E2F1 Mediates SOX17 Deficiency-Induced Pulmonary Hypertension.

BACKGROUND: Rare genetic variants and genetic variation at loci in an enhancer in SOX17 (SRY-box transcription factor 17) are identified in patients with idiopathic pulmonary arterial hypertension (PAH) and PAH with congenital heart disease. However, the exact role of genetic variants or mutations in SOX17 in PAH pathogenesis has not been reported. METHODS: SOX17 expression was evaluated in the lungs and pulmonary endothelial cells (ECs) of patients with idiopathic PAH. Mice with Tie2Cre-mediated Sox17 knockdown and EC-specific Sox17 deletion were generated to determine the role of SOX17 deficiency in the pathogenesis of PAH. Human pulmonary ECs were cultured to understand the role of SOX17 deficiency. Single-cell RNA sequencing, RNA-sequencing analysis, and luciferase assay were performed to understand the underlying molecular mechanisms of SOX17 deficiency-induced PAH. E2F1 (E2F transcription factor 1) inhibitor HLM006474 was used in EC-specific Sox17 mice. RESULTS: SOX17 expression was downregulated in the lung and pulmonary ECs from patients with idiopathic PAH. Mice with Tie2Cre-mediated Sox17 knockdown and EC-specific Sox17 deletion induced spontaneously mild pulmonary hypertension. Loss of endothelial Sox17 in EC exacerbated hypoxia-induced pulmonary hypertension in mice. Loss of SOX17 in lung ECs induced endothelial dysfunctions including upregulation of cell cycle programming, proliferative and antiapoptotic phenotypes, augmentation of paracrine effect on pulmonary arterial smooth muscle cells, impaired cellular junction, and BMP (bone morphogenetic protein) signaling. E2F1 signaling was shown to mediate the SOX17 deficiency-induced EC dysfunction. Pharmacological inhibition of E2F1 in Sox17 EC-deficient mice attenuated pulmonary hypertension development. CONCLUSIONS: Our study demonstrated that endothelial SOX17 deficiency induces pulmonary hypertension through E2F1. Thus, targeting E2F1 signaling represents a promising approach in patients with PAH.

The Role of a Lung Vascular Endothelium Enriched Gene TMEM100.

Transmembrane protein 100 (TMEM100) is a crucial factor in the development and maintenance of the vascular system. The protein is involved in several processes such as angiogenesis, vascular morphogenesis, and integrity. Furthermore, TMEM100 is a downstream target of the BMP9/10 and BMPR2/ALK1 signaling pathways, which are key regulators of vascular development. Our recent studies have shown that TMEM100 is a lung endothelium enriched gene and plays a significant role in lung vascular repair and regeneration. The importance of TMEM100 in endothelial cells' regeneration was demonstrated when Tmem100 was specifically deleted in endothelial cells, causing an impairment in their regenerative ability. However, the role of TMEM100 in various conditions and diseases is still largely unknown, making it an interesting area of research. This review summarizes the current knowledge of TMEM100, including its expression pattern, function, molecular signaling, and clinical implications, which could be valuable in the development of novel therapies for the treatment of cardiovascular and pulmonary diseases.

E2F1 Mediates SOX17 Deficiency-Induced Pulmonary Hypertension.

Rationale: Rare genetic variants and genetic variation at loci in an enhancer in SRY-Box Transcription Factor 17 (SOX17) are identified in patients with idiopathic pulmonary arterial hypertension (PAH) and PAH with congenital heart disease. However, the exact role of genetic variants or mutation in SOX17 in PAH pathogenesis has not been reported. Objectives: To investigate the role of SOX17 deficiency in pulmonary hypertension (PH) development. Methods: Human lung tissue and endothelial cells (ECs) from IPAH patients were used to determine the expression of SOX17. Tie2Cre-mediated and EC-specific deletion of Sox17 mice were assessed for PH development. Single-cell RNA sequencing analysis, human lung ECs, and smooth muscle cell culture were performed to determine the role and mechanisms of SOX17 deficiency. A pharmacological approach was used in Sox17 deficiency mice for therapeutic implication. Measurement and Main Results: SOX17 expression was downregulated in the lungs and pulmonary ECs of IPAH patients. Mice with Tie2Cre mediated Sox17 knockdown and EC-specific Sox17 deletion developed spontaneously mild PH. Loss of endothelial Sox17 in EC exacerbated hypoxia-induced PH in mice. Loss of SOX17 in lung ECs induced endothelial dysfunctions including upregulation of cell cycle programming, proliferative and anti-apoptotic phenotypes, augmentation of paracrine effect on pulmonary arterial smooth muscle cells, impaired cellular junction, and BMP signaling. E2F Transcription Factor 1 (E2F1) signaling was shown to mediate the SOX17 deficiency-induced EC dysfunction and PH development. Conclusions: Our study demonstrated that endothelial SOX17 deficiency induces PH through E2F1 and targeting E2F1 signaling represents a promising approach in PAH patients.

Suppression of BMP signaling by PHD2 deficiency in Pulmonary Arterial hypertension.

BMP signaling deficiency is evident in the lungs of patients with pulmonary arterial hypertension. We demonstrated that PHD2 deficiency suppresses BMP signaling in the lung endothelial cells, suggesting the novel mechanisms of dysregulated BMP signaling in the development of pulmonary arterial hypertension.

Identification of Specific Substances in the FAIMS Spectra of Complex Mixtures Using Deep Learning.

High-field asymmetric ion mobility spectrometry (FAIMS) spectra of single chemicals are easy to interpret but identifying specific chemicals within complex mixtures is difficult. This paper demonstrates that the FAIMS system can detect specific chemicals in complex mixtures. A homemade FAIMS system is used to analyze pure ethanol, ethyl acetate, acetone, 4-methyl-2-pentanone, butanone, and their mixtures in order to create datasets. An EfficientNetV2 discriminant model was constructed, and a blind test set was used to verify whether the deep-learning model is capable of the required task. The results show that the pre-trained EfficientNetV2 model completed convergence at a learning rate of 0.1 as well as 200 iterations. Specific substances in complex mixtures can be effectively identified using the trained model and the homemade FAIMS system. Accuracies of 100%, 96.7%, and 86.7% are obtained for ethanol, ethyl acetate, and acetone in the blind test set, which are much higher than conventional methods. The deep learning network provides higher accuracy than traditional FAIMS spectral analysis methods. This simplifies the FAIMS spectral analysis process and contributes to further development of FAIMS systems.