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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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Teles Filho ACDA, Sanchez DJD, Viana AGA, Sheheryar S, Guerreiro DD, Bustamante-Filho IC, Martins AMA, Sousa MV, Ricart CAO, Fontes W, Moura AA. A prospective study of the proteome of equine pre-implantation embryo. Reprod Domest Anim 2024; 59:e14663. [PMID: 38990011 DOI: 10.1111/rda.14663] [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: 01/14/2024] [Revised: 06/11/2024] [Accepted: 06/24/2024] [Indexed: 07/12/2024]
Abstract
The present study was conducted to investigate the global proteome of 8-day-old equine blastocysts. Follicular dynamics of eight adult mares were monitored by ultrasonography and inseminated 24 h after the detection of a preovulatory follicle. Four expanded blastocysts were recovered, pooled, and subjected to protein extraction and mass spectrometry. Protein identification was conducted based on four database searches (PEAKS, Proteome Discoverer software, SearchGUI software, and PepExplorer). Enrichment analysis was performed using g:Profiler, Panther, and String platforms. After the elimination of identification redundancies among search tools (at three levels, based on identifiers, peptides, and cross-database mapping), 1977 proteins were reliably identified in the samples of equine embryos. Proteomic analysis unveiled robust metabolic activity in the 8-day equine embryo, highlighted by an abundance of proteins engaged in key metabolic pathways like the TCA cycle, ATP biosynthesis, and glycolysis. The prevalence of chaperones among highly abundant proteins suggests that regulation of protein folding, and degradation is a key process during embryo development. These findings pave the way for developing new strategies to improve equine embryo media and optimize in vitro fertilization techniques.
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Affiliation(s)
| | - Deisy J D Sanchez
- Laboratório de Biotecnologia, Universidad Cooperativa de Colombia, Bucaramanga, Colombia
| | | | - Sheheryar Sheheryar
- Department of Animal Science, Federal University of Ceará, Fortaleza, Brazil
| | - Denise D Guerreiro
- Department of Animal Science, Federal University of Ceará, Fortaleza, Brazil
| | | | - Aline M A Martins
- Laboratory of Protein Chemistry and Biochemistry, University of Brasília, Brasilia, Brazil
| | - Marcelo V Sousa
- Laboratory of Protein Chemistry and Biochemistry, University of Brasília, Brasilia, Brazil
| | - Carlos A O Ricart
- Laboratory of Protein Chemistry and Biochemistry, University of Brasília, Brasilia, Brazil
| | - Wagner Fontes
- Laboratory of Protein Chemistry and Biochemistry, University of Brasília, Brasilia, Brazil
| | - Arlindo A Moura
- Department of Animal Science, Federal University of Ceará, Fortaleza, Brazil
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Do K, Mehta S, Wagner R, Bhuming D, Rajczewski AT, Skubitz APN, Johnson JE, Griffin TJ, Jagtap PD. A novel clinical metaproteomics workflow enables bioinformatic analysis of host-microbe dynamics in disease. mSphere 2024; 9:e0079323. [PMID: 38780289 DOI: 10.1128/msphere.00793-23] [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: 12/18/2023] [Accepted: 04/17/2024] [Indexed: 05/25/2024] Open
Abstract
Clinical metaproteomics has the potential to offer insights into the host-microbiome interactions underlying diseases. However, the field faces challenges in characterizing microbial proteins found in clinical samples, usually present at low abundance relative to the host proteins. As a solution, we have developed an integrated workflow coupling mass spectrometry-based analysis with customized bioinformatic identification, quantification, and prioritization of microbial proteins, enabling targeted assay development to investigate host-microbe dynamics in disease. The bioinformatics tools are implemented in the Galaxy ecosystem, offering the development and dissemination of complex bioinformatic workflows. The modular workflow integrates MetaNovo (to generate a reduced protein database), SearchGUI/PeptideShaker and MaxQuant [to generate peptide-spectral matches (PSMs) and quantification], PepQuery2 (to verify the quality of PSMs), Unipept (for taxonomic and functional annotation), and MSstatsTMT (for statistical analysis). We have utilized this workflow in diverse clinical samples, from the characterization of nasopharyngeal swab samples to bronchoalveolar lavage fluid. Here, we demonstrate its effectiveness via analysis of residual fluid from cervical swabs. The complete workflow, including training data and documentation, is available via the Galaxy Training Network, empowering non-expert researchers to utilize these powerful tools in their clinical studies. IMPORTANCE 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.
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Affiliation(s)
- Katherine Do
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, Minnesota, USA
| | - Subina Mehta
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, Minnesota, USA
| | - Reid Wagner
- Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota, USA
| | - Dechen Bhuming
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, Minnesota, USA
| | - Andrew T Rajczewski
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, Minnesota, USA
| | - Amy P N Skubitz
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, Minnesota, USA
| | - James E Johnson
- Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota, USA
| | - Timothy J Griffin
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, Minnesota, USA
| | - Pratik D Jagtap
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, Minnesota, USA
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Chen J, Cai Y, Wang Z, Xu Z, Zhuang W, Liu D, Lv Y, Wang S, Xu J, Ying H. Solid-state fermentation of corn straw using synthetic microbiome to produce fermented feed: The feed quality and conversion mechanism. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 920:171034. [PMID: 38369147 DOI: 10.1016/j.scitotenv.2024.171034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Revised: 02/11/2024] [Accepted: 02/15/2024] [Indexed: 02/20/2024]
Abstract
Straw is a typical biomass resource which can be converted into high nutritional value feed via microbial fermentation. The degradation and conversion of straw using a synthetic microbial community (SMC-8) was functionally investigated to characterise its nitrogen conversion and carbon metabolism. Four species of bacteria were found to utilise >20 % of the inorganic nitrogen within 15 h, and the ratio of the diameter of fungal transparent circles (D) to the diameter of the colony (d) of the four fungal species was >1. Solid-state fermentation of corn straw increased the total amino acid (AA) content by 41.69 %. The absolute digestibility of fermented corn straw dry weight (DW) and true protein was 34.34 % and 45.29 %, respectively. Comprehensive analysis of functional proteins revealed that Aspergillus niger, Trichoderma viride, Cladosporium cladosporioides, Bacillus subtilis and Acinetobacter johnsonii produce a complex enzyme system during corn straw fermentation, which plays a key role in the degradation of lignocellulose. This study provided a new insight in utilizing corn straw.
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Affiliation(s)
- Jinmeng Chen
- School of Chemical Engineering, Zhengzhou University, 100 Ke xue Dadao, Zhengzhou 450001, China
| | - Yafan Cai
- School of Chemical Engineering, Zhengzhou University, 100 Ke xue Dadao, Zhengzhou 450001, China
| | - Zhi Wang
- School of Chemical Engineering, Zhengzhou University, 100 Ke xue Dadao, Zhengzhou 450001, China
| | - Zhengzhong Xu
- School of Chemical Engineering, Zhengzhou University, 100 Ke xue Dadao, Zhengzhou 450001, China
| | - Wei Zhuang
- School of Chemical Engineering, Zhengzhou University, 100 Ke xue Dadao, Zhengzhou 450001, China; National Engineering Technique Research Center for Biotechnology, State Key Laboratory of Materials Oriented Chemical Engineering, College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, Nanjing 210009, China
| | - Dong Liu
- School of Chemical Engineering, Zhengzhou University, 100 Ke xue Dadao, Zhengzhou 450001, China; National Engineering Technique Research Center for Biotechnology, State Key Laboratory of Materials Oriented Chemical Engineering, College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, Nanjing 210009, China
| | - Yongkun Lv
- School of Chemical Engineering, Zhengzhou University, 100 Ke xue Dadao, Zhengzhou 450001, China
| | - Shilei Wang
- School of Chemical Engineering, Zhengzhou University, 100 Ke xue Dadao, Zhengzhou 450001, China.
| | - Jingliang Xu
- School of Chemical Engineering, Zhengzhou University, 100 Ke xue Dadao, Zhengzhou 450001, China
| | - Hanjie Ying
- School of Chemical Engineering, Zhengzhou University, 100 Ke xue Dadao, Zhengzhou 450001, China; National Engineering Technique Research Center for Biotechnology, State Key Laboratory of Materials Oriented Chemical Engineering, College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, Nanjing 210009, China
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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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Salvato F, Kleiner M. A Complete Metaproteomic Workflow for Arabidopsis Roots Inoculated by Synthetic Bacteria. Methods Mol Biol 2024; 2820:57-65. [PMID: 38941015 DOI: 10.1007/978-1-0716-3910-8_7] [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] [Indexed: 06/29/2024]
Abstract
Root metaproteome analysis can reveal the functions that govern plant-microbe and microbe-microbe interactions under specific environmental conditions. Efficient protein extraction method from microbes associated with plant roots is crucial for the comprehensive analysis of the metaproteome. In this chapter, a straightforward protein extraction method for roots of Arabidopsis inoculated with a microbial community that uses only milligrams of tissue is outlined. In addition, the plant inoculation using a synthetic community (SynCom) and the methods for a nanoflow liquid chromatography coupled to a high-resolution/high-accuracy mass spectrometer (LC-MS/MS) are described.
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Affiliation(s)
- Fernanda Salvato
- North Carolina State University, Plant and Microbial Biology Department, Raleigh, NC, USA
- Thermo Fisher Scientific, San Jose, CA, USA
| | - Manuel Kleiner
- North Carolina State University, Plant and Microbial Biology Department, Raleigh, NC, USA
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Do K, Mehta S, Wagner R, Bhuming D, Rajczewski AT, Skubitz APN, Johnson JE, Griffin TJ, Jagtap PD. A novel clinical metaproteomics workflow enables bioinformatic analysis of host-microbe dynamics in disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.21.568121. [PMID: 38045370 PMCID: PMC10690215 DOI: 10.1101/2023.11.21.568121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
Clinical metaproteomics has the potential to offer insights into the host-microbiome interactions underlying diseases. However, the field faces challenges in characterizing microbial proteins found in clinical samples, which are usually present at low abundance relative to the host proteins. As a solution, we have developed an integrated workflow coupling mass spectrometry-based analysis with customized bioinformatic identification, quantification and prioritization of microbial and host proteins, enabling targeted assay development to investigate host-microbe dynamics in disease. The bioinformatics tools are implemented in the Galaxy ecosystem, offering the development and dissemination of complex bioinformatic workflows. The modular workflow integrates MetaNovo (to generate a reduced protein database), SearchGUI/PeptideShaker and MaxQuant (to generate peptide-spectral matches (PSMs) and quantification), PepQuery2 (to verify the quality of PSMs), and Unipept and MSstatsTMT (for taxonomy and functional annotation). We have utilized this workflow in diverse clinical samples, from the characterization of nasopharyngeal swab samples to bronchoalveolar lavage fluid. Here, we demonstrate its effectiveness via analysis of residual fluid from cervical swabs. The complete workflow, including training data and documentation, is available via the Galaxy Training Network, empowering non-expert researchers to utilize these powerful tools in their clinical studies.
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De La Toba EA, Anapindi KDB, Sweedler JV. Assessment and Comparison of Database Search Engines for Peptidomic Applications. J Proteome Res 2023; 22:3123-3134. [PMID: 36809008 PMCID: PMC10440370 DOI: 10.1021/acs.jproteome.2c00307] [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] [Indexed: 02/23/2023]
Abstract
Protein database search engines are an integral component of mass spectrometry-based peptidomic analyses. Given the unique computational challenges of peptidomics, many factors must be taken into consideration when optimizing search engine selection, as each platform has different algorithms by which tandem mass spectra are scored for subsequent peptide identifications. In this study, four different database search engines, PEAKS, MS-GF+, OMSSA, and X! Tandem, were compared with Aplysia californica and Rattus norvegicus peptidomics data sets, and various metrics were assessed such as the number of unique peptide and neuropeptide identifications, and peptide length distributions. Given the tested conditions, PEAKS was found to have the highest number of peptide and neuropeptide identifications out of the four search engines in both data sets. Furthermore, principal component analysis and multivariate logistic regression were employed to determine whether specific spectral features contribute to false C-terminal amidation assignments by each search engine. From this analysis, it was found that the primary features influencing incorrect peptide assignments were the precursor and fragment ion m/z errors. Finally, an assessment employing a mixed species protein database was performed to evaluate search engine precision and sensitivity when searched against an enlarged search space containing human proteins.
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Affiliation(s)
- Eduardo A. De La Toba
- Beckman Institute of Advanced Science and Technology, University of Illinois at Urbana-Champaign, 61801
- Department of Chemistry, University of Illinois at Urbana-Champaign, 61801
| | - Krishna D. B. Anapindi
- Beckman Institute of Advanced Science and Technology, University of Illinois at Urbana-Champaign, 61801
- Department of Chemistry, University of Illinois at Urbana-Champaign, 61801
| | - Jonathan V. Sweedler
- Beckman Institute of Advanced Science and Technology, University of Illinois at Urbana-Champaign, 61801
- Department of Chemistry, University of Illinois at Urbana-Champaign, 61801
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Lee EM, Srinivasan S, Purvine SO, Fiedler TL, Leiser OP, Proll SC, Minot SS, Deatherage Kaiser BL, Fredricks DN. Optimizing metaproteomics database construction: lessons from a study of the vaginal microbiome. mSystems 2023; 8:e0067822. [PMID: 37350639 PMCID: PMC10469846 DOI: 10.1128/msystems.00678-22] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 04/06/2023] [Indexed: 06/24/2023] Open
Abstract
Metaproteomics, a method for untargeted, high-throughput identification of proteins in complex samples, provides functional information about microbial communities and can tie functions to specific taxa. Metaproteomics often generates less data than other omics techniques, but analytical workflows can be improved to increase usable data in metaproteomic outputs. Identification of peptides in the metaproteomic analysis is performed by comparing mass spectra of sample peptides to a reference database of protein sequences. Although these protein databases are an integral part of the metaproteomic analysis, few studies have explored how database composition impacts peptide identification. Here, we used cervicovaginal lavage (CVL) samples from a study of bacterial vaginosis (BV) to compare the performance of databases built using six different strategies. We evaluated broad versus sample-matched databases, as well as databases populated with proteins translated from metagenomic sequencing of the same samples versus sequences from public repositories. Smaller sample-matched databases performed significantly better, driven by the statistical constraints on large databases. Additionally, large databases attributed up to 34% of significant bacterial hits to taxa absent from the sample, as determined orthogonally by 16S rRNA gene sequencing. We also tested a set of hybrid databases which included bacterial proteins from NCBI RefSeq and translated bacterial genes from the samples. These hybrid databases had the best overall performance, identifying 1,068 unique human and 1,418 unique bacterial proteins, ~30% more than a database populated with proteins from typical vaginal bacteria and fungi. Our findings can help guide the optimal identification of proteins while maintaining statistical power for reaching biological conclusions. IMPORTANCE Metaproteomic analysis can provide valuable insights into the functions of microbial and cellular communities by identifying a broad, untargeted set of proteins. The databases used in the analysis of metaproteomic data influence results by defining what proteins can be identified. Moreover, the size of the database impacts the number of identifications after accounting for false discovery rates (FDRs). Few studies have tested the performance of different strategies for building a protein database to identify proteins from metaproteomic data and those that have largely focused on highly diverse microbial communities. We tested a range of databases on CVL samples and found that a hybrid sample-matched approach, using publicly available proteins from organisms present in the samples, as well as proteins translated from metagenomic sequencing of the samples, had the best performance. However, our results also suggest that public sequence databases will continue to improve as more bacterial genomes are published.
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Affiliation(s)
- Elliot M. Lee
- Fred Hutchinson Cancer Research Center, Seattle, Washington, DC, USA
- University of Washington, Seattle, Washington, DC, USA
| | | | - Samuel O. Purvine
- Pacific Northwest National Laboratory, Richland, Washington, DC, USA
| | - Tina L. Fiedler
- Fred Hutchinson Cancer Research Center, Seattle, Washington, DC, USA
| | - Owen P. Leiser
- Pacific Northwest National Laboratory, Richland, Washington, DC, USA
| | - Sean C. Proll
- Fred Hutchinson Cancer Research Center, Seattle, Washington, DC, USA
| | - Samuel S. Minot
- Fred Hutchinson Cancer Research Center, Seattle, Washington, DC, USA
| | | | - David N. Fredricks
- Fred Hutchinson Cancer Research Center, Seattle, Washington, DC, USA
- University of Washington, Seattle, Washington, DC, USA
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11
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Potgieter MG, Nel AJM, Fortuin S, Garnett S, Wendoh JM, Tabb DL, Mulder NJ, Blackburn JM. MetaNovo: An open-source pipeline for probabilistic peptide discovery in complex metaproteomic datasets. PLoS Comput Biol 2023; 19:e1011163. [PMID: 37327214 DOI: 10.1371/journal.pcbi.1011163] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 05/08/2023] [Indexed: 06/18/2023] Open
Abstract
BACKGROUND Microbiome research is providing important new insights into the metabolic interactions of complex microbial ecosystems involved in fields as diverse as the pathogenesis of human diseases, agriculture and climate change. Poor correlations typically observed between RNA and protein expression datasets make it hard to accurately infer microbial protein synthesis from metagenomic data. Additionally, mass spectrometry-based metaproteomic analyses typically rely on focused search sequence databases based on prior knowledge for protein identification that may not represent all the proteins present in a set of samples. Metagenomic 16S rRNA sequencing only targets the bacterial component, while whole genome sequencing is at best an indirect measure of expressed proteomes. Here we describe a novel approach, MetaNovo, that combines existing open-source software tools to perform scalable de novo sequence tag matching with a novel algorithm for probabilistic optimization of the entire UniProt knowledgebase to create tailored sequence databases for target-decoy searches directly at the proteome level, enabling metaproteomic analyses without prior expectation of sample composition or metagenomic data generation and compatible with standard downstream analysis pipelines. RESULTS We compared MetaNovo to published results from the MetaPro-IQ pipeline on 8 human mucosal-luminal interface samples, with comparable numbers of peptide and protein identifications, many shared peptide sequences and a similar bacterial taxonomic distribution compared to that found using a matched metagenome sequence database-but simultaneously identified many more non-bacterial peptides than the previous approaches. MetaNovo was also benchmarked on samples of known microbial composition against matched metagenomic and whole genomic sequence database workflows, yielding many more MS/MS identifications for the expected taxa, with improved taxonomic representation, while also highlighting previously described genome sequencing quality concerns for one of the organisms, and identifying an experimental sample contaminant without prior expectation. CONCLUSIONS By estimating taxonomic and peptide level information directly on microbiome samples from tandem mass spectrometry data, MetaNovo enables the simultaneous identification of peptides from all domains of life in metaproteome samples, bypassing the need for curated sequence databases to search. We show that the MetaNovo approach to mass spectrometry metaproteomics is more accurate than current gold standard approaches of tailored or matched genomic sequence database searches, can identify sample contaminants without prior expectation and yields insights into previously unidentified metaproteomic signals, building on the potential for complex mass spectrometry metaproteomic data to speak for itself.
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Affiliation(s)
- Matthys G Potgieter
- Computational Biology Division, Department of Integrative Biomedical Sciences, University of Cape Town, Cape Town, South Africa
- Division of Chemical and Systems Biology, Department of Integrative Biomedical Sciences, University of Cape Town, Cape Town, South Africa
| | - Andrew J M Nel
- Division of Chemical and Systems Biology, Department of Integrative Biomedical Sciences, University of Cape Town, Cape Town, South Africa
| | - Suereta Fortuin
- Division of Chemical and Systems Biology, Department of Integrative Biomedical Sciences, University of Cape Town, Cape Town, South Africa
| | - Shaun Garnett
- Division of Chemical and Systems Biology, Department of Integrative Biomedical Sciences, University of Cape Town, Cape Town, South Africa
| | - Jerome M Wendoh
- Division of Immunology, Department of Pathology, University of Cape Town, Cape Town, South Africa
| | - David L Tabb
- Division of Chemical and Systems Biology, Department of Integrative Biomedical Sciences, University of Cape Town, Cape Town, South Africa
- Division of Molecular Biology and Human Genetics, Department of Biomedical Sciences; African Microbiome Institute; South African Tuberculosis Bioinformatics Initiative; Stellenbosch University, Cape Town, South Africa
| | - Nicola J Mulder
- Computational Biology Division, Department of Integrative Biomedical Sciences, University of Cape Town, Cape Town, South Africa
- Institute of Infectious Disease & Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Jonathan M Blackburn
- Division of Chemical and Systems Biology, Department of Integrative Biomedical Sciences, University of Cape Town, Cape Town, South Africa
- Institute of Infectious Disease & Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
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12
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Nowatzky Y, Benner P, Reinert K, Muth T. Mistle: bringing spectral library predictions to metaproteomics with an efficient search index. Bioinformatics 2023; 39:btad376. [PMID: 37294786 PMCID: PMC10313348 DOI: 10.1093/bioinformatics/btad376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 05/11/2023] [Accepted: 06/08/2023] [Indexed: 06/11/2023] Open
Abstract
MOTIVATION Deep learning has moved to the forefront of tandem mass spectrometry-driven proteomics and authentic prediction for peptide fragmentation is more feasible than ever. Still, at this point spectral prediction is mainly used to validate database search results or for confined search spaces. Fully predicted spectral libraries have not yet been efficiently adapted to large search space problems that often occur in metaproteomics or proteogenomics. RESULTS In this study, we showcase a workflow that uses Prosit for spectral library predictions on two common metaproteomes and implement an indexing and search algorithm, Mistle, to efficiently identify experimental mass spectra within the library. Hence, the workflow emulates a classic protein sequence database search with protein digestion but builds a searchable index from spectral predictions as an in-between step. We compare Mistle to popular search engines, both on a spectral and database search level, and provide evidence that this approach is more accurate than a database search using MSFragger. Mistle outperforms other spectral library search engines in terms of run time and proves to be extremely memory efficient with a 4- to 22-fold decrease in RAM usage. This makes Mistle universally applicable to large search spaces, e.g. covering comprehensive sequence databases of diverse microbiomes. AVAILABILITY AND IMPLEMENTATION Mistle is freely available on GitHub at https://github.com/BAMeScience/Mistle.
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Affiliation(s)
- Yannek Nowatzky
- Section S.3 eScience, Federal Institute for Materials Research and Testing (BAM), Berlin 12205, Germany
| | - Philipp Benner
- Section S.3 eScience, Federal Institute for Materials Research and Testing (BAM), Berlin 12205, Germany
| | - Knut Reinert
- Department of Mathematics and Computer Science, FU Berlin, Berlin 14195, Germany
- Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Berlin 14195, Germany
| | - Thilo Muth
- Section S.3 eScience, Federal Institute for Materials Research and Testing (BAM), Berlin 12205, Germany
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13
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Mazuecos L, Alberdi P, Hernández-Jarguín A, Contreras M, Villar M, Cabezas-Cruz A, Simo L, González-García A, Díaz-Sánchez S, Neelakanta G, Bonnet SI, Fikrig E, de la Fuente J. Frankenbacteriosis targeting interactions between pathogen and symbiont to control infection in the tick vector. iScience 2023; 26:106697. [PMID: 37168564 PMCID: PMC10165458 DOI: 10.1016/j.isci.2023.106697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 01/23/2023] [Accepted: 04/13/2023] [Indexed: 05/13/2023] Open
Abstract
Tick microbiota can be targeted for the control of tick-borne diseases such as human granulocytic anaplasmosis (HGA) caused by model pathogen, Anaplasma phagocytophilum. Frankenbacteriosis is inspired by Frankenstein and defined here as paratransgenesis of tick symbiotic/commensal bacteria to mimic and compete with tick-borne pathogens. Interactions between A. phagocytophilum and symbiotic Sphingomonas identified by metaproteomics analysis in Ixodes scapularis midgut showed competition between both bacteria. Consequently, Sphingomonas was selected for frankenbacteriosis for the control of A. phagocytophilum infection and transmission. The results showed that Franken Sphingomonas producing A. phagocytophilum major surface protein 4 (MSP4) mimic pathogen and reduce infection in ticks by competition and interaction with cell receptor components of infection. Franken Sphingomonas-MSP4 transovarial and trans-stadial transmission suggests that tick larvae with genetically modified Franken Sphingomonas-MSP4 could be produced in the laboratory and released in the field to compete and replace the wildtype populations with associated reduction in pathogen infection/transmission and HGA disease risks.
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Affiliation(s)
- Lorena Mazuecos
- SaBio. Instituto de Investigación en Recursos Cinegéticos IREC-CSIC-UCLM-JCCM, Ronda de Toledo 12, 13005 Ciudad Real, Spain
| | - Pilar Alberdi
- SaBio. Instituto de Investigación en Recursos Cinegéticos IREC-CSIC-UCLM-JCCM, Ronda de Toledo 12, 13005 Ciudad Real, Spain
| | - Angélica Hernández-Jarguín
- SaBio. Instituto de Investigación en Recursos Cinegéticos IREC-CSIC-UCLM-JCCM, Ronda de Toledo 12, 13005 Ciudad Real, Spain
| | - Marinela Contreras
- SaBio. Instituto de Investigación en Recursos Cinegéticos IREC-CSIC-UCLM-JCCM, Ronda de Toledo 12, 13005 Ciudad Real, Spain
| | - Margarita Villar
- SaBio. Instituto de Investigación en Recursos Cinegéticos IREC-CSIC-UCLM-JCCM, Ronda de Toledo 12, 13005 Ciudad Real, Spain
| | - Alejandro Cabezas-Cruz
- ANSES, INRAE, Ecole Nationale Vétérinaire d’Alfort, UMR BIPAR, Laboratoire de Santé Animale, 94700 Maisons-Alfort, France
| | - Ladislav Simo
- ANSES, INRAE, Ecole Nationale Vétérinaire d’Alfort, UMR BIPAR, Laboratoire de Santé Animale, 94700 Maisons-Alfort, France
| | - Almudena González-García
- SaBio. Instituto de Investigación en Recursos Cinegéticos IREC-CSIC-UCLM-JCCM, Ronda de Toledo 12, 13005 Ciudad Real, Spain
| | - Sandra Díaz-Sánchez
- SaBio. Instituto de Investigación en Recursos Cinegéticos IREC-CSIC-UCLM-JCCM, Ronda de Toledo 12, 13005 Ciudad Real, Spain
| | - Girish Neelakanta
- Department of Biomedical and Diagnostic Sciences, College of Veterinary Medicine, University of Tennessee, Knoxville, TN 37996, USA
- Department of Biological Sciences, Old Dominion University, Norfolk, VA 23529, USA
| | - Sarah I. Bonnet
- Functional Genetics of Infectious Diseases Unit, Institut Pasteur, CNRS UMR 2000, Université de Paris, 75015 Paris, France
- Animal Health Department, INRAE, 37380 Nouzilly, France
| | - Erol Fikrig
- Section of Infectious Diseases, Yale University School of Medicine, New Haven, CT 208022, USA
| | - José de la Fuente
- SaBio. Instituto de Investigación en Recursos Cinegéticos IREC-CSIC-UCLM-JCCM, Ronda de Toledo 12, 13005 Ciudad Real, Spain
- Department of Veterinary Pathobiology, Center for Veterinary Health Sciences, Oklahoma State University, Stillwater, OK 74078, USA
- Corresponding author
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14
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Mappa C, Alpha-Bazin B, Pible O, Armengaud J. Mix24X, a Lab-Assembled Reference to Evaluate Interpretation Procedures for Tandem Mass Spectrometry Proteotyping of Complex Samples. Int J Mol Sci 2023; 24:ijms24108634. [PMID: 37239979 DOI: 10.3390/ijms24108634] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 05/09/2023] [Accepted: 05/10/2023] [Indexed: 05/28/2023] Open
Abstract
Correct identification of the microorganisms present in a complex sample is a crucial issue. Proteotyping based on tandem mass spectrometry can help establish an inventory of organisms present in a sample. Evaluation of bioinformatics strategies and tools for mining the recorded datasets is essential to establish confidence in the results obtained and to improve these pipelines in terms of sensitivity and accuracy. Here, we propose several tandem mass spectrometry datasets recorded on an artificial reference consortium comprising 24 bacterial species. This assemblage of environmental and pathogenic bacteria covers 20 different genera and 5 bacterial phyla. The dataset comprises difficult cases, such as the Shigella flexneri species, which is closely related to Escherichia coli, and several highly sequenced clades. Different acquisition strategies simulate real-life scenarios: from rapid survey sampling to exhaustive analysis. We provide access to individual proteomes of each bacterium separately to provide a rational basis for evaluating the assignment strategy of MS/MS spectra when recorded from complex mixtures. This resource should provide an interesting common reference for developers who wish to compare their proteotyping tools and for those interested in evaluating protein assignment when dealing with complex samples, such as microbiomes.
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Affiliation(s)
- Charlotte Mappa
- Département Médicaments et Technologies pour la Santé (DMTS), Université Paris-Saclay, CEA, INRAE, SPI, 30200 Bagnols-sur-Cèze, France
- Laboratoire Innovations Technologiques Pour la Détection et le Diagnostic (Li2D), Université de Montpellier, 30207 Bagnols sur Cèze, France
| | - Béatrice Alpha-Bazin
- Département Médicaments et Technologies pour la Santé (DMTS), Université Paris-Saclay, CEA, INRAE, SPI, 30200 Bagnols-sur-Cèze, France
| | - Olivier Pible
- Département Médicaments et Technologies pour la Santé (DMTS), Université Paris-Saclay, CEA, INRAE, SPI, 30200 Bagnols-sur-Cèze, France
| | - Jean Armengaud
- Département Médicaments et Technologies pour la Santé (DMTS), Université Paris-Saclay, CEA, INRAE, SPI, 30200 Bagnols-sur-Cèze, France
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15
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Porcheddu M, Abbondio M, De Diego L, Uzzau S, Tanca A. Meta4P: A User-Friendly Tool to Parse Label-Free Quantitative Metaproteomic Data and Taxonomic/Functional Annotations. J Proteome Res 2023. [PMID: 37116187 DOI: 10.1021/acs.jproteome.2c00803] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/30/2023]
Abstract
We present Meta4P (MetaProteins-Peptides-PSMs Parser), an easy-to-use bioinformatic application designed to integrate label-free quantitative metaproteomic data with taxonomic and functional annotations. Meta4P can retrieve, filter, and process identification and quantification data from three levels of inputs (proteins, peptides, PSMs) in different file formats. Abundance data can be combined with taxonomic and functional information and aggregated at different and customizable levels, including taxon-specific functions and pathways. Meta4P output tables, available in various formats, are ready to be used as inputs for downstream statistical analyses. This user-friendly tool is expected to provide a useful contribution to the field of metaproteomic data analysis, helping make it more manageable and straightforward.
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Affiliation(s)
- Massimo Porcheddu
- Department of Biomedical Sciences, University of Sassari, Viale San Pietro 43/B, 07100 Sassari, Italy
| | - Marcello Abbondio
- Department of Biomedical Sciences, University of Sassari, Viale San Pietro 43/B, 07100 Sassari, Italy
| | - Laura De Diego
- Department of Biomedical Sciences, University of Sassari, Viale San Pietro 43/B, 07100 Sassari, Italy
| | - Sergio Uzzau
- Department of Biomedical Sciences, University of Sassari, Viale San Pietro 43/B, 07100 Sassari, Italy
| | - Alessandro Tanca
- Department of Biomedical Sciences, University of Sassari, Viale San Pietro 43/B, 07100 Sassari, Italy
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16
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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: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [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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17
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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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18
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Armengaud J. Metaproteomics to understand how microbiota function: The crystal ball predicts a promising future. Environ Microbiol 2023; 25:115-125. [PMID: 36209500 PMCID: PMC10091800 DOI: 10.1111/1462-2920.16238] [Citation(s) in RCA: 24] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Accepted: 09/30/2022] [Indexed: 01/21/2023]
Abstract
In the medical, environmental, and biotechnological fields, microbial communities have attracted much attention due to their roles and numerous possible applications. The study of these communities is challenging due to their diversity and complexity. Innovative methods are needed to identify the taxonomic components of individual microbiota, their changes over time, and to determine how microoorganisms interact and function. Metaproteomics is based on the identification and quantification of proteins, and can potentially provide this full picture. Due to the wide molecular panorama and functional insights it provides, metaproteomics is gaining momentum in microbiome and holobiont research. Its full potential should be unleashed in the coming years with progress in speed and cost of analyses. In this exploratory crystal ball exercise, I discuss the technical and conceptual advances in metaproteomics that I expect to drive innovative research over the next few years in microbiology. I also debate the concepts of 'microbial dark matter' and 'Metaproteomics-Assembled Proteomes (MAPs)' and present some long-term prospects for metaproteomics in clinical diagnostics and personalized medicine, environmental monitoring, agriculture, and biotechnology.
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Affiliation(s)
- Jean Armengaud
- Département Médicaments et Technologies pour la Santé (DMTS), Université Paris-Saclay, CEA, INRAE, Bagnols-sur-Cèze, France
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19
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Biosa G, Bonelli P, Pisanu S, Ghisaura S, Santucciu C, Peruzzu A, Garippa G, Uzzau S, Masala G, Pagnozzi D. Proteomic characterization of Echinococcus granulosus sensu stricto, Taenia hydatigena and Taenia multiceps metacestode cyst fluids. Acta Trop 2022; 226:106253. [PMID: 34822852 DOI: 10.1016/j.actatropica.2021.106253] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 10/25/2021] [Accepted: 11/17/2021] [Indexed: 12/28/2022]
Abstract
Cystic echinococcosis (CE) diagnosis by means of serological assays is hampered by the presence of parasites closely related to Echinococcus granulosus sensu lato (s.l.), responsible of the zoonotic disease and with which share cross-reacting antigens. Thus, improvements on the characterization of Echinococcus specific antigens expressed in the larval stage are required, in order to provide useful information for the development of immunological assays for the serodiagnosis of CE in sheep. Here, the proteome of the hydatid cyst fluids (HFs) of Echinococcus granulosus (hydatid fluid, EgHF) and other ovine parasites cyst fluids (CFs), Taenia hydatigena (ThCF) and Taenia multiceps (TmCF) were analyzed by a shotgun proteomic approach. Parasite and host protein profiles in the three types of cyst fluids were characterized and compared. Among the identified proteins, differential parasitic markers with serodiagnostic potential, due to their well-known immunoreactivity in human, included Ag5, AgB proteins, 8-kDa glycoproteins, hydatid disease diagnostic antigen P29 and major egg antigen P40. In particular, seven proteoforms of AgB and 8-kDa glycoprotein resulted to be the most promising diagnostic biomarkers, as they might predict CE in ovine and discriminate between different types of parasites.
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20
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Blakeley-Ruiz JA, Kleiner M. Considerations for Constructing a Protein Sequence Database for Metaproteomics. Comput Struct Biotechnol J 2022; 20:937-952. [PMID: 35242286 PMCID: PMC8861567 DOI: 10.1016/j.csbj.2022.01.018] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 01/14/2022] [Accepted: 01/18/2022] [Indexed: 12/14/2022] Open
Abstract
Mass spectrometry-based metaproteomics has emerged as a prominent technique for interrogating the functions of specific organisms in microbial communities, in addition to total community function. Identifying proteins by mass spectrometry requires matching mass spectra of fragmented peptide ions to a database of protein sequences corresponding to the proteins in the sample. This sequence database determines which protein sequences can be identified from the measurement, and as such the taxonomic and functional information that can be inferred from a metaproteomics measurement. Thus, the construction of the protein sequence database directly impacts the outcome of any metaproteomics study. Several factors, such as source of sequence information and database curation, need to be considered during database construction to maximize accurate protein identifications traceable to the species of origin. In this review, we provide an overview of existing strategies for database construction and the relevant studies that have sought to test and validate these strategies. Based on this review of the literature and our experience we provide a decision tree and best practices for choosing and implementing database construction strategies.
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Affiliation(s)
- J. Alfredo Blakeley-Ruiz
- Department of Plant and Microbial Biology, North Carolina State University, Raleigh, NC, USA
- Center for Gastrointestinal Biology and Disease, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Corresponding authors at: Department of Plant and Microbial Biology, North Carolina State University, Raleigh, NC, USA.
| | - Manuel Kleiner
- Department of Plant and Microbial Biology, North Carolina State University, Raleigh, NC, USA
- Corresponding authors at: Department of Plant and Microbial Biology, North Carolina State University, Raleigh, NC, USA.
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21
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Simopoulos CMA, Figeys D, Lavallée-Adam M. Novel Bioinformatics Strategies Driving Dynamic Metaproteomic Studies. Methods Mol Biol 2022; 2456:319-338. [PMID: 35612752 DOI: 10.1007/978-1-0716-2124-0_22] [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] [Indexed: 10/18/2022]
Abstract
Constant improvements in mass spectrometry technologies and laboratory workflows have enabled the proteomics investigation of biological samples of growing complexity. Microbiomes represent such complex samples for which metaproteomics analyses are becoming increasingly popular. Metaproteomics experimental procedures create large amounts of data from which biologically relevant signal must be efficiently extracted to draw meaningful conclusions. Such a data processing requires appropriate bioinformatics tools specifically developed for, or capable of handling metaproteomics data. In this chapter, we outline current and novel tools that can perform the most commonly used steps in the analysis of cutting-edge metaproteomics data, such as peptide and protein identification and quantification, as well as data normalization, imputation, mining, and visualization. We also provide details about the experimental setups in which these tools should be used.
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Affiliation(s)
- Caitlin M A Simopoulos
- Department of Biochemistry, Microbiology and Immunology and Ottawa Institute of Systems Biology, University of Ottawa, Ottawa, ON, Canada
| | - Daniel Figeys
- Department of Biochemistry, Microbiology and Immunology and Ottawa Institute of Systems Biology, University of Ottawa, Ottawa, ON, Canada
- School of Pharmaceutical Sciences, University of Ottawa, Ottawa, ON, Canada
| | - Mathieu Lavallée-Adam
- Department of Biochemistry, Microbiology and Immunology and Ottawa Institute of Systems Biology, University of Ottawa, Ottawa, ON, Canada.
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22
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Van Den Bossche T, Kunath BJ, Schallert K, Schäpe SS, Abraham PE, Armengaud J, Arntzen MØ, Bassignani A, Benndorf D, Fuchs S, Giannone RJ, Griffin TJ, Hagen LH, Halder R, Henry C, Hettich RL, Heyer R, Jagtap P, Jehmlich N, Jensen M, Juste C, Kleiner M, Langella O, Lehmann T, Leith E, May P, Mesuere B, Miotello G, Peters SL, Pible O, Queiros PT, Reichl U, Renard BY, Schiebenhoefer H, Sczyrba A, Tanca A, Trappe K, Trezzi JP, Uzzau S, Verschaffelt P, von Bergen M, Wilmes P, Wolf M, Martens L, Muth T. Critical Assessment of MetaProteome Investigation (CAMPI): a multi-laboratory comparison of established workflows. Nat Commun 2021; 12:7305. [PMID: 34911965 PMCID: PMC8674281 DOI: 10.1038/s41467-021-27542-8] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 11/24/2021] [Indexed: 12/17/2022] Open
Abstract
Metaproteomics has matured into a powerful tool to assess functional interactions in microbial communities. While many metaproteomic workflows are available, the impact of method choice on results remains unclear. Here, we carry out a community-driven, multi-laboratory comparison in metaproteomics: the critical assessment of metaproteome investigation study (CAMPI). Based on well-established workflows, we evaluate the effect of sample preparation, mass spectrometry, and bioinformatic analysis using two samples: a simplified, laboratory-assembled human intestinal model and a human fecal sample. We observe that variability at the peptide level is predominantly due to sample processing workflows, with a smaller contribution of bioinformatic pipelines. These peptide-level differences largely disappear at the protein group level. While differences are observed for predicted community composition, similar functional profiles are obtained across workflows. CAMPI demonstrates the robustness of present-day metaproteomics research, serves as a template for multi-laboratory studies in metaproteomics, and provides publicly available data sets for benchmarking future developments.
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Affiliation(s)
- Tim Van Den Bossche
- VIB - UGent Center for Medical Biotechnology, VIB, Ghent, Belgium
- Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium
| | - Benoit J Kunath
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Kay Schallert
- Bioprocess Engineering, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
| | - Stephanie S Schäpe
- Department of Molecular Systems Biology, Helmholtz-Centre for Environmental Research - UFZ GmbH, Leipzig, Germany
| | - Paul E Abraham
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Jean Armengaud
- Département Médicaments et Technologies pour la Santé (DMTS), Université Paris Saclay, CEA, INRAE, SPI, 30200, Bagnols-sur-Cèze, France
| | - Magnus Ø Arntzen
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences (NMBU), Ås, Norway
| | - Ariane Bassignani
- INRAE, AgroParisTech, Micalis Institute, Université Paris-Saclay, 78350, Jouy-en-Josas, France
| | - Dirk Benndorf
- Bioprocess Engineering, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
- Microbiology, Department of Applied Biosciences and Process Technology, Anhalt University of Applied Sciences, Köthen, Germany
- Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
| | - Stephan Fuchs
- Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, Berlin, Germany
| | | | - Timothy J Griffin
- Department of Biochemistry Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA
| | - Live H Hagen
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences (NMBU), Ås, Norway
| | - Rashi Halder
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Céline Henry
- INRAE, AgroParisTech, Micalis Institute, Université Paris-Saclay, 78350, Jouy-en-Josas, France
| | - Robert L Hettich
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Robert Heyer
- Bioprocess Engineering, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
| | - Pratik Jagtap
- Department of Biochemistry Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA
| | - Nico Jehmlich
- Department of Molecular Systems Biology, Helmholtz-Centre for Environmental Research - UFZ GmbH, Leipzig, Germany
| | - Marlene Jensen
- Department of Plant & Microbial Biology, North Carolina State University, Raleigh, USA
| | - Catherine Juste
- INRAE, AgroParisTech, Micalis Institute, Université Paris-Saclay, 78350, Jouy-en-Josas, France
| | - Manuel Kleiner
- Department of Plant & Microbial Biology, North Carolina State University, Raleigh, USA
| | - Olivier Langella
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE - Le Moulon, 91190, Gif-sur-Yvette, France
| | - Theresa Lehmann
- Bioprocess Engineering, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
| | - Emma Leith
- Department of Biochemistry Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA
| | - Patrick May
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Bart Mesuere
- VIB - UGent Center for Medical Biotechnology, VIB, Ghent, Belgium
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
| | - Guylaine Miotello
- Département Médicaments et Technologies pour la Santé (DMTS), Université Paris Saclay, CEA, INRAE, SPI, 30200, Bagnols-sur-Cèze, France
| | - Samantha L Peters
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Olivier Pible
- Département Médicaments et Technologies pour la Santé (DMTS), Université Paris Saclay, CEA, INRAE, SPI, 30200, Bagnols-sur-Cèze, France
| | - Pedro T Queiros
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Udo Reichl
- Bioprocess Engineering, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
- Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
| | - Bernhard Y Renard
- Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, Berlin, Germany
- Data Analytics and Computational Statistics, Hasso-Plattner-Institute, Faculty of Digital Engineering, University of Potsdam, Potsdam, Germany
| | - Henning Schiebenhoefer
- Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, Berlin, Germany
- Data Analytics and Computational Statistics, Hasso-Plattner-Institute, Faculty of Digital Engineering, University of Potsdam, Potsdam, Germany
| | | | - Alessandro Tanca
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Kathrin Trappe
- Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, Berlin, Germany
| | - Jean-Pierre Trezzi
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Integrated Biobank of Luxembourg, Luxembourg Institute of Health, 1, rue Louis Rech, L-3555, Dudelange, Luxembourg
| | - Sergio Uzzau
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Pieter Verschaffelt
- VIB - UGent Center for Medical Biotechnology, VIB, Ghent, Belgium
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
| | - Martin von Bergen
- Department of Molecular Systems Biology, Helmholtz-Centre for Environmental Research - UFZ GmbH, Leipzig, Germany
| | - Paul Wilmes
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Department of Life Sciences and Medicine, Faculty of Science, Technology and Medicine, University of Luxembourg, 6 avenue du Swing, L-4367, Belvaux, Luxembourg
| | - Maximilian Wolf
- Bioprocess Engineering, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
| | - Lennart Martens
- VIB - UGent Center for Medical Biotechnology, VIB, Ghent, Belgium.
- Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium.
| | - Thilo Muth
- Section eScience (S.3), Federal Institute for Materials Research and Testing, Berlin, Germany
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23
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Van Den Bossche T, Kunath BJ, Schallert K, Schäpe SS, Abraham PE, Armengaud J, Arntzen MØ, Bassignani A, Benndorf D, Fuchs S, Giannone RJ, Griffin TJ, Hagen LH, Halder R, Henry C, Hettich RL, Heyer R, Jagtap P, Jehmlich N, Jensen M, Juste C, Kleiner M, Langella O, Lehmann T, Leith E, May P, Mesuere B, Miotello G, Peters SL, Pible O, Queiros PT, Reichl U, Renard BY, Schiebenhoefer H, Sczyrba A, Tanca A, Trappe K, Trezzi JP, Uzzau S, Verschaffelt P, von Bergen M, Wilmes P, Wolf M, Martens L, Muth T. Critical Assessment of MetaProteome Investigation (CAMPI): a multi-laboratory comparison of established workflows. Nat Commun 2021; 12:7305. [PMID: 34911965 DOI: 10.1101/2021.03.05.433915] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 11/24/2021] [Indexed: 05/21/2023] Open
Abstract
Metaproteomics has matured into a powerful tool to assess functional interactions in microbial communities. While many metaproteomic workflows are available, the impact of method choice on results remains unclear. Here, we carry out a community-driven, multi-laboratory comparison in metaproteomics: the critical assessment of metaproteome investigation study (CAMPI). Based on well-established workflows, we evaluate the effect of sample preparation, mass spectrometry, and bioinformatic analysis using two samples: a simplified, laboratory-assembled human intestinal model and a human fecal sample. We observe that variability at the peptide level is predominantly due to sample processing workflows, with a smaller contribution of bioinformatic pipelines. These peptide-level differences largely disappear at the protein group level. While differences are observed for predicted community composition, similar functional profiles are obtained across workflows. CAMPI demonstrates the robustness of present-day metaproteomics research, serves as a template for multi-laboratory studies in metaproteomics, and provides publicly available data sets for benchmarking future developments.
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Affiliation(s)
- Tim Van Den Bossche
- VIB - UGent Center for Medical Biotechnology, VIB, Ghent, Belgium
- Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium
| | - Benoit J Kunath
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Kay Schallert
- Bioprocess Engineering, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
| | - Stephanie S Schäpe
- Department of Molecular Systems Biology, Helmholtz-Centre for Environmental Research - UFZ GmbH, Leipzig, Germany
| | - Paul E Abraham
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Jean Armengaud
- Département Médicaments et Technologies pour la Santé (DMTS), Université Paris Saclay, CEA, INRAE, SPI, 30200, Bagnols-sur-Cèze, France
| | - Magnus Ø Arntzen
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences (NMBU), Ås, Norway
| | - Ariane Bassignani
- INRAE, AgroParisTech, Micalis Institute, Université Paris-Saclay, 78350, Jouy-en-Josas, France
| | - Dirk Benndorf
- Bioprocess Engineering, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
- Microbiology, Department of Applied Biosciences and Process Technology, Anhalt University of Applied Sciences, Köthen, Germany
- Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
| | - Stephan Fuchs
- Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, Berlin, Germany
| | | | - Timothy J Griffin
- Department of Biochemistry Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA
| | - Live H Hagen
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences (NMBU), Ås, Norway
| | - Rashi Halder
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Céline Henry
- INRAE, AgroParisTech, Micalis Institute, Université Paris-Saclay, 78350, Jouy-en-Josas, France
| | - Robert L Hettich
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Robert Heyer
- Bioprocess Engineering, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
| | - Pratik Jagtap
- Department of Biochemistry Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA
| | - Nico Jehmlich
- Department of Molecular Systems Biology, Helmholtz-Centre for Environmental Research - UFZ GmbH, Leipzig, Germany
| | - Marlene Jensen
- Department of Plant & Microbial Biology, North Carolina State University, Raleigh, USA
| | - Catherine Juste
- INRAE, AgroParisTech, Micalis Institute, Université Paris-Saclay, 78350, Jouy-en-Josas, France
| | - Manuel Kleiner
- Department of Plant & Microbial Biology, North Carolina State University, Raleigh, USA
| | - Olivier Langella
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE - Le Moulon, 91190, Gif-sur-Yvette, France
| | - Theresa Lehmann
- Bioprocess Engineering, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
| | - Emma Leith
- Department of Biochemistry Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA
| | - Patrick May
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Bart Mesuere
- VIB - UGent Center for Medical Biotechnology, VIB, Ghent, Belgium
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
| | - Guylaine Miotello
- Département Médicaments et Technologies pour la Santé (DMTS), Université Paris Saclay, CEA, INRAE, SPI, 30200, Bagnols-sur-Cèze, France
| | - Samantha L Peters
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Olivier Pible
- Département Médicaments et Technologies pour la Santé (DMTS), Université Paris Saclay, CEA, INRAE, SPI, 30200, Bagnols-sur-Cèze, France
| | - Pedro T Queiros
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Udo Reichl
- Bioprocess Engineering, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
- Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
| | - Bernhard Y Renard
- Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, Berlin, Germany
- Data Analytics and Computational Statistics, Hasso-Plattner-Institute, Faculty of Digital Engineering, University of Potsdam, Potsdam, Germany
| | - Henning Schiebenhoefer
- Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, Berlin, Germany
- Data Analytics and Computational Statistics, Hasso-Plattner-Institute, Faculty of Digital Engineering, University of Potsdam, Potsdam, Germany
| | | | - Alessandro Tanca
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Kathrin Trappe
- Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, Berlin, Germany
| | - Jean-Pierre Trezzi
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Integrated Biobank of Luxembourg, Luxembourg Institute of Health, 1, rue Louis Rech, L-3555, Dudelange, Luxembourg
| | - Sergio Uzzau
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Pieter Verschaffelt
- VIB - UGent Center for Medical Biotechnology, VIB, Ghent, Belgium
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
| | - Martin von Bergen
- Department of Molecular Systems Biology, Helmholtz-Centre for Environmental Research - UFZ GmbH, Leipzig, Germany
| | - Paul Wilmes
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Department of Life Sciences and Medicine, Faculty of Science, Technology and Medicine, University of Luxembourg, 6 avenue du Swing, L-4367, Belvaux, Luxembourg
| | - Maximilian Wolf
- Bioprocess Engineering, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
| | - Lennart Martens
- VIB - UGent Center for Medical Biotechnology, VIB, Ghent, Belgium.
- Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium.
| | - Thilo Muth
- Section eScience (S.3), Federal Institute for Materials Research and Testing, Berlin, Germany
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24
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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: 17] [Impact Index Per Article: 5.7] [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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25
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Karaduta O, Dvanajscak Z, Zybailov B. Metaproteomics-An Advantageous Option in Studies of Host-Microbiota Interaction. Microorganisms 2021; 9:microorganisms9050980. [PMID: 33946610 PMCID: PMC8147213 DOI: 10.3390/microorganisms9050980] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 04/29/2021] [Accepted: 04/30/2021] [Indexed: 12/20/2022] Open
Abstract
Gut microbiome contributes to host health by maintaining homeostasis, increasing digestive efficiency, and facilitating the development of the immune system. Manipulating gut microbiota is being recognized as a therapeutic target to manage various chronic diseases. The therapeutic manipulation of the intestinal microbiome is achieved through diet modification, the administration of prebiotics, probiotics, or antibiotics, and more recently, fecal microbiome transplantation (FMT). In this opinion paper, we give a perspective on the current status of application of multi-omics technologies in the analysis of host-microbiota interactions. The aim of this paper was to highlight the strengths of metaproteomics, which integrates with and often relies on other approaches.
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Affiliation(s)
- Oleg Karaduta
- Department of Biochemistry and Molecular Biology, UAMS, Little Rock, AR 72205, USA;
- Correspondence: ; Tel.: +1-501-251-5381
| | | | - Boris Zybailov
- Department of Biochemistry and Molecular Biology, UAMS, Little Rock, AR 72205, USA;
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26
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Huang W, Kane MA. MAPLE: A Microbiome Analysis Pipeline Enabling Optimal Peptide Search and Comparative Taxonomic and Functional Analysis. J Proteome Res 2021; 20:2882-2894. [PMID: 33848166 DOI: 10.1021/acs.jproteome.1c00114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Metaproteomics by mass spectrometry (MS) is a powerful approach to profile a large number of proteins expressed by all organisms in a highly complex biological or ecological sample, which is able to provide a direct and quantitative assessment of the functional makeup of a microbiota. The human gastrointestinal microbiota has been found playing important roles in human physiology and health, and metaproteomics has been shown to shed light on multiple novel associations between microbiota and diseases. MS-powered proteomics generally relies on genome data to define search space. However, metaproteomics, which simultaneously analyzes all proteins from hundreds to thousands of species, faces significant challenges regarding database search and interpretation of results. To overcome these obstacles, we have developed a user-friendly microbiome analysis pipeline (MAPLE, freely downloadable at http://maple.rx.umaryland.edu/), which is able to define an optimal search space by inferring proteomes specific to samples following the principle of parsimony. MAPLE facilitates highly comparable or better peptide identification compared to a sample-specific metagenome-guided search. In addition, we implemented an automated peptide-centric enrichment analysis function in MAPLE to address issues of traditional protein-centric comparison, enabling straightforward and comprehensive comparison of taxonomic and functional makeup between microbiota.
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Affiliation(s)
- Weiliang Huang
- Department of Pharmaceutical Sciences, University of Maryland, School of Pharmacy, Baltimore, Maryland 21201, United States
| | - Maureen A Kane
- Department of Pharmaceutical Sciences, University of Maryland, School of Pharmacy, Baltimore, Maryland 21201, United States
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27
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Stamboulian M, Li S, Ye Y. Using high-abundance proteins as guides for fast and effective peptide/protein identification from human gut metaproteomic data. MICROBIOME 2021; 9:80. [PMID: 33795009 PMCID: PMC8017886 DOI: 10.1186/s40168-021-01035-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Accepted: 02/11/2021] [Indexed: 05/23/2023]
Abstract
BACKGROUND A few recent large efforts significantly expanded the collection of human-associated bacterial genomes, which now contains thousands of entities including reference complete/draft genomes and metagenome assembled genomes (MAGs). These genomes provide useful resource for studying the functionality of the human-associated microbiome and their relationship with human health and diseases. One application of these genomes is to provide a universal reference for database search in metaproteomic studies, when matched metagenomic/metatranscriptomic data are unavailable. However, a greater collection of reference genomes may not necessarily result in better peptide/protein identification because the increase of search space often leads to fewer spectrum-peptide matches, not to mention the drastic increase of computation time. Video Abstract METHODS: Here, we present a new approach that uses two steps to optimize the use of the reference genomes and MAGs as the universal reference for human gut metaproteomic MS/MS data analysis. The first step is to use only the high-abundance proteins (HAPs) (i.e., ribosomal proteins and elongation factors) for metaproteomic MS/MS database search and, based on the identification results, to derive the taxonomic composition of the underlying microbial community. The second step is to expand the search database by including all proteins from identified abundant species. We call our approach HAPiID (HAPs guided metaproteomics IDentification). RESULTS We tested our approach using human gut metaproteomic datasets from a previous study and compared it to the state-of-the-art reference database search method MetaPro-IQ for metaproteomic identification in studying human gut microbiota. Our results show that our two-steps method not only performed significantly faster but also was able to identify more peptides. We further demonstrated the application of HAPiID to revealing protein profiles of individual human-associated bacterial species, one or a few species at a time, using metaproteomic data. CONCLUSIONS The HAP guided profiling approach presents a novel effective way for constructing target database for metaproteomic data analysis. The HAPiID pipeline built upon this approach provides a universal tool for analyzing human gut-associated metaproteomic data.
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Affiliation(s)
- Moses Stamboulian
- Luddy School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN, 47408 United States
| | - Sujun Li
- Luddy School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN, 47408 United States
| | - Yuzhen Ye
- Luddy School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN, 47408 United States
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28
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Salvato F, Hettich RL, Kleiner M. Five key aspects of metaproteomics as a tool to understand functional interactions in host-associated microbiomes. PLoS Pathog 2021; 17:e1009245. [PMID: 33630960 PMCID: PMC7906368 DOI: 10.1371/journal.ppat.1009245] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Affiliation(s)
- Fernanda Salvato
- Department of Plant and Microbial Biology, North Carolina State University, Raleigh, North Carolina, United States of America
- * E-mail: (FS); (MK)
| | - Robert L. Hettich
- Oak Ridge National Laboratory, Biosciences Division, Oak Ridge, Tennessee, United States of America
| | - Manuel Kleiner
- Department of Plant and Microbial Biology, North Carolina State University, Raleigh, North Carolina, United States of America
- * E-mail: (FS); (MK)
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29
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Freire MS, Oliveira NG, Lima SMF, Porto WF, Martins DCM, Silva ON, Chaves SB, Sousa MV, Ricart CAO, Castro MS, Fontes W, Franco OL, Rezende TMB. IL-4 absence triggers distinct pathways in apical periodontitis development. J Proteomics 2020; 233:104080. [PMID: 33338687 DOI: 10.1016/j.jprot.2020.104080] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Revised: 11/17/2020] [Accepted: 12/12/2020] [Indexed: 12/18/2022]
Abstract
Dental pulp is a specialized tissue able to respond to infectious processes. Nevertheless, infection progress and root canal colonization trigger an immune-inflammatory response in tooth-surrounding tissues, leading to apical periodontitis and bone tissue destruction, further contributing to tooth loss. In order to shed some light on the effects of IL-4 on periradicular pathology development modulation, microtomographic, histological and proteomic analyses were performed using 60 mice, 30 wild type and 30 IL-4-/-. For that, 5 animals were used for microtomographic and histological analysis, and another 5 for proteomic analysis for 0, 7 and 21 days with/without pulp exposure. The periapical lesions were established in WT and IL-4-/- mice without statistical differences in their volume, and the value of p < 0.05 was adopted as significant in microtomographic and histological analyses. Regarding histological analysis, IL-4-/- mice show aggravation of pulp inflammation compared to WT. By using proteomic analysis, we have identified 32 proteins with increased abundance and 218 proteins with decreased abundance in WT animals after 21 days of pulp exposure, compared to IL-4-/- animals. However, IL-4-/- mice demonstrated faster development of apical periodontitis. These animals developed a compensatory mechanism to overcome IL-4 absence, putatively based on the identification of upregulated proteins related to immune system signaling pathways. Significance: IL-4 might play a protective role in diseases involving bone destruction and its activity may contribute to host protection, mainly due to its antiosteoclastogenic action.
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Affiliation(s)
- Mirna S Freire
- Programa de Pós-Graduação em Biotecnologia e Biodiversidade, Universidade de Brasília, Brasília, DF, Brazil; Centro de Análises Proteômicas e Bioquímicas, Programa de Pós-Graduação em Ciências Genômicas e Biotecnologia, Universidade Católica de Brasília, Brasília, DF, Brazil; Curso de Odontologia, Centro Universitário do Planalto Central Apparecido dos Santos, UNICEPLAC, Brasília, DF, Brazil
| | - Nelson G Oliveira
- Centro de Análises Proteômicas e Bioquímicas, Programa de Pós-Graduação em Ciências Genômicas e Biotecnologia, Universidade Católica de Brasília, Brasília, DF, Brazil
| | - Stella M F Lima
- Centro de Análises Proteômicas e Bioquímicas, Programa de Pós-Graduação em Ciências Genômicas e Biotecnologia, Universidade Católica de Brasília, Brasília, DF, Brazil; Curso de Odontologia, Universidade Católica de Brasília, UCB, Brasília, DF, Brazil
| | - William F Porto
- Centro de Análises Proteômicas e Bioquímicas, Programa de Pós-Graduação em Ciências Genômicas e Biotecnologia, Universidade Católica de Brasília, Brasília, DF, Brazil; Porto Reports, Brasília, DF, Brazil
| | - Danilo C M Martins
- Centro de Análises Proteômicas e Bioquímicas, Programa de Pós-Graduação em Ciências Genômicas e Biotecnologia, Universidade Católica de Brasília, Brasília, DF, Brazil; Programa de Pós-Graduação em Ciências da Saúde, Faculdade de Ciências da Saúde, Universidade de Brasília, UnB, Brasília, DF, Brazil
| | - Osmar N Silva
- Programa de Pós-graduacao em Ciências Farmacêuticas. Centro Universitário de Anápolis - UniEVANGELICA, Anápolis, GO, Brazil
| | - Sacha B Chaves
- Departamento de nanotecnologia, Universidade de Brasília, Brazil
| | - Marcelo V Sousa
- Laboratório de Bioquímica e Química de Proteínas, Departamento de Biologia Celular, Universidade de Brasília, Brazil
| | - Carlos A O Ricart
- Laboratório de Bioquímica e Química de Proteínas, Departamento de Biologia Celular, Universidade de Brasília, Brazil
| | - Mariana S Castro
- Laboratório de Bioquímica e Química de Proteínas, Departamento de Biologia Celular, Universidade de Brasília, Brazil
| | - Wagner Fontes
- Laboratório de Bioquímica e Química de Proteínas, Departamento de Biologia Celular, Universidade de Brasília, Brazil
| | - Octavio L Franco
- Programa de Pós-Graduação em Biotecnologia e Biodiversidade, Universidade de Brasília, Brasília, DF, Brazil; Centro de Análises Proteômicas e Bioquímicas, Programa de Pós-Graduação em Ciências Genômicas e Biotecnologia, Universidade Católica de Brasília, Brasília, DF, Brazil; Programa de Pós-Graduação em Ciências da Saúde, Faculdade de Ciências da Saúde, Universidade de Brasília, UnB, Brasília, DF, Brazil; Programa de Pós-Graduação em Patologia Molecular, Universidade de Brasília, Brasília, DF, Brazil.
| | - Taia M B Rezende
- Centro de Análises Proteômicas e Bioquímicas, Programa de Pós-Graduação em Ciências Genômicas e Biotecnologia, Universidade Católica de Brasília, Brasília, DF, Brazil; Curso de Odontologia, Universidade Católica de Brasília, UCB, Brasília, DF, Brazil; Programa de Pós-Graduação em Ciências da Saúde, Faculdade de Ciências da Saúde, Universidade de Brasília, UnB, Brasília, DF, Brazil.
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30
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Bostanci N, Grant M, Bao K, Silbereisen A, Hetrodt F, Manoil D, Belibasakis GN. Metaproteome and metabolome of oral microbial communities. Periodontol 2000 2020; 85:46-81. [PMID: 33226703 DOI: 10.1111/prd.12351] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
The emergence of high-throughput technologies for the comprehensive measurement of biomolecules, also referred to as "omics" technologies, has helped us gather "big data" and characterize microbial communities. In this article, we focus on metaproteomic and metabolomic approaches that support hypothesis-driven investigations on various oral biologic samples. Proteomics reveals the working units of the oral milieu and metabolomics unveils the reactions taking place; and so these complementary techniques can unravel the functionality and underlying regulatory processes within various oral microbial communities. Current knowledge of the proteomic interplay and metabolic interactions of microorganisms within oral biofilm and salivary microbiome communities is presented and discussed, from both clinical and basic research perspectives. Communities indicative of, or from, health, caries, periodontal diseases, and endodontic lesions are represented. Challenges, future prospects, and examples of best practice are given.
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Affiliation(s)
- Nagihan Bostanci
- Division of Oral Diseases, Department of Dental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Melissa Grant
- Biological Sciences, School of Dentistry, Institute of Clinical Sciences, University of Birmingham, Birmingham, UK
| | - Kai Bao
- Division of Oral Diseases, Department of Dental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Angelika Silbereisen
- Division of Oral Diseases, Department of Dental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Franziska Hetrodt
- Division of Oral Diseases, Department of Dental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Daniel Manoil
- Division of Oral Diseases, Department of Dental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Georgios N Belibasakis
- Division of Oral Diseases, Department of Dental Medicine, Karolinska Institutet, Stockholm, Sweden
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31
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Gouveia D, Miotello G, Gallais F, Gaillard JC, Debroas S, Bellanger L, Lavigne JP, Sotto A, Grenga L, Pible O, Armengaud J. Proteotyping SARS-CoV-2 Virus from Nasopharyngeal Swabs: A Proof-of-Concept Focused on a 3 Min Mass Spectrometry Window. J Proteome Res 2020; 19:4407-4416. [PMID: 32697082 PMCID: PMC7640971 DOI: 10.1021/acs.jproteome.0c00535] [Citation(s) in RCA: 78] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Indexed: 12/13/2022]
Abstract
Rapid but yet sensitive, specific, and high-throughput detection of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in clinical samples is key to diagnose infected people and to better control the spread of the virus. Alternative methodologies to PCR and immunodiagnostics that would not require specific reagents are worthy to investigate not only for fighting the COVID-19 pandemic but also to detect other emergent pathogenic threats. Here, we propose the use of tandem mass spectrometry to detect SARS-CoV-2 marker peptides in nasopharyngeal swabs. We documented that the signal from the microbiota present in such samples is low and can be overlooked when interpreting shotgun proteomic data acquired on a restricted window of the peptidome landscape. In this proof-of-concept study, simili nasopharyngeal swabs spiked with different quantities of purified SARS-CoV-2 viral material were used to develop a nanoLC-MS/MS acquisition method, which was then successfully applied on COVID-19 clinical samples. We argue that peptides ADETQALPQR and GFYAQGSR from the nucleocapsid protein are of utmost interest as their signal is intense and their elution can be obtained within a 3 min window in the tested conditions. These results pave the way for the development of time-efficient viral diagnostic tests based on mass spectrometry.
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Affiliation(s)
- Duarte Gouveia
- INRAE,
Département Médicaments et Technologies pour la Santé
(DMTS), SPI, Université Paris Saclay,
CEA, 30200 Bagnols-sur-Cèze, France
| | - Guylaine Miotello
- INRAE,
Département Médicaments et Technologies pour la Santé
(DMTS), SPI, Université Paris Saclay,
CEA, 30200 Bagnols-sur-Cèze, France
| | - Fabrice Gallais
- INRAE,
Département Médicaments et Technologies pour la Santé
(DMTS), SPI, Université Paris Saclay,
CEA, 30200 Bagnols-sur-Cèze, France
| | - Jean-Charles Gaillard
- INRAE,
Département Médicaments et Technologies pour la Santé
(DMTS), SPI, Université Paris Saclay,
CEA, 30200 Bagnols-sur-Cèze, France
| | - Stéphanie Debroas
- INRAE,
Département Médicaments et Technologies pour la Santé
(DMTS), SPI, Université Paris Saclay,
CEA, 30200 Bagnols-sur-Cèze, France
| | - Laurent Bellanger
- INRAE,
Département Médicaments et Technologies pour la Santé
(DMTS), SPI, Université Paris Saclay,
CEA, 30200 Bagnols-sur-Cèze, France
| | - Jean-Philippe Lavigne
- U1047,
Institut National de la Santé et de la Recherche Médicale, Université Montpellier, Montpellier, France
- VBMI,
INSERM U1047, Université de Montpellier, Service de Microbiologie
et Hygiène Hospitalière, CHU
Nîmes, Nîmes, France
| | - Albert Sotto
- VBMI,
INSERM U1047, Université de Montpellier, Service des Maladies
Infectieuses et Tropicales, CHU Nîmes, Nîmes, France
| | - Lucia Grenga
- INRAE,
Département Médicaments et Technologies pour la Santé
(DMTS), SPI, Université Paris Saclay,
CEA, 30200 Bagnols-sur-Cèze, France
| | - Olivier Pible
- INRAE,
Département Médicaments et Technologies pour la Santé
(DMTS), SPI, Université Paris Saclay,
CEA, 30200 Bagnols-sur-Cèze, France
| | - Jean Armengaud
- INRAE,
Département Médicaments et Technologies pour la Santé
(DMTS), SPI, Université Paris Saclay,
CEA, 30200 Bagnols-sur-Cèze, France
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32
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Djemiel C, Goulas E, Badalato N, Chabbert B, Hawkins S, Grec S. Targeted Metagenomics of Retting in Flax: The Beginning of the Quest to Harness the Secret Powers of the Microbiota. Front Genet 2020; 11:581664. [PMID: 33193706 PMCID: PMC7652851 DOI: 10.3389/fgene.2020.581664] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Accepted: 09/21/2020] [Indexed: 12/13/2022] Open
Abstract
The mechanical and chemical properties of natural plant fibers are determined by many different factors, both intrinsic and extrinsic to the plant, during growth but also after harvest. A better understanding of how all these factors exert their effect and how they interact is necessary to be able to optimize fiber quality for use in different industries. One important factor is the post-harvest process known as retting, representing the first step in the extraction of bast fibers from the stem of species such as flax and hemp. During this process microorganisms colonize the stem and produce hydrolytic enzymes that target cell wall polymers thereby facilitating the progressive destruction of the stem and fiber bundles. Recent advances in sequencing technology have allowed researchers to implement targeted metagenomics leading to a much better characterization of the microbial communities involved in retting, as well as an improved understanding of microbial dynamics. In this paper we review how our current knowledge of the microbiology of retting has been improved by targeted metagenomics and discuss how related '-omics' approaches might be used to fully characterize the functional capability of the retting microbiome.
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Affiliation(s)
- Christophe Djemiel
- Univ. Lille, CNRS, UMR 8576 - UGSF - Unité de Glycobiologie Structurale et Fonctionnelle, Lille, France
| | - Estelle Goulas
- Univ. Lille, CNRS, UMR 8576 - UGSF - Unité de Glycobiologie Structurale et Fonctionnelle, Lille, France
| | - Nelly Badalato
- Univ. Lille, CNRS, UMR 8576 - UGSF - Unité de Glycobiologie Structurale et Fonctionnelle, Lille, France
| | - Brigitte Chabbert
- Université de Reims Champagne Ardenne, INRAE, UMR FARE A 614, Reims, France
| | - Simon Hawkins
- Univ. Lille, CNRS, UMR 8576 - UGSF - Unité de Glycobiologie Structurale et Fonctionnelle, Lille, France
| | - Sébastien Grec
- Univ. Lille, CNRS, UMR 8576 - UGSF - Unité de Glycobiologie Structurale et Fonctionnelle, Lille, France
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Schiebenhoefer H, Schallert K, Renard BY, Trappe K, Schmid E, Benndorf D, Riedel K, Muth T, Fuchs S. A complete and flexible workflow for metaproteomics data analysis based on MetaProteomeAnalyzer and Prophane. Nat Protoc 2020; 15:3212-3239. [PMID: 32859984 DOI: 10.1038/s41596-020-0368-7] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Accepted: 05/29/2020] [Indexed: 12/14/2022]
Abstract
Metaproteomics, the study of the collective protein composition of multi-organism systems, provides deep insights into the biodiversity of microbial communities and the complex functional interplay between microbes and their hosts or environment. Thus, metaproteomics has become an indispensable tool in various fields such as microbiology and related medical applications. The computational challenges in the analysis of corresponding datasets differ from those of pure-culture proteomics, e.g., due to the higher complexity of the samples and the larger reference databases demanding specific computing pipelines. Corresponding data analyses usually consist of numerous manual steps that must be closely synchronized. With MetaProteomeAnalyzer and Prophane, we have established two open-source software solutions specifically developed and optimized for metaproteomics. Among other features, peptide-spectrum matching is improved by combining different search engines and, compared to similar tools, metaproteome annotation benefits from the most comprehensive set of available databases (such as NCBI, UniProt, EggNOG, PFAM, and CAZy). The workflow described in this protocol combines both tools and leads the user through the entire data analysis process, including protein database creation, database search, protein grouping and annotation, and results visualization. To the best of our knowledge, this protocol presents the most comprehensive, detailed and flexible guide to metaproteomics data analysis to date. While beginners are provided with robust, easy-to-use, state-of-the-art data analysis in a reasonable time (a few hours, depending on, among other factors, the protein database size and the number of identified peptides and inferred proteins), advanced users benefit from the flexibility and adaptability of the workflow.
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Affiliation(s)
- Henning Schiebenhoefer
- Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, Berlin, Germany
- Hasso Plattner Institute, Faculty for Digital Engineering, University of Potsdam, Potsdam, Germany
| | - Kay Schallert
- Bioprocess Engineering, Otto von Guericke University, Magdeburg, Germany
| | - Bernhard Y Renard
- Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, Berlin, Germany
- Hasso Plattner Institute, Faculty for Digital Engineering, University of Potsdam, Potsdam, Germany
| | - Kathrin Trappe
- Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, Berlin, Germany
| | - Emanuel Schmid
- ID Computational & Data Science Support, Eidgenössische Technische Hochschule, Zurich, Switzerland
| | - Dirk Benndorf
- Bioprocess Engineering, Otto von Guericke University, Magdeburg, Germany
- Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
| | - Katharina Riedel
- Center for Functional Genomics of Microbes (CFGM), Institute of Microbiology, University of Greifswald, Greifswald, Germany
| | - Thilo Muth
- Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, Berlin, Germany
- Section S.3 eScience, Federal Institute for Materials Research and Testing (BAM), Berlin, Germany
| | - Stephan Fuchs
- Department of Infectious Diseases, Robert Koch Institute, Wernigerode, Germany.
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34
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Combining proteogenomics and metaproteomics for deep taxonomic and functional characterization of microbiomes from a non-sequenced host. NPJ Biofilms Microbiomes 2020; 6:23. [PMID: 32504001 PMCID: PMC7275042 DOI: 10.1038/s41522-020-0133-2] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2019] [Accepted: 05/07/2020] [Indexed: 02/06/2023] Open
Abstract
Metaproteomics of gut microbiomes from animal hosts lacking a reference genome is challenging. Here we describe a strategy combining high-resolution metaproteomics and host RNA sequencing (RNA-seq) with generalist database searching to survey the digestive tract of Gammarus fossarum, a small crustacean used as a sentinel species in ecotoxicology. This approach provides a deep insight into the full range of biomasses and metabolic activities of the holobiont components, and differentiates between the intestine and hepatopancreatic caecum.
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35
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Mooradian AD, van der Post S, Naegle KM, Held JM. ProteoClade: A taxonomic toolkit for multi-species and metaproteomic analysis. PLoS Comput Biol 2020; 16:e1007741. [PMID: 32150535 PMCID: PMC7082058 DOI: 10.1371/journal.pcbi.1007741] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2019] [Revised: 03/19/2020] [Accepted: 02/18/2020] [Indexed: 01/06/2023] Open
Abstract
We present ProteoClade, a Python toolkit that performs taxa-specific peptide assignment, protein inference, and quantitation for multi-species proteomics experiments. ProteoClade scales to hundreds of millions of protein sequences, requires minimal computational resources, and is open source, multi-platform, and accessible to non-programmers. We demonstrate its utility for processing quantitative proteomic data derived from patient-derived xenografts and its speed and scalability enable a novel de novo proteomic workflow for complex microbiota samples. The exponential growth of the number of available reference protein sequences has provided an opportunity to taxonomically annotate and quantify complex mixtures of organisms using bottom-up proteomics. However, the ability to annotate relevant taxa to proteomics data is computationally challenging when data sets generate millions of candidate sequences and the reference database contains billions of peptide sequences. Here, we provide a software tool that enables users to perform taxon-specific quantitation on large proteomic data sets without requiring high performance computing. This tool flexibly enables users to match the reference database settings to their experimental conditions, and can scale from two-organism studies to the entire UniProt repository. In addition, we provide a de novo analysis workflow that enables the identification of organisms in the sample without prior specification, analogous to 16S rRNA sequencing.
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Affiliation(s)
- Arshag D. Mooradian
- Department of Medicine, Washington University School of Medicine in St. Louis, St. Louis, Missouri, United States of America
| | - Sjoerd van der Post
- Department of Medicine, Washington University School of Medicine in St. Louis, St. Louis, Missouri, United States of America
| | - Kristen M. Naegle
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
| | - Jason M. Held
- Department of Medicine, Washington University School of Medicine in St. Louis, St. Louis, Missouri, United States of America
- Department of Anesthesiology, Washington University School of Medicine in St. Louis, St. Louis, Missouri, United States of America
- Siteman Cancer Center, Washington University School of Medicine in St. Louis, St. Louis, Missouri, United States of America
- * E-mail:
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36
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Alves G, Yu YK. Robust Accurate Identification and Biomass Estimates of Microorganisms via Tandem Mass Spectrometry. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2020; 31:85-102. [PMID: 32881514 PMCID: PMC10501333 DOI: 10.1021/jasms.9b00035] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Rapid and accurate identification of microorganisms and estimation of their biomasses are of extreme importance to public health. Mass spectrometry has become an important technique for these purposes. Previously we published a workflow named Microorganism Classification and Identification (MiCId v.12.26.2017) that was shown to perform no worse than other workflows. This manuscript presents MiCId v.12.13.2018 that, in comparison with the earlier version v.12.26.2017, allows for biomass estimates, provides more accurate microorganism identifications (better controls the number of false positives), and is robust against database size increase. This significant advance is made possible by several new ingredients introduced: first, we apply a modified expectation-maximization method to compute for each taxon considered a prior probability, which can be used for biomass estimate; second, we introduce a new concept called ownership, through which the participation ratio is computed and use it as the number of taxa to be kept within a cluster of closely related taxa; third, based on confidently identified peptides, we calculate for each taxon its degree of independence from the rest of taxa considered to determine whether or not to split this taxon off the cluster. Using 270 data files, each containing a large number of MS/MS spectra, we show that, in comparison with v.12.26.2017, version v.12.13.2018 yields superior retrieval results. We also show that MiCId v.12.13.2018 can estimate species biomass reasonably well. The new MiCId v.12.13.2018, designed to run in Linux environment, is freely available for download at https://www.ncbi.nlm.nih.gov/CBBresearch/Yu/downloads.html.
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Affiliation(s)
- Gelio Alves
- National Center for Biotehnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland 20894, United States
| | - Yi-Kuo Yu
- National Center for Biotehnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland 20894, United States
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37
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Mikan MP, Harvey HR, Timmins-Schiffman E, Riffle M, May DH, Salter I, Noble WS, Nunn BL. Metaproteomics reveal that rapid perturbations in organic matter prioritize functional restructuring over taxonomy in western Arctic Ocean microbiomes. THE ISME JOURNAL 2020; 14:39-52. [PMID: 31492961 PMCID: PMC6908719 DOI: 10.1038/s41396-019-0503-z] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Revised: 07/31/2019] [Accepted: 08/06/2019] [Indexed: 02/05/2023]
Abstract
We examined metaproteome profiles from two Arctic microbiomes during 10-day shipboard incubations to directly track early functional and taxonomic responses to a simulated algal bloom and an oligotrophic control. Using a novel peptide-based enrichment analysis, significant changes (p-value < 0.01) in biological and molecular functions associated with carbon and nitrogen recycling were observed. Within the first day under both organic matter conditions, Bering Strait surface microbiomes increased protein synthesis, carbohydrate degradation, and cellular redox processes while decreasing C1 metabolism. Taxonomic assignments revealed that the core microbiome collectively responded to algal substrates by assimilating carbon before select taxa utilize and metabolize nitrogen intracellularly. Incubations of Chukchi Sea bottom water microbiomes showed similar, but delayed functional responses to identical treatments. Although 24 functional terms were shared between experimental treatments, the timing, and degree of the remaining responses were highly variable, showing that organic matter perturbation directs community functionality prior to alterations to the taxonomic distribution at the microbiome class level. The dynamic responses of these two oceanic microbial communities have important implications for timing and magnitude of responses to organic perturbations within the Arctic Ocean and how community-level functions may forecast biogeochemical gradients in oceans.
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Affiliation(s)
- Molly P Mikan
- Ocean, Earth and Atmospheric Sciences, Old Dominion University, 406 Oceanography & Physical Sciences Building, Norfolk, VA, 23529, USA
| | - H Rodger Harvey
- Ocean, Earth and Atmospheric Sciences, Old Dominion University, 406 Oceanography & Physical Sciences Building, Norfolk, VA, 23529, USA
| | - Emma Timmins-Schiffman
- Department of Genome Sciences, University of Washington, William H. Foege Hall, 3720 15th Ave NE, Seattle, WA, 98195, USA
| | - Michael Riffle
- Department of Biochemistry, University of Washington, 1705 NE Pacific St., Seattle, WA, USA
| | - Damon H May
- Department of Genome Sciences, University of Washington, William H. Foege Hall, 3720 15th Ave NE, Seattle, WA, 98195, USA
| | - Ian Salter
- Faroese Marine Research Institute, Nóatún 1, FO-100, Tórshavn, Faroe Islands
- Alfred Wegener Institute Helmholtz Center for Polar and Marine Research, Bremerhaven, Germany
| | - William S Noble
- Department of Genome Sciences, University of Washington, William H. Foege Hall, 3720 15th Ave NE, Seattle, WA, 98195, USA
| | - Brook L Nunn
- Department of Genome Sciences, University of Washington, William H. Foege Hall, 3720 15th Ave NE, Seattle, WA, 98195, USA.
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38
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Géron A, Werner J, Wattiez R, Lebaron P, Matallana-Surget S. Deciphering the Functioning of Microbial Communities: Shedding Light on the Critical Steps in Metaproteomics. Front Microbiol 2019; 10:2395. [PMID: 31708885 PMCID: PMC6821674 DOI: 10.3389/fmicb.2019.02395] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2019] [Accepted: 10/03/2019] [Indexed: 11/13/2022] Open
Abstract
Unraveling the complex structure and functioning of microbial communities is essential to accurately predict the impact of perturbations and/or environmental changes. From all molecular tools available today to resolve the dynamics of microbial communities, metaproteomics stands out, allowing the establishment of phenotype-genotype linkages. Despite its rapid development, this technology has faced many technical challenges that still hamper its potential power. How to maximize the number of protein identification, improve quality of protein annotation, and provide reliable ecological interpretation are questions of immediate urgency. In our study, we used a robust metaproteomic workflow combining two protein fractionation approaches (gel-based versus gel-free) and four protein search databases derived from the same metagenome to analyze the same seawater sample. The resulting eight metaproteomes provided different outcomes in terms of (i) total protein numbers, (ii) taxonomic structures, and (iii) protein functions. The characterization and/or representativeness of numerous proteins from ecologically relevant taxa such as Pelagibacterales, Rhodobacterales, and Synechococcales, as well as crucial environmental processes, such as nutrient uptake, nitrogen assimilation, light harvesting, and oxidative stress response, were found to be particularly affected by the methodology. Our results provide clear evidences that the use of different protein search databases significantly alters the biological conclusions in both gel-free and gel-based approaches. Our findings emphasize the importance of diversifying the experimental workflow for a comprehensive metaproteomic study.
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Affiliation(s)
- Augustin Géron
- Division of Biological and Environmental Sciences, Faculty of Natural Sciences, University of Stirling, Stirling, United Kingdom
- Department of Proteomic and Microbiology, University of Mons, Mons, Belgium
| | - Johannes Werner
- Department of Biological Oceanography, Leibniz Institute for Baltic Sea Research, Rostock, Germany
| | - Ruddy Wattiez
- Department of Proteomic and Microbiology, University of Mons, Mons, Belgium
| | - Philippe Lebaron
- Sorbonne Universités, UPMC Université Paris 06, USR 3579, LBBM, Observatoire Océanologique, Banyuls-sur-Mer, France
| | - Sabine Matallana-Surget
- Division of Biological and Environmental Sciences, Faculty of Natural Sciences, University of Stirling, Stirling, United Kingdom
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39
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Machado KCT, Fortuin S, Tomazella GG, Fonseca AF, Warren RM, Wiker HG, de Souza SJ, de Souza GA. On the Impact of the Pangenome and Annotation Discrepancies While Building Protein Sequence Databases for Bacteria Proteogenomics. Front Microbiol 2019; 10:1410. [PMID: 31281302 PMCID: PMC6596428 DOI: 10.3389/fmicb.2019.01410] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2019] [Accepted: 06/05/2019] [Indexed: 01/19/2023] Open
Abstract
In proteomics, peptide information within mass spectrometry (MS) data from a specific organism sample is routinely matched against a protein sequence database that best represent such organism. However, if the species/strain in the sample is unknown or genetically poorly characterized, it becomes challenging to determine a database which can represent such sample. Building customized protein sequence databases merging multiple strains for a given species has become a strategy to overcome such restrictions. However, as more genetic information is publicly available and interesting genetic features such as the existence of pan- and core genes within a species are revealed, we questioned how efficient such merging strategies are to report relevant information. To test this assumption, we constructed databases containing conserved and unique sequences for 10 different species. Features that are relevant for probabilistic-based protein identification by proteomics were then monitored. As expected, increase in database complexity correlates with pangenomic complexity. However, Mycobacterium tuberculosis and Bordetella pertussis generated very complex databases even having low pangenomic complexity. We further tested database performance by using MS data from eight clinical strains from M. tuberculosis, and from two published datasets from Staphylococcus aureus. We show that by using an approach where database size is controlled by removing repeated identical tryptic sequences across strains/species, computational time can be reduced drastically as database complexity increases.
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Affiliation(s)
- Karla C T Machado
- Bioinformatics Multidisciplinary Environment, Universidade Federal do Rio Grande do Norte, Natal, Brazil
| | - Suereta Fortuin
- DST/NRF Centre of Excellence for Biomedical Tuberculosis Research/SAMRC Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Stellenbosch, South Africa
| | - Gisele Guicardi Tomazella
- Bioinformatics Multidisciplinary Environment, Universidade Federal do Rio Grande do Norte, Natal, Brazil
- The Gade Research Group for Infection and Immunity, Department of Clinical Science, University of Bergen, Bergen, Norway
- The Institute of Bioinformatics and Biotechnology, Natal, Brazil
| | - Andre F Fonseca
- Bioinformatics Multidisciplinary Environment, Universidade Federal do Rio Grande do Norte, Natal, Brazil
| | - Robin Mark Warren
- DST/NRF Centre of Excellence for Biomedical Tuberculosis Research/SAMRC Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Stellenbosch, South Africa
| | - Harald G Wiker
- The Gade Research Group for Infection and Immunity, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Sandro Jose de Souza
- Bioinformatics Multidisciplinary Environment, Universidade Federal do Rio Grande do Norte, Natal, Brazil
- The Brain Institute, Universidade Federal do Rio Grande do Norte, Natal, Brazil
| | - Gustavo Antonio de Souza
- Bioinformatics Multidisciplinary Environment, Universidade Federal do Rio Grande do Norte, Natal, Brazil
- Department of Biochemistry, Federal University of Rio Grande do Norte (UFRN), Natal, Brazil
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40
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Abstract
Metaproteomics is the large-scale identification and quantification of proteins from microbial communities and thus provides direct insight into the phenotypes of microorganisms on the molecular level. Initially, metaproteomics was mainly used to assess the "expressed" metabolism and physiology of microbial community members. However, recently developed metaproteomic tools allow quantification of per-species biomass to determine community structure, in situ carbon sources of community members, and the uptake of labeled substrates by community members. In this perspective, I provide a brief overview of the questions that we can currently address, as well as new metaproteomics-based approaches that we and others are developing to address even more questions in the study of microbial communities and plant and animal microbiota. I also highlight some areas and technologies where I anticipate developments and potentially major breakthroughs in the next 5 years and beyond.
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Levi Mortera S, Soggiu A, Vernocchi P, Del Chierico F, Piras C, Carsetti R, Marzano V, Britti D, Urbani A, Roncada P, Putignani L. Metaproteomic investigation to assess gut microbiota shaping in newborn mice: A combined taxonomic, functional and quantitative approach. J Proteomics 2019; 203:103378. [PMID: 31102759 DOI: 10.1016/j.jprot.2019.103378] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2019] [Revised: 04/23/2019] [Accepted: 05/13/2019] [Indexed: 12/16/2022]
Abstract
Breastfeeding is nowadays known to be one of the most critical factors contributing to the development of an efficient immune system. In the last decade, a consistent number of pieces of evidence demonstrated the relationship between a healthy organism and its gut microbiota. However, this link is still not fully understood and requires further investigation. We recently adopted a murine model to describe the impact of either maternal milk or parental genetic background, on the composition of the gut microbial population in the first weeks of life. A metaproteomic approach to such complex environments is a big challenge that requires a strong effort in both data production and analysis, including the set-up of dedicated multitasking bioinformatics pipelines. Herein we present an LC-MS/MS based investigation to monitor mouse gut microbiota in the early life, aiming at characterizing its functions and metabolic activities together with a taxonomic description in terms of operational taxonomic units. We provided a quantitative evaluation of bacterial metaproteins, taking into account differential expression results in relation to the functional and taxonomic classification, particularly with proteins from orthologues groups. This allowed the reduction of the bias arising from the presence of a high number of shared peptides, and proteins, among different bacterial species. We also focused on host mucosal proteome and its modulation, according to different microbiota composition. SIGNIFICANCE: This paper would represent a reference work for investigations on gut microbiota in early life, from both a microbiological and a functional proteomic point of view. We focused on the shaping of the mouse gut microbiota in dependence on the feeding modality, defining a reliable taxonomic description, highlighting some functional characteristics of the microbial community, and performing a first quantitative evaluation by data independent analysis in metaproteomics.
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Affiliation(s)
| | - Alessio Soggiu
- Department of Veterinary Medicine, University of Milan, Milan, Italy
| | - Pamela Vernocchi
- Human Microbiome Unit, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
| | | | - Cristian Piras
- Department of Veterinary Medicine, University of Milan, Milan, Italy
| | - Rita Carsetti
- B cell Pathophysiology Unit, Immunology Research Area and Unit of Diagnostic Immunology, Department of Laboratories, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
| | - Valeria Marzano
- Human Microbiome Unit, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
| | - Domenico Britti
- C.I.S. - Interdepartmental Services Centre of Veterinary for Human and Animal Health, University of Catanzaro "Magna Græcia", Catanzaro, Italy.; Department of Health Sciences, University of Catanzaro "Magna Græcia", Catanzaro, Italy
| | - Andrea Urbani
- Catholic University of Sacred Heart, Rome, Italy; Fondazione Policlinico Universitario A. Gemelli, IRCCS, Rome, Italy
| | - Paola Roncada
- Department of Health Sciences, University of Catanzaro "Magna Græcia", Catanzaro, Italy
| | - Lorenza Putignani
- Parasitology Unit and Human Microbiome Unit, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy.
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Schiebenhoefer H, Van Den Bossche T, Fuchs S, Renard BY, Muth T, Martens L. Challenges and promise at the interface of metaproteomics and genomics: an overview of recent progress in metaproteogenomic data analysis. Expert Rev Proteomics 2019; 16:375-390. [PMID: 31002542 DOI: 10.1080/14789450.2019.1609944] [Citation(s) in RCA: 54] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
INTRODUCTION The study of microbial communities based on the combined analysis of genomic and proteomic data - called metaproteogenomics - has gained increased research attention in recent years. This relatively young field aims to elucidate the functional and taxonomic interplay of proteins in microbiomes and its implications on human health and the environment. Areas covered: This article reviews bioinformatics methods and software tools dedicated to the analysis of data from metaproteomics and metaproteogenomics experiments. In particular, it focuses on the creation of tailored protein sequence databases, on the optimal use of database search algorithms including methods of error rate estimation, and finally on taxonomic and functional annotation of peptide and protein identifications. Expert opinion: Recently, various promising strategies and software tools have been proposed for handling typical data analysis issues in metaproteomics. However, severe challenges remain that are highlighted and discussed in this article; these include: (i) robust false-positive assessment of peptide and protein identifications, (ii) complex protein inference against a background of highly redundant data, (iii) taxonomic and functional post-processing of identification data, and finally, (iv) the assessment and provision of metrics and tools for quantitative analysis.
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Affiliation(s)
- Henning Schiebenhoefer
- a Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure , Robert Koch Institute , Berlin , Germany
| | - Tim Van Den Bossche
- b VIB - UGent Center for Medical Biotechnology, VIB , Ghent , Belgium.,c Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences , Ghent University , Ghent , Belgium
| | - Stephan Fuchs
- d FG13 Division of Nosocomial Pathogens and Antibiotic Resistances , Robert Koch Institute , Wernigerode , Germany
| | - Bernhard Y Renard
- a Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure , Robert Koch Institute , Berlin , Germany
| | - Thilo Muth
- a Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure , Robert Koch Institute , Berlin , Germany
| | - Lennart Martens
- b VIB - UGent Center for Medical Biotechnology, VIB , Ghent , Belgium.,c Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences , Ghent University , Ghent , Belgium
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Evaluating Metagenomic Prediction of the Metaproteome in a 4.5-Year Study of a Patient with Crohn's Disease. mSystems 2019; 4:mSystems00337-18. [PMID: 30801026 PMCID: PMC6372841 DOI: 10.1128/msystems.00337-18] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Accepted: 01/17/2019] [Indexed: 02/07/2023] Open
Abstract
Although genetic approaches are the standard in microbiome analysis, proteome-level information is largely absent. This discrepancy warrants a better understanding of the relationship between gene copy number and protein abundance, as this is crucial information for inferring protein-level changes from metagenomic data. As it remains unknown how metaproteomic systems evolve during dynamic disease states, we leveraged a 4.5-year fecal time series using samples from a single patient with colonic Crohn's disease. Utilizing multiplexed quantitative proteomics and shotgun metagenomic sequencing of eight time points in technical triplicate, we quantified over 29,000 protein groups and 110,000 genes and compared them to five protein biomarkers of disease activity. Broad-scale observations were consistent between data types, including overall clustering by principal-coordinate analysis and fluctuations in Gene Ontology terms related to Crohn's disease. Through linear regression, we determined genes and proteins fluctuating in conjunction with inflammatory metrics. We discovered conserved taxonomic differences relevant to Crohn's disease, including a negative association of Faecalibacterium and a positive association of Escherichia with calprotectin. Despite concordant associations of genera, the specific genes correlated with these metrics were drastically different between metagenomic and metaproteomic data sets. This resulted in the generation of unique functional interpretations dependent on the data type, with metaproteome evidence for previously investigated mechanisms of dysbiosis. An example of one such mechanism was a connection between urease enzymes, amino acid metabolism, and the local inflammation state within the patient. This proof-of-concept approach prompts further investigation of the metaproteome and its relationship with the metagenome in biologically complex systems such as the microbiome. IMPORTANCE A majority of current microbiome research relies heavily on DNA analysis. However, as the field moves toward understanding the microbial functions related to healthy and disease states, it is critical to evaluate how changes in DNA relate to changes in proteins, which are functional units of the genome. This study tracked the abundance of genes and proteins as they fluctuated during various inflammatory states in a 4.5-year study of a patient with colonic Crohn's disease. Our results indicate that despite a low level of correlation, taxonomic associations were consistent in the two data types. While there was overlap of the data types, several associations were uniquely discovered by analyzing the metaproteome component. This case study provides unique and important insights into the fundamental relationship between the genes and proteins of a single individual's fecal microbiome associated with clinical consequences.
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Challenges in Clinical Metaproteomics Highlighted by the Analysis of Acute Leukemia Patients with Gut Colonization by Multidrug-Resistant Enterobacteriaceae. Proteomes 2019; 7:proteomes7010002. [PMID: 30626002 PMCID: PMC6473847 DOI: 10.3390/proteomes7010002] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Revised: 12/20/2018] [Accepted: 01/03/2019] [Indexed: 12/25/2022] Open
Abstract
The microbiome has a strong impact on human health and disease and is, therefore, increasingly studied in a clinical context. Metaproteomics is also attracting considerable attention, and such data can be efficiently generated today owing to improvements in mass spectrometry-based proteomics. As we will discuss in this study, there are still major challenges notably in data analysis that need to be overcome. Here, we analyzed 212 fecal samples from 56 hospitalized acute leukemia patients with multidrug-resistant Enterobactericeae (MRE) gut colonization using metagenomics and metaproteomics. This is one of the largest clinical metaproteomic studies to date, and the first metaproteomic study addressing the gut microbiome in MRE colonized acute leukemia patients. Based on this substantial data set, we discuss major current limitations in clinical metaproteomic data analysis to provide guidance to researchers in the field. Notably, the results show that public metagenome databases are incomplete and that sample-specific metagenomes improve results. Furthermore, biological variation is tremendous which challenges clinical study designs and argues that longitudinal measurements of individual patients are a valuable future addition to the analysis of patient cohorts.
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45
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Gurdeep Singh R, Tanca A, Palomba A, Van der Jeugt F, Verschaffelt P, Uzzau S, Martens L, Dawyndt P, Mesuere B. Unipept 4.0: Functional Analysis of Metaproteome Data. J Proteome Res 2018; 18:606-615. [PMID: 30465426 DOI: 10.1021/acs.jproteome.8b00716] [Citation(s) in RCA: 83] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Unipept ( https://unipept.ugent.be ) is a web application for metaproteome data analysis, with an initial focus on tryptic-peptide-based biodiversity analysis of MS/MS samples. Because the true potential of metaproteomics lies in gaining insight into the expressed functions of complex environmental samples, the 4.0 release of Unipept introduces complementary functional analysis based on GO terms and EC numbers. Integration of this new functional analysis with the existing biodiversity analysis is an important asset of the extended pipeline. As a proof of concept, a human faecal metaproteome data set from 15 healthy subjects was reanalyzed with Unipept 4.0, yielding fast, detailed, and straightforward characterization of taxon-specific catalytic functions that is shown to be consistent with previous results from a BLAST-based functional analysis of the same data.
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Affiliation(s)
- Robbert Gurdeep Singh
- Department of Applied Mathematics, Computer Science and Statistics , Ghent University , Ghent B-9000 , Belgium
| | - Alessandro Tanca
- Porto Conte Ricerche, Science and Technology Park of Sardinia , Tramariglio, Alghero 07041 , Italy
| | - Antonio Palomba
- Porto Conte Ricerche, Science and Technology Park of Sardinia , Tramariglio, Alghero 07041 , Italy
| | - Felix Van der Jeugt
- Department of Applied Mathematics, Computer Science and Statistics , Ghent University , Ghent B-9000 , Belgium
| | - Pieter Verschaffelt
- Department of Applied Mathematics, Computer Science and Statistics , Ghent University , Ghent B-9000 , Belgium
| | - Sergio Uzzau
- Porto Conte Ricerche, Science and Technology Park of Sardinia , Tramariglio, Alghero 07041 , Italy
| | - Lennart Martens
- VIB-UGent Center for Medical Biotechnology , VIB , Ghent B-9000 , Belgium.,Department of Biochemistry , Ghent University , Ghent B-9000 , Belgium
| | - Peter Dawyndt
- Department of Applied Mathematics, Computer Science and Statistics , Ghent University , Ghent B-9000 , Belgium
| | - Bart Mesuere
- Department of Applied Mathematics, Computer Science and Statistics , Ghent University , Ghent B-9000 , Belgium.,VIB-UGent Center for Medical Biotechnology , VIB , Ghent B-9000 , Belgium.,Department of Biochemistry , Ghent University , Ghent B-9000 , Belgium
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46
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Alves G, Wang G, Ogurtsov AY, Drake SK, Gucek M, Sacks DB, Yu YK. Rapid Classification and Identification of Multiple Microorganisms with Accurate Statistical Significance via High-Resolution Tandem Mass Spectrometry. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2018; 29:1721-1737. [PMID: 29873019 PMCID: PMC6061032 DOI: 10.1007/s13361-018-1986-y] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/13/2017] [Revised: 03/30/2018] [Accepted: 04/25/2018] [Indexed: 05/30/2023]
Abstract
Rapid and accurate identification and classification of microorganisms is of paramount importance to public health and safety. With the advance of mass spectrometry (MS) technology, the speed of identification can be greatly improved. However, the increasing number of microbes sequenced is complicating correct microbial identification even in a simple sample due to the large number of candidates present. To properly untwine candidate microbes in samples containing one or more microbes, one needs to go beyond apparent morphology or simple "fingerprinting"; to correctly prioritize the candidate microbes, one needs to have accurate statistical significance in microbial identification. We meet these challenges by using peptide-centric representations of microbes to better separate them and by augmenting our earlier analysis method that yields accurate statistical significance. Here, we present an updated analysis workflow that uses tandem MS (MS/MS) spectra for microbial identification or classification. We have demonstrated, using 226 MS/MS publicly available data files (each containing from 2500 to nearly 100,000 MS/MS spectra) and 4000 additional MS/MS data files, that the updated workflow can correctly identify multiple microbes at the genus and often the species level for samples containing more than one microbe. We have also shown that the proposed workflow computes accurate statistical significances, i.e., E values for identified peptides and unified E values for identified microbes. Our updated analysis workflow MiCId, a freely available software for Microorganism Classification and Identification, is available for download at https://www.ncbi.nlm.nih.gov/CBBresearch/Yu/downloads.html . Graphical Abstract ᅟ.
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Affiliation(s)
- Gelio Alves
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20894, USA
| | - Guanghui Wang
- Proteomics Core, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Aleksey Y Ogurtsov
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20894, USA
| | - Steven K Drake
- Critical Care Medicine Department, Clinical Center, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Marjan Gucek
- Proteomics Core, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - David B Sacks
- Department of Laboratory Medicine, Clinical Center, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Yi-Kuo Yu
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20894, USA.
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47
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Integrated metatranscriptomics and metaproteomics for the characterization of bacterial microbiota in unfed Ixodes ricinus. Ticks Tick Borne Dis 2018; 9:1241-1251. [DOI: 10.1016/j.ttbdis.2018.04.020] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2018] [Revised: 04/28/2018] [Accepted: 04/29/2018] [Indexed: 12/12/2022]
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48
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Sandrin TR, Demirev PA. Characterization of microbial mixtures by mass spectrometry. MASS SPECTROMETRY REVIEWS 2018; 37:321-349. [PMID: 28509357 DOI: 10.1002/mas.21534] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2016] [Revised: 03/09/2017] [Accepted: 03/09/2017] [Indexed: 05/27/2023]
Abstract
MS applications in microbiology have increased significantly in the past 10 years, due in part to the proliferation of regulator-approved commercial MALDI MS platforms for rapid identification of clinical infections. In parallel, with the expansion of MS technologies in the "omics" fields, novel MS-based research efforts to characterize organismal as well as environmental microbiomes have emerged. Successful characterization of microorganisms found in complex mixtures of other organisms remains a major challenge for researchers and clinicians alike. Here, we review recent MS advances toward addressing that challenge. These include sample preparation methods and protocols, and established, for example, MALDI, as well as newer, for example, atmospheric pressure ionization (API) techniques. MALDI mass spectra of intact cells contain predominantly information on the highly expressed house-keeping proteins used as biomarkers. The API methods are applicable for small biomolecule analysis, for example, phospholipids and lipopeptides, and facilitate species differentiation. MS hardware and techniques, for example, tandem MS, including diverse ion source/mass analyzer combinations are discussed. Relevant examples for microbial mixture characterization utilizing these combinations are provided. Chemometrics and bioinformatics methods and algorithms, including those applied to large scale MS data acquisition in microbial metaproteomics and MS imaging of biofilms, are highlighted. Select MS applications for polymicrobial culture analysis in environmental and clinical microbiology are reviewed as well.
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Affiliation(s)
- Todd R Sandrin
- School of Mathematical and Natural Sciences, Arizona State University, Phoenix, Arizona
| | - Plamen A Demirev
- Applied Physics Laboratory, Johns Hopkins University, Laurel, Maryland
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49
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Xiao J, Tanca A, Jia B, Yang R, Wang B, Zhang Y, Li J. Metagenomic Taxonomy-Guided Database-Searching Strategy for Improving Metaproteomic Analysis. J Proteome Res 2018; 17:1596-1605. [DOI: 10.1021/acs.jproteome.7b00894] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Affiliation(s)
- Jinqiu Xiao
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, People’s Republic of China
| | - Alessandro Tanca
- Porto Conte Ricerche, Science and Technology Park of Sardinia, Tramariglio, Alghero, Italy
| | - Ben Jia
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, People’s Republic of China
| | - Runqing Yang
- College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, People’s Republic of China
| | - Bo Wang
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, People’s Republic of China
| | - Yu Zhang
- Institute of Oceanography, Shanghai Jiao Tong University, Shanghai 200240, People’s Republic of China
| | - Jing Li
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, People’s Republic of China
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50
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Blank C, Easterly C, Gruening B, Johnson J, Kolmeder CA, Kumar P, May D, Mehta S, Mesuere B, Brown Z, Elias JE, Hervey WJ, McGowan T, Muth T, Nunn B, Rudney J, Tanca A, Griffin TJ, Jagtap PD. Disseminating Metaproteomic Informatics Capabilities and Knowledge Using the Galaxy-P Framework. Proteomes 2018; 6:proteomes6010007. [PMID: 29385081 PMCID: PMC5874766 DOI: 10.3390/proteomes6010007] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2017] [Revised: 01/26/2018] [Accepted: 01/26/2018] [Indexed: 01/12/2023] Open
Abstract
The impact of microbial communities, also known as the microbiome, on human health and the environment is receiving increased attention. Studying translated gene products (proteins) and comparing metaproteomic profiles may elucidate how microbiomes respond to specific environmental stimuli, and interact with host organisms. Characterizing proteins expressed by a complex microbiome and interpreting their functional signature requires sophisticated informatics tools and workflows tailored to metaproteomics. Additionally, there is a need to disseminate these informatics resources to researchers undertaking metaproteomic studies, who could use them to make new and important discoveries in microbiome research. The Galaxy for proteomics platform (Galaxy-P) offers an open source, web-based bioinformatics platform for disseminating metaproteomics software and workflows. Within this platform, we have developed easily-accessible and documented metaproteomic software tools and workflows aimed at training researchers in their operation and disseminating the tools for more widespread use. The modular workflows encompass the core requirements of metaproteomic informatics: (a) database generation; (b) peptide spectral matching; (c) taxonomic analysis and (d) functional analysis. Much of the software available via the Galaxy-P platform was selected, packaged and deployed through an online metaproteomics "Contribution Fest" undertaken by a unique consortium of expert software developers and users from the metaproteomics research community, who have co-authored this manuscript. These resources are documented on GitHub and freely available through the Galaxy Toolshed, as well as a publicly accessible metaproteomics gateway Galaxy instance. These documented workflows are well suited for the training of novice metaproteomics researchers, through online resources such as the Galaxy Training Network, as well as hands-on training workshops. Here, we describe the metaproteomics tools available within these Galaxy-based resources, as well as the process by which they were selected and implemented in our community-based work. We hope this description will increase access to and utilization of metaproteomics tools, as well as offer a framework for continued community-based development and dissemination of cutting edge metaproteomics software.
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Affiliation(s)
- Clemens Blank
- Bioinformatics Group, Department of Computer Science, University of Freiburg, 79110 Freiburg im Breisgau, Germany.
| | - Caleb Easterly
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA.
| | - Bjoern Gruening
- Bioinformatics Group, Department of Computer Science, University of Freiburg, 79110 Freiburg im Breisgau, Germany.
| | - James Johnson
- Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN 55455, USA.
| | - Carolin A Kolmeder
- Institute of Biotechnology, University of Helsinki, 00014 Helsinki, Finland.
| | - Praveen Kumar
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA.
| | - Damon May
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA.
| | - Subina Mehta
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA.
| | - Bart Mesuere
- Computational Biology Group, Ghent University, Krijgslaan 281, B-9000 Ghent, Belgium.
| | - Zachary Brown
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA.
| | - Joshua E Elias
- Department of Chemical & Systems Biology, Stanford University, Stanford, CA 94305, USA.
| | - W Judson Hervey
- Center for Bio/Molecular Science & Engineering, Naval Research Laboratory, Washington, DC 20375, USA.
| | - Thomas McGowan
- Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN 55455, USA.
| | - Thilo Muth
- Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, 13353 Berlin, Germany.
| | - Brook Nunn
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA.
| | - Joel Rudney
- Department of Diagnostic and Biological Sciences, University of Minnesota, Minneapolis, MN 55455, USA.
| | - Alessandro Tanca
- Porto Conte Ricerche Science and Technology Park of Sardinia, 07041 Alghero, Italy.
| | - Timothy J Griffin
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA.
| | - Pratik D Jagtap
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA.
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