In vivo reprogramming of NG2 glia enables adult neurogenesis and functional recovery following spinal cord injury

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Loss of FoxO3a prevents aortic aneurysm formation through maintenance of VSMC homeostasis

Vascular smooth muscle cell (VSMC) phenotypic switching plays a critical role in the formation of abdominal aortic aneurysms (AAAs). FoxO3a is a key suppressor of VSMC homeostasis. We found that in human and animal AAA tissues, FoxO3a was upregulated, SM22α and α-smooth muscle actin (α-SMA) proteins were downregulated and synthetic phenotypic markers were upregulated, indicating that VSMC phenotypic switching occurred in these diseased tissues. In addition, in cultured VSMCs, significant enhancement of FoxO3a expression was found during angiotensin II (Ang II)-induced VSMC phenotypic switching. In vivo, FoxO3a overexpression in C57BL/6J mice treated with Ang II increased the formation of AAAs, whereas FoxO3a knockdown exerted an inhibitory effect on AAA formation in ApoE^−/− mice infused with Ang II. Mechanistically, FoxO3a overexpression significantly inhibited the expression of differentiated smooth muscle cell (SMC) markers, activated autophagy, the essential repressor of VSMC homeostasis, and promoted AAA formation. Our study revealed that FoxO3a promotes VSMC phenotypic switching to accelerate AAA formation through the P62/LC3BII autophagy signaling pathway and that therapeutic approaches that decrease FoxO3a expression may prevent AAA formation.Subject terms: Cell biology, Diseases     

Conformation-dependent recognition of mutant HTT (huntingtin) proteins by selective autophagy

Protein misfolding is the common theme for neurodegenerative disorders including Huntington disease (HD), which is mainly caused by cytotoxicity of the mutant HTT (huntingtin) protein (mHTT). The soluble mHTT has an expanded polyglutamine (polyQ) stretch that may adopt multiple conformations, among which the one recognized by the polyQ antibody 3B5H10 is the most toxic due to unknown mechanisms. In a recent study, we showed that the 3B5H10-recognized mHTT species has a slower degradation rate due to its resistance to selective macroautophagy/autophagy. In HD mouse brain tissues as well as HD patient fibroblasts and post-mortem brain tissues, the 3B5H10-recognized mHTT species lacks Lys63-polyubiquitination and SQSTM1/p62 interaction, which are essential for cargo recognition by selective autophagy. Collectively, we discovered that the mHTT protein is subject to conformation-dependent recognition by selective autophagy, which is more selective than what we perceived: the process can be selective among different conformations of the same protein, leading to conformation-dependent differences in protein degradation and toxicity.     

DNA-templated photo-induced silver nanowires: Fabrication and use in detection of relative humidity

A very simple and novel approach of fabricating Ag–DNA network is herein reported. The Ag–DNA network can be formed from reduction of silver ion absorbed on DNA template by sunlight. Mesh size of the Ag–DNA network and the diameter of the Ag–DNA nanowire can be controlled by adjusting the concentration of DNA and irradiation time, respectively. Furthermore, the Ag–DNA network placed onto comb-like gold electrodes can be utilized as a sensor for humidity, which presents a good response to the detection of relative humidity (RH).     

Machine learning of diffraction image patterns for accurate classification of cells modeled with different nuclear sizes

Measurement of nuclear‐to‐cytoplasm (N:C) ratios plays an important role in detection of atypical and tumor cells. Yet, current clinical methods rely heavily on immunofluroescent staining and manual reading. To achieve the goal of rapid and label‐free cell classification, realistic optical cell models (OCMs) have been developed for simulation of diffraction imaging by single cells. A total of 1892 OCMs were obtained with varied nuclear volumes and orientations to calculate cross‐polarized diffraction image (p‐DI) pairs divided into three nuclear size groups of OCM_S, OCM_O and OCM_L based on three prostate cell structures. Binary classifications were conducted among the three groups with image parameters extracted by the algorithm of gray‐level co‐occurrence matrix. The averaged accuracy of support vector machine (SVM) classifier on test dataset of p‐DI was found to be 98.8% and 97.5% respectively for binary classifications of OCM_S vs OCM_O and OCM_O vs OCM_L for the prostate cancer cell structure. The values remain about the same at 98.9% and 97.8% for the smaller prostate normal cell structures. The robust performance of SVM over clustering classifiers suggests that the high‐order correlations of diffraction patterns are potentially useful for label‐free detection of single cells with large N:C ratios.