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A resource for integrated genomic analysis of the human liver.

ABSTRACT: In this study, we generated whole-transcriptome RNA-Seq from n = 192 genotyped liver samples and used these data with existing data from the GTEx Project (RNA-Seq) and previous liver eQTL (microarray) studies to create an enhanced transcriptomic sequence resource in the human liver. Analyses of genotype-expression associations show pronounced enrichment of associations with genes of drug response. The associations are primarily consistent across the two RNA-Seq datasets, with some modest variation, indicating the importance of obtaining multiple datasets to produce a robust resource. We further used an empirical Bayesian model to compare eQTL patterns in liver and an additional 20 GTEx tissues, finding that MHC genes, and especially class II genes, are enriched for liver-specific eQTL patterns. To illustrate the utility of the resource to augment GWAS analysis with small sample sizes, we developed a novel meta-analysis technique to combine several liver eQTL data sources. We also illustrate its application using a transcriptome-enhanced re-analysis of a study of neutropenia in pancreatic cancer patients. The associations of genotype with liver expression, including splice variation and its genetic associations, are made available in a searchable genome browser.

SUBMITTER: Zhou YH

PROVIDER: S-EPMC9452507 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature

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A resource for integrated genomic analysis of the human liver.

Zhou Yi-Hui YH Gallins Paul J PJ Etheridge Amy S AS Jima Dereje D Scholl Elizabeth E Wright Fred A FA Innocenti Federico F

Scientific reports 20220907 1

In this study, we generated whole-transcriptome RNA-Seq from n = 192 genotyped liver samples and used these data with existing data from the GTEx Project (RNA-Seq) and previous liver eQTL (microarray) studies to create an enhanced transcriptomic sequence resource in the human liver. Analyses of genotype-expression associations show pronounced enrichment of associations with genes of drug response. The associations are primarily consistent across the two RNA-Seq datasets, with some modest variati ...[more]

PMID: 36071064

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Project description:BackgroundThe mitochondrial genome is transcribed as continuous polycistrons of RNA containing multiple genes. As a consequence, post-transcriptional events are critical for the regulation of gene expression and therefore all aspects of mitochondrial function. One particularly important process is the m1A/m1G RNA methylation of the ninth position of different mitochondrial tRNAs, which allows efficient processing of mitochondrial mRNAs and protein translation, and de-regulation of genes involved in these processes has been associated with altered mitochondrial function. Although mitochondria play a key role in cancer, the status of mitochondrial RNA processing in tumorigenesis is unknown.MethodsWe measure and assess mitochondrial RNA processing using integrated genomic analysis of RNA sequencing and genotyping data from 1226 samples across 12 different cancer types. We focus on the levels of m1A and m1G RNA methylation in mitochondrial tRNAs in normal and tumor samples and use supervised and unsupervised statistical analysis to compare the levels of these modifications to patient whole genome genotypes, nuclear gene expression, and survival outcomes.ResultsWe find significant changes to m1A and m1G RNA methylation levels in mitochondrial tRNAs in tumor tissues across all cancers. Pathways of RNA processing are strongly associated with methylation levels in normal tissues (P = 3.27 × 10-31), yet these associations are lost in tumors. Furthermore, we report 18 gene-by-disease-state interactions where altered RNA methylation levels occur under cancer status conditional on genotype, implicating genes associated with mitochondrial function or cancer (e.g., CACNA2D2, LMO2, and FLT3) and suggesting that nuclear genetic variation can potentially modulate an individual's ability to maintain unaltered rates of mitochondrial RNA processing under cancer status. Finally, we report a significant association between the magnitude of methylation level changes in tumors and patient survival outcomes.ConclusionsWe report widespread variation of mitochondrial RNA processing between normal and tumor tissues across all cancer types investigated and show that these alterations are likely modulated by patient genotype and may impact patient survival outcomes. These results highlight the potential clinical relevance of altered mitochondrial RNA processing and provide broad new insights into the importance and complexity of these events in cancer.

Project description:Transcriptome studies have shown the pervasive nature of transcription, demonstrating almost all the genes undergo alternative splicing. Accurately annotating all transcripts of a gene is crucial. It is needed to understand the impact of mutations on phenotypes, to shed light on genetic and epigenetic regulation of mRNAs and more generally to widen our knowledge about cell functionality and tissue diversity. RNA-sequencing (RNA-Seq), and the other applications of the next-generation sequencing, provides precious data to improve annotations' accuracy, simultaneously creating issues related to the variety, complexity and the size of produced data. In this 'scenario', the lack of user-friendly resources, easily accessible to researchers with low skills in bioinformatics, makes difficult to retrieve complete information about one or few genes without browsing a jungle of databases. Concordantly, the increasing amount of data from 'omics' technologies imposes to develop integrated databases merging different data formats coming from distinct but complementary sources. In light of these considerations, and given the wide interest in studying Down syndrome-a genetic condition due to the trisomy of human chromosome 21 (HSA21)-we developed an integrated relational database and a web interface, named ALE-HSA21 (AnaLysis of Expression on HSA21), accessible at http://bioinfo.na.iac.cnr.it/ALE-HSA21. This comprehensive and user-friendly web resource integrates-for all coding and noncoding transcripts of chromosome 21-existing gene annotations and transcripts identified de novo through RNA-Seq analysis with predictive computational analysis of regulatory sequences. Given the role of noncoding RNAs and untranslated regions of coding genes in key regulatory mechanisms, ALE-HSA21 is also an interesting web-based platform to investigate such processes. The 'transcript-centric' and easily-accessible nature of ALE-HSA21 makes this resource a valuable tool to rapidly retrieve data at the isoform level, rather than at gene level, useful to investigate any disease, molecular pathway or cell process involving chromosome 21 genes. Database URL: http://bioinfo.na.iac.cnr.it/ALE-HSA21/.

Dataset Information

A resource for integrated genomic analysis of the human liver.

Publications

A resource for integrated genomic analysis of the human liver.

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