Literature DB >> 31235611

metaQuantome: An Integrated, Quantitative Metaproteomics Approach Reveals Connections Between Taxonomy and Protein Function in Complex Microbiomes.

Caleb W Easterly1, Ray Sajulga1, Subina Mehta1, James Johnson2, Praveen Kumar3, Shane Hubler1, Bart Mesuere4, Joel Rudney5, Timothy J Griffin1, Pratik D Jagtap6.   

Abstract

Microbiome research offers promising insights into the impact of microorganisms on biological systems. Metaproteomics, the study of microbial proteins at the community level, integrates genomic, transcriptomic, and proteomic data to determine the taxonomic and functional state of a microbiome. However, standard metaproteomics software is subject to several limitations, commonly supporting only spectral counts, emphasizing exploratory analysis rather than hypothesis testing and rarely offering the ability to analyze the interaction of function and taxonomy - that is, which taxa are responsible for different processes.Here we present metaQuantome, a novel, multifaceted software suite that analyzes the state of a microbiome by leveraging complex taxonomic and functional hierarchies to summarize peptide-level quantitative information, emphasizing label-free intensity-based methods. For experiments with multiple experimental conditions, metaQuantome offers differential abundance analysis, principal components analysis, and clustered heat map visualizations, as well as exploratory analysis for a single sample or experimental condition. We benchmark metaQuantome analysis against standard methods, using two previously published datasets: (1) an artificially assembled microbial community dataset (taxonomy benchmarking) and (2) a dataset with a range of recombinant human proteins spiked into an Escherichia coli background (functional benchmarking). Furthermore, we demonstrate the use of metaQuantome on a previously published human oral microbiome dataset.In both the taxonomic and functional benchmarking analyses, metaQuantome quantified taxonomic and functional terms more accurately than standard summarization-based methods. We use the oral microbiome dataset to demonstrate metaQuantome's ability to produce publication-quality figures and elucidate biological processes of the oral microbiome. metaQuantome enables advanced investigation of metaproteomic datasets, which should be broadly applicable to microbiome-related research. In the interest of accessible, flexible, and reproducible analysis, metaQuantome is open source and available on the command line and in Galaxy.
© 2019 Easterly et al.

Entities:  

Keywords:  Bioinformatics Software; Computational Biology; Functional Inference; Mass Spectrometry; Microbiology; Microbiome; Multiomics; Quantification; Taxonomy

Mesh:

Substances:

Year:  2019        PMID: 31235611      PMCID: PMC6692774          DOI: 10.1074/mcp.RA118.001240

Source DB:  PubMed          Journal:  Mol Cell Proteomics        ISSN: 1535-9476            Impact factor:   5.911


  46 in total

Review 1.  The global ocean microbiome.

Authors:  Mary Ann Moran
Journal:  Science       Date:  2015-12-11       Impact factor: 47.728

2.  Bridging the Chromosome-centric and Biology/Disease-driven Human Proteome Projects: Accessible and Automated Tools for Interpreting the Biological and Pathological Impact of Protein Sequence Variants Detected via Proteogenomics.

Authors:  Ray Sajulga; Subina Mehta; Praveen Kumar; James E Johnson; Candace R Guerrero; Michael C Ryan; Rachel Karchin; Pratik D Jagtap; Timothy J Griffin
Journal:  J Proteome Res       Date:  2018-09-05       Impact factor: 4.466

Review 3.  Microbial genome analysis: the COG approach.

Authors:  Michael Y Galperin; David M Kristensen; Kira S Makarova; Yuri I Wolf; Eugene V Koonin
Journal:  Brief Bioinform       Date:  2019-07-19       Impact factor: 11.622

4.  Biopython: freely available Python tools for computational molecular biology and bioinformatics.

Authors:  Peter J A Cock; Tiago Antao; Jeffrey T Chang; Brad A Chapman; Cymon J Cox; Andrew Dalke; Iddo Friedberg; Thomas Hamelryck; Frank Kauff; Bartek Wilczynski; Michiel J L de Hoon
Journal:  Bioinformatics       Date:  2009-03-20       Impact factor: 6.937

Review 5.  Direct sequencing of the human microbiome readily reveals community differences.

Authors:  Justin Kuczynski; Elizabeth K Costello; Diana R Nemergut; Jesse Zaneveld; Christian L Lauber; Dan Knights; Omry Koren; Noah Fierer; Scott T Kelley; Ruth E Ley; Jeffrey I Gordon; Rob Knight
Journal:  Genome Biol       Date:  2010-05-05       Impact factor: 13.583

6.  Structure, function and diversity of the healthy human microbiome.

Authors: 
Journal:  Nature       Date:  2012-06-13       Impact factor: 49.962

7.  The NCBI Taxonomy database.

Authors:  Scott Federhen
Journal:  Nucleic Acids Res       Date:  2011-12-01       Impact factor: 16.971

8.  ETE 3: Reconstruction, Analysis, and Visualization of Phylogenomic Data.

Authors:  Jaime Huerta-Cepas; François Serra; Peer Bork
Journal:  Mol Biol Evol       Date:  2016-02-26       Impact factor: 16.240

9.  Gene Ontology Consortium: going forward.

Authors: 
Journal:  Nucleic Acids Res       Date:  2014-11-26       Impact factor: 19.160

Review 10.  Use of Metatranscriptomics in Microbiome Research.

Authors:  Stavros Bashiardes; Gili Zilberman-Schapira; Eran Elinav
Journal:  Bioinform Biol Insights       Date:  2016-04-20
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  7 in total

1.  Proteomics Is Not an Island: Multi-omics Integration Is the Key to Understanding Biological Systems.

Authors:  Bing Zhang; Bernhard Kuster
Journal:  Mol Cell Proteomics       Date:  2019-08-09       Impact factor: 5.911

2.  Novel Bioinformatics Strategies Driving Dynamic Metaproteomic Studies.

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

Review 3.  The Human Oral Microbiome in Health and Disease: From Sequences to Ecosystems.

Authors:  Jesse R Willis; Toni Gabaldón
Journal:  Microorganisms       Date:  2020-02-23

Review 4.  Challenges and Perspective in Integrated Multi-Omics in Gut Microbiota Studies.

Authors:  Eric Banan-Mwine Daliri; Fred Kwame Ofosu; Ramachandran Chelliah; Byong H Lee; Deog-Hwan Oh
Journal:  Biomolecules       Date:  2021-02-17

5.  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

6.  Survey of metaproteomics software tools for functional microbiome analysis.

Authors:  Ray Sajulga; Caleb Easterly; Michael Riffle; Bart Mesuere; Thilo Muth; Subina Mehta; Praveen Kumar; James Johnson; Bjoern Andreas Gruening; Henning Schiebenhoefer; Carolin A Kolmeder; Stephan Fuchs; Brook L Nunn; Joel Rudney; Timothy J Griffin; Pratik D Jagtap
Journal:  PLoS One       Date:  2020-11-10       Impact factor: 3.240

7.  Precursor Intensity-Based Label-Free Quantification Software Tools for Proteomic and Multi-Omic Analysis within the Galaxy Platform.

Authors:  Subina Mehta; Caleb W Easterly; Ray Sajulga; Robert J Millikin; Andrea Argentini; Ignacio Eguinoa; Lennart Martens; Michael R Shortreed; Lloyd M Smith; Thomas McGowan; Praveen Kumar; James E Johnson; Timothy J Griffin; Pratik D Jagtap
Journal:  Proteomes       Date:  2020-07-08
  7 in total

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