy of cranberry proanthocyanidins (PAC, 200 mg/kg/day for 12 weeks) in mitigating MetS pathophysiology in male mice subjected to standard Chow or high-fat/high-fructose (HFHF) diets while unravelling intricate mechanisms. The administration of PAC, in conjunction with an HFHF diet, significantly averted obesity, evidenced by reductions in body weight, adiposity across various fat depots, and adipocyte hypertrophy. Similarly, PAC prevented HFHF-induced hyperglycemia and hyperinsulinemia while also lessening IR. Furthermore, PAC proved effective in alleviating key risk factors associated with cardiovascular diseases by diminishing plasma saturated fatty acids, as well as levels of triglycerides, cholesterol, and non-HDL-C levels. The rise in adiponectin and drop in circulating levels of inflammatory markers showcased PAC's protective role against inflammation. To better clarify the mechanisms behind PAC actions, gut-liver axis parameters were examined, showing significant enhancements in gut microbiota composition, microbiota-derived metabolites, and marked reductions in intestinal and hepatic inflammation, liver steatosis, and key biomarkers associated with endoplasmic reticulum (ER) stress and lipid metabolism. This study enhances our understanding of the complex mechanisms underlying the development of MetS and provides valuable insights into how PAC may alleviate cardiometabolic dysfunction in HFHF mice.

Background Early-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 maternal separation stress, on gut microbiota composition, short-chain fatty acid (SCFA) production, hypothalamic-pituitary-adrenal (HPA) axis activity, and systemic, mucosal, and neuroimmune responses. Results Broad-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. Conclusions These 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.

The gut microbiome’s 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 32,419 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 interactions 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.