The Landscape and Perspectives of the Human Gut Metaproteomics.

The human gut microbiome is closely associated with human health and diseases. Metaproteomics has emerged as a valuable tool for studying the functionality of the gut microbiome by analyzing the entire proteins present in microbial communities. Recent advancements in liquid chromatography and tandem mass spectrometry (LC-MS/MS) techniques have expanded the detection range of metaproteomics. However, the overall coverage of the proteome in metaproteomics is still limited. While metagenomics studies have revealed substantial microbial diversity and functional potential of the human gut microbiome, few studies have summarized and studied the human gut microbiome landscape revealed with metaproteomics. In this article, we present the current landscape of human gut metaproteomics studies by re-analyzing the identification results from 15 published studies. We quantified the limited proteome coverage in metaproteomics and revealed a high proportion of annotation coverage of metaproteomics-identified proteins. We conducted a preliminary comparison between the metaproteomics view and the metagenomics view of the human gut microbiome, identifying key areas of consistency and divergence. Based on the current landscape of human gut metaproteomics, we discuss the feasibility of using metaproteomics to study functionally unknown proteins and propose a whole workflow peptide-centric analysis. Additionally, we suggest enhancing metaproteomics analysis by refining taxonomic classification and calculating confidence scores, as well as developing tools for analyzing the interaction between taxonomy and function.

Staufen1 controls mitochondrial metabolism via HIF2α in embryonal rhabdomyosarcoma and promotes tumorigenesis.

Elevated mitochondrial metabolism promotes tumorigenesis of Embryonal Rhabdomyosarcomas (ERMS). Accordingly, targeting oxidative phosphorylation (OXPHOS) could represent a therapeutic strategy for ERMS. We previously demonstrated that genetic reduction of Staufen1 (STAU1) levels results in the inhibition of ERMS tumorigenicity. Here, we examined STAU1-mediated mechanisms in ERMS and focused on its potential involvement in regulating OXPHOS. We report the novel and differential role of STAU1 in mitochondrial metabolism in cancerous versus non-malignant skeletal muscle cells (NMSkMCs). Specifically, our data show that STAU1 depletion reduces OXPHOS and inhibits proliferation of ERMS cells. Our findings further reveal the binding of STAU1 to several OXPHOS mRNAs which affects their stability. Indeed, STAU1 depletion reduced the stability of OXPHOS mRNAs, causing inhibition of mitochondrial metabolism. In parallel, STAU1 depletion impacted negatively the HIF2α pathway which further modulates mitochondrial metabolism. Exogenous expression of HIF2α in STAU1-depleted cells reversed the mitochondrial inhibition and induced cell proliferation. However, opposite effects were observed in NMSkMCs. Altogether, these findings revealed the impact of STAU1 in the regulation of mitochondrial OXPHOS in cancer cells as well as its differential role in NMSkMCs. Overall, our results highlight the therapeutic potential of targeting STAU1 as a novel approach for inhibiting mitochondrial metabolism in ERMS.

MetaPep: A core peptide database for faster human gut metaproteomics database searches.

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

Revealing proteome-level functional redundancy in the human gut microbiome using ultra-deep metaproteomics.

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

BRK confers tamoxifen-resistance in breast cancer via regulation of tyrosine phosphorylation of CDK1.

Tamoxifen (Tam) has been the first-line therapy for estrogen receptor-positive breast cancer since its FDA-approval in 1998. Tam-resistance, however, presents a challenge and the mechanisms that drive it have yet to be fully elucidated. The non-receptor tyrosine kinase BRK/PTK6 is a promising candidate as previous research has shown that BRK knockdown resensitizes Tam-resistant breast cancer cells to the drug. However, the specific mechanisms that drive its importance to resistance remain to be investigated. Here, we investigate the role and mechanism of action of BRK in Tam-resistant (TamR), ER+, and T47D breast cancer cells using phosphopeptide enrichment and high throughput phopshoproteomics analysis. We conducted BRK-specific shRNA knockdown in TamR T47D cells and compared phosphopeptides identified in these cells with their Tam-resistant counterpart and parental, Tam-sensitive cells (Par). A total of 6492 STY phosphosites were identified. Of these sites, 3739 high-confidence pST sites and 118 high-confidence pY sites were analyzed for significant changes in phosphorylation levels to identify pathways that were differentially regulated in TamR versus Par and to investigate changes in these pathways when BRK is knocked down in TamR. We observed and validated increased CDK1 phosphorylation at Y15 in TamR cells compared to BRK-depleted TamR cells. Our data suggest that BRK is a potential Y15-directed CDK1 regulatory kinase in Tam-resistant breast cancer.

MetaLab-MAG: A Metaproteomic Data Analysis Platform for Genome-Level Characterization of Microbiomes from the Metagenome-Assembled Genomes Database.

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

An economic and robust TMT labeling approach for high throughput proteomic and metaproteomic analysis.

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

Assessing the Dark Field of Metaproteome.

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

MetaProClust-MS1: an MS1 Profiling Approach for Large-Scale Microbiome Screening.

Metaproteomics is used to explore the functional dynamics of microbial communities. However, acquiring metaproteomic data by tandem mass spectrometry (MS/MS) is time-consuming and resource-intensive, and there is a demand for computational methods that can be used to reduce these resource requirements. We present MetaProClust-MS1, a computational framework for microbiome feature screening developed to prioritize samples for follow-up MS/MS. In this proof-of-concept study, we tested and compared MetaProClust-MS1 results on gut microbiome data, from fecal samples, acquired using short 15-min MS1-only chromatographic gradients and MS1 spectra from longer 60-min gradients to MS/MS-acquired data. We found that MetaProClust-MS1 identified robust gut microbiome responses caused by xenobiotics with significantly correlated cluster topologies of comparable data sets. We also used MetaProClust-MS1 to reanalyze data from both a clinical MS/MS diagnostic study of pediatric patients with inflammatory bowel disease and an experiment evaluating the therapeutic effects of a small molecule on the brain tissue of Alzheimer's disease mouse models. MetaProClust-MS1 clusters could distinguish between inflammatory bowel disease diagnoses (ulcerative colitis and Crohn's disease) using samples from mucosal luminal interface samples and identified hippocampal proteome shifts of Alzheimer's disease mouse models after small-molecule treatment. Therefore, we demonstrate that MetaProClust-MS1 can screen both microbiomes and single-species proteomes using only MS1 profiles, and our results suggest that this approach may be generalizable to any proteomics experiment. MetaProClust-MS1 may be especially useful for large-scale metaproteomic screening for the prioritization of samples for further metaproteomic characterization, using MS/MS, for instance, in addition to being a promising novel approach for clinical diagnostic screening. IMPORTANCE Growing evidence suggests that human gut microbiome composition and function are highly associated with health and disease. As such, high-throughput metaproteomic studies are becoming more common in gut microbiome research. However, using a conventional long liquid chromatography (LC)-MS/MS gradient metaproteomics approach as an initial screen in large-scale microbiome experiments can be slow and expensive. To combat this challenge, we introduce MetaProClust-MS1, a computational framework for microbiome screening using MS1-only profiles. In this proof-of-concept study, we show that MetaProClust-MS1 identifies clusters of gut microbiome treatments using MS1-only profiles similar to those identified using MS/MS. Our approach allows researchers to prioritize samples and treatments of interest for further metaproteomic analyses and may be generally applicable to any proteomic analysis. In particular, this approach may be especially useful for large-scale metaproteomic screening or in clinical settings where rapid diagnostic evidence is required.

Comprehensive Assessment of Functional Effects of Commonly Used Sugar Substitute Sweeteners on Ex Vivo Human Gut Microbiome.

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

Evaluating live microbiota biobanking using an ex vivo microbiome assay and metaproteomics.

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

Structural analysis of Atopobium parvulum SufS cysteine desulfurase linked to Crohn's disease.

Crohn's disease (CD) is characterized by the chronic inflammation of the gastrointestinal tract. A dysbiotic microbiome and a defective immune system are linked to CD, where hydrogen sulfide (H2 S) microbial producers positively correlate with the severity of the disease. Atopobium parvulum is a key H2 S producer from the microbiome of CD patients. In this study, the biochemical characterization of two Atopobium parvulum cysteine desulfurases, ApSufS and ApCsdB, shows that the enzymes are allosterically regulated. Structural analyses reveal that ApSufS forms a dimer with conserved characteristics observed in type II cysteine desulfurases. Four residues surrounding the active site are essential to catalyse cysteine desulfurylation, and a segment of short-chain residues grant access for substrate binding. A better understanding of ApSufS will help future avenues for CD treatment.

Elevated colonic microbiota-associated paucimannosidic and truncated N-glycans in pediatric ulcerative colitis.

Pediatric ulcerative colitis (UC) is a distinct type of inflammatory bowel disease with severe disease activity and rapid progression, which can lead to detrimental life-long consequences. The pathogenesis of pediatric UC remains unclear, although dysbiosis of the gut microbiota has been considered an important factor. In this study, we collected intestinal mucosal-luminal interface microbiota samples from a cohort of treatment-naïve pediatric UC or control patients and used two different mass spectrometry-based glycomic approaches to examine the N-glycans that were associated with the microbiota. We observed abundant small N-glycans that were associated with the microbiota and found that the pediatric UC microbiota samples contained significantly higher levels of these atypical N-glycans compared to those of controls. Four paucimannosidic or other truncated N-glycans were identified to successfully segregate UC from control patients with an area under the ROC curve of ≥0.9. This study indicates that the aberrant metabolism of glycans in the intestinal by gut microbiota may be involved in the pathogenesis of UC and intestinal N-glycans, including small glycans, can act as novel biomarker candidates for pediatric UC. SIGNIFICANCE: There is no cure for pediatric ulcerative colitis (UC) due to its unclear pathogenesis and the diagnosis of UC in children still largely depends on invasive colonoscopic examination. Recent evidence suggests that the dysbiosis of intestinal microbiota is associated with the onset and development of UC, however how the microbiota interact with the host remains unclear. This study used two different mass spectrometry-based glycomic approaches to quantitatively examine N-glycans that are associated with colonic mucosal-luminal interface microbiota of pediatric UC or control patients. To the best of our knowledge, this is the first comprehensive glycomic study of intestinal microbiota samples in UC, which demonstrated that intestinal microbiota was associated with abundant atypical small N-glycans with elevated levels in UC than controls. This study also identified four intestinal paucimannosidic or other truncated N-glycans as promising biomarker candidates for pediatric UC. These findings shed light on the mechanism study of host-microbiome interactions in UC and indicate that atypical glycans present in the gut can be a source for UC biomarker discovery.

Examining the Effects of an Anti-Salmonella Bacteriophage Preparation, BAFASAL®, on Ex-Vivo Human Gut Microbiome Composition and Function Using a Multi-Omics Approach.

Salmonella infections (salmonellosis) pose serious health risks to humans, usually via food-chain contamination. This foodborne pathogen causes major food losses and human illnesses, with significant economic impacts. Overuse of antibiotics in the food industry has led to multidrug-resistant strains of bacteria, and governments are now restricting their use, leading the food industry to search for alternatives to secure food chains. Bacteriophages, viruses that infect and kill bacteria, are currently being investigated and used as replacement treatments and prophylactics due to their specificity and efficacy. They are generally regarded as safe alternatives to antibiotics, as they are natural components of the ecosystem. However, when specifically used in the industry, they can also make their way into humans through our food chain or exposure, as is the case for antibiotics. In particular, agricultural workers could be repeatedly exposed to bacteriophages supplemented to animal feeds. To our knowledge, no studies have investigated the effects of such exposure to bacteriophages on the human gut microbiome. In this study, we used a novel in-vitro assay called RapidAIM to investigate the effect of a bacteriophage mixture, BAFASAL®, used in poultry farming on five individual human gut microbiomes. Multi-omics analyses, including 16S rRNA gene sequencing and metaproteomic, revealed that ex-vivo human gut microbiota composition and function were unaffected by BAFASAL® treatment, providing an additional measure for its safety. Due to the critical role of the gut microbiome in human health and the known role of bacteriophages in regulation of microbiome composition and function, we suggest assaying the impact of bacteriophage-cocktails on the human gut microbiome as a part of their safety assessment.

Associations between Cellular Energy and Pediatric Inflammatory Bowel Disease Patient Response to Treatment.

Inflammatory bowel diseases (IBDs), including Crohn's disease (CD) and ulcerative colitis, are chronic diseases of the gastrointestinal tract, with an unknown etiology, that affect over 6.8 million people worldwide. To characterize disease pathogenesis, proteomic and bioinformatic analyses were performed on colon biopsies collected during diagnostic endoscopy from 119 treatment-naïve pediatric patients, including from 78 IBD patients and 41 non-IBD patients who served as controls. Due to the presence of noninflamed and/or inflamed regions in IBD patients, up to two biopsies were obtained from IBD patients as compared to a single noninflamed biopsy from non-IBD pediatric control patients. Additional biopsies were obtained and analyzed from 33 of the IBD patients after IBD-directed therapeutic intervention for comparison of pre- and post-treatment proteomes. SuperSILAC was utilized to perform quantitative analysis of homogenized tissues, which were processed by filter-aided sample preparation. Hierarchical clustering and principal component analyses revealed proteomic patterns that distinguished inflamed from noninflamed tissues independent of therapy. Gene ontology revealed that proteins downregulated in inflammation are associated with metabolism, whereas upregulated proteins contribute to protein processing. A comparison of pre- and post-treatment proteomes from CD patients identified over 100 proteins that are significantly different between patients who responded and those who did not respond to therapy, including creatine kinase B and basigin.

Exploring the Microbiome-Wide Lysine Acetylation, Succinylation, and Propionylation in Human Gut Microbiota.

Lysine acylations are important post-translational modifications that are present in both eukaryotes and prokaryotes and regulate diverse cellular functions. Our knowledge of the microbiome lysine acylation remains limited due to the lack of efficient analytical and bioinformatics methods for complex microbial communities. Here, we show that the serial enrichment using motif antibodies successfully captures peptides containing lysine acetylation, propionylation, and succinylation from human gut microbiome samples. A new bioinformatic workflow consisting of an unrestricted database search confidently identified >60,000 acetylated, and ∼20,000 propionylated and succinylated gut microbial peptides. The characterization of these identified modification-specific metaproteomes, i.e., meta-PTMomes, demonstrates that lysine acylations are differentially distributed in microbial species with different metabolic capabilities. This study provides an analytical framework for the study of lysine acylations in the microbiome, which enables functional microbiome studies at the post-translational level.

A functional ecological network based on metaproteomics responses of individual gut microbiomes to resistant starches.

Resistant starches (RS) are dietary compounds processed by the gut microbiota into metabolites, such as butyrate, that are beneficial to the host. The production of butyrate by the microbiome appears to be affected by the plant source and type of RS as well as the individual's microbiota. In this study, we used in vitro culture and metaproteomic methods to explore individual microbiome's functional responses to RS2 (enzymatically-resistant starch), RS3 (retrograded starch) and RS4 (chemically-modified starch). Results showed that RS2 and RS3 significantly altered the protein expressions in the individual gut microbiomes, while RS4 did not result in significant protein changes. Significantly elevated protein groups were enriched in carbohydrate metabolism and transport functions of families Eubacteriaceae, Lachnospiraceae and Ruminococcaceae. In addition, Bifidobacteriaceae was significantly increased in response to RS3. We also observed taxon-specific enrichments of starch metabolism and pentose phosphate pathways corresponding to this family. Functions related to starch utilization, ABC transporters and pyruvate metabolism pathways were consistently increased in the individual microbiomes in response to RS2 and RS3. Given that these taxon-specific responses depended on the type of carbohydrate sources, we constructed a functional ecological network to gain a system-level insight of functional organization. Our results suggest that while some microbes tend to be functionally independent, there are subsets of microbes that are functionally co-regulated by environmental changes, potentially by alterations of trophic interactions.

Studying the Temporal Dynamics of the Gut Microbiota Using Metabolic Stable Isotope Labeling and Metaproteomics.

The gut microbiome and its metabolic processes are dynamic systems. Surprisingly, our understanding of gut microbiome dynamics is limited. Here, we report a metaproteomic workflow that involves protein stable isotope probing (protein-SIP) and identification/quantification of partially labeled peptides. We also developed a package, which we call MetaProfiler, that corrects for false identifications and performs phylogenetic and time series analysis for the study of microbiome dynamics. From the stool sample of five mice that were fed with 15N hydrolysate from Ralstonia eutropha, we identified 12 326 nonredundant unlabeled peptides, of which 8256 of their heavy counterparts were quantified. These peptides revealed incorporation profiles over time that were different between and within taxa, as well as between and within clusters of orthologous groups (COGs). Our study helps unravel the complex dynamics of protein synthesis and bacterial dynamics in the mouse microbiome. MetaProfiler and the bioinformatic pipeline are available at https://github.com/northomics/MetaProfiler.git.

Widespread protein lysine acetylation in gut microbiome and its alterations in patients with Crohn's disease.

Lysine acetylation (Kac), an abundant post-translational modification (PTM) in prokaryotes, regulates various microbial metabolic pathways. However, no studies have examined protein Kac at the microbiome level, and it remains unknown whether Kac level is altered in patient microbiomes. Herein, we use a peptide immuno-affinity enrichment strategy coupled with mass spectrometry to characterize protein Kac in the microbiome, which successfully identifies 35,200 Kac peptides from microbial or human proteins in gut microbiome samples. We demonstrate that Kac is widely distributed in gut microbial metabolic pathways, including anaerobic fermentation to generate short-chain fatty acids. Applying to the analyses of microbiomes of patients with Crohn's disease identifies 52 host and 136 microbial protein Kac sites that are differentially abundant in disease versus controls. This microbiome-wide acetylomic approach aids in advancing functional microbiome research.

MealTime-MS: A Machine Learning-Guided Real-Time Mass Spectrometry Analysis for Protein Identification and Efficient Dynamic Exclusion.

Mass spectrometry-based proteomics technologies are prime methods for the high-throughput identification of proteins in complex biological samples. Nevertheless, there are still technical limitations that hinder the ability of mass spectrometry to identify low abundance proteins in complex samples. Characterizing such proteins is essential to provide a comprehensive understanding of the biological processes taking place in cells and tissues. Still today, most mass spectrometry-based proteomics approaches use a data-dependent acquisition strategy, which favors the collection of mass spectra from proteins of higher abundance. Since the computational identification of proteins from proteomics data is typically performed after mass spectrometry analysis, large numbers of mass spectra are typically redundantly acquired from the same abundant proteins, and little to no mass spectra are acquired for proteins of lower abundance. We therefore propose a novel supervised learning algorithm, MealTime-MS, that identifies proteins in real-time as mass spectrometry data are acquired and prevents further data collection from confidently identified proteins to ultimately free mass spectrometry resources to improve the identification sensitivity of low abundance proteins. We use real-time simulations of a previously performed mass spectrometry analysis of a HEK293 cell lysate to show that our approach can identify 92.1% of the proteins detected in the experiment using 66.2% of the MS2 spectra. We also demonstrate that our approach outperforms a previously proposed method, is sufficiently fast for real-time mass spectrometry analysis, and is flexible. Finally, MealTime-MS' efficient usage of mass spectrometry resources will provide a more comprehensive characterization of proteomes in complex samples.