A simple and accurate method to quantify real-time contraction of vascular smooth muscle cell in vitro.

Vascular smooth muscle cells (VSMCs) constitute the medial layer of the blood vessel wall. Their contractile state regulates blood flow in physiological and pathological conditions. Current methods for assessing the contractility of VSMCs are not amenable to the high-throughput screening of pharmaceutical compounds. This study aimed to develop a method to address this shortcoming in the field. Real-time contraction was visualized in living VSMCs using the exogenous expression of green fluorescent protein (GFP). Image-Pro Plus software (IPPS) was used to measure various morphological cell indices. In phenylephrine-treated VSMCs, GFP fluorescence imaging was more accurate than brightfield imaging or phalloidin staining in representing VSMC morphology, as measured using IPPS. Among the multiple indices of VSMC shape, area and mean-diameter were more sensitive than length in reflecting the morphological changes in VSMC. We developed a new index, compound length, by combining the mean-diameter and length to differentiate contracted and uncontracted VSMCs. Based on the compound length, we further generated a contraction index to define a single-VSMC contractile status as single-VSMC contraction-index (SVCI). Finally, compound length and SVCI were validated to effectively assess cell contraction in VSMCs challenged with U46619 and KCl. In conclusion, GFP-based indices of compound length and SVCI can accurately quantify the real-time contraction of VSMCs. In future, the new method will be applied to high-throughput drug screening or basic cardiovascular research.

Excessive expression of miR-1a by statin causes skeletal injury through targeting mitogen-activated protein kinase kinase kinase 1.

BACKGROUNDS: A major side effect of statin, a widely used drug to treat hyperlipidemia, is skeletal myopathy through cell apoptosis. The aim of this study is to investigate the roles of microRNA in statin-induced injury. METHODS: Apolipoprotein E knockout (ApoE-/-) mice were administered with simvastatin (20 mg/kg/day) for 8 weeks. Exercise capacity was evaluated by hanging grid test, forelimb grip strength, and running tolerance test. RESULTS: In cultured skeletal muscle cells, statin increased the levels of miR-1a but decreased the levels of mitogen-activated protein kinase kinase kinase 1 (MAP3K1) in a time or dose dependent manner. Both computational target-scan analysis and luciferase gene reporter assay indicated that MAP3K1 is the target gene of miR-1a. Statin induced cell apoptosis of skeletal muscle cells, but abolished by downregulating of miR-1a or upregulation of MAP3K1. Further, the effects of miR-1a inhibition on statin-induced cell apoptosis were ablated by MAP3K1 siRNA. In ApoE-/- mice, statin induced cell apoptosis of skeletal muscle cells and decreased exercise capacity in mice infected with vector, but not in mice with lentivirus-mediated miR-1a gene silence. CONCLUSION: Statin causes skeletal injury through induction of miR-1a excessive expression to decrease MAP3K1 gene expression.

Machine Learning Model for Predicting Acute Respiratory Failure in Individuals With Moderate-to-Severe Traumatic Brain Injury.

Background: There is a high incidence of acute respiratory failure (ARF) in moderate or severe traumatic brain injury (M-STBI), worsening outcomes. This study aimed to design a predictive model for ARF. Methods: Adult patients with M-STBI [3 ≤ Glasgow Coma Scale (GCS) ≤ 12] with a definite history of brain trauma and abnormal head on CT images, obtained from September 2015 to May 2017, were included. Patients with age >80 years or <18 years, multiple injuries with TBI upon admission, or pregnancy (in women) were excluded. Two models based on machine learning extreme gradient boosting (XGBoost) or logistic regression, respectively, were developed for predicting ARF within 48 h upon admission. These models were evaluated by out-of-sample validation. The samples were assigned to the training and test sets at a ratio of 3:1. Results: In total, 312 patients were analyzed including 132 (42.3%) patients who had ARF. The GCS and the Marshall CT score, procalcitonin (PCT), and C-reactive protein (CRP) on admission significantly predicted ARF. The novel machine learning XGBoost model was superior to logistic regression model in predicting ARF [area under the receiver operating characteristic (AUROC) = 0.903, 95% CI, 0.834-0.966 vs. AUROC = 0.798, 95% CI, 0.697-0.899; p < 0.05]. Conclusion: The XGBoost model could better predict ARF in comparison with logistic regression-based model. Therefore, machine learning methods could help to develop and validate novel predictive models.

Potential roles of GATA binding protein 5 in cardiovascular diseases.

It is known that functional defects of GATA binding protein 5 (GATA5), an important member of GATA transcription factor family, could cause multiple congenital defects. However, the mechanisms of this transcription factor in cardiovascular diseases are still little known. Finding a genetic approach should help with understanding the possible roles of GATA5 in different cardiovascular diseases and purpose it as a possible therapeutic agent. Hence, this review is divided into three chapters to summarize the roles and main regulatory mechanisms of GATA5 in hypertension, arrhythmia and congenital heart disease, respectively. In each chapter, this review firstly introduces the roles of GATA5 mutations, and then discusses the main regulatory mechanisms of GATA5 in the corresponding diseases (Such as the endothelial dysfunction signaling pathway in the chapter of hypertension, GATA5-NaV1.5 signaling pathway in the chapter of arrhythmia, GATA5-HEY2 and GATA5-Nodal signaling pathway in the chapter of congenital heart disease). Additionally, based on these regulatory networks, it is also speculated that abnormal methylation of the GATA5 gene promoter may lead to cardiovascular diseases such as congenital heart disease. This conjecture is proposed to enrich the regulatory networks of GATA5 and provide a theoretical basis for diagnosis and treatment of cardiovascular diseases.

Flavanomarein inhibits high glucose-stimulated epithelial-mesenchymal transition in HK-2 cells via targeting spleen tyrosine kinase.

Flavanomarein (FM) is a major natural compound of Coreopsis tinctoria Nutt with protective effects against diabetic nephropathy (DN). In this study, we investigated the effects of FM on epithelial-mesenchymal transition (EMT) in high glucose (HG)-stimulated human proximal tubular epithelial cells (HK-2) and the underlying mechanisms, including both direct targets and downstream signal-related proteins. The influence of FM on EMT marker proteins was evaluated via western blot. Potential target proteins of FM were searched using Discovery Studio 2017 R2. Gene Ontology (GO) analysis was conducted to enrich the proteins within the protein-protein interaction (PPI) network for biological processes. Specific binding of FM to target proteins was examined via molecular dynamics and surface plasmon resonance analyses (SPR). FM promoted the proliferation of HK-2 cells stimulated with HG and inhibited EMT through the Syk/TGF-ß1/Smad signaling pathway. Spleen tyrosine kinase (Syk) was predicted to be the most likely directly interacting protein with FM. Combined therapy with a Syk inhibitor and FM presents significant potential as an effective novel therapeutic strategy for DN.