Construction and Analysis of Functional Networks in the Gut Microbiome of Type 2 Diabetes Patients

Lianshuo Li,Zicheng Wang,Peng He,Shining Ma,Jie Du,Rui Jiang

doi:10.1016/j.gpb.2016.02.005

Lianshuo Li, Zicheng Wang + Show 4 more

Open Access

https://doi.org/10.1016/j.gpb.2016.02.005

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Abstract

Although networks of microbial species have been widely used in the analysis of 16S rRNA sequencing data of a microbiome, the construction and analysis of a complete microbial gene network are in general problematic because of the large number of microbial genes in metagenomics studies. To overcome this limitation, we propose to map microbial genes to functional units, including KEGG orthologous groups and the evolutionary genealogy of genes: Non-supervised Orthologous Groups (eggNOG) orthologous groups, to enable the construction and analysis of a microbial functional network. We devised two statistical methods to infer pairwise relationships between microbial functional units based on a deep sequencing dataset of gut microbiome from type 2 diabetes (T2D) patients as well as healthy controls. Networks containing such functional units and their significant interactions were constructed subsequently. We conducted a variety of analyses of global properties, local properties, and functional modules in the resulting functional networks. Our data indicate that besides the observations consistent with the current knowledge, this study provides novel biological insights into the gut microbiome associated with T2D.

Highlights

Advancement of the next-generation sequencing technology has made it possible to sequence all genetic materials of a microbiome and to assemble millions of microbial genes from the data, resulting in the recent explosion of large-scale metagenomic studies in soil [1,2,3], air [4,5], marine environments [6,7,8], and humans [9,10,11], as well as in many other fields [12]
We propose a framework for constructing and analyzing functional networks of the human gut microbiome
We find that the networks constructed using different methods and from different samples may capture different aspects of biological meanings

Summary

Introduction

Advancement of the next-generation sequencing technology has made it possible to sequence all genetic materials of a microbiome and to assemble millions of microbial genes from the data, resulting in the recent explosion of large-scale metagenomic studies in soil [1,2,3], air [4,5], marine environments [6,7,8], and humans [9,10,11], as well as in many other fields [12]. Li L et al / Functional Networks of Gut Microbiome in Type 2 Diabetes Patients rRNA gene in whole or its hypervariable regions selectively can be employed to profile the taxonomic composition of a microbiome. This approach, together with the powerful network analysis methodology [13], has a variety of applications, such as identification of co-occurrence networks of microbial species in soil, marine environments [14], and, more recently, of humans [15,16,17]. In order to study functions of a microbial community, it is necessary to know which genes are in a community and how these genes interact with one another to support a complicated biological function

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