Computational methodology for predicting the landscape of the human-microbial interactome region level influence

J Bioinform Comput Biol. 2015 Oct;13(5):1550023. doi: 10.1142/S0219720015500237. Epub 2015 Aug 11.

Abstract

Microbial communities thrive in close association among themselves and with the host, establishing protein-protein interactions (PPIs) with the latter, and thus being able to benefit (positively impact) or disturb (negatively impact) biological events in the host. Despite major collaborative efforts to sequence the Human microbiome, there is still a great lack of understanding their impact. We propose a computational methodology to predict the impact of microbial proteins in human biological events, taking into account the abundance of each microbial protein and its relation to all other microbial and human proteins. This alternative methodology is centered on an improved impact estimation algorithm that integrates PPIs between human and microbial proteins with Reactome pathway data. This methodology was applied to study the impact of 24 microbial phyla over different cellular events, within 10 different human microbiomes. The results obtained confirm findings already described in the literature and explore new ones. We believe the Human microbiome can no longer be ignored as not only is there enough evidence correlating microbiome alterations and disease states, but also the return to healthy states once these alterations are reversed.

Keywords: Protein–protein interactions; computational prediction; host–pathogen interactions.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Computational Biology / methods*
  • Computing Methodologies
  • Databases, Protein
  • Female
  • Genetic Variation
  • Host-Pathogen Interactions
  • Humans
  • Male
  • Metagenomics / statistics & numerical data
  • Microbiota*
  • Organ Specificity
  • Phylogeny
  • Protein Interaction Mapping / statistics & numerical data*