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Xue M, Xie Y, Zang X, Zhong Y, Ma X, Sun H, Liu J. Deciphering functional groups of rumen microbiome and their underlying potentially causal relationships in shaping host traits. IMETA 2024; 3:e225. [PMID: 39135684 PMCID: PMC11316931 DOI: 10.1002/imt2.225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/31/2024] [Revised: 06/25/2024] [Accepted: 06/26/2024] [Indexed: 08/15/2024]
Abstract
Over the years, microbiome research has achieved tremendous advancements driven by culture-independent meta-omics approaches. Despite extensive research, our understanding of the functional roles and causal effects of the microbiome on phenotypes remains limited. In this study, we focused on the rumen metaproteome, combining it with metatranscriptome and metabolome data to accurately identify the active functional distributions of rumen microorganisms and specific functional groups that influence feed efficiency. By integrating host genetics data, we established the potentially causal relationships between microbes-proteins/metabolites-phenotype, and identified specific patterns in which functional groups of rumen microorganisms influence host feed efficiency. We found a causal link between Selenomonas bovis and rumen carbohydrate metabolism, potentially mediated by bacterial chemotaxis and a two-component regulatory system, impacting feed utilization efficiency of dairy cows. Our study on the nutrient utilization functional groups in the rumen of high-feed-efficiency dairy cows, along with the identification of key microbiota functional proteins and their potentially causal relationships, will help move from correlation to causation in rumen microbiome research. This will ultimately enable precise regulation of the rumen microbiota for optimized ruminant production.
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Affiliation(s)
- Ming‐Yuan Xue
- Institute of Dairy Science, College of Animal SciencesZhejiang UniversityHangzhouChina
- Xianghu LaboratoryHangzhouChina
| | - Yun‐Yi Xie
- Institute of Dairy Science, College of Animal SciencesZhejiang UniversityHangzhouChina
| | - Xin‐Wei Zang
- Institute of Dairy Science, College of Animal SciencesZhejiang UniversityHangzhouChina
| | - Yi‐Fan Zhong
- Institute of Dairy Science, College of Animal SciencesZhejiang UniversityHangzhouChina
| | - Xiao‐Jiao Ma
- Institute of Dairy Science, College of Animal SciencesZhejiang UniversityHangzhouChina
| | - Hui‐Zeng Sun
- Institute of Dairy Science, College of Animal SciencesZhejiang UniversityHangzhouChina
- Ministry of Education Key Laboratory of Molecular Animal NutritionZhejiang UniversityHangzhouChina
| | - Jian‐Xin Liu
- Institute of Dairy Science, College of Animal SciencesZhejiang UniversityHangzhouChina
- Ministry of Education Key Laboratory of Molecular Animal NutritionZhejiang UniversityHangzhouChina
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2
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WU E, QIAO L. [Microbial metaproteomics--From sample processing to data acquisition and analysis]. Se Pu 2024; 42:658-668. [PMID: 38966974 PMCID: PMC11224941 DOI: 10.3724/sp.j.1123.2024.02009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Indexed: 07/06/2024] Open
Abstract
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.
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Wu E, Mallawaarachchi V, Zhao J, Yang Y, Liu H, Wang X, Shen C, Lin Y, Qiao L. Contigs directed gene annotation (ConDiGA) for accurate protein sequence database construction in metaproteomics. MICROBIOME 2024; 12:58. [PMID: 38504332 PMCID: PMC10949615 DOI: 10.1186/s40168-024-01775-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Accepted: 02/05/2024] [Indexed: 03/21/2024]
Abstract
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.
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Affiliation(s)
- Enhui Wu
- Department of Chemistry, and Shanghai Stomatological Hospital, Fudan University, Shanghai, 200000, China
| | - Vijini Mallawaarachchi
- School of Computing, College of Engineering, Computing and Cybernetics, The Australian National University, Canberra, ACT, 2600, Australia
- Flinders Accelerator for Microbiome Exploration, College of Science and Engineering, Flinders University, Bedford Park, SA, 5042, Australia
| | - Jinzhi Zhao
- Department of Chemistry, and Shanghai Stomatological Hospital, Fudan University, Shanghai, 200000, China
| | - Yi Yang
- Department of Chemistry, and Shanghai Stomatological Hospital, Fudan University, Shanghai, 200000, China
| | - Hebin Liu
- Shanghai Omicsolution Co., Ltd, Shanghai, 200000, China
| | - Xiaoqing Wang
- Shanghai Omicsolution Co., Ltd, Shanghai, 200000, China
| | - Chengpin Shen
- Shanghai Omicsolution Co., Ltd, Shanghai, 200000, China
| | - Yu Lin
- School of Computing, College of Engineering, Computing and Cybernetics, The Australian National University, Canberra, ACT, 2600, Australia
| | - Liang Qiao
- Department of Chemistry, and Shanghai Stomatological Hospital, Fudan University, Shanghai, 200000, China.
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Wu E, Yang Y, Zhao J, Zheng J, Wang X, Shen C, Qiao L. High-Abundance Protein-Guided Hybrid Spectral Library for Data-Independent Acquisition Metaproteomics. Anal Chem 2024; 96:1029-1037. [PMID: 38180447 DOI: 10.1021/acs.analchem.3c03255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2024]
Abstract
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.
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Affiliation(s)
- Enhui Wu
- Department of Chemistry, and Shanghai Stomatological Hospital, Fudan University, Shanghai 200000, China
| | - Yi Yang
- Department of Chemistry, and Shanghai Stomatological Hospital, Fudan University, Shanghai 200000, China
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou 310000, China
| | - Jinzhi Zhao
- Department of Chemistry, and Shanghai Stomatological Hospital, Fudan University, Shanghai 200000, China
| | - Jianxujie Zheng
- Department of Chemistry, and Shanghai Stomatological Hospital, Fudan University, Shanghai 200000, China
| | - Xiaoqing Wang
- Shanghai Omicsolution Co., Ltd., Shanghai 200000, China
| | - Chengpin Shen
- Shanghai Omicsolution Co., Ltd., Shanghai 200000, China
| | - Liang Qiao
- Department of Chemistry, and Shanghai Stomatological Hospital, Fudan University, Shanghai 200000, China
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Klaes S, Madan S, Deobald D, Cooper M, Adrian L. GroEL-Proteotyping of Bacterial Communities Using Tandem Mass Spectrometry. Int J Mol Sci 2023; 24:15692. [PMID: 37958676 PMCID: PMC10649880 DOI: 10.3390/ijms242115692] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 10/24/2023] [Accepted: 10/25/2023] [Indexed: 11/15/2023] Open
Abstract
Profiling bacterial populations in mixed communities is a common task in microbiology. Sequencing of 16S small subunit ribosomal-RNA (16S rRNA) gene amplicons is a widely accepted and functional approach but relies on amplification primers and cannot quantify isotope incorporation. Tandem mass spectrometry proteotyping is an effective alternative for taxonomically profiling microorganisms. We suggest that targeted proteotyping approaches can complement traditional population analyses. Therefore, we describe an approach to assess bacterial community compositions at the family level using the taxonomic marker protein GroEL, which is ubiquitously found in bacteria, except a few obligate intracellular species. We refer to our method as GroEL-proteotyping. GroEL-proteotyping is based on high-resolution tandem mass spectrometry of GroEL peptides and identification of GroEL-derived taxa via a Galaxy workflow and a subsequent Python-based analysis script. Its advantage is that it can be performed with a curated and extendable sample-independent database and that GroEL can be pre-separated by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) to reduce sample complexity, improving GroEL identification while simultaneously decreasing the instrument time. GroEL-proteotyping was validated by employing it on a comprehensive raw dataset obtained through a metaproteome approach from synthetic microbial communities as well as real human gut samples. Our data show that GroEL-proteotyping enables fast and straightforward profiling of highly abundant taxa in bacterial communities at reasonable taxonomic resolution.
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Affiliation(s)
- Simon Klaes
- Department of Environmental Biotechnology, Helmholtz Centre for Environmental Research (UFZ), 04318 Leipzig, Germany; (S.K.); (D.D.)
- Faculty III Process Sciences, Institute of Biotechnology, Chair of Geobiotechnology, Technische Universität Berlin, 13355 Berlin, Germany
| | - Shobhit Madan
- Department of Environmental Biotechnology, Helmholtz Centre for Environmental Research (UFZ), 04318 Leipzig, Germany; (S.K.); (D.D.)
- Faculty of Engineering, Ansbach University of Applied Sciences, 91522 Ansbach, Germany
| | - Darja Deobald
- Department of Environmental Biotechnology, Helmholtz Centre for Environmental Research (UFZ), 04318 Leipzig, Germany; (S.K.); (D.D.)
| | - Myriel Cooper
- Faculty III Process Sciences, Institute of Environmental Technology, Chair of Environmental Microbiology, Technische Universität Berlin, 10587 Berlin, Germany
| | - Lorenz Adrian
- Department of Environmental Biotechnology, Helmholtz Centre for Environmental Research (UFZ), 04318 Leipzig, Germany; (S.K.); (D.D.)
- Faculty III Process Sciences, Institute of Biotechnology, Chair of Geobiotechnology, Technische Universität Berlin, 13355 Berlin, Germany
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Canderan J, Stamboulian M, Ye Y. MetaProD: A Highly-Configurable Mass Spectrometry Analyzer for Multiplexed Proteomic and Metaproteomic Data. J Proteome Res 2023; 22:442-453. [PMID: 36688801 PMCID: PMC9903327 DOI: 10.1021/acs.jproteome.2c00614] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Indexed: 01/24/2023]
Abstract
The microbiome has been shown to be important for human health because of its influence on disease and the immune response. Mass spectrometry is an important tool for evaluating protein expression and species composition in the microbiome but is technically challenging and time-consuming. Multiplexing has emerged as a way to make spectrometry workflows faster while improving results. Here, we present MetaProD (MetaProteomics in Django) as a highly configurable metaproteomic data analysis pipeline supporting label-free and multiplexed mass spectrometry. The pipeline is open-source, uses fully open-source tools, and is integrated with Django to offer a web-based interface for configuration and data access. Benchmarking of MetaProD using multiple metaproteomics data sets showed that MetaProD achieved fast and efficient identification of peptides and proteins. Application of MetaProD to a multiplexed cancer data set resulted in identification of more differentially expressed human proteins in cancer tissues versus healthy tissues as compared to previous studies; in addition, MetaProD identified bacterial proteins in those samples, some of which are differentially abundant.
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Affiliation(s)
- Jamie Canderan
- Informatics
Department, Luddy School of Informatics, Computing and Engineering, Indiana University, Bloomington, Indiana 47408, United States
| | - Moses Stamboulian
- Informatics
Department, Luddy School of Informatics, Computing and Engineering, Indiana University, Bloomington, Indiana 47408, United States
| | - Yuzhen Ye
- Computer
Science Department, Luddy School of Informatics, Computing and Engineering, Indiana University, Bloomington, Indiana 47408, United States
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Cheng K, Ning Z, Li L, Zhang X, Serrana JM, Mayne J, Figeys D. MetaLab-MAG: A Metaproteomic Data Analysis Platform for Genome-Level Characterization of Microbiomes from the Metagenome-Assembled Genomes Database. J Proteome Res 2023; 22:387-398. [PMID: 36508259 PMCID: PMC9903328 DOI: 10.1021/acs.jproteome.2c00554] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Indexed: 12/14/2022]
Abstract
The studies of microbial communities have drawn increased attention in various research fields such as agriculture, environment, and human health. Recently, metaproteomics has become a powerful tool to interpret the roles of the community members by investigating the expressed proteins of the microbes. However, analyzing the metaproteomic data sets at genome resolution is still challenging because of the lack of efficient bioinformatics tools. Here we develop MetaLab-MAG, a specially designed tool for the characterization of microbiomes from metagenome-assembled genomes databases. MetaLab-MAG was evaluated by analyzing various human gut microbiota data sets and performed comparably or better than searching the gene catalog protein database directly. MetaLab-MAG can quantify the genome-level microbiota compositions and supports both label-free and isobaric labeling-based quantification strategies. MetaLab-MAG removes the obstacles of metaproteomic data analysis and provides the researchers with in-depth and comprehensive information from the microbiomes.
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Affiliation(s)
- Kai Cheng
- School of Pharmaceutical Sciences, Faculty of Medicine, University of Ottawa, Ottawa, Ontario K1H 8M5, Canada
| | - Zhibin Ning
- School of Pharmaceutical Sciences, Faculty of Medicine, University of Ottawa, Ottawa, Ontario K1H 8M5, Canada
| | - Leyuan Li
- School of Pharmaceutical Sciences, Faculty of Medicine, University of Ottawa, Ottawa, Ontario K1H 8M5, Canada
| | - Xu Zhang
- School of Pharmaceutical Sciences, Faculty of Medicine, University of Ottawa, Ottawa, Ontario K1H 8M5, Canada
| | - Joeselle M. Serrana
- School of Pharmaceutical Sciences, Faculty of Medicine, University of Ottawa, Ottawa, Ontario K1H 8M5, Canada
| | - Janice Mayne
- School of Pharmaceutical Sciences, Faculty of Medicine, University of Ottawa, Ottawa, Ontario K1H 8M5, Canada
| | - Daniel Figeys
- School of Pharmaceutical Sciences, Faculty of Medicine, University of Ottawa, Ottawa, Ontario K1H 8M5, Canada
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8
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Zhao J, Yang Y, Xu H, Zheng J, Shen C, Chen T, Wang T, Wang B, Yi J, Zhao D, Wu E, Qin Q, Xia L, Qiao L. Data-independent acquisition boosts quantitative metaproteomics for deep characterization of gut microbiota. NPJ Biofilms Microbiomes 2023; 9:4. [PMID: 36693863 PMCID: PMC9873935 DOI: 10.1038/s41522-023-00373-9] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Accepted: 01/11/2023] [Indexed: 01/26/2023] Open
Abstract
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.
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Affiliation(s)
- Jinzhi Zhao
- Department of Chemistry, Shanghai Stomatological Hospital, Fudan University, 200000, Shanghai, China
| | - Yi Yang
- Department of Chemistry, Shanghai Stomatological Hospital, Fudan University, 200000, Shanghai, China
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, 311200, Hangzhou, China
| | - Hua Xu
- Department of Core Facility of Basic Medical Sciences, and Department of Psychiatry of Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, 200000, Shanghai, China
| | - Jianxujie Zheng
- Department of Chemistry, Shanghai Stomatological Hospital, Fudan University, 200000, Shanghai, China
| | - Chengpin Shen
- Shanghai Omicsolution Co., Ltd, 201100, Shanghai, China
| | - Tian Chen
- Changhai Hospital, The Naval Military Medical University, 200433, Shanghai, China
| | - Tao Wang
- Department of Core Facility of Basic Medical Sciences, and Department of Psychiatry of Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, 200000, Shanghai, China
| | - Bing Wang
- College of Food Science and Technology, Shanghai Ocean University, 201306, Shanghai, China
| | - Jia Yi
- Department of Chemistry, Shanghai Stomatological Hospital, Fudan University, 200000, Shanghai, China
| | - Dan Zhao
- Department of Chemistry, Shanghai Stomatological Hospital, Fudan University, 200000, Shanghai, China
| | - Enhui Wu
- Department of Chemistry, Shanghai Stomatological Hospital, Fudan University, 200000, Shanghai, China
| | - Qin Qin
- Changhai Hospital, The Naval Military Medical University, 200433, Shanghai, China.
| | - Li Xia
- Department of Core Facility of Basic Medical Sciences, and Department of Psychiatry of Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, 200000, Shanghai, China.
| | - Liang Qiao
- Department of Chemistry, Shanghai Stomatological Hospital, Fudan University, 200000, Shanghai, China.
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9
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Gut Microbiome Proteomics in Food Allergies. Int J Mol Sci 2023; 24:ijms24032234. [PMID: 36768555 PMCID: PMC9917015 DOI: 10.3390/ijms24032234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 01/17/2023] [Accepted: 01/19/2023] [Indexed: 01/26/2023] Open
Abstract
Food allergies (FA) have dramatically increased in recent years, particularly in developed countries. It is currently well-established that food tolerance requires the strict maintenance of a specific microbial consortium in the gastrointestinal (GI) tract microbiome as alterations in the gut microbiota can lead to dysbiosis, causing inflammation and pathogenic intestinal conditions that result in the development of FA. Although there is currently not enough knowledge to fully understand how the interactions between gut microbiota, host responses and the environment cause food allergies, recent advances in '-omics' technologies (i.e., proteomics, genomics, metabolomics) and in approaches involving systems biology suggest future headways that would finally allow the scientific understanding of the relationship between gut microbiome and FA. This review summarizes the current knowledge in the field of FA and insights into the future advances that will be achieved by applying proteomic techniques to study the GI tract microbiome in the field of FA and their medical treatment. Metaproteomics, a proteomics experimental approach of great interest in the study of GI tract microbiota, aims to analyze and identify all the proteins in complex environmental microbial communities; with shotgun proteomics, which uses liquid chromatography (LC) for separation and tandem mass spectrometry (MS/MS) for analysis, as it is the most promising technique in this field.
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10
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Jiang X, Zhang Y, Wang H, Wang Z, Hu S, Cao C, Xiao H. In-Depth Metaproteomics Analysis of Oral Microbiome for Lung Cancer. Research (Wash D C) 2022; 2022:9781578. [PMID: 36320634 PMCID: PMC9590273 DOI: 10.34133/2022/9781578] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 09/22/2022] [Indexed: 11/12/2022] Open
Abstract
The human oral microbiome correlates with numerous diseases, including lung cancer. Identifying the functional changes by metaproteomics helps understand the disease-related dysbiosis, yet characterizing low-abundant bacteria is challenging. Here, we developed a free-flow isoelectric focusing electrophoresis-mass spectrometry- (FFIEF-MS-) based metaproteomics strategy to reduce host interferences and enrich low-abundant bacteria for in-depth interpretation of the oral microbiome. With our method, the number of interfering peptides decreased by 52.87%, whereas the bacterial peptides and species increased by 94.97% and 44.90%, respectively, compared to the conventional metaproteomics approach. We identified 3647 bacterial proteins, which is the most comprehensive oral metaproteomics study to date. Lung cancer-associated bacteria were validated among an independent cohort. The imbalanced Fusobacterium nucleatum and Prevotella histicola and their dysregulated functions in inhibiting immune response and maintaining cell redox homeostasis were revealed. The FFIEF-MS may serve as a valuable strategy to study the mechanisms between human diseases and microbiomes with broader applications.
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Affiliation(s)
- Xiaoteng Jiang
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yan Zhang
- School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Huiyu Wang
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Zeyuan Wang
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Shen Hu
- School of Dentistry and Jonsson Comprehensive Cancer Center, University of California-Los Angeles, Los Angeles 90095, USA
| | - Chengxi Cao
- Department of Instrument Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Hua Xiao
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
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11
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Stamboulian M, Canderan J, Ye Y. Metaproteomics as a tool for studying the protein landscape of human-gut bacterial species. PLoS Comput Biol 2022; 18:e1009397. [PMID: 35302987 PMCID: PMC8967034 DOI: 10.1371/journal.pcbi.1009397] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 03/30/2022] [Accepted: 02/15/2022] [Indexed: 12/26/2022] Open
Abstract
Host-microbiome interactions and the microbial community have broad impact in human health and diseases. Most microbiome based studies are performed at the genome level based on next-generation sequencing techniques, but metaproteomics is emerging as a powerful technique to study microbiome functional activity by characterizing the complex and dynamic composition of microbial proteins. We conducted a large-scale survey of human gut microbiome metaproteomic data to identify generalist species that are ubiquitously expressed across all samples and specialists that are highly expressed in a small subset of samples associated with a certain phenotype. We were able to utilize the metaproteomic mass spectrometry data to reveal the protein landscapes of these species, which enables the characterization of the expression levels of proteins of different functions and underlying regulatory mechanisms, such as operons. Finally, we were able to recover a large number of open reading frames (ORFs) with spectral support, which were missed by de novo protein-coding gene predictors. We showed that a majority of the rescued ORFs overlapped with de novo predicted protein-coding genes, but on opposite strands or in different frames. Together, these demonstrate applications of metaproteomics for the characterization of important gut bacterial species. Many reference genomes for studying human gut microbiome are available, but knowledge about how microbial organisms work is limited. Identification of proteins at individual species or community level provides direct insight into the functionality of microbial organisms. By analyzing more than a thousand metaproteomics datasets, we examined protein landscapes of more than two thousands of microbial species that may be important to human health and diseases. This work demonstrated new applications of metaproteomic datasets for studying individual genomes. We made the analysis results available through a website (called GutBac), which we believe will become a resource for studying microbial species important for human health and diseases.
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Affiliation(s)
- Moses Stamboulian
- Computer Science Department, Indiana University, Bloomington, Indiana, United States of America
| | - Jamie Canderan
- Computer Science Department, Indiana University, Bloomington, Indiana, United States of America
| | - Yuzhen Ye
- Computer Science Department, Indiana University, Bloomington, Indiana, United States of America
- * E-mail:
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12
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Jouffret V, Miotello G, Culotta K, Ayrault S, Pible O, Armengaud J. Increasing the power of interpretation for soil metaproteomics data. MICROBIOME 2021; 9:195. [PMID: 34587999 PMCID: PMC8482631 DOI: 10.1186/s40168-021-01139-1] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Accepted: 07/29/2021] [Indexed: 05/07/2023]
Abstract
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.
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Affiliation(s)
- Virginie Jouffret
- Université Paris-Saclay, CEA, INRAE, Département Médicaments et Technologies pour la Santé (DMTS), SPI, F-30200, Bagnols-sur-Cèze, France
- Laboratoire des Sciences et de l'Environnement (LSCE-IPSL), UMR 8212 (CEA/CNRS/UVSQ), CEA Saclay, Université Paris-Saclay, Orme des Merisiers, F-91191, Gif-sur-Yvette, France
- Laboratoire Innovations technologiques pour la Détection et le Diagnostic (Li2D), Université de Montpellier, F-30207, Bagnols-sur-Cèze, France
| | - Guylaine Miotello
- Université Paris-Saclay, CEA, INRAE, Département Médicaments et Technologies pour la Santé (DMTS), SPI, F-30200, Bagnols-sur-Cèze, France
| | - Karen Culotta
- Université Paris-Saclay, CEA, INRAE, Département Médicaments et Technologies pour la Santé (DMTS), SPI, F-30200, Bagnols-sur-Cèze, France
| | - Sophie Ayrault
- Laboratoire des Sciences et de l'Environnement (LSCE-IPSL), UMR 8212 (CEA/CNRS/UVSQ), CEA Saclay, Université Paris-Saclay, Orme des Merisiers, F-91191, Gif-sur-Yvette, France
| | - Olivier Pible
- Université Paris-Saclay, CEA, INRAE, Département Médicaments et Technologies pour la Santé (DMTS), SPI, F-30200, Bagnols-sur-Cèze, France
| | - Jean Armengaud
- Université Paris-Saclay, CEA, INRAE, Département Médicaments et Technologies pour la Santé (DMTS), SPI, F-30200, Bagnols-sur-Cèze, France.
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