Juelvittrup9437

Interestingly, the genes perlucin and MGAT1, both involved in the biomineralization process (deposition of aragonite and calcite crystals), also showed differential methylation, suggesting that a possible difference in the physical/spatial organization of the crystals could cause darkening (iridescence or transparency modification of the biomineral). These findings are of great interest for the pearl production industry, since wholly black pearls and their opposite, the palest pearls, command a higher value on several markets. They also open the route of epigenetic improvement as a new means for pearl production improvement.Carcass and meat quality are two important attributes for the beef industry because they drive profitability and consumer demand. These traits are of even greater importance in crossbred cattle used in subtropical and tropical regions for their superior adaptability because they tend to underperform compared to their purebred counterparts. Many of these traits are challenging and expensive to measure and unavailable until late in life or after the animal is harvested, hence unrealistic to improve through traditional phenotypic selection, but perfect candidates for genomic selection. Before genomic selection can be implemented in crossbred populations, it is important to explore if pleiotropic effects exist between carcass and meat quality traits. Therefore, the objective of this study was to identify genomic regions with pleiotropic effects on carcass and meat quality traits in a multibreed Angus-Brahman population that included purebred and crossbred animals. Data included phenotypes for 10 carcass and meat and tenderness in beef cattle, including calcium-related processes, cell signaling, and modulation of cell-cell adhesion. These results contribute with novel information about the complex genetic architecture and pleiotropic effects of carcass and meat quality traits in crossbred beef cattle.Low and high egg producing hens exhibit gene expression differences related to ovarian steroidogenesis. High egg producing hens display increased expression of genes involved in progesterone and estradiol production, in the granulosa layer of the largest follicle (F1G) and small white follicles (SWF), respectively, whereas low egg producing hens display increased expression of genes related to progesterone and androgen production in the granulosa (F5G) and theca interna layer (F5I) of the fifth largest follicle, respectively. Transcriptome analysis was performed on F1G, F5G, F5I, and SWF samples from low and high egg producing hens to identify novel regulators of ovarian steroidogenesis. In total, 12,221 differentially expressed genes (DEGs) were identified between low and high egg producing hens across the four cell types examined. Pathway analysis implied differential regulation of the hypothalamo-pituitary-thyroid (HPT) axis, particularly thyroid hormone transporters and thyroid hormone receptors, and of estradiol signaling in low and high egg producing hens. The HPT axis showed up-regulation in high egg producing hens in less mature follicles but up-regulation in low egg producing hens in more mature follicles. Estradiol signaling exclusively exhibited up-regulation in high egg producing hens. Treatment of SWF cells from low and high egg producing hens with thyroid hormone in vitro decreased estradiol production in cells from high egg producing hens to the levels seen in cells from low egg producing hens, whereas thyroid hormone treatment did not impact estradiol production in cells from low egg producing hens. Transcriptome analysis of the major cell types involved in steroidogenesis inferred the involvement of the HPT axis and estradiol signaling in the regulation of differential steroid hormone production seen among hens with different egg production levels.Breast cancer is a complex, highly heterogeneous disease at multiple levels ranging from its genetic origins and molecular processes to clinical manifestations. This heterogeneity has given rise to the so-called intrinsic or molecular breast cancer subtypes. Aside from classification, these subtypes have set a basis for differential prognosis and treatment. Multiple regulatory mechanisms-involving a variety of biomolecular entities-suffer from alterations leading to the diseased phenotypes. Information theoretical approaches have been found to be useful in the description of these complex regulatory programs. In this work, we identified the interactions occurring between three main mechanisms of regulation of the gene expression program transcription factor regulation, regulation via noncoding RNA, and epigenetic regulation through DNA methylation. Using data from The Cancer Genome Atlas, we inferred probabilistic multilayer networks, identifying key regulatory circuits able to (partially) explain the alterations that lead from a healthy phenotype to different manifestations of breast cancer, as captured by its molecular subtype classification. We also found some general trends in the topology of the multi-omic regulatory networks Tumor subtype networks present longer shortest paths than their normal tissue counterpart; epigenomic regulation has frequently focused on genes enriched for certain biological processes; CpG methylation and miRNA interactions are often part of a regulatory core of conserved interactions. AG-1478 The use of probabilistic measures to infer information regarding theoretical-derived multilayer networks based on multi-omic high-throughput data is hence presented as a useful methodological approach to capture some of the molecular heterogeneity behind regulatory phenomena in breast cancer, and potentially other diseases.Breast cancer represents the number one cause of cancer-associated mortality globally. The most aggressive molecular subtype is triple negative breast cancer (TNBC), of which limited therapeutic options are available. It is well known that breast cancer prognosis and tumor sensitivity toward immunotherapy are dictated by the tumor microenvironment. Breast cancer gene expression profiles were extracted from the METABRIC dataset and two TNBC clusters displaying unique immune features were identified. Activated immune cells formed a large proportion of cells in the high infiltration cluster, which correlated to a good prognosis. Differentially expressed genes (DEGs) extracted between two heterogeneous subtypes were used to further explore the underlying immune mechanism and to identify prognostic biomarkers. Functional enrichment analysis revealed that the DEGs were predominately related to some processes involved in activation and regulation of innate immune signaling. Using network analysis, we identified two modules in which genes were selected for further prognostic investigation.