Improving biological functions of endothelial progenitor cells (EPCs) is beneficial to maintaining endothelium homeostasis and promoting vascular re-endothelialization. Because macroautophagy/ autophagy has been documented as a double-edged sword in cell functions, its effects on EPCs remain to be elucidated. This study was designed to explore the role and molecular mechanisms of store-operated calcium entry (SOCE)-activated autophagy in proliferation of EPCs under hypercholesterolemia. We employed oxidized low-density lipoprotein (ox-LDL) to mimic hypercholesterolemia in bone marrowderived EPCs from rat. Ox-LDL dose-dependently activated autophagy flux, while inhibiting EPC proliferation. Importantly, inhibition of autophagy either by silencing Atg7 or by 3-methyladenine treatment, further aggravated proliferative inhibition by ox-LDL, suggesting the protective effects of autophagy against ox-LDL. Interestingly, ox-LDL increased STIM1 expression and intracellular Ca 2C concentration. Either Ca 2C chelators or deficiency in STIM1 attenuated ox-LDL-induced autophagy activation, confirming the involvement of SOCE in the process. Furthermore, CAMKK2 (calcium/ calmodulin-dependent protein kinase kinase 2, b) activation and MTOR (mechanistic target of rapamycin [serine/threonine kinase]) deactivation were associated with autophagy modulation. Together, our results reveal a novel signaling pathway of SOCE-CAMKK2 in the regulation of autophagy and offer new insights into the important roles of autophagy in maintaining proliferation and promoting the survival capability of EPCs. This may be beneficial to improving EPC transplantation efficacy and enhancing vascular reendothelialization in patients with hypercholesterolemia.

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BackgroundIdentification of the recombination hot/cold spots is critical for understanding the mechanism of recombination as well as the genome evolution process. However, experimental identification of recombination spots is both time-consuming and costly. Developing an accurate and automated method for reliably and quickly identifying recombination spots is thus urgently needed.ResultsHere we proposed a novel approach by fusing features from pseudo nucleic acid composition (PseNAC), including NAC, n-tier NAC and pseudo dinucleotide composition (PseDNC). A recursive feature extraction by linear kernel support vector machine (SVM) was then used to rank the integrated feature vectors and extract optimal features. SVM was adopted for identifying recombination spots based on these optimal features. To evaluate the performance of the proposed method, jackknife cross-validation test was employed on a benchmark dataset. The overall accuracy of this approach was 84.09%, which was higher (from 0.37% to 3.79%) than those of state-of-the-art tools.ConclusionsComparison results suggested that linear kernel SVM is a useful vehicle for identifying recombination hot/cold spots.

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