Expression of estrogen receptor 1 and progesterone receptor in primary goat mammary epithelial cells

Milk transcriptome biomarker identification to enhance feed efficiency and reduce nutritional costs in dairy ewes

In recent years, rising prices for high-quality protein-based feeds have significantly increased nutrition costs. Consequently, investigating strategies to reduce these expenses and improve feed efficiency (FE) have become increasingly important for the dairy sheep industry. This research investigates the impact of nutritional protein restriction (NPR) during prepuberty and FE on the milk transcriptome of dairy Assaf ewes (sampled during the first lactation). To this end, we first compared transcriptomic differences between NPR and control ewes. Subsequently, we evaluated gene expression differences between ewes with divergent FE, using feed conversion ratio (FCR), residual feed intake (RFI), and consensus classifications of high- and low-FE animals for both indices. Lastly, we assess milk gene expression as a predictor of FE phenotype using random forest. No effect was found for the prepubertal NPR on milk performance or FE. Moreover, at the milk transcriptome level, only one gene, HBB, was differentially expressed between the NPR (n = 14) and the control group (n = 14). Further, the transcriptomic analysis between divergent FE sheep revealed 114 differentially expressed genes (DEGs) for RFI index (high-FERFI = 10 vs low-FERFI = 10), 244 for FCR (high-FEFCR = 10 vs low-FEFCR = 10), and 1 016 DEGs between divergent consensus ewes for both indices (high-FEconsensus = 8 vs low-FEconsensus = 8). These results underscore the critical role of selected FE indices for RNA-Seq analyses, revealing that consensus divergent animals for both indices maximise differences in transcriptomic responses. Genes overexpressed in high-FEconsensus ewes were associated with milk production and mammary gland development, while low-FEconsensus genes were linked to higher metabolic expenditure for tissue organisation and repair. The best prediction accuracy for FE phenotype using random forest was obtained for a set of 44 genes consistently differentially expressed across lactations, with Spearman correlations of 0.37 and 0.22 for FCR and RFI, respectively. These findings provide insights into potential sustainability strategies for dairy sheep, highlighting the utility of transcriptomic markers as FE proxies.

Microproteomic-Based Analysis of the Goat Milk Protein Synthesis Network and Casein Production Evaluation

Goat milk has been consumed by humans since ancient times and is highly nutritious. Its quality is mainly determined by its casein content. Milk protein synthesis is controlled by a complex network with many signal pathways. Therefore, the aim of our study is to clearly depict the signal pathways involved in milk protein synthesis in goat mammary epithelial cells (GMECs) using state-of-the-art microproteomic techniques and to identify the key genes involved in the signal pathway. The microproteomic analysis identified more than 2253 proteins, with 323 pathways annotated from the identified proteins. Knockdown of IRS1 expression significantly influenced goat casein composition (α, β, and κ); therefore, this study also examined the insulin receptor substrate 1 (IRS1) gene more closely. A total of 12 differential expression proteins (DEPs) were characterized as upregulated or downregulated in the IRS1-silenced sample compared to the negative control. The enrichment and signal pathways of these DEPs in GMECs were identified using GO annotation and KEGG, as well as KOG analysis. Our findings expand our understanding of the functional genes involved in milk protein synthesis in goats, paving the way for new approaches for modifying casein content for the dairy goat industry and milk product development.

Scavenger receptor B1 facilitates the endocytosis of Escherichia coli via TLR4 signaling in mammary gland infection

SCARB1 belongs to class B of Scavenger receptors (SRs) that are known to be involved in binding and endocytosis of various pathogens. SRs have emerging role in regulating innate immunity and host-pathogen interactions by acting in co-ordination with Toll-like receptors.Query Little is known about the function of SCARB1 in milk-derived mammary epithelial cells (MECs). This study reports the role of SCARB1 in infection and its potential association in TLR4 signaling on bacterial challenge in Goat mammary epithelial cells (GMECs). The novelty in the establishment of MEC culture lies in the method that aims to enhance the viability of the cells with intact characteristics upto a higher passage number. We represent MEC culture to be used as a potential infection model for deeper understanding of animal physiology especially around the mammary gland. On E.coli challenge the expression of SCARB1 was significant in induced GMECs at 6 h. Endoribonuclease-esiRNA based silencing of SCARB1 affects the expression of TLR4 and its pathways i.e. MyD88 and TRIF pathways on infection. Knockdown also affected the endocytosis of E.coli in GMECs demonstrating that E.coli uses SCARB1 function to gain entry in cells. Furthermore, we predict 3 unique protein structures of uncharacterized SCARB1 (Capra hircus) protein. Overall, we highlight SCARB1 as a main participant in host defence and its function in antibacterial advances to check mammary gland infections. Video Abstract.

TF-Prioritizer: a Java pipeline to prioritize condition-specific transcription factors

BACKGROUND

Eukaryotic gene expression is controlled by cis-regulatory elements (CREs), including promoters and enhancers, which are bound by transcription factors (TFs). Differential expression of TFs and their binding affinity at putative CREs determine tissue- and developmental-specific transcriptional activity. Consolidating genomic datasets can offer further insights into the accessibility of CREs, TF activity, and, thus, gene regulation. However, the integration and analysis of multimodal datasets are hampered by considerable technical challenges. While methods for highlighting differential TF activity from combined chromatin state data (e.g., chromatin immunoprecipitation [ChIP], ATAC, or DNase sequencing) and RNA sequencing data exist, they do not offer convenient usability, have limited support for large-scale data processing, and provide only minimal functionality for visually interpreting results.

RESULTS

We developed TF-Prioritizer, an automated pipeline that prioritizes condition-specific TFs from multimodal data and generates an interactive web report. We demonstrated its potential by identifying known TFs along with their target genes, as well as previously unreported TFs active in lactating mouse mammary glands. Additionally, we studied a variety of ENCODE datasets for cell lines K562 and MCF-7, including 12 histone modification ChIP sequencing as well as ATAC and DNase sequencing datasets, where we observe and discuss assay-specific differences.

CONCLUSION

TF-Prioritizer accepts ATAC, DNase, or ChIP sequencing and RNA sequencing data as input and identifies TFs with differential activity, thus offering an understanding of genome-wide gene regulation, potential pathogenesis, and therapeutic targets in biomedical research.