Search Results (4)

Growth traits are the most important economic traits in red tilapia (Oreochromis spp.) production, and are the main targets for its genetic improvement. Increasing salinity levels in the environment are affecting the growth, development, and molecular processes of aquatic animals. Red tilapia tolerates saline water to some degree. However, few credible genetic markers or potential genes are available for choosing fast-growth traits in salt-tolerant red tilapia. This work used genome-wide association study (GWAS) and RNA-sequencing (RNA-seq) to discover genes related to four growth traits in red tilapia cultured in saline water. Through genotyping, it was determined that 22 chromosomes have 12,776,921 high-quality single-nucleotide polymorphisms (SNPs). One significant SNP and eight suggestive SNPs were obtained, explaining 0.0019% to 0.3873% of phenotypic variance. A significant SNP peak associated with red tilapia growth traits was located on chr7 (chr7-47464467), and plxnb2 was identified as the candidate gene in this region. A total of 501 differentially expressed genes (DEGs) were found in the muscle of fast-growing individuals compared to those of slow-growing ones, according to a transcriptome analysis. Combining the findings of the GWAS and RNA-seq analysis, 11 candidate genes were identified, namely galnt9, esrrg, map7, mtfr2, kcnj8, fhit, dnm1, cald1, plxnb2, nuak1, and bpgm. These genes were involved in ‘other types of O-glycan biosynthesis’, ‘glycine, serine and threonine metabolism’, ‘glycolysis/gluconeogenesis’, ‘mucin-type O-glycan biosynthesis’ and ‘purine metabolism signaling’ pathways. We have developed molecular markers to genetically breed red tilapia that grow quickly in salty water. Our study lays the foundation for the future marker-assisted selection of growth traits in salt-tolerant red tilapia. Full article

The grey prediction model is designed to characterize systems comprising both partially known information (referred to as white) and partially unknown dynamics (referred to as black). However, traditional GM(1,1) models are based on linear differential equations, which limits their capacity to capture nonlinear and non-stationary behaviors. To address this issue, this paper develops a generalized grey differential prediction approach based on the Bernoulli equation framework. We incorporate the Bernoulli mechanism with a nonlinear exponent n and a dynamic polynomial-driven term. In this work, we propose a new model designated as BPGM(1,1). Another key innovation of this work is the adoption of a nonlinear least squares direct parameter identification strategy to calculate the exponent and polynomial parameters in the Bernoulli equation, which achieves a higher degree of freedom in parameter selection and effectively circumvents the model distortion issues caused by traditional background value estimation. Furthermore, the Euler discretization method is utilized for numerical solving, reducing the reliance on traditional analytical solutions for linear structures. Numerical experiments indicate that BPGM(1,1) surpasses GM(1,1), NFBM(1,1), and their improved versions. By leveraging the synergistic mechanism between Bernoulli-type nonlinear regulation and polynomial-driven external excitation, this framework significantly enhances prediction accuracy for systems characterized by non-stationary behaviors and multi-scale trends. Full article

Aortic dissection (AD) is a life-threatening vascular condition with limited pharmacological options, and shared risk factors with cardiac disease include hypertension, atherosclerosis, smoking, and dyslipidemia. This study investigated Ferrostatin-1 (Fer-1), a ferroptosis inhibitor, in a BAPN/Ang-II-induced mouse model of AD, revealing significant therapeutic potential. Fer-1 significantly reduced AD incidence and mortality by preserving aortic wall integrity. RNA sequencing identified 922 differentially expressed genes, with 416 upregulated and 506 downregulated. Bioinformatics analysis revealed that Fer-1 modulates key regulators, such as MEF2C and KDM5A, impacting immune responses, oxidative stress, apoptosis, and lipid metabolism. Additionally, Fer-1 alters miRNA expression, with the upregulation of miR-361-5p and downregulation of miR-3151-5p, targeting pathways involved in inflammation, oxidative stress, and smooth muscle cell (SMC) phenotypic stability. Functional pathway analysis highlighted the inhibition of actin cytoskeleton, ILK, and IL-17 signaling, essential for SMC differentiation and extracellular matrix remodeling. Gene interaction network analysis identified 21 central molecules, including CXCR3, ACACA, and BPGM, associated with lipid metabolism, inflammation, and vascular remodeling. This research elucidates the mechanism of ferroptosis in AD pathogenesis and establishes Fer-1 as a promising therapeutic intervention. AD and cardiac diseases share molecular mechanisms, risk factors, and pathological processes, positioning AD within the broader scope of cardiovascular pathology. By attenuating lipid peroxidation, oxidative stress, and inflammation, Fer-1 may have cardioprotective effects beyond AD, providing a foundation for future translational research in cardiovascular medicine. Full article

Obesity is associated with an increased risk of developing cardiovascular disease (CVD), with limited alterations in cardiac genomic characteristics known. Cardiac transcriptome analysis was conducted to profile gene signatures in high-fat diet (HFD)-induced obese mice. A total of 184 differentially expressed genes (DEGs) were identified between groups. Based on the gene ontology (GO) term enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs, the critical role of closely interlocked glucose metabolism was determined in HFD-induced cardiac remodeling DEGs, including Nr4a1, Fgf21, Slc2a3, Pck1, Gck, Hmgcs2, and Bpgm. Subsequently, the expression levels of these DEGs were evaluated in both the myocardium and palmitic acid (PA)-stimulated H9c2 cardiomyocytes using qPCR. Nr4a1 was highlighted according to its overexpression resulting from the HFD. Additionally, inhibition of Nr4a1 by siRNA reversed the PA-induced altered expression of glucose metabolism-related DEGs and hexokinase 2 (HK2), the rate-limiting enzyme in glycolysis, thus indicating that Nr4a1 could modulate glucose metabolism homeostasis by regulating the expression of key enzymes in glycolysis, which may subsequently influence cardiac function in obesity. Overall, we provide a comprehensive understanding of the myocardium transcript molecular framework influenced by HFD and propose Nr4a1 as a key glucose metabolism target in obesity-induced CVD. Full article