Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.
Department of Internal Medicine, Wake Forest University School of Medicine, Winston Salem, North Carolina.
Physiol Genomics. 2022 Jun 1;54(6):206-219. doi: 10.1152/physiolgenomics.00172.2021. Epub 2022 Apr 25.
Transcriptomic analysis in metabolically active tissues allows a systems genetics approach to identify causal genes and networks involved in metabolic disease. Outbred heterogeneous stock (HS) rats are used for genetic mapping of complex traits, but to-date, a systems genetics analysis of metabolic tissues has not been done. We investigated whether adiposity-associated genes and gene coexpression networks in outbred heterogeneous stock (HS) rats overlap those found in humans. We analyzed RNAseq data from adipose tissue of 415 male HS rats, correlated these transcripts with body weight (BW) and compared transcriptome signatures to two human cohorts: the "African American Genetics of Metabolism and Expression" and "Metabolic Syndrome in Men." We used weighted gene coexpression network analysis to identify adiposity-associated gene networks and mediation analysis to identify genes under genetic control whose expression drives adiposity. We identified 554 orthologous "consensus genes" whose expression correlates with BW in the rat and with body mass index (BMI) in both human cohorts. Consensus genes fell within eight coexpressed networks and were enriched for genes involved in immune system function, cell growth, extracellular matrix organization, and lipid metabolic processes. We identified 19 consensus genes for which genetic variation may influence BW via their expression, including those involved in lipolysis (e.g., , inflammation (e.g., ), adipogenesis (e.g., ), or no previously known role in obesity (e.g., and Ms4a6a). Strong concordance between HS rat and human BW/BMI associated transcripts demonstrates translational utility of the rat model, while identification of novel genes expands our knowledge of the genetics underlying obesity.
在代谢活跃的组织中进行转录组分析，可以采用系统遗传学方法来鉴定与代谢疾病相关的因果基因和网络。近交系异质品系（HS）大鼠被用于复杂性状的遗传作图，但迄今为止，尚未对代谢组织进行系统遗传学分析。我们研究了肥胖相关基因和基因共表达网络在近交系异质品系（HS）大鼠中的表达情况，是否与人类相似。我们分析了 415 只雄性 HS 大鼠脂肪组织的 RNAseq 数据，将这些转录物与体重（BW）相关联，并将转录组特征与两个人类队列进行比较：“非洲裔美国人代谢和表达的遗传学”和“男性代谢综合征”。我们使用加权基因共表达网络分析来鉴定与肥胖相关的基因网络，并使用中介分析来鉴定受遗传控制的基因，其表达驱动肥胖。我们鉴定了 554 个与大鼠 BW 相关的“共识基因”，这些基因在人类两个队列中与体重指数（BMI）相关。共识基因位于八个共表达网络中，并且富集了参与免疫系统功能、细胞生长、细胞外基质组织和脂质代谢过程的基因。我们鉴定了 19 个共识基因，其遗传变异可能通过其表达影响 BW，包括那些参与脂肪分解（例如，炎症（例如），脂肪生成（例如），或在肥胖中尚无先前已知作用的基因（例如，和 Ms4a6a）。HS 大鼠和人类 BW/BMI 相关转录本之间的强一致性表明了大鼠模型的转化应用价值，而新基因的鉴定扩展了我们对肥胖遗传基础的认识。
