Considerations for Constructing a Protein Sequence Database for Metaproteomics

Cultivated genome references for protein database construction and high-resolution taxonomic annotation in metaproteomics

Metaproteomics offers a profound understanding of the functional dynamics of the gut microbiome, which is crucial for personalized healthcare strategies. The selection of an appropriate database is a critical step for the identification of peptides and proteins, as well as for the provision of accurate taxonomic and functional annotations. The matched metagenomic-derived database is considered to be the best, but its limitations include the identification of low-abundance organisms and taxonomic resolution. Herein, we constructed a protein database (DBCGR2) based on Cultivated Genome Reference 2 (CGR2) and developed a complete peptide-centric analysis workflow for database searching and for the annotation of taxonomy and function. This workflow was subsequently appraised in comparison with metagenomics-derived databases for the analysis of metaproteomic data. Our findings suggested that the performance of DBCGR2 in identification was comparable with metagenomics-derived databases with improvement in identification rates of peptides from low-abundance species. The database searching results could be fully annotated using the pepTaxa taxonomic annotation approach developed in this study, and the taxonomic resolution was enhanced to strain level. Additionally, the results demonstrated that the sensitivity of functional annotation could be enhanced by employing DBCGR2. Overall, the DBCGR2 combined with pepTaxa can be considered an alternative for metaproteomic data analysis with superior analysis performances.IMPORTANCEMass spectrometry-based metaproteomics offers a profound understanding of the gut microbial taxonomy and functionality. The databases utilized in the analysis of metaproteomic data are crucial, as they determine the identification of proteins that can be recognized and linked to overall human health, in addition to the quality of taxonomic and functional annotation. Among the most effective approaches for constructing protein databases is the utilization of metagenomic sequencing to create matched databases. However, the database, derived from isolated genomes, has yet to undergo rigorous testing for their efficacy and accuracy in protein identification and taxonomic and functional annotation. Here, we constructed a protein database DBCGR2 derived from Cultivated Genome Reference 2 (CGR2) and a complete workflow for data analysis. We compared the performances of DBCGR2 and metagenomics-derived databases. Our results indicated that DBCGR2 can be regarded as an alternative to metagenomics-derived databases, which contribute to metaproteomic data analysis.

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.

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.

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

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

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.

Stable isotope fingerprinting can directly link intestinal microorganisms with their carbon source and captures diet-induced substrate switching in vivo

Diet has strong impacts on the composition and function of the gut microbiota with implications for host health. Therefore, it is critical to identify the dietary components that support growth of specific microorganisms in vivo. We used protein-based stable isotope fingerprinting (Protein-SIF) to link microbial species in gut microbiota to their carbon sources by measuring each microbe's natural 13C content (δ13C) and matching it to the 13C content of available substrates. We fed gnotobiotic mice, inoculated with a 13 member microbiota, diets in which the 13C content of all components was known. We varied the source of protein, fiber or fat to observe 13C signature changes in microbial consumers of these substrates. We observed significant changes in the δ13C values and abundances of specific microbiota species, as well as host proteins, in response to changes in 13C signature or type of protein, fiber, and fat sources. Using this approach we were able to show that upon switching dietary source of protein, fiber, or fat (1) some microbial species continued to obtain their carbon from the same dietary component (e.g., protein); (2) some species switched their main substrate type (e.g., from protein to carbohydrates); and (3) some species might derive their carbon through foraging on host compounds. Our results demonstrate that Protein-SIF can be used to identify the dietary-derived substrates assimilated into proteins by microbes in the intestinal tract; this approach holds promise for the analysis of microbiome substrate usage in humans without the need of substrate labeling.

Significance

The gut microbiota plays a critical role in the health of animals including humans, influencing metabolism, the immune system, and even behavior. Diet is one of the most significant factors in determining the function and composition of the gut microbiota, but our understanding of how specific dietary components directly impact individual microbes remains limited. We present the application of an approach that measures the carbon isotope "fingerprint" of proteins in biological samples. This fingerprint is similar to the fingerprint of the substrate used to make the proteins. We describe how we used this approach in mice to determine which dietary components specific intestinal microbes use as carbon sources to make their proteins. This approach can directly identify components of an animal's diet that are consumed by gut microbes.

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.

LineageFilter: Improved Proteotyping of Complex Samples Using Metaproteomics and Machine Learning

Metaproteomics is a powerful tool to characterize how microbiota function by analyzing their proteic content by tandem mass spectrometry. Given the complexity of these samples, accurately assessing their taxonomical composition without prior information based solely on peptide sequences remains a challenge. Here, we present LineageFilter, a new python-based AI software for refined proteotyping of complex samples using metaproteomics interpreted data and machine learning. Given a tentative list of taxa, their abundances, and the scores associated with their identified peptides, LineageFilter computes a comprehensive set of features for each identified taxon at all taxonomical ranks. Its machine-learning model then assesses the likelihood of each taxon's presence based on these features, enabling improved proteotyping and sample-specific database construction.

Metaproteomics reveals diet-induced changes in gut microbiome function according to Crohn's disease location

BACKGROUND

Crohn's disease (CD) is characterized by chronic intestinal inflammation. Diet is a key modifiable factor influencing the gut microbiome (GM) and a risk factor for CD. However, the impact of diet modulation on GM function in CD patients is understudied. Herein, we evaluated the effect of a high-fiber, low-fat diet (the Mi-IBD diet) on GM function in CD patients. All participants were instructed to follow the Mi-IBD diet for 8 weeks. One group of CD patients received one-time diet counseling only (Gr1); catered food was supplied for the other three groups, including CD patients (Gr2) and dyads of CD patients and healthy household controls (HHCs) residing within the same household (Gr3-HHC dyads). Stool samples were collected at baseline, week 8, and week 36, and analyzed by liquid chromatography-tandem mass spectrometry.

RESULTS

At baseline, the metaproteomic profiles of CD patients and HHCs differed. The Mi-IBD diet significantly increased carbohydrate and iron transport and metabolism. The predicted microbial composition underlying the metaproteomic changes differed between patients with ileal only disease (ICD) or colonic involvement: ICD was characterized by decreased Faecalibacterium abundance. Even on the Mi-IBD diet, the CD patient metaproteome displayed significant underrepresentation of carbohydrate and purine/pyrimidine synthesis pathways compared to that of HHCs. Human immune-related proteins were upregulated in CD patients compared to HHCs.

CONCLUSIONS

The Mi-IBD diet changed the microbial function of CD patients and enhanced carbohydrate metabolism. Our metaproteomic results highlight functional differences in the microbiome according to disease location. Notably, our dietary intervention yielded the most benefit for CD patients with colonic involvement compared to ileal-only disease. Video Abstract.

Posttranslational modifications: an emerging functional layer of diet-host-microbe interactions

The microbiome plays a vital role in human health, with changes in its composition impacting various aspects of the body. Posttranslational modification (PTM) regulates protein activity by attaching chemical groups to amino acids in an enzymatic or non-enzymatic manner. PTMs offer fast and dynamic regulation of protein expression and can be influenced by specific dietary components that induce PTM events in gut microbiomes and their hosts. PTMs on microbiome proteins have been found to contribute to host-microbe interactions. For example, in Escherichia coli, S-sulfhydration of tryptophanase regulates uremic toxin production and chronic kidney disease in mice. On a broader microbial scale, the microbiomes of patients with inflammatory bowel disease exhibit distinct PTM patterns in their metaproteomes. Moreover, pathogens and commensals can alter host PTM profiles through protein secretion and diet-regulated metabolic shifts. The emerging field of metaPTMomics focuses on understanding PTM profiles in the microbiota, their association with lifestyle factors like diet, and their functional effects on host-microbe interactions.

Metaproteomic analysis decodes trophic interactions of microorganisms in the dark ocean

Proteins in the open ocean represent a significant source of organic matter, and their profiles reflect the metabolic activities of marine microorganisms. Here, by analyzing metaproteomic samples collected from the Pacific, Atlantic and Southern Ocean, we reveal size-fractionated patterns of the structure and function of the marine microbiota protein pool in the water column, particularly in the dark ocean (>200 m). Zooplankton proteins contributed three times more than algal proteins to the deep-sea community metaproteome. Gammaproteobacteria exhibited high metabolic activity in the deep-sea, contributing up to 30% of bacterial proteins. Close virus-host interactions of this taxon might explain the dominance of gammaproteobacterial proteins in the dissolved fraction. A high urease expression in nitrifiers suggested links between their dark carbon fixation and zooplankton urea production. In summary, our results uncover the taxonomic contribution of the microbiota to the oceanic protein pool, revealing protein fluxes from particles to the dissolved organic matter pool.

An integrated metaproteomics workflow for studying host-microbe dynamics in bronchoalveolar lavage samples applied to cystic fibrosis disease

Airway microbiota are known to contribute to lung diseases, such as cystic fibrosis (CF), but their contributions to pathogenesis are still unclear. To improve our understanding of host-microbe interactions, we have developed an integrated analytical and bioinformatic mass spectrometry (MS)-based metaproteomics workflow to analyze clinical bronchoalveolar lavage (BAL) samples from people with airway disease. Proteins from BAL cellular pellets were processed and pooled together in groups categorized by disease status (CF vs. non-CF) and bacterial diversity, based on previously performed small subunit rRNA sequencing data. Proteins from each pooled sample group were digested and subjected to liquid chromatography tandem mass spectrometry (MS/MS). MS/MS spectra were matched to human and bacterial peptide sequences leveraging a bioinformatic workflow using a metagenomics-guided protein sequence database and rigorous evaluation. Label-free quantification revealed differentially abundant human peptides from proteins with known roles in CF, like neutrophil elastase and collagenase, and proteins with lesser-known roles in CF, including apolipoproteins. Differentially abundant bacterial peptides were identified from known CF pathogens (e.g., Pseudomonas), as well as other taxa with potentially novel roles in CF. We used this host-microbe peptide panel for targeted parallel-reaction monitoring validation, demonstrating for the first time an MS-based assay effective for quantifying host-microbe protein dynamics within BAL cells from individual CF patients. Our integrated bioinformatic and analytical workflow combining discovery, verification, and validation should prove useful for diverse studies to characterize microbial contributors in airway diseases. Furthermore, we describe a promising preliminary panel of differentially abundant microbe and host peptide sequences for further study as potential markers of host-microbe relationships in CF disease pathogenesis.IMPORTANCEIdentifying microbial pathogenic contributors and dysregulated human responses in airway disease, such as CF, is critical to understanding disease progression and developing more effective treatments. To this end, characterizing the proteins expressed from bacterial microbes and human host cells during disease progression can provide valuable new insights. We describe here a new method to confidently detect and monitor abundance changes of both microbe and host proteins from challenging BAL samples commonly collected from CF patients. Our method uses both state-of-the art mass spectrometry-based instrumentation to detect proteins present in these samples and customized bioinformatic software tools to analyze the data and characterize detected proteins and their association with CF. We demonstrate the use of this method to characterize microbe and host proteins from individual BAL samples, paving the way for a new approach to understand molecular contributors to CF and other diseases of the airway.

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.

Database selection for shotgun metaproteomic of low-diversity dairy microbiomes

The metaproteomics field has recently gained more and more interest as a valuable tool for studying both the taxonomy and function of microbiomes, including those used in food fermentations. One crucial step in the metaproteomics pipeline is selecting a database to obtain high-quality taxonomical and functional information from microbial communities. One of the best strategies described for building protein databases is using sample-specific or study-specific protein databases obtained from metagenomic sequencing. While this is true for high-diversity microbiomes (such as gut and soil), there is still a lack of validation for different database construction strategies in low-diversity microbiomes, such as those found in fermented dairy products where starter cultures containing few species are used. In this study, we assessed the performance of various database construction strategies applied to metaproteomics on two low-diversity microbiomes obtained from cheese production using commercial starter cultures and analyzed by LC-MS/MS. Substantial differences were detected between the strategies, and the best performance in terms of the number of peptides and proteins identified from the spectra was achieved by metagenomic-derived databases. However, extensive databases constructed from a high number of available online genomes obtained a similar taxonomical and functional annotation of the metaproteome compared to the metagenomic-derived databases. Our results indicate that, in the case of low-diversity dairy microbiomes, the use of publically available genomes to construct protein databases can be considered as an alternative to metagenome-derived databases.

Mass spectrometry-based proteomic applications in dental implants research

Dental implants have been established as successful treatment options for missing teeth with steadily increasing demands. Today, the primary areas of research in dental implantology revolve around osseointegration, soft and hard tissue grafting as well as peri-implantitis diagnostics, prevention, and treatment. This review provides a comprehensive overview of the current literature on the application of MS-based proteomics in dental implant research, highlights how explorative proteomics provided insights into the biology of peri-implant soft and hard tissues and how proteomics facilitated the stratification between healthy and diseased implants, enabling the identification of potential new diagnostic markers. Additionally, this review illuminates technical aspects, and provides recommendations for future study designs based on the current evidence.

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.

Environmental proteomics as a useful methodology for early-stage detection of stress in anammox engineered systems

Anammox bacteria are widely applied worldwide for denitrification of urban wastewater. Differently, their application in the case of industrial effluents has been more limited. Those frequently present high loads of contaminants, demanding an individual evaluation of their treatability by anammox technologies. Bioreactors setting up and recovery after contaminants-derived perturbations are slow. Also, toxicity is frequently not acute but cumulative, which causes negative macroscopic effects to appear only after medium or long-term operations. All these particularities lead to relevant economic and time losses. We hypothesized that contaminants cause changes at anammox proteome level before perturbations in the engineered systems are detectable by macroscopic analyses. In this study, we explored the usefulness of short-batch tests combined with environmental proteomics for the early detection of those changes. Copper was used as a model of stressor contaminant, and anammox granules were exposed to increasing copper concentrations including previously reported IC50 values. The proteomic results revealed that specific anammox proteins involved in stress response (bacterioferritin, universal stress protein, or superoxide dismutase) were overexpressed in as short a time as 28 h at the higher copper concentrations. Consequently, EPS production was also increased, as indicated by the alginate export family protein, polysaccharide biosynthesis protein, and sulfotransferase increased expression. The described workflow can be applied to detect early-stage stress biomarkers of the negative effect of other metals, organics, or even changes in physical-chemical parameters such as pH or temperature on anammox-engineered systems. On an industrial level, it can be of great value for decision-making, especially before dealing with new effluents on facilities, deriving important economic and time savings.

gNOMO2: a comprehensive and modular pipeline for integrated multi-omics analyses of microbiomes

BACKGROUND

In recent years, omics technologies have offered an exceptional chance to gain a deeper insight into the structural and functional characteristics of microbial communities. As a result, there is a growing demand for user-friendly, reproducible, and versatile bioinformatic tools that can effectively harness multi-omics data to provide a holistic understanding of microbiomes. Previously, we introduced gNOMO, a bioinformatic pipeline tailored to analyze microbiome multi-omics data in an integrative manner. In response to the evolving demands within the microbiome field and the growing necessity for integrated multi-omics data analysis, we have implemented substantial enhancements to the gNOMO pipeline.

RESULTS

Here, we present gNOMO2, a comprehensive and modular pipeline that can seamlessly manage various omics combinations, ranging from 2 to 4 distinct omics data types, including 16S ribosomal RNA (rRNA) gene amplicon sequencing, metagenomics, metatranscriptomics, and metaproteomics. Furthermore, gNOMO2 features a specialized module for processing 16S rRNA gene amplicon sequencing data to create a protein database suitable for metaproteomics investigations. Moreover, it incorporates new differential abundance, integration, and visualization approaches, enhancing the toolkit for a more insightful analysis of microbiomes. The functionality of these new features is showcased through the use of 4 microbiome multi-omics datasets encompassing various ecosystems and omics combinations. gNOMO2 not only replicated most of the primary findings from these studies but also offered further valuable perspectives.

CONCLUSIONS

gNOMO2 enables the thorough integration of taxonomic and functional analyses in microbiome multi-omics data, offering novel insights in both host-associated and free-living microbiome research. gNOMO2 is available freely at https://github.com/muzafferarikan/gNOMO2.

Bioinformatic Workflows for Metaproteomics

The strong influence of microbiomes on areas such as ecology and human health has become widely recognized in the past years. Accordingly, various techniques for the investigation of the composition and function of microbial community samples have been developed. Metaproteomics, the comprehensive analysis of the proteins from microbial communities, allows for the investigation of not only the taxonomy but also the functional and quantitative composition of microbiome samples. Due to the complexity of the investigated communities, methods developed for single organism proteomics cannot be readily applied to metaproteomic samples. For this purpose, methods specifically tailored to metaproteomics are required. In this work, a detailed overview of current bioinformatic solutions and protocols in metaproteomics is given. After an introduction to the proteomic database search, the metaproteomic post-processing steps are explained in detail. Ten specific bioinformatic software solutions are focused on, covering various steps including database-driven identification and quantification as well as taxonomic and functional assignment.

Multi-omic approaches for host-microbiome data integration

The gut microbiome interacts with the host through complex networks that affect physiology and health outcomes. It is becoming clear that these interactions can be measured across many different omics layers, including the genome, transcriptome, epigenome, metabolome, and proteome, among others. Multi-omic studies of the microbiome can provide insight into the mechanisms underlying host-microbe interactions. As more omics layers are considered, increasingly sophisticated statistical methods are required to integrate them. In this review, we provide an overview of approaches currently used to characterize multi-omic interactions between host and microbiome data. While a large number of studies have generated a deeper understanding of host-microbiome interactions, there is still a need for standardization across approaches. Furthermore, microbiome studies would also benefit from the collection and curation of large, publicly available multi-omics datasets.

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.

Large Quantities of Bacterial DNA and Protein in Common Dietary Protein Source Used in Microbiome Studies

Diet has been shown to greatly impact the intestinal microbiota. To understand the role of individual dietary components, defined diets with purified components are frequently used in diet-microbiota studies. Many of the frequently used defined diets use purified casein as the protein source. Previous work indicated that this casein contains microbial DNA potentially impacting results of microbiome studies. Other diet-based microbially derived molecules that may impact microbiome measurements, such as proteins detected by metaproteomics, have not been determined for casein. Additionally, other protein sources used in microbiome studies have not been characterized for their microbial content. We used metagenomics and metaproteomics to identify and quantify microbial DNA and protein in a casein-based defined diet to better understand potential impacts on metagenomic and metaproteomic microbiome studies. We further tested six additional defined diets with purified protein sources with an integrated metagenomic-metaproteomic approach and show that contaminating microbial protein is unique to casein within the tested set as microbial protein was not identified in diets with other protein sources. We also illustrate the contribution of diet-derived microbial protein in diet-microbiota studies by metaproteomic analysis of stool samples from germ-free mice (GF) and mice with a conventional microbiota (CV) following consumption of diets with casein and non-casein protein. This study highlights a potentially confounding factor in diet-microbiota studies that must be considered through evaluation of the diet itself within a given study.

GroEL-Proteotyping of Bacterial Communities Using Tandem Mass Spectrometry

Profiling bacterial populations in mixed communities is a common task in microbiology. Sequencing of 16S small subunit ribosomal-RNA (16S rRNA) gene amplicons is a widely accepted and functional approach but relies on amplification primers and cannot quantify isotope incorporation. Tandem mass spectrometry proteotyping is an effective alternative for taxonomically profiling microorganisms. We suggest that targeted proteotyping approaches can complement traditional population analyses. Therefore, we describe an approach to assess bacterial community compositions at the family level using the taxonomic marker protein GroEL, which is ubiquitously found in bacteria, except a few obligate intracellular species. We refer to our method as GroEL-proteotyping. GroEL-proteotyping is based on high-resolution tandem mass spectrometry of GroEL peptides and identification of GroEL-derived taxa via a Galaxy workflow and a subsequent Python-based analysis script. Its advantage is that it can be performed with a curated and extendable sample-independent database and that GroEL can be pre-separated by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) to reduce sample complexity, improving GroEL identification while simultaneously decreasing the instrument time. GroEL-proteotyping was validated by employing it on a comprehensive raw dataset obtained through a metaproteome approach from synthetic microbial communities as well as real human gut samples. Our data show that GroEL-proteotyping enables fast and straightforward profiling of highly abundant taxa in bacterial communities at reasonable taxonomic resolution.

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

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

Integrated multi-omics analyses of microbial communities: a review of the current state and future directions

Integrated multi-omics analyses of microbiomes have become increasingly common in recent years as the emerging omics technologies provide an unprecedented opportunity to better understand the structural and functional properties of microbial communities. Consequently, there is a growing need for and interest in the concepts, approaches, considerations, and available tools for investigating diverse environmental and host-associated microbial communities in an integrative manner. In this review, we first provide a general overview of each omics analysis type, including a brief history, typical workflow, primary applications, strengths, and limitations. Then, we inform on both experimental design and bioinformatics analysis considerations in integrated multi-omics analyses, elaborate on the current approaches and commonly used tools, and highlight the current challenges. Finally, we discuss the expected key advances, emerging trends, potential implications on various fields from human health to biotechnology, and future directions.

Algorithms for de-novo sequencing of peptides by tandem mass spectrometry: A review

Peptide sequencing is of great significance to fundamental and applied research in the fields such as chemical, biological, medicinal and pharmaceutical sciences. With the rapid development of mass spectrometry and sequencing algorithms, de-novo peptide sequencing using tandem mass spectrometry (MS/MS) has become the main method for determining amino acid sequences of novel and unknown peptides. Advanced algorithms allow the amino acid sequence information to be accurately obtained from MS/MS spectra in short time. In this review, algorithms from exhaustive search to the state-of-art machine learning and neural network for high-throughput and automated de-novo sequencing are introduced and compared. Impacts of datasets on algorithm performance are highlighted. The current limitations and promising direction of de-novo peptide sequencing are also discussed in this review.

Metalloenzymes play major roles to achieve high-rate nitrogen removal in N-damo communities: Lessons from metaproteomics

Nitrite-driven anaerobic methane oxidation (N-damo) is a promising biological process to achieve carbon-neutral wastewater treatment solutions, aligned with the sustainable development goals. Here, the enzymatic activities in a membrane bioreactor highly enriched in N-damo bacteria operated at high nitrogen removal rates were investigated. Metaproteomic analyses, with a special focus on metalloenzymes, revealed the complete enzymatic route of N-damo including their unique nitric oxide dismutases. The relative protein abundance evidenced that "Ca. Methylomirabilis lanthanidiphila" was the predominant N-damo species, attributed to the induction of its lanthanide-binding methanol dehydrogenase in the presence of cerium. Metaproteomics also disclosed the activity of the accompanying taxa in denitrification, methylotrophy and methanotrophy. The most abundant functional metalloenzymes from this community require copper, iron, and cerium as cofactors which was correlated with the metal consumptions in the bioreactor. This study highlights the usefulness of metaproteomics for evaluating the enzymatic activities in engineering systems to optimize microbial management.

StandEnA: a customizable workflow for standardized annotation and generating a presence-absence matrix of proteins

Motivation

Several genome annotation tools standardize annotation outputs for comparability. During standardization, these tools do not allow user-friendly customization of annotation databases; limiting their flexibility and applicability in downstream analysis.

Results

StandEnA is a user-friendly command-line tool for Linux that facilitates the generation of custom databases by retrieving protein sequences from multiple databases. Directed by a user-defined list of standard names, StandEnA retrieves synonyms to search for corresponding sequences in a set of public databases. Custom databases are used in prokaryotic genome annotation to generate standardized presence-absence matrices and reference files containing standard database identifiers. To showcase StandEnA, we applied it to six metagenome-assembled genomes to analyze three different pathways.

Availability and implementation

StandEnA is an open-source software available at https://github.com/mdsufz/StandEnA.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Detection of Mutant Peptides of SARS-CoV-2 Variants by LC/MS in the DDA Approach Using an In-House Database

Equipped with a dramatically high mutation rate, which happens to be a signature of RNA viruses, SARS-CoV-2 trampled across the globe infecting individuals of all ages and ethnicities. As the variants of concern (VOC) loomed large, definitive detection of SARS-CoV-2 strains became a matter of utmost importance in epidemiological and clinical research. Besides, unveiling the disease pathogenesis at the molecular level and deciphering the therapeutic targets became key priorities since the emergence of the pandemic. Mass spectrometry has been largely used in this regard. A critical part of mass spectrometric analyses is the proteome database required for the identification of peptides. Presently, the mutational information on proteins available on SARS-CoV-2 databases cannot be used to analyze data extracted from mass spectrometers. Hence, we developed the novel Mutant Peptide Database (MPD) for the mass spectrometry (MS)-based identification of mutated peptides, which contains information from 11 proteins of SARS-CoV-2 from a total of 21,549 SARS-CoV-2 variants across different regions of India. The database was validated using clinical samples, and its applicability was also demonstrated with the mutated peptides extracted from the literature. We believe that MPD will support broad-spectrum MS-based studies like viral detection, disease pathogenesis, and therapeutics with respect to SARS-CoV-2 and its variants.

Moving beyond DNA: towards functional analysis of the vaginal microbiome by non-sequencing-based methods

Over the last two decades, sequencing-based methods have revolutionised our understanding of niche-specific microbial complexity. In the lower female reproductive tract, these approaches have enabled identification of bacterial compositional structures associated with health and disease. Application of metagenomics and metatranscriptomics strategies have provided insight into the putative function of these communities but it is increasingly clear that direct measures of microbial and host cell function are required to understand the contribution of microbe-host interactions to pathophysiology. Here we explore and discuss current methods and approaches, many of which rely upon mass-spectrometry, being used to capture functional insight into the vaginal mucosal interface. In addition to improving mechanistic understanding, these methods offer innovative solutions for the development of diagnostic and therapeutic strategies designed to improve women's health.

Ultra-sensitive isotope probing to quantify activity and substrate assimilation in microbiomes

BACKGROUND

Stable isotope probing (SIP) approaches are a critical tool in microbiome research to determine associations between species and substrates, as well as the activity of species. The application of these approaches ranges from studying microbial communities important for global biogeochemical cycling to host-microbiota interactions in the intestinal tract. Current SIP approaches, such as DNA-SIP or nanoSIMS allow to analyze incorporation of stable isotopes with high coverage of taxa in a community and at the single cell level, respectively, however they are limited in terms of sensitivity, resolution or throughput.

RESULTS

Here, we present an ultra-sensitive, high-throughput protein-based stable isotope probing approach (Protein-SIP), which cuts cost for labeled substrates by 50-99% as compared to other SIP and Protein-SIP approaches and thus enables isotope labeling experiments on much larger scales and with higher replication. The approach allows for the determination of isotope incorporation into microbiome members with species level resolution using standard metaproteomics liquid chromatography-tandem mass spectrometry (LC-MS/MS) measurements. At the core of the approach are new algorithms to analyze the data, which have been implemented in an open-source software ( https://sourceforge.net/projects/calis-p/ ). We demonstrate sensitivity, precision and accuracy using bacterial cultures and mock communities with different labeling schemes. Furthermore, we benchmark our approach against two existing Protein-SIP approaches and show that in the low labeling range used our approach is the most sensitive and accurate. Finally, we measure translational activity using 18O heavy water labeling in a 63-species community derived from human fecal samples grown on media simulating two different diets. Activity could be quantified on average for 27 species per sample, with 9 species showing significantly higher activity on a high protein diet, as compared to a high fiber diet. Surprisingly, among the species with increased activity on high protein were several Bacteroides species known as fiber consumers. Apparently, protein supply is a critical consideration when assessing growth of intestinal microbes on fiber, including fiber-based prebiotics.

CONCLUSIONS

We demonstrate that our Protein-SIP approach allows for the ultra-sensitive (0.01 to 10% label) detection of stable isotopes of elements found in proteins, using standard metaproteomics data.

Current progress and critical challenges to overcome in the bioinformatics of mass spectrometry-based metaproteomics

Metaproteomics is a relatively young field that has only been studied for approximately 15 years. Nevertheless, it has the potential to play a key role in disease research by elucidating the mechanisms of communication between the human host and the microbiome. Although it has been useful in developing an understanding of various diseases, its analytical strategies remain limited to the extended application of proteomics. The sequence databases in metaproteomics must be large because of the presence of thousands of species in a typical sample, which causes problems unique to large databases. In this review, we demonstrate the usefulness of metaproteomics in disease research through examples from several studies. Additionally, we discuss the challenges of applying metaproteomics to conventional proteomics analysis methods and introduce studies that may provide clues to the solutions. We also discuss the need for a standard false discovery rate control method for metaproteomics to replace common target-decoy search approaches in proteomics and a method to ensure the reliability of peptide spectrum match.

Taxonomical composition and functional analysis of biofilms sampled from a nuclear storage pool

Sampling small amounts of biofilm from harsh environments such as the biofilm present on the walls of a radioactive material storage pool offers few analytical options if taxonomic characterization and estimation of the different biomass contributions are the objectives. Although 16S/18S rRNA amplification on extracted DNA and sequencing is the most widely applied method, its reliability in terms of quantitation has been questioned as yields can be species-dependent. Here, we propose a tandem-mass spectrometry proteotyping approach consisting of acquiring peptide data and interpreting then against a generalist database without any a priori. The peptide sequence information is transformed into useful taxonomical information that allows to obtain the different biomass contributions at different taxonomical ranks. This new methodology is applied for the first time to analyze the composition of biofilms from minute quantities of material collected from a pool used to store radioactive sources in a nuclear facility. For these biofilms, we report the identification of three genera, namely Sphingomonas, Caulobacter, and Acidovorax, and their functional characterization by metaproteomics which shows that these organisms are metabolic active. Differential expression of Gene Ontology GOslim terms between the two main microorganisms highlights their metabolic specialization.

Tool and techniques study to plant microbiome current understanding and future needs: an overview

Microorganisms are present in the universe and they play role in beneficial and harmful to human life, society, and environments. Plant microbiome is a broad term in which microbes are present in the rhizo, phyllo, or endophytic region and play several beneficial and harmful roles with the plant. To know of these microorganisms, it is essential to be able to isolate purification and identify them quickly under laboratory conditions. So, to improve the microbial study, several tools and techniques such as microscopy, rRNA, or rDNA sequencing, fingerprinting, probing, clone libraries, chips, and metagenomics have been developed. The major benefits of these techniques are the identification of microbial community through direct analysis as well as it can apply in situ. Without tools and techniques, we cannot understand the roles of microbiomes. This review explains the tools and their roles in the understanding of microbiomes and their ecological diversity in environments.

Advances in experimental and computational methodologies for the study of microbial-surface interactions at different omics levels

The study of the biological response of microbial cells interacting with natural and synthetic interfaces has acquired a new dimension with the development and constant progress of advanced omics technologies. New methods allow the isolation and analysis of nucleic acids, proteins and metabolites from complex samples, of interest in diverse research areas, such as materials sciences, biomedical sciences, forensic sciences, biotechnology and archeology, among others. The study of the bacterial recognition and response to surface contact or the diagnosis and evolution of ancient pathogens contained in archeological tissues require, in many cases, the availability of specialized methods and tools. The current review describes advances in in vitro and in silico approaches to tackle existing challenges (e.g., low-quality sample, low amount, presence of inhibitors, chelators, etc.) in the isolation of high-quality samples and in the analysis of microbial cells at genomic, transcriptomic, proteomic and metabolomic levels, when present in complex interfaces. From the experimental point of view, tailored manual and automatized methodologies, commercial and in-house developed protocols, are described. The computational level focuses on the discussion of novel tools and approaches designed to solve associated issues, such as sample contamination, low quality reads, low coverage, etc. Finally, approaches to obtain a systems level understanding of these complex interactions by integrating multi omics datasets are presented.

Bioinformatic Analysis to Investigate Metaproteome Composition Using Trans-Proteomic Pipeline

With evidence emerging that the microbiome has a role in the onset of many human diseases, including cancer, analyzing these microbial communities and their proteins (i.e., the metaproteome) has become a powerful research tool. The Trans-Proteomic Pipeline (TPP) is a free, comprehensive software suite that facilitates the analysis of mass spectrometry (MS) data. By utilizing available microbial proteomes, TPP can identify microbial proteins and species, with an acceptable peptide false-discovery rate (FDR). An application to a publicly available oral cancer dataset is presented as an example to identify the viral metaproteome on the oral cancer invasive tumor front. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Collection of data and resources Basic Protocol 2: Analysis of MS data using TPP Basic Protocol 3: Analysis of TPP output using R in RStudio.