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

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

The gut microbiome's pivotal role in health and disease is well established. SARS-CoV-2 infection often causes gastrointestinal symptoms and is associated with changes of the microbiome in both human and animal studies. While hamsters serve as important animal models for coronavirus research, there exists a notable void in the functional characterization of their microbiomes with metaproteomics. In this study, we present a workflow for analyzing the hamster gut microbiome, including a metagenomics-derived hamster gut microbial protein database and a data-independent acquisition metaproteomics method. Using this workflow, we identified 32,419 protein groups from the fecal microbiomes of young and old hamsters infected with SARS-CoV-2. We showed age-specific changes in the expressions of microbiome functions and host proteins associated with microbiomes, providing further functional insight into the interactions between the microbiome and host in SARS-CoV-2 infection. Altogether, this study established and demonstrated the capability of metaproteomics for the study of hamster microbiomes.

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.

Metaproteomics in the One Health framework for unraveling microbial effectors in microbiomes

One Health seeks to integrate and balance the health of humans, animals, and environmental systems, which are intricately linked through microbiomes. These microbial communities exchange microbes and genes, influencing not only human and animal health but also key environmental, agricultural, and biotechnological processes. Preventing the emergence of pathogens as well as monitoring and controlling the composition of microbiomes through microbial effectors including virulence factors, toxins, antibiotics, non-ribosomal peptides, and viruses holds transformative potential. However, the mechanisms by which these microbial effectors shape microbiomes and their broader functional consequences for host and ecosystem health remain poorly understood. Metaproteomics offers a novel methodological framework as it provides insights into microbial dynamics by quantifying microbial biomass composition, metabolic functions, and detecting effectors like viruses, antimicrobial resistance proteins, and non-ribosomal peptides. Here, we highlight the potential of metaproteomics in elucidating microbial effectors and their impact on microbiomes and discuss their potential for modulating microbiomes to foster desired functions.

An Approach to Integrate Metagenomics, Metatranscriptomics and Metaproteomics Data in Public Data Resources

The availability of public metaproteomics, metagenomics and metatranscriptomics data in public resources such as MGnify (for metagenomics/metatranscriptomics) and the PRIDE database (for metaproteomics), continues to increase. When these omics techniques are applied to the same samples, their integration offers new opportunities to understand the structure (metagenome) and functional expression (metatranscriptome and metaproteome) of the microbiome. Here, we describe a pilot study aimed at integrating public multi-meta-omics datasets from studies based on human gut and marine hatchery samples. Reference search databases (search DBs) were built using assembled metagenomic (and metatranscriptomic, where available) sequence data followed by de novo gene calling, using both data from the same sampling event and from independent samples. The resulting protein sets were evaluated for their utility in metaproteomics analysis. In agreement with previous studies, the highest number of peptide identifications was generally obtained when using search DBs created from the same samples. Data integration of the multi-omics results was performed in MGnify. For that purpose, the MGnify website was extended to enable the visualisation of the resulting peptide/protein information from three reanalysed metaproteomics datasets. A workflow (https://github.com/PRIDE-reanalysis/MetaPUF) has been developed allowing researchers to perform equivalent data integration, using paired multi-omics datasets. This is the first time that a data integration approach for multi-omics datasets has been implemented from public data available in the world-leading MGnify and PRIDE resources.

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.

SysQuan: Repurposing SILAC Mice for the Cost-Effective Absolute Quantitation of the Human Proteome

Relative quantitation, used by most mass spectrometry-based proteomics laboratories to determine protein fold-changes, requires samples being processed and analyzed together for best comparability through minimizing batch differences. This limits the adoption of mass spectrometry-based proteomics in population-wide studies and the detection of subtle but relevant changes in heterogeneous samples. Absolute quantitation circumvents these limitations and enables comparison of results across laboratories, studies, and over time. However, high cost of the essential stable isotope labeled (SIL) standards prevents widespread access and limits the number of quantifiable proteins. Our new approach, called "SysQuan", repurposes SILAC mouse tissues/biofluids as system-wide internal standards for matched human samples to enable absolute quantitation of, theoretically, two-thirds of the human proteome using 157,086 shared tryptic peptides, of which 73,901 with lysine on the c terminus. We demonstrate that SysQuan enables quantification of 70% and 31% of the liver and plasma proteomes, respectively. We demonstrate for 14 metabolic proteins that abundant SIL mouse tissues enable cost-effective reverse absolute quantitation in, theoretically, 1000s of human samples. Moreover, 10,000s of light/heavy doublets in untargeted SysQuan datasets enable unique postacquisition absolute quantitation. SysQuan empowers researchers to replace relative quantitation with affordable absolute quantitation at scale, making data comparable across laboratories, diseases and tissues, enabling completely novel study designs and increasing reusability of data in repositories.

Metagenome-informed metaproteomics of the human gut microbiome, host, and dietary exposome uncovers signatures of health and inflammatory bowel disease

Host-microbiome-dietary interactions play crucial roles in regulating human health, yet their direct functional assessment remains challenging. We adopted metagenome-informed metaproteomics (MIM), in mice and humans, to non-invasively explore species-level microbiome-host interactions during commensal and pathogen colonization, nutritional modification, and antibiotic-induced perturbation. Simultaneously, fecal MIM accurately characterized the nutritional exposure landscape in multiple clinical and dietary contexts. Implementation of MIM in murine auto-inflammation and in human inflammatory bowel disease (IBD) characterized a "compositional dysbiosis" and a concomitant species-specific "functional dysbiosis" driven by suppressed commensal responses to inflammatory host signals. Microbiome transfers unraveled early-onset kinetics of these host-commensal cross-responsive patterns, while predictive analyses identified candidate fecal host-microbiome IBD biomarker protein pairs outperforming S100A8/S100A9 (calprotectin). Importantly, a simultaneous fecal nutritional MIM assessment enabled the determination of IBD-related consumption patterns, dietary treatment compliance, and small intestinal digestive aberrations. Collectively, a parallelized dietary-bacterial-host MIM assessment functionally uncovers trans-kingdom interactomes shaping gastrointestinal ecology while offering personalized diagnostic and therapeutic insights into microbiome-associated disease.

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.

The right tool for the job: Chemical biology and microbiome science

Microbiomes exist in ecological niches ranging from the ocean and soil to inside of larger organisms like plants and animals. Within these niches, microbes play key roles in biochemical processes that impact larger phenomena, such as biogeochemical cycling or health. By understanding of how these processes occur at the molecular level, it may be possible to develop new interventions to address global problems. The complexity of these systems poses challenges to more traditional techniques. Chemical biology can help overcome these challenges by providing tools that are broadly applicable and can obtain molecular-level information about complex systems. This primer is intended to serve as a brief introduction to chemical biology and microbiome science, to highlight some of the ways that these two disciplines complement each other, and to encourage dialog and collaboration between these fields.

Proteomic analysis of egg production peak and senescence in the ovaries of Taihe black-boned silky fowl (Gallus gallus domesticus Brisson)

BACKGROUND

The Taihe black-boned silky fowl, a distinguished indigenous breed of chicken, is renowned for its dual utility in both traditional medicinal and culinary applications. However, the breed faces significant challenges due to its suboptimal reproductive capabilities and a notably brief egg-laying period, which have impeded its broader development and cultivation. In this research endeavor, we employed an advanced, rapid DIA (Data independent acquisition) quantitative proteomics method on the Astral platform to meticulously analyze the ovarian proteome of these chickens. By analyzing the ovarian proteomic information of Taihe black-boned silky fowl during peak and decline egg-laying periods, we aim to identify potential reproductive candidate proteins and the molecular mechanisms underlying egg-laying decline. This could enable us to implement interventions to improve the reproductive efficiency of this valuable breed.

RESULT

In this study, a total of 8,281 proteins were identified within the ovarian proteome of the Taihe black-boned silky fowl. Among these, 303 proteins exhibited significant differential expression, with 98 proteins significantly up-regulated and 205 proteins significantly down-regulated. The functional annotation of these proteins illuminated their crucial roles in the steroid hormone synthesis pathways, which are pivotal during the peak of egg production. Furthermore, during the later stages of laying, there was a noticeable upregulation of proteins associated with inflammatory senescence and oxidative stress. This change suggests an increase in reproductive stress within the ovary, highlighting the physiological shifts that affect productivity as the chickens age.

CONCLUSION

This study identified key candidate protein markers in the Taihe black-boned silky fowl during critical phases of their reproductive cycle, specifically peak and late egg-laying periods. These findings contribute valuable new scientific insights that can be utilized for the breeding optimization and effective management of this unique breed. By elucidating the protein dynamics during different laying phases, the research offers potential strategies aimed at enhancing reproductive performance and extending the reproductive lifespan of the Taihe black-boned silky fowl. This could lead to significant improvements in both the sustainability and profitability of farming this indigenous breed.

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.

Proteomics analysis reveals the differential protein expression of female and male adult Toxocara canis using Orbitrap Astral analyzer

BACKGROUND

Toxocara canis, the most prevalent helminth in dogs and other canines, is one of the socioeconomically important zoonotic parasites, particularly affecting pediatric and adolescent populations in impoverished communities. However, limited information is available regarding the proteomes of female and male adult T. canis. To address this knowledge gap, we performed a comprehensive proteomic analysis to identify the proteins with differential abundance (PDAs) and gender-specifically expressed proteins between the two sexes adult T. canis.

METHODS

The comparative proteomic analysis was carried out by the Orbitrap mass spectrometry (MS) with asymmetric track lossless (Astral) analyzer. The difference analysis was conducted using t-test and the proteins verification was achieved through parallel reaction monitoring (PRM). The potential biological functions of identified adult T. canis proteins and PDAs were predicted by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. The domain, transcription factor and subcellular localization of the identified proteins and PDAs were analyzed by InterPro, AnimalTFDB 4.0 and Cell-mPLOC 2.0 databases, respectively.

RESULTS

A total of 8565 somatic proteins of adult T. canis were identified. Compared to male adult, 682 up-regulated PDAs and 844 down-regulated PDAs were identified in female adult with P-values < 0.05 and |log2FC| > 1, including 139 proteins exclusively expressed in female and 272 proteins exclusively expressed in male. The GO annotation analysis using all PDAs revealed that the main biological processes, cellular components and molecular functions corresponded to aminoglycan metabolic process, extracellular region and protein tyrosine phosphatase activity, respectively. The KEGG analysis using all PDAs showed that the pathways were mainly associated with adipocytokine signaling pathway, proximal tubule bicarbonate reclamation and PPAR signaling pathway.

CONCLUSIONS

This study reveals the differential protein expression between female and male adult T. canis, providing valuable resource for developing the novel intervention strategies against T. canis infection in humans and animals, especially from the perspective of sexual development and reproduction.

Universal Identification of Pathogenic Viruses by Liquid Chromatography Coupled with Tandem Mass Spectrometry Proteotyping

Accurate and rapid identification of viruses is crucial for an effective medical diagnosis when dealing with infections. Conventional methods, including DNA amplification techniques or lateral-flow assays, are constrained to a specific set of targets to search for. In this study, we introduce a novel tandem mass spectrometry proteotyping-based method that offers a universal approach for the identification of pathogenic viruses and other components, eliminating the need for a priori knowledge of the sample composition. Our protocol relies on a time and cost-efficient peptide sample preparation, followed by an analysis with liquid chromatography coupled to high-resolution tandem mass spectrometry. As a proof of concept, we first assessed our method on publicly available shotgun proteomics datasets obtained from virus preparations and fecal samples of infected individuals. Successful virus identification was achieved with 53 public datasets, spanning 23 distinct viral species. Furthermore, we illustrated the method's capability to discriminate closely related viruses within the same sample, using alphaviruses as an example. The clinical applicability of our method was demonstrated by the accurate detection of the vaccinia virus in spiked saliva, a matrix of paramount clinical significance due to its non-invasive and easily obtainable nature. This innovative approach represents a significant advancement in pathogen detection and paves the way for enhanced diagnostic capabilities.

An Inflection Point in High-Throughput Proteomics with Orbitrap Astral: Analysis of Biofluids, Cells, and Tissues

This Technical Note presents a comprehensive proteomics workflow for the new combination of Orbitrap and Astral mass analyzers across biofluids, cells, and tissues. Central to our workflow is the integration of Adaptive Focused Acoustics (AFA) technology for cells and tissue lysis to ensure robust and reproducible sample preparation in a high-throughput manner. Furthermore, we automated the detergent-compatible single-pot, solid-phase-enhanced sample Preparation (SP3) method for protein digestion. The synergy of these advanced methodologies facilitates a robust and high-throughput approach for cell and tissue analysis, an important consideration in translational research. This work disseminates our platform workflow, analyzes the effectiveness, demonstrates the reproducibility of the results, and highlights the potential of these technologies in biomarker discovery and disease pathology. For cells and tissues (heart, liver, lung, and intestine) proteomics analysis by data-independent acquisition mode, identifications exceeding 10,000 proteins can be achieved with a 24 min active gradient. In 200 ng injections of HeLa digest across multiple gradients, an average of more than 80% of proteins have a CV less than 20%, and a 45 min run covers ∼90% of the expressed proteome. This complete workflow allows for large swaths of the proteome to be identified and is compatible with diverse sample types.

Surface-Shaving of Staphylococcus aureus Strains and Quantitative Proteomic Analysis Reveal Differences in Protein Abundance of the Surfaceome

Staphylococcus aureus is a pathogen known to cause a wide range of infections. To find new targets for identification and to understand host-pathogen interactions, many studies have focused on surface proteins. We performed bacterial-cell surface-shaving, followed by tandem mass tag for quantitative mass spectrometry proteomics, to examine the surfaceome of S. aureus. Two steps were performed, the first step including surface protein-deficient mutants of S. aureus Newman strain lacking important virulence genes (clfA and spa, important for adhesion and immune evasion and srtAsrtB, linking surface-associated virulence factors to the surface) and the second step including isolates of different clinical origin. All strains were compared to the Newman strain. In Step 1, altogether, 7880 peptides were identified, corresponding to 1290 proteins. In Step 2, 4949 peptides were identified, corresponding to 919 proteins and for each strain, approximately 20 proteins showed differential expression compared to the Newman strain. The identified surface proteins were related to host-cell-adherence and immune-system-evasion, biofilm formation, and survival under harsh conditions. The results indicate that surface-shaving of intact S. aureus bacterial strains in combination with quantitative proteomics is a useful tool to distinguish differences in protein abundance of the surfaceome, including the expression of virulence factors.

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

Aim: This study aims to evaluate the impact of experimental workflow on fecal metaproteomic observations, including the recovery of small and antimicrobial proteins often overlooked in metaproteomic studies. The overarching goal is to provide guidance for optimized metaproteomic experimental design, considering the emerging significance of the gut microbiome in human health, disease, and therapeutic interventions. Methods: Mouse feces were utilized as the experimental model. Fecal sample pre-processing methods (differential centrifugation and non-differential centrifugation), protein digestion techniques (in-solution and filter-aided), data acquisition modes (data-dependent and data-independent, or DDA and DIA) when combined with parallel accumulation-serial fragmentation (PASEF), and different bioinformatic workflows were assessed. Results: We showed that, in DIA-PASEF metaproteomics, the library-free search using protein sequence database generated from DDA-PASEF data achieved better identifications than using the generated spectral library. Compared to DDA, DIA-PASEF identified more microbial peptides, quantified more proteins with fewer missing values, and recovered more small antimicrobial proteins. We did not observe any obvious impacts of protein digestion methods on both taxonomic and functional profiles. However, differential centrifugation decreased the recovery of small and antimicrobial proteins, biased the taxonomic observation with a marked overestimation of Muribaculum species, and altered the measured functional compositions of metaproteome. Conclusion: This study underscores the critical impact of experimental choices on metaproteomic outcomes and sheds light on the potential biases introduced at different stages of the workflow. The comprehensive methodological comparisons serve as a valuable guide for researchers aiming to enhance the accuracy and completeness of metaproteomic analyses.

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.

An inflection point in high-throughput proteomics with Orbitrap Astral: analysis of biofluids, cells, and tissues

This technical note presents a comprehensive proteomics workflow for the new combination of Orbitrap and Astral mass analyzers across biofluids, cells, and tissues. Central to our workflow is the integration of Adaptive Focused Acoustics (AFA) technology for cells and tissue lysis, to ensure robust and reproducible sample preparation in a high-throughput manner. Furthermore, we automated the detergent-compatible single-pot, solid-phase-enhanced sample Preparation (SP3) method for protein digestion, a technique that streamlines the process by combining purification and digestion steps, thereby reducing sample loss and improving efficiency. The synergy of these advanced methodologies facilitates a robust and high-throughput approach for cells and tissue analysis, an important consideration in translational research. This work disseminates our platform workflow, analyzes the effectiveness, demonstrates reproducibility of the results, and highlights the potential of these technologies in biomarker discovery and disease pathology. For cells and tissues (heart, liver, lung, and intestine) proteomics analysis by data-independent acquisition mode, identifications exceeding 10,000 proteins can be achieved with a 24-minute active gradient. In 200ng injections of HeLa digest across multiple gradients, an average of more than 80% of proteins have a CV less than 20%, and a 45-minute run covers ~90% of the expressed proteome. In plasma samples including naive, depleted, perchloric acid precipitated, and Seer nanoparticle captured, all with a 24-minute gradient length, we identified 87, 108, 96 and 137 out of 216 FDA approved circulating protein biomarkers, respectively. This complete workflow allows for large swaths of the proteome to be identified and is compatible across diverse sample types.

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.