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Journal of Perinatal Medicine

Official Journal of the World Association of Perinatal Medicine

Editor-in-Chief: Dudenhausen, MD, FRCOG, Joachim W.

Ed. by Bancalari, Eduardo / Chappelle, Joseph / Chervenak, Frank A. / D'Addario , Vincenzo / Genc, Mehmet R. / Greenough, Anne / Grunebaum, Amos / Konje, Justin C. / Kurjak M.D., Asim / Romero, Roberto / Zalud, MD PhD, Ivica

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Volume 46, Issue 5


Methylation differences reveal heterogeneity in preterm pathophysiology: results from bipartite network analyses

Suresh K. Bhavnani
  • Corresponding author
  • Institute for Translational Sciences, University of Texas Medical Branch, 301 University Blvd, 6.168 Research Building 6, Galveston, TX, USA
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/ Bryant Dang / Varun Kilaru
  • Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, Georgia
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/ Maria Caro / Shyam Visweswaran / George Saade
  • Department of Obstetrics and Gynecology, Division of Maternal Fetal-Medicine Perinatal Research, University of Texas Medical Branch, Galveston, TX, USA
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/ Alicia K. Smith
  • Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, Georgia
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/ Ramkumar Menon
  • Corresponding author
  • Department of Obstetrics and Gynecology, Division of Maternal Fetal-Medicine Perinatal Research, The University of Texas Medical Branch, MRB 11.138, 301 University Blvd, Galveston, TX 77555, USA
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Published Online: 2017-06-30 | DOI: https://doi.org/10.1515/jpm-2017-0126



Recent studies have shown that epigenetic differences can increase the risk of spontaneous preterm birth (PTB). However, little is known about heterogeneity underlying such epigenetic differences, which could lead to hypotheses for biological pathways in specific patient subgroups, and corresponding targeted interventions critical for precision medicine. Using bipartite network analysis of fetal DNA methylation data we demonstrate a novel method for classification of PTB.


The data consisted of DNA methylation across the genome (HumanMethylation450 BeadChip) in cord blood from 50 African-American subjects consisting of 22 cases of early spontaneous PTB (24–34 weeks of gestation) and 28 controls (>39 weeks of gestation). These data were analyzed using a combination of (1) a supervised method to select the top 10 significant methylation sites, (2) unsupervised “subject-variable” bipartite networks to visualize and quantitatively analyze how those 10 methylation sites co-occurred across all the subjects, and across only the cases with the goal of analyzing subgroups and their underlying pathways, and (3) a simple linear regression to test whether there was an association between the total methylation in the cases, and gestational age.


The bipartite network analysis of all subjects and significant methylation sites revealed statistically significant clustering consisting of an inverse symmetrical relationship in the methylation profiles between a case-enriched subgroup and a control-enriched subgroup: the former was predominantly hypermethylated across seven methylation sites, and hypomethylated across three methylation sites, whereas the latter was predominantly hypomethylated across the above seven methylation sites and hypermethylated across the three methylation sites. Furthermore, the analysis of only cases revealed one subgroup that was predominantly hypomethylated across seven methylation sites, and another subgroup that was hypomethylated across all methylation sites suggesting the presence of heterogeneity in PTB pathophysiology. Finally, the analysis found a strong inverse linear relationship between total methylation and gestational age suggesting that methylation differences could be used as predictive markers for gestational length.


The results demonstrate that unsupervised bipartite networks helped to identify a complex but comprehensible data-driven hypotheses related to patient subgroups and inferences about their underlying pathways, and therefore were an effective complement to supervised approaches currently used.

Keywords: Bipartite networks; epigenetics; network analysis; preterm; visual analytics; visualization


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About the article

Received: 2017-04-19

Accepted: 2017-05-26

Published Online: 2017-06-30

Published in Print: 2018-07-26

Author’s statement

Conflict of interest: Authors state no conflict of interest.

Material and methods: Informed consent: Informed consent has been obtained from all individuals included in this study.

Ethical approval: The research related to human subject use has complied with all the relevant national regulations, and institutional policies, and is in accordance with the tenets of the Helsinki Declaration, and has been approved by the authors’ institutional review board or equivalent committee.

Author contributions: Conceived and designed the analysis: SKB BD RM. Extracted the data: RM VK. Analyzed the data: SKB BD VK AKS. Wrote the paper: SKB BD VK MC SV GS SEP AKS RM.

Data availability: The data used in this study were extracted from epigenetic studies conducted by Dr. Menon (ram.menon@utmb.edu), and Dr. Smith (alicia.smith@emory.edu). These data can be obtained through email request.

Funding: This study was funded in part by a Clinical and Translational Science Award (UL1 TR001439) from the National Center for Advancing Translational Sciences, National Institutes of Health, and in part by the Rising STARs award from the University of Texas System. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies.

Competing interests: The authors have declared that no competing interests exist.

Citation Information: Journal of Perinatal Medicine, Volume 46, Issue 5, Pages 509–521, ISSN (Online) 1619-3997, ISSN (Print) 0300-5577, DOI: https://doi.org/10.1515/jpm-2017-0126.

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