The Metaproteomics Initiative: a coordinated approach for propelling the functional characterization of microbiomes

Lactobacillus rhamnosus-derived extracellular vesicles influence calcium deposition in a model of breast cancer intraductal calcium stress

Extracellular calcium export by the breast ductal epithelium is crucial during lactation and plays a significant role in breast cancer progression. Intraductal calcium deposition is a hallmark of aggressive premalignant lesions. This study tested the hypothesis that microbiome-derived extracellular vesicles (EVs) influence calcium modulation in premalignant breast cancer lesions. Based on the analysis of plasma, serum, saliva, and tissue collected from breast cancer patients and controls (N = 150), Lactobacillus rhamnosus (Lr) was chosen as the model microbiota. In a BT-474 human breast cancer cell line monolayer culture under acute calcium stress, Lr EVs enhanced intracellular calcium intake. In a BT474 3D spheroid model under chronic calcium stress, Lr EVs increased extracellular calcium deposition and mRNA expression of calcium export channel plasma membrane calcium-transporting ATPase 2 (PMCA2) and stromal interaction molecule 1 (STIM1) in a dose-dependent manner. We propose that Lr EVs influence calcium regulation and mineral deposition, thereby affecting premalignant breast cancer progression.

The dawn of the revolution that will allow us to precisely describe how microbiomes function

The community of microorganisms inhabiting a specific environment, such as the human gut - including bacteria, fungi, archaea, viruses, protozoa, and others - is known as the microbiota. A holobiont, in turn, refers to an integrated ecological unit where microbial communities function and interact with their host, thus is a more integrative concept. To understand the processes involved, the diversity of microorganisms present must be identified and their molecular components quantified, especially proteins. Indeed, proteins - through their roles as catalytic units, structural components, and signaling molecules - are the main drivers of biological processes. Metagenomics has significantly expanded what we know about the genetic material present in microbiota, revealing their functional potential; metabolomics delivers an overall snapshot of the metabolites produced by the community. But metaproteomics offers a complementary approach to explore microbiome and holobiont functionality by focusing on the active proteins and functional pathways from each taxon. Significant recent advances in high-resolution tandem mass spectrometry have greatly expanded the catalog of peptide sequences accessible in each sample, creating the conditions for unprecedented taxonomical profiling, while also providing more accurate biomass quantification, more detailed protein characterization, and a greater capacity to monitor abundance and distinguish host biomarkers. By integrating artificial intelligence into the metaproteomics pipeline, extended datasets can now be efficiently mined to gain a more comprehensive functional view of complex biological systems, paving the way for next-generation metaproteomics. In this perspective, I discuss the transformative potential of this methodology. We are on the cusp of a remarkable omic revolution that promises to uncover the intricate workings of microbiomes by producing a vast array of new knowledge with multiple applications. SIGNIFICANCE: Metaproteomics provides a powerful lens to investigate microbiome and holobiont functionality by identifying and quantifying active proteins and functional pathways within each taxon. Recent breakthroughs in high-resolution tandem mass spectrometry have dramatically expanded the repertoire of peptide sequences detectable per sample. This progress enables unprecedented taxonomic resolution for microbial identification, more precise biomass quantification, comprehensive protein characterization, abundance monitoring, and the unique identification of host biomarkers. In this commentary, I delve into the distinctive features that make metaproteomics a transformative tool. I discuss the recent advancements in tandem mass spectrometry and argue that the primary challenge in analyzing complex samples is shifting from data acquisition to data interpretation. With the integration of artificial intelligence, I believe next-generation metaproteomics is poised to become the next Big Thing in microbiome research, unlocking profound insights into microbial functionality and ecosystem dynamics.

Novel model organisms and proteomics for a better biological understanding

The concept of « model organisms » is being revisited in the light of the latest advances in multi-omics technologies that can now capture the full range of molecular events that occur over time, regardless of the organism studied. Classic, well-studied models, such as Escherichia coli, Saccharomyces cerevisiae, to name a few, have long been valuable for hypothesis testing, reproducibility, and sharing common platforms among researchers. However, they are not suitable for all types of research. The complexity of unanswered questions in biology demands more elaborated systems, particularly to study plant and animal biodiversity, microbial ecosystems and their interactions with their hosts if any. More integrated systems, known as « holobionts », are emerging to describe and unify host organisms and associated microorganisms, providing an overview of all their possible interactions and trajectories. Comparative evolutionary proteomics offers interesting prospects for extrapolating knowledge from a few selected model organisms to others. This approach enables a deeper characterization of the diversity of proteins and proteoforms across the three branches of the tree of life, i.e. Bacteria, Archaea, and Eukarya. It also provides a powerful means to address remaining biological questions, such as identifying the key molecular players in organisms when they are confronted to environmental challenges, like anthropogenic toxicants, pathogens, dietary shifts or climate stressors, and proposing long-term sustainable solutions. SIGNIFICANCE: In this commentary, we reevaluated the concept of "model organisms" in light of advancements in multi-omics technologies. Traditional models have proven invaluable for hypothesis testing, reproducibility, and fostering shared research frameworks. However, we discussed that they are not universally applicable. To address complexities such as biodiversity and understand microbial ecosystems and their host interactions, integrated systems like "holobionts," which encompass host organisms and their associated microbes, are gaining prominence. Comparative evolutionary proteomics further enhances our understanding by enabling detailed exploration of protein diversity across organisms. This approach also facilitates the identification of critical molecular players in organisms facing environmental challenges, such as pollutants, pathogens, dietary changes, or climate stress, and contributes to developing sustainable long-term solutions.

Bacteria in the brain: do they have a role in the pathogenesis of Alzheimer's disease?

PURPOSE OF REVIEW

Worldwide efforts continue to unravel the complex pathological pathways that lead to Alzheimer's disease. The gut-brain-microbiome axis, a communication pathway between the gut, brain and microorganisms, is emerging as a potential mechanism involved in Alzheimer's disease pathogenesis. While the gut microbiome's role in Alzheimer's disease has gained significant attention, the brain microbiome remains relatively unexplored. This review summarizes the latest research on the brain microbiome in Alzheimer's disease.

RECENT FINDINGS

In the past 4 years, four out of five studies have found bacteria, such as Streptococcus pneumoniae , in postmortem samples of both control and Alzheimer's disease brains, supporting the idea that the brain is not a sterile environment. Two studies report the overabundance of several bacterial phyla, including Proteobacteria and Actinomycetes, in postmortem Alzheimer's disease brains versus controls. One study reported the presence of Borrelia burgdorferi in a subset of Alzheimer's disease cases compared to controls.

SUMMARY

Limitations and challenges persist in studying the brain microbiome, including the lack of standardized assays and data analysis methods, small sample sizes, and inconsistent use of controls for environmental microbial contamination during sample processing. Well designed studies that employ reproducible and rigorous methods are required to elucidate whether microbes are involved in the pathogenesis of Alzheimer's disease.

A Non-Metaproteomics Researchers' View on Metaproteomics in Microbiome Research

Metaproteomics, an emerging field among the omic techniques, holds great promise for unraveling the function of microbiomes in host health and our environment. Metaproteomics can also be a valuable addition to multiomics studies of the microbiome, complementing genome-resolved metagenomics, metatranscriptomics, and metabolomics. The potential advancements from metaproteomics and multiomics research touch a breadth of disciplines, including ecology, biochemistry, immunology, medical microbiology, cell physiology, and medicine, and could lead to both fundamental and applied discoveries. However, there are significant roadblocks to widespread adoption of metaproteomics among microbiome researchers. In this Viewpoint article, we highlight the pivotal role of metaproteomics in microbiome research by showcasing its advantages, exploring opportunities to overcome challenges, and paving the way for its broader adoption as a mainstream technique. We hope that the recommendations provided in this Viewpoint article will inspire new, beneficial collaborations between proteomics experts, algorithm and infrastructure developers, biochemists, cell biologists, and microbiologists, enabling the construction of a knowledge base of microbiome function that can have an immediate and direct impact on host health and the environment.

New Therapeutic Challenges in Pediatric Gastroenterology: A Narrative Review

Pediatric gastroenterology is entering a pivotal phase marked by significant challenges and emerging opportunities in treating conditions like celiac disease (CeD), eosinophilic esophagitis (EoE), inflammatory bowel disease (IBD), and autoimmune hepatitis (AIH) pose significant clinical hurdles, but new therapeutic avenues are emerging. Advances in precision medicine, particularly proteomics, are reshaping care by tailoring treatments to individual patient characteristics. For CeD, therapies like gluten-degrading enzymes (latiglutenase, Kuma030) and zonulin inhibitors (larazotide acetate) show promise, though clinical outcomes are inconsistent. Immunotherapy and microbiota modulation, including probiotics and fecal microbiota transplantation (FMT), are also under exploration, with potential benefits in symptom management. Transglutaminase 2 inhibitors like ZED-1227 could help prevent gluten-induced damage. Monoclonal antibodies targeting immune pathways, such as AMG 714 and larazotide acetate, require further validation in pediatric populations. In EoE, biologics like dupilumab, cendakimab, dectrekumab (IL-13 inhibitors), and mepolizumab, reslizumab, and benralizumab (IL-5/IL-5R inhibitors) show varying efficacy, while thymic stromal lymphopoietin (TSLP) inhibitors like tezepelumab are also being investigated. These therapies require more pediatric-specific research to optimize their use. For IBD, biologics like vedolizumab, ustekinumab, and risankizumab, as well as small molecules like tofacitinib, etrasimod, and upadacitinib, are emerging treatments. New medications for individuals with refractory or steroid-dependent AIH have been explored. Personalized therapy, integrating precision medicine, therapeutic drug monitoring, and lifestyle changes, is increasingly guiding pediatric IBD management. This narrative review explores recent breakthroughs in treating CeD, EoE, IBD, and AIH, with a focus on pediatric studies when available, and discusses the growing role of proteomics in advancing personalized gastroenterological care.

Advanced computational tools, artificial intelligence and machine-learning approaches in gut microbiota and biomarker identification

The microbiome of the gut is a complex ecosystem that contains a wide variety of microbial species and functional capabilities. The microbiome has a significant impact on health and disease by affecting endocrinology, physiology, and neurology. It can change the progression of certain diseases and enhance treatment responses and tolerance. The gut microbiota plays a pivotal role in human health, influencing a wide range of physiological processes. Recent advances in computational tools and artificial intelligence (AI) have revolutionized the study of gut microbiota, enabling the identification of biomarkers that are critical for diagnosing and treating various diseases. This review hunts through the cutting-edge computational methodologies that integrate multi-omics data-such as metagenomics, metaproteomics, and metabolomics-providing a comprehensive understanding of the gut microbiome's composition and function. Additionally, machine learning (ML) approaches, including deep learning and network-based methods, are explored for their ability to uncover complex patterns within microbiome data, offering unprecedented insights into microbial interactions and their link to host health. By highlighting the synergy between traditional bioinformatics tools and advanced AI techniques, this review underscores the potential of these approaches in enhancing biomarker discovery and developing personalized therapeutic strategies. The convergence of computational advancements and microbiome research marks a significant step forward in precision medicine, paving the way for novel diagnostics and treatments tailored to individual microbiome profiles. Investigators have the ability to discover connections between the composition of microorganisms, the expression of genes, and the profiles of metabolites. Individual reactions to medicines that target gut microbes can be predicted by models driven by artificial intelligence. It is possible to obtain personalized and precision medicine by first gaining an understanding of the impact that the gut microbiota has on the development of disease. The application of machine learning allows for the customization of treatments to the specific microbial environment of an individual.

Advancing Gut Microbiome Research: The Shift from Metagenomics to Multi-Omics and Future Perspectives

The gut microbiome, a dynamic and integral component of human health, has co-evolved with its host, playing essential roles in metabolism, immunity, and disease prevention. Traditional microbiome studies, primarily focused on microbial composition, have provided limited insights into the functional and mechanistic interactions between microbiota and their host. The advent of multi-omics technologies has transformed microbiome research by integrating genomics, transcriptomics, proteomics, and metabolomics, offering a comprehensive, systems-level understanding of microbial ecology and host-microbiome interactions. These advances have propelled innovations in personalized medicine, enabling more precise diagnostics and targeted therapeutic strategies. This review highlights recent breakthroughs in microbiome research, demonstrating how these approaches have elucidated microbial functions and their implications for health and disease. Additionally, it underscores the necessity of standardizing multi-omics methodologies, conducting large-scale cohort studies, and developing novel platforms for mechanistic studies, which are critical steps toward translating microbiome research into clinical applications and advancing precision medicine.

PathwayPilot: A User-Friendly Tool for Visualizing and Navigating Metabolic Pathways

Metaproteomics, the study of collective proteomes in environmental communities, plays a crucial role in understanding microbial functionalities affecting ecosystems and human health. Pathway analysis offers structured insights into the biochemical processes within these communities. However, no existing tool effectively combines pathway analysis with peptide- or protein-level data. We here introduce PathwayPilot, a web-based application designed to improve metaproteomic data analysis by integrating pathway analysis with peptide- and protein-level data, filling a critical gap in current metaproteomics bioinformatics tools. By allowing users to compare functional annotations across different samples or multiple organisms within a sample, PathwayPilot provides valuable insights into microbial functions. In the re-analysis of a study examining the effects of caloric restriction on gut microbiota, the tool successfully identified shifts in enzyme expressions linked to short-chain fatty acid biosynthesis, aligning with its original findings. PathwayPilot's user-friendly interface and robust capabilities make it a significant advancement in metaproteomics, with the potential for widespread application in microbial ecology and health sciences. All code is open source under the Apache2 license and is available at https://pathwaypilot.ugent.be.

Metaproteomics Beyond Databases: Addressing the Challenges and Potentials of De Novo Sequencing

Metaproteomics enables the large-scale characterization of microbial community proteins, offering crucial insights into their taxonomic composition, functional activities, and interactions within their environments. By directly analyzing proteins, metaproteomics offers insights into community phenotypes and the roles individual members play in diverse ecosystems. Although database-dependent search engines are commonly used for peptide identification, they rely on pre-existing protein databases, which can be limiting for complex, poorly characterized microbiomes. De novo sequencing presents a promising alternative, which derives peptide sequences directly from mass spectra without requiring a database. Over time, this approach has evolved from manual annotation to advanced graph-based, tag-based, and deep learning-based methods, significantly improving the accuracy of peptide identification. This Viewpoint explores the evolution, advantages, limitations, and future opportunities of de novo sequencing in metaproteomics. We highlight recent technological advancements that have improved its potential for detecting unsequenced species and for providing deeper functional insights into microbial communities.

Integrating the milk microbiome signatures in mastitis: milk-omics and functional implications

Mammalian milk contains a variety of complex bioactive and nutritional components and microorganisms. These microorganisms have diverse compositions and functional roles that impact host health and disease pathophysiology, especially mastitis. The advent and use of high throughput omics technologies, including metagenomics, metatranscriptomics, metaproteomics, metametabolomics, as well as culturomics in milk microbiome studies suggest strong relationships between host phenotype and milk microbiome signatures in mastitis. While single omics studies have undoubtedly contributed to our current understanding of milk microbiome and mastitis, they often provide limited information, targeting only a single biological viewpoint which is insufficient to provide system-wide information necessary for elucidating the biological footprints and molecular mechanisms driving mastitis and milk microbiome dysbiosis. Therefore, integrating a multi-omics approach in milk microbiome research could generate new knowledge, improve the current understanding of the functional and structural signatures of the milk ecosystem, and provide insights for sustainable mastitis control and microbiome management.

Fecal Metaproteomics as a Tool to Monitor Functional Modifications Induced in the Gut Microbiota by Ketogenic Diet: A Case Study

Metaproteomics is a valuable approach to characterize the biological functions involved in the gut microbiota (GM) response to dietary interventions. Ketogenic diets (KDs) are very effective in controlling seizure severity and frequency in drug-resistant epilepsy (DRE) and in the weight loss management in obese/overweight individuals. This case study provides proof of concept for the suitability of metaproteomics to monitor changes in taxonomic and functional GM features in an individual on a short-term very low-calorie ketogenic diet (VLCKD, 4 weeks), followed by a low-calorie diet (LCD). A marked increase in Akkermansia and Pseudomonadota was observed during VLCKD and reversed after the partial reintroduction of carbohydrates (LCD), in agreement with the results of previous metagenomic studies. In functional terms, the relative increase in Akkermansia was associated with an increased production of proteins involved in response to stress and biosynthesis of gamma-aminobutyric acid. In addition, VLCKD caused a relative increase in enzymes involved in the synthesis of the beta-ketoacid acetoacetate and of the ketogenic amino acid leucine. Our data support the potential of fecal metaproteomics to investigate the GM-dependent effect of KD as a therapeutic option in obese/overweight individuals and DRE patients.

JA, Meyer A, Vintila S, McIlvin MR, Van Den Bossche T, Searle BC, Griffin TJ, Saito MA, Kleiner M, Jagtap PD. Data-Independent Acquisition Mass Spectrometry as a Tool for Metaproteomics: Interlaboratory Comparison Using a Model Microbiome

Mass spectrometry (MS)-based metaproteomics is used to identify and quantify proteins in microbiome samples, with the frequently used methodology being Data-Dependent Acquisition mass spectrometry (DDA-MS). However, DDA-MS is limited in its ability to reproducibly identify and quantify lower abundant peptides and proteins. To address DDA-MS deficiencies, proteomics researchers have started using Data-Independent Acquisition Mass Spectrometry (DIA-MS) for reproducible detection and quantification of peptides and proteins. We sought to evaluate the reproducibility and accuracy of DIA-MS metaproteomic measurements relative to DDA-MS using a mock community of known taxonomic composition. Artificial microbial communities of known composition were analyzed independently in three laboratories using DDA- and DIA-MS acquisition methods. DIA-MS yielded more protein and peptide identifications than DDA-MS in each laboratory. In addition, the protein and peptide identifications were more reproducible in all laboratories and provided an accurate quantification of proteins and taxonomic groups in the samples. We also identified some limitations of current DIA tools when applied to metaproteomic data, highlighting specific needs to improve DIA tools enabling analysis of metaproteomic datasets from complex microbiomes. Ultimately, DIA-MS represents a promising strategy for MS-based metaproteomics due to its large number of detected proteins and peptides, reproducibility, deep sequencing capabilities, and accurate quantitation.

Insights on Wet and Dry Workflows for Human Gut Metaproteomics

The human gut microbiota (GM) is a community of microorganisms that resides in the gastrointestinal (GI) tract. Recognized as a critical element of human health, the functions of the GM extend beyond GI well-being to influence overall systemic health and susceptibility to disease. Among the other omic sciences, metaproteomics highlights additional facets that make it a highly valuable discipline in the study of GM. Indeed, it allows the protein inventory of complex microbial communities. Proteins with associated taxonomic membership and function are identified and quantified from their constituent peptides by liquid chromatography coupled to mass spectrometry analyses and by querying specific databases (DBs). The aim of this review was to compile comprehensive information on metaproteomic studies of the human GM, with a focus on the bacterial component, to assist newcomers in understanding the methods and types of research conducted in this field. The review outlines key steps in a metaproteomic-based study, such as protein extraction, DB selection, and bioinformatic workflow. The importance of standardization is emphasized. In addition, a list of previously published studies is provided as hints for researchers interested in investigating the role of GM in health and disease states.

Construction of Protein Sequence Databases for Metaproteomics: A Review of the Current Tools and Databases

In metaproteomics studies, constructing a reference protein sequence database that is both comprehensive and not overly large is critical for the peptide identification step. Therefore, the availability of well-curated reference databases and tools for custom database construction is essential to enhance the performance of metaproteomics analyses. In this review, we first provide an overview of metaproteomics by presenting a concise historical background, outlining a typical experimental and bioinformatics workflow, emphasizing the crucial step of constructing a protein sequence database for metaproteomics. We then delve into the current tools available for building such databases, highlighting their individual approaches, utility, and advantages and limitations. Next, we examine existing protein sequence databases, detailing their scope and relevance in metaproteomics research. Then, we provide practical recommendations for constructing protein sequence databases for metaproteomics, along with an overview of the current challenges in this area. We conclude with a discussion of anticipated advancements, emerging trends, and future directions in the construction of protein sequence databases for metaproteomics.

Microbial Gatekeepers of Fertility in the Female Reproductive Microbiome of Cattle

This review paper delves into the intricate relationship between the genital microbiome and fertility outcomes in livestock, with a specific focus on cattle. Drawing upon insights derived from culture-independent metagenomics studies, the paper meticulously examines the composition and dynamics of the genital microbiome. Through advanced techniques such as high-throughput sequencing, the review illuminates the temporal shifts in microbial communities and their profound implications for reproductive health. The analysis underscores the association between dysbiosis-an imbalance in microbial communities-and the development of reproductive diseases, shedding light on the pivotal role of microbial gatekeepers in livestock fertility. Furthermore, the paper emphasizes the need for continued exploration of uncharted dimensions of the female reproductive microbiome to unlock new insights into its impact on fertility. By elucidating the complex interplay between microbial communities and reproductive health, this review underscores the importance of innovative strategies aimed at enhancing fertility and mitigating reproductive diseases in livestock populations.

Activity-based metaproteomics driven discovery and enzymological characterization of potential α-galactosidases in the mouse gut microbiome

The gut microbiota offers an extensive resource of enzymes, but many remain uncharacterized. To distinguish the activities of similar annotated proteins and mine the potentially applicable ones in the microbiome, we applied an effective Activity-Based Metaproteomics (ABMP) strategy using a specific activity-based probe (ABP) to screen the entire gut microbiome for directly discovering active enzymes and their potential applications, not for exploring host-microbiome interactions. By using an activity-based cyclophellitol aziridine probe specific to α-galactosidases (AGAL), we successfully identified and characterized several gut microbiota enzymes possessing AGAL activities. Cryo-electron microscopy analysis of a newly characterized enzyme (AGLA5) revealed the covalent binding conformations between the AGAL5 active site and the cyclophellitol aziridine ABP, which could provide insights into the enzyme's catalytic mechanism. The four newly characterized AGALs have diverse potential activities, including raffinose family oligosaccharides (RFOs) hydrolysis and enzymatic blood group transformation. Collectively, we present a ABMP platform that facilitates gut microbiota AGALs discovery, biochemical activity annotations and potential industrial or biopharmaceutical applications.

Proceedings of the EuBIC-MS developers meeting 2023

The 2023 European Bioinformatics Community for Mass Spectrometry (EuBIC-MS) Developers Meeting was held from January 15th to January 20th, 2023, in Congressi Stefano Franscin at Monte Verità in Ticino, Switzerland. The participants were scientists and developers working in computational mass spectrometry (MS), metabolomics, and proteomics. The 5-day program was split between introductory keynote lectures and parallel hackathon sessions focusing on "Artificial Intelligence in proteomics" to stimulate future directions in the MS-driven omics areas. During the latter, the participants developed bioinformatics tools and resources addressing outstanding needs in the community. The hackathons allowed less experienced participants to learn from more advanced computational MS experts and actively contribute to highly relevant research projects. We successfully produced several new tools applicable to the proteomics community by improving data analysis and facilitating future research.

SMMP: A Deep-Coverage Marine Metaproteome Method for Microbial Community Analysis throughout the Water Column Using 1 L of Seawater

Marine microbes drive pivotal transformations in planetary-scale elemental cycles and have crucial impacts on global biogeochemical processes. Metaproteomics is a powerful tool for assessing the metabolic diversity and function of marine microbes. However, hundreds of liters of seawater are required for normal metaproteomic analysis due to the sparsity of microbial populations in seawater, which poses a substantial challenge to the widespread application of marine metaproteomics, particularly for deep seawater. Herein, a sensitive marine metaproteomics workflow, named sensitive marine metaproteome analysis (SMMP), was developed by integrating polycarbonate filter-assisted microbial enrichment, solid-phase alkylation-based anti-interference sample preparation, and narrow-bore nanoLC column for trace peptide separation and characterization. The method provided more than 8500 proteins from 1 L of bathypelagic seawater samples, which covered diverse microorganisms and crucial functions, e.g., the detection of key enzymes associated with the Wood-Ljungdahl pathway. Then, we applied SMMP to investigate vertical variations in the metabolic expression patterns of marine microorganisms from the euphotic zone to the bathypelagic zone. Methane oxidation and carbon monoxide (CO) oxidation were active processes, especially in the bathypelagic zone, which provided a remarkable energy supply for the growth and proliferation of heterotrophic microorganisms. In addition, marker protein profiles detected related to ammonia transport, ammonia oxidation, and carbon fixation highlighted that Thaumarchaeota played a critical role in primary production based on the coupled carbon-nitrogen process, contributing to the storage of carbon and nitrogen in the bathypelagic regions. SMMP has low microbial input requirements and yields in-depth metaproteome analysis, making it a prospective approach for comprehensive marine metaproteomic investigations.

Benchmarking low- and high-throughput protein cleanup and digestion methods for human fecal metaproteomics

The application of fecal metaproteomics to large-scale studies of the gut microbiota requires high-throughput analysis and standardized experimental protocols. Although high-throughput protein cleanup and digestion methods are increasingly used in shotgun proteomics, no studies have yet critically compared such protocols using human fecal samples. In this study, human fecal protein extracts were processed using several different protocols based on three main approaches: filter-aided sample preparation (FASP), solid-phase-enhanced sample preparation (SP3), and suspension trapping (S-Trap). These protocols were applied in both low-throughput (i.e., microtube-based) and high-throughput (i.e., microplate-based) formats, and the final peptide mixtures were analyzed by liquid chromatography coupled to high-resolution tandem mass spectrometry. The FASP-based methods and the combination of SP3 with in-StageTips (iST) yielded the best results in terms of the number of peptides identified through a database search against gut microbiome and human sequences. The efficiency of protein digestion, the ability to preserve hydrophobic peptides and high molecular weight proteins, and the reproducibility of the methods were also evaluated for the different protocols. Other relevant variables, including interindividual variability of stool, duration of protocols, and total costs, were considered and discussed. In conclusion, the data presented here can significantly contribute to the optimization and standardization of sample preparation protocols in human fecal metaproteomics. Furthermore, the promising results obtained with the high-throughput methods are expected to encourage the development of automated workflows and their application to large-scale gut microbiome studies.IMPORTANCEFecal metaproteomics is an experimental approach that allows the investigation of gut microbial functions, which are involved in many different physiological and pathological processes. Standardization and automation of sample preparation protocols in fecal metaproteomics are essential for its application in large-scale studies. Here, we comparatively evaluated different methods, available also in a high-throughput format, enabling two key steps of the metaproteomics analytical workflow (namely, protein cleanup and digestion). The results of our study provide critical information that may be useful for the optimization of metaproteomics experimental pipelines and their implementation in laboratory automation systems.

Metaproteomic portrait of the healthy human gut microbiota

Gut metaproteomics can provide direct evidence of microbial functions actively expressed in the colonic environments, contributing to clarify the role of the gut microbiota in human physiology. In this study, we re-analyzed 10 fecal metaproteomics datasets of healthy individuals from different continents and countries, with the aim of identifying stable and variable gut microbial functions and defining the contribution of specific bacterial taxa to the main metabolic pathways. The "core" metaproteome included 182 microbial functions and 83 pathways that were identified in all individuals analyzed. Several enzymes involved in glucose and pyruvate metabolism, along with glutamate dehydrogenase, acetate kinase, elongation factors G and Tu and DnaK, were the proteins with the lowest abundance variability in the cohorts under study. On the contrary, proteins involved in chemotaxis, response to stress and cell adhesion were among the most variable functions. Random-effect meta-analysis of correlation trends between taxa, functions and pathways revealed key ecological and molecular associations within the gut microbiota. The contribution of specific bacterial taxa to the main biological processes was also investigated, finding that Faecalibacterium is the most stable genus and the top contributor to anti-inflammatory butyrate production in the healthy gut microbiota. Active production of other mucosal immunomodulators facilitating host tolerance was observed, including Roseburia flagellin and lipopolysaccharide biosynthetic enzymes expressed by members of Bacteroidota. Our study provides a detailed picture of the healthy human gut microbiota, contributing to unveil its functional mechanisms and its relationship with nutrition, immunity, and environmental stressors.

A novel clinical metaproteomics workflow enables bioinformatic analysis of host-microbe dynamics in disease

Clinical metaproteomics has the potential to offer insights into the host-microbiome interactions underlying diseases. However, the field faces challenges in characterizing microbial proteins found in clinical samples, usually present at low abundance relative to the host proteins. As a solution, we have developed an integrated workflow coupling mass spectrometry-based analysis with customized bioinformatic identification, quantification, and prioritization of microbial proteins, enabling targeted assay development to investigate host-microbe dynamics in disease. The bioinformatics tools are implemented in the Galaxy ecosystem, offering the development and dissemination of complex bioinformatic workflows. The modular workflow integrates MetaNovo (to generate a reduced protein database), SearchGUI/PeptideShaker and MaxQuant [to generate peptide-spectral matches (PSMs) and quantification], PepQuery2 (to verify the quality of PSMs), Unipept (for taxonomic and functional annotation), and MSstatsTMT (for statistical analysis). We have utilized this workflow in diverse clinical samples, from the characterization of nasopharyngeal swab samples to bronchoalveolar lavage fluid. Here, we demonstrate its effectiveness via analysis of residual fluid from cervical swabs. The complete workflow, including training data and documentation, is available via the Galaxy Training Network, empowering non-expert researchers to utilize these powerful tools in their clinical studies.

IMPORTANCE

Clinical metaproteomics has immense potential to offer functional insights into the microbiome and its contributions to human disease. However, there are numerous challenges in the metaproteomic analysis of clinical samples, including handling of very large protein sequence databases for sensitive and accurate peptide and protein identification from mass spectrometry data, as well as taxonomic and functional annotation of quantified peptides and proteins to enable interpretation of results. To address these challenges, we have developed a novel clinical metaproteomics workflow that provides customized bioinformatic identification, verification, quantification, and taxonomic and functional annotation. This bioinformatic workflow is implemented in the Galaxy ecosystem and has been used to characterize diverse clinical sample types, such as nasopharyngeal swabs and bronchoalveolar lavage fluid. Here, we demonstrate its effectiveness and availability for use by the research community via analysis of residual fluid from cervical swabs.

Untargeted metabolomics and metagenomics reveal signatures for intramammary ceftiofur treatment and lactation stage in the cattle hindgut

The gut microbiota in cattle is essential for protein, energy, and vitamin production and hence, microbiota perturbations can affect cattle performance. This study evaluated the effect of intramammary (IMM) ceftiofur treatment and lactation stage on the functional gut microbiome and metabolome. Forty dairy cows were enrolled at dry-off. Half received IMM ceftiofur and a non-antibiotic teat sealant containing bismuth subnitrate (cases), while the other half received the teat sealant (controls). Fecal samples were collected before treatment at dry off, during the dry period (weeks 1 and 5) and the first week after calving (week 9). Shotgun metagenomic sequencing was applied to predict microbial metabolic pathways whereas untargeted metabolomics was used identify polar and nonpolar metabolites. Compared to controls, long-term changes were observed in the cows given ceftiofur, including a lower abundance of microbial pathways linked to energy production, amino acid biosynthesis, and other vital molecules. The metabolome of treated cows had elevated levels of stachyose, phosphatidylethanolamine diacylglycerol (PE-DAG), and inosine a week after the IMM ceftiofur application, indicating alterations in microbial fermentation, lipid metabolism, energy, and cellular signaling. Differences were also observed by sampling, with cows in late lactation having more diverse metabolic pathways and a unique metabolome containing higher levels of histamine and histamine-producing bacteria. These data illustrate how IMM ceftiofur treatment can alter the functionality of the hindgut metabolome and microbiome. Understanding how antibiotics and lactation stages, which are each characterized by unique diets and physiology, impact the function of resident microbes is critical to define normal gut function in dairy cattle.

Critical steps in an environmental metaproteomics workflow

Environmental metaproteomics is a rapidly advancing field that provides insights into the structure, dynamics, and metabolic activity of microbial communities. As the field is still maturing, it lacks consistent workflows, making it challenging for non-expert researchers to navigate. This review aims to introduce the workflow of environmental metaproteomics. It outlines the standard practices for sample collection, processing, and analysis, and offers strategies to overcome the unique challenges presented by common environmental matrices such as soil, freshwater, marine environments, biofilms, sludge, and symbionts. The review also highlights the bottlenecks in data analysis that are specific to metaproteomics samples and provides suggestions for researchers to obtain high-quality datasets. It includes recent benchmarking studies and descriptions of software packages specifically built for metaproteomics analysis. The article is written without assuming the reader's familiarity with single-organism proteomic workflows, making it accessible to those new to proteomics or mass spectrometry in general. This primer for environmental metaproteomics aims to improve accessibility to this exciting technology and empower researchers to tackle challenging and ambitious research questions. While it is primarily a resource for those new to the field, it should also be useful for established researchers looking to streamline or troubleshoot their metaproteomics experiments.

Proteomic scrutiny of nasal microbiomes: implications for the clinic

INTRODUCTION

The nasal cavity is the initial site of the human respiratory tract and is one of the habitats where microorganisms colonize. The findings from a growing number of studies have shown that the nasal microbiome is an important factor for human disease and health. 16S rRNA sequencing and metagenomic next-generation sequencing (mNGS) are the most commonly used means of microbiome evaluation. Among them, 16S rRNA sequencing is the primary method used in previous studies of nasal microbiomes. However, neither 16S rRNA sequencing nor mNGS can be used to analyze the genes specifically expressed by nasal microorganisms and their functions. This problem can be addressed by proteomic analysis of the nasal microbiome.

AREAS COVERED

In this review, we summarize current advances in research on the nasal microbiome, introduce the methods for proteomic evaluation of the nasal microbiome, and focus on the important roles of proteomic evaluation of the nasal microbiome in the diagnosis and treatment of related diseases.

EXPERT OPINION

The detection method for microbiome-expressed proteins is known as metaproteomics. Metaproteomic analysis can help us dig deeper into the nasal microbiomes and provide new targets and ideas for clinical diagnosis and treatment of many nasal dysbiosis-related diseases.

The astounding exhaustiveness and speed of the Astral mass analyzer for highly complex samples is a quantum leap in the functional analysis of microbiomes

BACKGROUND

By analyzing the proteins which are the workhorses of biological systems, metaproteomics allows us to list the taxa present in any microbiota, monitor their relative biomass, and characterize the functioning of complex biological systems.

RESULTS

Here, we present a new strategy for rapidly determining the microbial community structure of a given sample and designing a customized protein sequence database to optimally exploit extensive tandem mass spectrometry data. This approach leverages the capabilities of the first generation of Quadrupole Orbitrap mass spectrometer incorporating an asymmetric track lossless (Astral) analyzer, offering rapid MS/MS scan speed and sensitivity. We took advantage of data-dependent acquisition and data-independent acquisition strategies using a peptide extract from a human fecal sample spiked with precise amounts of peptides from two reference bacteria.

CONCLUSIONS

Our approach, which combines both acquisition methods, proves to be time-efficient while processing extensive generic databases and massive datasets, achieving a coverage of more than 122,000 unique peptides and 38,000 protein groups within a 30-min DIA run. This marks a significant departure from current state-of-the-art metaproteomics methodologies, resulting in broader coverage of the metabolic pathways governing the biological system. In combination, our strategy and the Astral mass analyzer represent a quantum leap in the functional analysis of microbiomes. Video Abstract.

The 2023 Report on the Proteome from the HUPO Human Proteome Project

Since 2010, the Human Proteome Project (HPP), the flagship initiative of the Human Proteome Organization (HUPO), has pursued two goals: (1) to credibly identify the protein parts list and (2) to make proteomics an integral part of multiomics studies of human health and disease. The HPP relies on international collaboration, data sharing, standardized reanalysis of MS data sets by PeptideAtlas and MassIVE-KB using HPP Guidelines for quality assurance, integration and curation of MS and non-MS protein data by neXtProt, plus extensive use of antibody profiling carried out by the Human Protein Atlas. According to the neXtProt release 2023-04-18, protein expression has now been credibly detected (PE1) for 18,397 of the 19,778 neXtProt predicted proteins coded in the human genome (93%). Of these PE1 proteins, 17,453 were detected with mass spectrometry (MS) in accordance with HPP Guidelines and 944 by a variety of non-MS methods. The number of neXtProt PE2, PE3, and PE4 missing proteins now stands at 1381. Achieving the unambiguous identification of 93% of predicted proteins encoded from across all chromosomes represents remarkable experimental progress on the Human Proteome parts list. Meanwhile, there are several categories of predicted proteins that have proved resistant to detection regardless of protein-based methods used. Additionally there are some PE1-4 proteins that probably should be reclassified to PE5, specifically 21 LINC entries and ∼30 HERV entries; these are being addressed in the present year. Applying proteomics in a wide array of biological and clinical studies ensures integration with other omics platforms as reported by the Biology and Disease-driven HPP teams and the antibody and pathology resource pillars. Current progress has positioned the HPP to transition to its Grand Challenge Project focused on determining the primary function(s) of every protein itself and in networks and pathways within the context of human health and disease.

Metagenomics-Based Microbial Ecological Community Threshold and Indicators of Anthropogenic Disturbances in Estuarine Sediments

Assessing the impacts of cumulative anthropogenic disturbances on estuarine ecosystem health is challenging. Using spatially distributed sediments from the Pearl River Estuary (PRE) in southern China, which are significantly influenced by anthropogenic activities, we demonstrated that metagenomics-based surveillance of benthic microbial communities is a robust approach to assess anthropogenic impacts on estuarine benthic ecosystems. Correlational and threshold analyses between microbial compositions and environmental conditions indicated that anthropogenic disturbances in the PRE sediments drove the taxonomic and functional variations in the benthic microbial communities. An ecological community threshold of anthropogenic disturbances was identified, which delineated the PRE sediments into two groups (H and L) with distinct taxa and functional traits. Group H, located nearshore and subjected to a higher level of anthropogenic disturbances, was enriched with pollutant degraders, putative human pathogens, fecal pollution indicators, and functional traits related to stress tolerance. In contrast, Group L, located offshore and subjected to a lower level of anthropogenic disturbances, was enriched with halotolerant and oligotrophic taxa and functional traits related to growth and resource acquisition. The machine learning random forest model identified a number of taxonomic and functional indicators that could differentiate PRE sediments between Groups H and L. The identified ecological community threshold and microbial indicators highlight the utility of metagenomics-based microbial surveillance in assessing the adverse impacts of anthropogenic disturbances in estuarine sediments, which can assist environmental management to better protect ecosystem health.

Metaproteomics for Coinfections in the Upper Respiratory Tract: The Case of COVID-19

The upper respiratory tract (URT) is home to a diverse range of microbial species. Respiratory infections disturb the microbial flora in the URT, putting people at risk of secondary infections. The potential dangers and clinical effects of bacterial and fungal coinfections with SARS-CoV-2 support the need to investigate the microbiome of the URT using clinical samples. Mass spectrometry (MS)-based metaproteomics analysis of microbial proteins is a novel approach to comprehensively assess the clinical specimens with complex microbial makeup. The coronavirus that causes severe acute respiratory syndrome (SARS-CoV-2) is responsible for the COVID-19 pandemic resulting in a plethora of microbial coinfections impeding therapy, prognosis, and overall disease management. In this chapter, the corresponding workflows for MS-based shotgun proteomics and metaproteomic analysis are illustrated.

NovoLign: metaproteomics by sequence alignment

Tremendous advances in mass spectrometric and bioinformatic approaches have expanded proteomics into the field of microbial ecology. The commonly used spectral annotation method for metaproteomics data relies on database searching, which requires sample-specific databases obtained from whole metagenome sequencing experiments. However, creating these databases is complex, time-consuming, and prone to errors, potentially biasing experimental outcomes and conclusions. This asks for alternative approaches that can provide rapid and orthogonal insights into metaproteomics data. Here, we present NovoLign, a de novo metaproteomics pipeline that performs sequence alignment of de novo sequences from complete metaproteomics experiments. The pipeline enables rapid taxonomic profiling of complex communities and evaluates the taxonomic coverage of metaproteomics outcomes obtained from database searches. Furthermore, the NovoLign pipeline supports the creation of reference sequence databases for database searching to ensure comprehensive coverage. We assessed the NovoLign pipeline for taxonomic coverage and false positive annotations using a wide range of in silico and experimental data, including pure reference strains, laboratory enrichment cultures, synthetic communities, and environmental microbial communities. In summary, we present NovoLign, a de novo metaproteomics pipeline that employs large-scale sequence alignment to enable rapid taxonomic profiling, evaluation of database searching outcomes, and the creation of reference sequence databases. The NovoLign pipeline is publicly available via: https://github.com/hbckleikamp/NovoLign.

Biodiversity Analysis of Metaproteomics Samples with Unipept: A Comprehensive Tutorial

Metaproteomics has become a crucial omics technology for studying microbiomes. In this area, the Unipept ecosystem, accessible at https://unipept.ugent.be , has emerged as a valuable resource for analyzing metaproteomic data. It offers in-depth insights into both taxonomic distributions and functional characteristics of complex ecosystems. This tutorial explains essential concepts like Lowest Common Ancestor (LCA) determination and the handling of peptides with missed cleavages. It also provides a detailed, step-by-step guide on using the Unipept Web application and Unipept Desktop for thorough metaproteomics analyses. By integrating theoretical principles with practical methodologies, this tutorial empowers researchers with the essential knowledge and tools needed to fully utilize metaproteomics in their microbiome studies.

Examination of sex-specific interactions between gut microbiota and host metabolism after 12-week combined polyphenol supplementation in individuals with overweight or obesity

Polyphenols exert beneficial effects on host metabolism, which may be mediated by the gut microbiota. We investigated sex-specific differences in microbiota composition and interactions with cardiometabolic parameters after polyphenol supplementation in individuals with overweight/obesity. In a double-blind, randomized, placebo-controlled trial, 19 women and 18 men with normal glucose tolerance and body mass index >25 kg/m2 received epigallocatechin-3-gallate and resveratrol (EGCG+RES, 282 + 80 mg/d) or placebo supplements for 12 weeks. Fecal microbiota composition (16S rRNA gene amplicon sequencing, V3-V4 region), in vivo whole-body fat oxidation (indirect calorimetry), and mitochondrial respiration in permeabilized skeletal muscle fibers (SkM-Ox; ex vivo respirometry) were determined pre- and post-intervention. Overall, EGCG+RES supplementation did not affect gut microbiota composition. Akkermansia, Ruminococcaceae UCG-002, Subdoligranulum, and Lachnospiraceae UCG-004 were more abundant, while Veillonella, Tyzzerella 4, Clostridium innocuum group, Ruminococcus gnavus group, Escherichia-Shigella, and an uncultured Ruminococcaceae family genus were less abundant in women compared to men. In women, only baseline Eubacterium ventriosum group abundance correlated with EGCG+RES-induced changes in SkM-Ox. In men, low Dorea, Barnsiella, Anaerotruncus, Ruminococcus, Subdoligranulum, Coprococcus, Eubacterium ventriosum group, Ruminococcaceae UCG-003, and a Ruminococcaceae family genus abundance, and high Blautia abundance at baseline were associated with improvements in SkM-Ox. Changes in whole-body fat oxidation were not associated with gut microbiota features. We conclude that baseline microbiota composition predicts changes in SkM-Ox as a result of EGCG+RES supplementation in men but not in women. Men may be more prone to diet-induced, gut microbiota-related improvements in cardiometabolic health. These sex-differences should be further investigated in future precision-based intervention studies.

Extraction of Proteins from Microbiome of Livestocks

The development of high throughput methods has enabled the study of hundreds of samples and metaproteomics is not the exception. However, the study of thousands of proteins of different organisms represents different challenges from the protein extraction to the bioinformatic analysis. Here, the sample preparation, protein extraction and protein purification for livestock microbiome research throughout metaproteomics are described. These methods are essential because the quality of the final protein pool depends on them. For that reason, the following workflow is a combination of different chemical and physical methods that intend an initial separation of the microbial organisms from the host cells and other organic materials, as well as the extraction of high concentrate pure samples.

The metaproteome of the gut microbiota in pediatric patients affected by COVID-19

Introduction

The gut microbiota (GM) play a significant role in the infectivity and severity of COVID-19 infection. However, the available literature primarily focuses on adult patients and it is known that the microbiota undergoes changes throughout the lifespan, with significant alterations occurring during infancy and subsequently stabilizing during adulthood. Moreover, children have exhibited milder symptoms of COVID-19 disease, which has been associated with the abundance of certain protective bacteria. Here, we examine the metaproteome of pediatric patients to uncover the biological mechanisms that underlie this protective effect of the GM.

Methods

We performed nanoliquid chromatography coupled with tandem mass spectrometry on a high resolution analytical platform, resulting in label free quantification of bacterial protein groups (PGs), along with functional annotations via COG and KEGG databases by MetaLab-MAG. Additionally, taxonomic assignment was possible through the use of the lowest common ancestor algorithm provided by Unipept software.

Results

A COVID-19 GM functional dissimilarity respect to healthy subjects was identified by univariate analysis. The alteration in COVID-19 GM function is primarily based on bacterial pathways that predominantly involve metabolic processes, such as those related to tryptophan, butanoate, fatty acid, and bile acid biosynthesis, as well as antibiotic resistance and virulence.

Discussion

These findings highlight the mechanisms by which the pediatric GM could contribute to protection against the more severe manifestations of the disease in children. Uncovering these mechanisms can, therefore, have important implications in the discovery of novel adjuvant therapies for severe COVID-19.

A novel clinical metaproteomics workflow enables bioinformatic analysis of host-microbe dynamics in disease

Clinical metaproteomics has the potential to offer insights into the host-microbiome interactions underlying diseases. However, the field faces challenges in characterizing microbial proteins found in clinical samples, which are usually present at low abundance relative to the host proteins. As a solution, we have developed an integrated workflow coupling mass spectrometry-based analysis with customized bioinformatic identification, quantification and prioritization of microbial and host proteins, enabling targeted assay development to investigate host-microbe dynamics in disease. The bioinformatics tools are implemented in the Galaxy ecosystem, offering the development and dissemination of complex bioinformatic workflows. The modular workflow integrates MetaNovo (to generate a reduced protein database), SearchGUI/PeptideShaker and MaxQuant (to generate peptide-spectral matches (PSMs) and quantification), PepQuery2 (to verify the quality of PSMs), and Unipept and MSstatsTMT (for taxonomy and functional annotation). We have utilized this workflow in diverse clinical samples, from the characterization of nasopharyngeal swab samples to bronchoalveolar lavage fluid. Here, we demonstrate its effectiveness via analysis of residual fluid from cervical swabs. The complete workflow, including training data and documentation, is available via the Galaxy Training Network, empowering non-expert researchers to utilize these powerful tools in their clinical studies.

Human gut microbiome and metabolite dynamics under simulated microgravity

The Artificial Gravity Bed Rest - European Space Agency (AGBRESA) study was the first joint bed rest study by ESA, DLR, and NASA that examined the effect of simulated weightlessness on the human body and assessed the potential benefits of artificial gravity as a countermeasure in an analog of long-duration spaceflight. In this study, we investigated the impact of simulated microgravity on the gut microbiome of 12 participants during a 60-day head-down tilt bed rest at the :envihab facilities. Over 60 days of simulated microgravity resulted in a mild change in the gut microbiome, with distinct microbial patterns and pathway expression in the feces of the countermeasure group compared to the microgravity simulation-only group. Additionally, we found that the countermeasure protocols selectively increased the abundance of beneficial short-chain fatty acids in the gut, such as acetate, butyrate, and propionate. Some physiological signatures also included the modulation of taxa reported to be either beneficial or opportunistic, indicating a mild adaptation in the microbiome network balance. Our results suggest that monitoring the gut microbial catalog along with pathway clustering and metabolite profiling is an informative synergistic strategy to determine health disturbances and the outcome of countermeasure protocols for future space missions.

Unveiling the Gut Microbiome: How Junk Food Impacts the Gut

The human gut microbiome, a complex community of microorganisms, profoundly influences human health and disease. Bacteroidetes and Firmicutes make up the majority of the normal human gut microbiota. These microorganisms wield considerable influence over our physiological functions, impacting both our well-being and our susceptibility to disease. The surge of interest in the gut microbiome over the past decade has been remarkable. Once overlooked, the gastrointestinal tract's microbiota has gained recognition for its significance in maintaining optimal health. The food industry has capitalized on this, flooding the market with "probiotic" and "fermented" products. This article aims to provide a critical review of the current literature on the gut microbiome and its significance in human health, with a particular focus on the impact of dietary choices, especially junk food, on the composition and function of the gut microbiota. Microbes possess the remarkable ability to unlock nutrients from otherwise indigestible substances. The gut microbiome of individuals who consume healthy foods and those who prefer junk food varies significantly. Healthy diets promote a diverse and beneficial gut microbiome, while junk food consumption often leads to a less diverse microbiome with negative consequences for health.

lesSDRF is more: maximizing the value of proteomics data through streamlined metadata annotation

Public proteomics data often lack essential metadata, limiting its potential. To address this, we present lesSDRF, a tool to simplify the process of metadata annotation, thereby ensuring that data leave a lasting, impactful legacy well beyond its initial publication.

Evaluation of protein extraction methods to improve meta-proteomics analysis of treated wastewater biofilms

Metaproteomics can be used to study functionally active biofilm-based bacterial populations in reclaimed water distribution systems, which in turn result in bacterial regrowth that impacts the water quality. However, existing protein extraction methods have differences in their protein recovery and have not been evaluated for their efficacies in reclaimed water biofilm samples. In this study, we first evaluated six different protein extraction methods with diverse chemical and physical properties on a mixture of bacterial cell culture. Based on a weighting scores-based evaluation, the extraction protocols in order of decreasing performance are listed as B-PER > RIPA > PreOmics > SDS > AllPrep > Urea. The highest four optimal methods on cell culture were further tested against treated wastewater non-chlorinated and chlorinated effluent biofilms. In terms of protein yield, our findings showed that RIPA performed the best; however, the highest number of proteins were extracted from SDS and PreOmics. Furthermore, SDS and PreOmics worked best to rupture gram-positive and gram-negative bacterial cell walls. Considering the five evaluation factors, PreOmics obtained highest weighted score, indicating its potential effectiveness in extracting proteins from biofilms. This study provides the first insight into evaluating protein extraction methods to facilitate metaproteomics for complex reclaimed water matrices.

Blood-borne extracellular vesicles of bacteria and intestinal cells in patients with psychotic disorders

BACKGROUND

Human cells and bacteria secrete extracellular vesicles (EV) which play a role in intercellular communication. EV from the host intestinal epithelium are involved in the regulation of bacterial gene expression and growth. Bacterial EV (bactEV) produced in the intestine can pass to various tissues where they deliver biomolecules to many kinds of cells, including neurons. Emerging data indicate that gut microbiota is altered in patients with psychotic disorders. We hypothesized that the amount and content of blood-borne EV from intestinal cells and bactEV in psychotic patients would differ from healthy controls.

METHODS

We analyzed for human intestinal proteins by proteomics, for bactEV by metaproteomic analysis, and by measuring the level of lipopolysaccharide (LPS) in blood-borne EV from patients with psychotic disorders (n = 25), tested twice, in the acute phase of psychosis and after improvement, with age- and sex-matched healthy controls (n = 25).

RESULTS

Patients with psychotic disorders had lower LPS levels in their EV compared to healthy controls (p = .027). Metaproteome analyses confirmed LPS finding and identified Firmicutes and Bacteroidetes as dominating phyla. Total amounts of human intestine proteins in EV isolated from blood was lower in patients compared to controls (p = .02).

CONCLUSIONS

Our results suggest that bactEV and host intestinal EV are decreased in patients with psychosis and that this topic is worthy of further investigation given potential pathophysiological implications. Possible mechanisms involve dysregulation of the gut microbiota by host EV, altered translocation of bactEV to systemic circulation where bactEV can interact with both the brain and the immune system.

The Association of Biomolecular Resource Facilities Proteome Informatics Research Group Study on Metaproteomics (iPRG-2020)

Metaproteomics research using mass spectrometry data has emerged as a powerful strategy to understand the mechanisms underlying microbiome dynamics and the interaction of microbiomes with their immediate environment. Recent advances in sample preparation, data acquisition, and bioinformatics workflows have greatly contributed to progress in this field. In 2020, the Association of Biomolecular Research Facilities Proteome Informatics Research Group launched a collaborative study to assess the bioinformatics options available for metaproteomics research. The study was conducted in 2 phases. In the first phase, participants were provided with mass spectrometry data files and were asked to identify the taxonomic composition and relative taxa abundances in the samples without supplying any protein sequence databases. The most challenging question asked of the participants was to postulate the nature of any biological phenomena that may have taken place in the samples, such as interactions among taxonomic species. In the second phase, participants were provided a protein sequence database composed of the species present in the sample and were asked to answer the same set of questions as for phase 1. In this report, we summarize the data processing methods and tools used by participants, including database searching and software tools used for taxonomic and functional analysis. This study provides insights into the status of metaproteomics bioinformatics in participating laboratories and core facilities.

Label-Free Multiplex Proteotyping of Microbial Isolates

To meet clinical diagnostic needs and for general microbiological screening, it is essential to be able to accurately and rapidly identify any microorganisms from complex microbiota. To gain insight into the individual components of microbiota, culturomics has been proposed as a means to systematically test hundreds of possible cultivation conditions and generate numerous microbial isolates with very distinct characteristics. High-throughput identification methods must now be developed to quickly screen these isolates. Currently, most multiplexing methods involve labeling, which comes at a cost. In this paper, we present an innovative label-free multiplexing method for the identification of microorganisms using tandem mass spectrometry. The method is based on offline reversed-phase fractionation of individual peptidomes. Multiplexing is achieved by mixing fractions of staged hydrophobicity; thus, each sample is mapped to specific elution times. In this proof-of-concept study, multiplexed samples were analyzed by tandem mass spectrometry in a single run and microorganisms present in the mixture were resolved by phylopeptidomics proteotyping. Using this methodology, up to 21 microorganisms could be identified in a single 60 min run performed with a Q-Exactive HF high-resolution mass spectrometer, resulting in a rate of one microorganism identified per 3 min of mass spectrometry, without any need for the use of labeling reagents. This approach opens new perspectives for the application of high-throughput proteotyping of bacteria using tandem mass spectrometry in large culturomics projects.

Metaproteomic Analysis of Nasopharyngeal Swab Samples to Identify Microbial Peptides in COVID-19 Patients

During the COVID-19 pandemic, impaired immunity and medical interventions resulted in cases of secondary infections. The clinical difficulties and dangers associated with secondary infections in patients necessitate the exploration of their microbiome. Metaproteomics is a powerful approach to study the taxonomic composition and functional status of the microbiome under study. In this study, the mass spectrometry (MS)-based data of nasopharyngeal swab samples from COVID-19 patients was used to investigate the metaproteome. We have established a robust bioinformatics workflow within the Galaxy platform, which includes (a) generation of a tailored database of the common respiratory tract pathogens, (b) database search using multiple search algorithms, and (c) verification of the detected microbial peptides. The microbial peptides detected in this study, belong to several opportunistic pathogens such as Streptococcus pneumoniae, Klebsiella pneumoniae, Rhizopus microsporus, and Syncephalastrum racemosum. Microbial proteins with a role in stress response, gene expression, and DNA repair were found to be upregulated in severe patients compared to negative patients. Using parallel reaction monitoring (PRM), we confirmed some of the microbial peptides in fresh clinical samples. MS-based clinical metaproteomics can serve as a powerful tool for detection and characterization of potential pathogens, which can significantly impact the diagnosis and treatment of patients.

Unipept Desktop 2.0: Construction of Targeted Reference Protein Databases for Metaproteogenomics Analyses

Unipept Desktop 2.0 is the most recent iteration of the Unipept Desktop tool that adds support for the analysis of metaproteogenomics datasets. Unipept Desktop now supports the automatic construction of targeted protein reference databases that only contain proteins (originating from the UniProtKB resource) associated with a predetermined list of taxa. This improves both the taxonomic and functional resolution of a metaproteomic analysis and yields several technical advantages. By limiting the proteins present in a reference database, it is also possible to perform (meta)proteogenomics analyses. Since the protein reference database resides on the user's local machine, they have complete control over the database used during an analysis. Data no longer need to be transmitted over the Internet, decreasing the time required for an analysis and better safeguarding privacy-sensitive data. As a proof of concept, we present a case study in which a human gut metaproteome dataset is analyzed with Unipept Desktop 2.0 using different targeted databases based on matched 16S rRNA gene sequencing data.

A bibliometric analysis of the global impact of metaproteomics research

Background

Metaproteomics is a subfield in meta-omics that is used to characterize the proteome of a microbial community. Despite its importance and the plethora of publications in different research area, scientists struggle to fully comprehend its functional impact on the study of microbiomes. In this study, bibliometric analyses are used to evaluate the current state of metaproteomic research globally as well as evaluate the specific contribution of Africa to this burgeoning research area. In this study, we use bibliometric analyses to evaluate the current state of metaproteomic research globally, identify research frontiers and hotspots, and further predict future trends in metaproteomics. The specific contribution of Africa to this research area was evaluated.

Methods

Relevant documents from 2004 to 2022 were extracted from the Scopus database. The documents were subjected to bibliometric analyses and visualization using VOS viewer and Biblioshiny package in R. Factors such as the trends in publication, country and institutional cooperation networks, leading scientific journals, author's productivity, and keywords analyses were conducted. The African publications were ranked using Field-Weighted Citation Impact (FWCI) scores.

Results

A total of 1,138 documents were included and the number of publications increased drastically from 2004 to 2022 with more publications (170) reported in 2021. In terms of publishers, Frontiers in Microbiology had the highest number of total publications (62). The United States of America (USA), Germany, China, and Canada, together with other European countries were the most productive. Institution-wise, the Helmholtz Zentrum für Umweltforschung, Germany had more publications while Max Plank Institute had the highest total collaborative link strength. Jehmlich N. was the most productive author whereas Hettich RL had the highest h-index of 63. Regarding Africa, only 2.2% of the overall publications were from the continent with more publication outputs from South Africa. More than half of the publications from the continent had an FWCI score ≥ 1.

Conclusion

The scientific outputs of metaproteomics are rapidly evolving with developed countries leading the way. Although Africa showed prospects for future progress, this could only be accelerated by providing funding, increased collaborations, and mentorship programs.

Mix24X, a Lab-Assembled Reference to Evaluate Interpretation Procedures for Tandem Mass Spectrometry Proteotyping of Complex Samples

Correct identification of the microorganisms present in a complex sample is a crucial issue. Proteotyping based on tandem mass spectrometry can help establish an inventory of organisms present in a sample. Evaluation of bioinformatics strategies and tools for mining the recorded datasets is essential to establish confidence in the results obtained and to improve these pipelines in terms of sensitivity and accuracy. Here, we propose several tandem mass spectrometry datasets recorded on an artificial reference consortium comprising 24 bacterial species. This assemblage of environmental and pathogenic bacteria covers 20 different genera and 5 bacterial phyla. The dataset comprises difficult cases, such as the Shigella flexneri species, which is closely related to Escherichia coli, and several highly sequenced clades. Different acquisition strategies simulate real-life scenarios: from rapid survey sampling to exhaustive analysis. We provide access to individual proteomes of each bacterium separately to provide a rational basis for evaluating the assignment strategy of MS/MS spectra when recorded from complex mixtures. This resource should provide an interesting common reference for developers who wish to compare their proteotyping tools and for those interested in evaluating protein assignment when dealing with complex samples, such as microbiomes.

Interrogating the role of the milk microbiome in mastitis in the multi-omics era

There is growing interest in a functional understanding of milk-associated microbiota as there is ample evidence that host-associated microbial communities play an active role in host health and phenotype. Mastitis, characterized by painful inflammation of the mammary gland, is prevalent among lactating humans and agricultural animals and is associated with significant clinical and economic consequences. The etiology of mastitis is complex and polymicrobial and correlative studies have indicated alterations in milk microbial community composition. Recent evidence is beginning to suggest that a causal relationship may exist between the milk microbiota and host phenotype in mastitis. Multi-omic approaches can be leveraged to gain a mechanistic, molecular level understanding of how the milk microbiome might modulate host physiology, thereby informing strategies to prevent and ameliorate mastitis. In this paper, we review existing studies that have utilized omics approaches to investigate the role of the milk microbiome in mastitis. We also summarize the strengths and challenges associated with the different omics techniques including metagenomics, metatranscriptomics, metaproteomics, metabolomics and lipidomics and provide perspective on the integration of multiple omics technologies for a better functional understanding of the milk microbiome.

Metaproteomics to understand how microbiota function: The crystal ball predicts a promising future

In the medical, environmental, and biotechnological fields, microbial communities have attracted much attention due to their roles and numerous possible applications. The study of these communities is challenging due to their diversity and complexity. Innovative methods are needed to identify the taxonomic components of individual microbiota, their changes over time, and to determine how microoorganisms interact and function. Metaproteomics is based on the identification and quantification of proteins, and can potentially provide this full picture. Due to the wide molecular panorama and functional insights it provides, metaproteomics is gaining momentum in microbiome and holobiont research. Its full potential should be unleashed in the coming years with progress in speed and cost of analyses. In this exploratory crystal ball exercise, I discuss the technical and conceptual advances in metaproteomics that I expect to drive innovative research over the next few years in microbiology. I also debate the concepts of 'microbial dark matter' and 'Metaproteomics-Assembled Proteomes (MAPs)' and present some long-term prospects for metaproteomics in clinical diagnostics and personalized medicine, environmental monitoring, agriculture, and biotechnology.