Browsing by Author "Mahaffey, Spencer"

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    Integration of evidence across human and model organism studies: A meeting report
    (Wiley, 2021-04-23) Palmer, Rohan H.C.; Johnson, Emma C.; Won, Hyejung; Polimanti, Renato; Kapoor, Manav; Chitre, Apurva; Bogue, Molly A.; Benca-Bachman, Chelsie E.; Parker, Clarissa C.; Verm, Anurag; Reynolds, Timothy; Ernst, Jason; Bray, Michael; Kwon, Soo Bin; Lai, Dongbing; Quach, Bryan C.; Gaddis, Nathan C.; Saba, Laura; Chen, Hao; Hawrylycz, Michael; Zhang, Shan; Zhou, Yuan; Mahaffey, Spencer; Fischer, Christian; Sanchez-Roige, Sandra; Bandrowski, Anita; Lu, Qing; Shen, Li; Philip, Vivek; Gelernter, Joel; Bierut, Laura J.; Hancock, Dana B.; Edenberg, Howard J.; Johnson, Eric O.; Nestler, Eric J.; Barr, Peter B.; Prins, Pjotr; Smith, Desmond J.; Akbarian, Schahram; Thorgeirsson, Thorgeir; Walton, Dave; Baker, Erich; Jacobson, Daniel; Palmer, Abraham A.; Miles, Michael; Chesler, Elissa J.; Emerson, Jake; Agrawal, Arpana; Martone, Maryann; Williams, Robert W.; Medical and Molecular Genetics, School of Medicine
    The National Institute on Drug Abuse and Joint Institute for Biological Sciences at the Oak Ridge National Laboratory hosted a meeting attended by a diverse group of scientists with expertise in substance use disorders (SUDs), computational biology, and FAIR (Findability, Accessibility, Interoperability, and Reusability) data sharing. The meeting's objective was to discuss and evaluate better strategies to integrate genetic, epigenetic, and 'omics data across human and model organisms to achieve deeper mechanistic insight into SUDs. Specific topics were to (a) evaluate the current state of substance use genetics and genomics research and fundamental gaps, (b) identify opportunities and challenges of integration and sharing across species and data types, (c) identify current tools and resources for integration of genetic, epigenetic, and phenotypic data, (d) discuss steps and impediment related to data integration, and (e) outline future steps to support more effective collaboration-particularly between animal model research communities and human genetics and clinical research teams. This review summarizes key facets of this catalytic discussion with a focus on new opportunities and gaps in resources and knowledge on SUDs.
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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.