Polytropic Influence of TRIB3 rs2295490 Genetic Polymorphism on Response to Antihypertensive Agents in Patients With Essential Hypertension

Tribbles homolog 3 (TRIB3) mediating signaling pathways are closely related to blood pressure regulation. Our previous findings suggested a greater benefit on vascular outcomes in patients carrying TRIB3 (251, A > G, rs2295490) G allele with good glucose and blood pressure control. And TRIB3 (rs2295490) AG/GG genotypes were found to reduce primary vascular events in type 2 diabetic patients who received intensive glucose treatment as compared to those receiving standard glucose treatment. However, the effect of TRIB3 genetic variation on antihypertensives was not clear in essential hypertension patients. A total of 368 patients treated with conventional dosage of antihypertensives (6 groups, grouped by atenolol/bisoprolol, celiprolol, doxazosin, azelnidipine/nitrendipine, imidapril, and candesartan/irbesartan) were enrolled in our study. Genetic variations were successfully identified by sanger sequencing. A linear mixed model analysis was performed to evaluate blood pressures among TRIB3 (251, A > G) genotypes and adjusted for baseline age, gender, body mass index, systolic blood pressure (SBP), diastolic blood pressure (DBP), total cholesterol and other biochemical factors appropriately. Our data suggested that TRIB3 (251, A > G) AA genotype carriers showed better antihypertensive effect than the AG/GG genotype carriers [P = 0.014 for DBP and P = 0.042 for mean arterial pressure (MAP)], with a maximal reduction of DBP by 4.2 mmHg and MAP by 3.56 mmHg after azelnidipine or nitrendipine treatment at the 4th week. Similar tendency of DBP-change and MAP-change was found for imidapril (ACEI) treatment, in which marginally significances were achieved (P = 0.073 and 0.075, respectively). Against that, we found that TRIB3 (251, A > G) AG/GG genotype carriers benefited from antihypertensive therapy of ARBs with a larger DBP-change during the period of observation (P = 0.036). Additionally, stratified analysis revealed an obvious difference of the maximal blood pressure change (13 mmHg for the MAP between male and female patients with AA genotype who took ARBs). Although no significant difference in antihypertensive effect between TRIB3 (251, A > G) genotypes in patients treated with α, β-ADRs was observed, we found significant difference in age-, sex-dependent manner related to α, β-ADRs. In conclusion, our data supported that TRIB3 (251, A > G) genetic polymorphism may serve as a useful biomarker in the treatment of hypertension

Sprouty genes regulate activated fibroblasts in mammary epithelial development and breast cancer.

Stromal fibroblasts are a major stem cell niche component essential for organ formation and cancer development. Fibroblast heterogeneity, as revealed by recent advances in single-cell techniques, has raised important questions about the origin, differentiation, and function of fibroblast subtypes. In this study, we show in mammary stromal fibroblasts that loss of the receptor tyrosine kinase (RTK) negative feedback regulators encoded by Spry1, Spry2, and Spry4 causes upregulation of signaling in multiple RTK pathways and increased extracellular matrix remodeling, resulting in accelerated epithelial branching. Single-cell transcriptomic analysis demonstrated that increased production of FGF10 due to Sprouty (Spry) loss results from expansion of a functionally distinct subgroup of fibroblasts with the most potent branching-promoting ability. Compared to their three independent lineage precursors, fibroblasts in this subgroup are activated, as they are located immediately adjacent to the epithelium that is actively undergoing branching and invasion. Spry genes are downregulated, and activated fibroblasts are expanded, in all three of the major human breast cancer subtypes. Together, our data highlight the regulation of a functional subtype of mammary fibroblasts by Spry genes and their essential role in epithelial morphogenesis and cancer development

Fas-Associated Factor 1 Promotes Hepatic Insulin Resistance via JNK Signaling Pathway

Fas-associated factor 1 (FAF1), a member of the Fas death-inducing signaling complex, is reported to interact potentially with diverse proteins and function in diverse cellular possesses. It remains unclear, however, whether FAF1 is involved in hepatic metabolic disorder and insulin resistance. This study aims to elucidate the role and the molecular mechanism of FAF1 in hepatic insulin resistance. Rats treated with high-fat diets are used as hepatic insulin resistance animal models. Quantitative real-time PCR, immunohistochemistry, and immunofluorescence assay are utilized to detect the FAF1 expression. The expression of relevant proteins is detected by Western blotting. We determine ROS production, lipid accumulation, and glucose uptake by using flow cytometry. Immunoprecipitation is employed to investigate protein-protein interaction. We find that increased expression of FAF1 occurred in the livers of insulin-resistant rats. Using gain-of-function and loss-of-function approaches, we observe dramatic exacerbation of insulin resistance, upregulated gluconeogenesis genes, downregulated glucose transport genes, and enhanced ROS production by FAF1 overexpression, whereas downregulation of FAF1 leads to a completely opposite phenotype. Mechanistically, FAF1 interacts directly with c-Jun N-terminal kinase (JNK) and activates its phosphorylation, thereby blocking the downstream insulin signaling pathway and leading to insulin resistance. Our data indicate that FAF1 is a potent regulator in hepatic metabolic disorder and insulin resistance

Unprecedented snow darkening and melting in New Zealand due to 2019–2020 Australian wildfires

Wildfire events have recently shown a rapid increase in frequency and scale due to the warmer present-day climate; however, their potential effects on the cryosphere are difficult to assess. Catastrophic wildfires in Australia during 2019–2020 emitted large amounts of light-absorbing particles (LAPs) to the atmosphere. Satellite observations indicate that these LAPs caused unprecedented snow-darkening of glaciers in New Zealand through long-range transport and deposition, with their effects lasting for up to three months in January–March 2020, influencing >90% of total glacier/snow and leading to a mean broadband snow-reflectance reduction of 0.08 ± 0.03. This snow darkening accelerated snowmelt by ~0.41 ± 0.2 cm day–1 during the southern summer, equivalent to that caused by a ~1.8 °C increase in air temperature. This indicates the significant impact of the 2019–2020 Australian wildfires on the hydrologic cycle in New Zealand, exceeding that of the local climate warming of ~1.5 °C since the preindustrial period. Wildfire-induced snow darkening is not limited to New Zealand. Future projections of wildfire incidence indicate widespread effects of snow darkening on the global cryosphere

Which global reanalysis dataset has better representativeness in snow cover on the Tibetan Plateau?

<jats:p>Abstract. The extensive snow cover across the Tibetan Plateau (TP) has a major influence on the climate and water supply for over 1 billion downstream inhabitants. However, an adequate evaluation of variability in the snow cover fraction (SCF) over the TP simulated by multiple reanalysis datasets has yet to be undertaken. In this study, we used the Snow Property Inversion from Remote Sensing (SPIReS) SCF dataset for the water years (WYs) 2001–2017 to evaluate the capabilities of eight reanalysis datasets (HMASR, MERRA2, ERA5, ERA5L, JRA55, CFSR, CRAL, and GLDAS) in simulating the spatial and temporal variability in SCF in the TP. CFSR, GLDAS, CRAL, and HMASR are good in simulating the spatial pattern of climatological SCF, with lower bias and higher correlation and Taylor skill score (SS). By contrast, ERA5L, JRA55, and ERA5 have a relatively good performance in terms of SCF annual trends among eight reanalysis datasets. The biases in SCF simulations across reanalysis datasets are influenced by a combination of meteorological forcings, including snowfall and temperature, as well as by the SCF parameterization methods. However, the primary influencing factors vary among the reanalysis datasets. Additionally, averaging multiple reanalysis datasets can enhance the spatiotemporal accuracy of SCF simulations, but this enhancement effect does not consistently increase with the number of reanalysis datasets used. </jats:p&gt