The role of alternative promoter usage in tissue-specific gene expression has been well established; however, its role in complex diseases is poorly understood. We performed cap analysis of gene expression (CAGE) sequencing from the left ventricle of a rat model of hypertension, the spontaneously hypertensive rat (SHR), and a normotensive strain, Brown Norway to understand the role of alternative promoter usage in complex disease. We identified 26,560 CAGE-defined transcription start sites in the rat left ventricle, including 1,970 novel cardiac transcription start sites. We identified 28 genes with alternative promoter usage between SHR and Brown Norway, which could lead to protein isoforms differing at the amino terminus between two strains and 475 promoter switching events altering the length of the 5' UTR. We found that the shift in Insr promoter usage was significantly associated with insulin levels and blood pressure within a panel of HXB/BXH recombinant inbred rat strains, suggesting that hyperinsulinemia due to insulin resistance might lead to hypertension in SHR. Our study provides a preliminary evidence of alternative promoter usage in complex diseases.

• MeSH
• Transcription, Genetic genetics   MeSH
• Hypertension * genetics   metabolism   MeSH
• Rats MeSH
• Rats, Inbred SHR MeSH
• Promoter Regions, Genetic genetics   MeSH
• Sequence Analysis, RNA methods   MeSH
• Gene Expression Profiling methods   MeSH
• Animals MeSH
• Check Tag
• Rats MeSH
• Male MeSH
• Female MeSH
• Animals MeSH
• Publication type
• Journal Article MeSH

BACKGROUND: A statistical pipeline was developed and used for determining candidate genes and candidate gene coexpression networks involved in 2 alcohol (i.e., ethanol [EtOH]) metabolism phenotypes, namely alcohol clearance and acetate area under the curve in a recombinant inbred (RI) (HXB/BXH) rat panel. The approach was also used to provide an indication of how EtOH metabolism can impact the normal function of the identified networks. METHODS: RNA was extracted from alcohol-naïve liver tissue of 30 strains of HXB/BXH RI rats. The reconstructed transcripts were quantitated, and data were used to construct gene coexpression modules and networks. A separate group of rats, comprising the same 30 strains, were injected with EtOH (2 g/kg) for measurement of blood EtOH and acetate levels. These data were used for quantitative trait loci (QTL) analysis of the rate of EtOH disappearance and circulating acetate levels. The analysis pipeline required calculation of the module eigengene values, the correction of these values with EtOH metabolism rates and acetate levels across the rat strains, and the determination of the eigengene QTLs. For a module to be considered a candidate for determining phenotype, the module eigengene values had to have significant correlation with the strain phenotypic values and the module eigengene QTLs had to overlap the phenotypic QTLs. RESULTS: Of the 658 transcript coexpression modules generated from liver RNA sequencing data, a single module satisfied all criteria for being a candidate for determining the alcohol clearance trait. This module contained 2 alcohol dehydrogenase genes, including the gene whose product was previously shown to be responsible for the majority of alcohol elimination in the rat. This module was also the only module identified as a candidate for influencing circulating acetate levels. This module was also linked to the process of generation and utilization of retinoic acid as related to the autonomous immune response. CONCLUSIONS: We propose that our analytical pipeline can successfully identify genetic regions and transcripts which predispose a particular phenotype and our analysis provides functional context for coexpression module components.

• Keywords
• Alcohol Metabolism, HXB/BXH Recombinant Inbred Rat Panel, Liver, Quantitative Trait Locus Mapping, RNA Sequencing, Weighted Gene Coexpression Network Analysis,
• MeSH
• Ethanol administration & dosage   metabolism   MeSH
• Liver drug effects   metabolism   MeSH
• Rats MeSH
• Metabolic Clearance Rate drug effects   physiology   MeSH
• Multifactorial Inheritance drug effects   physiology   MeSH
• Alcohol Drinking genetics   metabolism   MeSH
• Rats, Inbred BN MeSH
• Rats, Inbred SHR MeSH
• Rats, Transgenic MeSH
• Unsupervised Machine Learning * MeSH
• Systems Biology methods   MeSH
• Animals MeSH
• Check Tag
• Rats MeSH
• Male MeSH
• Animals MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Research Support, N.I.H., Extramural MeSH
• Names of Substances
• Ethanol MeSH

Gene co-expression analysis has proven to be a powerful tool for ascertaining the organization of gene products into networks that are important for organ function. An organ, such as the liver, engages in a multitude of functions important for the survival of humans, rats, and other animals; these liver functions include energy metabolism, metabolism of xenobiotics, immune system function, and hormonal homeostasis. With the availability of organ-specific transcriptomes, we can now examine the role of RNA transcripts (both protein-coding and non-coding) in these functions. A systems genetic approach for identifying and characterizing liver gene networks within a recombinant inbred panel of rats was used to identify genetically regulated transcriptional networks (modules). For these modules, biological consensus was found between functional enrichment analysis and publicly available phenotypic quantitative trait loci (QTL). In particular, the biological function of two liver modules could be linked to immune response. The eigengene QTLs for these co-expression modules were located at genomic regions coincident with highly significant phenotypic QTLs; these phenotypes were related to rheumatoid arthritis, food preference, and basal corticosterone levels in rats. Our analysis illustrates that genetically and biologically driven RNA-based networks, such as the ones identified as part of this research, provide insight into the genetic influences on organ functions. These networks can pinpoint phenotypes that manifest through the interaction of many organs/tissues and can identify unannotated or under-annotated RNA transcripts that play a role in these phenotypes.

• MeSH
• Gene Ontology MeSH
• Immune System metabolism   MeSH
• Liver immunology   metabolism   MeSH
• Lod Score MeSH
• Quantitative Trait Loci MeSH
• Rats, Inbred SHR MeSH
• RNA genetics   metabolism   MeSH
• Sequence Analysis, RNA MeSH
• Transcriptome MeSH
• Linkage Disequilibrium MeSH
• Animals MeSH
• Check Tag
• Male MeSH
• Female MeSH
• Animals MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Research Support, N.I.H., Extramural MeSH
• Names of Substances
• RNA MeSH