Bhavnani Suresh K, Dang Bryant, Kilaru Varun, Caro Maria, Visweswaran Shyam, Saade George, Smith Alicia K, Menon Ramkumar
Institute for Translational Sciences, University of Texas Medical Branch, 301 University Blvd, 6.168 Research Building 6, Galveston, TX, USA.
Institute for Translational Sciences, University of Texas Medical Branch, Galveston, TX, USA.
J Perinat Med. 2018 Jul 26;46(5):509-521. doi: 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.
近期研究表明，表观遗传差异会增加自发性早产（PTB）的风险。然而，对于这些表观遗传差异背后的异质性知之甚少，而异质性可能会引出特定患者亚组中生物学途径的假设以及对精准医学至关重要的相应靶向干预措施。通过对胎儿DNA甲基化数据进行二分网络分析，我们展示了一种用于PTB分类的新方法。
数据包括来自50名非裔美国受试者脐带血中全基因组的DNA甲基化（HumanMethylation450 BeadChip），其中包括22例早期自发性PTB（妊娠24 - 34周）病例和28名对照（妊娠>39周）。这些数据通过以下方法进行分析：（1）一种监督方法，用于选择前10个显著的甲基化位点；（2）无监督的“受试者 - 变量”二分网络，以可视化和定量分析这10个甲基化位点在所有受试者中以及仅在病例中的共现情况，目的是分析亚组及其潜在途径；（3）简单线性回归，以测试病例中的总甲基化与胎龄之间是否存在关联。
对所有受试者和显著甲基化位点的二分网络分析揭示了具有统计学意义的聚类，在病例富集亚组和对照富集亚组之间的甲基化谱中存在反对称关系：前者在七个甲基化位点上主要为高甲基化，在三个甲基化位点上为低甲基化，而后者在上述七个甲基化位点上主要为低甲基化，在三个甲基化位点上为高甲基化。此外，仅对病例的分析揭示了一个亚组在七个甲基化位点上主要为低甲基化，另一个亚组在所有甲基化位点上均为低甲基化，这表明PTB病理生理学中存在异质性。最后，分析发现总甲基化与胎龄之间存在强烈的负线性关系，这表明甲基化差异可作为妊娠长度的预测标志物。
结果表明无监督二分网络有助于识别与患者亚组相关的复杂但可理解的数据驱动假设及其潜在途径的推断，因此是当前使用的监督方法的有效补充。
