Suresh K Bhavnani1, Bryant Dang2, Varun Kilaru3, Maria Caro2, Shyam Visweswaran4, George Saade5, Alicia K Smith3, Ramkumar Menon6. 1. Institute for Translational Sciences, University of Texas Medical Branch, 301 University Blvd, 6.168 Research Building 6, Galveston, TX, USA. 2. Institute for Translational Sciences, University of Texas Medical Branch, Galveston, TX, USA. 3. Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, Georgia. 4. Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA. 5. Department of Obstetrics and Gynecology, Division of Maternal Fetal-Medicine Perinatal Research, University of Texas Medical Branch, Galveston, TX, USA. 6. 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.
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.
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.
Authors: Suresh K Bhavnani; Sundar Victor; William J Calhoun; William W Busse; Eugene Bleecker; Mario Castro; Hyunsu Ju; Regina Pillai; Numan Oezguen; Gowtham Bellala; Allan R Brasier Journal: J Biomed Inform Date: 2011-10-01 Impact factor: 6.317
Authors: Stephen J Fortunato; Ramkumar Menon; Digna R Velez; Poul Thorsen; Scott M Williams Journal: Am J Obstet Gynecol Date: 2008-06 Impact factor: 8.661