Literature DB >> 31755272

Data-Independent Acquisition Mass Spectrometry in Metaproteomics of Gut Microbiota-Implementation and Computational Analysis.

Juhani Aakko1, Sami Pietilä1, Tomi Suomi1, Mehrad Mahmoudian1,2, Raine Toivonen3, Petri Kouvonen1, Anne Rokka1, Arno Hänninen3,4, Laura L Elo1.   

Abstract

Metagenomic approaches focus on taxonomy or gene annotation but lack power in defining functionality of gut microbiota. Therefore, metaproteomics approaches have been introduced to overcome this limitation. However, the common metaproteomics approach uses data-dependent acquisition mass spectrometry, which is known to have limited reproducibility when analyzing samples with complex microbial composition. In this work, we provide a proof of concept for data-independent acquisition (DIA) metaproteomics. To this end, we analyze metaproteomes using DIA mass spectrometry and introduce an open-source data analysis software package, diatools, which enables accurate and consistent quantification of DIA metaproteomics data. We demonstrate the feasibility of our approach in gut microbiota metaproteomics using laboratory-assembled microbial mixtures as well as human fecal samples.

Entities:  

Keywords:  bioinformatics; data analysis; data-independent acquisition; human gut microbiota; mass spectrometry; metaproteomics; microbiota functionality; proteomics; software

Mesh:

Year:  2019        PMID: 31755272     DOI: 10.1021/acs.jproteome.9b00606

Source DB:  PubMed          Journal:  J Proteome Res        ISSN: 1535-3893            Impact factor:   4.466


  7 in total

1.  Novel Bioinformatics Strategies Driving Dynamic Metaproteomic Studies.

Authors:  Caitlin M A Simopoulos; Daniel Figeys; Mathieu Lavallée-Adam
Journal:  Methods Mol Biol       Date:  2022

Review 2.  Metabolomics: The Key to Unraveling the Role of the Microbiome in Visceral Pain Neurotransmission.

Authors:  Adam Shute; Dominique G Bihan; Ian A Lewis; Yasmin Nasser
Journal:  Front Neurosci       Date:  2022-06-23       Impact factor: 5.152

3.  Metaproteomics-An Advantageous Option in Studies of Host-Microbiota Interaction.

Authors:  Oleg Karaduta; Zeljko Dvanajscak; Boris Zybailov
Journal:  Microorganisms       Date:  2021-04-30

Review 4.  Proteomics and Metaproteomics Add Functional, Taxonomic and Biomass Dimensions to Modeling the Ecosystem at the Mucosal-luminal Interface.

Authors:  Leyuan Li; Daniel Figeys
Journal:  Mol Cell Proteomics       Date:  2020-06-24       Impact factor: 5.911

5.  A carbohydrate-active enzyme (CAZy) profile links successful metabolic specialization of Prevotella to its abundance in gut microbiota.

Authors:  Juhani Aakko; Sami Pietilä; Raine Toivonen; Anne Rokka; Kati Mokkala; Kirsi Laitinen; Laura Elo; Arno Hänninen
Journal:  Sci Rep       Date:  2020-07-24       Impact factor: 4.379

6.  Five key aspects of metaproteomics as a tool to understand functional interactions in host-associated microbiomes.

Authors:  Fernanda Salvato; Robert L Hettich; Manuel Kleiner
Journal:  PLoS Pathog       Date:  2021-02-25       Impact factor: 6.823

Review 7.  Considerations for constructing a protein sequence database for metaproteomics.

Authors:  J Alfredo Blakeley-Ruiz; Manuel Kleiner
Journal:  Comput Struct Biotechnol J       Date:  2022-01-21       Impact factor: 7.271

  7 in total

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