Network analysis of drug effect on triglyceride-associated DNA methylation

Lim et al. BMC Proceedings 2018, 12(Suppl 9):27 https://doi.org/10.1186/s12919-018-0130-0 BMC Proceedings PROCEEDINGS Open Access Network analysis of drug effect on triglyceride-associated DNA methylation Elise Lim1*†, Hanfei Xu1†, Peitao Wu1, Daniel Posner1, Jiayi Wu2, Gina M. Peloso1, Achilleas N. Pitsillides1, Anita L. DeStefano1, L. Adrienne Cupples1 and Ching-Ti Liu1 From Genetic Analysis Workshop 20 San Diego, CA, USA. 4 - 8 March 2017 Abstract Background: DNA methylation, an epigenetic modification, can be affected by environmental factors and thus regulate gene expression levels that can lead to alterations of certain phenotypes. Network analysis has been used successfully to discover gene sets that are expressed differently across multiple disease states and suggest possible pathways of disease progression. We applied this framework to compare DNA methylation levels before and after lipid-lowering medication and to identify modules that differ topologically between the two time points, revealing the association between lipid medication and these triglyceride-related methylation sites. Methods: We performed quality control using beta-mixture quantile normalization on 463,995 cytosine-phosphateguanine (CpG) sites and deleted problematic sites, resulting in 423,004 probes. We identified 14,850 probes that were nominally associated with triglycerides prior to treatment and performed weighted gene correlation network analysis (WGCNA) to construct pre- and posttreatment methylation networks of these probes. We then applied both WGCNA module preservation and generalized Hamming distance (GHD) to identify modules with topological differences between the pre- and posttreatment. For modules with structural changes between 2 time points, we performed pathway-enrichment analysis to gain further insight into the biological function of the genes from these modules. Results: Six triglyceride-associated modules were identified using pretreatment methylation probes. The same 3 modules were not preserved in posttreatment data using both the module-preservation and the GHD methods. Topenriched pathways for the 3 differentially methylated modules are sphingolipid signaling pathway, proteoglycans in cancer, and metabolic pathways (p values < 0.005). One module in particular included an enrichment of lipid-related pathways among the top results. Conclusions: The same 3 modules, which were differentially methylated between pre- and posttreatment, were identified using both WGCNA module-preservation and GHD methods. Pathway analysis revealed that triglycerideassociated modules contain groups of genes that are involved in lipid signaling and metabolism. These 3 modules may provide insight into the effect of fenofibrate on changes in triglyceride levels and these methylation sites. * Correspondence: † Elise Lim and Hanfei Xu contributed equally to this work. 1 Department of Biostatistics, Boston University, 801 Massachusetts Avenue 3rd Floor, Boston, MA 02118, USA Full list of author information is available at the end of the article © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Lim et al. BMC Proceedings 2018, 12(Suppl 9):27 Background Epigenetic changes are biochemical modifications in chromosomes that do not alter the DNA sequence [1]. DNA methylation is one such epigenetic process implicated in human disease. DNA methylation involves the addition of a methyl group to DNA, which usually occurs at cytosine-phosphate-guanine (CpG) dinucleotides in the promoter region or within genes [1]. It is known to regulate gene expression levels by changing the chromatin structure, thereby preventing transcription factors from binding to the gene promoter, which can lead to alterations of phenotypes [2]. DNA methylation can be modulated by external factors, such as smoking or exposure to toxins [3]. As such, epigenetic information is considered to be fundamental in understanding the interaction between the human genome and the environment. Recent research has unveiled the potential involvement of DNA methylation on the regulation of fasting blood lipids [4–6]. One way to visualize interactions and changes in the DNA methylation profile is to construct methylation networks and compare their topology. Structural changes resulting from external stimuli can be detected with network comparison algorithms that can identify subnetworks that are either preserved or structurally different. Several papers have performed network-based methods to identify trait-related modules [7–9] and examined the preservation of such modules, either between different tissues or different data sets. For example, Horvath et al. [7] conducted weighted gene correlation network analysis (WGCNA) to examine the effect of aging on DNA methylation modules in humans and reported a robustly defined age-related comethylation module that is present in multiple human issues including blood and brain. Rickabaugh et al. [8] also found a preserved methylation module that was associated with age and HIV-1 status in 2 different HIV data sets via WGCNA. However, to our best knowledge, there are no previous studies using network-based approaches to identify triglyceride-associated modules and assess topological differences for each module between 2 time points. We investigated the topological differences of triglyceride-associated methylation networks constructed before and after the administration of fenofibrate. Methods Quality control Data from GAW20 was used and included epigenetic and pharmacogenomics data for 188 unique families [10]. A total of 995 individuals with pretreatment DNA methylation profiles at visit 2 and 530 individuals with posttreatment DNA methylation profiles at visit 4 (with 446 individuals overlapping during the 3-week treatment period) were used to construct, separately, a pre- and posttreatment network. Because of systemic differences in the range of Infinium I and Infinium II probe methylation Page 110 of 258 values, we performed beta-mixture quantile normalization (BMIQ) using the bmiq function from wateRmelon package in R on a total of 463,995 methylation probes to adjust the beta values of Type II probes to align with the distribution of Type I probes [11, 12]. Probes with single nucleotide polymorphisms (SNPs) under the actual CpG sites, SNPs at the nucleotide right next to a CpG site, or cross-reactive probes that have a target sequence simila (...truncated)