Integrative DNA methylation and gene expression analyses identify DNA packaging and epigenetic regulatory genes associated with low motility sperm
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE26982 | GEO | 2011/06/01
SECONDARY ACCESSION(S): PRJNA135929
REPOSITORIES: GEO
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Project description:In previous studies using candidate gene approaches, low sperm count (oligospermia) has been associated with altered sperm mRNA content and DNA methylation in both imprinted and non-imprinted genes. We performed a genome-wide analysis of sperm DNA methylation and mRNA content to test for associations with sperm function.Sperm DNA and mRNA were isolated from 21 men with a range of semen parameters presenting to a tertiary male reproductive health clinic. DNA methylation was measured with the Illumina Infinium array at 27,578 CpG loci. Unsupervised clustering of methylation data differentiated the 21 sperm samples by their motility values. Recursively partitioned mixture modeling (RPMM) of methylation data resulted in four distinct methylation profiles that were significantly associated with sperm motility (P?=?0.01). Linear models of microarray analysis (LIMMA) was performed based on motility and identified 9,189 CpG loci with significantly altered methylation (Q<0.05) in the low motility samples. In addition, the majority of these disrupted CpG loci (80%) were hypomethylated. Of the aberrantly methylated CpGs, 194 were associated with imprinted genes and were almost equally distributed into hypermethylated (predominantly paternally expressed) and hypomethylated (predominantly maternally expressed) groups. Sperm mRNA was measured with the Human Gene 1.0 ST Affymetrix GeneChip Array. LIMMA analysis identified 20 candidate transcripts as differentially present in low motility sperm, including HDAC1 (NCBI 3065), SIRT3 (NCBI 23410), and DNMT3A (NCBI 1788). There was a trend among altered expression of these epigenetic regulatory genes and RPMM DNA methylation class.Using integrative genome-wide approaches we identified CpG methylation profiles and mRNA alterations associated with low sperm motility.
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2011-06-01 | E-GEOD-26982 | biostudies-arrayexpress
Project description:DNA methylation is an epigenetic modification modulating the structure of DNA molecule and the interactions with its binding proteins. Accumulating large-scale methylation data motivates the development of analytic tools to facilitate methylome data mining. One critical phenomenon associated with dynamic DNA methylation is the altered DNA binding affinity of transcription factors, which plays key roles in gene expression regulation. In this study, we conceived an algorithm to predict epigenetic regulatory modules through recursive motif analyses on differentially methylated loci. A two-step procedure was implemented to first group differentially methylated loci into clusters according to their correlations in methylation profiles and then to repeatedly identify the transcription factor binding motifs significantly enriched in each cluster. We applied this tool on methylome datasets generated for mouse brains which have a lack of DNA demethylation enzymes TET1 or TET2. Compared with wild type control, the differentially methylated CpG sites identified in TET1 knockout mouse brains differed significantly from those determined for TET2 knockout. Transcription factors with zinc finger DNA binding domains including Egr1, Zic3, and Zeb1 were predicted to be associated with TET1 mediated brain methylome programming, while Lhx family members with Homeobox domains were predicted to be associated with TET2 function. Interestingly, genomic loci from a co-methylated cluster often host motifs for transcription factors sharing the same DNA binding domains. Altogether, our study provided a systematic approach for epigenetic regulatory module identification and will help throw light on the interplay of DNA methylation and transcription factors.
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Project description:How do epigenetic modifications change across species and how do these modifications affect evolution? These are fundamental questions at the forefront of our evolutionary epigenomic understanding. Our previous work investigated human and chimpanzee brain methylomes, but it was limited by the lack of outgroup data which is critical for comparative (epi)genomic studies. Here, we compared whole genome DNA methylation maps from brains of humans, chimpanzees and also rhesus macaques (outgroup) to elucidate DNA methylation changes during human brain evolution. Moreover, we validated that our approach is highly robust by further examining 38 human-specific DMRs using targeted deep genomic and bisulfite sequencing in an independent panel of 37 individuals from five primate species. Our unbiased genome-scan identified human brain differentially methylated regions (DMRs), irrespective of their associations with annotated genes. Remarkably, over half of the newly identified DMRs locate in intergenic regions or gene bodies. Nevertheless, their regulatory potential is on par with those of promoter DMRs. An intriguing observation is that DMRs are enriched in active chromatin loops, suggesting human-specific evolutionary remodeling at a higher-order chromatin structure. These findings indicate that there is substantial reprogramming of epigenomic landscapes during human brain evolution involving noncoding regions.
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Project description:Glioblastoma (GBM) ranks the most common and aggressive primary brain malignant tumor worldwide. However, the survival rates of patients remain very poor. Therefore, molecular oncology of GBM are urgently needed. In this study, we performed an integrative analysis of DNA methylation and gene expression to identify key epigenetic genes in GBM. The methylation and gene expression of GBM patients in The Cancer Genome Atlas (TCGA) database were downloaded. After data preprocessing, we identified 4,881 differentially expressed genes (DEGs) between tumor and normal samples, including 1,111 upregulated and 3,770 downregulated genes. Then, we randomly separated all samples into training set (n = 69) and testing set (n = 69). We next obtained 11,269 survival-methylation sites by univariate and multivariate Cox regression analyses. In the correlation analysis, we defined 198 low promoter methylation with high gene expression as epigenetically induced (EI) genes and 111 high promoter methylation with low gene expression as epigenetically suppressed (ES) genes. Key markers including C1orf61 and FAM50B were selected with a Pearson correlation coefficient greater than 0.75. Further, we chose the 20 CpG methylation sites of above two genes in unsupervised clustering analysis using the Euclidean distance. We found that the prognosis of the hypomethylated group was significantly better than that in the hypermethylated group (log-rank test p-value = 0.011). Based on the validation in the TCGA testing set and GEO dataset, we validated the prognostic value of our signature (p-value = 0.02 in TCGA and 0.012 in GEO). In conclusion, our findings provided predictive and prognostic value as methylation-based biomarkers for the diagnosis and treatment of GBM.
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Project description:Sperm is one of the most morphologically diverse cell types in nature, yet they also exhibit remarkable behavioural variation, including the formation of collective groups of cells that swim together for motility or transport through the female reproductive tract. Here, we take advantage of natural variation in sperm traits observed across Peromyscus mice to test the hypothesis that the morphology of the sperm head influences their sperm aggregation behaviour. Using both manual and automated morphometric approaches to quantify their complex shapes, and then statistical modelling and machine learning to analyse their features, we show that the aspect ratio of the sperm head is the most distinguishing morphological trait and statistically associates with collective sperm movements obtained from in vitro observations. We then successfully use neural network analysis to predict the size of sperm aggregates from sperm head morphology and show that species with relatively wider sperm heads form larger aggregates, which is consistent with the theoretical prediction that an adhesive region around the equatorial region of the sperm head mediates these unique gametic interactions. Together these findings advance our understanding of how even subtle variation in sperm design can drive differences in sperm function and performance.
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Project description:AimOsteoarthritis (OA) is caused by complex interactions between genetic and environmental factors. Epigenetic mechanisms control the expression of genes and are likely to regulate the OA transcriptome. We performed integrative genomic analyses to define methylation-gene expression relationships in osteoarthritic cartilage.Patients and methodsGenome-wide DNA methylation profiling of articular cartilage from five patients with OA of the knee and five healthy controls was conducted using the Illumina Infinium HumanMethylation450 BeadChip (Illumina, San Diego, California). Other independent genome-wide mRNA expression profiles of articular cartilage from three patients with OA and three healthy controls were obtained from the Gene Expression Omnibus (GEO) database. Integrative pathway enrichment analysis of DNA methylation and mRNA expression profiles was performed using integrated analysis of cross-platform microarray and pathway software. Gene ontology (GO) analysis was conducted using the Database for Annotation, Visualization and Integrated Discovery (DAVID).ResultsWe identified 1265 differentially methylated genes, of which 145 are associated with significant changes in gene expression, such as DLX5, NCOR2 and AXIN2 (all p-values of both DNA methylation and mRNA expression < 0.05). Pathway enrichment analysis identified 26 OA-associated pathways, such as mitogen-activated protein kinase (MAPK) signalling pathway (p = 6.25 × 10-4), phosphatidylinositol (PI) signalling system (p = 4.38 × 10-3), hypoxia-inducible factor 1 (HIF-1) signalling pathway (p = 8.63 × 10-3 pantothenate and coenzyme A (CoA) biosynthesis (p = 0.017), ErbB signalling pathway (p = 0.024), inositol phosphate (IP) metabolism (p = 0.025), and calcium signalling pathway (p = 0.032).ConclusionWe identified a group of genes and biological pathwayswhich were significantly different in both DNA methylation and mRNA expression profiles between patients with OA and controls. These results may provide new clues for clarifying the mechanisms involved in the development of OA.Cite this article: A. He, Y. Ning, Y. Wen, Y. Cai, K. Xu, Y. Cai, J. Han, L. Liu, Y. Du, X. Liang, P. Li, Q. Fan, J. Hao, X. Wang, X. Guo, T. Ma, F. Zhang. Use of integrative epigenetic and mRNA expression analyses to identify significantly changed genes and functional pathways in osteoarthritic cartilage. Bone Joint Res 2018;7:343-350. DOI: 10.1302/2046-3758.75.BJR-2017-0284.R1.
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Project description:Aberrant DNA methylation profiles have been associated with male infertility and some semen parameters. However, few studies systematically surveyed DNA methylation profiles associated with sperm motility in normozoospermia and asthenozoospermia. In this study, based on promoter targeted bisulfite sequencing technology, we provided a quantitative description on global DNA methylation profiles. The average global methylation values were 24.7% and the inter-individual variance was about 14.4%, while the intra-individual variance was about 3.9%. The difference between different motile sperm population or different participant groups was subtle and not significant. Furthermore, we identified 134 differentially methylated CpGs and 134 differentially variable CpGs in low motile sperm from asthenozoospermic patient (P < 0.05). Based on the literature, we further found 16 differentially methylated or variable genes which were required for spermatogenesis or sperm motility or dominantly expressed in testis. This study will provide potential markers for clinical diagnosis and a promising basis for understanding the effect of DNA methylation on asthenozoospermia.
2015-10-01 | GSE68971 | GEO
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