metaQuantome: An Integrated, Quantitative Metaproteomics Approach Reveals Connections Between Taxonomy and Protein Function in Complex Microbiomes
| Autor: | James R. Johnson, Shane L. Hubler, Joel D. Rudney, Timothy J. Griffin, Pratik D. Jagtap, Ray Sajulga, Subina Mehta, Caleb Easterly, Bart Mesuere, Praveen Kumar |
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| Rok vydání: | 2019 |
| Předmět: |
Proteomics
DATABASE Computer science Dental Plaque BIOLOGY Computational biology Biochemistry Analytical Chemistry NORMALIZATION 03 medical and health sciences Escherichia coli Humans Microbiome Child Molecular Biology 030304 developmental biology Statistical hypothesis testing 0303 health sciences Software suite Microbiota Research 030302 biochemistry & molecular biology Biology and Life Sciences LABEL-FREE QUANTIFICATION Benchmarking Automatic summarization Principal component analysis Metaproteomics Dysbiosis Oral Microbiome Mouth Diseases Peptides Software |
| Zdroj: | MOLECULAR & CELLULAR PROTEOMICS |
| ISSN: | 1535-9476 1535-9484 |
| DOI: | 10.1074/mcp.ra118.001240 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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