Genome-wide DNA methylation and gene expression analyses in monozygotic twins identify potential biomarkers of depression

AbstractDepression is currently the leading cause of disability around the world. We conducted an epigenome-wide association study (EWAS) in a sample of 58 depression score-discordant monozygotic twin pairs, aiming to detect specific epigenetic variants potentially related to depression and further integrate with gene expression profile data. Association between the methylation level of each CpG site and depression score was tested by applying a linear mixed effect model. Weighted gene co-expression network analysis (WGCNA) was performed for gene expression data. The association of DNA methylation levels of 66 CpG sites with depression score reached the level of P < 1 × 10−4. These top CpG sites were located at 34 genes, especially PTPRN2, HES5, GATA2, PRDM7, and KCNIP1. Many ontology enrichments were highlighted, including Notch signaling pathway, Huntington disease, p53 pathway by glucose deprivation, hedgehog signaling pathway, DNA binding, and nucleic acid metabolic process. We detected 19 differentially methylated regions (DMRs), some of which were located at GRIK2, DGKA, and NIPA2. While integrating with gene expression data, HELZ2, PTPRN2, GATA2, and ZNF624 were differentially expressed. In WGCNA, one specific module was positively correlated with depression score (r = 0.62, P = 0.002). Some common genes (including BMP2, PRDM7, KCNIP1, and GRIK2) and enrichment terms (including complement and coagulation cascades pathway, DNA binding, neuron fate specification, glial cell differentiation, and thyroid gland development) were both identified in methylation analysis and WGCNA. Our study identifies specific epigenetic variations which are significantly involved in regions, functional genes, biological function, and pathways that mediate depression disorder.

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Genome-wide DNA methylation analysis of pulmonary function in middle and old-aged Chinese monozygotic twins

Respiratory Research ◽

10.1186/s12931-021-01896-5 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Tong Wang ◽

Weijing Wang ◽

Weilong Li ◽

Haiping Duan ◽

Chunsheng Xu ◽

...

Keyword(s):

Gene Expression ◽

Dna Methylation ◽

Pulmonary Function ◽

Monozygotic Twins ◽

Linear Mixed Effect Model ◽

Sequencing Analysis ◽

Mixed Effect ◽

Cpg Sites ◽

Genome Wide ◽

Genomic Regions

Abstract Background Previous studies have determined the epigenetic association between DNA methylation and pulmonary function among various ethnics, whereas this association is largely unknown in Chinese adults. Thus, we aimed to explore epigenetic relationships between genome-wide DNA methylation levels and pulmonary function among middle-aged Chinese monozygotic twins. Methods The monozygotic twin sample was drawn from the Qingdao Twin Registry. Pulmonary function was measured by three parameters including forced expiratory volume the first second (FEV1), forced vital capacity (FVC), and FEV1/FVC ratio. Linear mixed effect model was used to regress the methylation level of CpG sites on pulmonary function. After that, we applied Genomic Regions Enrichment of Annotations Tool (GREAT) to predict the genomic regions enrichment, and used comb-p python library to detect differentially methylated regions (DMRs). Gene expression analysis was conducted to validate the results of differentially methylated analyses. Results We identified 112 CpG sites with the level of P < 1 × 10–4 which were annotated to 40 genes. We identified 12 common enriched pathways of three pulmonary function parameters. We detected 39 DMRs located at 23 genes, of which PRDM1 was related to decreased pulmonary function, and MPL, LTB4R2, and EPHB3 were related to increased pulmonary function. The gene expression analyses validated DIP2C, ASB2, SLC6A5, and GAS6 related to decreased pulmonary function. Conclusion Our DNA methylation sequencing analysis on identical twins provides new references for the epigenetic regulation on pulmonary function. Several CpG sites, genes, biological pathways and DMRs are considered as possible crucial to pulmonary function.

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A Linear Regression and Deep Learning Approach for Detecting Reliable Genetic Alterations in Cancer Using DNA Methylation and Gene Expression Data

Genes ◽

10.3390/genes11080931 ◽

2020 ◽

Vol 11 (8) ◽

pp. 931 ◽

Cited By ~ 1

Author(s):

Saurav Mallik ◽

Soumita Seth ◽

Tapas Bhadra ◽

Zhongming Zhao

Keyword(s):

Gene Expression ◽

Cervical Cancer ◽

Dna Methylation ◽

Deep Learning ◽

Linear Regression ◽

Differential Expression ◽

Gene Expression Data ◽

Uterine Cervical Cancer ◽

Expression Data ◽

Cpg Sites

DNA methylation change has been useful for cancer biomarker discovery, classification, and potential treatment development. So far, existing methods use either differentially methylated CpG sites or combined CpG sites, namely differentially methylated regions, that can be mapped to genes. However, such methylation signal mapping has limitations. To address these limitations, in this study, we introduced a combinatorial framework using linear regression, differential expression, deep learning method for accurate biological interpretation of DNA methylation through integrating DNA methylation data and corresponding TCGA gene expression data. We demonstrated it for uterine cervical cancer. First, we pre-filtered outliers from the data set and then determined the predicted gene expression value from the pre-filtered methylation data through linear regression. We identified differentially expressed genes (DEGs) by Empirical Bayes test using Limma. Then we applied a deep learning method, “nnet” to classify the cervical cancer label of those DEGs to determine all classification metrics including accuracy and area under curve (AUC) through 10-fold cross validation. We applied our approach to uterine cervical cancer DNA methylation dataset (NCBI accession ID: GSE30760, 27,578 features covering 63 tumor and 152 matched normal samples). After linear regression and differential expression analysis, we obtained 6287 DEGs with false discovery rate (FDR) <0.001. After performing deep learning analysis, we obtained average classification accuracy 90.69% (±1.97%) of the uterine cervical cancerous labels. This performance is better than that of other peer methods. We performed in-degree and out-degree hub gene network analysis using Cytoscape. We reported five top in-degree genes (PAIP2, GRWD1, VPS4B, CRADD and LLPH) and five top out-degree genes (MRPL35, FAM177A1, STAT4, ASPSCR1 and FABP7). After that, we performed KEGG pathway and Gene Ontology enrichment analysis of DEGs using tool WebGestalt(WEB-based Gene SeT AnaLysis Toolkit). In summary, our proposed framework that integrated linear regression, differential expression, deep learning provides a robust approach to better interpret DNA methylation analysis and gene expression data in disease study.

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