Exploring systemic RNA interference in insects: a genome-wide survey for RNAi genes in Tribolium

Tribolium resembles C. elegans in showing a robust systemic RNAi response, but does not have C. elegans-type RNAi mechanisms; insect systemic RNAi probably uses a different mechanism

Differentiated glioblastoma cells accelerate tumor progression by shaping the tumor microenvironment via CCN1-mediated macrophage infiltration

Glioblastoma (GBM) is the most lethal primary brain tumor characterized by significant cellular heterogeneity, namely tumor cells, including GBM stem-like cells (GSCs) and differentiated GBM cells (DGCs), and non-tumor cells such as endothelial cells, vascular pericytes, macrophages, and other types of immune cells. GSCs are essential to drive tumor progression, whereas the biological roles of DGCs are largely unknown. In this study, we focused on the roles of DGCs in the tumor microenvironment. To this end, we extracted DGC-specific signature genes from transcriptomic profiles of matched pairs of in vitro GSC and DGC models. By evaluating the DGC signature using single cell data, we confirmed the presence of cell subpopulations emulated by in vitro culture models within a primary tumor. The DGC signature was correlated with the mesenchymal subtype and a poor prognosis in large GBM cohorts such as The Cancer Genome Atlas and Ivy Glioblastoma Atlas Project. In silico signaling pathway analysis suggested a role of DGCs in macrophage infiltration. Consistent with in silico findings, in vitro DGC models promoted macrophage migration. In vivo, coimplantation of DGCs and GSCs reduced the survival of tumor xenograft-bearing mice and increased macrophage infiltration into tumor tissue compared with transplantation of GSCs alone. DGCs exhibited a significant increase in YAP/TAZ/TEAD activity compared with GSCs. CCN1, a transcriptional target of YAP/TAZ, was selected from the DGC signature as a candidate secreted protein involved in macrophage recruitment. In fact, CCN1 was secreted abundantly from DGCs, but not GSCs. DGCs promoted macrophage migration in vitro and macrophage infiltration into tumor tissue in vivo through secretion of CCN1. Collectively, these results demonstrate that DGCs contribute to GSC-dependent tumor progression by shaping a mesenchymal microenvironment via CCN1-mediated macrophage infiltration. This study provides new insight into the complex GBM microenvironment consisting of heterogeneous cells

Non-HDL-C and CVD

Aims: We aimed to investigate the association between non-high-density lipoprotein cholesterol (non-HDL-C) levels and the risk of cardiovascular disease (CVD) and its subtypes. Methods: In this contemporary cohort study, we analyzed the data of 63,814 Japanese employees aged ≥ 30 years, without known CVD in 2012 and who were followed up for up to 8 years. The non-HDL-C level was divided into 5 groups: ＜110, 110-129, 130-149, 150-169, and ≥ 170 mg/dL. The Cox proportional hazards model was used to calculate the hazard ratios (HRs) and the corresponding 95% confidence intervals (CIs) for CVD and its subtypes associated with each non-HDL-C group, considering 130-149 mg/dL as the reference group. Results: During the study period, 271 participants developed CVD, including 78 myocardial infarctions and 193 strokes (102 ischemic strokes, 89 hemorrhagic strokes, and 2 unknowns). A U-shaped association between non-HDL-C and stroke was observed. In the analysis of stroke subtypes, the multivariable-adjusted HR (95% CI) for hemorrhagic stroke was 2.61 (1.19–5.72), 2.02 (0.95–4.29), 2.10 (1.01–4.36), and 1.98 (0.96-4.08), while that for ischemic stroke was 1.54 (0.77-3.07), 0.91 (0.46-1.80), 0.73 (0.38-1.41), and 1.50 (0.87-2.56) in the ＜110, 110-129, 150-169, and ≥ 170 mg/dL groups, respectively. Individuals with elevated non-HDL-C levels had a higher risk of myocardial infarction. Conclusions: High non-HDL-C levels were associated with an increased risk of myocardial infarction. Moreover, high and low non-HDL-C levels were associated with a high risk of stroke and its subtypes among Japanese workers

Optimal waist circumference cut-off points and ability of different metabolic syndrome criteria for predicting diabetes in Japanese men and women: Japan Epidemiology Collaboration on Occupational Health Study

Abstract Background We sought to establish the optimal waist circumference (WC) cut-off point for predicting diabetes mellitus (DM) and to compare the predictive ability of the metabolic syndrome (MetS) criteria of the Joint Interim Statement (JIS) and the Japanese Committee of the Criteria for MetS (JCCMS) for DM in Japanese. Methods Participants of the Japan Epidemiology Collaboration on Occupational Health Study, who were aged 20–69 years and free of DM at baseline (n = 54,980), were followed-up for a maximum of 6 years. Time-dependent receiver operating characteristic analysis was used to determine the optimal cut-off points of WC for predicting DM. Time-dependent sensitivity, specificity, and positive and negative predictive values for the prediction of DM were compared between the JIS and JCCMS MetS criteria. Results During 234,926 person-years of follow-up, 3180 individuals developed DM. Receiver operating characteristic analysis suggested that the most suitable cut-off point of WC for predicting incident DM was 85 cm for men and 80 cm for women. MetS was associated with 3–4 times increased hazard for developing DM in men and 7–9 times in women. Of the MetS criteria tested, the JIS criteria using our proposed WC cut-off points (85 cm for men and 80 cm for women) had the highest sensitivity (54.5 % for men and 43.5 % for women) for predicting DM. The sensitivity and specificity of the JCCMS MetS criteria were ~37.7 and 98.9 %, respectively. Conclusion Data from the present large cohort of workers suggest that WC cut-offs of 85 cm for men and 80 cm for women may be appropriate for predicting DM for Japanese. The JIS criteria can detect more people who later develop DM than does the JCCMS criteria

RNA interference in Lepidoptera: an overview of successful and unsuccessful studies and implications for experimental design

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