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Browsing by Author "Cho, Michael H."
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Item Allele-specific control of rodent and human lncRNA KMT2E-AS1 promotes hypoxic endothelial pathology in pulmonary hypertension(American Association for the Advancement of Science, 2024) Tai, Yi-Yin; Yu, Qiujun; Tang, Ying; Sun, Wei; Kelly, Neil J.; Okawa, Satoshi; Zhao, Jingsi; Schwantes-An, Tae-Hwi; Lacoux, Caroline; Torrino, Stephanie; Al Aaraj, Yassmin; El Khoury, Wadih; Negi, Vinny; Liu, Mingjun; Corey, Catherine G.; Belmonte, Frances; Vargas, Sara O.; Schwartz, Brian; Bhat, Bal; Chau, B. Nelson; Karnes, Jason H.; Satoh, Taijyu; Barndt, Robert J.; Wu, Haodi; Parikh, Victoria N.; Wang, Jianrong; Zhang, Yingze; McNamara, Dennis; Li, Gang; Speyer, Gil; Wang, Bing; Shiva, Sruti; Kaufman, Brett; Kim, Seungchan; Gomez, Delphine; Mari, Bernard; Cho, Michael H.; Boueiz, Adel; Pauciulo, Michael W.; Southgate, Laura; Trembath, Richard C.; Sitbon, Olivier; Humbert, Marc; Graf, Stefan; Morrell, Nicholas W.; Rhodes, Christopher J.; Wilkins, Martin R.; Nouraie, Mehdi; Nichols, William C.; Desai, Ankit A.; Bertero, Thomas; Chan, Stephen Y.; Medicine, School of MedicineHypoxic reprogramming of vasculature relies on genetic, epigenetic, and metabolic circuitry, but the control points are unknown. In pulmonary arterial hypertension (PAH), a disease driven by hypoxia inducible factor (HIF)-dependent vascular dysfunction, HIF-2α promoted expression of neighboring genes, long noncoding RNA (lncRNA) histone lysine N-methyltransferase 2E-antisense 1 (KMT2E-AS1) and histone lysine N-methyltransferase 2E (KMT2E). KMT2E-AS1 stabilized KMT2E protein to increase epigenetic histone 3 lysine 4 trimethylation (H3K4me3), driving HIF-2α-dependent metabolic and pathogenic endothelial activity. This lncRNA axis also increased HIF-2α expression across epigenetic, transcriptional, and posttranscriptional contexts, thus promoting a positive feedback loop to further augment HIF-2α activity. We identified a genetic association between rs73184087, a single-nucleotide variant (SNV) within a KMT2E intron, and disease risk in PAH discovery and replication patient cohorts and in a global meta-analysis. This SNV displayed allele (G)-specific association with HIF-2α, engaged in long-range chromatin interactions, and induced the lncRNA-KMT2E tandem in hypoxic (G/G) cells. In vivo, KMT2E-AS1 deficiency protected against PAH in mice, as did pharmacologic inhibition of histone methylation in rats. Conversely, forced lncRNA expression promoted more severe PH. Thus, the KMT2E-AS1/KMT2E pair orchestrates across convergent multi-ome landscapes to mediate HIF-2α pathobiology and represents a key clinical target in pulmonary hypertension.Item NHLBI-CMREF Workshop Report on Pulmonary Vascular Disease Classification: JACC State-of-the-Art Review(Elsevier, 2021) Oldham, William M.; Hemnes, Anna R.; Aldred, Micheala A.; Barnard, John; Brittain, Evan L.; Chan, Stephen Y.; Cheng, Feixiong; Cho, Michael H.; Desai, Ankit A.; Garcia, Joe G.N.; Geraci, Mark W.; Ghiassian, Susan D.; Hall, Kathryn T.; Horn, Evelyn M.; Jain, Mohit; Kelly, Rachel S.; Leopold, Jane A.; Lindstrom, Sara; Modena, Brian D.; Nichols, William C.; Rhodes, Christopher J.; Sun, Wei; Sweatt, Andrew J.; Vanderpool, Rebecca R.; Wilkins, Martin R.; Wilmot, Beth; Zamanian, Roham T.; Fessel, Joshua P.; Aggarwal, Neil R.; Loscalzo, Joseph; Xiao, Lei; Medicine, School of MedicineThe National Heart, Lung, and Blood Institute and the Cardiovascular Medical Research and Education Fund held a workshop on the application of pulmonary vascular disease omics data to the understanding, prevention, and treatment of pulmonary vascular disease. Experts in pulmonary vascular disease, omics, and data analytics met to identify knowledge gaps and formulate ideas for future research priorities in pulmonary vascular disease in line with National Heart, Lung, and Blood Institute Strategic Vision goals. The group identified opportunities to develop analytic approaches to multiomic datasets, to identify molecular pathways in pulmonary vascular disease pathobiology, and to link novel phenotypes to meaningful clinical outcomes. The committee suggested support for interdisciplinary research teams to develop and validate analytic methods, a national effort to coordinate biosamples and data, a consortium of preclinical investigators to expedite target evaluation and drug development, longitudinal assessment of molecular biomarkers in clinical trials, and a task force to develop a master clinical trials protocol for pulmonary vascular disease.Item Unsupervised representation learning improves genomic discovery and risk prediction for respiratory and circulatory functions and diseases(medRxiv, 2023-08-29) Yun, Taedong; Cosentino, Justin; Behsaz, Babak; McCaw, Zachary R.; Hill, Davin; Luben, Robert; Lai, Dongbing; Bates, John; Yang, Howard; Schwantes-An, Tae-Hwi; Zhou, Yuchen; Khawaja, Anthony P.; Carroll, Andrew; Hobbs, Brian D.; Cho, Michael H.; McLean, Cory Y.; Hormozdiari, Farhad; Medical and Molecular Genetics, School of MedicineHigh-dimensional clinical data are becoming more accessible in biobank-scale datasets. However, effectively utilizing high-dimensional clinical data for genetic discovery remains challenging. Here we introduce a general deep learning-based framework, REpresentation learning for Genetic discovery on Low-dimensional Embeddings (REGLE), for discovering associations between genetic variants and high-dimensional clinical data. REGLE uses convolutional variational autoencoders to compute a non-linear, low-dimensional, disentangled embedding of the data with highly heritable individual components. REGLE can incorporate expert-defined or clinical features and provides a framework to create accurate disease-specific polygenic risk scores (PRS) in datasets which have minimal expert phenotyping. We apply REGLE to both respiratory and circulatory systems: spirograms which measure lung function and photoplethysmograms (PPG) which measure blood volume changes. Genome-wide association studies on REGLE embeddings identify more genome-wide significant loci than existing methods and replicate known loci for both spirograms and PPG, demonstrating the generality of the framework. Furthermore, these embeddings are associated with overall survival. Finally, we construct a set of PRSs that improve predictive performance of asthma, chronic obstructive pulmonary disease, hypertension, and systolic blood pressure in multiple biobanks. Thus, REGLE embeddings can quantify clinically relevant features that are not currently captured in a standardized or automated way.