In Brief metaQuantome enables quantitative analysis of the taxonomic and functional state of a microbiome. Leveraging quantitative mass spectrometry data generated from metaproteomic samples along with taxonomic and functional annotations, metaQuantome unravels the complex and hierarchical data structure of taxonomic and functional ontologies. As a result, metaQuantome enables data exploration, tests hypotheses, and generates high-quality visualizations. metaQuantome deciphers the contribution of taxa to a functional process and vice versa. Its accessibility will pave the way for advanced multi-omic analysis of diverse microbiomes.
Multi-omics approaches focused on mass-spectrometry (MS)-based data, such as metaproteomics, utilize genomic and/or transcriptomic sequencing data to generate a comprehensive protein sequence database. These databases can be very large, containing millions of sequences, which reduces the sensitivity of matching tandem mass spectrometry (MS/MS) data to sequences to generate peptide spectrum matches (PSMs). Here, we describe a sectioning method for generating an enriched database for those protein sequences that are most likely present in the sample. Our evaluation demonstrates how this method helps to increase the sensitivity of PSMs while maintaining acceptable false discovery rate statistics. We demonstrate increased true positive PSM identifications using the sectioning method when compared to the traditional large database searching method, whereas it helped in reducing the false PSM identifications when compared to a previously described two-step method for reducing database size. The sectioning method for large sequence databases enables generation of an enriched protein sequence database and promotes increased sensitivity in identifying PSMs, while maintaining acceptable and manageable FDR. Furthermore, implementation in the Galaxy platform provides access to a usable and automated workflow for carrying out the method. Our results show the utility of this methodology for a wide-range of applications where genome-guided, large sequence databases are required for MS-based proteomics data analysis.
Sequence variation in the HLA-B gene is critically linked to differential immune responses. A dimorphism at -21 of HLA-B exon 1 gives rise to leader peptides that are markers for risk of acute graft-versus-host disease (GVHD), relapse, and mortality after unrelated donor and cord blood transplantation. To optimize the selection of stem cell transplant sources based on the HLA-B leader, an HLA-B Leader Assessment Tool ("BLEAT") was developed to automate the assignment of leader genotypes, define HLA-B leader match statuses, and rank order candidate stem cell sources according to clinical risk. The base cohort consisted of 9,417,614 registered donors from the Be The Match Registry® with HLA-B typing. Among these donors, the performance of BLEAT was assessed in 1,098,358 donors with sequence data for HLA-B exon 1 (2,196,716 haplotypes). The accuracy of leader assignment was then assessed in a second cohort of 1,259 patients and their unrelated transplant donors. We furthermore established the frequencies of HLA-B leader genotype (MM, MT, TT) representations in broad racial categories in the 9.42 million donors. BLEAT has direct applications for the selection of optimal stem cell sources for transplantation and broad utility in basic and clinical research in pharmacogenomics, vaccine development, and cancer and infectious disease studies of human populations.
Pulmonary involvement occurs in up to 95% of sarcoidosis cases. In this pilot study, we examine lung compartment-specific protein expression to identify pathways linked to development and progression of pulmonary sarcoidosis. We characterized bronchoalveolar lavage (BAL) cells and fluid (BALF) proteins in recently diagnosed sarcoidosis cases. We identified 4,306 proteins in BAL cells, of which 272 proteins were differentially expressed in sarcoidosis compared to controls. These proteins map to novel pathways such as integrin-linked kinase and IL-8 signaling and previously implicated pathways in sarcoidosis, including phagosome maturation, clathrin-mediated endocytic signaling and redox balance. In the BALF, the differentially expressed proteins map to several pathways identified in the BAL cells. The differentially expressed BALF proteins also map to aryl hydrocarbon signaling, communication between innate and adaptive immune response, integrin, PTEN and phospholipase C signaling, serotonin and tryptophan metabolism, autophagy, and B cell receptor signaling. Additional pathways that were different between progressive and non-progressive sarcoidosis in the BALF included CD28 signaling and PFKFB4 signaling. Our studies demonstrate the power of contemporary proteomics to reveal novel mechanisms operational in sarcoidosis. Application of our workflows in well-phenotyped large cohorts maybe beneficial to identify biomarkers for diagnosis and prognosis and therapeutically tenable molecular mechanisms.
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.
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