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Raba G, Luis AS, Schneider H, Morell I, Jin C, Adamberg S, Hansson GC, Adamberg K, Arike L. Metaproteomics reveals parallel utilization of colonic mucin glycans and dietary fibers by the human gut microbiota. iScience 2024; 27:110093. [PMID: 38947523 PMCID: PMC11214529 DOI: 10.1016/j.isci.2024.110093] [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: 11/15/2023] [Revised: 02/29/2024] [Accepted: 05/21/2024] [Indexed: 07/02/2024] Open
Abstract
A diet lacking dietary fibers promotes the expansion of gut microbiota members that can degrade host glycans, such as those on mucins. The microbial foraging on mucin has been associated with disruptions of the gut-protective mucus layer and colonic inflammation. Yet, it remains unclear how the co-utilization of mucin and dietary fibers affects the microbiota composition and metabolic activity. Here, we used 14 dietary fibers and porcine colonic and gastric mucins to study the dynamics of mucin and dietary fiber utilization by the human fecal microbiota in vitro. Combining metaproteome and metabolites analyses revealed the central role of the Bacteroides genus in the utilization of complex fibers together with mucin while Akkermansia muciniphila was the main utilizer of sole porcine colonic mucin but not gastric mucin. This study gives a broad overview of the colonic environment in response to dietary and host glycan availability.
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Affiliation(s)
- Grete Raba
- Department of Chemistry and Biotechnology, Tallinn University of Technology, 12618 Tallinn, Estonia
- Department of Medical Biochemistry and Cell Biology, University of Gothenburg, 41390 Gothenburg, Sweden
| | - Ana S. Luis
- Department of Medical Biochemistry and Cell Biology, University of Gothenburg, 41390 Gothenburg, Sweden
- SciLifeLab, University of Gothenburg, 41390 Gothenburg, Sweden
| | - Hannah Schneider
- Department of Medical Biochemistry and Cell Biology, University of Gothenburg, 41390 Gothenburg, Sweden
| | - Indrek Morell
- Center of Food and Fermentation Technologies, 12618 Tallinn, Estonia
| | - Chunsheng Jin
- Department of Medical Biochemistry and Cell Biology, University of Gothenburg, 41390 Gothenburg, Sweden
| | - Signe Adamberg
- Department of Chemistry and Biotechnology, Tallinn University of Technology, 12618 Tallinn, Estonia
| | - Gunnar C. Hansson
- Department of Medical Biochemistry and Cell Biology, University of Gothenburg, 41390 Gothenburg, Sweden
| | - Kaarel Adamberg
- Department of Chemistry and Biotechnology, Tallinn University of Technology, 12618 Tallinn, Estonia
- Center of Food and Fermentation Technologies, 12618 Tallinn, Estonia
| | - Liisa Arike
- Department of Medical Biochemistry and Cell Biology, University of Gothenburg, 41390 Gothenburg, Sweden
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2
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Chabas M, Pible O, Armengaud J, Alpha-Bazin B. Label-Free Multiplex Proteotyping of Microbial Isolates. Anal Chem 2023; 95:13163-13171. [PMID: 37590279 DOI: 10.1021/acs.analchem.3c01975] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/19/2023]
Abstract
To meet clinical diagnostic needs and for general microbiological screening, it is essential to be able to accurately and rapidly identify any microorganisms from complex microbiota. To gain insight into the individual components of microbiota, culturomics has been proposed as a means to systematically test hundreds of possible cultivation conditions and generate numerous microbial isolates with very distinct characteristics. High-throughput identification methods must now be developed to quickly screen these isolates. Currently, most multiplexing methods involve labeling, which comes at a cost. In this paper, we present an innovative label-free multiplexing method for the identification of microorganisms using tandem mass spectrometry. The method is based on offline reversed-phase fractionation of individual peptidomes. Multiplexing is achieved by mixing fractions of staged hydrophobicity; thus, each sample is mapped to specific elution times. In this proof-of-concept study, multiplexed samples were analyzed by tandem mass spectrometry in a single run and microorganisms present in the mixture were resolved by phylopeptidomics proteotyping. Using this methodology, up to 21 microorganisms could be identified in a single 60 min run performed with a Q-Exactive HF high-resolution mass spectrometer, resulting in a rate of one microorganism identified per 3 min of mass spectrometry, without any need for the use of labeling reagents. This approach opens new perspectives for the application of high-throughput proteotyping of bacteria using tandem mass spectrometry in large culturomics projects.
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Affiliation(s)
- Madisson Chabas
- 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, F-30207 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
| | - Béatrice Alpha-Bazin
- Département Médicaments et Technologies pour la Santé (DMTS), Université Paris-Saclay, CEA, INRAE, SPI, 30200 Bagnols-sur-Cèze, France
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3
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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; 24:8634. [PMID: 37239979 PMCID: PMC10218423 DOI: 10.3390/ijms24108634] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [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 (O.P.)
- 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 (O.P.)
| | - Olivier Pible
- Département Médicaments et Technologies pour la Santé (DMTS), Université Paris-Saclay, CEA, INRAE, SPI, 30200 Bagnols-sur-Cèze, France (O.P.)
| | - Jean Armengaud
- Département Médicaments et Technologies pour la Santé (DMTS), Université Paris-Saclay, CEA, INRAE, SPI, 30200 Bagnols-sur-Cèze, France (O.P.)
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4
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Mappa C, Alpha-Bazin B, Pible O, Armengaud J. Evaluation of the Limit of Detection of Bacteria by Tandem Mass Spectrometry Proteotyping and Phylopeptidomics. Microorganisms 2023; 11:1170. [PMCID: PMC10223342 DOI: 10.3390/microorganisms11051170] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 04/20/2023] [Accepted: 04/22/2023] [Indexed: 06/01/2023] Open
Abstract
Shotgun proteomics has proven to be an attractive alternative for identifying a pathogen and characterizing the antimicrobial resistance genes it produces. Because of its performance, proteotyping of microorganisms by tandem mass spectrometry is expected to become an essential tool in modern healthcare. Proteotyping microorganisms that have been isolated from the environment by culturomics is also a cornerstone for the development of new biotechnological applications. Phylopeptidomics is a new strategy that estimates the phylogenetic distances between the organisms present in the sample and calculates the ratio of their shared peptides, thus improving the quantification of their contributions to the biomass. Here, we established the limit of detection of tandem mass spectrometry proteotyping based on MS/MS data recorded for several bacteria. The limit of detection for Salmonella bongori with our experimental set-up is 4 × 104 colony-forming units from a sample volume of 1 mL. This limit of detection is directly related to the amount of protein per cell and therefore depends on the shape and size of the microorganism. We have demonstrated that identification of bacteria by phylopeptidomics is independent of their growth stage and that the limit of detection of the method is not degraded in presence of additional bacteria in the same proportion.
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Affiliation(s)
- Charlotte Mappa
- Université Paris-Saclay, CEA, INRAE, Département Médicaments et Technologies pour la Santé (DMTS), 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
- Université Paris-Saclay, CEA, INRAE, Département Médicaments et Technologies pour la Santé (DMTS), SPI, 30200 Bagnols-sur-Cèze, France
| | - Olivier Pible
- Université Paris-Saclay, CEA, INRAE, Département Médicaments et Technologies pour la Santé (DMTS), SPI, 30200 Bagnols-sur-Cèze, France
| | - Jean Armengaud
- Université Paris-Saclay, CEA, INRAE, Département Médicaments et Technologies pour la Santé (DMTS), SPI, 30200 Bagnols-sur-Cèze, France
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5
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Barlin M, Erdmann-Gilmore P, Mudd JL, Zhang Q, Seymour RW, Guo Z, Miessner JR, Goedegebuure SP, Bi Y, Osorio OA, Alexander-Brett J, Li S, Ma CX, Fields RC, Townsend RR, Held JM. Proteins in Tumor-Derived Plasma Extracellular Vesicles Indicate Tumor Origin. Mol Cell Proteomics 2023; 22:100476. [PMID: 36470535 PMCID: PMC9801135 DOI: 10.1016/j.mcpro.2022.100476] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 10/12/2022] [Accepted: 11/28/2022] [Indexed: 12/09/2022] Open
Abstract
Cancer-derived extracellular vesicles (EVs) promote tumorigenesis, premetastatic niche formation, and metastasis via their protein cargo. However, the proteins packaged by patient tumors into EVs cannot be determined in vivo because of the presence of EVs derived from other tissues. We therefore developed a cross-species proteomic method to quantify the human tumor-derived proteome of plasma EVs produced by patient-derived xenografts of four cancer types. Proteomic profiling revealed individualized packaging of novel protein cargo, and machine learning accurately classified the type of the underlying tumor.
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Affiliation(s)
- Meltem Barlin
- Department of Medicine, Washington University School of Medicine in St Louis, St Louis, Missouri, USA
| | - Petra Erdmann-Gilmore
- Department of Medicine, Washington University School of Medicine in St Louis, St Louis, Missouri, USA; Siteman Cancer Center, Washington University School of Medicine in St Louis, St Louis, Missouri, USA
| | - Jacqueline L Mudd
- Siteman Cancer Center, Washington University School of Medicine in St Louis, St Louis, Missouri, USA; Department of Surgery, Washington University School of Medicine in St Louis, St Louis, Missouri, USA
| | - Qiang Zhang
- Department of Medicine, Washington University School of Medicine in St Louis, St Louis, Missouri, USA; Siteman Cancer Center, Washington University School of Medicine in St Louis, St Louis, Missouri, USA
| | - Robert W Seymour
- Department of Medicine, Washington University School of Medicine in St Louis, St Louis, Missouri, USA
| | - Zhanfang Guo
- Department of Medicine, Washington University School of Medicine in St Louis, St Louis, Missouri, USA
| | - Julia R Miessner
- Siteman Cancer Center, Washington University School of Medicine in St Louis, St Louis, Missouri, USA; Department of Surgery, Washington University School of Medicine in St Louis, St Louis, Missouri, USA
| | - S Peter Goedegebuure
- Siteman Cancer Center, Washington University School of Medicine in St Louis, St Louis, Missouri, USA; Department of Surgery, Washington University School of Medicine in St Louis, St Louis, Missouri, USA
| | - Ye Bi
- Siteman Cancer Center, Washington University School of Medicine in St Louis, St Louis, Missouri, USA; Department of Surgery, Washington University School of Medicine in St Louis, St Louis, Missouri, USA
| | - Omar A Osorio
- Department of Medicine, Washington University School of Medicine in St Louis, St Louis, Missouri, USA
| | - Jennifer Alexander-Brett
- Department of Medicine, Washington University School of Medicine in St Louis, St Louis, Missouri, USA; Department of Pathology and Immunology, Washington University School of Medicine in St Louis, St Louis, Missouri, USA
| | - Shunqiang Li
- Department of Medicine, Washington University School of Medicine in St Louis, St Louis, Missouri, USA; Siteman Cancer Center, Washington University School of Medicine in St Louis, St Louis, Missouri, USA
| | - Cynthia X Ma
- Department of Medicine, Washington University School of Medicine in St Louis, St Louis, Missouri, USA; Siteman Cancer Center, Washington University School of Medicine in St Louis, St Louis, Missouri, USA
| | - Ryan C Fields
- Siteman Cancer Center, Washington University School of Medicine in St Louis, St Louis, Missouri, USA; Department of Surgery, Washington University School of Medicine in St Louis, St Louis, Missouri, USA
| | - R Reid Townsend
- Department of Medicine, Washington University School of Medicine in St Louis, St Louis, Missouri, USA; Siteman Cancer Center, Washington University School of Medicine in St Louis, St Louis, Missouri, USA
| | - Jason M Held
- Department of Medicine, Washington University School of Medicine in St Louis, St Louis, Missouri, USA; Siteman Cancer Center, Washington University School of Medicine in St Louis, St Louis, Missouri, USA; Department of Anesthesiology, Washington University School of Medicine in St Louis, St Louis, Missouri, USA.
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6
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Singh A, Ambaru B, Bandsode V, Ahmed N. Panomics to decode virulence and fitness in Gram-negative bacteria. Front Cell Infect Microbiol 2022; 12:1061596. [PMID: 36478674 PMCID: PMC9719987 DOI: 10.3389/fcimb.2022.1061596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 10/26/2022] [Indexed: 11/22/2022] Open
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7
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Leskoske K, Garcia-Mansfield K, Sharma R, Krishnan A, Rusert JM, Mesirov JP, Wechsler-Reya RJ, Pirrotte P. Subgroup-Enriched Pathways and Kinase Signatures in Medulloblastoma Patient-Derived Xenografts. J Proteome Res 2022; 21:2124-2136. [PMID: 35977718 PMCID: PMC9442791 DOI: 10.1021/acs.jproteome.2c00203] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Indexed: 11/30/2022]
Abstract
Medulloblastoma (MB) is the most common malignant pediatric brain tumor. MB is classified into four primary molecular subgroups: wingless (WNT), sonic hedgehog (SHH), Group 3 (G3), and Group 4 (G4), and further genomic and proteomic subtypes have been reported. Subgroup heterogeneity and few actionable mutations have hindered the development of targeted therapies, especially for G3 MB, which has a particularly poor prognosis. To identify novel therapeutic targets for MB, we performed mass spectrometry-based deep expression proteomics and phosphoproteomics in 20 orthotopic patient-derived xenograft (PDX) models of MB comprising SHH, G3, and G4 subgroups. We found that the proteomic profiles of MB PDX tumors are closely aligned with those of primary human MB tumors illustrating the utility of PDX models. SHH PDXs were enriched for NFκB and p38 MAPK signaling, while G3 PDXs were characterized by MYC activity. Additionally, we found a significant association between actinomycin D sensitivity and increased abundance of MYC and MYC target genes. Our results highlight several candidate pathways that may serve as targets for new MB therapies. Mass spectrometry data are available via ProteomeXchange with identifier PXD035070.
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Affiliation(s)
- Kristin
L. Leskoske
- Cancer
and Cell Biology Division, Translational
Genomics Research Institute, Phoenix, Arizona 85004, United States
| | - Krystine Garcia-Mansfield
- Cancer
and Cell Biology Division, Translational
Genomics Research Institute, Phoenix, Arizona 85004, United States
- Integrated
Mass Spectrometry Shared Resource, City of Hope Comprehensive Cancer
Center, Duarte, California 91010, United States
| | - Ritin Sharma
- Cancer
and Cell Biology Division, Translational
Genomics Research Institute, Phoenix, Arizona 85004, United States
- Integrated
Mass Spectrometry Shared Resource, City of Hope Comprehensive Cancer
Center, Duarte, California 91010, United States
| | - Aparna Krishnan
- Cancer
and Cell Biology Division, Translational
Genomics Research Institute, Phoenix, Arizona 85004, United States
| | - Jessica M. Rusert
- Tumor
Initiation and Maintenance Program, NCI-Designated Cancer Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, California 92037, United States
| | - Jill P. Mesirov
- Department
of Medicine, University of California San
Diego, La Jolla, California 92093, United States
- Moores
Cancer Center, University of California
San Diego, La Jolla, California 92093, United States
| | - Robert J. Wechsler-Reya
- Tumor
Initiation and Maintenance Program, NCI-Designated Cancer Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, California 92037, United States
| | - Patrick Pirrotte
- Cancer
and Cell Biology Division, Translational
Genomics Research Institute, Phoenix, Arizona 85004, United States
- Integrated
Mass Spectrometry Shared Resource, City of Hope Comprehensive Cancer
Center, Duarte, California 91010, United States
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8
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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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9
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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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10
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Graw S, Chappell K, Washam CL, Gies A, Bird J, Robeson MS, Byrum SD. Multi-omics data integration considerations and study design for biological systems and disease. Mol Omics 2021; 17:170-185. [PMID: 33347526 PMCID: PMC8058243 DOI: 10.1039/d0mo00041h] [Citation(s) in RCA: 62] [Impact Index Per Article: 20.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
With the advancement of next-generation sequencing and mass spectrometry, there is a growing need for the ability to merge biological features in order to study a system as a whole. Features such as the transcriptome, methylome, proteome, histone post-translational modifications and the microbiome all influence the host response to various diseases and cancers. Each of these platforms have technological limitations due to sample preparation steps, amount of material needed for sequencing, and sequencing depth requirements. These features provide a snapshot of one level of regulation in a system. The obvious next step is to integrate this information and learn how genes, proteins, and/or epigenetic factors influence the phenotype of a disease in context of the system. In recent years, there has been a push for the development of data integration methods. Each method specifically integrates a subset of omics data using approaches such as conceptual integration, statistical integration, model-based integration, networks, and pathway data integration. In this review, we discuss considerations of the study design for each data feature, the limitations in gene and protein abundance and their rate of expression, the current data integration methods, and microbiome influences on gene and protein expression. The considerations discussed in this review should be regarded when developing new algorithms for integrating multi-omics data.
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Affiliation(s)
- Stefan Graw
- Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, 4301 West Markham Street (slot 516), Little Rock, AR 72205-7199, USA.
| | - Kevin Chappell
- Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, 4301 West Markham Street (slot 516), Little Rock, AR 72205-7199, USA.
| | - Charity L Washam
- Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, 4301 West Markham Street (slot 516), Little Rock, AR 72205-7199, USA. and Arkansas Children's Research Institute, 13 Children's Way, Little Rock, AR 72202, USA
| | - Allen Gies
- Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, 4301 West Markham Street (slot 516), Little Rock, AR 72205-7199, USA.
| | - Jordan Bird
- Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, 4301 West Markham Street (slot 516), Little Rock, AR 72205-7199, USA.
| | - Michael S Robeson
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA.
| | - Stephanie D Byrum
- Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, 4301 West Markham Street (slot 516), Little Rock, AR 72205-7199, USA. and Arkansas Children's Research Institute, 13 Children's Way, Little Rock, AR 72202, USA
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11
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van der Post S, Arike L. Metaproteomics Analysis of Host-Microbiota Interfaces. Methods Mol Biol 2021; 2259:167-179. [PMID: 33687714 DOI: 10.1007/978-1-0716-1178-4_10] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Metaproteomics of host-microbiome interfaces comprises the analysis of complex mixtures of bacteria, archaea, fungi, and viruses in combination with its host cells. Microbial niches can be found all over the host including the skin, oral cavity, and the intestine and are considered to be essential for the homeostasis. The complex interactions between the host and diverse commensal microbiota are poorly characterized while of great interest as dysbiosis is associated with the development of various inflammatory and metabolic diseases. The metaproteomics workflows to study these interfaces are currently being established, and many challenges remain. The major challenge is the large diversity in species composition that make up the microbiota, which results in complex samples that require extended mass spectrometry analysis time. In addition, current database search strategies are not developed to the size of the search space required for unbiased microbial protein identification.Here, we describe a workflow for the proteomics analysis of microbial niches with a focus on intestinal mucus layer. We will cover step-by-step the sample collection, sample preparation, liquid chromatography-mass spectrometry, and data analysis.
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Affiliation(s)
- Sjoerd van der Post
- Department of Medical Biochemistry and Cell Biology, University of Gothenburg, Gothenburg, Sweden
| | - Liisa Arike
- Department of Medical Biochemistry and Cell Biology, University of Gothenburg, Gothenburg, Sweden.
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12
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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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