A 3-dimensional mathematical model of microbial proliferation that generates the characteristic cumulative relative abundance distributions in gut microbiomes

PLoS One. 2017 Aug 8;12(8):e0180863. doi: 10.1371/journal.pone.0180863. eCollection 2017.

Abstract

The gut microbiome is highly variable among individuals, largely due to differences in host lifestyle and physiology. However, little is known about the underlying processes or rules that shape the complex microbial community. In this paper, we show that the cumulative relative abundance distribution (CRAD) of microbial species can be approximated by a power law function, and found that the power exponent of CRADs generated from 16S rRNA gene and metagenomic data for normal gut microbiomes of humans and mice was similar consistently with ∼0.9. A similarly robust power exponent was observed in CRADs of gut microbiomes during dietary interventions and several diseases. However, the power exponent was found to be ∼0.6 in CRADs from gut microbiomes characterized by lower species richness, such as those of human infants and the small intestine of mice. In addition, the CRAD of gut microbiomes of mice treated with antibiotics differed slightly from those of infants and the small intestines of mice. Based on these observations, in addition to data on the spatial distribution of microbes in the digestive tract, we developed a 3-dimensional mathematical model of microbial proliferation that reproduced the experimentally observed CRAD patterns. Our model indicated that the CRAD may be determined by the ratio of emerging to pre-existing species during non-uniform spatially competitive proliferation, independent of species composition.

MeSH terms

  • Animals
  • Anti-Bacterial Agents / pharmacology
  • Cell Proliferation* / physiology
  • Colon / drug effects
  • Colon / metabolism
  • Colon / microbiology
  • Colon / pathology
  • Computer Simulation
  • Diabetes Mellitus, Type 2 / metabolism
  • Diabetes Mellitus, Type 2 / microbiology
  • Diet, Vegetarian
  • Feces / microbiology
  • Gastrointestinal Microbiome* / physiology
  • Humans
  • Infant
  • Infant, Newborn
  • Inflammatory Bowel Diseases / metabolism
  • Inflammatory Bowel Diseases / microbiology
  • Intestine, Small / drug effects
  • Intestine, Small / metabolism
  • Intestine, Small / microbiology
  • Intestine, Small / pathology
  • Mice
  • Models, Biological*
  • Multiple Sclerosis / metabolism
  • Multiple Sclerosis / microbiology
  • RNA, Ribosomal, 16S / metabolism

Substances

  • Anti-Bacterial Agents
  • RNA, Ribosomal, 16S

Grants and funding

The work was supported by a Grant-in-Aid for Japan Society for the Promotion of Science (JSPS) Fellows to L. T. (Grant Number: 26-7826, HP: https://www.jsps.go.jp/english/). Sony Computer Science Laboratories, Inc. provided support in the form of salary for author H.T., but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of this author are articulated in the ‘author contributions’ section.