Hansenchristiansen3045

Our findings provided the genomic evidence of the complex genetic mechanisms of adaptation to hot arid and harsh environments in chickens. These results may improve our understanding of thermal, drought, and harsh environment acclimation in chickens and may serve as a valuable resource for developing new biotechnological tools to breed stress-tolerant chicken lines and or breeds in the future.The phenotype of carcass traits in beef cattle are affected by random genetic and non-genetic effects, which both can be modulated by an environmental variable such as Temperature-Humidity Index (THI), a key environmental factor in cattle production. In this study, a multivariate reaction norm model (MRNM) was used to assess if the random genetic and non-genetic (i.e., residual) effects of carcass weight (CW), back fat thickness (BFT), eye muscle area (EMA), and marbling score (MS) were modulated by THI, using 9,318 Hanwoo steers (N = 8,964) and cows (N = 354) that were genotyped on the Illumina Bovine SNP50 BeadChip (50K). THI was measured based on the period of 15-45 days before slaughter. Both the correlation and the interaction between THI and random genetic and non-genetic effects were accounted for in the model. In the analyses, it was shown that the genetic effects of EMA and the non-genetic effects of CW and MS were significantly modulated by THI. No significant THI modulation of such effects was found for BFT. These results highlight the relevance of THI changes for the genetic and non-genetic variation of CW, EMA, and MS in Hanwoo beef cattle. Compound Library molecular weight Importantly, heritability estimates for CW, EMA, and MS from additive models without considering THI interactions were underestimated. Moreover, the significance of interaction can be biased if not properly accounting for the correlation between THI and genetic and non-genetic effects. Thus, we argue that the estimation of genetic parameters should be based on appropriate models to avoid any potential bias of estimates. Our finding should serve as a basis for future studies aiming at revealing genotype by environment interaction in estimation and genomic prediction of breeding values.Stem cells from fetal tissue protect against aging and possess greater proliferative capacity than their adult counterparts. These cells can more readily expand in vitro and senesce later in culture. However, the underlying molecular mechanisms for these differences are still not fully understood. In this study, we used a robust rank aggregation (RRA) method to discover robust differentially expressed genes (DEGs) between fetal bone marrow mesenchymal stem cells (fMSCs) and aged adult bone marrow mesenchymal stem cells (aMSCs). Multiple methods, including gene set enrichment analysis (GSEA), Gene Ontology (GO) analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed for functional annotation of the robust DEGs, and the results were visualized using the R software. The hub genes and other genes with which they interacted directly were detected by protein-protein interaction (PPI) network analysis. Correlation of gene expression was measured by Pearson correlation coefficient. A total of 388 up-regulated and 289 down-regulated DEGs were identified between aMSCs and fMSCs. We found that the down-regulated genes were mainly involved in the cell cycle, telomerase activity, and stem cell proliferation. The up-regulated DEGs were associated with cell adhesion molecules, extracellular matrix (ECM)-receptor interactions, and the immune response. We screened out four hub genes, MYC, KIF20A, HLA-DRA, and HLA-DPA1, through PPI-network analysis. The MYC gene was negatively correlated with TXNIP, an age-related gene, and KIF20A was extensively involved in the cell cycle. The results suggested that MSCs derived from the bone marrow of an elderly donor present a pro-inflammatory phenotype compared with that of fMSCs, and the HLA-DRA and HLA-DPA1 genes are related to the immune response. These findings provide new insights into the differences between aMSCs and fMSCs and may suggest novel strategies for ex vivo expansion and application of adult MSCs.The emergence of a new coronavirus (CoV), severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), responsible for severe respiratory disease in humans termed coronavirus disease of 2019 (COVID-19), became a new global threat for health and the economy. The SARS-CoV-2 genome is about a 29,800-nucleotide-long plus-strand RNA that can form functionally important secondary and higher-order structures called cis-acting RNA elements. These elements can interact with viral proteins, host proteins, or other RNAs and be involved in regulating translation and replication processes of the viral genome and encapsidation of the virus. However, the cis-acting RNA elements and their biological roles in SARS-CoV-2 as well as their comparative analysis in the closely related viral genome have not been well explored, which is very important to understand the molecular mechanism of viral infection and pathogenies. In this study, we used a bioinformatics approach to identify the cis-acting RNA elements in the SARS-CoV-2 geof COVID-19. It is imperative to further characterize these cis-acting RNA elements experimentally for a better mechanistic understanding of SARS-CoV-2 infection and therapeutic intervention.The first evidence of individual targeting medicine appeared in ancient times thousands of years ago. Various therapeutic approaches have been established since then. However, even nowadays, conventional therapies do not take into consideration individuals' idiosyncrasy and genetic make-up, failing thus to be effective in some cases. Over time, the necessity of a more precise and effective treatment resulted in the development of a scientific field currently known as "personalized medicine." The numerous technological breakthroughs in this field have acknowledged personalized medicine as the next generation of diagnosis and treatment. Although personalized medicine has attracted a lot of attention the last years, there are still several obstacles hindering its application in clinical practice. These limitations have come to light recently, due to the COVID-19 pandemic. This review describes the "journey" of personalized medicine over time, emphasizing on important milestones achieved through time. Starting from the treatment of malaria, as a first more personalized therapeutic approach, it highlights the need of new diagnostic tools and therapeutic regimens based on individuals' genetic background.