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Analysis of cancer metabolism with high-throughput technologies.

ABSTRACT: Recent advances in genomics and proteomics have allowed us to study the nuances of the Warburg effect--a long-standing puzzle in cancer energy metabolism--at an unprecedented level of detail. While modern next-generation sequencing technologies are extremely powerful, the lack of appropriate data analysis tools makes this study difficult. To meet this challenge, we developed a novel application for comparative analysis of gene expression and visualization of RNA-Seq data.We analyzed two biological samples (normal human brain tissue and human cancer cell lines) with high-energy, metabolic requirements. We calculated digital topology and the copy number of every expressed transcript. We observed subtle but remarkable qualitative and quantitative differences between the citric acid (TCA) cycle and glycolysis pathways. We found that in the first three steps of the TCA cycle, digital expression of aconitase 2 (ACO2) in the brain exceeded both citrate synthase (CS) and isocitrate dehydrogenase 2 (IDH2), while in cancer cells this trend was quite the opposite. In the glycolysis pathway, all genes showed higher expression levels in cancer cell lines; and most notably, digital gene expression of glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and enolase (ENO) were considerably increased when compared to the brain sample.The variations we observed should affect the rates and quantities of ATP production. We expect that the developed tool will provide insights into the subtleties related to the causality between the Warburg effect and neoplastic transformation. Even though we focused on well-known and extensively studied metabolic pathways, the data analysis and visualization pipeline that we developed is particularly valuable as it is global and pathway-independent.

SUBMITTER: Markovets AA

PROVIDER: S-EPMC3236851 | biostudies-literature | 2011 Oct

REPOSITORIES: biostudies-literature

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Analysis of cancer metabolism with high-throughput technologies.

Markovets Aleksandra A AA Herman Damir D

BMC bioinformatics 20111018

<h4>Background</h4>Recent advances in genomics and proteomics have allowed us to study the nuances of the Warburg effect--a long-standing puzzle in cancer energy metabolism--at an unprecedented level of detail. While modern next-generation sequencing technologies are extremely powerful, the lack of appropriate data analysis tools makes this study difficult. To meet this challenge, we developed a novel application for comparative analysis of gene expression and visualization of RNA-Seq data.<h4>R ...[more]

PMID: 22166000

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Project description:Background and objectiveAsthma is the most common chronic respiratory disease in the world with an estimated heritability between 50% and 60%, and recent studies have shown that epigenetic mechanisms play an important role in its development. Many cutting-edge epigenetic research techniques have been applied to the study of the pathogenesis of asthma, which has promoted the development of asthma etiology and brought new possibilities for treatment. We summarized recent advances in epigenetic research of the pathogenesis of asthma, especially from the perspective of high-throughput analysis techniques, to find potential epigenetic biomarkers and possible molecular targets for the future intervention and treatment of the disease.MethodsWe reviewed and summarized recent progress in epigenomic studies of asthma on a "pre-transcriptional level", including DNA methylation, histone modification, and chromatin remodeling, and on a "post-transcriptional level" with a focus on non-coding RNA, from the perspective of high-throughput analysis technologies.Key content and findingsWe have summarized the progress of different kinds of recent epigenetic studies in asthma, including DNA methylation studies [candidate genes methylation studies and epigenome-wide association study (EWAS)], histone modification studies (histone acetylation/deacetylation studies and histone methylation studies), non-coding RNA studies [microRNAs (miRNAs), long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs)], to help the readers to gain a comprehensive insight into the epigenetic research fields for asthma. The application of high-throughput analysis techniques in asthma research, including EWAS (DNA methylation chips), chromatin immunoprecipitation sequencing (CHIP-seq), microRNA sequencing, whole transcriptome sequencing, co-expression network and competing endogenous RNA (ceRNA) analyses, were introduced accompany with the main findings. And the potential epigenetic biomarkers and possible molecular targets identified via high-throughput analyses were also discussed.ConclusionsEpigenetic research has become a hotspot in research on the pathogenesis of asthma. The combination of high-throughput epigenetic analysis technologies and traditional biological function and clinical studies will bring new breakthrough in the pathogenesis study of asthma, which will improve the genetic interpretation of the disease and bring more possibilities for the development of precision medicine to treat it.

Project description:BackgroundThe recent development within high-throughput technologies for expression profiling has allowed for parallel analysis of transcriptomes and proteomes in biological systems such as comparative analysis of transcript and protein levels of tissue regulated genes. Until now, such studies of have only included microarray or short length sequence tags for transcript profiling. Furthermore, most comparisons of transcript and protein levels have been based on absolute expression values from within the same tissue and not relative expression values based on tissue ratios.ResultsPresented here is a novel study of two porcine tissues based on integrative analysis of data from expression profiling of identical samples using cDNA microarray, 454-sequencing and iTRAQ-based proteomics. Sequence homology identified 2.541 unique transcripts that are detectable by both microarray hybridizations and 454-sequencing of 1.2 million cDNA tags. Both transcript-based technologies showed high reproducibility between sample replicates of the same tissue, but the correlation across these two technologies was modest. Thousands of genes being differentially expressed were identified with microarray. Out of the 306 differentially expressed genes, identified by 454-sequencing, 198 (65%) were also found by microarray. The relationship between the regulation of transcript and protein levels was analyzed by integrating iTRAQ-based proteomics data. Protein expression ratios were determined for 354 genes, of which 148 could be mapped to both microarray and 454-sequencing data. A comparison of the expression ratios from the three technologies revealed that differences in transcript and protein levels across heart and muscle tissues are positively correlated.ConclusionWe show that the reproducibility within cDNA microarray and 454-sequencing is high, but that the agreement across these two technologies is modest. We demonstrate that the regulation of transcript and protein levels across identical tissue samples is positively correlated when the tissue expression ratios are used for comparison. The results presented are of interest in systems biology research in terms of integration and analysis of high-throughput expression data from mammalian tissues.

Dataset Information

Analysis of cancer metabolism with high-throughput technologies.

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Analysis of cancer metabolism with high-throughput technologies.

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