metaQuantome

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

Caleb W. Easterly, Ray Sajulga, Subina Mehta, James E Johnson, Praveen Kumar, Shane Hubler, Bart Mesuere, Joel D Rudney, Timothy J Griffin, Pratik D Jagtap

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Pages (from-to)S82-S91
JournalMolecular & cellular proteomics : MCP
Volume18
Issue number8
DOIs
StatePublished - Aug 9 2019

Fingerprint

Microbiota
Taxonomies
Benchmarking
Proteins
Galaxies
Biological systems
Microorganisms
Principal component analysis
Escherichia coli
Labels
Visualization
Software
Peptides
Biological Phenomena
Testing
Principal Component Analysis
Research
Recombinant Proteins
Proteomics
Experiments

Keywords

  • Bioinformatics Software
  • Computational Biology
  • Functional Inference
  • Mass Spectrometry
  • Microbiology
  • Microbiome
  • Multiomics
  • Quantification
  • Taxonomy

Cite this

metaQuantome : An Integrated, Quantitative Metaproteomics Approach Reveals Connections Between Taxonomy and Protein Function in Complex Microbiomes. / Easterly, Caleb W.; Sajulga, Ray; Mehta, Subina; Johnson, James E; Kumar, Praveen; Hubler, Shane; Mesuere, Bart; Rudney, Joel D; Griffin, Timothy J; Jagtap, Pratik D.

In: Molecular & cellular proteomics : MCP, Vol. 18, No. 8, 09.08.2019, p. S82-S91.

Research output: Contribution to journalArticle

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