1
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Xue M, Xie Y, Zang X, Zhong Y, Ma X, Sun H, Liu J. Deciphering functional groups of rumen microbiome and their underlying potentially causal relationships in shaping host traits. IMETA 2024; 3:e225. [PMID: 39135684 PMCID: PMC11316931 DOI: 10.1002/imt2.225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/31/2024] [Revised: 06/25/2024] [Accepted: 06/26/2024] [Indexed: 08/15/2024]
Abstract
Over the years, microbiome research has achieved tremendous advancements driven by culture-independent meta-omics approaches. Despite extensive research, our understanding of the functional roles and causal effects of the microbiome on phenotypes remains limited. In this study, we focused on the rumen metaproteome, combining it with metatranscriptome and metabolome data to accurately identify the active functional distributions of rumen microorganisms and specific functional groups that influence feed efficiency. By integrating host genetics data, we established the potentially causal relationships between microbes-proteins/metabolites-phenotype, and identified specific patterns in which functional groups of rumen microorganisms influence host feed efficiency. We found a causal link between Selenomonas bovis and rumen carbohydrate metabolism, potentially mediated by bacterial chemotaxis and a two-component regulatory system, impacting feed utilization efficiency of dairy cows. Our study on the nutrient utilization functional groups in the rumen of high-feed-efficiency dairy cows, along with the identification of key microbiota functional proteins and their potentially causal relationships, will help move from correlation to causation in rumen microbiome research. This will ultimately enable precise regulation of the rumen microbiota for optimized ruminant production.
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Affiliation(s)
- Ming‐Yuan Xue
- Institute of Dairy Science, College of Animal SciencesZhejiang UniversityHangzhouChina
- Xianghu LaboratoryHangzhouChina
| | - Yun‐Yi Xie
- Institute of Dairy Science, College of Animal SciencesZhejiang UniversityHangzhouChina
| | - Xin‐Wei Zang
- Institute of Dairy Science, College of Animal SciencesZhejiang UniversityHangzhouChina
| | - Yi‐Fan Zhong
- Institute of Dairy Science, College of Animal SciencesZhejiang UniversityHangzhouChina
| | - Xiao‐Jiao Ma
- Institute of Dairy Science, College of Animal SciencesZhejiang UniversityHangzhouChina
| | - Hui‐Zeng Sun
- Institute of Dairy Science, College of Animal SciencesZhejiang UniversityHangzhouChina
- Ministry of Education Key Laboratory of Molecular Animal NutritionZhejiang UniversityHangzhouChina
| | - Jian‐Xin Liu
- Institute of Dairy Science, College of Animal SciencesZhejiang UniversityHangzhouChina
- Ministry of Education Key Laboratory of Molecular Animal NutritionZhejiang UniversityHangzhouChina
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2
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da Silva Duarte V, de Paula Dias Moreira L, Skeie SB, Svalestad F, Øyaas J, Porcellato D. Database selection for shotgun metaproteomic of low-diversity dairy microbiomes. Int J Food Microbiol 2024; 418:110706. [PMID: 38696985 DOI: 10.1016/j.ijfoodmicro.2024.110706] [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: 02/15/2024] [Revised: 04/12/2024] [Accepted: 04/13/2024] [Indexed: 05/04/2024]
Abstract
The metaproteomics field has recently gained more and more interest as a valuable tool for studying both the taxonomy and function of microbiomes, including those used in food fermentations. One crucial step in the metaproteomics pipeline is selecting a database to obtain high-quality taxonomical and functional information from microbial communities. One of the best strategies described for building protein databases is using sample-specific or study-specific protein databases obtained from metagenomic sequencing. While this is true for high-diversity microbiomes (such as gut and soil), there is still a lack of validation for different database construction strategies in low-diversity microbiomes, such as those found in fermented dairy products where starter cultures containing few species are used. In this study, we assessed the performance of various database construction strategies applied to metaproteomics on two low-diversity microbiomes obtained from cheese production using commercial starter cultures and analyzed by LC-MS/MS. Substantial differences were detected between the strategies, and the best performance in terms of the number of peptides and proteins identified from the spectra was achieved by metagenomic-derived databases. However, extensive databases constructed from a high number of available online genomes obtained a similar taxonomical and functional annotation of the metaproteome compared to the metagenomic-derived databases. Our results indicate that, in the case of low-diversity dairy microbiomes, the use of publically available genomes to construct protein databases can be considered as an alternative to metagenome-derived databases.
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Affiliation(s)
- Vinícius da Silva Duarte
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences, NMBU, P.O. Box 5003, N-1432 Ås, Norway
| | - Luiza de Paula Dias Moreira
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences, NMBU, P.O. Box 5003, N-1432 Ås, Norway
| | - Siv B Skeie
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences, NMBU, P.O. Box 5003, N-1432 Ås, Norway
| | | | - Jorun Øyaas
- TINE SA, P.O. Box 7, Kalbakken, N-0902 Oslo, Norway
| | - Davide Porcellato
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences, NMBU, P.O. Box 5003, N-1432 Ås, Norway.
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3
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Griffiths JA, Yoo BB, Thuy-Boun P, Cantu VJ, Weldon KC, Challis C, Sweredoski MJ, Chan KY, Thron TM, Sharon G, Moradian A, Humphrey G, Zhu Q, Shaffer JP, Wolan DW, Dorrestein PC, Knight R, Gradinaru V, Mazmanian SK. Peripheral neuronal activation shapes the microbiome and alters gut physiology. Cell Rep 2024; 43:113953. [PMID: 38517896 PMCID: PMC11132177 DOI: 10.1016/j.celrep.2024.113953] [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: 09/30/2022] [Revised: 12/07/2023] [Accepted: 02/27/2024] [Indexed: 03/24/2024] Open
Abstract
The gastrointestinal (GI) tract is innervated by intrinsic neurons of the enteric nervous system (ENS) and extrinsic neurons of the central nervous system and peripheral ganglia. The GI tract also harbors a diverse microbiome, but interactions between the ENS and the microbiome remain poorly understood. Here, we activate choline acetyltransferase (ChAT)-expressing or tyrosine hydroxylase (TH)-expressing gut-associated neurons in mice to determine effects on intestinal microbial communities and their metabolites as well as on host physiology. The resulting multi-omics datasets support broad roles for discrete peripheral neuronal subtypes in shaping microbiome structure, including modulating bile acid profiles and fungal colonization. Physiologically, activation of either ChAT+ or TH+ neurons increases fecal output, while only ChAT+ activation results in increased colonic contractility and diarrhea-like fluid secretion. These findings suggest that specific subsets of peripherally activated neurons differentially regulate the gut microbiome and GI physiology in mice without involvement of signals from the brain.
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Affiliation(s)
- Jessica A Griffiths
- Division of Biology & Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA; Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA
| | - Bryan B Yoo
- Division of Biology & Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Peter Thuy-Boun
- Departments of Molecular Medicine and Integrative Structural and Computational Biology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037, USA
| | - Victor J Cantu
- Department of Pediatrics, University of California, San Diego, San Diego, CA, USA
| | - Kelly C Weldon
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, San Diego, CA, USA; UCSD Center for Microbiome Innovation, University of California, San Diego, San Diego, CA, USA
| | - Collin Challis
- Division of Biology & Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Michael J Sweredoski
- Division of Biology & Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Ken Y Chan
- Division of Biology & Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Taren M Thron
- Division of Biology & Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Gil Sharon
- Division of Biology & Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Annie Moradian
- Division of Biology & Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Gregory Humphrey
- Department of Pediatrics, University of California, San Diego, San Diego, CA, USA
| | - Qiyun Zhu
- Department of Pediatrics, University of California, San Diego, San Diego, CA, USA
| | - Justin P Shaffer
- Department of Pediatrics, University of California, San Diego, San Diego, CA, USA
| | - Dennis W Wolan
- Departments of Molecular Medicine and Integrative Structural and Computational Biology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037, USA
| | - Pieter C Dorrestein
- Department of Pediatrics, University of California, San Diego, San Diego, CA, USA; Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, San Diego, CA, USA; UCSD Center for Microbiome Innovation, University of California, San Diego, San Diego, CA, USA
| | - Rob Knight
- Department of Pediatrics, University of California, San Diego, San Diego, CA, USA; UCSD Center for Microbiome Innovation, University of California, San Diego, San Diego, CA, USA; Department of Computer Science and Engineering, University of California, San Diego, San Diego, CA, USA; Shu Chien-Gene Lay Department of Engineering, University of California, San Diego, San Diego, CA, USA; Halıcıoğlu Data Science Institute, University of California, San Diego, San Diego, CA, USA
| | - Viviana Gradinaru
- Division of Biology & Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA; Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA
| | - Sarkis K Mazmanian
- Division of Biology & Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA; Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA.
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4
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Chiang Y, Welker F, Collins MJ. Spectra without stories: reporting 94% dark and unidentified ancient proteomes. OPEN RESEARCH EUROPE 2024; 4:71. [PMID: 38903702 PMCID: PMC11187534 DOI: 10.12688/openreseurope.17225.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 03/15/2024] [Indexed: 06/22/2024]
Abstract
Background Data-dependent, bottom-up proteomics is widely used for identifying proteins and peptides. However, one key challenge is that 70% of fragment ion spectra consistently fail to be assigned by conventional database searching. This 'dark matter' of bottom-up proteomics seems to affect fields where non-model organisms, low-abundance proteins, non-tryptic peptides, and complex modifications may be present. While palaeoproteomics may appear as a niche field, understanding and reporting unidentified ancient spectra require collaborative innovation in bioinformatics strategies. This may advance the analysis of complex datasets. Methods 14.97 million high-impact ancient spectra published in Nature and Science portfolios were mined from public repositories. Identification rates, defined as the proportion of assigned fragment ion spectra, were collected as part of deposited database search outputs or parsed using open-source python packages. Results and Conclusions We report that typically 94% of the published ancient spectra remain unidentified. This phenomenon may be caused by multiple factors, notably the limitations of database searching and the selection of user-defined reference data with advanced modification patterns. These 'spectra without stories' highlight the need for widespread data sharing to facilitate methodological development and minimise the loss of often irreplaceable ancient materials. Testing and validating alternative search strategies, such as open searching and de novo sequencing, may also improve overall identification rates. Hence, lessons learnt in palaeoproteomics may benefit other fields grappling with challenging data.
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Affiliation(s)
- Yun Chiang
- Globe Institute, University of Copenhagen, Copenhagen, Denmark
- The Nice Institute of Chemistry, Universite Cote d'Azur, Nice, France
| | - Frido Welker
- Globe Institute, University of Copenhagen, Copenhagen, Denmark
| | - Matthew James Collins
- Globe Institute, University of Copenhagen, Copenhagen, Denmark
- McDonald Institute for Archaeological Research, University of Cambridge, Cambridge, England, UK
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5
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Jagtap PD, Hoopmann MR, Neely BA, Harvey A, Käll L, Perez-Riverol Y, Abajorga MK, Thomas JA, Weintraub ST, Palmblad M. The Association of Biomolecular Resource Facilities Proteome Informatics Research Group Study on Metaproteomics (iPRG-2020). J Biomol Tech 2023; 34:3fc1f5fe.a058bad4. [PMID: 37969874 PMCID: PMC10644979 DOI: 10.7171/3fc1f5fe.a058bad4] [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: 11/17/2023]
Abstract
Metaproteomics research using mass spectrometry data has emerged as a powerful strategy to understand the mechanisms underlying microbiome dynamics and the interaction of microbiomes with their immediate environment. Recent advances in sample preparation, data acquisition, and bioinformatics workflows have greatly contributed to progress in this field. In 2020, the Association of Biomolecular Research Facilities Proteome Informatics Research Group launched a collaborative study to assess the bioinformatics options available for metaproteomics research. The study was conducted in 2 phases. In the first phase, participants were provided with mass spectrometry data files and were asked to identify the taxonomic composition and relative taxa abundances in the samples without supplying any protein sequence databases. The most challenging question asked of the participants was to postulate the nature of any biological phenomena that may have taken place in the samples, such as interactions among taxonomic species. In the second phase, participants were provided a protein sequence database composed of the species present in the sample and were asked to answer the same set of questions as for phase 1. In this report, we summarize the data processing methods and tools used by participants, including database searching and software tools used for taxonomic and functional analysis. This study provides insights into the status of metaproteomics bioinformatics in participating laboratories and core facilities.
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Affiliation(s)
| | | | - Benjamin A. Neely
- National Institute of Standards and TechnologyCharlestonSouth Carolina29412USA
| | | | - Lukas Käll
- Royal Institute of Technology114 28StockholmSweden
| | - Yasset Perez-Riverol
- European Molecular Biology LaboratoryEuropean Bioinformatics InstituteWellcome Trust Genome CampusHinxtonCambridgeCB10 1SDUnited Kingdom
| | | | | | | | - Magnus Palmblad
- Center for Proteomics and MetabolomicsLeiden University Medical Center2000 RC LeidenThe Netherlands
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Shen K, Din AU, Sinha B, Zhou Y, Qian F, Shen B. Translational informatics for human microbiota: data resources, models and applications. Brief Bioinform 2023; 24:7152256. [PMID: 37141135 DOI: 10.1093/bib/bbad168] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 04/07/2023] [Accepted: 04/11/2023] [Indexed: 05/05/2023] Open
Abstract
With the rapid development of human intestinal microbiology and diverse microbiome-related studies and investigations, a large amount of data have been generated and accumulated. Meanwhile, different computational and bioinformatics models have been developed for pattern recognition and knowledge discovery using these data. Given the heterogeneity of these resources and models, we aimed to provide a landscape of the data resources, a comparison of the computational models and a summary of the translational informatics applied to microbiota data. We first review the existing databases, knowledge bases, knowledge graphs and standardizations of microbiome data. Then, the high-throughput sequencing techniques for the microbiome and the informatics tools for their analyses are compared. Finally, translational informatics for the microbiome, including biomarker discovery, personalized treatment and smart healthcare for complex diseases, are discussed.
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Affiliation(s)
- Ke Shen
- Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610212, China
| | - Ahmad Ud Din
- Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610212, China
| | - Baivab Sinha
- Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610212, China
| | - Yi Zhou
- Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610212, China
| | - Fuliang Qian
- Center for Systems Biology, Suzhou Medical College of Soochow University, Suzhou 215123, China
- Jiangsu Province Engineering Research Center of Precision Diagnostics and Therapeutics Development, Suzhou 215123, China
| | - Bairong Shen
- Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610212, China
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7
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Wright AT, Hudson LA, Garcia WL. Activity‐Based Protein Profiling – Enabling Phenotyping of Host‐Associated and Environmental Microbiomes. Isr J Chem 2023. [DOI: 10.1002/ijch.202200099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Affiliation(s)
- Aaron T. Wright
- Department of Biology Baylor University Waco Texas 76798 USA
- Department of Chemistry and Biochemistry Baylor University Waco Texas 76798 USA
| | - LaRae A. Hudson
- Department of Biology Baylor University Waco Texas 76798 USA
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8
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Lee JY, Mitchell HD, Burnet MC, Wu R, Jenson SC, Merkley ED, Nakayasu ES, Nicora CD, Jansson JK, Burnum-Johnson KE, Payne SH. Uncovering Hidden Members and Functions of the Soil Microbiome Using De Novo Metaproteomics. J Proteome Res 2022; 21:2023-2035. [PMID: 35793793 PMCID: PMC9361346 DOI: 10.1021/acs.jproteome.2c00334] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
Abstract
![]()
Metaproteomics has
been increasingly utilized for high-throughput
characterization of proteins in complex environments and has been
demonstrated to provide insights into microbial composition and functional
roles. However, significant challenges remain in metaproteomic data
analysis, including creation of a sample-specific protein sequence
database. A well-matched database is a requirement for successful
metaproteomics analysis, and the accuracy and sensitivity of PSM identification
algorithms suffer when the database is incomplete or contains extraneous
sequences. When matched DNA sequencing data of the sample is unavailable
or incomplete, creating the proteome database that accurately represents
the organisms in the sample is a challenge. Here, we leverage a de novo peptide sequencing approach to identify the sample
composition directly from metaproteomic data. First, we created a
deep learning model, Kaiko, to predict the peptide sequences from
mass spectrometry data and trained it on 5 million peptide–spectrum
matches from 55 phylogenetically diverse bacteria. After training,
Kaiko successfully identified organisms from soil isolates and synthetic
communities directly from proteomics data. Finally, we created a pipeline
for metaproteome database generation using Kaiko. We tested the pipeline
on native soils collected in Kansas, showing that the de novo sequencing model can be employed as an alternative and complementary
method to construct the sample-specific protein database instead of
relying on (un)matched metagenomes. Our pipeline identified all highly
abundant taxa from 16S rRNA sequencing of the soil samples and uncovered
several additional species which were strongly represented only in
proteomic data.
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Affiliation(s)
- Joon-Yong Lee
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Hugh D Mitchell
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Meagan C Burnet
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Ruonan Wu
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Sarah C Jenson
- Signature Sciences and Technology Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Eric D Merkley
- Signature Sciences and Technology Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Ernesto S Nakayasu
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Carrie D Nicora
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Janet K Jansson
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Kristin E Burnum-Johnson
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Samuel H Payne
- Biology Department, Brigham Young University, Provo, Utah 84602, United States
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Thuy-Boun PS, Wang AY, Crissien-Martinez A, Xu JH, Chatterjee S, Stupp GS, Su AI, Coyle WJ, Wolan DW. Quantitative metaproteomics and activity-based protein profiling of patient fecal microbiome identifies host and microbial serine-type endopeptidase activity associated with ulcerative colitis. Mol Cell Proteomics 2022; 21:100197. [PMID: 35033677 PMCID: PMC8941213 DOI: 10.1016/j.mcpro.2022.100197] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 01/10/2022] [Accepted: 01/11/2022] [Indexed: 12/12/2022] Open
Abstract
The gut microbiota plays an important yet incompletely understood role in the induction and propagation of ulcerative colitis (UC). Organism-level efforts to identify UC-associated microbes have revealed the importance of community structure, but less is known about the molecular effectors of disease. We performed 16S rRNA gene sequencing in parallel with label-free data-dependent LC-MS/MS proteomics to characterize the stool microbiomes of healthy (n = 8) and UC (n = 10) patients. Comparisons of taxonomic composition between techniques revealed major differences in community structure partially attributable to the additional detection of host, fungal, viral, and food peptides by metaproteomics. Differential expression analysis of metaproteomic data identified 176 significantly enriched protein groups between healthy and UC patients. Gene ontology analysis revealed several enriched functions with serine-type endopeptidase activity overrepresented in UC patients. Using a biotinylated fluorophosphonate probe and streptavidin-based enrichment, we show that serine endopeptidases are active in patient fecal samples and that additional putative serine hydrolases are detectable by this approach compared with unenriched profiling. Finally, as metaproteomic databases expand, they are expected to asymptotically approach completeness. Using ComPIL and de novo peptide sequencing, we estimate the size of the probable peptide space unidentified (“dark peptidome”) by our large database approach to establish a rough benchmark for database sufficiency. Despite high variability inherent in patient samples, our analysis yielded a catalog of differentially enriched proteins between healthy and UC fecal proteomes. This catalog provides a clinically relevant jumping-off point for further molecular-level studies aimed at identifying the microbial underpinnings of UC. Identified 176 significantly altered protein groups between healthy and UC patients. Serine-type endopeptidase activity is overrepresented in UC patients. Fluorophosphonate ABPP shows that endopeptidases are active in fecal samples. ABPP enrichment helps identify additional putative serine hydrolases in samples. De novo sequencing used to estimate number of MS2 spectra unidentified by ComPIL.
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Affiliation(s)
- Peter S Thuy-Boun
- Department of Molecular Medicine, The Scripps Research Institute, La Jolla, CA 92037
| | - Ana Y Wang
- Department of Molecular Medicine, The Scripps Research Institute, La Jolla, CA 92037
| | | | - Janice H Xu
- Department of Molecular Medicine, The Scripps Research Institute, La Jolla, CA 92037
| | - Sandip Chatterjee
- Department of Molecular Medicine, The Scripps Research Institute, La Jolla, CA 92037
| | - Gregory S Stupp
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037
| | - Andrew I Su
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037
| | - Walter J Coyle
- Scripps Clinic Gastroenterology Division, La Jolla, CA 92037
| | - Dennis W Wolan
- Department of Molecular Medicine, The Scripps Research Institute, La Jolla, CA 92037; Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037.
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10
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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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11
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Pettersen VK, Antunes LCM, Dufour A, Arrieta MC. Inferring early-life host and microbiome functions by mass spectrometry-based metaproteomics and metabolomics. Comput Struct Biotechnol J 2021; 20:274-286. [PMID: 35024099 PMCID: PMC8718658 DOI: 10.1016/j.csbj.2021.12.012] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2021] [Revised: 12/08/2021] [Accepted: 12/08/2021] [Indexed: 12/17/2022] Open
Abstract
Humans have a long-standing coexistence with microorganisms. In particular, the microbial community that populates the human gastrointestinal tract has emerged as a critical player in governing human health and disease. DNA and RNA sequencing techniques that map taxonomical composition and genomic potential of the gut community have become invaluable for microbiome research. However, deriving a biochemical understanding of how activities of the gut microbiome shape host development and physiology requires an expanded experimental design that goes beyond these approaches. In this review, we explore advances in high-throughput techniques based on liquid chromatography-mass spectrometry. These omics methods for the identification of proteins and metabolites have enabled direct characterisation of gut microbiome functions and the crosstalk with the host. We discuss current metaproteomics and metabolomics workflows for producing functional profiles, the existing methodological challenges and limitations, and recent studies utilising these techniques with a special focus on early life gut microbiome.
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Affiliation(s)
- Veronika Kuchařová Pettersen
- Research Group for Host-Microbe Interactions, Department of Medical Biology, UiT The Arctic University of Norway, Tromsø, Norway
- Pediatric Research Group, Department of Clinical Medicine, UiT The Arctic University of Norway, Tromsø, Norway
- Centre for New Antibacterial Strategies, UiT The Arctic University of Norway, Tromsø, Norway
| | - Luis Caetano Martha Antunes
- Oswaldo Cruz Institute, Oswaldo Cruz Foundation, Rio de Janeiro, RJ, Brazil
- National Institute of Science and Technology of Innovation on Diseases of Neglected Populations, Center for Technological Development in Health, Oswaldo Cruz Foundation, Rio de Janeiro, RJ, Brazil
| | - Antoine Dufour
- Department of Physiology & Pharmacology, University of Calgary, Calgary, Canada
| | - Marie-Claire Arrieta
- Department of Physiology & Pharmacology, University of Calgary, Calgary, Canada
- Department of Pediatrics, University of Calgary, Calgary, AB, Canada
- International Microbiome Centre, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
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12
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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: 37] [Impact Index Per Article: 12.3] [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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13
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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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14
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D'haeseleer P, Collette NM, Lao V, Segelke BW, Branda SS, Franco M. Shotgun Immunoproteomic Approach for the Discovery of Linear B-Cell Epitopes in Biothreat Agents Francisella tularensis and Burkholderia pseudomallei. Front Immunol 2021; 12:716676. [PMID: 34659206 PMCID: PMC8513525 DOI: 10.3389/fimmu.2021.716676] [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: 05/29/2021] [Accepted: 09/02/2021] [Indexed: 11/13/2022] Open
Abstract
Peptide-based subunit vaccines are coming to the forefront of current vaccine approaches, with safety and cost-effective production among their top advantages. Peptide vaccine formulations consist of multiple synthetic linear epitopes that together trigger desired immune responses that can result in robust immune memory. The advantages of linear compared to conformational epitopes are their simple structure, ease of synthesis, and ability to stimulate immune responses by means that do not require complex 3D conformation. Prediction of linear epitopes through use of computational tools is fast and cost-effective, but typically of low accuracy, necessitating extensive experimentation to verify results. On the other hand, identification of linear epitopes through experimental screening has been an inefficient process that requires thorough characterization of previously identified full-length protein antigens, or laborious techniques involving genetic manipulation of organisms. In this study, we apply a newly developed generalizable screening method that enables efficient identification of B-cell epitopes in the proteomes of pathogenic bacteria. As a test case, we used this method to identify epitopes in the proteome of Francisella tularensis (Ft), a Select Agent with a well-characterized immunoproteome. Our screen identified many peptides that map to known antigens, including verified and predicted outer membrane proteins and extracellular proteins, validating the utility of this approach. We then used the method to identify seroreactive peptides in the less characterized immunoproteome of Select Agent Burkholderia pseudomallei (Bp). This screen revealed known Bp antigens as well as proteins that have not been previously identified as antigens. Although B-cell epitope prediction tools Bepipred 2.0 and iBCE-EL classified many of our seroreactive peptides as epitopes, they did not score them significantly higher than the non-reactive tryptic peptides in our study, nor did they assign higher scores to seroreactive peptides from known Ft or Bp antigens, highlighting the need for experimental data instead of relying on computational epitope predictions alone. The present workflow is easily adaptable to detecting peptide targets relevant to the immune systems of other mammalian species, including humans (depending upon the availability of convalescent sera from patients), and could aid in accelerating the discovery of B-cell epitopes and development of vaccines to counter emerging biological threats.
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Affiliation(s)
- Patrik D'haeseleer
- Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory, Livermore, CA, United States
| | - Nicole M Collette
- Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory, Livermore, CA, United States
| | - Victoria Lao
- Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory, Livermore, CA, United States
| | - Brent W Segelke
- Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory, Livermore, CA, United States
| | - Steven S Branda
- Molecular and Microbiology Department, Sandia National Laboratories, Livermore, CA, United States
| | - Magdalena Franco
- Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory, Livermore, CA, United States
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15
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Sethupathy S, Morales GM, Li Y, Wang Y, Jiang J, Sun J, Zhu D. Harnessing microbial wealth for lignocellulose biomass valorization through secretomics: a review. BIOTECHNOLOGY FOR BIOFUELS 2021; 14:154. [PMID: 34225772 PMCID: PMC8256616 DOI: 10.1186/s13068-021-02006-9] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 06/26/2021] [Indexed: 05/10/2023]
Abstract
The recalcitrance of lignocellulosic biomass is a major constraint to its high-value use at industrial scale. In nature, microbes play a crucial role in biomass degradation, nutrient recycling and ecosystem functioning. Therefore, the use of microbes is an attractive way to transform biomass to produce clean energy and high-value compounds. The microbial degradation of lignocelluloses is a complex process which is dependent upon multiple secreted enzymes and their synergistic activities. The availability of the cutting edge proteomics and highly sensitive mass spectrometry tools make possible for researchers to probe the secretome of microbes and microbial consortia grown on different lignocelluloses for the identification of hydrolytic enzymes of industrial interest and their substrate-dependent expression. This review summarizes the role of secretomics in identifying enzymes involved in lignocelluloses deconstruction, the development of enzyme cocktails and the construction of synthetic microbial consortia for biomass valorization, providing our perspectives to address the current challenges.
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Affiliation(s)
- Sivasamy Sethupathy
- School of the Environment and Safety Engineering, Biofuels Institute, Jiangsu University, Zhenjiang, 212013, Jiangsu, China
| | - Gabriel Murillo Morales
- School of the Environment and Safety Engineering, Biofuels Institute, Jiangsu University, Zhenjiang, 212013, Jiangsu, China
| | - Yixuan Li
- School of the Environment and Safety Engineering, Biofuels Institute, Jiangsu University, Zhenjiang, 212013, Jiangsu, China
| | - Yongli Wang
- School of the Environment and Safety Engineering, Biofuels Institute, Jiangsu University, Zhenjiang, 212013, Jiangsu, China
| | - Jianxiong Jiang
- School of the Environment and Safety Engineering, Biofuels Institute, Jiangsu University, Zhenjiang, 212013, Jiangsu, China
| | - Jianzhong Sun
- School of the Environment and Safety Engineering, Biofuels Institute, Jiangsu University, Zhenjiang, 212013, Jiangsu, China
| | - Daochen Zhu
- School of the Environment and Safety Engineering, Biofuels Institute, Jiangsu University, Zhenjiang, 212013, Jiangsu, China.
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16
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Thuy-Boun PS, Mehta S, Gruening B, McGowan T, Nguyen A, Rajczewski A, Johnson JE, Griffin TJ, Wolan DW, Jagtap PD. Metaproteomics Analysis of SARS-CoV-2-Infected Patient Samples Reveals Presence of Potential Coinfecting Microorganisms. J Proteome Res 2021; 20:1451-1454. [PMID: 33393790 PMCID: PMC7805602 DOI: 10.1021/acs.jproteome.0c00822] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Indexed: 01/06/2023]
Abstract
In this Letter, we reanalyze published mass spectrometry data sets of clinical samples with a focus on determining the coinfection status of individuals infected with SARS-CoV-2 coronavirus. We demonstrate the use of ComPIL 2.0 software along with a metaproteomics workflow within the Galaxy platform to detect cohabitating potential pathogens in COVID-19 patients using mass spectrometry-based analysis. From a sample collected from gargling solutions, we detected Streptococcus pneumoniae (opportunistic and multidrug-resistant pathogen) and Lactobacillus rhamnosus (a probiotic component) along with SARS-Cov-2. We could also detect Pseudomonas sps. Bc-h from COVID-19 positive samples and Acinetobacter ursingii and Pseudomonas monteilii from COVID-19 negative samples collected from oro- and nasopharyngeal samples. We believe that the early detection and characterization of coinfections by using metaproteomics from COVID-19 patients will potentially impact the diagnosis and treatment of patients affected by SARS-CoV-2 infection.
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Affiliation(s)
| | | | - Bjoern Gruening
- Bioinformatics Group, Department of Computer Science, University of Freiburg, 79110 Freiburg im Breisgau, Germany
| | | | - An Nguyen
- University of Minnesota, Minneapolis, MN, USA
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17
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Romanis CS, Pearson LA, Neilan BA. Cyanobacterial blooms in wastewater treatment facilities: Significance and emerging monitoring strategies. J Microbiol Methods 2020; 180:106123. [PMID: 33316292 DOI: 10.1016/j.mimet.2020.106123] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Revised: 12/06/2020] [Accepted: 12/08/2020] [Indexed: 12/30/2022]
Abstract
Municipal wastewater treatment facilities (WWTFs) are prone to the proliferation of cyanobacterial species which thrive in stable, nutrient-rich environments. Dense cyanobacterial blooms frequently disrupt treatment processes and the supply of recycled water due to their production of extracellular polymeric substances, which hinder microfiltration, and toxins, which pose a health risk to end-users. A variety of methods are employed by water utilities for the identification and monitoring of cyanobacteria and their toxins in WWTFs, including microscopy, flow cytometry, ELISA, chemoanalytical methods, and more recently, molecular methods. Here we review the literature on the occurrence and significance of cyanobacterial blooms in WWTFs and discuss the pros and cons of the various strategies for monitoring these potentially hazardous events. Particular focus is directed towards next-generation metagenomic sequencing technologies for the development of site-specific cyanobacterial bloom management strategies. Long-term multi-omic observations will enable the identification of indicator species and the development of site-specific bloom dynamics models for the mitigation and management of cyanobacterial blooms in WWTFs. While emerging metagenomic tools could potentially provide deep insight into the diversity and flux of problematic cyanobacterial species in these systems, they should be considered a complement to, rather than a replacement of, quantitative chemoanalytical approaches.
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Affiliation(s)
- Caitlin S Romanis
- School of Environmental and Life Sciences, University of Newcastle, Newcastle 2308, Australia
| | - Leanne A Pearson
- School of Environmental and Life Sciences, University of Newcastle, Newcastle 2308, Australia
| | - Brett A Neilan
- School of Environmental and Life Sciences, University of Newcastle, Newcastle 2308, Australia.
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18
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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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Rumen metaproteomics: Closer to linking rumen microbial function to animal productivity traits. Methods 2020; 186:42-51. [PMID: 32758682 DOI: 10.1016/j.ymeth.2020.07.011] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Revised: 06/12/2020] [Accepted: 07/27/2020] [Indexed: 12/28/2022] Open
Abstract
The rumen microbiome constitutes a dense and complex mixture of anaerobic bacteria, archaea, protozoa, virus and fungi. Collectively, rumen microbial populations interact closely in order to degrade and ferment complex plant material into nutrients for host metabolism, a process which also produces other by-products, such as methane gas. Our understanding of the rumen microbiome and its functions are of both scientific and industrial interest, as the metabolic functions are connected to animal health and nutrition, but at the same time contribute significantly to global greenhouse gas emissions. While many of the major microbial members of the rumen microbiome are acknowledged, advances in modern culture-independent meta-omic techniques, such as metaproteomics, enable deep exploration into active microbial populations involved in essential rumen metabolic functions. Meaningful and accurate metaproteomic analyses are highly dependent on representative samples, precise protein extraction and fractionation, as well as a comprehensive and high-quality protein sequence database that enables precise protein identification and quantification. This review focuses on the application of rumen metaproteomics, and its potential towards understanding the complex rumen microbiome and its metabolic functions. We present and discuss current methods in sample handling, protein extraction and data analysis for rumen metaproteomics, and finally emphasize the potential of (meta)genome-integrated metaproteomics for accurate reconstruction of active microbial populations in the rumen.
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Peters DL, Wang W, Zhang X, Ning Z, Mayne J, Figeys D. Metaproteomic and Metabolomic Approaches for Characterizing the Gut Microbiome. Proteomics 2019; 19:e1800363. [PMID: 31321880 DOI: 10.1002/pmic.201800363] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Revised: 06/27/2019] [Indexed: 12/14/2022]
Abstract
The gut microbiome has been shown to play a significant role in human healthy and diseased states. The dynamic signaling that occurs between the host and microbiome is critical for the maintenance of host homeostasis. Analyzing the human microbiome with metaproteomics, metabolomics, and integrative multi-omics analyses can provide significant information on markers for healthy and diseased states, allowing for the eventual creation of microbiome-targeted treatments for diseases associated with dysbiosis. Metaproteomics enables functional activity information to be gained from the microbiome samples, while metabolomics provides insight into the overall metabolic states affecting/representing the host-microbiome interactions. Combining these functional -omic platforms together with microbiome composition profiling allows for a holistic overview on the functional and metabolic state of the microbiome and its influence on human health. Here the benefits of metaproteomics, metabolomics, and the integrative multi-omic approaches to investigating the gut microbiome in the context of human health and diseases are reviewed.
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Affiliation(s)
- Danielle L Peters
- Ottawa Institute of Systems Biology and Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, 451 Smyth Road, Ottawa, ON, KIH 8M5, Canada
| | - Wenju Wang
- Ottawa Institute of Systems Biology and Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, 451 Smyth Road, Ottawa, ON, KIH 8M5, Canada
| | - Xu Zhang
- Ottawa Institute of Systems Biology and Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, 451 Smyth Road, Ottawa, ON, KIH 8M5, Canada
| | - Zhibin Ning
- Ottawa Institute of Systems Biology and Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, 451 Smyth Road, Ottawa, ON, KIH 8M5, Canada
| | - Janice Mayne
- Ottawa Institute of Systems Biology and Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, 451 Smyth Road, Ottawa, ON, KIH 8M5, Canada
| | - Daniel Figeys
- Ottawa Institute of Systems Biology and Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, 451 Smyth Road, Ottawa, ON, KIH 8M5, Canada.,Canadian Institute for Advanced Research, 661 University Ave, Toronto, ON, M5G 1M1, Canada.,The University of Ottawa and Shanghai Institute of Materia Medica Joint Research Center on Systems and Personalized Pharmacology, 451 Smyth Road, Ottawa, ON, KIH 8M5, Canada
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