The integrative metabolomic-transcriptomic landscape of glioblastome multiforme.
Ontology highlight
ABSTRACT:
SUBMITTER: Heiland DH
PROVIDER: S-EPMC5564759 | biostudies-other | 2017 Jul
REPOSITORIES: biostudies-other
Similar Datasets
Project description:Akebia trifoliata fruit is prone to crack after ripening, but little is known about the mechanism underlying the cracking process. This study integrated transcriptomic and metabolomic data, revealing significant changes in 398 metabolites and 8414 genes during ripening and cracking, mainly impacting cell-wall metabolism. Multi-omics joint analysis indicated that genes related to polygalacturonase, pectate lyase, α-amylase, and glycogen phosphorylase were up-regulated after cracking, degrading cell wall and starch. Concurrently, diminished photosynthetic metabolism and heightened phenylpropanoid metabolism suggested alterations in cuticle structure, potentially impacting cell-wall robustness. Numerous auxin and abscisic acid signaling-related genes were expressed, and we assume that they contributed to the promoting peel growth. These alterations collectively might compromise peel strength and elevate expanding pressure, potentially leading to A. trifoliata cracking. Transcription factors, predominantly ethylene response factors and helix-loop-helix family members, appeared to regulate these metabolic shifts. These findings provide valuable insights into A. trifoliata cracking mechanisms; however, direct experimental validation of these assumptions is necessary to strengthen these conclusions and expedite their commercial utilization.
| S-EPMC10706055 | biostudies-literature
Project description:While recent integrative analyses of copy number and gene expression data in breast cancer have revealed a complex molecular landscape with multiple subtypes and many oncogenic/tumour suppressor driver events, much less is known about the role of DNA methylation in shaping breast cancer taxonomy and defining driver events.Here, we applied a powerful integrative network algorithm to matched DNA methylation and RNA-Seq data for 724 estrogen receptor (ER)-positive (ER+) breast cancers and 111 normal adjacent tissue specimens from The Cancer Genome Atlas (TCGA) project, in order to identify putative epigenetic driver events and to explore the resulting molecular taxonomy. This revealed the existence of nine functionally deregulated epigenetic hotspots encompassing a total of 146 genes, which we were able to validate in independent data sets encompassing over 1000 ER+ breast cancers. Integrative clustering of the matched messenger RNA (mRNA) and DNA methylation data over these genes resulted in only two clusters, which correlated very strongly with the luminal-A and luminal B subtypes. Overall, luminal-A and luminal-B breast cancers shared the same epigenetically deregulated hotspots but with luminal-B cancers exhibiting increased aberrant DNA methylation patterns relative to normal tissue. We show that increased levels of DNA methylation and mRNA expression deviation from the normal state define a marker of poor prognosis. Our data further implicates epigenetic silencing of WNT signalling antagonists and bone morphogenetic proteins (BMP) as key events underlying both luminal subtypes but specially of luminal-B breast cancer. Finally, we show that DNA methylation changes within the identified epigenetic interactome hotspots do not exhibit mutually exclusive patterns within the same cancer sample, instead exhibiting coordinated changes within the sample.Our results indicate that the integrative DNA methylation and transcriptomic landscape of ER+ breast cancer is surprisingly homogeneous, defining two main subtypes which strongly correlate with luminal-A/B subtype status. In particular, we identify WNT and BMP signalling as key epigenetically deregulated tumour suppressor pathways in luminal ER+ breast cancer, with increased deregulation seen in luminal-B breast cancer.
| S-EPMC4673726 | biostudies-other
Project description:Metabolism reprogramming is considered a hallmark of cancer. The study of bladder cancer (BC) metabolism could be the key to developing new strategies for diagnosis and therapy. This work aimed to identify tissue and urinary metabolic signatures as biomarkers of BC and get further insight into BC tumor biology through the study of gene-metabolite networks and the integration of metabolomics and transcriptomics data. BC and control tissue samples (n = 44) from the same patients were analyzed by High-Resolution Magic Angle Spinning Nuclear Magnetic Resonance and microarrays techniques. Besides, urinary profiling study (n = 35) was performed in the same patients to identify a metabolomic profile, linked with BC tissue hallmarks, as a potential non-invasive approach for BC diagnosis. The metabolic profile allowed for the classification of BC tissue samples with a sensitivity and specificity of 100%. The most discriminant metabolites for BC tissue samples reflected alterations in amino acids, glutathione, and taurine metabolic pathways. Transcriptomic data supported metabolomic results and revealed a predominant downregulation of metabolic genes belonging to phosphorylative oxidation, tricarboxylic acid cycle, and amino acid metabolism. The urinary profiling study showed a relation with taurine and other amino acids perturbed pathways observed in BC tissue samples, and classified BC from non-tumor urine samples with good sensitivities (91%) and specificities (77%). This urinary profile could be used as a non-invasive tool for BC diagnosis and follow-up.
| S-EPMC6562847 | biostudies-literature
Project description:BackgroundSingle omic analyses have provided some insight into the basis of lung function in children with asthma, but the underlying biologic pathways are still poorly understood.MethodsWeighted gene coexpression network analysis (WGCNA) was used to identify modules of coregulated gene transcripts and metabolites in blood among 325 children with asthma from the Genetic Epidemiology of Asthma in Costa Rica study. The biology of modules associated with lung function as measured by FEV1, the FEV1/FVC ratio, bronchodilator response, and airway responsiveness to methacholine was explored. Significantly correlated gene-metabolite module pairs were then identified, and their constituent features were analyzed for biologic pathway enrichments.ResultsWGCNA clustered 25,060 gene probes and 8,185 metabolite features into eight gene modules and eight metabolite modules, where four and six, respectively, were associated with lung function (P ≤ .05). The gene modules were enriched for immune, mitotic, and metabolic processes and asthma-associated microRNA targets. The metabolite modules were enriched for lipid and amino acid metabolism. Integration of correlated gene-metabolite modules expanded the single omic findings, linking the FEV1/FVC ratio with ORMDL3 and dysregulated lipid metabolism. This finding was replicated in an independent population.ConclusionsThe results of this hypothesis-generating study suggest a mechanistic basis for multiple asthma genes, including ORMDL3, and a role for lipid metabolism. They demonstrate that integrating multiple omic technologies may provide a more informative picture of asthmatic lung function biology than single omic analyses.
| S-EPMC6689076 | biostudies-literature
Project description:Next-generation sequencing of primary tumors is now standard for transcriptomic studies, but microarray-based data still constitute the majority of available information on other clinically valuable samples, including archive material. Using prostate cancer (PC) as a model, we developed a robust analytical framework to integrate data across different technical platforms and disease subtypes to connect distinct disease stages and reveal potentially relevant genes not identifiable from single studies alone. We reconstructed the molecular profile of PC to yield the first comprehensive insight into its development, by tracking changes in mRNA levels from normal prostate to high-grade prostatic intraepithelial neoplasia, and metastatic disease. A total of nine previously unreported stage-specific candidate genes with prognostic significance were also found. Here, we integrate gene expression data from disparate sample types, disease stages and technical platforms into one coherent whole, to give a global view of the expression changes associated with the development and progression of PC from normal tissue through to metastatic disease. Summary and individual data are available online at the Prostate Integrative Expression Database (PIXdb), a user-friendly interface designed for clinicians and laboratory researchers to facilitate translational research.
| S-EPMC7838904 | biostudies-literature
Project description:In multiple myeloma, novel treatments with proteasome inhibitors (PIs) and immunomodulatory agents (IMiDs) have prolonged survival but the disease remains incurable. At relapse, next-generation sequencing has shown occasional mutations of drug targets but has failed to identify unifying features that underlie chemotherapy resistance. We studied 42 patients refractory to both PIs and IMiDs. Whole-exome sequencing was performed in 40 patients, and RNA sequencing (RNA-seq) was performed in 27. We found more mutations than were reported at diagnosis and more subclonal mutations, which implies ongoing evolution of the genome of myeloma cells during treatment. The mutational landscape was different from that described in published studies on samples taken at diagnosis. The TP53 pathway was the most frequently inactivated (in 45% of patients). Conversely, point mutations of genes associated with resistance to IMiDs were rare and were always subclonal. Refractory patients were uniquely characterized by having a mutational signature linked to exposure to alkylating agents, whose role in chemotherapy resistance and disease progression remains to be elucidated. RNA-seq analysis showed that treatment or mutations had no influence on clustering, which was instead influenced by karyotypic events. We describe a cluster with both amp(1q) and del(13) characterized by CCND2 upregulation and also overexpression of MCL1, which represents a novel target for experimental treatments. Overall, high-risk features were found in 65% of patients. However, only amp(1q) predicted survival. Gene mutations of IMiD and PI targets are not a preferred mode of drug resistance in myeloma. Chemotherapy resistance of the bulk tumor population is likely attained through differential, yet converging evolution of subclones that are overall variable from patient to patient and within the same patient.
| S-EPMC7065476 | biostudies-literature
Project description:Regeneration requires cells to regulate proliferation and patterning according to their spatial position. Positional memory is a property that enables regenerating cells to recall spatial information from the uninjured tissue. Positional memory is hypothesized to rely on gradients of molecules, few of which have been identified. Here, we quantified the global abundance of transcripts, proteins, and metabolites along the proximodistal axis of caudal fins of uninjured and regenerating adult zebrafish. Using this approach, we uncovered complex overlapping expression patterns for hundreds of molecules involved in diverse cellular functions, including development, bioelectric signaling, and amino acid and lipid metabolism. Moreover, 32 genes differentially expressed at the RNA level had concomitant differential expression of the encoded proteins. Thus, the identification of proximodistal differences in levels of RNAs, proteins, and metabolites will facilitate future functional studies of positional memory during appendage regeneration.
| S-EPMC5293114 | biostudies-literature
Project description:State of the art high-throughput technologies allow comprehensive experimental studies of organism metabolism and induce the need for a convenient presentation of large heterogeneous datasets. Especially, the combined analysis and visualization of data from different high-throughput technologies remains a key challenge in bioinformatics. We present here the MarVis-Graph software for integrative analysis of metabolic and transcriptomic data. All experimental data is investigated in terms of the full metabolic network obtained from a reference database. The reactions of the network are scored based on the associated data, and sub-networks, according to connected high-scoring reactions, are identified. Finally, MarVis-Graph scores the detected sub-networks, evaluates them by means of a random permutation test and presents them as a ranked list. Furthermore, MarVis-Graph features an interactive network visualization that provides researchers with a convenient view on the results. The key advantage of MarVis-Graph is the analysis of reactions detached from their pathways so that it is possible to identify new pathways or to connect known pathways by previously unrelated reactions. The MarVis-Graph software is freely available for academic use and can be downloaded at: http://marvis.gobics.de/marvis-graph.
| S-EPMC3961162 | biostudies-literature
Project description:Gossypol, a yellow polyphenolic compound found in the Gossypium genus, is toxic to animals that ingest cotton-derived feed materials. However, ruminants display a notable tolerance to gossypol, attributed to the pivotal role of ruminal microorganisms in its degradation. The mechanisms of how rumen microorganisms degrade and tolerate gossypol remain unclear. Therefore, in this study, Enterobacter sp. GD5 was isolated from rumen fluid, and the effects of gossypol on its metabolism and gene expression were investigated using liquid chromatography-mass spectrometry (LC-MS) and RNA analyses. The LC-MS results revealed that gossypol significantly altered the metabolic profiles of 15 metabolites (eight upregulated and seven downregulated). The Kyoto Encyclopedia of Genes and Genomes analysis results showed that significantly different metabolites were associated with glutathione metabolism in both positive and negative ion modes, where gossypol significantly affected the biosynthesis of amino acids in the negative ion mode. Transcriptomic analysis indicated that gossypol significantly affected 132 genes (104 upregulated and 28 downregulated), with significant changes observed in the expression of catalase peroxidase, glutaredoxin-1, glutathione reductase, thioredoxin 2, thioredoxin reductase, and alkyl hydroperoxide reductase subunit F, which are related to antioxidative stress. Furthermore, Gene Ontology analysis revealed significant changes in homeostatic processes following gossypol supplementation. Overall, these results indicate that gossypol induces oxidative stress, resulting in the increased expression of antioxidative stress-related genes in Enterobacter sp. GD5, which may partially explain its tolerance to gossypol.
| S-EPMC11302909 | biostudies-literature
Project description:Feed efficiency (FE) is an economically important trait. Thus, reliable predictors would help to reduce the production cost and provide sustainability to the pig industry. We carried out metabolome-transcriptome integration analysis on 40 purebred Duroc and Landrace uncastrated male pigs to identify potential gene-metabolite interactions and explore the molecular mechanisms underlying FE. To this end, we applied untargeted metabolomics and RNA-seq approaches to the same animals. After data quality control, we used a linear model approach to integrate the data and find significant differently correlated gene-metabolite pairs separately for the breeds (Duroc and Landrace) and FE groups (low and high FE) followed by a pathway over-representation analysis. We identified 21 and 12 significant gene-metabolite pairs for each group. The valine-leucine-isoleucine biosynthesis/degradation and arginine-proline metabolism pathways were associated with unique metabolites. The unique genes obtained from significant metabolite-gene pairs were associated with sphingolipid catabolism, multicellular organismal process, cGMP, and purine metabolic processes. While some of the genes and metabolites identified were known for their association with FE, others are novel and provide new avenues for further research. Further validation of genes, metabolites, and gene-metabolite interactions in larger cohorts will elucidate the regulatory mechanisms and pathways underlying FE.
| S-EPMC7408121 | biostudies-literature