The P/N (Positive-to-Negative Links) Ratio in Complex Networks—A Promising In Silico Biomarker for Detecting Changes Occurring in the Human Microbiome
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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.
KeywordsP/N ratio Human microbiome In silico biomarker Microbiome network Personalized diagnosis
(Human Microbiome Project)
(Operational Taxonomic Unit)
(Most Abundant OTU)
- P/N Ratio
The ratio of positive links to the number of negative links in the microbial SCN
Periodontitis with Bleeding
Periodontitis with no Bleeding
Species abundance distribution
Species Correlation Network
I thank Prof. Ian McHardy, UCLA Department of Pathology and Laboratory Medicine, UCLA, USA, and Prof. Vladimir Lazarevic, Genomic Research Laboratory, Geneva University Hospitals, Switzerland, for their generous help by providing us the OTU tables from their original studies. We are also indebted to the help from Jie Li and Lianwei Li of the Computational Biology and Medical Ecology Lab, Chinese Academy of Sciences, for their helps in performing the data analysis.
Z Ma designed and conducted the study, and wrote the paper.
The study received funding from the National Natural Science Foundation of China (No. 71473243), an International Cooperative Grant and a Key Project of Biomedicine and Health from Yunnan Province, China.
Compliance with Ethical Standards
Consent to Publish
Conflict of Interest
I declare no conflict of interests.
Availability of Data and Materials
The datasets utilized in this study are available in the original studies cited in Table 1.
- 4.Benjamini Y, Hochberg Y (1995) Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. J R Stat Soc. 57:289–300Google Scholar
- 8.Csardi G, Nepusz T (2005) The Igraph Software Package for Complex Network Research. Int J Compl Syst 1695Google Scholar
- 10.Faust K, Lahti L, Gonze D, de Vos WM, Raes J (2015) Metagenomics meets time series analysis: unraveling microbial community dynamics. Curr OpinMicrobiol. 25:56–66Google Scholar
- 22.Kong HH, Oh J, Deming C, Conlan S, Grice EA, Beatson MA, Nomicos E, Polley EC, Komarow HD, Program NCS, Murray PR, Turner ML, Segre JA (2012) Temporal shifts in the skin microbiome associated with disease flares and treatment in children with atopic dermatitis. Genome Res. 22:850–859CrossRefPubMedPubMedCentralGoogle Scholar
- 26.Ma B, Forney LJ, Ravel J (2012) Vaginal Microbiome: Rethinking Health and Disease. Annu Rev Microbiol. 66:371–389. https://doi.org/10.1146/annurev-micro-092611-150157 CrossRefPubMedPubMedCentralGoogle Scholar
- 28.Ma Z, Zhang C, Zhang Q, Li J, Li L, Qi L, Yang X (2016) A Brief Review on the Ecological Network Analysis with Applications in the Emerging Medical Ecology. In: McGenity TJ, Timmis KN, Nogales Fernández B, Fernández BN (eds) Hydrocarbon and Lipid Microbiology Protocols. Springer Protocols Handbooks. Springer, Berlin, pp. 7–39Google Scholar
- 31.Morin PJ 1999 Community ecology, 2nd Edition. Publisher Wiley-Blackwell, New York, pp410Google Scholar
- 34.Ramayo-Caldas Y, Mach N, Lepage P, Levenez F, Denis C, Lemonnier G, Leplat J-J, Billon Y, Berri M, Doré J, Rogel-Gaillard C, Estellé J (2016) Phylogenetic network analysis applied to pig gut microbiota identifies an ecosystem structure linked with growth traits. ISME J 10:2973–2977CrossRefPubMedPubMedCentralGoogle Scholar
- 36.Servant N, Gravier E, Gestraud P, Laurent C, Paccard C, Biton A, Mandel J, Asselain B, Barillot E, Hupe P (2016) EMA: easy microarray data analysis. https://cran.r-project.org/web/packages/EMA/index.html
- 38.Srinivasan S, Hoffman NG, Morgan MT, Matsen FA, Fiedler TL, Hall RW, Ross FJ, McCoy CO, Bumgarner R, Marrazzo JM, Fredricks DN (2012) Bacterial Communities in Women with Bacterial Vaginosis: High Resolution Phylogenetic Analyses Reveal Relationships of Microbiota to Clinical Criteria. PLoS One 7:e37818CrossRefPubMedPubMedCentralGoogle Scholar