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

  • Suresh K. Bhavnani EMAIL logo , Bryant Dang , Varun Kilaru , Maria Caro , Shyam Visweswaran , George Saade , Alicia K. Smith und Ramkumar Menon EMAIL logo
Veröffentlicht/Copyright: 30. Juni 2017

Abstract

Background:

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.

Methods:

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.

Results:

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.

Conclusions:

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.

Acknowledgments

We thank Rohit Divekar for feedback and support on the analysis and results.

  1. Author’s statement

  2. Conflict of interest: Authors state no conflict of interest.

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

  4. 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.

  5. 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.

  6. 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.

  7. 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.

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

References

[1] Beck S, Wojdyla D, Say L, Betran AP, Merialdi M, Requejo JH, et al. The worldwide incidence of preterm birth: a systematic review of maternal mortality and morbidity. Bull World Health Organ. 2010;88:31–8.10.2471/BLT.08.062554Suche in Google Scholar PubMed PubMed Central

[2] Chmurzynska A. Fetal programming: link between early nutrition, DNA methylation, and complex diseases. Nutr Rev. 2010;68:87–98.10.1111/j.1753-4887.2009.00265.xSuche in Google Scholar PubMed

[3] Barker DJ. The fetal and infant origins of adult disease. Br Med J. 1990;301:1111.10.1136/bmj.301.6761.1111Suche in Google Scholar PubMed PubMed Central

[4] Barker DJ, Gelow J, Thornburg K, Osmond C, Kajantie E, Eriksson JG. The early origins of chronic heart failure: impaired placental growth and initiation of insulin resistance in childhood. Eur J Heart Fail. 2010;12:819–25.10.1093/eurjhf/hfq069Suche in Google Scholar PubMed PubMed Central

[5] Champagne FA, Curley JP. Epigenetic mechanisms mediating the long-term effects of maternal care on development. Neurosci Biobehav Rev. 2009;33:593–600.10.1016/j.neubiorev.2007.10.009Suche in Google Scholar PubMed

[6] Burdge GC, Hanson MA, Slater-Jefferies JL, Lillycrop KA. Epigenetic regulation of transcription: a mechanism for inducing variations in phenotype (fetal programming) by differences in nutrition during early life? Br J Nutr. 2007;97:1036–46.10.1017/S0007114507682920Suche in Google Scholar PubMed PubMed Central

[7] Menon R. Spontaneous preterm birth, a clinical dilemma: etiologic, pathophysiologic and genetic heterogeneities and racial disparity. Acta Obstet Gynecol Scandinavica. 2008;87:590–600.10.1080/00016340802005126Suche in Google Scholar PubMed

[8] Koullali B, Oudijk MA, Nijman TA, Mol BW, Pajkrt E. Risk assessment and management to prevent preterm birth. Semin Fetal Neonatal Med. 2016;21:80–8.10.1016/j.siny.2016.01.005Suche in Google Scholar PubMed

[9] Mortensen LH, Helweg-Larsen K, Andersen AM. Socioeconomic differences in perinatal health and disease. Scand J Public Health. 2011;39:110–4.10.1177/1403494811405096Suche in Google Scholar PubMed

[10] Yee LM, Truong YN, Caughey AB, Cheng YW. The association between interdelivery interval and adverse perinatal outcomes in a diverse US population. J Perinatol. 2016;36:593–7.10.1038/jp.2016.54Suche in Google Scholar PubMed

[11] Romero R, Dey SK, Fisher SJ. Preterm labor: one syndrome, many causes. Science 2014;345:760–5.10.1126/science.1251816Suche in Google Scholar PubMed PubMed Central

[12] Coleman T, Chamberlain C, Davey MA, Cooper SE, Leonardi-Bee J. Pharmacological interventions for promoting smoking cessation during pregnancy. Cochrane Database Syst Rev. 2012;9:Cd010078.10.1002/14651858.CD010078Suche in Google Scholar PubMed

[13] Messer LC, Kaufman JS, Mendola P, Laraia BA. Black-white preterm birth disparity: a marker of inequality. Ann Epidemiol. 2008;18:851–8.10.1016/j.annepidem.2008.06.007Suche in Google Scholar PubMed

[14] Behnia F, Parets SE, Kechichian T, Yin H, Dutta EH, Saade GR, et al. Fetal DNA methylation of autism spectrum disorders candidate genes: association with spontaneous preterm birth. Am J Obstet Gynecol. 2015;212:533.e1–9.10.1016/j.ajog.2015.02.011Suche in Google Scholar PubMed

[15] Monangi NK, Brockway HM, House M, Zhang G, Muglia LJ. The genetics of preterm birth: Progress and promise. Semin Perinatol. 2015;39:574–83.10.1053/j.semperi.2015.09.005Suche in Google Scholar PubMed

[16] Parets SE, Conneely KN, Kilaru V, Fortunato SJ, Syed TA, Saade G, et al. Fetal DNA methylation associates with early spontaneous preterm birth and gestational age. PLoS One. 2013;8:e67489.10.1371/journal.pone.0067489Suche in Google Scholar PubMed PubMed Central

[17] Newman MEJ. Networks: an Introduction. Oxford: Oxford University Press; 2010.10.1093/acprof:oso/9780199206650.001.0001Suche in Google Scholar

[18] Bhavnani SK, Dang B, Bellala G, Divekar R, Visweswaran S, Brasier A, et al. Unlocking proteomic heterogeneity in complex diseases through visual analytics. Proteomics 2015;15:1405–18.10.1002/pmic.201400451Suche in Google Scholar PubMed PubMed Central

[19] Lacy ME, Wellenius GA, Carnethon MR, Loucks EB, Carson AP, Luo X, et al. Racial differences in the performance of existing risk prediction models for incident type 2 diabetes: The CARDIA study. Diabetes Care. 2015;39:285–91.10.2337/dc15-0509Suche in Google Scholar PubMed PubMed Central

[20] Baker JJ. Medicare payment system for hospital inpatients: diagnosis-related groups. J Health Care Finance. 2002;28:1–13.Suche in Google Scholar

[21] Lipkovich I, Dmitrienko A, Denne J, Enas G. Subgroup identification based on differential effect search – a recursive partitioning method for establishing response to treatment in patient subpopulations. Stat Med. 2011;30:2601–21.10.1002/sim.4289Suche in Google Scholar PubMed

[22] Kehl V, Ulm K. Responder identification in clinical trials with censored data. Comput Stat Data Anal. 2006;50:1338–55.10.1016/j.csda.2004.11.015Suche in Google Scholar

[23] Hastie T, Tibshirani R, Friedman J. The elements of statistical learning. New York, NY: Springer New York Inc.; 2001.10.1007/978-0-387-21606-5Suche in Google Scholar

[24] Fitzpatrick AM, Teague WG, Meyers DA, Peters SP, Li X, Li H, et al. Heterogeneity of severe asthma in childhood: confirmation by cluster analysis of children in the National Institutes of Health/National Heart, Lung, and Blood Institute Severe Asthma Research Program. J Allergy Clin immunol. 2011;127:382–9.e1–13.10.1016/j.jaci.2010.11.015Suche in Google Scholar PubMed PubMed Central

[25] Abu-jamous B, Fa R, Nandi AK. Integrative cluster analysis in bioinformatics. Wiley; 2015.10.1002/9781118906545Suche in Google Scholar

[26] Lochner KA, Cox CS. Prevalence of multiple chronic conditions among Medicare beneficiaries, United States, 2010. Prev Chronic Dis. 2013;10:E61.10.5888/pcd10.120137Suche in Google Scholar PubMed PubMed Central

[27] Shabalin AA, Weigman VJ, Perou CM, Nobel AB. Finding large average submatrices in high dimensional data. Ann Appl Stat. 2009;3:985–1012.10.1214/09-AOAS239Suche in Google Scholar

[28] Odibat O, Reddy CK. Efficient mining of discriminative co-clusters from gene expression data. Knowl Inf Sys. 2014;41:667–96.10.1007/s10115-013-0684-0Suche in Google Scholar PubMed PubMed Central

[29] Casanova R, Saldana S, Chew EY, Danis RP, Greven CM, Ambrosius WT. Application of random forests methods to diabetic retinopathy classification analyses. PLoS One. 2014;9:e98587.10.1371/journal.pone.0098587Suche in Google Scholar PubMed PubMed Central

[30] Thomas JJ, Cook KA, editors. Illuminating the path: the R&D agenda for visual analytics. IEEE Press; 2005.Suche in Google Scholar

[31] Cramer AO, Waldorp LJ, van der Maas HL, Borsboom D. Comorbidity: a network perspective. Behav Brain Sci. 2010;33:137–50.10.1017/S0140525X09991567Suche in Google Scholar PubMed

[32] Jensen LJ, Kuhn M, Stark M, Chaffron S, Creevey C, Muller J, et al. STRING 8 – a global view on proteins and their functional interactions in 630 organisms. Nucleic Acids Res. 2009;37:D412–6.10.1093/nar/gkn760Suche in Google Scholar PubMed PubMed Central

[33] Christakis NA, Fowler JH. Social network sensors for early detection of contagious outbreaks. PLoS One. 2010;5:e12948.10.1371/journal.pone.0012948Suche in Google Scholar PubMed PubMed Central

[34] Kamada T, Kawai S. An algorithm for drawing general undirected graphs. Inform Process Lett. 1989;31:7–15.10.1142/9789814434478_0005Suche in Google Scholar

[35] Bhavnani SK, Bellala G, Victor S, Bassler KE, Visweswaran S. The role of complementary bipartite visual analytical representations in the analysis of SNPs: a case study in ancestral informative markers. J Am Med Inform Assoc. 2012;19:e5–12.10.1136/amiajnl-2011-000745Suche in Google Scholar PubMed PubMed Central

[36] Bhavnani SK, Dang B, Caro M, Bellala G, Visweswaran S, Mejias A, et al. Heterogeneity within and across pediatric pulmonary infections: from bipartite networks to at-risk subphenotypes. AMIA Jt Summits Transl Sci Proc. 2014;2014:29–34.Suche in Google Scholar

[37] Bhavnani SK, Dang B, Visweswaran S, Divekar R, Tan A, Karmarkar A, et al. How comorbidities co-occur in readmitted hip fracture patients: from bipartite networks to insights for post-discharge planning. AMIA Jt Summits Transl Sci Proc. 2015;2015:36–40.Suche in Google Scholar

[38] Bhavnani SK, Drake J, Bellala G, Dang B, Peng BH, Oteo JA, et al. How cytokines co-occur across rickettsioses patients: from bipartite visual analytics to mechanistic inferences of a cytokine storm. AMIA Jt Summits Transl Sci Proc. 2013;2013:15–9.Suche in Google Scholar

[39] Bhavnani SK, Drake J, Divekar R. The role of visual analytics in asthma phenotyping and biomarker discovery. Adv Exp Med Biol. 2014;795:289–305.10.1007/978-1-4614-8603-9_18Suche in Google Scholar PubMed

[40] Bhavnani SK, Victor S, Calhoun WJ, Busse WW, Bleecker E, Castro M, et al. How cytokines co-occur across asthma patients: from bipartite network analysis to a molecular-based classification. J Biomed Inform. 2011;44:S24–30.10.1016/j.jbi.2011.09.006Suche in Google Scholar PubMed PubMed Central

[41] Johnson RA, Wichern DW, editors. Applied multivariate statistical analysis. Upper Saddle River, NJ, USA: Prentice-Hall, Inc.; 1988.10.2307/2531616Suche in Google Scholar

[42] Parets SE, Conneely KN, Kilaru V, Menon R, Smith AK. DNA methylation provides insight into intergenerational risk for preterm birth in African Americans. Epigenetics 2015;10:784–92.10.1080/15592294.2015.1062964Suche in Google Scholar PubMed PubMed Central

[43] Menon R, Velez DR, Simhan H, Ryckman K, Jiang L, Thorsen P, et al. Multilocus interactions at maternal tumor necrosis factor-alpha, tumor necrosis factor receptors, interleukin-6 and interleukin-6 receptor genes predict spontaneous preterm labor in European-American women. Am J Obstet Gynecol. 2006;194:1616–24.10.1016/j.ajog.2006.03.059Suche in Google Scholar PubMed

[44] Menon R, Williams SM, Fortunato SJ. Amniotic fluid interleukin-1beta and interleukin-8 concentrations: racial disparity in preterm birth. Reprod Sci. 2007;14:253–9.10.1177/1933719107301336Suche in Google Scholar PubMed

[45] Menon R, Fortunato SJ, Edwards DR, Williams SM. Association of genetic variants, ethnicity and preterm birth with amniotic fluid cytokine concentrations. Ann Hum Genet. 2010;74:165–83.10.1111/j.1469-1809.2010.00562.xSuche in Google Scholar PubMed

[46] Menon R, Pearce B, Velez DR, Merialdi M, Williams SM, Fortunato SJ, et al. Racial disparity in pathophysiologic pathways of preterm birth based on genetic variants. Reprod Biol Endocrinol. 2009;7:62.10.1186/1477-7827-7-62Suche in Google Scholar PubMed PubMed Central

[47] Menon R, Velez DR, Morgan N, Lombardi SJ, Fortunato SJ, Williams SM. Genetic regulation of amniotic fluid TNF-alpha and soluble TNF receptor concentrations affected by race and preterm birth. Hum Genet. 2008;124:243–53.10.1007/s00439-008-0547-zSuche in Google Scholar PubMed

[48] Velez DR, Fortunato SJ, Thorsen P, Lombardi SJ, Williams SM, Menon R. Preterm birth in Caucasians is associated with coagulation and inflammation pathway gene variants. PLoS One. 2008;3:e3283.10.1371/journal.pone.0003283Suche in Google Scholar PubMed PubMed Central

[49] Velez DR, Fortunato SJ, Morgan N, Edwards TL, Lombardi SJ, Williams SM, et al. Patterns of cytokine profiles differ with pregnancy outcome and ethnicity. Hum Reprod. 2008;23:1902–9.10.1093/humrep/den170Suche in Google Scholar PubMed PubMed Central

[50] Fortunato SJ, Menon R, Velez DR, Thorsen P, Williams SM. Racial disparity in maternal-fetal genetic epistasis in spontaneous preterm birth. Am J Obstet Gynecol. 2008;198:666.e1–9; discussion.e9–10.10.1016/j.ajog.2008.02.003Suche in Google Scholar PubMed

[51] Nooy W, Mrvar A, Batagelj V. Exploratory social network analysis with Pajek. 2nd ed. New York, NY: Cambridge University Press; 2011.10.1017/CBO9780511996368Suche in Google Scholar

[52] Menon R, Behnia F, Polettini J, Saade GR, Campisi J, Velarde M. Placental membrane aging and HMGB1 signaling associated with human parturition. Aging 2016;8:216–30.10.18632/aging.100891Suche in Google Scholar PubMed PubMed Central

[53] Behnia F, Taylor BD, Woodson M, Kacerovsky M, Hawkins H, Fortunato SJ, et al. Chorioamniotic membrane senescence: a signal for parturition? Am J Obstet Gynecol. 2015;213:359.e1–16.10.1016/j.ajog.2015.05.041Suche in Google Scholar PubMed

[54] Dutta EH, Behnia F, Boldogh I, Saade GR, Taylor BD, Kacerovsky M, et al. Oxidative stress damage-associated molecular signaling pathways differentiate spontaneous preterm birth and preterm premature rupture of the membranes. Mol Hum Reprod. 2016;22:143–57.10.1093/molehr/gav074Suche in Google Scholar PubMed

[55] Menon R, Yu J, Basanta-Henry P, Brou L, Berga SL, Fortunato SJ, et al. Short fetal leukocyte telomere length and preterm prelabor rupture of the membranes. PLoS One. 2012;7:e31136.10.1371/journal.pone.0031136Suche in Google Scholar PubMed PubMed Central

[56] Readings in information visualization: using vision to think. Burlington, MA, USA: Morgan Kaufmann Publishers Inc.; 1999.Suche in Google Scholar

[57] Halford GS, Baker R, McCredden JE, Bain JD. How many variables can humans process? Psychol Sci. 2005;16:70–6.10.1111/j.0956-7976.2005.00782.xSuche in Google Scholar PubMed

[58] Wagemans J, Elder JH, Kubovy M, Palmer SE, Peterson MA, Singh M, et al. A century of gestalt psychology in visual perception I. perceptual grouping and figure-ground organization. Psychol Bull. 2012;138:1172–217.10.1037/a0029333Suche in Google Scholar PubMed PubMed Central

[59] Tufte ER. The visual display of quantitative information. Cheshire, CT: Graphics Press; 1986. 197 p.Suche in Google Scholar

[60] Zhang J, Norman AD. Representations in distributed cognitive tasks. Cogn Sci. 1994;18:87–122.10.1207/s15516709cog1801_3Suche in Google Scholar

[61] Larkin JH, Simon HA. Why a diagram is (sometimes) worth ten thousand words. Cogn Sci. 1987;11:65–9.10.1111/j.1551-6708.1987.tb00863.xSuche in Google Scholar

Received: 2017-4-19
Accepted: 2017-5-26
Published Online: 2017-6-30
Published in Print: 2018-7-26

©2018 Walter de Gruyter GmbH, Berlin/Boston

Artikel in diesem Heft

  1. Frontmatter
  2. Editorial
  3. A transformative icon for modern perinatology
  4. Highlight: Preterm Labor
  5. Editorial
  6. What’s new in preterm birth prediction and prevention?
  7. Review articles
  8. Pulmo uterinus: a history of ideas on fetal respiration
  9. Mid-trimester preterm premature rupture of membranes (PPROM): etiology, diagnosis, classification, international recommendations of treatment options and outcome
  10. Highlight articles
  11. A soft cervix, categorized by shear-wave elastography, in women with short or with normal cervical length at 18–24 weeks is associated with a higher prevalence of spontaneous preterm delivery
  12. Association between genital mycoplasmas, acute chorioamnionitis and fetal pneumonia in spontaneous abortions
  13. Methylation differences reveal heterogeneity in preterm pathophysiology: results from bipartite network analyses
  14. Understanding fetal factors that contribute to preterm birth: Sjögren-Larsson syndrome as a model
  15. Safety and efficacy of the cervical pessary combined with vaginal progesterone for the prevention of spontaneous preterm birth
  16. Risk of preterm birth by maternal age at first and second pregnancy and race/ethnicity
  17. Infant mortality and causes of death by birth weight for gestational age in non-malformed singleton infants: a 2002–2012 population-based study
  18. Perinatal outcomes after previable preterm premature rupture of membranes before 24 weeks of gestation
  19. Letter to the Editor
  20. Fundal pressure: risk factors in uterine rupture. The issue of liability: complication or malpractice?
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