Empagliflozin rescues pro-arrhythmic and Ca2+ homeostatic effects of transverse aortic constriction in intact murine hearts.

We explored physiological effects of the sodium-glucose co-transporter-2 inhibitor empagliflozin on intact experimentally hypertrophic murine hearts following transverse aortic constriction (TAC). Postoperative drug (2-6 weeks) challenge resulted in reduced late Na+ currents, and increased phosphorylated (p-)CaMK-II and Nav1.5 but not total (t)-CaMK-II, and Na+/Ca2+ exchanger expression, confirming previous cardiomyocyte-level reports. It rescued TAC-induced reductions in echocardiographic ejection fraction and fractional shortening, and diastolic anterior and posterior wall thickening. Dual voltage- and Ca2+-optical mapping of Langendorff-perfused hearts demonstrated that empagliflozin rescued TAC-induced increases in action potential durations at 80% recovery (APD80), Ca2+ transient peak signals and durations at 80% recovery (CaTD80), times to peak Ca2+ (TTP100) and Ca2+ decay constants (Decay30-90) during regular 10-Hz stimulation, and Ca2+ transient alternans with shortening cycle length. Isoproterenol shortened APD80 in sham-operated and TAC-only hearts, shortening CaTD80 and Decay30-90 but sparing TTP100 and Ca2+ transient alternans in all groups. All groups showed similar APD80, and TAC-only hearts showed greater CaTD80, heterogeneities following isoproterenol challenge. Empagliflozin abolished or reduced ventricular tachycardia and premature ventricular contractions and associated re-entrant conduction patterns, in isoproterenol-challenged TAC-operated hearts following successive burst pacing episodes. Empagliflozin thus rescues TAC-induced ventricular hypertrophy and systolic functional, Ca2+ homeostatic, and pro-arrhythmogenic changes in intact hearts.

Development of a whole-slide-level segmentation-based dMMR/pMMR deep learning detector for colorectal cancer.

To investigate whole-slide-level prediction in the field of artificial intelligence identification of dMMR/pMMR from hematoxylin and eosin (H&E) in colorectal cancer (CRC), we established a segmentation-based dMMR/pMMR deep learning detector (SPEED). Our model was approximately 1,700 times faster than that of the classification-based model. For the internal validation cohort, our model yielded an overall AUC of 0.989. For the external validation cohort, the model exhibited a high performance, with an AUC of 0.865. The human‒machine strategy further improved the model performance for external validation by an AUC up to 0.988. Our whole-slide-level prediction model provided an approach for dMMR/pMMR detection from H&E whole slide images with excellent predictive performance and less computer processing time in patients with CRC.

The characteristics and molecular targets of antiarrhythmic natural products.

Arrhythmia is one of the most common cardiovascular diseases. The search for new drugs to suppress various types of cardiac arrhythmias has always been the focus of attention. In the past decade, the screening of antiarrhythmic active substances from plants has received extensive attention. These natural compounds have obvious antiarrhythmic effects, and chemical modifications based on natural compounds have greatly increased their pharmacological properties. The chemical modification of botanical antiarrhythmic drugs is closely related to the development of new and promising drugs. Therefore, the structural characteristics and action targets of natural compounds with antiarrhythmic effects are reviewed in this paper, so that pharmacologists can select antiarrhythmic lead compounds from natural compounds based on the disease target - chemical structural characteristics.

Single-cell RNA sequencing of murine hearts for studying the development of the cardiac conduction system.

The development of the cardiac conduction system (CCS) is essential for correct heart function. However, critical details on the cell types populating the CCS in the mammalian heart during the development remain to be resolved. Using single-cell RNA sequencing, we generated a large dataset of transcriptomes of ~0.5 million individual cells isolated from murine hearts at six successive developmental corresponding to the early, middle and late stages of heart development. The dataset provides a powerful library for studying the development of the heart's CCS and other cardiac components. Our initial analysis identified distinct cell types between 20 to 26 cell types across different stages, of which ten are involved in forming the CCS. Our dataset allows researchers to reuse the datasets for data mining and a wide range of analyses. Collectively, our data add valuable transcriptomic resources for further study of cardiac development, such as gene expression, transcriptional regulation and functional gene activity in developing hearts, particularly the CCS.

SETD8 promotes glycolysis in colorectal cancer via regulating HIF1α/HK2 axis.

Glycolysis is one of the factors influencing cancer cell growth and metastasis. Here, we aimed to investigate the role of SETD8 gene, which is a pro-oncogene. Using bioinformatics tools including Ualcan, Timer, GEPIA, and PrognoScan to study the expression of SETD8 in colorectal cancer, we found that SETD8 expression was higher in colon cancer tissues than that in normal tissues. Higher levels of SETD8 predicted poorer survival of patients. This piqued our interest, so we transfected SETD8 knockdown and overexpression plasmids into colorectal cancer cells and found that SETD8 overexpression enhanced proliferation and glycolysis in colon cancer cells, while SETD8 knockdown decreased cell proliferation and glycolysis. Mechanistically, we examined the expression of HIF1α and HK2 protein by western-blot assay and found that SETD8 activated the HIF1α/HK2 pathway. Then, we treated SETD8-overexpressed cells with HIF1α inhibitor and found that the pro-tumor growth and glycolytic effects of SETD8 were reversed, indicating that SETD8 promoted the growths of colorectal cancer cells by upregulating the HIF1α /HK2 pathway.

ALKBH2 inhibition alleviates malignancy in colorectal cancer by regulating BMI1-mediated activation of NF-κB pathway.

BACKGROUND: The alkB homolog 2, alpha-ketoglutarate-dependent dioxygenase (ALKBH2) gene is involved in DNA repair and is expressed in different types of malignancies. However, the role of ALKBH2 in colorectal carcinoma (CRC) remains unclear. This study aimed to explore the potential mechanism of ALKBH2 and its function in CRC. METHODS: The expression levels of ALKBH2 in CRC tissues and cells were determined by qRT-PCR. Following that, the role of ALKBH2 in cell proliferation, invasion, and epithelial-mesenchymal transition (EMT) in CRC cells (Caco-2 and LOVO) were assessed by Cell Counting Kit-8 (CCK-8), transwell assays, and Western blotting, respectively. The effect of ALKBH2 on B cell-specific Moloney murine leukemia virus integration site 1 (BMI1) and downstream NF-κB pathway was determined by Western blotting and luciferase reporter assay. RESULTS: The expression of ALKBH2 was significantly upregulated both in CRC tissues and cells. Further experiments demonstrated that reduction of ALKBH2 suppressed Caco-2 and LOVO cell proliferation and invasion. Moreover, ALKBH2 knockdown also suppressed EMT, which increased E-cadherin expression and reduced N-cadherin expression. Besides, ALKBH2 silencing inhibited BMI1 expression and reduced nuclear accumulation of the NF-κB p65 protein, as well as the luciferase activity of NF-κB p65. Upregulation of BMI1 reversed the effect of ALKBH2 knockdown on the proliferation and invasion in CRC cells. CONCLUSIONS: Our findings suggest that suppression of ALKBH2 alleviates malignancy in CRC by regulating BMI1-mediated activation of NF-κB pathway. ALKBH2 may serve as a potential treatment target for human CRC.

Network­based gene function inference method to predict optimal gene functions associated with fetal growth restriction.

The guilt by association (GBA) principle has been widely used to predict gene functions, and a network­based approach may enhance the confidence and stability of the analysis compared with focusing on individual genes. Fetal growth restriction (FGR), is the second primary cause of perinatal mortality. Therefore, the present study aimed to predict the optimal gene functions for FGR using a network­based GBA method. The method was comprised of four parts: Identification of differentially­expressed genes (DEGs) between patients with FGR and normal controls based on gene expression data; construction of a co­expression network (CEN) dependent on DEGs, using the Spearman correlation coefficient algorithm; collection of gene ontology (GO) data on the basis of a known confirmed database and DEGs; and prediction of optimal gene functions using the GBA algorithm, for which the area under the receiver operating characteristic curve (AUC) was obtained for each GO term. A total of 115 DEGs and 109 GO terms were obtained for subsequent analysis. All DEGs were mapped to the CEN and formed 6,555 edges. The results of GBA algorithm demonstrated that 78 GO terms had a good classification performance with AUC >0.5. In particular, the AUC for 5 of the GO terms was >0.7, and these were defined as optimal gene functions, including defense response, immune system process, response to stress, cellular response to chemical stimulus and positive regulation of biological process. In conclusion, the results of the present study provided insights into the pathological mechanism underlying FGR, and provided potential biomarkers for early detection and targeted treatment of this disease. However, the interactions between the 5 GO terms remain unclear, and further studies are required.

Design, synthesis, and antitumor evaluation of quinoline-imidazole derivatives.

A series of compounds bearing quinoline-imidazole (8a-e, 9a-e, 10a-e, 11a-e, and 12a-e) not reported previously were designed and synthesized. The target compounds were evaluated for antitumor activity against A549, PC-3, HepG2, and MCF-7 cells by the MTT method, with NVP-BEZ235 being the positive control. Most compounds showed moderate activity and compound 12a showed the best activity against HepG2, A549, and PC-3 cells, with half-maximal inhibitory concentration (IC50 ) values of 2.42 ± 1.02 µM, 6.29 ± 0.99 µM, and 5.11 ± 1.00 µM, respectively, which was equal to NVP-BEZ235 (0.54 ± 0.13 µM, 0.36 ± 0.06 µM, 0.20 ± 0.01 µM). Besides, the IC50 value of 12a against the cell line WI-38 (human fetal lung fibroblasts) was 32.8 ± 1.23 µM, indicating that the target compounds were selective for cancer cells. So, 11a and 12a were evaluated against PI3Kα and mTOR to find out if the compounds acted through the PI3K-Akt-mTOR signal transduction pathway. The inhibition ratios to PI3Kα and mTOR were slightly lower than that of NVP-BEZ235, suggesting there may be some other mechanisms of action. The structure-activity relationships and docking study of 11a and 12a revealed that the latter was superior. Moreover, the target compounds showed better in vitro anticancer activity when the C-6 of the quinoline ring was replaced by a bromine atom.