Mechanically sensitive HSF1 is a key regulator of left-right symmetry breaking in zebrafish embryos.

The left-right symmetry breaking of vertebrate embryos requires nodal flow. However, the molecular mechanisms that mediate the asymmetric gene expression regulation under nodal flow remain elusive. Here, we report that heat shock factor 1 (HSF1) is asymmetrically activated in the Kupffer's vesicle of zebrafish embryos in the presence of nodal flow. Deficiency in HSF1 expression caused a significant situs inversus and disrupted gene expression asymmetry of nodal signaling proteins in zebrafish embryos. Further studies demonstrated that HSF1 is a mechanosensitive protein. The mechanical sensation ability of HSF1 is conserved in a variety of mechanical stimuli in different cell types. Moreover, cilia and Ca2+-Akt signaling axis are essential for the activation of HSF1 under mechanical stress in vitro and in vivo. Considering the conserved expression of HSF1 in organisms, these findings unveil a fundamental mechanism of gene expression regulation by mechanical clues during embryonic development and other physiological and pathological transformations.

Prediction of the Risk Distributions for Anopheles sinensis, a Vector for Malaria in Shanghai, China.

Malaria is a parasitic disease caused by Plasmodium, and Anopheles sinensis is a vector of malaria. Although malaria is no longer indigenous to China, a high risk remains for local transmission of imported malaria. This study aimed to identify the risk distribution of vector An. sinensis and malaria transmission. Using data collected from routine monitoring in Shanghai from 2010 to 2020, online databases for An. sinensis and malaria, and environmental variables including climate, geography, vegetation, and hosts, we constructed 10 algorithms and developed ensemble models. The ensemble models combining multiple algorithms (An. sinensis: area under the curve [AUC] = 0.981, kappa = 0.920; malaria: AUC = 0.959, kappa = 0.800), with the best out-of-sample performance, were used to identify important environmental predictors for the risk distributions of An. sinensis and malaria transmission. For An. sinensis, the most important predictor in the ensemble model was moisture index, which reflected degree of wetness; the risk of An. sinensis decreased with higher degrees of wetness. For malaria transmission, the most important predictor in the ensemble model was the normalized differential vegetation index, which reflected vegetation cover; the risk of malaria transmission decreased with more vegetation cover. Risk levels for An. sinensis and malaria transmission for each district of Shanghai were presented; however, there was a mismatch between the risk classification maps of An. sinensis and malaria transmission. Facing the challenge of malaria transmission in Shanghai, in addition to precise An. sinensis monitoring in risk areas of malaria transmission, malaria surveillance should occur even in low-risk areas for An. sinensis.