The P/N (Positive-to-Negative Links) Ratio in Complex NetworksA Promising In Silico Biomarker for Detecting Changes Occurring in the Human Microbiome

Human Microbiome

Abstract

Relatively little progress in the methodology for differentiating between the healthy and diseased microbiomes, beyond comparing microbial community diversities with traditional species richness or Shannon index, has been made. Network analysis has increasingly been called for the task, but most currently available microbiome datasets only allows for the construction of simple species correlation networks (SCNs). The main results from SCN analysis are a series of network properties such as network degree and modularity, but the metrics for these network properties often produce inconsistent evidence. We propose a simple new network property, the P/N ratio, defined as the ratio of positive links to the number of negative links in the microbial SCN. We postulate that the P/N ratio should reflect the balance between facilitative and inhibitive interactions among microbial species, possibly one of the most important changes occurring in diseased microbiome. We tested our hypothesis with five datasets representing five major human microbiome sites and discovered that the P/N ratio exhibits contrasting differences between healthy and diseased microbiomes and may be harnessed as an in silico biomarker for detecting disease-associated changes in the human microbiome, and may play an important role in personalized diagnosis of the human microbiome-associated diseases.

Keywords

P/N ratio Human microbiome In silico biomarker Microbiome network Personalized diagnosis 

Abbreviations

AD

Atopic Dermatitis

ART

Antiretroviral Therapy

BV

Bacterial Vaginosis

CF

Cystic Fibrosis

HMP

(Human Microbiome Project)

OTU

(Operational Taxonomic Unit)

MAO

(Most Abundant OTU)

P/N Ratio

The ratio of positive links to the number of negative links in the microbial SCN

PB

Periodontitis with Bleeding

PnB

Periodontitis with no Bleeding

SAD

Species abundance distribution

SCN

Species Correlation Network

Supplementary material

248_2017_1079_MOESM1_ESM.zip (124 kb)
ESM 1(ZIP 281 kb)

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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Computational Biology and Medical Ecology Lab, State Key Lab of Genetic Resources and Evolution, Kunming Institute of ZoologyChinese Academy of SciencesKunmingChina

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