scholarly journals Human methylome variation across Infinium 450K data on the Gene Expression Omnibus

2020 ◽  
Author(s):  
Sean K. Maden ◽  
Reid F. Thompson ◽  
Kasper D. Hansen ◽  
Abhinav Nellore
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Cancer Patients ◽  
Gene Expression Omnibus ◽  
Controlled Vocabulary ◽  
Epigenetic Mark ◽  
Regular Expressions ◽  
Array Data ◽  
Epigenetic Age ◽  
Quality Assessments

AbstractWhile DNA methylation (DNAm) is the most-studied epigenetic mark, few recent studies probe the breadth of publicly available DNAm array samples. We collectively analyzed 35,360 Illumina Infinium HumanMethylation450K DNAm array samples published on the Gene Expression Omnibus (GEO). We learned a controlled vocabulary of sample labels by applying regular expressions to metadata and used existing models to predict various sample properties including epigenetic age. We found approximately two-thirds of samples were from blood, one-quarter were from brain, and one-third were from cancer patients. 19% of samples failed at least one of Illumina’s 17 prescribed quality assessments; signal distributions across samples suggest modifying manufacturer-recommended thresholds for failure would make these assessments more informative. We further analyzed DNAm variances in seven tissues (adipose, nasal, blood, brain, buccal, sperm, and liver) and characterized specific probes distinguishing them. Finally, we compiled DNAm array data and metadata, including our learned and predicted sample labels, into database files accessible via the recountmethylation R/Bioconductor companion package. Its vignettes walk the user through some analyses contained in this paper.

scholarly journals Human methylome variation across Infinium 450K data on the Gene Expression Omnibus

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Sean K Maden ◽  
Reid F Thompson ◽  
Kasper D Hansen ◽  
Abhinav Nellore
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Cancer Patients ◽  
Gene Expression Omnibus ◽  
Controlled Vocabulary ◽  
Epigenetic Mark ◽  
Regular Expressions ◽  
Array Data ◽  
Epigenetic Age ◽  
Quality Assessments

Abstract While DNA methylation (DNAm) is the most-studied epigenetic mark, few recent studies probe the breadth of publicly available DNAm array samples. We collectively analyzed 35 360 Illumina Infinium HumanMethylation450K DNAm array samples published on the Gene Expression Omnibus. We learned a controlled vocabulary of sample labels by applying regular expressions to metadata and used existing models to predict various sample properties including epigenetic age. We found approximately two-thirds of samples were from blood, one-quarter were from brain and one-third were from cancer patients. About 19% of samples failed at least one of Illumina’s 17 prescribed quality assessments; signal distributions across samples suggest modifying manufacturer-recommended thresholds for failure would make these assessments more informative. We further analyzed DNAm variances in seven tissues (adipose, nasal, blood, brain, buccal, sperm and liver) and characterized specific probes distinguishing them. Finally, we compiled DNAm array data and metadata, including our learned and predicted sample labels, into database files accessible via the recountmethylation R/Bioconductor companion package. Its vignettes walk the user through some analyses contained in this paper.

Abstract 15: Glucose-Mediated Remodeling of Cardiac DNA Methylation

2017 ◽  
Vol 121 (suppl_1) ◽  
Author(s):  
Mark E Pepin ◽  
David K Crossman ◽  
Joseph P Barchue ◽  
Salpy V Pamboukian ◽  
Steven M Pogwizd ◽  
...  
Keyword(s):  
Gene Expression ◽  
Heart Failure ◽  
Dna Methylation ◽  
Diabetic Cardiomyopathy ◽  
Left Ventricular ◽  
Epigenetic Mark ◽  
Gene Transcript ◽  
Transcript Levels ◽  
Glycemic Memory

To identify the role of glucose in the development of diabetic cardiomyopathy, we had directly assessed glucose delivery to the intact heart on alterations of DNA methylation and gene expression using both an inducible heart-specific transgene (glucose transporter 4; mG4H) and streptozotocin-induced diabetes (STZ) mouse models. We aimed to determine whether long-lasting diabetic complications arise from prior transient exposure to hyperglycemia via a process termed “glycemic memory.” We had identified DNA methylation changes associated with significant gene expression regulation. Comparing our results from STZ, mG4H, and the modifications which persist following transgene silencing, we now provide evidence for cardiac DNA methylation as a persistent epigenetic mark contributing to glycemic memory. To begin to determine which changes contribute to human heart failure, we measured both RNA transcript levels and whole-genome DNA methylation in heart failure biopsy samples (n = 12) from male patients collected at left ventricular assist device placement using RNA-sequencing and Methylation450 assay, respectively. We hypothesized that epigenetic changes such as DNA methylation distinguish between heart failure etiologies. Our findings demonstrated that type 2 diabetic heart failure patients (n = 6) had an overall signature of hypomethylation, whereas patients listed as ischemic (n = 5) had a distinct hypermethylation signature for regulated transcripts. The focus of this initial analysis was on promoter-associated CpG islands with inverse changes in gene transcript levels, from which diabetes (14 genes; e.g. IGFBP4) and ischemic (12 genes; e.g. PFKFB3) specific targets emerged with significant regulation of both measures. By combining our mouse and human molecular analyses, we provide evidence that diabetes mellitus governs direct regulation of cellular function by DNA methylation and the corresponding gene expression in diabetic mouse and human hearts. Importantly, many of the changes seen in either mouse type 1 diabetes or human type 2 diabetes were similar supporting a consistent mechanism of regulation. These studies are some of the first steps at defining mechanisms of epigenetic regulation in diabetic cardiomyopathy.

scholarly journals High Moesin Expression Is a Predictor of Poor Prognosis of Breast Cancer: Evidence From a Systematic Review With Meta-Analysis

2021 ◽  
Vol 11 ◽  
Author(s):  
Xiaoli Hu ◽  
Yang Liu ◽  
Zhitong Bing ◽  
Qian Ye ◽  
Chengcheng Li
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Systematic Review ◽  
Cancer Patients ◽  
Poor Prognosis ◽  
Data Extraction ◽  
Meta Analysis ◽  
Gene Expression Omnibus ◽  
Breast Cancer Patients ◽  
Node Metastases

Owing to metastases and drug resistance, the prognosis of breast cancer is still dismal. Therefore, it is necessary to find new prognostic markers to improve the efficacy of breast cancer treatment. Literature shows a controversy between moesin (MSN) expression and prognosis in breast cancer. Here, we aimed to conduct a systematic review and meta-analysis to evaluate the prognostic relationship between MSN and breast cancer. Literature retrieval was conducted in the following databases: PubMed, Web of Science, Embase, and Cochrane. Two reviewers independently performed the screening of studies and data extraction. The Gene Expression Omnibus (GEO) database including both breast cancer gene expression and follow-up datasets was selected to verify literature results. The R software was employed for the meta-analysis. A total of 9 articles with 3,039 patients and 16 datasets with 2,916 patients were ultimately included. Results indicated that there was a significant relationship between MSN and lymph node metastases (P < 0.05), and high MSN expression was associated with poor outcome of breast cancer patients (HR = 1.99; 95% CI 1.73–2.24). In summary, there is available evidence to support that high MSN expression has valuable importance for the poor prognosis in breast cancer patients.Systematic Review Registrationhttps://inplasy.com/inplasy-2020-8-0039/.

scholarly journals Gene activation precedes DNA demethylation in response to infection in human dendritic cells

2019 ◽  
Vol 116 (14) ◽  
pp. 6938-6943 ◽  
Author(s):  
Alain Pacis ◽  
Florence Mailhot-Léonard ◽  
Ludovic Tailleux ◽  
Haley E. Randolph ◽  
Vania Yotova ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Dendritic Cells ◽  
Transcriptional Activation ◽  
Time Course ◽  
Gene Activation ◽  
Dna Demethylation ◽  
Epigenetic Mark ◽  
Distal Enhancer ◽  
Human Dendritic Cells

DNA methylation is considered to be a relatively stable epigenetic mark. However, a growing body of evidence indicates that DNA methylation levels can change rapidly; for example, in innate immune cells facing an infectious agent. Nevertheless, the causal relationship between changes in DNA methylation and gene expression during infection remains to be elucidated. Here, we generated time-course data on DNA methylation, gene expression, and chromatin accessibility patterns during infection of human dendritic cells withMycobacterium tuberculosis. We found that the immune response to infection is accompanied by active demethylation of thousands of CpG sites overlapping distal enhancer elements. However, virtually all changes in gene expression in response to infection occur before detectable changes in DNA methylation, indicating that the observed losses in methylation are a downstream consequence of transcriptional activation. Footprinting analysis revealed that immune-related transcription factors (TFs), such as NF-κB/Rel, are recruited to enhancer elements before the observed losses in methylation, suggesting that DNA demethylation is mediated by TF binding to cis-acting elements. Collectively, our results show that DNA demethylation plays a limited role to the establishment of the core regulatory program engaged upon infection.

scholarly journals The role of FOXD2-AS1 in cancer: a comprehensive study based on data mining and published articles

2020 ◽  
Vol 40 (11) ◽  
Author(s):  
Yongping Zhang ◽  
Chaojie Liang ◽  
Yu Zhang ◽  
Zhinmin Wang ◽  
Ruihuan Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cancer Patients ◽  
Tumor Size ◽  
Meta Analysis ◽  
Disease Free Survival ◽  
Gene Expression Omnibus ◽  
Tnm Stage ◽  
Potential Biomarker ◽  
Tcga Dataset

Abstract Background and aims: Long non-coding RNA (lncRNA) FOXD2 adjacent opposite strand RNA 1 (FOXD2-AS1) is aberrantly expressed in various cancers and associated with cancer progression. A comprehensive meta-analysis was performed based on published literature and data in the Gene Expression Omnibus database, and then the Cancer Genome Atlas (TCGA) dataset was used to assess the clinicopathological and prognostic value of FOXD2-AS1 in cancer patients. Methods: Gene Expression Omnibus databases of microarray data and published articles were used for meta-analysis, and TCGA dataset was also explored using the GEPIA analysis program. Hazard ratios (HRs) and pooled odds ratios (ORs) with 95% confidence intervals (CIs) were used to assess the role of FOXD2-AS1 in cancers. Results: This meta-analysis included 21 studies with 2391 patients and 25 GEO datasets with 3311 patients. The pooled HRs suggested that highly expressed FOXD2-AS1 expression was correlated with poor overall survival (OS) and disease-free survival (DFS). Similar results were obtained by analysis of TCGA data for 9502 patients. The pooled results also indicated that FOXD2-AS1 expression was associated with bigger tumor size and advanced TNM stage, but was not related to age, gender, differentiation and lymph node metastasis. Conclusion: The present study demonstrated that FOXD2-AS1 is closely related to tumor size and TNM stage. Additionally, increased FOXD2-AS1 was a risk factor of OS and DFS in cancer patients, suggesting FOXD2-AS1 may be a potential biomarker in human cancers.

scholarly journals Predicting gene expression using DNA methylation in three human populations

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6757 ◽  
Author(s):  
Huan Zhong ◽  
Soyeon Kim ◽  
Degui Zhi ◽  
Xiangqin Cui
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Linear Regression ◽  
Human Population ◽  
Penalized Regression ◽  
Superior Performance ◽  
Gene Region ◽  
Human Populations ◽  
Epigenetic Mark ◽  
Lasso Regression

Background DNA methylation, an important epigenetic mark, is well known for its regulatory role in gene expression, especially the negative correlation in the promoter region. However, its correlation with gene expression across genome at human population level has not been well studied. In particular, it is unclear if genome-wide DNA methylation profile of an individual can predict her/his gene expression profile. Previous studies were mostly limited to association analyses between single CpG site methylation and gene expression. It is not known whether DNA methylation of a gene has enough prediction power to serve as a surrogate for gene expression in existing human study cohorts with DNA samples other than RNA samples. Results We examined DNA methylation in the gene region for predicting gene expression across individuals in non-cancer tissues of three human population datasets, adipose tissue of the Multiple Tissue Human Expression Resource Projects (MuTHER), peripheral blood mononuclear cell (PBMC) from Asthma and normal control study participates, and lymphoblastoid cell lines (LCL) from healthy individuals. Three prediction models were investigated, single linear regression, multiple linear regression, and least absolute shrinkage and selection operator (LASSO) penalized regression. Our results showed that LASSO regression has superior performance among these methods. However, the prediction power is generally low and varies across datasets. Only 30 and 42 genes were found to have cross-validation R2 greater than 0.3 in the PBMC and Adipose datasets, respectively. A substantially larger number of genes (258) were identified in the LCL dataset, which was generated from a more homogeneous cell line sample source. We also demonstrated that it gives better prediction power not to exclude any CpG probe due to cross hybridization or SNP effect. Conclusion In our three population analyses DNA methylation of CpG sites at gene region have limited prediction power for gene expression across individuals with linear regression models. The prediction power potentially varies depending on tissue, cell type, and data sources. In our analyses, the combination of LASSO regression and all probes not excluding any probe on the methylation array provides the best prediction for gene expression.

scholarly journals Epigenetic Age Acceleration of Stomach Adenocarcinoma Associated With Tumor Stemness Features, Immunoactivation, and Favorable Prognosis

2021 ◽  
Vol 12 ◽  
Author(s):  
Chunhong Hong ◽  
Shaohua Yang ◽  
Qiaojin Wang ◽  
Shiqiang Zhang ◽  
Wenhui Wu ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Correlation Analysis ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Chronological Age ◽  
Favorable Prognosis ◽  
Molecular Features ◽  
Epigenetic Age ◽  
Epigenetic Age Acceleration ◽  
Stomach Adenocarcinoma

Background: Abnormal DNA methylation (DNAm) age has been assumed to be an indicator for canceration and all-cause mortality. However, associations between DNAm age and molecular features of stomach adenocarcinoma (STAD), and its prognosis have not been systematically studied.Method: We calculated the DNAm age of 591 STAD samples and 115 normal stomach samples from The Cancer Genome Atlas (TCGA) and gene expression omnibus (GEO) database using the Horvath’s clock model. Meanwhile, we utilized survival analysis to evaluate the prognostic value of DNAm age and epigenetic age acceleration shift. In addition, we performed weighted gene co-expression network analysis (WGCNA) to identify DNAm age-associated gene modules and pathways. Finally, the association between DNAm age and molecular features was performed by correlation analysis.Results: DNA methylation age was significantly correlated with chronological age in normal gastric tissues (r = 0.85, p < 0.0001), but it was not associated with chronological age in STAD samples (r = 0.060, p = 0.2369). Compared with tumor adjacent normal tissue, the DNAm age of STAD tissues was significantly decreased. Meanwhile, chronological age in STAD samples was higher than its DNAm age. Both DNAm age and epigenetic acceleration shift were associated with the prognosis of STAD patients. By using correlation analysis, we also found that DNAm age was associated with immunoactivation and stemness in STAD samples.Conclusion: In summary, epigenetic age acceleration of STAD was associated with tumor stemness, immunoactivation, and favorable prognosis.

scholarly journals Methylation by multiple type I restriction modification systems avoids influencing gene regulation in uropathogenic Escherichia coli

2021 ◽  
Author(s):  
Kurosh S Mehershahi ◽  
Swaine Chen
Keyword(s):  
Gene Expression ◽  
Escherichia Coli ◽  
Dna Methylation ◽  
Gene Regulation ◽  
Detectable Effect ◽  
Epigenetic Mark ◽  
Type I ◽  
E Coli ◽  
Restriction Modification ◽  
Restriction Modification Systems

DNA methylation is a common epigenetic mark that influences transcriptional regulation, and therefore cellular phenotype, across all domains of life, extending also to bacterial virulence. Both orphan methyltransferases and those from restriction modification systems (RMSs) have been co-opted to regulate virulence epigenetically in many bacteria. However, the potential regulatory role of DNA methylation mediated by archetypal Type I systems in Escherichia coli has never been studied. We demonstrated that removal of DNA methylated mediated by three different Escherichia coli Type I RMSs in three distinct E. coli strains had no detectable effect on gene expression or growth in a screen of 1190 conditions. Additionally, deletion of the Type I RMS EcoUTI in UTI89, a prototypical cystitis strain of E. coli , which led to loss of methylation at >750 sites across the genome, had no detectable effect on virulence in a murine model of ascending urinary tract infection (UTI). Finally, introduction of two heterologous Type I RMSs into UTI89 also resulted in no detectable change in gene expression or growth phenotypes. These results stand in sharp contrast with many reports of RMSs regulating gene expression in other bacteria, leading us to propose the concept of “regulation avoidance” for these E. coli Type I RMSs. We hypothesize that regulation avoidance is a consequence of evolutionary adaptation of both the RMSs and the E. coli genome. Our results provide a clear and (currently) rare example of regulation avoidance for Type I RMSs in multiple strains of E. coli , further study of which may provide deeper insights into the evolution of gene regulation and horizontal gene transfer.

scholarly journals Different epigenetic clocks reflect distinct pathophysiological features of multiple sclerosis

Epigenomics ◽  
2019 ◽  
Vol 11 (12) ◽  
pp. 1429-1439 ◽  
Author(s):  
Eleftheria Theodoropoulou ◽  
Lars Alfredsson ◽  
Fredrik Piehl ◽  
Francesco Marabita ◽  
Maja Jagodic
Keyword(s):  
Multiple Sclerosis ◽  
Dna Methylation ◽  
Positive Association ◽  
Cell Types ◽  
Gene Expression Omnibus ◽  
Methylation Data ◽  
Accession Number ◽  
Age Related ◽  
Epigenetic Age ◽  
Epigenetic Age Acceleration

Aim: Accumulating evidence links epigenetic age to diseases and age-related conditions, but little is known about its association with multiple sclerosis (MS). Materials & methods: We estimated epigenetic age acceleration measures using DNA methylation from blood or sorted cells of MS patients and controls. Results: In blood, sex (p = 4.39E-05) and MS (p = 2.99E-03) explained the variation in age acceleration, and isolated blood cell types showed different epigenetic age. Intrinsic epigenetic age acceleration and extrinsic epigenetic age acceleration were only associated with sex (p = 2.52E-03 and p = 1.58E-04, respectively), while PhenoAge Acceleration displayed positive association with MS (p = 3.40E-02). Conclusion: Different age acceleration measures are distinctly influenced by phenotypic factors, and they might measure separate pathophysiological aspects of MS. Data deposition: DNA methylation data can be accessed at Gene Expression Omnibus database under accession number GSE35069, GSE43976, GSE106648, GSE130029, GSE130030.

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