Microbial Ecology

, Volume 75, Issue 4, pp 1063–1073 | Cite as

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

  • Zhanshan (Sam) MaEmail author
Human Microbiome


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.


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



Atopic Dermatitis


Antiretroviral Therapy


Bacterial Vaginosis


Cystic Fibrosis


(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.

Author Contributions

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


Not Applicable.

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.

Supplementary material (124 kb)
ESM 1 (ZIP 281 kb)


  1. 1.
    Abusleme L, Dupuy AK, Dutzan N, Silva N, Burleson JA, Strausbaugh LD, Gamonal J, Diaz PI (2013) The subgingival microbiome in health and periodontitis and its relationship with community biomass and inflammation. ISME J 7:1016–1025CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Barberán A, Casamayor EO, Fierer N (2014) The microbial contribution to macroecology., The microbial contribution to macroecology. Front. Microbiol. 5(5):203–203PubMedPubMedCentralGoogle Scholar
  3. 3.
    Barberán A, Bates ST, Casamayor EO, Fierer N (2012) Using network analysis to explore co-occurrence patterns in soil microbial communities. ISME J 6:343–351CrossRefPubMedGoogle Scholar
  4. 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
  5. 5.
    Chow C-ET, Kim DY, Sachdeva R, Caron DA, Fuhrman JA (2014) Top-down controls on bacterial community structure: microbial network analysis of bacteria, T4-like viruses and protists. ISME J 8:816–829CrossRefPubMedGoogle Scholar
  6. 6.
    Costello EK, Lauber CL, Hamady M, Fierer N, Gordon JI, Knight R (2009) Bacterial Community Variation in Human Body Habitats Across Space and Time. Science 326:1694–1697CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Costello EK, Stagaman K, Dethlefsen L, Bohannan BJM, Relman DA (2012) The Application of Ecological Theory Toward an Understanding of the Human Microbiome. Science 336:1255–1262CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Csardi G, Nepusz T (2005) The Igraph Software Package for Complex Network Research. Int J Compl Syst 1695Google Scholar
  9. 9.
    Duran-Pinedo AE, Paster B, Teles R, Frias-Lopez J (2011) Correlation Network Analysis Applied to Complex Biofilm Communities. PLoS One 6:e28438CrossRefPubMedPubMedCentralGoogle Scholar
  10. 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
  11. 11.
    Faust K, Raes J (2012) Microbial interactions: from networks to models. Nat Rev Microbiol. 10:538–550CrossRefPubMedGoogle Scholar
  12. 12.
    Faust K, Sathirapongsasuti JF, Izard J, Segata N, Gevers D, Raes J, Huttenhower C (2012) Microbial Co-occurrence Relationships in the Human Microbiome. PLoS Comput Biol. 8:e1002606CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Fernandez M, Riveros JD, Campos M, Mathee K, Narasimhan G (2015) Microbial ‘social networks’. BMC Genomics 16(Suppl 11):S6CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Fodor AA, Klem ER, Gilpin DF, Elborn JS, Boucher RC, Tunney MM, Wolfgang MC (2012) The Adult Cystic Fibrosis Airway Microbiota Is Stable over Time and Infection Type, and Highly Resilient to Antibiotic Treatment of Exacerbations. PLoS ONE 7(7):e45001–e45001CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Fredricks DN (2011) Molecular methods to describe the spectrum and dynamics of the vaginal microbiota. Anaerobe 17:191–195CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Gajer P, Brotman RM, Bai G, Sakamoto J, Schütte UME, Zhong X, Koenig SSK, Fu L, Ma Z(S), Zhou X, Abdo Z, Forney LJ, Ravel J (2012) Temporal Dynamics of the Human Vaginal Microbiota. Sci Transl Med. 4:132ra52CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Human Microbiome Project Consortium (2012a) A framework for human microbiome research. Nature 486:215–221CrossRefGoogle Scholar
  18. 18.
    Human Microbiome Project Consortium (2012b) Structure, function and diversity of the healthy human microbiome. Nature 486:207–214CrossRefGoogle Scholar
  19. 19.
    Hunt DE, Ward CS (2015) A network-based approach to disturbance transmission through microbial interactions. Front Microbiol. 6:1182CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Imangaliyev S, Keijser B, Crielaard W, Tsivtsivadze E (2015) Personalized microbial network inference via co-regularized spectral clustering. Methods 83:28–35CrossRefPubMedGoogle Scholar
  21. 21.
    Junker BH, Schreiber F (2008) Analysis of Biological Networks, 1 editionn. Wiley-Interscience, Hoboken, CrossRefGoogle Scholar
  22. 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
  23. 23.
    Koumans EH, Kendrick JS (2001) Preventing Adverse Sequelae of Bacterial Vaginosis: A Public Health Program and Research Agenda. Sex Transm Dis. 28(5):292–297CrossRefPubMedGoogle Scholar
  24. 24.
    Lozupone CA, Stombaugh JI, Gordon JI, Jansson JK, Knight R (2012) Diversity, stability and resilience of the human gut microbiota. Nature 489:220–230CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Ma ZS (2015) Power law analysis of the human microbiome. Mol Ecol. 24:5428–5445CrossRefPubMedGoogle Scholar
  26. 26.
    Ma B, Forney LJ, Ravel J (2012) Vaginal Microbiome: Rethinking Health and Disease. Annu Rev Microbiol. 66:371–389. CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Ma ZS, Guan Q, Ye C, Zhang C, Foster JA, Forney LJ (2015) Network analysis suggests a potentially ‘evil’ alliance of opportunistic pathogens inhibited by a cooperative network in human milk bacterial communities. Sci Rep 5:8275CrossRefGoogle Scholar
  28. 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
  29. 29.
    McHardy IH, Li X, Tong M, Ruegger P, Jacobs J, Borneman J, Anton P, Braun J (2013) HIV Infection is associated with compositional and functional shifts in the rectal mucosal microbiota. Microbiome 1:26CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Menezes AB, Prendergast-Miller MT, Richardson AE, Toscas P, Farrell M, Macdonald LM, Baker G, Wark T, Thrall PH (2015) Network analysis reveals that bacteria and fungi form modules that correlate independently with soil parameters. Environ Microbiol. 17:2677–2689CrossRefPubMedGoogle Scholar
  31. 31.
    Morin PJ 1999 Community ecology, 2nd Edition. Publisher Wiley-Blackwell, New York, pp410Google Scholar
  32. 32.
    Noble WS (2009) How does multiple testing correction work? Nat Biotechnol. 27:1135–1137CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Prosser JI, Bohannan BJM, Curtis TP, Ellis RJ, Firestone MK, Freckleton RP, Green JL, Green LE, Killham K, Lennon JJ, Osborn AM, Solan M, van der Gast CJ, Young JPW (2007) The role of ecological theory in microbial ecology. Nat Rev Microbiol. 5:384–392CrossRefPubMedGoogle Scholar
  34. 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
  35. 35.
    Ravel J, Gajer P, Abdo Z, Schneider GM, Koenig SSK, McCulle SL, Karlebach S, Gorle R, Russell J, Tacket CO, Brotman RM, Davis CC, Ault K, Peralta L, Forney LJ (2011) Vaginal microbiome of reproductive-age women. Proc Natl Acad Sci. 108:4680–4687CrossRefPubMedGoogle Scholar
  36. 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.
  37. 37.
    Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T (2003) Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks. Genome Res. 13:2498–2504CrossRefPubMedPubMedCentralGoogle Scholar
  38. 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

Copyright information

© Springer Science+Business Media, LLC 2017
Corrected publication November/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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