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.

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.

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.

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.

Mass spectrometry-based proteomics and metaproteomics have long been used in the study of human microbiomes, with the potential of metaproteomics only recently being fully harnessed. This progress is due to the advancements of high-performance mass spectrometers, innovative proteomics strategies, and the development of dedicated bioinformatics tools. In this review, we critically examine the recent technological developments that enhance the application of metaproteomics in clinical microbiome analysis. We also summarize significant advancements in the application of metaproteomics to study human microbiomes across various body sites under disease conditions. Despite these, the potential of metaproteomics remains underutilized due to typically small sample sizes and insufficient data mining. We thereby highlight some key aspects that could facilitate the broader and more effective application of mass spectrometry-based metaproteomics in clinical microbiome analysis, including the development of microbiome assays for translational research and application.

Aim: Our gut microbiome has its own functionalities which can be modulated by various xenobiotic and biotic components. The development and application of a high-throughput functional screening approach of individual gut microbiomes accelerates drug discovery and our understanding of microbiome-drug interactions. We previously developed the rapid assay of individual microbiome (RapidAIM), which combined an optimized culturing model with metaproteomics to study gut microbiome responses to xenobiotics. In this study, we aim to incorporate automation and multiplexing techniques into RapidAIM to develop a high-throughput protocol. Methods: To develop a 2.0 version of RapidAIM, we automated the protein analysis protocol, and introduced a tandem mass tag (TMT) multiplexing technique. To demonstrate the typical outcome of the protocol, we used RapidAIM 2.0 to evaluate the effect of prebiotic kestose on ex vivo individual human gut microbiomes biobanked with five different workflows. Results: We describe the protocol of RapidAIM 2.0 with extensive details on stool sample collection, biobanking, in vitro culturing and stimulation, sample processing, metaproteomics measurement, and data analysis. The analysis depth of 5,014 ± 142 protein groups per multiplexed sample was achieved. A test on five biobanking methods using RapidAIM 2.0 showed the minimal effect of sample processing on live microbiota functional responses to kestose. Conclusions: Depth and reproducibility of RapidAIM 2.0 are comparable to previous manual label-free metaproteomic analyses. In the meantime, the protocol realizes culturing and sample preparation of 320 samples in six days, opening the door to extensively understanding the effects of xenobiotic and biotic factors on our internal ecology.

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 data's 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. MetaLab's 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 MetaLab's 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. ### Competing Interest Statement D.F. is a co-founder of MedBiome Inc., a microbiome nutrition and therapeutic company. Other co-authors declare no conflict of interest.

Functional redundancy is a key ecosystem property representing the fact that different taxa contribute to an ecosystem in similar ways through the expression of redundant functions. The redundancy of potential functions (or genome-level functional redundancy FR_g ) of human microbiomes has been recently quantified using metagenomics data. Yet, the redundancy of expressed functions in the human microbiome has never been quantitatively explored. Here, we present an approach to quantify the proteome-level functional redundancy FR_p in the human gut microbiome using metaproteomics. Ultra-deep metaproteomics reveals high proteome-level functional redundancy and high nestedness in the human gut proteomic content networks (i.e., the bipartite graphs connecting taxa to functions). We find that the nested topology of proteomic content networks and relatively small functional distances between proteomes of certain pairs of taxa together contribute to high FR_p in the human gut microbiome. As a metric comprehensively incorporating the factors of presence/absence of each function, protein abundances of each function and biomass of each taxon, FR_p outcompetes diversity indices in detecting significant microbiome responses to environmental factors, including individuality, biogeography, xenobiotics, and disease. We show that gut inflammation and exposure to specific xenobiotics can significantly diminish the FR_p with no significant change in taxonomic diversity.

The studies of microbial communities have drawn increased attention in various research fields such as agriculture, environment, and human health. Recently, metaproteomics has become a powerful tool to interpret the roles of the community members by investigating the expressed proteins of the microbes. However, analyzing the metaproteomic data sets at genome resolution is still challenging because of the lack of efficient bioinformatics tools. Here we develop MetaLab-MAG, a specially designed tool for the characterization of microbiomes from metagenome-assembled genomes databases. MetaLab-MAG was evaluated by analyzing various human gut microbiota data sets and performed comparably or better than searching the gene catalog protein database directly. MetaLab-MAG can quantify the genome-level microbiota compositions and supports both label-free and isobaric labeling-based quantification strategies. MetaLab-MAG removes the obstacles of metaproteomic data analysis and provides the researchers with in-depth and comprehensive information from the microbiomes.

Metaproteomics has increasingly been applied to study functional changes in the human gut microbiome. Peptide identification is an important step in metaproteomics research, with sequence database search (SDS) and spectral library search (SLS) as the two main methods to identify peptides. However, the large search space in meta-proteomics studies causes significant challenges for both identification methods. Moreover, with the development of mass spectrometry, it is now feasible to perform metaproteomic projects involving 100-1000 individual microbiomes. These large-scale projects create a conundrum for searching large databases. In this study, we constructed MetaPep, a core peptide database (including both collections of peptide sequences and tandem MS spectra) greatly accelerating the peptide identifications. Raw files from fifteen metaproteomics projects were re-analyzed and the identified peptide-spectrum matches (PSMs) were used to construct the MetaPep database. The constructed MetaPep database achieved rapid and accurate identification of peptides for human gut meta-proteomics. MetaPep has a large collection of peptides and spectra that have been identified in published human gut metaproteomics datasets. MetaPep database can be used as an important resource in the current stage of human gut metaproteomics research. This study showed the possibility of applying a core peptide database as a generic metaproteomics workflow. MetaPep could also be an important resource for future human gut metaproteomics research, such as DIA (data-independent acquisition) analysis.

Multiplexed quantitative proteomics using tandem mass tag (TMT) is increasingly used in –omic study of complex samples. While TMT-based proteomics has the advantages of the higher quantitative accuracy, fewer missing values, and reduced instrument analysis time, it is limited by the increased cost due to the use of labeling reagents. In addition, current TMT labeling workflows involve repeated small volume pipetting of reagents in volatile organic solvents, which may increase the sample-to-sample variations and is not readily suitable for high throughput applications. In this study, we demonstrated that the TMT labeling procedures could be streamlined by using pre-aliquoted dry TMT reagents in a 96 well plate or 12-tube strip. As little as 50 μg dry TMT per channel effectively labels 6-12 μg peptides, yielding efficient TMT labeling efficiency (∼99%) in both microbiome and mammalian cell line samples. This streamlined workflow decreases reagent loss and reduces inter-sample variations. We applied this workflow to analyze 97 samples in a study to evaluate whether ice recrystallization inhibitors improve the cultivability and activity of frozen microbiota. The results demonstrated tight sample clustering corresponding to groups and consistent microbiome responses to prebiotic treatments. This study supports the use of TMT reagents that are pre-aliquoted, dried, and stored for streamlined and robust quantitative proteomics and metaproteomics in high throughput applications.

Membrane cholesterol-rich domains have been shown to be important for regulating a range of membrane protein activities. Low-density lipoprotein receptor (LDLR)-mediated internalization of cholesterol-rich LDL particles is tightly regulated by feedback mechanisms involving intracellular sterol sensors. Since LDLR plays a role in maintaining cellular cholesterol homeostasis, we explore the role that membrane domains may have in regulating LDLR activity. We expressed a fluorescent LDLR-mEGFP construct in HEK293T cells and imaged the unligated receptor or bound to an LDL/DiI fluorescent ligand using total internal reflection fluorescence microscopy. We studied the receptor's spatiotemporal dynamics using fluorescence fluctuation analysis methods. Image cross correlation spectroscopy reveals a lower LDL-to-LDLR binding fraction when membrane cholesterol concentrations are augmented using cholesterol esterase, and a higher binding fraction when the cells are treated with methyl-β-cyclodextrin) to lower membrane cholesterol. This suggests that LDLR's ability to metabolize LDL particles is negatively correlated to membrane cholesterol concentrations. We then tested if a change in activity is accompanied by a change in membrane localization. Image mean-square displacement analysis reveals that unligated LDLR-mEGFP and ligated LDLR-mEGFP/LDL-DiI constructs are transiently confined on the cell membrane, and the size of their confinement domains increases with augmented cholesterol concentrations. Receptor diffusion within the domains and their domain-escape probabilities decrease upon treatment with methyl-β-cyclodextrin, consistent with a change in receptor populations to more confined domains, likely clathrin-coated pits. We propose a feedback model to account for regulation of LDLR within the cell membrane: when membrane cholesterol concentrations are high, LDLR is sequestered in cholesterol-rich domains. These LDLR populations are attenuated in their efficacy to bind and internalize LDL. However, when membrane cholesterol levels drop, LDL has a higher binding affinity to its receptor and the LDLR transits to nascent clathrin-coated domains, where it diffuses at a slower rate while awaiting internalization.

The gut microbiome plays a critical role to all animals and humans health. Methods based on ex vivo cultures are time and cost-effective solutions for rapid evaluation of probiotic effects on microbiomes. In this study, we assessed whether the protein secretome from the potential probiotic Enterococcus durans LAB18S grown on fructoligosaccharides (FOS) and galactoligosaccharides (GOS) had specific effects on ex vivo cultured intestinal microbiome obtained from a healthy individual. Metaproteomics was used to evaluate changes in microbial communities of the human intestinal microbiome. Hierarchical clustering analysis revealed 654 differentially abundant proteins from the metaproteome samples, showing that gut microbial protein expression varied on the presence of different E. durans secretomes. Increased amount of Bacteroidetes phylum was observed in treatments with secretomes from E. durans cultures on FOS, GOS and albumin, resulting in a decrease of the Firmicutes to Bacteroidetes (F/B) ratio. The most functionally abundant bacterial taxa were Roseburia, Bacteroides, Alistipes and Faecalibacterium. The results suggest that the secretome of E. durans may have favorable effects on the intestinal microbial composition, stimulating growth and different protein expression of beneficial bacteria. These findings suggest that proteins secreted by E. durans growing on FOS and GOS have different effects on the modulation of gut microbiota functional activities during cultivation.

The studies of microbial communities have drawn increased attention in various research fields such as agriculture, environment, and human health. Recently, metaproteomics has become a powerful tool to interpret the roles of the community members by investigating the expressed proteins of the microbes. However, analyzing the metaproteomic data sets at genome resolution is still challenging because of the lack of efficient bioinformatics tools. Here we develop MetaLab-MAG, a specially designed tool for the characterization of microbiomes from metagenome-assembled genomes databases. MetaLab-MAG was evaluated by analyzing various human gut microbiota data sets and performed comparably or better than searching the gene catalog protein database directly. MetaLab-MAG can quantify the genome-level microbiota compositions and supports both label-free and isobaric labeling-based quantification strategies. MetaLab-MAG removes the obstacles of metaproteomic data analysis and provides the researchers with in-depth and comprehensive information from the microbiomes.

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder leading to dementia. The hippocampus, which is one of the sites where neural stem cells reside and new neurons are born, exhibits the most significant neuronal loss in AD. A decline in adult neurogenesis has been described in several animal models of AD. However, the age at which this defect first appears remains unknown. To determine at which stage, from birth to adulthood, the neurogenic deficits are found in AD, we used the triple transgenic mouse model of AD (3xTg). We show that defects in neurogenesis are present as early as postnatal stages, well before the onset of any neuropathology or behavioral deficits. We also show that 3xTg mice have significantly fewer neural stem/progenitor cells, with reduced proliferation and decreased numbers of newborn neurons at postnatal stages, consistent with reduced volumes of hippocampal structures. To determine whether there are early changes in the molecular signatures of neural stem/progenitor cells, we perform bulk RNA-seq on cells sorted directly from the hippocampus. We show significant changes in the gene expression profiles at one month of age, including genes of the Notch and Wnt pathways. These findings reveal impairments in neurogenesis very early in the 3xTg AD model, which provides new opportunities for early diagnosis and therapeutic interventions to prevent neurodegeneration in AD.

The human gut microbiome is a complex system composed of hundreds of species, and metaproteomics can be used to explore their expressed functions. However, many lower abundance species are not detected by current metaproteomic techniques and represent the dark field of metaproteomics. We do not know the minimal abundance of a bacterium in a microbiome(depth) that can be detected by shotgun metaproteomics. In this study, we spiked 15N-labeled E. coli peptides at different percentages into peptides mixture derived from the human gut microbiome to evaluate the depth that can be achieved by shotgun metaproteomics. We observed that the number of identified peptides and peptide intensity from 15N-labeled E. coli were linearly correlated with the spike-in levels even when 15N-labeled E. coli was down to 0.5% of the biomass. Below that level, it was not detected. Interestingly, the match-between-run strategy significantly increased the number of quantified peptides even when 15N-labeled E. coli peptides were at low abundance. This is indicative that in metaproteomics of complex gut microbiomes many peptides from low abundant species are likely observable in MS1 but are not selected for MS2 by standard shotgun strategies.

The composition and function of the human gut microbiome are often associated with health and disease status. Sugar substitute sweeteners are widely used food additives, although many studies using animal models have linked sweetener consumption to gut microbial changes and health issues. Whether sugar substitute sweeteners directly change the human gut microbiome functionality remains largely unknown. In this study, we systematically investigated the responses of five human gut microbiomes to 21 common sugar substitute sweeteners, using an approach combining high-throughput in vitro microbiome culturing and metaproteomic analyses to quantify functional changes in different taxa. Hierarchical clustering based on metaproteomic responses of individual microbiomes resulted in two clusters. The noncaloric artificial sweetener (NAS) cluster was composed of NASs and two sugar alcohols with shorter carbon backbones (4 or 5 carbon atoms), and the carbohydrate (CHO) cluster was composed of the remaining sugar alcohols. The metaproteomic functional responses of the CHO cluster were clustered with those of the prebiotics fructooligosaccharides and kestose. The sugar substitute sweeteners in the CHO cluster showed the ability to modulate the metabolism of Clostridia. This study provides a comprehensive evaluation of the direct effects of commonly used sugar substitute sweeteners on the functions of the human gut microbiome using a functional metaproteomic approach, improving our understanding of the roles of sugar substitute sweeteners on microbiome-associated human health and disease issues. IMPORTANCE The human gut microbiome is closely related to human health. Sugar substitute sweeteners as commonly used food additives are increasingly consumed and have potential impacts on microbiome functionality. Although many studies have evaluated the effects of a few sweeteners on gut microbiomes using animal models, the direct effect of sugar substitute sweeteners on the human gut microbiome remains largely unknown. Our results revealed that the sweetener-induced metaproteomic responses of individual microbiomes had two major patterns, which were associated with the chemical properties of the sweeteners. This study provided a comprehensive evaluation of the effects of commonly used sugar substitute sweeteners on the human gut microbiome.

Biobanking of live microbiota is becoming indispensable for mechanistic and clinical investigations of drug-microbiome interactions and fecal microbiota transplantation. However, there is a lack of methods to rapidly and systematically evaluate whether the biobanked microbiota maintains their cultivability and functional activity. In this study, we use a rapid ex vivo microbiome assay and metaproteomics to evaluate the cultivability and the functional responses of biobanked microbiota to treatment with a prebiotic (fructo-oligosaccharide, FOS). Our results indicate that the microbiota cultivability and their functional responses to FOS treatment were well maintained by freezing in a deoxygenated glycerol buffer at -80°C for 12 months. We also demonstrate that the fecal microbiota is functionally stable for 48 hours on ice in a deoxygenated glycerol buffer, allowing off-site fecal sample collection and shipping to laboratory for live microbiota biobanking. This study provides a method for rapid evaluation of the cultivability of biobanked live microbiota. Our results show minimal detrimental influences of long-term freezing in deoxygenated glycerol buffer on the cultivability of fecal microbiota.

Curtailing the Spring 2020 COVID-19 surge required sweeping and stringent interventions by governments across the world. Wastewater-based COVID-19 epidemiology programs have been initiated in many countries to provide public health agencies with a complementary disease tracking metric and non-discriminating surveillance tool. However, their efficacy in prospectively capturing resurgences following a period of low prevalence is unclear. In this study, the SARS-CoV-2 viral signal was measured in primary clarified sludge harvested every two days at the City of Ottawa's water resource recovery facility during the summer of 2020, when clinical testing recorded daily percent positivity below 1%. In late July, increases of >400% in normalized SARS-CoV-2 RNA signal in wastewater were identified 48 h prior to reported >300% increases in positive cases that were retrospectively attributed to community-acquired infections. During this resurgence period, SARS-CoV-2 RNA signal in wastewater preceded the reported >160% increase in community hospitalizations by approximately 96 h. This study supports wastewater-based COVID-19 surveillance of populations in augmenting the efficacy of diagnostic testing, which can suffer from sampling biases or timely reporting as in the case of hospitalization census.