Network analysis of a complex disease: the gut microbiota in the inflammatory bowel disease case

TL;DR

This study applies network science to analyze gut microbiota in IBD, revealing distinct microbial modules and network properties that differ among control, Crohn's disease, and ulcerative colitis subjects.

Contribution

It introduces a network-based approach to characterize microbiota differences in IBD, using correlation and co-expression networks with community detection and centrality measures.

Findings

Modules of Bacteroides are connected in controls

Faecalibacterium prausnitzii shows increased centrality in IBD

Escherichia coli modules vary position across diagnoses

Abstract

Inflammatory bowel diseases (IBD) are complex diseases in which the gut microbiota is attacked by the immune system of genetically predisposed subjects when they are exposed to yet unclear environmental factors. The complexity of this class of diseases makes them suitable to be represented and studied with network science. In the project, the metagenomic data of the gut microbiota of control, Crohn's disease, and ulcerative colitis subjects were divided in three ranges (prevalent, common, uncommon). Then, correlation networks and co-expression networks were used to represent this data. The former networks involved the calculation of the Pearson's correlation and the use of the percolation threshold to binarize the adjacency matrix, whereas the latter involved the construction of the bipartite networks and the monopartite projection after binarization of the biadjacency matrix. Then, centrality measures and community detection were used on the so-built networks. The main results obtained were about the modules of "Bacteroides", which were connected in control subjects' correlation network, "Faecalibacterium prausnitzii", where co-enzyme A became central in IBD correlation networks and "Escherichia coli", which module has different position in the different diagnoses networks.