MetGEMs Toolbox: Metagenome-scale models as integrative toolbox for uncovering metabolic functions and routes of human gut microbiome

PLoS Comput Biol. 2021 Jan 6;17(1):e1008487. doi: 10.1371/journal.pcbi.1008487. eCollection 2021 Jan.

Abstract

Investigating metabolic functional capability of a human gut microbiome enables the quantification of microbiome changes, which can cause a phenotypic change of host physiology and disease. One possible way to estimate the functional capability of a microbial community is through inferring metagenomic content from 16S rRNA gene sequences. Genome-scale models (GEMs) can be used as scaffold for functional estimation analysis at a systematic level, however up to date, there is no integrative toolbox based on GEMs for uncovering metabolic functions. Here, we developed the MetGEMs (metagenome-scale models) toolbox, an open-source application for inferring metabolic functions from 16S rRNA gene sequences to facilitate the study of the human gut microbiome by the wider scientific community. The developed toolbox was validated using shotgun metagenomic data and shown to be superior in predicting functional composition in human clinical samples compared to existing state-of-the-art tools. Therefore, the MetGEMs toolbox was subsequently applied for annotating putative enzyme functions and metabolic routes related in human disease using atopic dermatitis as a case study.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Bacteria* / enzymology
  • Bacteria* / genetics
  • Bacteria* / metabolism
  • Bacterial Proteins / classification
  • Bacterial Proteins / genetics
  • Bacterial Proteins / metabolism
  • DNA, Bacterial / genetics
  • Feces / microbiology
  • Gastrointestinal Microbiome / genetics*
  • Humans
  • Metagenome / genetics*
  • Metagenomics / methods*
  • RNA, Ribosomal, 16S / genetics
  • Software*

Substances

  • Bacterial Proteins
  • DNA, Bacterial
  • RNA, Ribosomal, 16S

Grants and funding

WV received funding supports from Kasetsart University Research and Development Institute (KURDI) at Kasetsart University, Department of Zoology, Faculty of Science, as well as Omics Center for Agriculture, Bioresources, Food, and Health, Kasetsart University (OmiKU). PP received funding supports from Interdisciplinary Graduate Program in Bioscience, Faculty of Science, Kasetsart University and International Affairs Division (IAD), Kasetsart University. NS received funding supports from the National Science and Technology Development Agency (NSTDA) (Grant No. P-17-50648) and Ratchadapisek Research Funds (Grant No. CU-GR(S)_61_38_30_03) Chulalongkorn University. GP would like to thank the Deutsche Forschungsgemeinschaft (DFG) CRC/Transregio 124 “Pathogenic fungi and their human host: Networks of interaction”, subprojects B5 and INF. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.