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Quantitative gene analysis of methylation and expression (Q-GAME) in fresh or fixed cells and tissues.

ABSTRACT: Epigenetic regulation of gene expression by DNA methylation is a central mechanism governing the silencing of tumor suppressor genes in many forms of cancer. Current methods have not proven optimal for the quantitative analysis of DNA methylation and corresponding in situ protein expression within cells in small specimens like skin biopsies. We have overcome this limitation by combining and modifying several techniques: target cell enrichment, DNA micro-isolation, one-step denaturation/bisulphite conversion/in-column desulphonation, specially designed PCR amplification, pyrosequencing and multispectral image analysis. Using this approach optimized for small samples, we can quantify minor alterations in gene methylation and protein expression using minimal amounts of tissue. Comparative studies of fresh and processed cells showed that our method is valid for DNA in both fresh and formalin-fixed, paraffin-embedded specimens. We can measure the effects of DNA methylation inhibitors, administered in vitro or in vivo, on the promoter methylation and protein expression of selected genes in specific cells. This novel approach should prove useful for a wide variety of investigative and clinical applications in dermatology and other specialties where the collection of small, routinely processed biopsy specimens is common. We refer to this method as Q-GAME (quantitative gene analysis of methylation and expression).

SUBMITTER: Wu J

PROVIDER: S-EPMC4331060 | biostudies-literature | 2014 May

REPOSITORIES: biostudies-literature

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Quantitative gene analysis of methylation and expression (Q-GAME) in fresh or fixed cells and tissues.

Wu Jianqiang J Salva Katrin A KA Stutz Nathalie N Longley B Jack BJ Spiegelman Vladimir S VS Wood Gary S GS

Experimental dermatology 20140501 5

Epigenetic regulation of gene expression by DNA methylation is a central mechanism governing the silencing of tumor suppressor genes in many forms of cancer. Current methods have not proven optimal for the quantitative analysis of DNA methylation and corresponding in situ protein expression within cells in small specimens like skin biopsies. We have overcome this limitation by combining and modifying several techniques: target cell enrichment, DNA micro-isolation, one-step denaturation/bisulphit ...[more]

PMID: 24646432

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Project description:BACKGROUND:The main bottleneck for genomic studies of tumors is the limited availability of fresh frozen (FF) samples collected from patients, coupled with comprehensive long-term clinical follow-up. This shortage could be alleviated by using existing large archives of routinely obtained and stored Formalin-Fixed Paraffin-Embedded (FFPE) tissues. However, since these samples are partially degraded, their RNA sequencing is technically challenging. RESULTS:In an effort to establish a reliable and practical procedure, we compared three protocols for RNA sequencing using pairs of FF and FFPE samples, both taken from the same breast tumor. In contrast to previous studies, we compared the expression profiles obtained from the two matched sample types, using the same protocol for both. Three protocols were tested on low initial amounts of RNA, as little as 100 ng, to represent the possibly limited availability of clinical samples. For two of the three protocols tested, poly(A) selection (mRNA-seq) and ribosomal-depletion, the total gene expression profiles of matched FF and FFPE pairs were highly correlated. For both protocols, differential gene expression between two FFPE samples was in agreement with their matched FF samples. Notably, although expression levels of FFPE samples by mRNA-seq were mainly represented by the 3'-end of the transcript, they yielded very similar results to those obtained by ribosomal-depletion protocol, which produces uniform coverage across the transcript. Further, focusing on clinically relevant genes, we showed that the high correlation between expression levels persists at higher resolutions. CONCLUSIONS:Using the poly(A) protocol for FFPE exhibited, unexpectedly, similar efficiency to the ribosomal-depletion protocol, with the latter requiring much higher (2-3 fold) sequencing depth to compensate for the relative low fraction of reads mapped to the transcriptome. The results indicate that standard poly(A)-based RNA sequencing of archived FFPE samples is a reliable and cost-effective alternative for measuring mRNA-seq on FF samples. Expression profiling of FFPE samples by mRNA-seq can facilitate much needed extensive retrospective clinical genomic studies.

Project description:BackgroundTranscriptional analysis is widely used to study the molecular biology of cancer and hold great biomarker potential for clinical patient stratification. Yet, accurate transcriptional profiling requires RNA of a high quality, which often cannot be retrieved from formalin-fixed, paraffin-embedded (FFPE) tumor tissue that is routinely collected and archived in clinical departments. To overcome this roadblock to clinical testing, we previously developed MethCORR, a method that infers gene expression from DNA methylation data, which is robustly retrieved from FFPE tissue. MethCORR was originally developed for colorectal cancer and with this study, we aim to: (1) extend the MethCORR method to 10 additional cancer types and (2) to illustrate that the inferred gene expression is accurate and clinically informative.ResultsRegression models to infer gene expression information from DNA methylation were developed for ten common cancer types using matched RNA sequencing and DNA methylation profiles (HumanMethylation450 BeadChip) from The Cancer Genome Atlas Project. Robust and accurate gene expression profiles were inferred for all cancer types: on average, the expression of 11,000 genes was modeled with good accuracy and an intra-sample correlation of R2 = 0.90 between inferred and measured gene expression was observed. Molecular pathway analysis and transcriptional subtyping were performed for breast, prostate, and lung cancer samples to illustrate the general usability of the inferred gene expression profiles: overall, a high correlation of r = 0.96 (Pearson) in pathway enrichment scores and a 76% correspondence in molecular subtype calls were observed when using measured and inferred gene expression as input. Finally, inferred expression from FFPE tissue correlated better with RNA sequencing data from matched fresh-frozen tissue than did RNA sequencing data from FFPE tissue (P < 0.0001; Wilcoxon rank-sum test).ConclusionsIn all cancers investigated, MethCORR enabled DNA methylation-based transcriptional analysis, thus enabling future analysis of cancer in situations where high-quality DNA, but not RNA, is available. Here, we provide the framework and resources for MethCORR modeling of ten common cancer types, thereby widely expanding the possibilities for transcriptional studies of archival FFPE material.

Project description:BackgroundNasal (airway) epithelial methylation profiles have been associated with asthma, but the effects of such profiles on expression of distant cis-genes are largely unknown.Research questionTo identify genes whose expression is associated with proximal and distal CpG probes (within 1 Mb), and to assess whether and how such genes are differentially expressed in atopic asthma.Study design and methodsGenome-wide expression quantitative trait methylation (eQTM) analysis in nasal epithelium from Puerto Rican subjects (aged 9-20 years) with (n = 219) and without (n = 236) asthma. After the eQTM analysis, a Gene Ontology Enrichment analysis was conducted for the top 500 eQTM genes, and mediation analyses were performed to identify paths from DNA methylation to atopic asthma through gene expression. Asthma was defined as physician-diagnosed asthma and wheeze in the previous year, and atopy was defined as at least one positive IgE to allergens. Atopic asthma was defined as the presence of both atopy and asthma.ResultsWe identified 16,867 significant methylation-gene expression pairs (false-discovery rate-adjusted P < .01) in nasal epithelium from study participants. Most eQTM methylation probes were distant (average distance, ∼378 kb) from their target genes, and also more likely to be located in enhancer regions of their target genes in lung tissue than control probes. The top 500 eQTM genes were enriched in pathways for immune processes and epithelial integrity and were more likely to have been previously identified as differentially expressed in atopic asthma. In a mediation analysis, we identified 5,934 paths through which methylation markers could affect atopic asthma through gene expression in nasal epithelium.InterpretationPrevious epigenome-wide association studies of asthma have estimated the effects of DNA methylation markers on expression of nearby genes in airway epithelium. Our findings suggest that distant epigenetic regulation of gene expression in airway epithelium plays a role in atopic asthma.

Dataset Information

Quantitative gene analysis of methylation and expression (Q-GAME) in fresh or fixed cells and tissues.

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Quantitative gene analysis of methylation and expression (Q-GAME) in fresh or fixed cells and tissues.

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