RESUMEN

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.

RESUMEN

The analysis of meta-omics data requires the utilization of several bioinformatics tools and proficiency in informatics. The integration of multiple meta-omics data is even more challenging, and the outputs of existing bioinformatics solutions are not always easy to interpret. Here, we present a meta-omics bioinformatics pipeline, Meta-Omics Software for Community Analysis (MOSCA), which aims to overcome these limitations. MOSCA was initially developed for analysing metagenomics (MG) and metatranscriptomics (MT) data. Now, it also performs MG and metaproteomics (MP) integrated analysis, and MG/MT analysis was upgraded with an additional iterative binning step, metabolic pathways mapping, and several improvements regarding functional annotation and data visualization. MOSCA handles raw sequencing data and mass spectra and performs pre-processing, assembly, annotation, binning and differential gene/protein expression analysis. MOSCA shows taxonomic and functional analysis in large tables, performs metabolic pathways mapping, generates Krona plots and shows gene/protein expression results in heatmaps, improving omics data visualization. MOSCA is easily run from a single command while also providing a web interface (MOSGUITO). Relevant features include an extensive set of customization options, allowing tailored analyses to suit specific research objectives, and the ability to restart the pipeline from intermediary checkpoints using alternative configurations. Two case studies showcased MOSCA results, giving a complete view of the anaerobic microbial communities from anaerobic digesters and insights on the role of specific microorganisms. MOSCA represents a pivotal advancement in meta-omics research, offering an intuitive, comprehensive, and versatile solution for researchers seeking to unravel the intricate tapestry of microbial communities.

RESUMEN

Methane emissions from enteric fermentation present a dual challenge globally: they not only contribute significantly to atmospheric greenhouse gases but also represent a considerable energy loss for ruminant animals. Utilizing high-throughput omics technologies to analyze rumen microbiome samples (meta-omics, i.e., metagenomics, metatranscriptomics, metaproteomics, metabolomics) holds vast potential for uncovering the intricate interplay between diet, microbiota, and methane emissions in these animals. The primary obstacle is the effective integration of diverse meta-omic approaches and their broader application across different ruminant species. Genetic variability significantly impacts methane production in ruminants, suggesting that genomic selection could be a viable strategy to reduce emissions. While substantial research has been conducted on the microbiological aspects of methane production, there remains a critical need to delineate the specific genetic interactions between the host and its microbiome. Advancements in meta-omics technologies are poised to shed light on these interactions, enhancing our understanding of the genetic factors that govern methane output. This review explores the potential of meta-omics to accelerate genetic advancements that could lead to reduced methane emissions in ruminants. By employing a systems biology approach, the integration of various omics technologies allows for the identification of key genomic regions and genetic markers linked to methane production. These markers can then be leveraged in selective breeding programs to cultivate traits associated with lower emissions. Moreover, the review addresses current challenges in applying genomic selection for this purpose and discusses how omics technologies can overcome these obstacles. The systematic integration and analysis of diverse biological data provide deeper insights into the genetic underpinnings and overall biology of methane production traits in ruminants. Ultimately, this comprehensive approach not only aids in reducing the environmental impact of agriculture but also contributes to the sustainability and efficiency of livestock management.

Asunto(s)

RESUMEN

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.

Asunto(s)

RESUMEN

Alterations in microbiotas and endogenous enzymes have been implicated in meat deterioration. However, the factors that mediate the interactions between meat quality and microbiome profile were inadequately investigated. In this study, we collected pork samples throughout the refrigeration period and employed metaproteomics to characterize both the pork and microbial proteins. Our findings demonstrated that pork proteins associated with the catabolic process are upregulated during storage compared to the initial stage. Pseudomonas, Clostridium, Goodfellowiella, and Gonapodya contribute to the spoilage process. Notably, we observed an elevated abundance of microbial proteins related to glycolytic enzymes in refrigerated pork, identifying numerous proteins linked to biogenic amine production, thus highlighting their essential role in microbial decay. Further, we reveal that many of these microbial proteins from Pseudomonas are ribosomal proteins, promoting enzyme synthesis by enhancing transcription and translation. This study provides intrinsic insights into the underlying mechanisms by which microorganisms contribute to meat spoilage.

Asunto(s)

RESUMEN

The taxonomy of marine plastisphere communities has been extensively studied, demonstrating the ubiquity of hydrocarbonoclastic bacteria of potential biotechnological significance. However, prokaryotic functioning on plastic surfaces has received limited attention, and the question of whether these microorganisms are active and expressing specific molecular mechanisms underpinning plastisphere colonisation remains to be addressed. The aim of this study was to investigate the plastic colonisation process, to identify the active taxa involved in biofilm formation and the mechanisms used to initiate colonisation. To achieve this, a marine plastisphere characterised by active hydrocarbonoclastic genera was used as the inoculum for a short-term microcosm experiment using virgin low-density polyethylene as the sole carbon source. Following incubation for 1 and 2 weeks (representing early and late colonisation, respectively), a taxonomic and comparative metaproteomic approach revealed a significant shift in plastisphere diversity and composition, yet highlighted stability in the predominance of active Proteobacteria spanning 16 genera, including Marinomonas, Pseudomonas, and Pseudoalteromonas. Relative quantification of 1762 proteins shared between the initial plastisphere inoculum, the microcosm plastisphere and the planktonic cells in the surrounding artificial seawater, provided insights into the differential regulation of proteins associated with plastisphere formation. This included the upregulation of proteins mediating cellular attachment in the plastisphere, for example flagellin expressed by Marinomonas, Cobetia, Pseudoalteromonas, and Pseudomonas, and curli expressed by Cobetia. In addition to the differential regulation of energy metabolism in Marinomonas, Psychrobacter, Pseudomonas and Cobetia within the plastisphere relative to the surrounding seawater. Further, we identified the upregulation of amino acid metabolism and transport, including glutamine hydrolysis to glutamate in Marinomonas and unclassified Halomonadaceae, potentially coupled to ammonia availability and oxidative stress experienced within the plastisphere. Our study provides novel insights into the dynamics of plastisphere formation and function, highlighting potential targets for regulating plastisphere growth to enhance plastic bioremediation processes.

Asunto(s)

RESUMEN

We report a metaproteomic analysis of the gut microbiota of eight infants with cystic fibrosis, during the first year of life. This is the first study in this disease that uses metaproteomics to analyze stool samples from patients at such a young age.

RESUMEN

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.

Asunto(s)

RESUMEN

The 2023 European Bioinformatics Community for Mass Spectrometry (EuBIC-MS) Developers Meeting was held from January 15th to January 20th, 2023, in Congressi Stefano Franscin at Monte Verità in Ticino, Switzerland. The participants were scientists and developers working in computational mass spectrometry (MS), metabolomics, and proteomics. The 5-day program was split between introductory keynote lectures and parallel hackathon sessions focusing on "Artificial Intelligence in proteomics" to stimulate future directions in the MS-driven omics areas. During the latter, the participants developed bioinformatics tools and resources addressing outstanding needs in the community. The hackathons allowed less experienced participants to learn from more advanced computational MS experts and actively contribute to highly relevant research projects. We successfully produced several new tools applicable to the proteomics community by improving data analysis and facilitating future research.

Asunto(s)

RESUMEN

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

Asunto(s)

RESUMEN

Weak magnetic field (WMF) has been recognized to promote biological denitrification processes; however, the underlying mechanisms remain largely unexplored, hindering the optimization of its effectiveness. Here, we systematically investigated the effects of WMF on denitrification performance, enzyme activity, microbial community, and metaproteome in packed bed bioreactors treating high nitrate wastewater under different WMF intensities and C:N ratios. Results showed that WMFs significantly promoted denitrification by consistently stimulating the activities of denitrifying reductases and NAD+/NADH biosynthesis across decreasing C:N ratios. Reductases and electron transfer enzymes involved in denitrification were overproduced due to the significantly enriched overexpression of ferromagnetic ion-containing (FIC) metalloproteins. We also observed WMFs' intensity-dependent selective pressure on microbial community structures despite the effects being limited compared to those caused by changing C:N ratios. By coupling genome-centric metaproteomics and structure prediction, we found the dominant denitrifier, Halomonas, was outcompeted by Pseudomonas and Azoarcus under WMFs, likely due to its structural deficiencies in iron uptake, suggesting that advantageous ferromagnetic ion acquisition capacity was necessary to satisfy the substrate demand for FIC metalloprotein overproduction. This study advances our understanding of the biomagnetic effects in the context of complex communities and highlights WMF's potential for manipulating FIC protein-associated metabolism and fine-tuning community structure.

Asunto(s)

RESUMEN

To reveal the key enzymes in the nitrogen removal pathway and to further elucidate the mechanism of the catalytic reaction, this study utilized metaproteomics combined with molecular dynamics and density functional theory calculation. K. stuttgartiensis provided the proteins up to 88.37 % in the anammox-based system. Hydrazine synthase (HZS) and hydrazine dehydrogenase (HDH) accounted for 15.94 % and 3.45 % of the total proteins expressed by K. stuttgartiensis, thus were considered as critical enzymes in the nitrogen removal pathway. The process of HZSγ binding to NO with lowest binding free energy of -4.91 ± 1.33 kJ/mol. The reaction catalyzed by HZSα was calculated to be the rate-limiting catalyzing step, because it transferred the proton from NH3 to ·OH by crossing an energy barrier of up to 190.29 kJ/mol. This study provided molecular level insights to enhance the performance of nitrogen removal in anammox-based system.

Asunto(s)

RESUMEN

Intestinal lavage fluid (IVF) containing the mucosa-associated microbiota instead of fecal samples was used to study the gut microbiota using different omics approaches. Focusing on the 63 IVF samples collected from healthy and hepatitis B virus-liver disease (HBV-LD), a question is prompted whether omics features could be extracted to distinguish these samples. The IVF-related microbiota derived from the omics data was classified into two enterotype sets, whereas the genomics-based enterotypes were poorly overlapped with the proteomics-based one in either distribution of microbiota or of IVFs. There is lack of molecular features in these enterotypes to specifically recognize healthy or HBV-LD. Running machine learning against the omics data sought the appropriate models to discriminate the healthy and HBV-LD IVFs based on selected genes or proteins. Although a single omics dataset is basically workable in such discrimination, integration of the two datasets enhances discrimination efficiency. The protein features with higher frequencies in the models are further compared between healthy and HBV-LD based on their abundance, bringing about three potential protein biomarkers. This study highlights that integration of metaomics data is beneficial for a molecular discriminator of healthy and HBV-LD, and reveals the IVF samples are valuable for microbiome in a small cohort.

RESUMEN

Metaproteomics has become a crucial omics technology for studying microbiomes. In this area, the Unipept ecosystem, accessible at https://unipept.ugent.be , has emerged as a valuable resource for analyzing metaproteomic data. It offers in-depth insights into both taxonomic distributions and functional characteristics of complex ecosystems. This tutorial explains essential concepts like Lowest Common Ancestor (LCA) determination and the handling of peptides with missed cleavages. It also provides a detailed, step-by-step guide on using the Unipept Web application and Unipept Desktop for thorough metaproteomics analyses. By integrating theoretical principles with practical methodologies, this tutorial empowers researchers with the essential knowledge and tools needed to fully utilize metaproteomics in their microbiome studies.

Asunto(s)

RESUMEN

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.

Asunto(s)

RESUMEN

The human gut microbiome plays a vital role in preserving individual health and is intricately involved in essential functions. Imbalances or dysbiosis within the microbiome can significantly impact human health and are associated with many diseases. Several metaproteomics platforms are currently available to study microbial proteins within complex microbial communities. In this study, we attempted to develop an integrated pipeline to provide deeper insights into both the taxonomic and functional aspects of the cultivated human gut microbiomes derived from clinical colon biopsies. We combined a rapid peptide search by MSFragger against the Unified Human Gastrointestinal Protein database and the taxonomic and functional analyses with Unipept Desktop and MetaLab-MAG. Across seven samples, we identified and matched nearly 36,000 unique peptides to approximately 300 species and 11 phyla. Unipept Desktop provided gene ontology, InterPro entries, and enzyme commission number annotations, facilitating the identification of relevant metabolic pathways. MetaLab-MAG contributed functional annotations through Clusters of Orthologous Genes and Non-supervised Orthologous Groups categories. These results unveiled functional similarities and differences among the samples. This integrated pipeline holds the potential to provide deeper insights into the taxonomy and functions of the human gut microbiome for interrogating the intricate connections between microbiome balance and diseases.

RESUMEN

Aim: Our gut microbiome has its own functionalities which can be modulated by various xenobiotic and biotic components. The development and application of a high-throughput functional screening approach of individual gut microbiomes accelerates drug discovery and our understanding of microbiome-drug interactions. We previously developed the rapid assay of individual microbiome (RapidAIM), which combined an optimized culturing model with metaproteomics to study gut microbiome responses to xenobiotics. In this study, we aim to incorporate automation and multiplexing techniques into RapidAIM to develop a high-throughput protocol. Methods: To develop a 2.0 version of RapidAIM, we automated the protein analysis protocol, and introduced a tandem mass tag (TMT) multiplexing technique. To demonstrate the typical outcome of the protocol, we used RapidAIM 2.0 to evaluate the effect of prebiotic kestose on ex vivo individual human gut microbiomes biobanked with five different workflows. Results: We describe the protocol of RapidAIM 2.0 with extensive details on stool sample collection, biobanking, in vitro culturing and stimulation, sample processing, metaproteomics measurement, and data analysis. The analysis depth of 5,014 ± 142 protein groups per multiplexed sample was achieved. A test on five biobanking methods using RapidAIM 2.0 showed the minimal effect of sample processing on live microbiota functional responses to kestose. Conclusions: Depth and reproducibility of RapidAIM 2.0 are comparable to previous manual label-free metaproteomic analyses. In the meantime, the protocol realizes culturing and sample preparation of 320 samples in six days, opening the door to extensively understanding the effects of xenobiotic and biotic factors on our internal ecology.

RESUMEN

BACKGROUND: Microbial communities are important drivers of global biogeochemical cycles, xenobiotic detoxification, as well as organic matter decomposition. Their major metabolic role in ecosystem functioning is ensured by a unique set of enzymes, providing a tremendous yet mostly hidden enzymatic potential. Exploring this enzymatic repertoire is therefore not only relevant for a better understanding of how microorganisms function in their natural environment, and thus for ecological research, but further turns microbial communities, in particular from extreme habitats, into a valuable resource for the discovery of novel enzymes with potential applications in biotechnology. Different strategies for their uncovering such as bioprospecting, which relies mainly on metagenomic approaches in combination with sequence-based bioinformatic analyses, have emerged; yet accurate function prediction of their proteomes and deciphering the in vivo activity of an enzyme remains challenging. RESULTS: Here, we present environmental activity-based protein profiling (eABPP), a multi-omics approach that extends genome-resolved metagenomics with mass spectrometry-based ABPP. This combination allows direct profiling of environmental community samples in their native habitat and the identification of active enzymes based on their function, even without sequence or structural homologies to annotated enzyme families. eABPP thus bridges the gap between environmental genomics, correct function annotation, and in vivo enzyme activity. As a showcase, we report the successful identification of active thermostable serine hydrolases from eABPP of natural microbial communities from two independent hot springs in Kamchatka, Russia. CONCLUSIONS: By reporting enzyme activities within an ecosystem in their native state, we anticipate that eABPP will not only advance current methodological approaches to sequence homology-guided enzyme discovery from environmental ecosystems for subsequent biocatalyst development but also contributes to the ecological investigation of microbial community interactions by dissecting their underlying molecular mechanisms.

RESUMEN

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.

Asunto(s)

RESUMEN

Wastewater treatment plants (WWTPs) are the main barrier to cope with the increased pressure of municipal and industrial wastewater on natural water resources in terms of both polluting load and produced volumes. For this reason, WWTP's efficiency should be the highest; thus, their monitoring becomes critical. In conventional WWTPs, biodegradation of pollutants mainly occurs in the biological reactors, and an increasing interest in a deeper characterization of the biomasses involved in these processes (made of biofilms, granules, and suspended activated sludge) rose up in recent years. In this sense, the meta-omics approaches were recently developed to investigate the entire set of biomolecules of a given class in a microbial community with the same general objective: the identification of the biomolecules through the sequence similarity of high degree in the already available databases. Particularly, metaproteomics concerns the identification of all proteins in a microbial community in a given moment or condition. In this chapter, a protocol for the extraction and separation of proteins from activate sludge sampled at WWTPs is proposed.

Asunto(s)