CACCT: An Automated Tool of Detecting Complicated Cardiac Malformations in Mouse Models

Single-cell RNA sequencing profiling of mouse cardiac cells in response to retinoic acid

Congenital heart disease (CHD) is the leading cause of birth defect-related mortality. CHD is a multifactorial, complex disease involving environmental factors playing important roles. To elucidate the cardiac cellular and molecular mechanisms of cardiac malformation, we administered pregnant mice with a single dose of all-trans retinoic acid (RA) at E8.5, as the CHD model. We performed single-cell RNA sequencing on cardiac cells from developing mouse hearts spanning from E8.5 to E17.5 after RA administration. A total of 69,447 cells were obtained from seven developmental stages ranging from E8.5 to E17.5. RA significantly impacted various CM subpopulations, particularly the outflow tract CMs at E9.0 by reduction of Tdgf1 expression. RA also influences the transition of endocardial-to-mesenchymal cells by decreasing the Stmn2 levels, which may contribute to abnormal valve development. In addition, RA altered the metabolic pattern of epicardial cells at E11.5 and promoted its differentiation potential. Taken together, these results are valuable for the development of preventive and therapeutic strategies for CHDs.

Integrating artificial intelligence into the modernization of traditional Chinese medicine industry: a review

Traditional Chinese medicine (TCM) is the practical experience and summary of the Chinese nation for thousands of years. It shows great potential in treating various chronic diseases, complex diseases and major infectious diseases, and has gradually attracted the attention of people all over the world. However, due to the complexity of prescription and action mechanism of TCM, the development of TCM industry is still in a relatively conservative stage. With the rise of artificial intelligence technology in various fields, many scholars began to apply artificial intelligence technology to traditional Chinese medicine industry and made remarkable progress. This paper comprehensively summarizes the important role of artificial intelligence in the development of traditional Chinese medicine industry from various aspects, including new drug discovery, data mining, quality standardization and industry technology of traditional Chinese medicine. The limitations of artificial intelligence in these applications are also emphasized, including the lack of pharmacological research, database quality problems and the challenges brought by human-computer interaction. Nevertheless, the development of artificial intelligence has brought new opportunities and innovations to the modernization of traditional Chinese medicine. Integrating artificial intelligence technology into the comprehensive application of Chinese medicine industry is expected to overcome the major problems faced by traditional Chinese medicine industry and further promote the modernization of the whole traditional Chinese medicine industry.

Omentin-1 drives cardiomyocyte cell cycle arrest and metabolic maturation by interacting with BMP7

Mammalian cardiomyocytes (CMs) undergo maturation during postnatal heart development to meet the increased demands of growth. Here, we found that omentin-1, an adipokine, facilitates CM cell cycle arrest and metabolic maturation. Deletion of omentin-1 causes mouse heart enlargement and dysfunction in adulthood and CM maturation retardation in juveniles, including delayed cell cycle arrest and reduced fatty acid oxidation. Through RNA sequencing, molecular docking analysis, and proximity ligation assays, we found that omentin-1 regulates CM maturation by interacting directly with bone morphogenetic protein 7 (BMP7). Omentin-1 prevents BMP7 from binding to activin type II receptor B (ActRIIB), subsequently decreasing the downstream pathways mothers against DPP homolog 1 (SMAD1)/Yes-associated protein (YAP) and p38 mitogen-activated protein kinase (p38 MAPK). In addition, omentin-1 is required and sufficient for the maturation of human embryonic stem cell-derived CMs. Together, our findings reveal that omentin-1 is a pro-maturation factor for CMs that is essential for postnatal heart development and cardiac function maintenance.

EZH2 controls epicardial cell migration during heart development

Enhancer of zeste homolog 2 (EZH2) is an important transcriptional regulator in development that catalyzes H3K27me3. The role of EZH2 in epicardial development is still unknown. In this study, we show that EZH2 is expressed in epicardial cells during both human and mouse heart development. Ezh2 epicardial deletion resulted in impaired epicardial cell migration, myocardial hypoplasia, and defective coronary plexus development, leading to embryonic lethality. By using RNA sequencing, we identified that EZH2 controls the transcription of tissue inhibitor of metalloproteinase 3 (TIMP3) in epicardial cells during heart development. Loss-of-function studies revealed that EZH2 promotes epicardial cell migration by suppressing TIMP3 expression. We also found that epicardial Ezh2 deficiency-induced TIMP3 up-regulation leads to extracellular matrix reconstruction in the embryonic myocardium by mass spectrometry. In conclusion, our results demonstrate that EZH2 is required for epicardial cell migration because it blocks Timp3 transcription, which is vital for heart development. Our study provides new insight into the function of EZH2 in cell migration and epicardial development.

How Parental Predictors Jointly Affect the Risk of Offspring Congenital Heart Disease: A Nationwide Multicenter Study Based on the China Birth Cohort

Objective

Congenital heart disease (CHD) is complex in its etiology. Its genetic causes have been investigated, whereas the non-genetic factor related studies are still limited. We aimed to identify dominant parental predictors and develop a predictive model and nomogram for the risk of offspring CHD.

Methods

This was a retrospective study from November 2017 to December 2021 covering 44,578 participants, of which those from 4 hospitals in eastern China were assigned to the development cohort and those from 5 hospitals in central and western China were used as the external validation cohort. Univariable and multivariable analyses were used to select the dominant predictors of CHD among demographic characteristics, lifestyle behaviors, environmental pollution, maternal disease history, and the current pregnancy information. Multivariable logistic regression analysis was used to construct the model and nomogram using the selected predictors. The predictive model and the nomogram were both validated internally and externally. A web-based nomogram was developed to predict patient-specific probability for CHD.

Results

Dominant risk factors for offspring CHD included increased maternal age [odds ratio (OR): 1.14, 95% CI: 1.10–1.19], increased paternal age (1.05, 95% CI: 1.02–1.09), maternal secondhand smoke exposure (2.89, 95% CI: 2.22–3.76), paternal drinking (1.41, 95% CI: 1.08–1.84), maternal pre-pregnancy diabetes (3.39, 95% CI: 1.95–5.87), maternal fever (3.35, 95% CI: 2.49–4.50), assisted reproductive technology (2.89, 95% CI: 2.13–3.94), and environmental pollution (1.61, 95% CI: 1.18–2.20). A higher household annual income (100,000–400,000 CNY: 0.47, 95% CI: 0.34–0.63; > 400,000 CNY: 0.23, 95% CI: 0.15–0.36), higher maternal education level (13–16 years: 0.68, 95% CI: 0.50–0.93; ≥ 17 years: 0.87, 95% CI: 0.55–1.37), maternal folic acid (0.21, 95% CI: 0.16–0.27), and multivitamin supplementation (0.33, 95% CI: 0.26–0.42) were protective factors. The nomogram showed good discrimination in both internal [area under the receiver-operating-characteristic curve (AUC): 0.843] and external validations (development cohort AUC: 0.849, external validation cohort AUC: 0.837). The calibration curves showed good agreement between the nomogram-predicted probability and actual presence of CHD.

Conclusion

We revealed dominant parental predictors and presented a web-based nomogram for the risk of offspring CHD, which could be utilized as an effective tool for quantifying the individual risk of CHD and promptly identifying high-risk population.