Leyuan Li

The high taxonomic and strain-level diversity of microbial communities makes it difficult for reference databases to fully represent the protein sequences present in metaproteomic samples, limiting database-dependent peptide identification. De novo peptide sequencing can recover peptide sequences directly from tandem mass spectra without relying on reference databases, providing complementary peptide evidence for metaproteomics. However, most existing de novo sequencing models were developed largely from non-microbial proteomic data and lack specific adaptation to microbial spectra. Here, we established a dedicated microbial spectral resource comprising more than 10 million annotated high-quality tandem mass spectra from 72 cultured microbial isolates, with peptide digests from each isolate separated into five high-pH reversed-phase fractions to increase the opportunity for deeper and more diverse peptide sampling. Using this resource, we developed π-MNovo, which combines adaptation to microbial spectra with evidence-guided candidate selection to improve full-length peptide sequencing precision. On a seven-species external benchmark, π-MNovo achieved 66.30% complete-peptide recall under the same residue-mass-based complete-peptide matching criterion applied to all models, exceeding four published models by 6.9–21.7% in relative recall, with consistent gains across species and peptide-length groups. In two independent metaproteomic datasets, π-MNovo increased reference-supported taxonomic coverage, while synthetic-community analysis recapitulated the designed abundance ranking. From pFind-unidentified spectra, π-MNovo recovered 1,587 pFind-unreported, reference-matched peptides that passed spectrum-evidence filtering. These results show that π-MNovo expands recoverable peptide evidence and enhances the utility of de novo sequencing for metaproteomic analysis.

De novo peptide sequencing, the mainstream technique for identifying novel peptides, has recently seen remarkable improvements due to deep learning approaches. However, existing models struggle to effectively enhance the encoding of mass spectra and the decoding of amino acids, which limits their overall performance. Moreover, these models lack peptide filtering for de novo peptides, and often present challenges for users without programming expertise. Here, we propose π-HelixNovo2, a de novo peptide sequencing model that integrates complementary spectrum and bidirectional decoding within a Transformer framework. We further propose a peptide filtering strategy to identify the correct peptide-spectrum matches from the results of π-HelixNovo2. Our experiments demonstrate that π-HelixNovo2 outperforms state-of-the-art models, offering reliable performance in identifying antibody peptides, multi-enzyme cleavage peptides, non-enzymatic peptides, and analyzing the gut metaproteome. Finally, we trained π-HelixNovo2 on the large-scale MassIVE-KB dataset, and present an open, user-friendly, and online computational platform to make π-HelixNovo2 freely available to all (https://openi.pcl.ac.cn/OpenI/pi-HelixNovo-NPU).

Metaproteomics is an effective tool for characterizing the functional profiles of microbial communities by directly identifying and quantifying abundances. However, prospective power analysis and sample-size estimation are often overlooked at the study design stage in metaproteomics, which can result in underpowered experiments and reduced ability to detect biologically meaningful effects. In this study, we present a practical, end-to-end workflow for conducting power analysis prior to data collection. We focus on three common experimental designs: between-group comparisons, parallelized perturbation experiments, and beta diversity analyses. To tailored these experimental designs, we consider three major statistical approaches for power estimation: parametric tests (e.g. t-test, ANOVA), non-parametric tests (e.g. Wilcoxon rank-sum test, Kruskal–Wallis test), and distance-based multivariate methods (e.g. PERMANOVA using Bray–Curtis). By presenting detailed case studies, we provide practical guidance on how to calculate effect sizes, generate simulated datasets, and estimate statistical power across varying sample sizes. We also supply corresponding visualizations for each scenario to support sample-size determination and power assessment. This framework is intended to help researchers optimize sample size, improve experimental efficiency, and reduce costs, thereby enabling more reliable and interpretable biological insights from metaproteomic studies.

Background: 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 bioinformatic stages hinder reproducibility and comparability, limiting integration with other omics data. Results: 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. Conclusions: 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.

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.

The human gut microbiome exhibits characteristics of complex ecosystems, including the ability to resist and compete with exogenous species or communities. Understanding the microbiome response that emerges from such competitive interactions is crucial, particularly for applications like fecal microbiota transplantation (FMT), where the success of treatment largely depends on the outcome of these microbial competitions. During these processes, microbial communities undergo coalescence, a phenomenon where distinct microbial communities combine and interact, leading to complex ecological outcomes that are still being uncovered. In this study, we examined the coalescent dynamics of 10 different pairs of human gut microbiota by co‐culturing the plateau‐phase communities of individual samples in vitro, and highlighted the critical role of metaproteomics in elucidating the competitive dynamics of co‐cultured human fecal samples. Results showed that microbiome changes observed after coalescent co‐culture were not straightforwardly an approximate average of the initial taxonomic or functional compositions of the two samples. Instead, both coalescent microbiotas behaved as cohesive structures, influencing the competitive outcome toward one of them. Although co‐cultured communities usually exhibited high degrees of taxonomic similarities to one of its parental samples, we found that 23% of the observed proteins still showed differential expression or abundance at the metaproteomic level. Interestingly, and somewhat counterintuitively, no specific microbial ecological characteristic could linearly determine which of the two initial microbiotas would act as the driving microbiota. Instead, we observed that the outcomes of the microbial co‐cultures resembled a “rock‐paper‐scissors”‐like dynamic. Through an analysis of co‐colonizing species in such “rock‐paper‐scissors”‐like triangle, we discovered that co‐colonizing species that contributed to winning each between‐community competition differed from one community pair to another. This suggests that no single species or function consistently dominates across all situations; instead, this involves more complex mechanisms, which require further in‐depth investigation in future studies. Our findings demonstrate that the complex competitive interactions between microbial communities make predicting success through a single parameter challenging, whereas pre‐co‐culturing shows promise as an effective method for predicting outcomes in ecological therapies such as FMT.Summary: This study underscores the critical importance of integrating metaproteomics with microbial systems ecology to gain a functional understanding of microbial coalescence. By addressing the ecological question of how two communities compete when they are brought into contact, we investigated the metaproteomic responses of pairs of coalescent co‐cultured human gut microbiotas. Our results revealed significant insights: post‐co‐culture microbiota changes were not merely a simple average of the initial compositions but instead exhibited distinct shifts toward one of the original samples. Notably, due to the observed rock‐paper‐scissors‐like cycle of winning, we argue that no single microbial ecological characteristic could straightforwardly predict which of the two samples would dominate as the driving microbiota. Overall, our findings suggest that during coalescence, microbial communities behave as cohesive structures both taxonomically and functionally, influencing competitive dynamics and ecosystem complexity, indicating that an in vitro coalescence pretest may help predict the success of therapies like FMT.

Metaproteomics is an emerging approach for studying microbiomes, offering the ability to characterize proteins that underpin microbial functionality within diverse ecosystems. As the primary catalytic and structural components of microbiomes, proteins provide unique insights into the active processes and ecological roles of microbial communities. By integrating metaproteomics with other omics disciplines, researchers can gain a comprehensive understanding of microbial ecology, interactions, and functional dynamics. This review, developed by the Metaproteomics Initiative (www.metaproteomics.org), serves as a practical guide for both microbiome and proteomics researchers, presenting key principles, state‐of‐the‐art methodologies, and analytical workflows essential to metaproteomics. Topics covered include experimental design, sample preparation, mass spectrometry techniques, data analysis strategies, and statistical approaches.

The human oral microbiome has been associated with multiple inflammatory conditions including inflammatory bowel disease (IBD). Identifying functional changes in oral microbiome by metaproteomics helps understanding the factors driving dysbiosis related to intestinal diseases. However, enriching bacterial cells from oral samples (such as saliva and mouth rinse) rich in host proteins is challenging. Here, we present an Optimized Salivary MetaProteomic sample analysis workflow (OSaMPle) to enrich salivary bacteria and reduce host-derived interferences for in-depth analysis of the oral metaproteome. Compared to a conventional approach, OSaMPle improved the identification of bacterial peptides and proteins by 3.2 folds and 1.7 folds, respectively. Furthermore, applying OSaMPle to analyze mouth rinse samples from IBD patients revealed significant alterations in bacterial protein expressions under disease conditions. Specifically, proteins involved in the fatty acid elongation pathway in Peptostreptococcus were significantly less abundant in IBD patients, whereas proteins associated with the TCA cycle in Neisseria were significantly more abundant. The OSaMPle workflow is capable of processing small-volume oral samples and adaptable to high-throughput automation. It holds promise as a strategy for investigating the functional responses of oral microbiomes under disease conditions and identifying disease-associated microbes with their proteins, providing critical insights for detecting disease-related biomarkers within the oral microbiome.