Transcriptome analyses identify five transcription factors differentially expressed in the hypothalamus of post- versus prepubertal Brahman heifers.

Puberty onset is a developmental process influenced by genetic determinants, environment, and nutrition. Mutations and regulatory gene networks constitute the molecular basis for the genetic determinants of puberty onset. The emerging knowledge of these genetic determinants presents opportunities for innovation in the breeding of early pubertal cattle. This paper presents new data on hypothalamic gene expression related to puberty in Bos indicus (Brahman) in age- and weight-matched heifers. Six postpubertal heifers were compared with 6 prepubertal heifers using whole-genome RNA sequencing methodology for quantification of global gene expression in the hypothalamus. Five transcription factors (TF) with potential regulatory roles in the hypothalamus were identified in this experiment: E2F8, NFAT5, SIX5, ZBTB38, and ZNF605. These TF genes were significantly differentially expressed in the hypothalamus of postpubertal versus prepubertal heifers and were also identified as significant according to the applied regulatory impact factor metric (P < 0.05). Two of these 5 TF, ZBTB38 and ZNF605, were zinc fingers, belonging to a gene family previously reported to have a central regulatory role in mammalian puberty. The SIX5 gene belongs to the family of homologues of Drosophila sine oculis (SIX) genes implicated in transcriptional regulation of gonadotrope gene expression. Tumor-related genes such as E2F8 and NFAT5 are known to affect basic cellular processes that are relevant in both cancer and developmental processes. Mutations in NFAT5 were associated with puberty in humans. Mutations in these TF, together with other genetic determinants previously discovered, could be used in genomic selection to predict the genetic merit of cattle (i.e., the likelihood of the offspring presenting earlier than average puberty for Brahman). Knowledge of key mutations involved in genetic traits is an advantage for genomic prediction because it can increase its accuracy. [ABSTRACT FROM AUTHOR]