Search

Abstract Ovarian cancer, among all gynecologic malignancies, exhibits the highest incidence and mortality rate, primarily because it is often presents with non-specific or no symptoms during its early stages. For the advancement of Ovarian Cancer Diagnosis, it is crucial to identify the potential molecular signatures that could significantly differentiate between healthy and ovarian cancerous tissues and can be used further as a diagnostic biomarker for detecting ovarian cancer. In this study, we investigated the genome-wide methylation patterns in ovarian cancer patients using Methylated DNA Immunoprecipitation (MeDIP-Seq) followed by NGS. Identified differentially methylated regions (DMRs) were further validated by targeted bisulfite sequencing for CpG site-specific methylation profiles. Furthermore, expression validation of six genes by Quantitative Reverse Transcriptase-PCR was also performed. Out of total 120 differentially methylated genes (DMGs), 68 genes were hypermethylated, and 52 were hypomethylated in their promoter region. After analysis, we identified the top 6 hub genes, namely POLR3B, PLXND1, GIGYF2, STK4, BMP2 and CRKL. Interestingly we observed Non-CpG site methylation in the case of POLR3B and CRKL which was statistically significant in discriminating ovarian cancer samples from normal controls. The most significant pathways identified were focal adhesion, the MAPK signaling pathway, and the Ras signaling pathway. Expression analysis of hypermethylated genes was correlated with the downregulation of the genes. POLR3B and GIGYF2 turned out to be the novel genes associated with the carcinogenesis of EOC. Our study demonstrated that methylation profiling through MeDIP-sequencing has effectively identified six potential hub genes and pathways that might exacerbate our understanding of underlying molecular mechanisms of ovarian carcinogenesis.

Abstract Background Colorectal cancer is the most common malignancy and the third leading cause of cancer-related death worldwide. This study aimed to identify potential diagnostic biomarkers for colorectal cancer by genome-wide plasma cell-free DNA (cfDNA) methylation analysis. Methods Peripheral blood from colorectal cancer patients and healthy controls was collected for cfDNA extraction. Genome-wide cfDNA methylation profiling, especially differential methylation profiling between colorectal cancer patients and healthy controls, was performed by methylated DNA immunoprecipitation coupled with high-throughput sequencing (MeDIP-seq). Logistic regression models were established, and the accuracy of this diagnostic model for colorectal cancer was verified using tissue-sourced data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) due to the lack of cfDNA methylation data in public datasets. Results Compared with the control group, 939 differentially methylated regions (DMRs) located in promoter regions were found in colorectal cancer patients; 16 of these DMRs were hypermethylated, and the remaining 923 were hypomethylated. In addition, these hypermethylated genes, mainly PRDM14, RALYL, ELMOD1, and TMEM132E, were validated and confirmed in colorectal cancer by using publicly available DNA methylation data. Conclusions MeDIP-seq can be used as an optimal approach for analyzing cfDNA methylomes, and 12 probes of four differentially methylated genes identified by MeDIP-seq (PRDM14, RALYL, ELMOD1, and TMEM132E) could serve as potential biomarkers for clinical application in patients with colorectal cancer.

Abstract Background Tibetan pigs, which inhabit the Tibetan Plateau, exhibit distinct phenotypic and physiological characteristics from those of lowland pigs and have adapted well to the extreme conditions at high altitude. However, the genetic and epigenetic mechanisms of hypoxic adaptation in animals remain unclear. Methods Whole-genome DNA methylation data were generated for heart tissues of Tibetan pigs grown in the highland (TH, n = 4) and lowland (TL, n = 4), as well as Yorkshire pigs grown in the highland (YH, n = 4) and lowland (YL, n = 4), using methylated DNA immunoprecipitation sequencing. Results We obtained 480 million reads and detected 280679, 287224, 259066, and 332078 methylation enrichment peaks in TH, YH, TL, and YL, respectively. Pairwise TH vs. YH, TL vs. YL, TH vs. TL, and YH vs. YL comparisons revealed 6829, 11997, 2828, and 1286 differentially methylated regions (DMRs), respectively. These DMRs contained 384, 619, 192, and 92 differentially methylated genes (DMGs), respectively. DMGs that were enriched in the hypoxia-inducible factor 1 signaling pathway and pathways involved in cancer and hypoxia-related processes were considered to be important candidate genes for high-altitude adaptation in Tibetan pigs. Conclusions This study elucidates the molecular and epigenetic mechanisms involved in hypoxic adaptation in pigs and may help further understand human hypoxia-related diseases.

Abstract Background DNA methylation of CpG dinucleotides is an essential epigenetic modification that plays a key role in transcription. Widely used DNA enrichment-based methods offer high coverage for measuring methylated CpG dinucleotides, with the lowest cost per CpG covered genome-wide. However, these methods measure the DNA enrichment of methyl-CpG binding, and thus do not provide information on absolute methylation levels. Further, the enrichment is influenced by various confounding factors in addition to methylation status, for example, CpG density. Computational models that can accurately derive absolute methylation levels from DNA enrichment data are needed. Results We developed “MeDEStrand,” a method that uses a sigmoid function to estimate and correct the CpG bias from enrichment results to infer absolute DNA methylation levels. Unlike previous methods, which estimate CpG bias based on reads mapped at the same genomic loci, MeDEStrand processes the reads for the positive and negative DNA strands separately. We compared the performance of MeDEStrand to that of three other state-of-the-art methods “MEDIPS,” “BayMeth,” and “QSEA” on four independent datasets generated using immortalized cell lines (GM12878 and K562) and human primary cells (foreskin fibroblasts and mammary epithelial cells). Based on the comparison of the inferred absolute methylation levels from MeDIP-seq data and the corresponding reduced-representation bisulfite sequencing data from each method, MeDEStrand showed the best performance at high resolution of 25, 50, and 100 base pairs. Conclusions The MeDEStrand tool can be used to infer whole-genome absolute DNA methylation levels at the same cost of enrichment-based methods with adequate accuracy and resolution. R package MeDEStrand and its tutorial is freely available for download at https://github.com/jxu1234/MeDEStrand.git.

Abstract Background Milk yield for Holstein cows has doubled over five decades due to genetic selection and changes to management, but the molecular mechanisms that facilitated this increase are mostly unknown. Epigenetic modifications to the cattle genome are a plausible molecular mechanism to cause variation in milk yield and our objective was to describe genome-wide DNA methylation patterns in peripheral blood mononuclear cells (PBMC) from mature Holstein dairy cows with variable milk yield. Results Whole genome MeDIP-seq was performed following DNA extraction from PBMC of 6 lactating dairy cows from 4 different herds that varied in milk yield from 13,556 kg to 23,105 kg per 305 day lactation. We describe methylation across the genome and for 13,677 protein coding genes. Repetitive element reads were primarily mapped to satellite (36.4%), SINE (29.1%), and LINE (23.7%) regions and the majority (78.4%) of CpG sites were sequenced at least once. DNA methylation was generally low upstream of genes with the nadir occurring 95 bp prior to the transcription start site (TSS). Methylation was lower in the first exon than in later exons, was highest for introns near the intron-exon junctions, and declined downstream as the distance from the gene increased. We identified 72 differentially methylated regions (DMR) between high milk yield cows and their control, and 252 DMR across herd environments. Conclusions This reference methylome for cattle with extreme variation in milk yield phenotype provides a resource to more fully evaluate relationships between DNA methylation and phenotype in populations subject to selection. The detection of DMR in cows of varying milk yield suggests potential to exploit epigenetic variation in cattle improvement programs.

Abstract Purpose We investigated the genomic DNA methylation profile of prostate cancer in transgenic adenocarcinoma of the mouse prostate (TRAMP) cancer model and to analyze the crosstalk among targeted genes and the related functional pathways. Methods Prostate DNA samples from 24-week-old TRAMP and C57BL/6 male mice were isolated. The DNA methylation profiles were analyzed by methylated DNA immunoprecipitation (MeDIP) followed by next-generation sequencing (MeDIP-seq). Canonical pathways, diseases and function and network analyses of the different samples were then performed using the Ingenuity® Pathway Analysis (IPA) software. Some target genes with significant difference in methylation were selected for validation using methylation specific primers (MSP) and qPCR. Results TRAMP mice undergo extensive aberrant CpG hyper- and hypo-methylation. There were 2147 genes with a significant (log2-change ≥ 2) change in CpG methylation between the two groups, as mapped by the IPA software. Among these genes, the methylation of 1105 and 1042 genes was significantly decreased and increased, respectively, in TRAMP prostate tumors. The top associated disease identified by IPA was adenocarcinoma; however, the cAMP response element-binding protein (CREB)-, histone deacetylase 2 (HDAC2)-, glutathione S-transferase pi (GSTP1)- and polyubiquitin-C (UBC)-related pathways showed significantly altered methylation profiles based on the canonical pathway and network analyses. MSP and qPCR results of genes of interests corroborated with MeDIP-seq findings. Conclusions This is the first MeDIP-seq with IPA analysis of the TRAMP model to provide novel insight into the genome-wide methylation profile of prostate cancer. Studies on epigenetics, such as DNA methylation, will potentially provide novel avenues and strategies for further development of biomarkers targeted for treatment and prevention approaches for prostate cancer.

Abstract Background Switchgrass is a crop with many desirable traits for bioenergy production. Plant genomes have high DNA methylation levels throughout genes and transposable elements and DNA methylation is known to play a role in silencing transposable elements. Here we analyzed methylomes in two switchgrass genotypes AP13 and VS16. AP13 is derived from a lowland ecotype and VS16, typically considered drought-tolerant, is derived from an upland ecotype, both genotypes are tetraploid (2n = 4× = 36). Results Methylated DNA immunoprecipitation-sequencing (MeDIP-seq) and bisulfite-sequencing (BS-seq) were used to profile DNA methylation in genomic features of AP13 and VS16. The methylation patterns in genes and transposable elements were similar to other plants, however, overall CHH methylation levels were comparatively low. Differentially methylated regions (DMRs) were assessed and a total of 1777 CG-DMRs, 573 CHG-DMRs, and 3 CHH-DMRs were detected between the two genotypes. TEs and their flanking regions were higher than that of genic regions. Different types of TEs had different methylation patterns, but the two LTRs (Copia and Gypsy) were similarly methylated, while LINEs and DNA transposons typically had different methylation patterns. MeDIP-seq data was compared to BS-seq data and most of the peaks generated by MeDIP-seq were confirmed to be highly methylated by BS-seq. Conclusions DNA methylation in switchgrass genotypes obtained from the two ecotypes were found similar. Collinear gene pairs in two subgenomes (A and B) were not significantly differentially methylated. Both BS-seq and MeDIP-seq methodologies were found effective. Methylation levels were highest at CG and least in CHH. Increased DNA methylation was seen in TEs compared to genic regions. Exploitation of TE methylations can be a viable option in future crop improvement.