The impact of sequence database choice on metaproteomic results in gut microbiota studies

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.

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.

Neuroretinal Cell Culture Model as a Tool for the Development of New Therapeutic Approaches for Oxidative Stress-Induced Ocular Diseases, with a Focus on Glaucoma

Glaucoma is a heterogeneous group of optic neuropathies characterized by a progressive degeneration of the retinal ganglion cells (RGCs), leading to irreversible vision loss. Nowadays, the traditional therapeutic approach to glaucoma consists of lowering the intraocular pressure (IOP), which does not address the neurodegenerative features of the disease. Besides animal models of glaucoma, there is a considerable need for in vitro experimental models to propose new therapeutic strategies for this ocular disease. In this study, we elucidated the pathological mechanisms leading to neuroretinal R28 cell death after exposure to glutamate and hydrogen peroxide (H2O2) in order to develop new therapeutic approaches for oxidative stress-induced retinal diseases, including glaucoma. We were able to show that glutamate and H2O2 can induce a decrease in R28 cell viability in a concentration-dependent manner. A cell viability of about 42% was found after exposure to 3 mM of glutamate and about 56% after exposure to 100 µM of H2O2 (n = 4). Label-free quantitative mass spectrometry analysis revealed differential alterations of 193 and 311 proteins in R28 cells exposed to 3 mM of glutamate and 100 µM of H2O2, respectively (FDR < 1%; p < 0.05). Bioinformatics analysis indicated that the protein changes were associated with the dysregulation of signaling pathways, which was similar to those observed in glaucoma. Thus, the proteomic alteration induced by glutamate was associated with the inhibition of the PI3K/AKT signaling pathway. On the other hand, H2O2-induced toxicity in R28 cells was linked to the activation of apoptosis signaling and the inhibition of the mTOR and ERK/MAPK signaling pathways. Furthermore, the data show a similarity in the inhibition of the EIF2 and AMPK signaling pathways and the activation of the sumoylation and WNT/β-catenin signaling pathways in both groups. Our findings suggest that the exposure of R28 cells to glutamate and H2O2 could induce glaucoma-like neurodegenerative features and potentially provide a suitable tool for the development of new therapeutic strategies for retinal diseases.

Contigs directed gene annotation (ConDiGA) for accurate protein sequence database construction in metaproteomics

BACKGROUND

Microbiota are closely associated with human health and disease. Metaproteomics can provide a direct means to identify microbial proteins in microbiota for compositional and functional characterization. However, in-depth and accurate metaproteomics is still limited due to the extreme complexity and high diversity of microbiota samples. It is generally recommended to use metagenomic data from the same samples to construct the protein sequence database for metaproteomic data analysis. Although different metagenomics-based database construction strategies have been developed, an optimization of gene taxonomic annotation has not been reported, which, however, is extremely important for accurate metaproteomic analysis.

RESULTS

Herein, we proposed an accurate taxonomic annotation pipeline for genes from metagenomic data, namely contigs directed gene annotation (ConDiGA), and used the method to build a protein sequence database for metaproteomic analysis. We compared our pipeline (ConDiGA or MD3) with two other popular annotation pipelines (MD1 and MD2). In MD1, genes were directly annotated against the whole bacterial genome database; in MD2, contigs were annotated against the whole bacterial genome database and the taxonomic information of contigs was assigned to the genes; in MD3, the most confident species from the contigs annotation results were taken as reference to annotate genes. Annotation tools, including BLAST, Kaiju, and Kraken2, were compared. Based on a synthetic microbial community of 12 species, it was found that Kaiju with the MD3 pipeline outperformed the others in the construction of protein sequence database from metagenomic data. Similar performance was also observed with a fecal sample, as well as in silico mixed datasets of the simulated microbial community and the fecal sample.

CONCLUSIONS

Overall, we developed an optimized pipeline for gene taxonomic annotation to construct protein sequence databases. Our study can tackle the current taxonomic annotation reliability problem in metagenomics-derived protein sequence database and can promote the in-depth metaproteomic analysis of microbiome. The unique metagenomic and metaproteomic datasets of the 12 bacterial species are publicly available as a standard benchmarking sample for evaluating various analysis pipelines. The code of ConDiGA is open access at GitHub for the analysis of microbiota samples. Video Abstract.

Metaproteomics analysis of anaerobic digestion of food waste by the addition of calcium peroxide and magnetite

Adding trace calcium peroxide and magnetite into a semi-continuous digester is a new method to effectively improve the anaerobic digestion of food waste. However, the microbial mechanism in this system has not been fully explored. Metaproteomics further revealed that the most active and significantly regulated genus u_p_Chloroflexi had formed a good cooperative relationship with Methanomicrobiales and Methanothrix in the system. u_p_Chloroflexi decomposed more organic compounds into CO2, acetate, amino acids, and other substances by alternating between short aerobic-anaerobic respiration. It perceived and adapted to the surrounding environment by producing biofilm, extracellular enzymes, and accelerating substrate transport, formed a respiratory barrier, and enhanced iron transport capacity by using highly expressed cytochrome C. The methanogens formed reactive oxygen species scavengers and reduced iron transport to prevent oxidative damage. This study provides new insight for improving the efficiency of anaerobic digestion of food waste and identifying key microorganisms and their regulated functional proteins in the calcium peroxide-magnetite digestion system.IMPORTANCEPrevious study has found that the combination of calcium peroxide and magnetite has a good promoting effect on the anaerobic digestion process of food waste. Through multiple omics approaches, information such as microbial population structure and changes in metabolites can be further analyzed. This study can help researchers gain a deeper understanding of the digestion pathway of food waste under the combined action of calcium peroxide and magnetite, further elucidate the impact mechanisms of calcium peroxide and magnetite at the microbial level, and provide theoretical guidance to improve the efficiency and stability of anaerobic digestion of food waste, as well as reduce operational costs. This research contributes to improving energy recovery efficiency, promoting sustainable management and development of food waste, and is of great significance to environmental protection.

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.

The Potential of Meta-Proteomics and Artificial Intelligence to Establish the Next Generation of Probiotics for Personalized Healthcare

The symbiosis of probiotic bacteria with humans has rendered various health benefits while providing nutrition and a suitable environment for their survival. However, the probiotics must survive unfavorable gut conditions to exert beneficial effects. The intrinsic resistance of probiotics to survive harsh conditions results from a myriad of proteins. Interaction of microbial proteins with the host is indispensable for modulating the gut microbiome, such as interaction with cell receptors and protective action against pathogens. The complex interplay of proteins should be unraveled by utilizing metaproteomic strategies. The contribution of probiotics to health is now widely accepted. However, due to the inconsistency of generalized probiotics, contemporary research toward precision probiotics has gained momentum for customized treatment. This review explores the application of metaproteomics and AI/ML algorithms in resolving multiomics data analysis and in silico prediction of microbial features for screening specific beneficial probiotic organisms. Implementing these integrative strategies could augment the potential of precision probiotics for personalized healthcare.

Metaproteomics, metagenomics and 16S rRNA sequencing provide different perspectives on the aerobic granular sludge microbiome

The tremendous progress in sequencing technologies has made DNA sequencing routine for microbiome studies. Additionally, advances in mass spectrometric techniques have extended conventional proteomics into the field of microbial ecology. However, systematic studies that provide a better understanding of the complementary nature of these 'omics' approaches, particularly for complex environments such as wastewater treatment sludge, are urgently needed. Here, we describe a comparative metaomics study on aerobic granular sludge from three different wastewater treatment plants. For this, we employed metaproteomics, whole metagenome, and 16S rRNA amplicon sequencing to study the same granule material with uniform size. We furthermore compare the taxonomic profiles using the Genome Taxonomy Database (GTDB) to enhance the comparability between the different approaches. Though the major taxonomies were consistently identified in the different aerobic granular sludge samples, the taxonomic composition obtained by the different omics techniques varied significantly at the lower taxonomic levels, which impacts the interpretation of the nutrient removal processes. Nevertheless, as demonstrated by metaproteomics, the genera that were consistently identified in all techniques cover the majority of the protein biomass. The established metaomics data and the contig classification pipeline are publicly available, which provides a valuable resource for further studies on metabolic processes in aerobic granular sludge.

Exercise and microbiome: From big data to therapy

Exercise is a vital component in maintaining optimal health and serves as a prospective therapeutic intervention for various diseases. The human microbiome, comprised of trillions of microorganisms, plays a crucial role in overall health. Given the advancements in microbiome research, substantial databases have been created to decipher the functionality and mechanisms of the microbiome in health and disease contexts. This review presents an initial overview of microbiomics development and related databases, followed by an in-depth description of the multi-omics technologies for microbiome. It subsequently synthesizes the research pertaining to exercise-induced modifications of the microbiome and diseases that impact the microbiome. Finally, it highlights the potential therapeutic implications of an exercise-modulated microbiome in intestinal disease, obesity and diabetes, cardiovascular disease, and immune/inflammation-related diseases.

Optimizing metaproteomics database construction: lessons from a study of the vaginal microbiome

Metaproteomics, a method for untargeted, high-throughput identification of proteins in complex samples, provides functional information about microbial communities and can tie functions to specific taxa. Metaproteomics often generates less data than other omics techniques, but analytical workflows can be improved to increase usable data in metaproteomic outputs. Identification of peptides in the metaproteomic analysis is performed by comparing mass spectra of sample peptides to a reference database of protein sequences. Although these protein databases are an integral part of the metaproteomic analysis, few studies have explored how database composition impacts peptide identification. Here, we used cervicovaginal lavage (CVL) samples from a study of bacterial vaginosis (BV) to compare the performance of databases built using six different strategies. We evaluated broad versus sample-matched databases, as well as databases populated with proteins translated from metagenomic sequencing of the same samples versus sequences from public repositories. Smaller sample-matched databases performed significantly better, driven by the statistical constraints on large databases. Additionally, large databases attributed up to 34% of significant bacterial hits to taxa absent from the sample, as determined orthogonally by 16S rRNA gene sequencing. We also tested a set of hybrid databases which included bacterial proteins from NCBI RefSeq and translated bacterial genes from the samples. These hybrid databases had the best overall performance, identifying 1,068 unique human and 1,418 unique bacterial proteins, ~30% more than a database populated with proteins from typical vaginal bacteria and fungi. Our findings can help guide the optimal identification of proteins while maintaining statistical power for reaching biological conclusions. IMPORTANCE Metaproteomic analysis can provide valuable insights into the functions of microbial and cellular communities by identifying a broad, untargeted set of proteins. The databases used in the analysis of metaproteomic data influence results by defining what proteins can be identified. Moreover, the size of the database impacts the number of identifications after accounting for false discovery rates (FDRs). Few studies have tested the performance of different strategies for building a protein database to identify proteins from metaproteomic data and those that have largely focused on highly diverse microbial communities. We tested a range of databases on CVL samples and found that a hybrid sample-matched approach, using publicly available proteins from organisms present in the samples, as well as proteins translated from metagenomic sequencing of the samples, had the best performance. However, our results also suggest that public sequence databases will continue to improve as more bacterial genomes are published.

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

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

MetaNovo: An open-source pipeline for probabilistic peptide discovery in complex metaproteomic datasets

BACKGROUND

Microbiome research is providing important new insights into the metabolic interactions of complex microbial ecosystems involved in fields as diverse as the pathogenesis of human diseases, agriculture and climate change. Poor correlations typically observed between RNA and protein expression datasets make it hard to accurately infer microbial protein synthesis from metagenomic data. Additionally, mass spectrometry-based metaproteomic analyses typically rely on focused search sequence databases based on prior knowledge for protein identification that may not represent all the proteins present in a set of samples. Metagenomic 16S rRNA sequencing only targets the bacterial component, while whole genome sequencing is at best an indirect measure of expressed proteomes. Here we describe a novel approach, MetaNovo, that combines existing open-source software tools to perform scalable de novo sequence tag matching with a novel algorithm for probabilistic optimization of the entire UniProt knowledgebase to create tailored sequence databases for target-decoy searches directly at the proteome level, enabling metaproteomic analyses without prior expectation of sample composition or metagenomic data generation and compatible with standard downstream analysis pipelines.

RESULTS

We compared MetaNovo to published results from the MetaPro-IQ pipeline on 8 human mucosal-luminal interface samples, with comparable numbers of peptide and protein identifications, many shared peptide sequences and a similar bacterial taxonomic distribution compared to that found using a matched metagenome sequence database-but simultaneously identified many more non-bacterial peptides than the previous approaches. MetaNovo was also benchmarked on samples of known microbial composition against matched metagenomic and whole genomic sequence database workflows, yielding many more MS/MS identifications for the expected taxa, with improved taxonomic representation, while also highlighting previously described genome sequencing quality concerns for one of the organisms, and identifying an experimental sample contaminant without prior expectation.

CONCLUSIONS

By estimating taxonomic and peptide level information directly on microbiome samples from tandem mass spectrometry data, MetaNovo enables the simultaneous identification of peptides from all domains of life in metaproteome samples, bypassing the need for curated sequence databases to search. We show that the MetaNovo approach to mass spectrometry metaproteomics is more accurate than current gold standard approaches of tailored or matched genomic sequence database searches, can identify sample contaminants without prior expectation and yields insights into previously unidentified metaproteomic signals, building on the potential for complex mass spectrometry metaproteomic data to speak for itself.

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.

Meta4P: A User-Friendly Tool to Parse Label-Free Quantitative Metaproteomic Data and Taxonomic/Functional Annotations

We present Meta4P (MetaProteins-Peptides-PSMs Parser), an easy-to-use bioinformatic application designed to integrate label-free quantitative metaproteomic data with taxonomic and functional annotations. Meta4P can retrieve, filter, and process identification and quantification data from three levels of inputs (proteins, peptides, PSMs) in different file formats. Abundance data can be combined with taxonomic and functional information and aggregated at different and customizable levels, including taxon-specific functions and pathways. Meta4P output tables, available in various formats, are ready to be used as inputs for downstream statistical analyses. This user-friendly tool is expected to provide a useful contribution to the field of metaproteomic data analysis, helping make it more manageable and straightforward.

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

Metaproteomics can provide valuable insights into the functions of human gut microbiota (GM), but is challenging due to the extreme complexity and heterogeneity of GM. Data-independent acquisition (DIA) mass spectrometry (MS) has been an emerging quantitative technique in conventional proteomics, but is still at the early stage of development in the field of metaproteomics. Herein, we applied library-free DIA (directDIA)-based metaproteomics and compared the directDIA with other MS-based quantification techniques for metaproteomics on simulated microbial communities and feces samples spiked with bacteria with known ratios, demonstrating the superior performance of directDIA by a comprehensive consideration of proteome coverage in identification as well as accuracy and precision in quantification. We characterized human GM in two cohorts of clinical fecal samples of pancreatic cancer (PC) and mild cognitive impairment (MCI). About 70,000 microbial proteins were quantified in each cohort and annotated to profile the taxonomic and functional characteristics of GM in different diseases. Our work demonstrated the utility of directDIA in quantitative metaproteomics for investigating intestinal microbiota and its related disease pathogenesis.

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.

Forages and pastures symposium: forage biodegradation: advances in ruminal microbial ecology

The rumen microbial ecosystem provides ruminants a selective advantage, the ability to utilize forages, allowing them to flourish worldwide in various environments. For many years, our understanding of the ruminal microbial ecosystem was limited to understanding the microbes (usually only laboratory-amenable bacteria) grown in pure culture, meaning that much of our understanding of ruminal function remained a "black box." However, the ruminal degradation of plant cell walls is performed by a consortium of bacteria, archaea, protozoa, and fungi that produces a wide variety of carbohydrate-active enzymes (CAZymes) that are responsible for the catabolism of cellulose, hemicellulose, and pectin. The past 15 years have seen the development and implementation of numerous next-generation sequencing (NGS) approaches (e.g., pyrosequencing, Illumina, and shotgun sequencing), which have contributed significantly to a greater level of insight regarding the microbial ecology of ruminants fed a variety of forages. There has also been an increase in the utilization of liquid chromatography and mass spectrometry that revolutionized transcriptomic approaches, and further improvements in the measurement of fermentation intermediates and end products have advanced with metabolomics. These advanced NGS techniques along with other analytic approaches, such as metaproteomics, have been utilized to elucidate the specific role of microbial CAZymes in forage degradation. Other methods have provided new insights into dynamic changes in the ruminal microbial population fed different diets and how these changes impact the assortment of products presented to the host animal. As more omics-based data has accumulated on forage-fed ruminants, the sequence of events that occur during fiber colonization by the microbial consortium has become more apparent, with fungal populations and fibrolytic bacterial populations working in conjunction, as well as expanding understanding of the individual microbial contributions to degradation of plant cell walls and polysaccharide components. In the future, the ability to predict microbial population and enzymatic activity and end products will be able to support the development of dynamic predictive models of rumen forage degradation and fermentation. Consequently, it is imperative to understand the rumen's microbial population better to improve fiber degradation in ruminants and, thus, stimulate more sustainable production systems.

Short-Term Omega-3 Supplementation Modulates Novel Neurovascular and Fatty Acid Metabolic Proteome Changes in the Retina and Ophthalmic Artery of Mice with Targeted Cyp2c44 Gene Deletion

Cytochrome P450 (CYP) gene mutations are a common predisposition associated with glaucoma. Although the molecular mechanisms are largely unknown, omega-3 polyunsaturated fatty acids (ω-3 PUFA) and their CYP-derived bioactive mediators play crucial roles in the ocular system. Here, we elucidated the proteome and cell-signalling alterations attributed to the main human CYP2C gene deficiency using a homologous murine model (Cyp2c44^−/−), and unravelled the effects of acute ω-3 PUFA supplementation in two ocular vascular beds comprising the retrobulbar ophthalmic artery (OA) and retina (R). Male Cyp2c44^−/− mice (KO) and their floxed littermates (WT) were gavaged daily for 7 days with 0.01 mL/g of ω-3 PUFA composed of menhaden fish oil. Another group in respective strains served as vehicle-treated controls. OA and R were isolated at day 8 post-treatment (n = 9/group) and subjected to mass spectrometry (MS)-based proteomics and in silico bioinformatics analyses. Cyp2c44^−/− resulted in significant detrimental proteome changes associated with compromised vascular integrity and degeneration in the OA and R, respectively. However, notable changes in the OA after ω-3 PUFA intake were associated with the maintenance of intercellular junctional and endothelial cell functions, as well as activation of the fatty acid metabolic pathway in the KO mice. Conversely, ω-3 PUFA supplementation profoundly influenced the regulation of a large majority of retinal proteins involved in the preservation of neuronal and phototransduction activities in WT mice, namely synaptophysin, phosducin and guanylate cyclase-1, while significantly abrogating degenerative processes in the KO mice via the regulation of, namely, synaptotagmin-1 and beta-crystallin B2. In gist, this study demonstrated that dietary supplementation with ω-3 PUFA for a short period of seven days regulated specific neuro-vasculoprotective mechanisms to preserve the functionality of the OA and R in the absence of Cyp2c44. The potential adjunct use of ω-3 PUFA for glaucoma therapy needs further investigation.

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

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

Morphine and high-fat diet differentially alter the gut microbiota composition and metabolic function in lean versus obese mice

There are known associations between opioids, obesity, and the gut microbiome, but the molecular connection/mediation of these relationships is not understood. To better clarify the interplay of physiological, genetic, and microbial factors, this study investigated the microbiome and host inflammatory responses to chronic opioid administration in genetically obese, diet-induced obese, and lean mice. Samples of feces, urine, colon tissue, and plasma were analyzed using targeted LC-MS/MS quantification of metabolites, immunoassays of inflammatory cytokine levels, genome-resolved metagenomics, and metaproteomics. Genetic obesity, diet-induced obesity, and morphine treatment in lean mice each showed increases in distinct inflammatory cytokines. Metagenomic assembly and binning uncovered over 400 novel gut bacterial genomes and species. Morphine administration impacted the microbiome's composition and function, with the strongest effect observed in lean mice. This microbiome effect was less pronounced than either diet or genetically driven obesity. Based on inferred microbial physiology from the metaproteome datasets, a high-fat diet transitioned constituent microbes away from harvesting diet-derived nutrients and towards nutrients present in the host mucosal layer. Considered together, these results identified novel host-dependent phenotypes, differentiated the effects of genetic obesity versus diet induced obesity on gut microbiome composition and function, and showed that chronic morphine administration altered the gut microbiome.

An analysis of proteogenomics and how and when transcriptome-informed reduction of protein databases can enhance eukaryotic proteomics

Background

Proteogenomics aims to identify variant or unknown proteins in bottom-up proteomics, by searching transcriptome- or genome-derived custom protein databases. However, empirical observations reveal that these large proteogenomic databases produce lower-sensitivity peptide identifications. Various strategies have been proposed to avoid this, including the generation of reduced transcriptome-informed protein databases, which only contain proteins whose transcripts are detected in the sample-matched transcriptome. These were found to increase peptide identification sensitivity. Here, we present a detailed evaluation of this approach.

Results

We establish that the increased sensitivity in peptide identification is in fact a statistical artifact, directly resulting from the limited capability of target-decoy competition to accurately model incorrect target matches when using excessively small databases. As anti-conservative false discovery rates (FDRs) are likely to hamper the robustness of the resulting biological conclusions, we advocate for alternative FDR control methods that are less sensitive to database size. Nevertheless, reduced transcriptome-informed databases are useful, as they reduce the ambiguity of protein identifications, yielding fewer shared peptides. Furthermore, searching the reference database and subsequently filtering proteins whose transcripts are not expressed reduces protein identification ambiguity to a similar extent, but is more transparent and reproducible.

Conclusions

In summary, using transcriptome information is an interesting strategy that has not been promoted for the right reasons. While the increase in peptide identifications from searching reduced transcriptome-informed databases is an artifact caused by the use of an FDR control method unsuitable to excessively small databases, transcriptome information can reduce the ambiguity of protein identifications.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13059-022-02701-2.

Multi-Omics Strategies for Investigating the Microbiome in Toxicology Research

Microbial communities on and within the host contact environmental pollutants, toxic compounds, and other xenobiotic compounds. These communities of bacteria, fungi, viruses, and archaea possess diverse metabolic potential to catabolize compounds and produce new metabolites. Microbes alter chemical disposition thus making the microbiome a natural subject of interest for toxicology. Sequencing and metabolomics technologies permit the study of microbiomes altered by acute or long-term exposure to xenobiotics. These investigations have already contributed to and are helping to re-interpret traditional understandings of toxicology. The purpose of this review is to provide a survey of the current methods used to characterize microbes within the context of toxicology. This will include discussion of commonly used techniques for conducting omic-based experiments, their respective strengths and deficiencies, and how forward-looking techniques may address present shortcomings. Finally, a perspective will be provided regarding common assumptions that currently impede microbiome studies from producing causal explanations of toxicologic mechanisms.

Proteomic traits vary across taxa in a coastal Antarctic phytoplankton bloom

Production and use of proteins is under strong selection in microbes, but it is unclear how proteome-level traits relate to ecological strategies. We identified and quantified proteomic traits of eukaryotic microbes and bacteria through an Antarctic phytoplankton bloom using in situ metaproteomics. Different taxa, rather than different environmental conditions, formed distinct clusters based on their ribosomal and photosynthetic proteomic proportions, and we propose that these characteristics relate to ecological differences. We defined and used a proteomic proxy for regulatory cost, which showed that SAR11 had the lowest regulatory cost of any taxa we observed at our summertime Southern Ocean study site. Haptophytes had lower regulatory cost than diatoms, which may underpin haptophyte-to-diatom bloom progression in the Ross Sea. We were able to make these proteomic trait inferences by assessing various sources of bias in metaproteomics, providing practical recommendations for researchers in the field. We have quantified several proteomic traits (ribosomal and photosynthetic proteomic proportions, regulatory cost) in eukaryotic and bacterial taxa, which can then be incorporated into trait-based models of microbial communities that reflect resource allocation strategies.

An integrated workflow for enhanced taxonomic and functional coverage of the mouse fecal metaproteome

Intestinal microbiota plays a key role in shaping host homeostasis by regulating metabolism, immune responses and behavior. Its dysregulation has been associated with metabolic, immune and neuropsychiatric disorders and is accompanied by changes in bacterial metabolic regulation. Although proteomics is well suited for analysis of individual microbes, metaproteomics of fecal samples is challenging due to the physical structure of the sample, presence of contaminating host proteins and coexistence of hundreds of taxa. Furthermore, there is a lack of consensus regarding preparation of fecal samples, as well as downstream bioinformatic analyses following metaproteomics data acquisition. Here we assess sample preparation and data analysis strategies applied to mouse feces in a typical mass spectrometry-based metaproteomic experiment. We show that subtle changes in sample preparation protocols may influence interpretation of biological findings. Two-step database search strategies led to significant underestimation of false positive protein identifications. Unipept software provided the highest sensitivity and specificity in taxonomic annotation of the identified peptides of unknown origin. Comparison of matching metaproteome and metagenome data revealed a positive correlation between protein and gene abundances. Notably, nearly all functional categories of detected protein groups were differentially abundant in the metaproteome compared to what would be expected from the metagenome, highlighting the need to perform metaproteomics when studying complex microbiome samples.

Quantitative metaproteomics and activity-based protein profiling of patient fecal microbiome identifies host and microbial serine-type endopeptidase activity associated with ulcerative colitis

The gut microbiota plays an important yet incompletely understood role in the induction and propagation of ulcerative colitis (UC). Organism-level efforts to identify UC-associated microbes have revealed the importance of community structure, but less is known about the molecular effectors of disease. We performed 16S rRNA gene sequencing in parallel with label-free data-dependent LC-MS/MS proteomics to characterize the stool microbiomes of healthy (n = 8) and UC (n = 10) patients. Comparisons of taxonomic composition between techniques revealed major differences in community structure partially attributable to the additional detection of host, fungal, viral, and food peptides by metaproteomics. Differential expression analysis of metaproteomic data identified 176 significantly enriched protein groups between healthy and UC patients. Gene ontology analysis revealed several enriched functions with serine-type endopeptidase activity overrepresented in UC patients. Using a biotinylated fluorophosphonate probe and streptavidin-based enrichment, we show that serine endopeptidases are active in patient fecal samples and that additional putative serine hydrolases are detectable by this approach compared with unenriched profiling. Finally, as metaproteomic databases expand, they are expected to asymptotically approach completeness. Using ComPIL and de novo peptide sequencing, we estimate the size of the probable peptide space unidentified (“dark peptidome”) by our large database approach to establish a rough benchmark for database sufficiency. Despite high variability inherent in patient samples, our analysis yielded a catalog of differentially enriched proteins between healthy and UC fecal proteomes. This catalog provides a clinically relevant jumping-off point for further molecular-level studies aimed at identifying the microbial underpinnings of UC.

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Identified 176 significantly altered protein groups between healthy and UC patients.

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Serine-type endopeptidase activity is overrepresented in UC patients.

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Fluorophosphonate ABPP shows that endopeptidases are active in fecal samples.

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ABPP enrichment helps identify additional putative serine hydrolases in samples.

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De novo sequencing used to estimate number of MS2 spectra unidentified by ComPIL.

Metaproteomic Profile of the Colonic Luminal Microbiota From Patients With Colon Cancer

Recent studies have provided evidence of interactions among the gut microbiota (GM), local host immune cells, and intestinal tissues in colon carcinogenesis. However, little is known regarding the functions exerted by the GM in colon cancer (CC), particularly with respect to tumor clinical classification and lymphocyte infiltration. In addition, stool, usually employed as a proxy of the GM, cannot fully represent the original complexity of CC microenvironment. Here, we present a pilot study aimed at characterizing the metaproteome of CC-associated colonic luminal contents and identifying its possible associations with CC clinicopathological features. Colonic luminal contents were collected from 24 CC tissue specimens immediately after surgery. Samples were analyzed by shotgun metaproteomics. Almost 30,000 microbial peptides were quantified in the samples, enabling the achievement of the taxonomic and functional profile of the tumor-associated colonic luminal metaproteome. Upon sample aggregation based on tumor stage, grade, or tumor-infiltrating lymphocytes (TILs), peptide sets enabling discrimination of sample groups were identified through discriminant analysis (DA). As a result, Bifidobacterium and Bacteroides fragilis were significantly enriched in high-stage and high-grade CC, respectively. Among metabolic functions, formate-tetrahydrofolate ligase was significantly associated with high-stage CC. Finally, based on the results of this pilot study, we assessed the optimal sample size for differential metaproteomic studies analyzing colonic luminal contents. In conclusion, we provide a detailed picture of the microbial and host components of the colonic luminal proteome and propose promising associations between GM taxonomic/functional features and CC clinicopathological features. Future studies will be needed to verify the prognostic value of these data and to fully exploit the potential of metaproteomics in enhancing our knowledge concerning CC progression.

Novel Bioinformatics Strategies Driving Dynamic Metaproteomic Studies

Constant improvements in mass spectrometry technologies and laboratory workflows have enabled the proteomics investigation of biological samples of growing complexity. Microbiomes represent such complex samples for which metaproteomics analyses are becoming increasingly popular. Metaproteomics experimental procedures create large amounts of data from which biologically relevant signal must be efficiently extracted to draw meaningful conclusions. Such a data processing requires appropriate bioinformatics tools specifically developed for, or capable of handling metaproteomics data. In this chapter, we outline current and novel tools that can perform the most commonly used steps in the analysis of cutting-edge metaproteomics data, such as peptide and protein identification and quantification, as well as data normalization, imputation, mining, and visualization. We also provide details about the experimental setups in which these tools should be used.

Considerations for Constructing a Protein Sequence Database for Metaproteomics

Mass spectrometry-based metaproteomics has emerged as a prominent technique for interrogating the functions of specific organisms in microbial communities, in addition to total community function. Identifying proteins by mass spectrometry requires matching mass spectra of fragmented peptide ions to a database of protein sequences corresponding to the proteins in the sample. This sequence database determines which protein sequences can be identified from the measurement, and as such the taxonomic and functional information that can be inferred from a metaproteomics measurement. Thus, the construction of the protein sequence database directly impacts the outcome of any metaproteomics study. Several factors, such as source of sequence information and database curation, need to be considered during database construction to maximize accurate protein identifications traceable to the species of origin. In this review, we provide an overview of existing strategies for database construction and the relevant studies that have sought to test and validate these strategies. Based on this review of the literature and our experience we provide a decision tree and best practices for choosing and implementing database construction strategies.

Evaluation of Sample Preservation and Storage Methods for Metaproteomics Analysis of Intestinal Microbiomes

A critical step in studies of the intestinal microbiome using meta-omics approaches is the preservation of samples before analysis. Preservation is essential for approaches that measure gene expression, such as metaproteomics, which is used to identify and quantify proteins in microbiomes. Intestinal microbiome samples are typically stored by flash-freezing and storage at -80°C, but some experimental setups do not allow for immediate freezing of samples. In this study, we evaluated methods to preserve fecal microbiome samples for metaproteomics analyses when flash-freezing is not possible. We collected fecal samples from C57BL/6 mice and stored them for 1 and 4 weeks using the following methods: flash-freezing in liquid nitrogen, immersion in RNAlater, immersion in 95% ethanol, immersion in a RNAlater-like buffer, and combinations of these methods. After storage, we extracted protein and prepared peptides for liquid chromatography with tandem mass spectrometry (LC-MS/MS) analysis to identify and quantify peptides and proteins. All samples produced highly similar metaproteomes, except for ethanol-preserved samples that were distinct from all other samples in terms of protein identifications and protein abundance profiles. Flash-freezing and RNAlater (or RNAlater-like treatments) produced metaproteomes that differed only slightly, with less than 0.7% of identified proteins differing in abundance. In contrast, ethanol preservation resulted in an average of 9.5% of the identified proteins differing in abundance between ethanol and the other treatments. Our results suggest that preservation at room temperature in RNAlater or an RNAlater-like solution performs as well as freezing for the preservation of intestinal microbiome samples before metaproteomics analyses. IMPORTANCE Metaproteomics is a powerful tool to study the intestinal microbiome. By identifying and quantifying a large number of microbial, dietary, and host proteins in microbiome samples, metaproteomics provides direct evidence of the activities and functions of microbial community members. A critical step for metaproteomics workflows is preserving samples before analysis because protein profiles are susceptible to fast changes in response to changes in environmental conditions (air exposure, temperature changes, etc.). This study evaluated the effects of different preservation treatments on the metaproteomes of intestinal microbiome samples. In contrast to prior work on preservation of fecal samples for metaproteomics analyses, we ensured that all steps of sample preservation were identical so that all differences could be attributed to the preservation method.

Critical Assessment of MetaProteome Investigation (CAMPI): a multi-laboratory comparison of established workflows

Metaproteomics has matured into a powerful tool to assess functional interactions in microbial communities. While many metaproteomic workflows are available, the impact of method choice on results remains unclear. Here, we carry out a community-driven, multi-laboratory comparison in metaproteomics: the critical assessment of metaproteome investigation study (CAMPI). Based on well-established workflows, we evaluate the effect of sample preparation, mass spectrometry, and bioinformatic analysis using two samples: a simplified, laboratory-assembled human intestinal model and a human fecal sample. We observe that variability at the peptide level is predominantly due to sample processing workflows, with a smaller contribution of bioinformatic pipelines. These peptide-level differences largely disappear at the protein group level. While differences are observed for predicted community composition, similar functional profiles are obtained across workflows. CAMPI demonstrates the robustness of present-day metaproteomics research, serves as a template for multi-laboratory studies in metaproteomics, and provides publicly available data sets for benchmarking future developments.

Critical Assessment of MetaProteome Investigation (CAMPI): a multi-laboratory comparison of established workflows

Metaproteomics has matured into a powerful tool to assess functional interactions in microbial communities. While many metaproteomic workflows are available, the impact of method choice on results remains unclear. Here, we carry out a community-driven, multi-laboratory comparison in metaproteomics: the critical assessment of metaproteome investigation study (CAMPI). Based on well-established workflows, we evaluate the effect of sample preparation, mass spectrometry, and bioinformatic analysis using two samples: a simplified, laboratory-assembled human intestinal model and a human fecal sample. We observe that variability at the peptide level is predominantly due to sample processing workflows, with a smaller contribution of bioinformatic pipelines. These peptide-level differences largely disappear at the protein group level. While differences are observed for predicted community composition, similar functional profiles are obtained across workflows. CAMPI demonstrates the robustness of present-day metaproteomics research, serves as a template for multi-laboratory studies in metaproteomics, and provides publicly available data sets for benchmarking future developments.

Integrated Metagenomic and Metaproteomic Analyses Unravel Ammonia Toxicity to Active Methanogens and Syntrophs, Enzyme Synthesis, and Key Enzymes in Anaerobic Digestion

During anaerobic digestion, the active microbiome synthesizes enzymes by transcription and translation, and then enzymes catalyze multistep bioconversions of substrates before methane being produced. However, little information is available on how ammonia affects truly active microbes containing the expressed enzymes, enzyme synthesis, and key enzymes. In this study, an integrated metagenomic and metaproteomic investigation showed that ammonia suppressed not only the obligate acetotrophic methanogens but also the syntrophic propionate and butyrate oxidation taxa and their assistant bacteria (genus Desulfovibrio), which declined the biotransformations of propionate and butyrate → acetate → methane. Although the total population of the hydrolyzing and acidifying bacteria was not affected by ammonia, the bacteria with ammonia resistance increased. Our study also revealed that ammonia restrained the enzyme synthesis process by inhibiting the RNA polymerase (subunits A' and D) during transcription and the ribosome (large (L3, L12, L13, L22, and L25) and small (S3, S3Ae, and S7) ribosomal subunits) and aminoacyl-tRNA synthesis (aspartate-tRNA synthetase) in translation. Further investigation suggested that methylmalonyl-CoA mutase, acetyl-CoA C-acetyltransferase, and CH₃-CoM reductase, which regulate propionate and butyrate oxidation and acetoclastic methanation, were significantly downregulated by ammonia. This study provides intrinsic insights into the fundamental mechanisms of how ammonia inhibits anaerobic digestion.

Increasing the power of interpretation for soil metaproteomics data

BACKGROUND

Soil and sediment microorganisms are highly phylogenetically diverse but are currently largely under-represented in public molecular databases. Their functional characterization by means of metaproteomics is usually performed using metagenomic sequences acquired for the same sample. However, such hugely diverse metagenomic datasets are difficult to assemble; in parallel, theoretical proteomes from isolates available in generic databases are of high quality. Both these factors advocate for the use of theoretical proteomes in metaproteomics interpretation pipelines. Here, we examined a number of database construction strategies with a view to increasing the outputs of metaproteomics studies performed on soil samples.

RESULTS

The number of peptide-spectrum matches was found to be of comparable magnitude when using public or sample-specific metagenomics-derived databases. However, numbers were significantly increased when a combination of both types of information was used in a two-step cascaded search. Our data also indicate that the functional annotation of the metaproteomics dataset can be maximized by using a combination of both types of databases.

CONCLUSIONS

A two-step strategy combining sample-specific metagenome database and public databases such as the non-redundant NCBI database and a massive soil gene catalog allows maximizing the metaproteomic interpretation both in terms of ratio of assigned spectra and retrieval of function-derived information. Video abstract.

Challenges, Strategies, and Perspectives for Reference-Independent Longitudinal Multi-Omic Microbiome Studies

In recent years, multi-omic studies have enabled resolving community structure and interrogating community function of microbial communities. Simultaneous generation of metagenomic, metatranscriptomic, metaproteomic, and (meta) metabolomic data is more feasible than ever before, thus enabling in-depth assessment of community structure, function, and phenotype, thus resulting in a multitude of multi-omic microbiome datasets and the development of innovative methods to integrate and interrogate those multi-omic datasets. Specifically, the application of reference-independent approaches provides opportunities in identifying novel organisms and functions. At present, most of these large-scale multi-omic datasets stem from spatial sampling (e.g., water/soil microbiomes at several depths, microbiomes in/on different parts of the human anatomy) or case-control studies (e.g., cohorts of human microbiomes). We believe that longitudinal multi-omic microbiome datasets are the logical next step in microbiome studies due to their characteristic advantages in providing a better understanding of community dynamics, including: observation of trends, inference of causality, and ultimately, prediction of community behavior. Furthermore, the acquisition of complementary host-derived omics, environmental measurements, and suitable metadata will further enhance the aforementioned advantages of longitudinal data, which will serve as the basis to resolve drivers of community structure and function to understand the biotic and abiotic factors governing communities and specific populations. Carefully setup future experiments hold great potential to further unveil ecological mechanisms to evolution, microbe-microbe interactions, or microbe-host interactions. In this article, we discuss the challenges, emerging strategies, and best-practices applicable to longitudinal microbiome studies ranging from sampling, biomolecular extraction, systematic multi-omic measurements, reference-independent data integration, modeling, and validation.

MAPLE: A Microbiome Analysis Pipeline Enabling Optimal Peptide Search and Comparative Taxonomic and Functional Analysis

Metaproteomics by mass spectrometry (MS) is a powerful approach to profile a large number of proteins expressed by all organisms in a highly complex biological or ecological sample, which is able to provide a direct and quantitative assessment of the functional makeup of a microbiota. The human gastrointestinal microbiota has been found playing important roles in human physiology and health, and metaproteomics has been shown to shed light on multiple novel associations between microbiota and diseases. MS-powered proteomics generally relies on genome data to define search space. However, metaproteomics, which simultaneously analyzes all proteins from hundreds to thousands of species, faces significant challenges regarding database search and interpretation of results. To overcome these obstacles, we have developed a user-friendly microbiome analysis pipeline (MAPLE, freely downloadable at http://maple.rx.umaryland.edu/), which is able to define an optimal search space by inferring proteomes specific to samples following the principle of parsimony. MAPLE facilitates highly comparable or better peptide identification compared to a sample-specific metagenome-guided search. In addition, we implemented an automated peptide-centric enrichment analysis function in MAPLE to address issues of traditional protein-centric comparison, enabling straightforward and comprehensive comparison of taxonomic and functional makeup between microbiota.

Bottom-Up Community Proteome Analysis of Saliva Samples and Tongue Swabs by Data-Dependent Acquisition Nano LC-MS/MS Mass Spectrometry

Analysis using mass spectrometry enables the characterization of metaproteomes in their native environments and overcomes the limitation of proteomics of pure cultures. Metaproteomics is a promising approach to link functions of currently actively expressed genes to the phylogenetic composition of the microbiome in their habitat. In this chapter, we describe the preparation of saliva samples and tongue swabs for nLC-MS/MS measurements and their bioinformatic analysis based on the Trans-Proteomic Pipeline and Prophane to study the oral microbiome .

Insects' potential: Understanding the functional role of their gut microbiome

The study of insect-associated microbial communities is a field of great importance in agriculture, principally because of the role insects play as pests. In addition, there is a recent focus on the potential of the insect gut microbiome in areas such as biotechnology, given some microorganisms produce molecules with biotechnological and industrial applications, and also in biomedicine, since some bacteria and fungi are a reservoir of antibiotic resistance genes (ARGs). To date, most studies aiming to characterize the role of the gut microbiome of insects have been based on high-throughput sequencing of the 16S rRNA gene and/or metagenomics. However, recently functional approaches such as metatranscriptomics, metaproteomics and metabolomics have also been employed. Besides providing knowledge about the taxonomic distribution of microbial populations, these techniques also reveal their functional and metabolic capabilities. This information is essential to gain a better understanding of the role played by microbes comprising the microbial communities in their hosts, as well as to indicate their possible exploitation. This review provides an overview of how far we have come in characterizing insect gut functionality through omics, as well as the challenges and future perspectives in this field.

A complete and flexible workflow for metaproteomics data analysis based on MetaProteomeAnalyzer and Prophane

Metaproteomics, the study of the collective protein composition of multi-organism systems, provides deep insights into the biodiversity of microbial communities and the complex functional interplay between microbes and their hosts or environment. Thus, metaproteomics has become an indispensable tool in various fields such as microbiology and related medical applications. The computational challenges in the analysis of corresponding datasets differ from those of pure-culture proteomics, e.g., due to the higher complexity of the samples and the larger reference databases demanding specific computing pipelines. Corresponding data analyses usually consist of numerous manual steps that must be closely synchronized. With MetaProteomeAnalyzer and Prophane, we have established two open-source software solutions specifically developed and optimized for metaproteomics. Among other features, peptide-spectrum matching is improved by combining different search engines and, compared to similar tools, metaproteome annotation benefits from the most comprehensive set of available databases (such as NCBI, UniProt, EggNOG, PFAM, and CAZy). The workflow described in this protocol combines both tools and leads the user through the entire data analysis process, including protein database creation, database search, protein grouping and annotation, and results visualization. To the best of our knowledge, this protocol presents the most comprehensive, detailed and flexible guide to metaproteomics data analysis to date. While beginners are provided with robust, easy-to-use, state-of-the-art data analysis in a reasonable time (a few hours, depending on, among other factors, the protein database size and the number of identified peptides and inferred proteins), advanced users benefit from the flexibility and adaptability of the workflow.

Pipeline for Targeted Meta-Proteomic Analyses to Assess the Diversity of Cattle Rumen Microbial Urease

In the rumen of cattle, urease produced by ureolytic bacteria catalyzes the hydrolysis of urea to ammonia, which plays an important role in nitrogen metabolism and animal production. A high diversity of rumen bacterial urease genes was observed in our previous study; however, information on urease protein diversity could not be determined due to technical limitations. Here, we developed a targeted meta-proteomic pipeline to analyze rumen urease protein diversity. Protein extraction (duration of cryomilling in liquid nitrogen), protein digestion state (in-solution or in-gel), and the digestion enzyme used (trypsin or Glu-C/Lys-C) were optimized, and the digested peptides were analyzed by LC-MS/MS. Four minutes was the best duration for cryomilling and yielded the highest urease activity. Trypsin digestion of in-gel proteins outperformed other digestion methods and yielded the greatest number of identifications and superior peptide performance in regards to the digestion efficiency and high-score peptide. The annotation of peptides by PEAKS software revealed diversity among urease proteins, with the predominant proteins being from Prochlorococcus, Helicobacter, and uncultured bacteria. In conclusion, trypsin digestion of in-gel proteins was the optimal method for the meta-proteomic pipeline analyzing rumen microbial ureases. This pipeline provides a guide for targeted meta-proteomic analyses in other ecosystems.

Metaproteomics insights into traditional fermented foods and beverages

Traditional fermented foods and beverages (TFFB) are important dietary components. Multi-omics techniques have been applied to all aspects of TFFB research to clarify the composition and nutritional value of TFFB, and to reveal the microbial community, microbial interactions, fermentative kinetics, and metabolic profiles during the fermentation process of TFFB. Because of the advantages of metaproteomics in providing functional information, this technology has increasingly been used in research to assess the functional diversity of microbial communities. Metaproteomics is gradually gaining attention in the field of TFFB research because it can reveal the nature of microorganism function at the protein level. This paper reviews the common methods of metaproteomics applied in TFFB research; systematically summarizes the results of metaproteomics research on TFFB, such as sauces, wines, fermented tea, cheese, and fermented fish; and compares the differences in conclusions reached through metaproteomics versus other omics methods. Metaproteomics has great advantages in revealing the microbial functions in TFFB and the interaction between the materials and microbial community. In the future, metaproteomics should be further applied to the study of functional protein markers and protein interaction in TFFB; multi-omics technology requires further integration to reveal the molecular nature of TFFB fermentation.

gNOMO: a multi-omics pipeline for integrated host and microbiome analysis of non-model organisms

The study of bacterial symbioses has grown exponentially in the recent past. However, existing bioinformatic workflows of microbiome data analysis do commonly not integrate multiple meta-omics levels and are mainly geared toward human microbiomes. Microbiota are better understood when analyzed in their biological context; that is together with their host or environment. Nevertheless, this is a limitation when studying non-model organisms mainly due to the lack of well-annotated sequence references. Here, we present gNOMO, a bioinformatic pipeline that is specifically designed to process and analyze non-model organism samples of up to three meta-omics levels: metagenomics, metatranscriptomics and metaproteomics in an integrative manner. The pipeline has been developed using the workflow management framework Snakemake in order to obtain an automated and reproducible pipeline. Using experimental datasets of the German cockroach Blattella germanica, a non-model organism with very complex gut microbiome, we show the capabilities of gNOMO with regard to meta-omics data integration, expression ratio comparison, taxonomic and functional analysis as well as intuitive output visualization. In conclusion, gNOMO is a bioinformatic pipeline that can easily be configured, for integrating and analyzing multiple meta-omics data types and for producing output visualizations, specifically designed for integrating paired-end sequencing data with mass spectrometry from non-model organisms.

Rumen metaproteomics: Closer to linking rumen microbial function to animal productivity traits

The rumen microbiome constitutes a dense and complex mixture of anaerobic bacteria, archaea, protozoa, virus and fungi. Collectively, rumen microbial populations interact closely in order to degrade and ferment complex plant material into nutrients for host metabolism, a process which also produces other by-products, such as methane gas. Our understanding of the rumen microbiome and its functions are of both scientific and industrial interest, as the metabolic functions are connected to animal health and nutrition, but at the same time contribute significantly to global greenhouse gas emissions. While many of the major microbial members of the rumen microbiome are acknowledged, advances in modern culture-independent meta-omic techniques, such as metaproteomics, enable deep exploration into active microbial populations involved in essential rumen metabolic functions. Meaningful and accurate metaproteomic analyses are highly dependent on representative samples, precise protein extraction and fractionation, as well as a comprehensive and high-quality protein sequence database that enables precise protein identification and quantification. This review focuses on the application of rumen metaproteomics, and its potential towards understanding the complex rumen microbiome and its metabolic functions. We present and discuss current methods in sample handling, protein extraction and data analysis for rumen metaproteomics, and finally emphasize the potential of (meta)genome-integrated metaproteomics for accurate reconstruction of active microbial populations in the rumen.

Bioenergetic shift and actin cytoskeleton remodelling as acute vascular adaptive mechanisms to angiotensin II in murine retina and ophthalmic artery

Ocular vascular dysfunction is a major contributing factor to the pathogenesis of glaucoma. In recent years, there has been a renewed interest in the role of angiotensin II (Ang II) in mediating the disease progression. Despite its (patho)physiological importance, the molecular mechanisms underlying Ang II-mediated oxidative stress remain largely unexplored in the ocular vasculature. Here, we provide the first direct evidence of the alterations of proteome and signalling pathways underlying Ang II-elicited oxidative insult independent of arterial pressure changes in the ophthalmic artery (OA) and retina (R) employing an in vitro experimental model. Both R and OA were isolated from male C57Bl/6J mice (n = 15/group; n = 5/biological replicate) and incubated overnight in medium containing either vehicle or Ang II (0.1 μM) at physiological conditions. Label-free quantitative mass spectrometry (MS)-based proteomics analysis identified a differential expression of 107 and 34 proteins in the R and OA, respectively. Statistical and bioinformatics analyses revealed that protein clusters involved in actin cytoskeleton and integrin-linked kinase signalling were significantly activated in the OA. Conversely, a large majority of differentially expressed retinal proteins were involved in dysregulation of numerous energy-producing and metabolic signalling pathways, hinting to a possible shift in retinal cell bioenergetics. Particularly, Ang II-mediated downregulation of septin-7 (Sept7; p < 0.01) and superoxide dismutase [Cu-Zn] (Sod1; p < 0.05), and upregulation of troponin T, fast skeletal muscle (Tnnt3; p < 0.05) and tropomyosin alpha-3 chain (Tpm3; p < 0.01) in the OA, and significant decreased expressions of two crystallin proteins (Cryab; p < 0.05 and Crybb2; p < 0.0001) in the R were verified at the mRNA level, corroborating our proteomics findings. In summary, these results demonstrated that exogenous application of Ang II over an acute time period caused impairment of retinal bioenergetics and cellular demise, and actin cytoskeleton-mediated vascular remodelling in the OA.

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Acute Ang II stimulation elicits oxidative stress in ocular vasculature without pressor effect. .

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Dysregulation of energy-producing and metabolic pathways are implicated in the retina. .

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Actin cytoskeleton remodelling are vascular adaptation processes in the ophthalmic artery. .

pepFunk: a tool for peptide-centric functional analysis of metaproteomic human gut microbiome studies

Motivation

Enzymatic digestion of proteins before mass spectrometry analysis is a key process in metaproteomic workflows. Canonical metaproteomic data processing pipelines typically involve matching spectra produced by the mass spectrometer to a theoretical spectra database, followed by matching the identified peptides back to parent-proteins. However, the nature of enzymatic digestion produces peptides that can be found in multiple proteins due to conservation or chance, presenting difficulties with protein and functional assignment.

Results

To combat this challenge, we developed pepFunk, a peptide-centric metaproteomic workflow focused on the analysis of human gut microbiome samples. Our workflow includes a curated peptide database annotated with Kyoto Encyclopedia of Genes and Genomes (KEGG) terms and a gene set variation analysis-inspired pathway enrichment adapted for peptide-level data. Analysis using our peptide-centric workflow is fast and highly correlated to a protein-centric analysis, and can identify more enriched KEGG pathways than analysis using protein-level data. Our workflow is open source and available as a web application or source code to be run locally.

Availability and implementation

pepFunk is available online as a web application at https://shiny.imetalab.ca/pepFunk/ with open-source code available from https://github.com/northomics/pepFunk.

Contact

dfigeys@uottawa.ca

Supplementary information

Supplementary data are available at Bioinformatics online.

Resources and tools for the high-throughput, multi-omic study of intestinal microbiota

The human gut microbiome impacts several aspects of human health and disease, including digestion, drug metabolism and the propensity to develop various inflammatory, autoimmune and metabolic diseases. Many of the molecular processes that play a role in the activity and dynamics of the microbiota go beyond species and genic composition and thus, their understanding requires advanced bioinformatics support. This article aims to provide an up-to-date view of the resources and software tools that are being developed and used in human gut microbiome research, in particular data integration and systems-level analysis efforts. These efforts demonstrate the power of standardized and reproducible computational workflows for integrating and analysing varied omics data and gaining deeper insights into microbe community structure and function as well as host-microbe interactions.

Function is what counts: how microbial community complexity affects species, proteome and pathway coverage in metaproteomics

Introduction: Metaproteomics is an established method to obtain a comprehensive taxonomic and functional view of microbial communities. After more than a decade, we are now able to describe the promise, reality, and perspectives of metaproteomics and provide useful information about the choice of method, applications, and potential improvement strategies.Areas covered: In this article, we will discuss current challenges of species and proteome coverage, and also highlight functional aspects of metaproteomics analysis of microbial communities with different levels of complexity. To do this, we re-analyzed data from microbial communities with low to high complexity (8, 72, 200 and >300 species). High species diversity leads to a reduced number of protein group identifications in a complex community, and thus the number of species resolved is underestimated. Ultimately, low abundance species remain undiscovered in complex communities. However, we observed that the main functional categories were better represented within complex microbiomes when compared to species coverage.Expert opinion: Our findings showed that even with low species coverage, metaproteomics has the potential to reveal habitat-specific functional features. Finally, we exploit this information to highlight future research avenues that are urgently needed to enhance our understanding of taxonomic composition and functions of complex microbiomes.

Robust Accurate Identification and Biomass Estimates of Microorganisms via Tandem Mass Spectrometry

Rapid and accurate identification of microorganisms and estimation of their biomasses are of extreme importance to public health. Mass spectrometry has become an important technique for these purposes. Previously we published a workflow named Microorganism Classification and Identification (MiCId v.12.26.2017) that was shown to perform no worse than other workflows. This manuscript presents MiCId v.12.13.2018 that, in comparison with the earlier version v.12.26.2017, allows for biomass estimates, provides more accurate microorganism identifications (better controls the number of false positives), and is robust against database size increase. This significant advance is made possible by several new ingredients introduced: first, we apply a modified expectation-maximization method to compute for each taxon considered a prior probability, which can be used for biomass estimate; second, we introduce a new concept called ownership, through which the participation ratio is computed and use it as the number of taxa to be kept within a cluster of closely related taxa; third, based on confidently identified peptides, we calculate for each taxon its degree of independence from the rest of taxa considered to determine whether or not to split this taxon off the cluster. Using 270 data files, each containing a large number of MS/MS spectra, we show that, in comparison with v.12.26.2017, version v.12.13.2018 yields superior retrieval results. We also show that MiCId v.12.13.2018 can estimate species biomass reasonably well. The new MiCId v.12.13.2018, designed to run in Linux environment, is freely available for download at https://www.ncbi.nlm.nih.gov/CBBresearch/Yu/downloads.html.