Data-independent acquisition boosts quantitative metaproteomics for deep characterization of gut microbiota

Wise Roles and Future Visionary Endeavors of Current Emperor: Advancing Dynamic Methods for Longitudinal Microbiome Meta-Omics Data in Personalized and Precision Medicine

Understanding the etiological complexity of diseases requires identifying biomarkers longitudinally associated with specific phenotypes. Advanced sequencing tools generate dynamic microbiome data, providing insights into microbial community functions and their impact on health. This review aims to explore the current roles and future visionary endeavors of dynamic methods for integrating longitudinal microbiome multi-omics data in personalized and precision medicine. This work seeks to synthesize existing research, propose best practices, and highlight innovative techniques. The development and application of advanced dynamic methods, including the unified analytical frameworks and deep learning tools in artificial intelligence, are critically examined. Aggregating data on microbes, metabolites, genes, and other entities offers profound insights into the interactions among microorganisms, host physiology, and external stimuli. Despite progress, the absence of gold standards for validating analytical protocols and data resources of various longitudinal multi-omics studies remains a significant challenge. The interdependence of workflow steps critically affects overall outcomes. This work provides a comprehensive roadmap for best practices, addressing current challenges with advanced dynamic methods. The review underscores the biological effects of clinical, experimental, and analytical protocol settings on outcomes. Establishing consensus on dynamic microbiome inter-studies and advancing reliable analytical protocols are pivotal for the future of personalized and precision medicine.

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.

Metaproteomic analysis from cervical biopsies and cytologies identifies proteinaceous biomarkers representing both human and microbial species

The detection of HPV infection and microbial colonization in cervical lesions is currently done through PCR-based viral or bacterial DNA amplification. Our objective was to develop a methodology to expand the metaproteomic landscape of cervical disease and determine if protein biomarkers from both human and microbes could be detected in distinct cervical samples. This would lead to the development of multi-species proteomics, which includes protein-based lateral flow diagnostics that can define patterns of microbes and/or human proteins relevant to disease status. In this study, we collected both non-frozen tissue biopsy and exfoliative non-fixed cytology samples to assess the consistency of detecting human proteomic signatures between the cytology and biopsy samples. Our results show that proteomics using biopsies or cytologies can detect both human and microbial organisms. Across patients, Lumican and Galectin-1 were most highly expressed human proteins in the tissue biopsy, whilst IL-36 and IL-1RA were most highly expressed human proteins in the cytology. We also used mass spectrometry to assess microbial proteomes known to reside based on prior 16S rRNA gene signatures. Lactobacillus spp. was the most highly expressed proteome in patient samples and specific abundant Lactobacillus proteins were identified. These methodological approaches can be used in future metaproteomic clinical studies to interrogate the vaginal human and microbiome structure and metabolic diversity in cytologies or biopsies from the same patients who have pre-invasive cervical intraepithelial neoplasia, invasive cervical cancer, as well as in healthy controls to assess how human and pathogenic proteins may correlate with disease presence and severity.

STAVER: a standardized benchmark dataset-based algorithm for effective variation reduction in large-scale DIA-MS data

Mass spectrometry (MS)-based proteomics has become instrumental in comprehensively investigating complex biological systems. Data-independent acquisition (DIA)-MS, utilizing hybrid spectral library search strategies, allows for the simultaneous quantification of thousands of proteins, showing promise in enhancing protein identification and quantification precision. However, low-quality profiles can considerably undermine quantitative precision, resulting in inaccurate protein quantification. To tackle this challenge, we introduced STAVER, a novel algorithm that leverages standardized benchmark datasets to reduce non-biological variation in large-scale DIA-MS analyses. By eliminating unwanted noise in MS signals, STAVER significantly improved protein quantification precision, especially in hybrid spectral library searches. Moreover, we validated STAVER's robustness and applicability across multiple large-scale DIA datasets, demonstrating significantly enhanced precision and reproducibility of protein quantification. STAVER offers an innovative and effective approach for enhancing the quality of large-scale DIA proteomic data, facilitating cross-platform and cross-laboratory comparative analyses. This advancement significantly enhances the consistency and reliability of findings in clinical research. The complete package is available at https://github.com/Ran485/STAVER.

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.

Deep Learning Powers Protein Identification from Precursor MS Information

Proteome analysis currently heavily relies on tandem mass spectrometry (MS/MS), which does not fully utilize MS1 features, as many precursors remain unselected for MS/MS fragmentation, especially in the cases of low abundance samples and wide abundance dynamic range samples. Therefore, leveraging MS1 features as a complement to MS/MS has become an attractive option to improve the coverage of feature identification. Herein, we propose MonoMS1, an approach combining deep learning-based retention time, ion mobility, detectability prediction, and logistic regression-based scoring for MS1 feature identification. The approach achieved a significant increase in MS1 feature identification based on an E. coli data set. Application of MonoMS1 to data sets with wide dynamic range, such as human serum proteome samples, and with low sample abundance, such as single-cell proteome samples, enabled substantial complementation of MS/MS-based peptide and protein identification. This method opens a new avenue for proteomic analysis and can boost proteomic research on complex samples.

Proteomics profiling reveals lipid metabolism abnormalities during oogenesis in unexplained recurrent pregnancy loss

Background

Unexplained recurrent pregnancy loss (URPL) is a clinical dilemma in reproductive fields. Its diagnosis is mainly exclusionary after extensive clinical examination, and some of the patients may still face the risk of miscarriage.

Methods

We analyzed follicular fluid (FF) from in vitro fertilization (IVF) in eight patients with URPL without endocrine abnormalities or verifiable causes of abortion and eight secondary infertility controls with no history of pregnancy loss who had experienced at least one normal pregnancy and delivery by direct data-independent acquisition (dDIA) quantitative proteomics to identify differentially expressed proteins (DEPs). In this study, bioinformatics analysis was performed using online software including g:profiler, String, and ToppGene. Cytoscape was used to construct the protein-protein interaction (PPI) network, and ELISA was used for validation.

Results

Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed that the DEPs are involved in the biological processes (BP) of complement and coagulation cascades. Apolipoproteins (APOs) are key proteins in the PPI network. ELISA confirmed that APOB was low-expressed in both the FF and peripheral blood of URPL patients.

Conclusion

Dysregulation of the immune network intersecting coagulation and inflammatory response is an essential feature of URPL, and this disequilibrium exists as early as the oogenesis stage. Therefore, earlier intervention is necessary to prevent the development of URPL. Moreover, aberrant lipoprotein regulation appears to be a key factor contributing to URPL. The mechanism by which these factors are involved in the complement and coagulation cascade pathways remains to be further investigated, which also provides new candidate targets for URPL treatment.

Full Native timsTOF PASEF-Enabled Quantitative Proteomics with the i2MassChroQ Software Package

Ion mobility mass spectrometry has become popular in proteomics lately, in particular because the Bruker timsTOF instruments have found significant adoption in proteomics facilities. The Bruker's implementation of the ion mobility dimension generates massive amounts of mass spectrometric data that require carefully designed software both to extract meaningful information and to perform processing tasks at reasonable speed. In a historical move, the Bruker company decided to harness the skills of the scientific software development community by releasing to the public the timsTOF data file format specification. As a proteomics facility that has been developing Free Open Source Software (FOSS) solutions since decades, we took advantage of this opportunity to implement the very first FOSS proteomics complete solution to natively read the timsTOF data, low-level process them, and explore them in an integrated quantitative proteomics software environment. We dubbed our software i2MassChroQ because it implements a (peptide)identification-(protein)inference-mass-chromatogram-quantification processing workflow. The software benchmarking results reported in this paper show that i2MassChroQ performed better than competing software on two critical characteristics: (1) feature extraction capability and (2) protein quantitative dynamic range. Altogether, i2MassChroQ yielded better quantified protein numbers, both in a technical replicate MS runs setting and in a differential protein abundance analysis setting.

Engineering of a Substrate Affinity Reduced S-Adenosyl-methionine Synthetase as a Novel Biosensor for Growth-Coupling Selection of L-Methionine Overproducers

Biosensors are powerful tools for monitoring specific metabolites or controlling metabolic flux towards the products in a single cell, which play important roles in microbial cell factory construction. Despite their potential role in metabolic flux monitoring, the development of biosensors for small molecules is still limited. Reported biosensors often exhibit bottlenecks of poor specificity and a narrow dynamic range. Moreover, fine-tuning the substrate binding affinity of a crucial enzyme can decrease its catalytic activity, which ultimately results in the repression of the corresponding essential metabolite biosynthesis and impairs cell growth. However, increasing intracellular substrate concentration can elevate the availability of the essential metabolite and may lead to restore cellular growth. Herein, a new strategy was proposed for constructing whole-cell biosensors based on enzyme encoded by essential gene that offer inherent specificity and universality. Specifically, S-adenosyl-methionine synthetase (MetK) in E. coli was chosen as the crucial enzyme, and a series of MetK variants were identified that were sensitive to L-methionine concentration. This occurrence enabled the engineered cell to sense L-methionine and exhibit L-methionine dose-dependent cell growth. To improve the biosensor's dynamic range, an S-adenosyl-methionine catabolic enzyme was overexpressed to reduce the intracellular availability of S-adenosyl-methionine. The resulting whole-cell biosensor effectively coupled the intracellular concentration of L-methionine with growth and was successfully applied to select strains with enhanced L-methionine biosynthesis from random mutagenesis libraries. Overall, our study presents a universal strategy for designing and constructing growth-coupled biosensors based on crucial enzyme, which can be applied to select strains overproducing high value-added metabolites in cellular metabolism.

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.

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.

[Microbial metaproteomics--From sample processing to data acquisition and analysis]

Microorganisms are closely associated with human diseases and health. Understanding the composition and function of microbial communities requires extensive research. Metaproteomics has recently become an important method for throughout and in-depth study of microorganisms. However, major challenges in terms of sample processing, mass spectrometric data acquisition, and data analysis limit the development of metaproteomics owing to the complexity and high heterogeneity of microbial community samples. In metaproteomic analysis, optimizing the preprocessing method for different types of samples and adopting different microbial isolation, enrichment, extraction, and lysis schemes are often necessary. Similar to those for single-species proteomics, the mass spectrometric data acquisition modes for metaproteomics include data-dependent acquisition (DDA) and data-independent acquisition (DIA). DIA can collect comprehensive peptide information from a sample and holds great potential for future development. However, data analysis for DIA is challenged by the complexity of metaproteome samples, which hinders the deeper coverage of metaproteomes. The most important step in data analysis is the construction of a protein sequence database. The size and completeness of the database strongly influence not only the number of identifications, but also analyses at the species and functional levels. The current gold standard for metaproteome database construction is the metagenomic sequencing-based protein sequence database. A public database-filtering method based on an iterative database search has been proven to have strong practical value. The peptide-centric DIA data analysis method is a mainstream data analysis strategy. The development of deep learning and artificial intelligence will greatly promote the accuracy, coverage, and speed of metaproteomic analysis. In terms of downstream bioinformatics analysis, a series of annotation tools that can perform species annotation at the protein, peptide, and gene levels has been developed in recent years to determine the composition of microbial communities. The functional analysis of microbial communities is a unique feature of metaproteomics compared with other omics approaches. Metaproteomics has become an important component of the multi-omics analysis of microbial communities, and has great development potential in terms of depth of coverage, sensitivity of detection, and completeness of data analysis.

Data-independent acquisition in metaproteomics

INTRODUCTION

Metaproteomics offers insights into the function of complex microbial communities, while it is also capable of revealing microbe-microbe and host-microbe interactions. Data-independent acquisition (DIA) mass spectrometry is an emerging technology, which holds great potential to achieve deep and accurate metaproteomics with higher reproducibility yet still facing a series of challenges due to the inherent complexity of metaproteomics and DIA data.

AREAS COVERED

This review offers an overview of the DIA metaproteomics approaches, covering aspects such as database construction, search strategy, and data analysis tools. Several cases of current DIA metaproteomics studies are presented to illustrate the procedures. Important ongoing challenges are also highlighted. Future perspectives of DIA methods for metaproteomics analysis are further discussed. Cited references are searched through and collected from Google Scholar and PubMed.

EXPERT OPINION

Considering the inherent complexity of DIA metaproteomics data, data analysis strategies specifically designed for interpretation are imperative. From this point of view, we anticipate that deep learning methods and de novo sequencing methods will become more prevalent in the future, potentially improving protein coverage in metaproteomics. Moreover, the advancement of metaproteomics also depends on the development of sample preparation methods, data analysis strategies, etc. These factors are key to unlocking the full potential of metaproteomics.

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.

Assessment of urine sample collection and processing variables for extracellular vesicle-based proteomics

Extracellular vesicles (EVs) in urine are a promising source for developing non-invasive biomarkers. However, urine concentration and content are highly variable and dynamic, and actual urine collection and handling often is nonideal. Furthermore, patients such as those with prostate diseases have challenges in sample collection due to difficulties in holding urine at designated time points. Here, we simulated the actual situation of clinical sample collection to examine the stability of EVs in urine under different circumstances, including urine collection time and temporary storage temperature, as well as daily urine sampling under different diet conditions. EVs were isolated using functionalized EVtrap magnetic beads and characterized by nanoparticle tracking analysis (NTA), western blotting, electron microscopy, and mass spectrometry (MS). EVs in urine remained relatively stable during temporary storage for 6 hours at room temperature and for 12 hours at 4 °C, while significant fluctuations were observed in EV amounts from urine samples collected at different time points from the same individuals, especially under certain diets. Sample normalization with creatinine reduced the coefficient of variation (CV) values among EV samples from 17% to approximately 6% and facilitated downstream MS analyses. Finally, based on the results, we applied them to evaluate potential biomarker panels in prostate cancer by data-independent acquisition (DIA) MS, presenting the recommendation that can facilitate biomarker discovery with nonideal handling conditions.

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.

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.

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.

Novel functional insights into the microbiome inhabiting marine plastic debris: critical considerations to counteract the challenges of thin biofilms using multi-omics and comparative metaproteomics

BACKGROUND

Microbial functioning on marine plastic surfaces has been poorly documented, especially within cold climates where temperature likely impacts microbial activity and the presence of hydrocarbonoclastic microorganisms. To date, only two studies have used metaproteomics to unravel microbial genotype-phenotype linkages in the marine 'plastisphere', and these have revealed the dominance of photosynthetic microorganisms within warm climates. Advancing the functional representation of the marine plastisphere is vital for the development of specific databases cataloging the functional diversity of the associated microorganisms and their peptide and protein sequences, to fuel biotechnological discoveries. Here, we provide a comprehensive assessment for plastisphere metaproteomics, using multi-omics and data mining on thin plastic biofilms to provide unique insights into plastisphere metabolism. Our robust experimental design assessed DNA/protein co-extraction and cell lysis strategies, proteomics workflows, and diverse protein search databases, to resolve the active plastisphere taxa and their expressed functions from an understudied cold environment.

RESULTS

For the first time, we demonstrate the predominance and activity of hydrocarbonoclastic genera (Psychrobacter, Flavobacterium, Pseudomonas) within a primarily heterotrophic plastisphere. Correspondingly, oxidative phosphorylation, the citrate cycle, and carbohydrate metabolism were the dominant pathways expressed. Quorum sensing and toxin-associated proteins of Streptomyces were indicative of inter-community interactions. Stress response proteins expressed by Psychrobacter, Planococcus, and Pseudoalteromonas and proteins mediating xenobiotics degradation in Psychrobacter and Pseudoalteromonas suggested phenotypic adaptations to the toxic chemical microenvironment of the plastisphere. Interestingly, a targeted search strategy identified plastic biodegradation enzymes, including polyamidase, hydrolase, and depolymerase, expressed by rare taxa. The expression of virulence factors and mechanisms of antimicrobial resistance suggested pathogenic genera were active, despite representing a minor component of the plastisphere community.

CONCLUSION

Our study addresses a critical gap in understanding the functioning of the marine plastisphere, contributing new insights into the function and ecology of an emerging and important microbial niche. Our comprehensive multi-omics and comparative metaproteomics experimental design enhances biological interpretations to provide new perspectives on microorganisms of potential biotechnological significance beyond biodegradation and to improve the assessment of the risks associated with microorganisms colonizing marine plastic pollution. Video Abstract.

High-Abundance Protein-Guided Hybrid Spectral Library for Data-Independent Acquisition Metaproteomics

Metaproteomics offers a direct avenue to identify microbial proteins in microbiota, enabling the compositional and functional characterization of microbiota. Due to the complexity and heterogeneity of microbial communities, in-depth and accurate metaproteomics faces tremendous limitations. One challenge in metaproteomics is the construction of a suitable protein sequence database to interpret the highly complex metaproteomic data, especially in the absence of metagenomic sequencing data. Herein, we present a high-abundance protein-guided hybrid spectral library strategy for in-depth data independent acquisition (DIA) metaproteomic analysis (HAPs-hyblibDIA). A dedicated high-abundance protein database of gut microbial species is constructed and used to mine the taxonomic information on microbiota samples. Then, a sample-specific protein sequence database is built based on the taxonomic information using Uniprot protein sequence for subsequent analysis of the DIA data using hybrid spectral library-based DIA analysis. We evaluated the accuracy and sensitivity of the method using synthetic microbial community samples and human gut microbiome samples. It was demonstrated that the strategy can successfully identify taxonomic compositions of microbiota samples and that the peptides identified by HAPs-hyblibDIA overlapped greatly with the peptides identified using a metagenomic sequencing-derived database. At the peptide and species level, our results can serve as a complement to the results obtained using a metagenomic sequencing-derived database. Furthermore, we validated the applicability of the HAPs-hyblibDIA strategy in a cohort of human gut microbiota samples of colorectal cancer patients and controls, highlighting its usability in biomedical research.

Progress in understanding of relationship between intestinal microecology and pancreatic cancer

In recent years, the association between the gut microbiota (GM) and pancreatic cancer (PC) has attracted extensive attention. Studies have shown that the oral, intestinal, and pancreatic microbiota of PC patients is different from that of healthy people, showing different characteristics. On this basis, the application of characteristic GM and its metabolites as biomarkers for early diagnosis and prognosis evaluation of PC holds great potential. Intestinal microecological therapy targeting the GM, such as probiotics and fecal microbiota transplantation, may affect the response to chemotherapy and immunotherapy by remodeling the tumor microenvironment, to improve the prognosis. In this paper, we review the role of the GM in PC development, early diagnosis, prognosis assessment, and treatment.

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.

Increasing taxonomic and functional characterization of host-microbiome interactions by DIA-PASEF metaproteomics

Introduction

Metaproteomics is a rapidly advancing field that offers unique insights into the taxonomic composition and the functional activity of microbial communities, and their effects on host physiology. Classically, data-dependent acquisition (DDA) mass spectrometry (MS) has been applied for peptide identification and quantification in metaproteomics. However, DDA-MS exhibits well-known limitations in terms of depth, sensitivity, and reproducibility. Consequently, methodological improvements are required to better characterize the protein landscape of microbiomes and their interactions with the host.

Methods

We present an optimized proteomic workflow that utilizes the information captured by Parallel Accumulation-Serial Fragmentation (PASEF) MS for comprehensive metaproteomic studies in complex fecal samples of mice.

Results and discussion

We show that implementing PASEF using a DDA acquisition scheme (DDA-PASEF) increased peptide quantification up to 5 times and reached higher accuracy and reproducibility compared to previously published classical DDA and data-independent acquisition (DIA) methods. Furthermore, we demonstrate that the combination of DIA, PASEF, and neuronal-network-based data analysis, was superior to DDA-PASEF in all mentioned parameters. Importantly, DIA-PASEF expanded the dynamic range towards low-abundant proteins and it doubled the quantification of proteins with unknown or uncharacterized functions. Compared to previous classical DDA metaproteomic studies, DIA-PASEF resulted in the quantification of up to 4 times more taxonomic units using 16 times less injected peptides and 4 times shorter chromatography gradients. Moreover, 131 additional functional pathways distributed across more and even uniquely identified taxa were profiled as revealed by a peptide-centric taxonomic-functional analysis. We tested our workflow on a validated preclinical mouse model of neuropathic pain to assess longitudinal changes in host-gut microbiome interactions associated with pain - an unexplored topic for metaproteomics. We uncovered the significant enrichment of two bacterial classes upon pain, and, in addition, the upregulation of metabolic activities previously linked to chronic pain as well as various hitherto unknown ones. Furthermore, our data revealed pain-associated dynamics of proteome complexes implicated in the crosstalk between the host immune system and the gut microbiome. In conclusion, the DIA-PASEF metaproteomic workflow presented here provides a stepping stone towards a deeper understanding of microbial ecosystems across the breadth of biomedical and biotechnological fields.

Metaproteomics profiling of the microbial communities in fermentation starters (Daqu) during multi-round production of Chinese liquor

Introduction

The special flavor and fragrance of Chinese liquor are closely related to microorganisms in the fermentation starter Daqu. The changes of microbial community can affect the stability of liquor yield and quality.

Methods

In this study, we used data-independent acquisition mass spectrometry (DIA-MS) for cohort study of the microbial communities of a total of 42 Daqu samples in six production cycles at different times of a year. The DIA MS data were searched against a protein database constructed by metagenomic sequencing.

Results

The microbial composition and its changes across production cycles were revealed. Functional analysis of the differential proteins was carried out and the metabolic pathways related to the differential proteins were explored. These metabolic pathways were related to the saccharification process in liquor fermentation and the synthesis of secondary metabolites to form the unique flavor and aroma in the Chinese liquor.

Discussion

We expect that the metaproteome profiling of Daqu from different production cycles will serve as a guide for the control of fermentation process of Chinese liquor in the future.

Transcriptome-wide identification of single-stranded RNA binding proteins

RNA-protein interactions are precisely regulated by RNA secondary structures in various biological processes. Large-scale identification of proteins that interact with particular RNA structure is important to the RBPome. Herein, a kethoxal assisted single-stranded RNA interactome capture (KASRIC) strategy was developed to globally identify single-stranded RNA binding proteins (ssRBPs). This approach combines RNA secondary structure probing technology with the conventional method of RNA-binding proteins profiling, realizing the transcriptome-wide identification of ssRBPs. Applying KASRIC, we identified 3180 candidate RBPs and 244 candidate ssRBPs in HeLa cells. Importantly, the 244 candidate ssRBPs contained 55 previously reported ssRBPs and 189 novel ssRBPs. Function analysis of the candidate ssRBPs exhibited enrichment in cellular processes related to RNA splicing and RNA degradation. The KASRIC strategy will facilitate the investigation of RNA-protein interactions.