Počet záznamů: 1  

Systems genetic analysis of brown adipose tissue function

  1. 1. 0489317 - FGU-C 2019 RIV US eng J - Článek v odborném periodiku
    Pravenec, Michal - Saba, L. M. - Zídek, Václav - Landa, Vladimír - Mlejnek, Petr - Šilhavý, Jan - Šimáková, Miroslava - Strnad, Hynek - Trnovská, J. - Škop, V. - Hüttl, M. - Marková, I. - Oliyarnyk, O. - Malínská, H. - Kazdová, L. - Smith, H. - Tabakoff, B.
    Systems genetic analysis of brown adipose tissue function.
    Physiological Genomics. Roč. 50, č. 1 (2018), s. 52-66. ISSN 1094-8341
    Grant CEP: GA ČR(CZ) GA13-04420S
    Institucionální podpora: RVO:67985823
    Klíčová slova: brown adipose tissue * coexpression modules * quantitative trait locus * recombinant inbred strains * spontaneously hypertensive rat
    Kód oboru RIV: EB - Genetika a molekulární biologie
    Obor OECD: Human genetics
    Impakt faktor: 2.782, rok: 2017

    Brown adipose tissue (BAT) has been suggested to play an important role in lipid and glucose metabolism in rodents and possibly also in humans. In the current study, we used genetic and correlation analyses in the BXH/HXB recombinant inbred (RI) strains, derived from Brown Norway (BN) and spontaneously hypertensive rats (SHR), to identify genetic determinants of BAT function. Linkage analyses revealed a quantitative trait locus (QTL) associated with interscapular BAT mass on chromosome 4 and two closely linked QTLs associated with glucose oxidation and glucose incorporation into BAT lipids on chromosome 2. Using weighted gene coexpression network analysis (WGCNA) we identified 1,147 gene coexpression modules in the BAT from BXH/HXB rats and mapped their module eigengene QTLs. Through an unsupervised analysis, we identified modules related to BAT relative mass and function. The Coral4.1 coexpression module is associated with BAT relative mass (includes Cd36 highly connected gene), and the Darkseagreen coexpression module is associated with glucose incorporation into BAT lipids (includes Hiat1, Fmo5, and Sort1 highly connected transcripts). Because multiple statistical criteria were used to identify candidate modules, significance thresholds for individual tests were not adjusted for multiple comparisons across modules. In summary, a systems genetic analysis using genomic and quantitative transcriptomic and physiological information has produced confirmation of several known genetic factors and significant insight into novel genetic components functioning in BAT and possibly contributing to traits characteristic of the metabolic syndrome.
    Trvalý link: http://hdl.handle.net/11104/0283756