Browsing by Author "Tabakoff, Boris"

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    A long non‐coding RNA (Lrap) modulates brain gene expression and levels of alcohol consumption in rats
    (Wiley, 2021-03) Saba, Laura M.; Hoffman, Paula L.; Homanics, Gregg E.; Mahaffey, Spencer; Daulatabad, Swapna Vidhur; Janga, Sarath Chandra; Tabakoff, Boris; BioHealth Informatics, School of Informatics and Computing
    LncRNAs are important regulators of quantitative and qualitative features of the transcriptome. We have used QTL and other statistical analyses to identify a gene coexpression module associated with alcohol consumption. The "hub gene" of this module, Lrap (Long non-coding RNA for alcohol preference), was an unannotated transcript resembling a lncRNA. We used partial correlation analyses to establish that Lrap is a major contributor to the integrity of the coexpression module. Using CRISPR/Cas9 technology, we disrupted an exon of Lrap in Wistar rats. Measures of alcohol consumption in wild type, heterozygous and knockout rats showed that disruption of Lrap produced increases in alcohol consumption/alcohol preference. The disruption of Lrap also produced changes in expression of over 700 other transcripts. Furthermore, it became apparent that Lrap may have a function in alternative splicing of the affected transcripts. The GO category of "Response to Ethanol" emerged as one of the top candidates in an enrichment analysis of the differentially expressed transcripts. We validate the role of Lrap as a mediator of alcohol consumption by rats, and also implicate Lrap as a modifier of the expression and splicing of a large number of brain transcripts. A defined subset of these transcripts significantly impacts alcohol consumption by rats (and possibly humans). Our work shows the pleiotropic nature of non-coding elements of the genome, the power of network analysis in identifying the critical elements influencing phenotypes, and the fact that not all changes produced by genetic editing are critical for the concomitant changes in phenotype.
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    The sequenced rat brain transcriptome--its use in identifying networks predisposing alcohol consumption
    (Wiley, 2015-09) Saba, Laura M.; Flink, Stephen C.; Vanderlinden, Lauren A.; Israel, Yedy; Tampier, Lutske; Colombo, Giancarlo; Kiianmaa, Kalervo; Bell, Richard L.; Printz, Morton P.; Flodman, Pamela; Koob, George; Richardson, Heather N.; Lombardo, Joseph; Hoffman, Paula L.; Tabakoff, Boris; Department of Psychiatry, IU School of Medicine
    A quantitative genetic approach, which involves correlation of transcriptional networks with the phenotype in a recombinant inbred (RI) population and in selectively bred lines of rats, and determination of coinciding QTLs for gene expression and the trait of interest, has been applied in the current study. In this analysis, a novel approach was used that combined DNA-Seq data, data from brain exon array analysis of HXB/BXH RI rat strains and six pairs of rat lines selectively bred for high and low alcohol preference, and RNA-Seq data (including rat brain transcriptome reconstruction) to quantify transcript expression levels, generate co-expression modules, and identify biological functions that contribute to the predisposition to consume varying amounts of alcohol. A gene co-expression module was identified in the RI rat strains that contained both annotated and unannotated transcripts expressed in brain, and was associated with alcohol consumption in the RI panel. This module was found to be enriched with differentially expressed genes from the selected lines of rats. The candidate genes within the module and differentially expressed genes between high and low drinking selected lines were associated with glia (microglia and astrocytes), and could be categorized as being related to immune function, energy metabolism and calcium homeostasis, and glial-neuronal communication. Our results illustrate that there are multiple combinations of genetic factors that can produce the same phenotypic outcome. While no single gene accounts for predisposition to a particular level of alcohol consumption in every animal model, coordinated differential expression of subsets of genes in the identified pathways produce similar phenotypic outcomes.