High fertility and early puberty in Bos indicus heifers are desirable and genetically correlated traits in beef production. The hypothalamus–pituitary–ovarian (HPO) axis synthesizes steroid hormones, which contribute to the shift from the pre-pubertal state into the post-pubertal state and influence subsequent fertility. Understanding variations in abundance of proteins that govern steroid synthesis and ovarian signaling pathways remains crucial to understanding puberty and fertility. We used whole ovaries of six pre-pubertal and six post-pubertal Brahman heifers to conduct differential abundance analyses of protein profiles between the two physiological states. Extracted proteins were digested into peptides followed by identification and quantification with massspectrometry (MS) by sequential window acquisition of all instances of theoretical fragment ion mass spectrometry (SWATH-MS). MS and statistical analysis identified 566 significantly differentially abundant (DA) proteins (adjusted p < 0.05), which were then analyzed for gene ontology and pathway enrichment. Our data indicated an up-regulation of steroidogenic proteins contributing to progesterone synthesis at luteal phase post-puberty. Proteins related to progesterone signaling, TGF-β, retinoic acid, extracellular matrix, cytoskeleton, and pleiotrophin signaling were DA in this study. The DA proteins probably relate to the formation and function of the corpus luteum, which is only present after ovulation, post-puberty. Some DA proteins might also be related to granulosa cells signaling, which regulates oocyte maturation or arrest in ovaries prior to ovulation. Ten DA proteins were coded by genes previously associated with reproductive traits according to the animal quantitative trait loci (QTL) database. In conclusion, the DA proteins and their pathways were related to ovarian activity in Bos indicus cattle. The genes that code for these proteins may explain some known QTLs and could be targeted in future genetic studies.

Co-expression networks tightly coordinate the spatiotemporal patterns of gene expression unfolding during development. Due to the dynamic nature of developmental processes simply overlaying gene expression patterns onto static representations of coexpression networks may be misleading. Here, we aim to formally quantitate topological changes of co-expression networks during embryonic development using a publicly available Drosophila melanogaster transcriptome data set comprising 14 time points. We deployed a network approach which inferred 10 discrete co-expression networks by smoothly sliding along from early to late development using 5 consecutive time points per window. Such an approach allows changing network structure, including the presence of hubs, modules and other topological parameters to be quantitated. To explore the dynamic aspects of gene expression captured by our approach, we focused on regulator genes with apparent influence over particular aspects of development. Those key regulators were selected using a differential network algorithm to contrast the first 7 (early) with the last 7 (late) developmental time points. This assigns high scores to genes whose connectivity to abundant differentially expressed target genes has changed dramatically between states. We have produced a list of key regulators-some increasing (e.g., Tusp, slbo, Sidpn, DCAF12, and chinmo) and some decreasing (Rfx, bap, Hmx, Awh, and mld) connectivity during development-which reflects their role in different stages of embryogenesis. The networks we have constructed can be explored and interpreted within Cytoscape software and provide a new systems biology approach for the Drosophila research community to better visualize and interpret developmental regulation of gene expression.

Puberty is a whole‐body event, driven by the hypothalamic integration of peripheral signals such as leptin or IGF‐1. In the process of puberty, reproductive development is simultaneous to growth, including muscle growth. To enhance our understanding of muscle function related to puberty, we performed transcriptome analyses of muscle samples from six pre‐ and six post‐pubertal Brahman heifers (Bos indicus). Our aims were to perform differential expression analyses and co‐expression analyses to derive a regulatory gene network associate with puberty. As a result, we identified 431 differentially expressed (DEx) transcripts (genes and non‐coding RNAs) when comparing pre‐ to post‐pubertal average gene expression. The DEx transcripts were compared with all expressed transcripts in our samples (over 14,000 transcripts) for functional enrichment analyses. The DEx transcripts were associated with “extracellular region,” “inflammatory response” and “hormone activity” (adjusted p < .05). Inflammatory response for muscle regeneration is a necessary aspect of muscle growth, which is accelerated during puberty. The term “hormone activity” may signal genes that respond to progesterone signalling in the muscle, as the presence of this hormone is an important difference between pre‐ and post‐pubertal heifers in our experimental design. The DEx transcript with the highest average expression difference was a mitochondrial gene, ENSBTAG00000043574 that might be another important link between energy metabolism and puberty. In the derived co‐expression gene network, we identified six hub genes: CDC5L, MYC, TCF3, RUNX2, ATF2 and CREB1. In the same network, 48 key regulators of DEx transcripts were identified, using a regulatory impact factor metric. The hub gene TCF3 was also a key regulator. The majority of the key regulators (22 genes) are members of the zinc finger family, which has been implicated in bovine puberty in other tissues. In conclusion, we described how puberty may affect muscle gene expression in cattle.

The hypothalamus and the pituitary gland are directly involved in the complex systemic changes that drive the onset of puberty in cattle. Here, we applied integrated bioinformatics to elucidate the critical proteins underlying puberty and uncover potential molecular mechanisms from the hypothalamus and pituitary gland of prepubertal (n = 6) and postpubertal (n = 6) cattle. Proteomic analysis in the hypothalamus and pituitary gland revealed 275 and 186 differentially abundant (DA) proteins, respectively (adjusted p-value &lt; 0.01). The proteome profiles found herein were integrated with previously acquired transcriptome profiles. These transcriptomic studies used the same tissues harvested from the same heifers at pre- and post-puberty. This comparison detected a small number of matched transcripts and protein changes at puberty in each tissue, suggesting the need for multiple omics analyses for interpreting complex biological systems. In the hypothalamus, upregulated DA proteins at post-puberty were enriched in pathways related to puberty, including GnRH, calcium and oxytocin signalling pathways, whereas downregulated proteins were observed in the estrogen signalling pathway, axon guidance and GABAergic synapse. Additionally, this study revealed that ribosomal pathway proteins in the pituitary were involved in the pubertal development of mammals. The reported molecules and derived protein-protein networks are a starting point for future experimental approaches that might dissect with more detail the role of each molecule to provide new insights into the mechanisms of puberty onset in cattle.