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Type 1 and type 2 diabetic patients are at increased risk of cardiomyopathy and heart failure is a major cause of death for these patients. Cardiomyopathy in diabetes is associated with a cluster of features including decreased diastolic compliance, interstitial fibrosis and myocyte hypertrophy. The mechanisms leading to diabetic cardiomyopathy remain uncertain. Diabetes is associated with most known risk factors for cardiac failure seen in the overall population, including obesity, dyslipidemia, thrombosis, infarction, hypertension, activation of multiple hormone and cytokine systems, autonomic neuropathy, endothelial dysfunction and coronary artery disease. In light of these common contributing pathologies it remains uncertain whether diabetic cardiomyopathy is a distinct disease. It is also uncertain which factors are most important to the overall incidence of heart failure in diabetic patients. This review focuses on factors that can have direct effects on diabetic cardiomyocytes: hyperglycemia, altered fuel use, and changes in the activity of insulin and angiotensin. Particular attention is given to the changes these factors can have on cardiac mitochondria and the role of reactive oxygen species in mediating injury to cardiomyocytes.

To obtain a comprehensive understanding of transcriptomic reprogramming under salt stress, we performed whole-transcriptome sequencing on the leaf and root of soybean seedlings subjected to salt treatment in a time-course experiment (0, 1, 2, 4, 24, and 48 hr). This time series dataset enabled us to identify important hubs and connections of gene expressions. We highlighted the analysis on phytohormone signaling pathways and their possible crosstalks. Differential expressions were also found among those genes involved in carbon and nitrogen metabolism. In general, the salt-treated seedlings slowed down their photosynthetic functions and ramped up sugar catabolism to provide extra energy for survival. Primary nitrogen assimilation was shut down whereas nitrogen resources were redistributed. Overall, the results from the transcriptomic analyses indicate that the plant uses a multipronged approach to overcome salt stress, with both fast-acting, immediate physiological responses, and longer term reactions that may involve metabolic adjustment.

Lasting activations of toll-like receptors (TLRs), MAPK and NF-κB pathways can support influenza A virus (IAV) infection and promote pneumonia. In this study, we have investigated the effect and mechanism of action of emodin on IAV infection using qRT-PCR, western blotting, ELISA, Nrf2 luciferase reporter, siRNA and plaque inhibition assays. The results showed that emodin could significantly inhibit IAV (ST169, H1N1) replication, reduce IAV-induced expressions of TLR2/3/4/7, MyD88 and TRAF6, decrease IAV-induced phosphorylations of p38/JNK MAPK and nuclear translocation of NF-κB p65. Emodin also activated the Nrf2 pathway, decreased ROS levels, increased GSH levelss and GSH/GSSG ratio, and upregulated the activities of SOD, GR, CAT and GSH-Px after IAV infection. Suppression of Nrf2 via siRNA markedly blocked the inhibitory effects of emodin on IAV-induced activations of TLR4, p38/JNK, and NF-κB pathways and on IAV-induced production of IL-1β, IL-6 and expression of IAV M2 protein. Emodin also dramatically increased the survival rate of mice, reduced lung edema, pulmonary viral titer and inflammatory cytokines, and improved lung histopathological changes. In conclusion, emodin can inhibit IAV replication and influenza viral pneumonia, at least in part, by activating Nrf2 signaling and inhibiting IAV-induced activations of the TLR4, p38/JNK MAPK and NF-κB pathways.

In many aspects of life, epigenetics, or the altering of phenotype without changes in sequences, play an essential role in biological function. A vast number of epigenomic datasets are emerging as a result of the advent of next‐generation sequencing. Annotation, comparison, visualization, and interpretation of epigenomic datasets remain key aspects of computational biology. ChIPseeker is a Bioconductor package for performing these analyses among variable epigenomic datasets. The fundamental functions of ChIPseeker, including data preparation, annotation, comparison, and visualization, are explained in this article. ChIPseeker is a freely available open‐source package that may be found at https://www.bioconductor.org/packages/ChIPseeker. © 2022 Wiley Periodicals LLC. Basic Protocol 1: ChIPseeker and epigenomic dataset preparation Basic Protocol 2: Annotation of epigenomic datasets Basic Protocol 3: Comparison of epigenomic datasets Basic Protocol 4: Visualization of annotated results Basic Protocol 5: Functional analysis of epigenomic datasets Basic Protocol 6: Genome‐wide and locus‐specific distribution of epigenomic datasets Basic Protocol 7: Heatmaps and metaplots of epigenomic datasets