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IntroductionSarcoidosis is a heterogeneous granulomatous disease with highly variable clinical trajectories, yet no validated biomarkers exist to distinguish progressive sarcoidosis (P-sarcoidosis) from non-progressive disease (NP-sarcoidosis). This lack of tractable biomarkers limits early risk stratification and impedes therapeutic decision-making. Preliminary data from our group suggest that P-sarcoidosis and NP-sarcoidosis may be differentiated by blood-derived and peripheral blood mononuclear cell (PBMC)-derived molecular signatures, as well as ex vivo granuloma biogenesis in response to putative disease-causing antigens. This protocol describes a multi-omic study aimed at identifying mechanistically grounded, clinically translatable biomarkers that distinguish P-sarcoidosis from NP-sarcoidosis.Methods and analysisWe will perform an integrative proteomic and transcriptomic analysis across three biological compartments: ex vivo granuloma model, PBMCs and plasma. Participants with clinically adjudicated P-sarcoidosis or NP-sarcoidosis will provide blood samples for multi-omic profiling. P-sarcoidosis versus NP-sarcoidosis phenotype will be assessed based on changes in spirometry, diffusing capacity for carbon monoxide, chest radiography and need for treatment for pulmonary symptoms. Patient-reported outcomes will also be recorded. Data-driven computational approaches will be used to identify molecular pathways associated with granuloma formation and disease persistence and to develop a classifier that distinguishes P-sarcoidosis from NP-sarcoidosis. Rigorous internal validation, feature-selection procedures and statistical controls for high-dimensional data will be applied. Candidate biomarkers emerging from multi-compartment integration will be prioritised based on biological coherence, reproducibility and clinical feasibility.Ethics and disseminationThe study protocol has been approved by the Biomedical Research Alliance of New York, serving as a single Institutional Review Board (IRB) for the project (IRB # 23–02-503), as well as at National Jewish Health (IRB# HS-4091), University of Minnesota (STUDY00020121/SITE00002051) and The Ohio State University (IRB# 2023X0140). All participants will provide informed consent prior to enrolment. Results will be disseminated through peer-reviewed publications, scientific conferences and presentations to patients and advocacy groups. De-identified datasets and analytic workflows will be shared in accordance with institutional policies and data-sharing agreements.

To gain a thorough appreciation of microbiome dynamics, researchers characterize the functional relevance of expressed microbial genes or proteins. This can be accomplished through metaproteomics, which characterizes the protein expression of microbiomes. Several software tools exist for analyzing microbiomes at the functional level by measuring their combined proteome-level response to environmental perturbations. In this survey, we explore the performance of six available tools, to enable researchers to make informed decisions regarding software choice based on their research goals. Tandem mass spectrometry-based proteomic data obtained from dental caries plaque samples grown with and without sucrose in paired biofilm reactors were used as representative data for this evaluation. Microbial peptides from one sample pair were identified by the X! tandem search algorithm via SearchGUI and subjected to functional analysis using software tools including eggNOG-mapper, MEGAN5, MetaGOmics, MetaProteomeAnalyzer (MPA), ProPHAnE, and Unipept to generate functional annotation through Gene Ontology (GO) terms. Among these software tools, notable differences in functional annotation were detected after comparing differentially expressed protein functional groups. Based on the generated GO terms of these tools we performed a peptide-level comparison to evaluate the quality of their functional annotations. A BLAST analysis against the NCBI non-redundant database revealed that the sensitivity and specificity of functional annotation varied between tools. For example, eggNOG-mapper mapped to the most number of GO terms, while Unipept generated more accurate GO terms. Based on our evaluation, metaproteomics researchers can choose the software according to their analytical needs and developers can use the resulting feedback to further optimize their algorithms. To make more of these tools accessible via scalable metaproteomics workflows, eggNOG-mapper and Unipept 4.0 were incorporated into the Galaxy platform.

IntroductionSarcoidosis is a multisystem disorder with variable presentation and disease course. Diagnosis requires the exclusion of other causes of granulomatous inflammation. Current clinical management is often fraught with diagnostic uncertainy and the lack of tools to predict pulmonary disease progression. To address these challenges, we designed a study using data from bronchoalveolar lavage (BAL) fluid and cells to develop diagnostic and prognostic tools in patients with pulmonary sarcoidosis.Methods and AnalysisThis multicentre study will include discovery and validation cohorts of healthy controls, interstitial lung disease controls and pulmonary sarcoidosis cases from three study sites. Sarcoidosis participants will be grouped into progressive and non-progressive pulmonary disease based on changes in pulmonary function testing, chest radiographs or treatment requirements. The discovery cohort consists of participants with existing BAL fluid, BAL cells, and clinical datasets; the validation cohort will be prospectively enrolled and participants will consent for BAL collection from either a clinical or research bronchoscopy. Untargeted proteomic profiling of BALF along with statistical modelling with variable selection techniques will generate a classifier for diagnosis and prognosis. Targeted proteomics using parallel reaction monitoring–mass spectrometry will be used for internal and external validation. Additionally, BAL cell single-cell gene-expression analysis using Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) will be integrated with proteome-wide data to elucidate cell-specific pathways implicated in the development and progression of sarcoidosis.Ethics and DisseminationThe study will be conducted in accordance with Good Clinical Practice and the Declaration of Helsinki. The protocol has been approved by the Biomedical Research Alliance of New York Institutional Review Board (IRB), which serves as the single IRB across all study sites. The findings of this study will be presented as abstracts at scientific meetings and summarised in peer-reviewed journal manuscripts.

Characterizing tumor-specific neoantigen peptides, derived from genomic or transcriptomic aberrations and presented to the immune system, is critical for immuno-oncology studies. To this end, the modular iPepGen immunopeptidogenomics pipeline provides these functions: (1) Neoantigen prediction and protein database generation from genomic or transcriptomic sequencing data; (2) Peptide identification (3) Verification from immunopeptidomic mass spectral data; (4) Neoantigen classification and visualization; (5) Candidate prioritization for further study. Easy access via a publicly available, scalable cloud-based gateway coupled with online, interactive training materials streamlines the adoption by cancer researchers who require immunopeptidogenomic analysis tools but lack advanced computational expertise and resources.

Intestinal proteases mediate digestion and immune signalling, while increased gut proteolytic activity disrupts the intestinal barrier and generates visceral hypersensitivity, which is common in irritable bowel syndrome (IBS). However, the mechanisms controlling protease function are unclear. Here we show that members of the gut microbiota suppress intestinal proteolytic activity through production of unconjugated bilirubin. This occurs via microbial β-glucuronidase-mediated conversion of bilirubin conjugates. Metagenomic analysis of faecal samples from patients with post-infection IBS ( n  = 52) revealed an altered gut microbiota composition, in particular a reduction in Alistipes taxa, and high gut proteolytic activity driven by specific host serine proteases compared with controls. Germ-free mice showed 10-fold higher proteolytic activity compared with conventional mice. Colonization with microbiota samples from high proteolytic activity IBS patients failed to suppress proteolytic activity in germ-free mice, but suppression of proteolytic activity was achieved with colonization using microbiota from healthy donors. High proteolytic activity mice had higher intestinal permeability, a higher relative abundance of Bacteroides and a reduction in Alistipes taxa compared with low proteolytic activity mice. High proteolytic activity IBS patients had lower fecal β-glucuronidase activity and end-products of bilirubin deconjugation. Mice treated with unconjugated bilirubin and β-glucuronidase-overexpressing E. coli significantly reduced proteolytic activity, while inhibitors of microbial β-glucuronidases increased proteolytic activity. Together, these data define a disease-relevant mechanism of host–microbial interaction that maintains protease homoeostasis in the gut. β-glucuronidases produced by gut microbiota members mediate proteolytic activity in the gut via the production of unconjugated bilirubin, which is dysregulated in irritable bowel syndrome.

Within ovarian cancer research, patient-derived xenograft (PDX) models recapitulate histologic features and genomic aberrations found in original tumors. However, conflicting data from published studies have demonstrated significant transcriptional differences between PDXs and original tumors, challenging the fidelity of these models. We employed a quantitative mass spectrometry-based proteomic approach coupled with generation of patient-specific databases using RNA-seq data to investigate the proteogenomic landscape of serially-passaged PDX models established from two patients with distinct subtypes of ovarian cancer. We demonstrate that the utilization of patient-specific databases guided by transcriptional profiles increases the depth of human protein identification in PDX models. Our data show that human proteomes of serially passaged PDXs differ significantly from their patient-derived tumor of origin. Analysis of differentially abundant proteins revealed enrichment of distinct biological pathways with major downregulated processes including extracellular matrix organization and the immune system. Finally, we investigated the relative abundances of ovarian cancer-related proteins identified from the Cancer Gene Census across serially passaged PDXs, and found their protein levels to be unstable across PDX models. Our findings highlight features of distinct and dynamic proteomes of serially-passaged PDX models of ovarian cancer.

Clinical metaproteomics has immense potential to offer functional insights into the microbiome and its contributions to human disease. However, there are numerous challenges in the metaproteomic analysis of clinical samples, including handling of very large protein sequence databases for sensitive and accurate peptide and protein identification from mass spectrometry data, as well as taxonomic and functional annotation of quantified peptides and proteins to enable interpretation of results. To address these challenges, we have developed a novel clinical metaproteomics workflow that provides customized bioinformatic identification, verification, quantification, and taxonomic and functional annotation. This bioinformatic workflow is implemented in the Galaxy ecosystem and has been used to characterize diverse clinical sample types, such as nasopharyngeal swabs and bronchoalveolar lavage fluid. Here, we demonstrate its effectiveness and availability for use by the research community via analysis of residual fluid from cervical swabs.

Background Obstructive lung disease (OLD) is increasingly prevalent among persons living with HIV (PLWH). However, the role of proteases in HIV-associated OLD remains unclear. Methods We combined proteomics and peptidomics to comprehensively characterize protease activities. We combined mass spectrometry (MS) analysis on bronchoalveolar lavage fluid (BALF) peptides and proteins from PLWH with OLD (n = 25) and without OLD (n = 26) with a targeted Somascan aptamer-based proteomic approach to quantify individual proteases and assess their correlation with lung function. Endogenous peptidomics mapped peptides to native proteins to identify substrates of protease activity. Using the MEROPS database, we identified candidate proteases linked to peptide generation based on binding site affinities which were assessed via z-scores. We used t-tests to compare average forced expiratory volume in 1 s per predicted value (FEV1pp) between samples with and without detection of each cleaved protein and adjusted for multiple comparisons by controlling the false discovery rate (FDR). Findings We identified 101 proteases, of which 95 had functional network associations and 22 correlated with FEV1pp. These included cathepsins, metalloproteinases (MMP), caspases and neutrophil elastase. We discovered 31 proteins subject to proteolytic cleavage that associate with FEV1pp, with the top pathways involved in small ubiquitin-like modifier mediated modification (SUMOylation). Proteases linked to protein cleavage included neutrophil elastase, granzyme, and cathepsin D. Interpretations In HIV-associated OLD, a significant number of proteases are up-regulated, many of which are involved in protein degradation. These proteases degrade proteins involved in cell cycle and protein stability, thereby disrupting critical biological functions.

The Earth Microbiome Project (EMP) aided in understanding the role of microbial communities and the influence of collective genetic material (the 'microbiome') and microbial diversity patterns across the habitats of our planet. With the evolution of new sequencing technologies, researchers can now investigate the microbiome and map its influence on the environment and human health. Advances in bioinformatics methods for next-generation sequencing (NGS) data analysis have helped researchers to gain an in-depth knowledge about the taxonomic and genetic composition of microbial communities. Metagenomic-based methods have been the most commonly used approaches for microbiome analysis; however, it primarily extracts information about taxonomic composition and genetic potential of the microbiome under study, lacking quantification of the gene products (RNA and proteins). On the other hand, metatranscriptomics, the study of a microbial community's RNA expression, can reveal the dynamic gene expression of individual microbial populations and the community as a whole, ultimately providing information about the active pathways in the microbiome.  In order to address the analysis of NGS data, the ASaiM analysis framework was previously developed and made available via the Galaxy platform. Although developed for both metagenomics and metatranscriptomics, the original publication demonstrated the use of ASaiM only for metagenomics, while thorough testing for metatranscriptomics data was lacking.  In the current study, we have focused on validating and optimizing the tools within ASaiM for metatranscriptomics data. As a result, we deliver a robust workflow that will enable researchers to understand dynamic functional response of the microbiome in a wide variety of metatranscriptomics studies. This improved and optimized ASaiM-metatranscriptomics (ASaiM-MT) workflow is publicly available via the ASaiM framework, documented and supported with training material so that users can interrogate and characterize metatranscriptomic data, as part of larger meta-omic studies of microbiomes.

Background ‘Omics methods have empowered scientists to tackle the complexity of microbial communities on a scale not attainable before. Individually, omics analyses can provide great insight; while combined as “meta-omics”, they enhance the understanding of which organisms occupy specific metabolic niches, how they interact, and how they utilize environmental nutrients. Here we present three integrative meta-omics workflows, developed in Galaxy, for enhanced analysis and integration of metagenomics, metatranscriptomics, and metaproteomics, combined with our newly developed web-application, ViMO (Visualizer for Meta-Omics) to analyse metabolisms in complex microbial communities. Results In this study, we applied the workflows on a highly efficient cellulose-degrading minimal consortium enriched from a biogas reactor to analyse the key roles of uncultured microorganisms in complex biomass degradation processes. Metagenomic analysis recovered metagenome-assembled genomes (MAGs) for several constituent populations including  Hungateiclostridium thermocellum , Thermoclostridium stercorarium and multiple heterogenic strains affiliated to  Coprothermobacter proteolyticus . The metagenomics workflow was developed as two modules, one standard, and one optimized for improving the MAG quality in complex samples by implementing a combination of single- and co-assembly, and dereplication after binning. The exploration of the active pathways within the recovered MAGs can be visualized in ViMO, which also provides an overview of the MAG taxonomy and quality (contamination and completeness), and information about carbohydrate-active enzymes (CAZymes), as well as KEGG annotations and pathways, with counts and abundances at both mRNA and protein level. To achieve this, the metatranscriptomic reads and metaproteomic mass-spectrometry spectra are mapped onto predicted genes from the metagenome to analyse the functional potential of MAGs, as well as the actual expressed proteins and functions of the microbiome, all visualized in ViMO. Conclusion Our three workflows for integrative meta-omics in combination with ViMO presents a progression in the analysis of ‘omics data, particularly within Galaxy, but also beyond. The optimized metagenomics workflow allows for detailed reconstruction of microbial community consisting of MAGs with high quality, and thus improves analyses of the metabolism of the microbiome, using the metatranscriptomics and metaproteomics workflows.