Project description:Well-balanced and orderly metabolism is a crucial prerequisite for promoting oogenesis. Involvement of single metabolites in oocyte development has been widely reported; however, the comprehensive metabolic framework controlling oocyte maturation is still lacking. In the present study, we employed an integrated temporal metabolomic and transcriptomic method to analyze metabolism in goat oocytes at key stages, revealing the global picture of the metabolic patterns during maturation. In particular, several significantly altered metabolic pathways during goat oocyte meiosis have been identified, including active serine metabolism, increased utilization of tryptophan, and marked accumulation of purine nucleotide. In summary, the current study not only provides multiple omics data resources for goat oocyte development, but also presents a novel perspective to understand the mechanisms regulating mammalian oogenesis.
Project description:The aim of the study was to investigate differences in the gene expression profiles of selected tissues in two most popular goat’s breeds in Poland: Polish White Improved (PWI) and Polish Fawn Improved (PFI). Three different types of tissue samples were selected: somatic cells isolated from goats’ milk (MSC), milk fat globules (MFG) and peripheral nuclear blood cells (PBNC) Since there were no earlier genetic studies focused on genetic differences between these two goat breeds we decided to evaluate hypothetical genomic differences assuming that such a differences should be the consequence of genetic differences. We created the hypothesis that if genomic differences exist they should be revealed in hierarchical clustering of transcriptomic profiles of selected tissues. Should the genomic differences exist the clusters obtained are grouping goat breeds and not goat’s tissues. The results of hierarchical clustering however show something completely different. The clusters are grouping goat tissues (milk fat globules, milk somatic cells, peripheral blood nuclear cells) without any relation with goat breed. So the analytical tool does not recognize the goat breed as a driver of transcriptomic difference. Moreover, we were not able to find significantly regulated genes between two breeds
Project description:MicroRNAs (miRNAs) are small non-coding RNAs that regulate the post transcriptional control of several pathway intermediates, and essential for regulation in skeletal muscle of many species, such as mice, cattle, pig and so on. However, a little number of miRNAs have been reported in the muscle development of goat. In this study, the longissimus dorsi transcripts of goat at 1- and 10-month-old were analyzed for RNA-seq and miRNA-seq. The results showed that 10-month-old Longlin goat expressed 327 up- and 419 down-regulated differentially expressed genes (DEGs) compared with the 1-month-old were founded. In addition, 20 co-up-regulated and 55 co-down-regulated miRNAs involved in muscle fiber hypertrophy of goat were identified in 10-month-old Longlin and Nubian goat compared with 1-month-old. Five miRNA–mRNA pairs (chi-let-7b-3p-MIRLET7A, chi-miR193b-3p-MMP14, chi-miR-355-5p-DGAT2, novel_128-LOC102178119, novel_140-SOD3) involved in the goat skeletal muscle development were identified by miRNA–mRNA negative correlation network analysis. Our results provided an insight into the functional roles of miRNAs of goat muscle-associated miRNAs, allowing us to better understand the transformation of miRNA roles during mammalian muscle development.
Project description:Periodontal pathogens (Porphyromonas gingivalis, Fusobacterium nucleatum) are known as one of several important bacterial pathogens associated with sporadic Alzheimer's disease (AD). It is suggested that the entry of Periodontal pathogens into the brain is due to the disruption of the tight junctions of endothelial cells by Periodontal pathogens. However, genomic alterations that occur as a result of Periodontal pathogens infection in endothelial cells are not well understood. Our goal was to identify genes associated with Periodontal pathogens infection-induced endothelial dysfunction by integrating gene expression data.