Accumulating evidence has shown that diabetes accelerates aging and endothelial cell senescence is involved in the pathogenesis of diabetic vascular complications, including diabetic retinopathy. Oxidative stress is recognized as a key factor in the induction of endothelial senescence and diabetic retinopathy. However, specific mechanisms involved in oxidative stress-induced endothelial senescence have not been elucidated. We hypothesized that Sirt6, which is a nuclear, chromatin-bound protein critically involved in many pathophysiologic processes such as aging and inflammation, may have a role in oxidative stress-induced vascular cell senescence. Measurement of Sirt6 expression in human endothelial cells revealed that H₂O₂treatment significantly reduced Sirt6 protein. The loss of Sirt6 was associated with an induction of a senescence phenotype in endothelial cells, including decreased cell growth, proliferation and angiogenic ability, and increased expression of senescence-associatedβ-galactosidase activity. Additionally, H₂O₂treatment reduced eNOS expression, enhanced p21 expression, and dephosphorylated (activated) retinoblastoma (Rb) protein. All of these alternations were attenuated by overexpression of Sirt6, while partial knockdown of Sirt6 expression by siRNA mimicked the effect of H₂O₂. In conclusion, these results suggest that Sirt6 is a critical regulator of endothelial senescence and oxidative stress-induced downregulation of Sirt6 is likely involved in the pathogenesis of diabetic retinopathy.

Aging is associated with an increased incidence and prevalence of renal glomerular diseases. Sirtuin (Sirt) 6, a nicotinamide adenine dinucleotide (NAD)-dependent histone deacetylase, has been shown to protect against multiple age-associated phenotypes; however it is unknown whether Sirt6 has a direct pathophysiologic role in the kidney. In the present study, we demonstrate that Sirt6 is expressed in the kidney and aging Sirt6-deficient mice exhibit renal hypertrophy with glomerular enlargement. Sirt6 deletion induces podocyte injury, including decreases in slit diaphragm proteins, foot process effacement, and cellular loss, resulting in proteinuria. Knockdown of Sirt6 in cultured primary murine podocytes induces shape changes with loss of process formation and cell apoptosis. Moreover, Sirt6 deficiency results in progressive renal inflammation and fibrosis. Collectively, these data provide compelling evidence that Sirt6 is important for podocyte homeostasis and maintenance of glomerular function, and warrant further investigation into the role of Sirt6 in age-associated kidney dysfunction.

Progressive loss of retinal ganglion cells (RGCs) leads to irreversible visual deficits in glaucoma. Here, we found that the level of cyclic AMP and the activity and expression of its mediator Epac1 were increased in retinas of two mouse models of ocular hypertension. Genetic depletion of Epac1 significantly attenuated ocular hypertension–induced detrimental effects in the retina, including vascular inflammation, neuronal apoptosis and necroptosis, thinning of ganglion cell complex layer, RGC loss, and retinal neuronal dysfunction. With bone marrow transplantation and various Epac1 conditional knockout mice, we further demonstrated that Epac1 in retinal neuronal cells (especially RGCs) was responsible for their death. Consistently, pharmacologic inhibition of Epac activity prevented RGC loss. Moreover, in vitro study on primary RGCs showed that Epac1 activation was sufficient to induce RGC death, which was mechanistically mediated by CaMKII activation. Taken together, these findings indicate that neuronal Epac1 plays a critical role in retinal neurodegeneration and suggest that Epac1 could be considered a target for neuroprotection in glaucoma.

Purpose: Retinal ischemia, a common cause of several vision-threatening diseases, contributes to the death of retinal neurons, particularly retinal ganglion cells (RGCs). Heat shock transcription factor 1 (HSF1), a stress-responsive protein, has been shown to be important in response to cellular stress stimuli, including ischemia. This study is to investigate whether HSF1 has a role in retinal neuronal injury in a mouse model of retinal ischemia-reperfusion (IR). Methods: IR was induced by inserting an infusion needle into the anterior chamber of the right eye and elevating a saline reservoir connected to the needle to raise the intraocular pressure to 110 mm Hg for 45 minutes. HSF1, Hsp70, molecules in the endoplasmic reticulum (ER) stress branches, tau phosphorylation, inflammatory molecules, and RGC injury were determined by immunohistochemistry, Western blot, or quantitative PCR. Results: HSF1 expression was significantly increased in the retina 6 hours after IR. Using our novel transgenic mice carrying full-length human HSF gene, we demonstrated that IR-induced retinal neuronal apoptosis and necroptosis were abrogated 12 hours after IR. RGCs and their function were preserved in the HSF1 transgenic mice 7 days after IR. Mechanistically, the beneficial effects of HSF1 may be mediated by its induction of chaperone protein Hsp70 and alleviation of ER stress, leading to decreased tau phosphorylation and attenuated inflammatory response 12 to 24 hours after IR. Conclusions: These data provide compelling evidence that HSF1 is neuroprotective against retinal IR injury, and boosting HSF1 expression may be a beneficial strategy to limit neuronal degeneration in retinal diseases.

Epac1 promotes pathological blood vessel formation and is a therapeutic target for vasoproliferative diseases.

Accumulating studies appear to suggest that the risk factors for venous thromboembolism (VTE) among young-middle-aged inpatients are different from those among elderly people. Therefore, the current prediction models for VTE are not applicable to young-middle-aged inpatients. The aim of this study was to develop and externally validate a new prediction model for young-middle-aged people using machine learning methods. The clinical data sets linked with 167 inpatients with deep venous thrombosis (DVT) and/or pulmonary embolism (PE) and 406 patients without DVT or PE were compared and analysed with machine learning techniques. Five algorithms, including logistic regression, decision tree, feed-forward neural network, support vector machine, and random forest, were used for training and preparing the models. The support vector machine model had the best performance, with AUC values of 0.806–0.944 for 95% CI, 59% sensitivity and 99% specificity, and an accuracy of 87%. Although different top predictors of adverse outcomes appeared in the different models, life-threatening illness, fibrinogen, RBCs, and PT appeared to be more consistently featured by the different models as top predictors of adverse outcomes. Clinical data sets of young and middle-aged inpatients can be used to accurately predict the risk of VTE with a support vector machine model.