TEA domain transcription factor 1(TEAD1) induces cardiac fibroblasts cells remodeling through BRD4/Wnt4 pathway.

Cardiac fibroblasts (CFs) are the primary cells tasked with depositing and remodeling collagen and significantly associated with heart failure (HF). TEAD1 has been shown to be essential for heart development and homeostasis. However, fibroblast endogenous TEAD1 in cardiac remodeling remains incompletely understood. Transcriptomic analyses revealed consistently upregulated cardiac TEAD1 expression in mice 4 weeks after transverse aortic constriction (TAC) and Ang-II infusion. Further investigation revealed that CFs were the primary cell type expressing elevated TEAD1 levels in response to pressure overload. Conditional TEAD1 knockout was achieved by crossing TEAD1-floxed mice with CFs- and myofibroblasts-specific Cre mice. Echocardiographic and histological analyses demonstrated that CFs- and myofibroblasts-specific TEAD1 deficiency and treatment with TEAD1 inhibitor, VT103, ameliorated TAC-induced cardiac remodeling. Mechanistically, RNA-seq and ChIP-seq analysis identified Wnt4 as a novel TEAD1 target. TEAD1 has been shown to promote the fibroblast-to-myofibroblast transition through the Wnt signalling pathway, and genetic Wnt4 knockdown inhibited the pro-transformation phenotype in CFs with TEAD1 overexpression. Furthermore, co-immunoprecipitation combined with mass spectrometry, chromatin immunoprecipitation, and luciferase assays demonstrated interaction between TEAD1 and BET protein BRD4, leading to the binding and activation of the Wnt4 promoter. In conclusion, TEAD1 is an essential regulator of the pro-fibrotic CFs phenotype associated with pathological cardiac remodeling via the BRD4/Wnt4 signalling pathway.

Downregulation of apoptotic repressor AVEN exacerbates cardiac injury after myocardial infarction.

Myocardial infarction (MI) is a leading cause of heart failure (HF), associated with morbidity and mortality worldwide. As an essential part of gene expression regulation, the role of alternative polyadenylation (APA) in post-MI HF remains elusive. Here, we revealed a global, APA-mediated, 3' untranslated region (3' UTR)-lengthening pattern in both human and murine post-MI HF samples. Furthermore, the 3' UTR of apoptotic repressor gene, AVEN, is lengthened after MI, contributing to its downregulation. AVEN knockdown increased cardiomyocyte apoptosis, whereas restoration of AVEN expression substantially improved cardiac function. Mechanistically, AVEN 3' UTR lengthening provides additional binding sites for miR-30b-5p and miR-30c-5p, thus reducing AVEN expression. Additionally, PABPN1 (poly(A)-binding protein 1) was identified as a potential regulator of AVEN 3' UTR lengthening after MI. Altogether, our findings revealed APA as a unique mechanism regulating cardiac injury in response to MI and also indicated that the APA-regulated gene, AVEN, holds great potential as a critical therapeutic target for treating post-MI HF.

Metabolic substrates, histone modifications, and heart failure.

Histone epigenetic modifications are chemical modification changes to histone amino acid residues that modulate gene expression without altering the DNA sequence. As both the phenotypic and causal factors, cardiac metabolism disorder exacerbates mitochondrial ATP generation deficiency, thus promoting pathological cardiac hypertrophy. Moreover, several concomitant metabolic substrates also promote the expression of hypertrophy-responsive genes via regulating histone modifications as substrates or enzyme-modifiers, indicating their dual roles as metabolic and epigenetic regulators. This review focuses on the cardiac acetyl-CoA-dependent histone acetylation, NAD+-dependent SIRT-mediated deacetylation, FAD+-dependent LSD-mediated, and α-KG-dependent JMJD-mediated demethylation after briefly addressing the pathological and physiological cardiac energy metabolism. Besides using an "iceberg model" to explain the dual role of metabolic substrates as both metabolic and epigenetic regulators, we also put forward that the therapeutic supplementation of metabolic substrates is promising to blunt HF via re-establishing histone modifications.

A deep learning approach to the diagnosis of atelectasis and attic retraction pocket in otitis media with effusion using otoscopic images.

BACKGROUND: This study aimed to develop and validate a deep learning (DL) model to identify atelectasis and attic retraction pocket in cases of otitis media with effusion (OME) using multi-center otoscopic images. METHOD: A total of 6393 OME otoscopic images from three centers were used to develop and validate a DL model for detecting atelectasis and attic retraction pocket. A threefold random cross-validation procedure was adopted to divide the dataset into training validation sets on a patient level. A team of otologists was assigned to diagnose and characterize atelectasis and attic retraction pocket in otoscopic images. Receiver operating characteristic (ROC) curves, including area under the ROC curve (AUC), accuracy, sensitivity, and specificity were used to assess the performance of the DL model. Class Activation Mapping (CAM) illustrated the discriminative regions in the otoscopic images. RESULTS: Among all OME otoscopic images, 3564 (55.74%) were identified with attic retraction pocket, and 2460 (38.48%) with atelectasis. The diagnostic DL model of attic retraction pocket and atelectasis achieved a threefold cross-validation accuracy of 89% and 79%, AUC of 0.89 and 0.87, a sensitivity of 0.93 and 0.71, and a specificity of 0.62 and 0.84, respectively. Larger and deeper cases of atelectasis and attic retraction pocket showed greater weight, based on the red color depicted in the heat map of CAM. CONCLUSION: The DL algorithm could be employed to identify atelectasis and attic retraction pocket in otoscopic images of OME, and as a tool to assist in the accurate diagnosis of OME.

A Deep Learning Approach to Predict Conductive Hearing Loss in Patients With Otitis Media With Effusion Using Otoscopic Images.

Importance: Otitis media with effusion (OME) is one of the most common causes of acquired conductive hearing loss (CHL). Persistent hearing loss is associated with poor childhood speech and language development and other adverse consequence. However, to obtain accurate and reliable hearing thresholds largely requires a high degree of cooperation from the patients. Objective: To predict CHL from otoscopic images using deep learning (DL) techniques and a logistic regression model based on tympanic membrane features. Design, Setting, and Participants: A retrospective diagnostic/prognostic study was conducted using 2790 otoscopic images obtained from multiple centers between January 2015 and November 2020. Participants were aged between 4 and 89 years. Of 1239 participants, there were 209 ears from children and adolescents (aged 4-18 years [16.87%]), 804 ears from adults (aged 18-60 years [64.89%]), and 226 ears from older people (aged >60 years, [18.24%]). Overall, 679 ears (54.8%) were from men. The 2790 otoscopic images were randomly assigned into a training set (2232 [80%]), and validation set (558 [20%]). The DL model was developed to predict an average air-bone gap greater than 10 dB. A logistic regression model was also developed based on otoscopic features. Main Outcomes and Measures: The performance of the DL model in predicting CHL was measured using the area under the receiver operating curve (AUC), accuracy, and F1 score (a measure of the quality of a classifier, which is the harmonic mean of precision and recall; a higher F1 score means better performance). In addition, these evaluation parameters were compared to results obtained from the logistic regression model and predictions made by three otologists. Results: The performance of the DL model in predicting CHL showed the AUC of 0.74, accuracy of 81%, and F1 score of 0.89. This was better than the results from the logistic regression model (ie, AUC of 0.60, accuracy of 76%, and F1 score of 0.82), and much improved on the performance of the 3 otologists; accuracy of 16%, 30%, 39%, and F1 scores of 0.09, 0.18, and 0.25, respectively. Furthermore, the DL model took 2.5 seconds to predict from 205 otoscopic images, whereas the 3 otologists spent 633 seconds, 645 seconds, and 692 seconds, respectively. Conclusions and Relevance: The model in this diagnostic/prognostic study provided greater accuracy in prediction of CHL in ears with OME than those obtained from the logistic regression model and otologists. This indicates great potential for the use of artificial intelligence tools to facilitate CHL evaluation when CHL is unable to be measured.