Accurate geometric modeling of the aortic valve from 3D CT images is essential for biomechanical analysis and patient-specific simulations to assess valve health or make a preoperative plan. However, it remains challenging to generate aortic valve meshes with both high-quality and consistency across different patients. Traditional approaches often produce triangular meshes with irregular topologies, which can result in poorly shaped elements and inconsistent correspondence due to inter-patient anatomical variation. In this work, we address these challenges by introducing a template-fitting pipeline with deep neural networks to generate structured quad (i.e., quadrilateral) meshes from 3D CT images to represent aortic valve geometries. By remeshing aortic valves of all patients with a common quad mesh template, we ensure a uniform mesh topology with consistent node-to-node and element-to-element correspondence across patients. This consistency enables us to simplify the learning objective of the deep neural networks, by employing a loss function with only two terms (i.e., a geometry reconstruction term and a smoothness regularization term), which is sufficient to preserve mesh smoothness and element quality. Our experiments demonstrate that the proposed approach produces high-quality aortic valve surface meshes with improved smoothness and shape quality, while requiring fewer explicit regularization terms compared to the traditional methods. These results highlight that using structured quad meshes for the template and neural network training not only ensures mesh correspondence and quality but also simplifies the training process, thus enhancing the effectiveness and efficiency of aortic valve modeling.

Background: Pulmonary artery (PA) hemodynamics are predictors of mortality, yet genetic mechanisms underlying increased PA stiffness in diseases such as pulmonary hypertension are poorly understood, and targeted therapies are limited. Evaluation of PA hemodynamics frequently requires invasive right heart catheterization, limiting large-scale studies. Our investigation leveraged cardiac magnetic resonance (CMR) imaging to non-invasively determine and characterize PA pulsatility, an established measure of pulmonary vascular compliance. Methods: A deep learning model measured CMR-derived PA pulsatility in 42,774 UK Biobank participants. Associations of PA pulsatility with demographics and comorbidities were assessed using multivariate logistic regression models, and its association with all-cause mortality was evaluated using a multivariate Cox proportional hazards model. A genome-wide association study (GWAS) was performed, followed by genomic and in vitro functional characterization of candidate effector genes. Results: The mean (SD) PA pulsatility was 0.36 (0.11), and pulsatility decreased by 0.035 for every decade increase in age. We observed an inverse association between PA pulsatility and odds of congestive heart failure (odds ratio [OR] 1.55; 95% CI 1.37-1.76), chronic obstructive pulmonary disease (OR 1.41; 95% CI 1.29-1.53), diabetes (OR 1.18; 95% CI 1.13-1.23), and age- and sex-adjusted mortality (hazard ratio [HR] 1.13; 95% CI 1.03-1.23). A genome-wide association study of 40,496 participants identified loci on chromosome 2 (top SNP: rs6738973) and chromosome 10 (rs2077218) associated with PA pulsatility at genome-wide significance (p < 5x10 -8 ). Multiple lines of evidence from integrative fine-mapping analyses identified PKDCC and PLCE1 among likely effector genes. Using mass spectrometry, differential binding on electrophoretic mobility shift assays of two factors, NONO and SFPQ, at the lead loci were identified and shown to regulate downstream expression of PKDCC and PLCE1. Conclusions: This is the first large-scale, population-based epidemiologic and genomics study of PA pulsatility. Lower PA pulsatility is associated with older age, higher rates of comorbidities, and higher mortality odds. The genes PKDCC and PLCE1 are among likely effectors of PA pulsatility and warrant further functional characterization.

Thoracic aortic aneurysm (TAA) represents a critical cardiovascular challenge, characterized by the silent, progressive dilation of the aorta that can lead to catastrophic rupture or dissection. Despite the significant clinical burden of TAA, current clinical risk stratification relies heavily on maximum diameter measurements derived from three-dimensional computed tomography (3D CT), a metric that often fails to capture the complex biomechanical environment precipitating failure. To address this, we developed a comprehensive computational pipeline for patient-specific modeling of thoracic aortic aneurysms, combining segmentation, mesh generation, and finite element simulation. A key innovation of this pipeline is the open-arch mesh with node and element correspondence among different patients, enabled by the templatefitting based meshing method, which preserves anatomical accuracy, enables statistical shape modeling, and facilitates FE simulations with all-hexahedral elements in PyTorch-FEA -capabilities not addressed in previous studies. Our pipeline captures clinically relevant biomechanical differences, showing that aneurysmal patients exhibit significantly higher maximum absolute principal stress compared to non-aneurysmal patients. This result identifies a quantitative biomechanical marker that extends beyond conventional geometric measurements and potentially aids in TAA diagnosis. Moreover, the pipeline quantifies stress distribution across different regions of the aorta, enabling more detailed biomechanical assessment and supporting personalized treatment planning. By streamlining the transition from routine imaging to advanced stress analysis, this pipeline facilitates large-scale population studies and paves the way for the integration of patient-specific biomechanical parameters into routine clinical decision-making.

Introduction: Hypoxia and ischemia drive pathogenic vascular remodeling and endothelial cell (EC) dysfunction, leading to the progression of vascular diseases, such as coronary artery disease (CAD). Emerging evidence highlights the importance of long-range genomic interactions between non-coding intergenic single nucleotide polymorphisms (SNPs) and gene promoters in EC reprogramming. However, their functional relevance to CAD remains unclear. Hypothesis: Hypoxia triggers chromatin remodeling in ECs, altering genomic interactions and gene expression and thereby promoting EC dysfunction in CAD. Methods and Results: We employed Micro-C analysis of cultured human ECs to assess chromatin architecture changes under hypoxic compared with normoxic conditions and integrated these results with RNA sequencing data to correlate genomic interactions with gene expression changes. Among 162477 interactions identified, the interaction between SNP rs115561468 and promoter region of Metastasis Lung Cancer Associated Transcript 1 (MALAT1) was significantly enriched in hypoxia (p=8.38x10 -25 ). A prior genome-wide association study (GWAS, N=2466) linked the T allele of this SNP to higher plasma trimethyllysine (TML) levels (p=3x10 -32 ), a metabolite linked to higher atherosclerosis risk. Our analysis of the All of Us database identified this allele as a risk factor for developing CAD (p=0.00356). Ex-vivo proteomic analysis and in-vitro chromatin immunoprecipitation followed by quantitative PCR revealed allele-specific protein binding patterns at the SNP, suggesting the presence of potential allele-specific transcriptional regulators of MALAT1, including SFPQ and HNRNPA3. Correspondingly, SFPQ and HNRNPA3 knockdown in cultured ECs increased MALAT1 expression in normoxic and hypoxic conditions. In CRISPR-edited isogenic, induced pluripotent stem cell-derived endothelial cells, MALAT1 was found upregulated under hypoxia in all genotypes, but the magnitude of induction was significantly greater in cells carrying the T allele. Functionally, exogenous TML drove endothelial pathophenotypes, including increased apoptosis and decreased proliferation. Conclusion: Our work reveals a hypoxia-sensitive, SNP-driven regulatory mechanism linking MALAT1 activity, TML metabolism to endothelial dysfunction, carrying broad implications for understanding the genetic predisposition to CAD pathogenesis, providing new insights into how genetic and metabolic factors converge to promote the disease.

Rationale: Pulmonary arterial hypertension (PAH) is a rare yet severe disease, closely linked to genetic susceptibility. The most extensive genome-wide association study (GWAS) to date identified Chr8q11.23 as a prominent genetic locus correlated with the risk of PAH (OR 1.8). As GWAS primarily reveals statistical associations, the mechanisms by which this genetic locus may regulate the pathogenesis of PAH remain unclear from both biological and mechanistic perspectives. We hypothesized that functional SNPs (fSNP) located within Chr8q11.23 control PAH susceptibility through transcriptional regulation of target genes, potentially via long-range chromatin interactions. Methods: We developed a post-GWAS approach to identify these candidate pathogenic genes that mediate PAH risk associated with this genetic locus. Based on linkage disequilibrium defined by r2>0.8, the haplotype SNPs that are closely linked to the GWAS-identified tag SNP(s) were collected and screened by allele-specific binding of nuclear regulatory proteins extracted from human pulmonary artery endothelial cells (PAECs). Via genome-wide chromatin contact (micro-C) analysis in human PAECs under normoxia and hypoxia, a prominent trigger of PAH, candidate target genes under putative SNP-dependent transcription control were defined by significant interactions between their promoter regions and the Chr8q11.23 SNPs. The pathogenic activity of the predicted target genes was studied in PAECs and PAH patients. Results: Electrophoretic mobility shift assay (EMSA) demonstrated allele-specific nuclear protein binding to the Chr8q11.23 SNP rs7844284, suggesting that it may serve as a functional SNP (fSNP) influencing PAH risk. Via micro-C promoter panel analysis, we identified a 6.9 Kbp segment (chr8: 54,325,934-54,332,889) containing this fSNP and exhibiting significant long-range interactions with the promotor of a target gene trimethylguanosine synthase 1 (TGS1). TGS1 was downregulated in both human PAECs exposed to hypoxia and those derived from PAH patients. TGS1 knockdown in PAECs upregulated inflammatory pathways and induced PAH-associated endothelial pathophenotypes, including impaired angiogenesis and apoptosis, increased proliferation, and enhanced lymphocyte adhesion. Notably, TGS1 catalyzes the cap hypermethylation and maturation of selenoprotein GPX1 mRNA. Knockdown of GPX1 in PAECs phenocopied the inflammatory activations seen with TGS1 deficiency, indicating that the endothelial effects of TGS1 deficiency are related to GPX1. Conclusion: Leveraging a comprehensive analysis of genome-wide chromatin interactions and post-GWAS function analysis, we define TGS1 deficiency as a pathogenic contributor to the association of Chr8q11.23 with risk of PAH. Thus, TGS1 could serve as a viable molecular target for the next generation of diagnostics and therapeutics in this deadly disease.

Background— Deficiencies of iron-sulfur (Fe-S) clusters, metal complexes that control redox state and mitochondrial metabolism, have been linked to pulmonary hypertension (PH), a deadly vascular disease with poorly defined molecular origins. The BolA Family Member 3 (BOLA3) regulates Fe-S biogenesis, and mutations in BOLA3 result in multiple mitochondrial dysfunction syndrome, a fatal disorder associated with PH. The mechanistic role of BOLA3 in PH remains undefined. Methods— In vitro assessment of BOLA3 regulation and gain and loss of function assays were performed in human pulmonary artery endothelial cells (PAECs) using siRNA and lentiviral vectors expressing the mitochondrial isoform of BOLA3. Polymeric nanoparticle 7C1 was utilized for lung endothelial-specific delivery of BOLA3 siRNA oligonucleotides in mice. Overexpression of pulmonary vascular BOLA3 was performed by orotracheal transgene delivery of adeno-associated virus in mouse models of PH. Results— In cultured hypoxic PAECs as well as lung from human Group 1 and 3 PH patients as well as multiple rodent models of PH, endothelial BOLA3 expression was down-regulated, which involved HIF-2 α -dependent transcriptional repression via HDAC-mediated histone deacetylation. In vitro gain and loss of function studies demonstrated that BOLA3 regulated Fe-S integrity, thus modulating lipoate-containing 2-oxoacid dehydrogenases with consequent control over glycolysis and mitochondrial respiration. In contexts of siRNA knockdown and naturally occurring human genetic mutation, cellular BOLA3 deficiency down-regulated the glycine cleavage system protein H (GCSH), thus bolstering intracellular glycine content. In the setting of these alterations of oxidative metabolism and glycine levels, BOLA3 deficiency increased endothelial proliferation, survival, and vasoconstriction, while decreasing angiogenic potential. In vivo, pharmacologic knockdown of endothelial BOLA3 and targeted overexpression of BOLA3 in mice demonstrated that BOLA3 deficiency promotes histologic and hemodynamic manifestations of PH. Notably, the therapeutic effects of BOLA3 expression were reversed by exogenous glycine supplementation. Conclusions— BOLA3 acts as a crucial lynchpin connecting Fe-S-dependent oxidative respiration and glycine homeostasis with endothelial metabolic re-programming critical to PH pathogenesis. These results provide a molecular explanation for the clinical associations linking PH with hyperglycinemic syndromes and mitochondrial disorders. These findings also identify novel metabolic targets, including those involved in epigenetics, iron-sulfur biogenesis, and glycine biology, for diagnostic and therapeutic development. provide crucial support for the of central dysregulation of Fe-S integrity a biogenesis

Aortic aneurysm disease ranks consistently in the top 20 causes of death in the U.S. population. Thoracic aortic aneurysm is manifested as an abnormal bulging of thoracic aortic wall and it is a leading cause of death in adults. From the perspective of biomechanics, rupture occurs when the stress acting on the aortic wall exceeds the wall strength. Wall stress distribution can be obtained by computational biomechanical analyses, especially structural Finite Element Analysis. For risk assessment, probabilistic rupture risk of TAA can be calculated by comparing stress with material strength using a material failure model. Although these engineering tools are currently available for TAA rupture risk assessment on patient specific level, clinical adoption has been limited due to two major barriers: labor intensive 3D reconstruction current patient specific anatomical modeling still relies on manual segmentation, making it time consuming and difficult to scale to a large patient population, and computational burden traditional FEA simulations are resource intensive and incompatible with time sensitive clinical workflows. The second barrier was successfully overcome by our team through the development of the PyTorch FEA library and the FEA DNN integration framework. By incorporating the FEA functionalities within PyTorch FEA and applying the principle of static determinacy, we reduced the FEA based stress computation time to approximately three minutes per case. Moreover, by integrating DNN and FEA through the PyTorch FEA library, our approach further decreases the computation time to only a few seconds per case. This work focuses on overcoming the first barrier through the development of an end to end deep neural network capable of generating patient specific finite element meshes of the aorta directly from 3D CT images.

Vascular inflammation regulates endothelial pathophenotypes, particularly in pulmonary arterial hypertension (PAH). Dysregulated lysosomal activity and cholesterol metabolism activate pathogenic inflammation, but their relevance to PAH is unclear. Nuclear receptor coactivator 7 ( NCOA7 ) deficiency in endothelium produced an oxysterol and bile acid signature through lysosomal dysregulation, promoting endothelial pathophenotypes. This oxysterol signature overlapped with a plasma metabolite signature associated with human PAH mortality. Mice deficient for endothelial Ncoa7 or exposed to an inflammatory bile acid developed worsened PAH. Genetic predisposition to NCOA7 deficiency was driven by single-nucleotide polymorphism rs11154337, which alters endothelial immunoactivation and is associated with human PAH mortality. An NCOA7-activating agent reversed endothelial immunoactivation and rodent PAH. Thus, we established a genetic and metabolic paradigm that links lysosomal biology and oxysterol processes to endothelial inflammation and PAH.

ObjectiveCurrently, the long-term outcomes of uncomplicated type B aortic dissection (TBAD) patients managed with optimal medical therapy (OMT) remain poor. Aortic expansion is a major factor that determines patient long-term survival. The objective of this study was to investigate the association between anatomic shape features and (i) OMT outcome; (ii) aortic growth rate for TBAD patients initially treated with OMT.Methods108 CT images of TBAD in the acute and chronic phases were collected from 46 patients who were initially treated with OMT. Statistical shape models (SSM) of TBAD were constructed to extract shape features from the earliest initial CT scans of each patient by using principal component analysis (PCA) and partial least square (PLS) regression. Additionally, conventional shape features (e.g., aortic diameter) were quantified from the earliest CT scans as a baseline for comparison. We identified conventional and SSM features that were significant in separating OMT “success” and failure patients. Moreover, the aortic growth rate was predicted by SSM and conventional features using linear and nonlinear regression with cross-validations.ResultsSize-related SSM and conventional features (mean aortic diameter: p=0.0484, centerline length: p=0.0112, PCA score c1: p=0.0192, and PLS scores t1: p=0.0004, t2: p=0.0274) were significantly different between OMT success and failure groups, but these features were incapable of predicting the aortic growth rate. SSM shape features showed superior results in growth rate prediction compared to conventional features. Using multiple linear regression, the conventional, PCA, and PLS shape features resulted in root mean square errors (RMSE) of 1.23, 0.85, and 0.84 mm/year, respectively, in leave-one-out cross-validations. Nonlinear support vector regression (SVR) led to improved RMSE of 0.99, 0.54, and 0.43 mm/year, for the conventional, PCA, and PLS features, respectively.ConclusionSize-related shape features of the earliest scan were correlated with OMT failure but led to large errors in the prediction of the aortic growth rate. SSM features in combination with nonlinear regression could be a promising avenue to predict the aortic growth rate.

Bicuspid aortic valve (BAV), the most common congenital heart disease, is prone to develop significant valvular dysfunction and aortic wall abnormalities such as ascending aortic aneurysm. Growing evidence has suggested that abnormal BAV hemodynamics could contribute to disease progression. In order to investigate BAV hemodynamics, we performed 3D patient-specific fluid-structure interaction (FSI) simulations with fully coupled blood flow dynamics and valve motion throughout the cardiac cycle. Results showed that the hemodynamics during systole can be characterized by a systolic jet and two counter-rotating recirculation vortices. At peak systole, the jet was usually eccentric, with asymmetric recirculation vortices and helical flow motion in the ascending aorta. The flow structure at peak systole was quantified using the vorticity, flow rate reversal ratio and local normalized helicity (LNH) at four locations from the aortic root to the ascending aorta. The systolic jet was evaluated with the peak velocity, normalized flow displacement, and jet angle. It was found that peak velocity and normalized flow displacement (rather than jet angle) gave a strong correlation with the vorticity and LNH in the ascending aorta, which suggests that these two metrics could be used for clinical noninvasive evaluation of abnormal blood flow patterns in BAV patients.

Acute type A aortic dissection remains a deadly and elusive condition, with risk factors such as hypertension, bicuspid aortic valves, and genetic predispositions. As existing guidelines for surgical intervention based solely on aneurysm diameter face scrutiny, there is a growing need to consider other predictors and parameters, including wall stress, in assessing dissection risk. Through our research, we aim to elucidate the biomechanical underpinnings of aortic dissection and provide valuable insights into its prediction and prevention.We applied finite element analysis (FEA) to assess stress distribution on a rare dataset comprising computed tomography (CT) images obtained from eight patients at three stages of aortic dissection: pre-dissection (preD), post-dissection (postD), and post-repair (postR). Our findings reveal significant increases in both mean and peak aortic wall stresses during the transition from the preD state to the postD state, reflecting the mechanical impact of dissection. Surgical repair effectively restores aortic wall diameter to pre-dissection levels, documenting its effectiveness in mitigating further complications. Furthermore, we identified stress concentration regions within the aortic wall that closely correlated with observed dissection borders, offering insights into high-risk areas.This study demonstrates the importance of considering biomechanical factors when assessing aortic dissection risk. Despite some limitations, such as uniform wall thickness assumptions and the absence of dynamic blood flow considerations, our patient-specific FEA approach provides valuable mechanistic insights into aortic dissection. These findings hold promise for improving predictive models and informing clinical decisions to enhance patient care.

Vascular inflammation critically regulates endothelial cell (EC) pathophenotypes, particularly in pulmonary arterial hypertension (PAH). Dysregulation of lysosomal activity and cholesterol metabolism have known inflammatory roles in disease, but their relevance to PAH is unclear. In human pulmonary arterial ECs and in PAH, we found that inflammatory cytokine induction of the nuclear receptor coactivator 7 (NCOA7) both preserved lysosomal acidification and served as a homeostatic brake to constrain EC immunoactivation. Conversely, NCOA7 deficiency promoted lysosomal dysfunction and proinflammatory oxysterol/bile acid generation that, in turn, contributed to EC pathophenotypes. In vivo, mice deficient for Ncoa7 or exposed to the inflammatory bile acid 7α-hydroxy-3-oxo-4-cholestenoic acid (7HOCA) displayed worsened PAH. Emphasizing this mechanism in human PAH, an unbiased, metabolome-wide association study (N=2,756) identified a plasma signature of the same NCOA7-dependent oxysterols/bile acids associated with PAH mortality (P<1.1x10-6). Supporting a genetic predisposition to NCOA7 deficiency, in genome-edited, stem cell-derived ECs, the common variant intronic SNP rs11154337 in NCOA7 regulated NCOA7 expression, lysosomal activity, oxysterol/bile acid production, and EC immunoactivation. Correspondingly, SNP rs11154337 was associated with PAH severity via six-minute walk distance and mortality in discovery (N=93, P=0.0250; HR=0.44, 95% CI [0.21-0.90]) and validation (N=630, P=2x10-4; HR=0.49, 95% CI [0.34-0.71]) cohorts. Finally, utilizing computational modeling of small molecule binding to NCOA7, we predicted and synthesized a novel activator of NCOA7 that prevented EC immunoactivation and reversed indices of rodent PAH. In summary, we have established a genetic and metabolic paradigm and a novel therapeutic agent that links lysosomal biology as well as oxysterol and bile acid processes to EC inflammation and PAH pathobiology. This paradigm carries broad implications for diagnostic and therapeutic development in PAH and in other conditions dependent upon acquired and innate immune regulation of vascular disease.

Hypoxic 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.

For the machine learning -assisted diagnosis of cardiac diseases, such as thoracic aortic aneurysm, the geometries of the heart and blood vessels need to be reconstructed from medical images, which is usually done by image segmentation followed by meshing. In this study, we applied U-Net (2D and 3D versions), a deep neural network with a U-shaped architecture, to segment human aorta from CT images. From our experiments, we have the following observations: (1) 2D U-Net, which segments each of the 2D slices of a 3D CT image independently, produced erroneous fragments (e.g., missing part of the aorta) and boundaries (i.e., aortic walls) in 3D; (2) 3D U-Net, which does segmentation in 3D regions of a 3D CT image, performed much better than 2D U-Net. We also observe the major weakness of the 3D U-Net: the reconstructed geometries of the aortic wall had large errors (measured by HD95) for some cases. The 3D U-Net in this study serves as a baseline for developing more advanced architectures of deep neural networks for more accurate geometry reconstruction of human aorta. ### Competing Interest Statement The authors have declared no competing interest.

Background: Chronic thromboembolic pulmonary hypertension (CTEPH) is a rare and mysterious complication of pulmonary embolism, characterized by chronic pulmonary arterial thrombosis, occlusions and vasculopathy. Emerging evidence has suggested genetic relevance of CTEPH, particularly to ABO blood type. Genome-wide association study (GWAS) has identified ABO gene as the most significant genetic locus associated with CTEPH. This locus, however, consists only intronic SNPs and there is no mechanistic explanation on how these non-coding SNPs control the risk of CTEPH. Methods: GWAS-identified CTEPH-associated ABO locus was analyzed, and haplotype SNPs were identified based on linkage disequilibrium (LD). Electrophoretic mobility shift assay (EMSA) was applied to screen functional SNPs (fSNPs) based on allele-imbalanced nuclear protein binding. SNP-binding nuclear transcription factors (TFs) were identified by proteomics. Hi-C chromatin-interaction analysis was utilized to determine potential target genes affected by fSNPs. Knockdown of fSNP-associated TFs in human pulmonary artery endothelial cells (hPAECs) was applied to validate the regulation of TFs on target genes. Results: Based on GWAS, the minor allele of tag SNP rs2519093 is associated with higher risk of CTEPH (Odds ratio 2.22). It defines 11 haplotype SNPs in a LD with R 2 > 0.8. Screened by EMSA with hPAEC nuclear proteins, intronic SNPs rs579459 and rs550057 were identified functional given their allele-specific protein binding. Proteomics analysis identified 3 candidate TFs that allele-specifically bind to fSNP rs579459: PBX1, CTCF and SAFB2. Candidate target gene ADAMTS13 is chosen based on its prominent interaction with fSNP revealed by Hi-C analysis, and the potential role in thromboembolism through vWF cleavage. TF knockdown by siRNA downregulates ADAMTS13 in hPAECs, suggesting their regulatory function as activators. Conclusion: Leveraging our post-GWAS functional genomics approach, we identified fSNPs in ABO locus that define a genetic architecture controlling the susceptibility of CTEPH. This genetic architecture, along with the newly identified fSNP-associated TFs and target gene ADAMTS13, provides a biological explanation for the pathogenesis of CTEPH.

Patient-specific finite element analysis (FEA) holds great promise in advancing the prognosis of cardiovascular diseases by providing detailed biomechanical insights such as high-fidelity stress and deformation on a patient-specific basis. Albeit feasible, FEA that incorporates three-dimensional, complex patient-specific geometry can be time-consuming and unsuitable for time-sensitive clinical applications. To mitigate this challenge, machine learning (ML) models, e.g., deep neural networks (DNNs), have been increasingly utilized as potential alternatives to finite element method (FEM) for biomechanical analysis. So far, efforts have been made in two main directions: (1) learning the input-to-output mapping of traditional FEM solvers and replacing FEM with data-driven ML surrogate models; (2) solving equilibrium equations using physics-informed loss functions of neural networks. While these two existing strategies have shown improved performance in terms of speed or scalability, ML models have not yet provided practical advantages over traditional FEM due to generalization issues. This has led us to the question: instead of abandoning or replacing the traditional FEM framework that can reliably solve biomechanical problems, can we integrate FEM and DNNs to enhance performance? In this study, we propose a synergistic integration of DNNs and FEM to overcome their individual limitations. Using biomechanical analysis of the human aorta as the test bed, we demonstrated two novel integrative strategies in forward and inverse problems. For the forward problem, we developed DNNs with state-of-the-art architectures to predict a nodal displacement field, and this initial DNN solution was then updated by a FEM-based refinement process, yielding a fast and accurate computing framework. For the inverse problem of heterogeneous material parameter identification, our method employs DNN as a regularizer of the spatial distribution of material parameters, aiding the optimizer in locating the optimal solution. In our demonstrative examples, despite that the DNN-only forward models yielded small displacement errors in most test cases; stress errors were considerably large, and for some test cases, the peak stress errors were greater than 50%. Our DNN-FEM integration eliminated these non-negligible errors in DNN-only models and was magnitudes faster than the FEM-only approach. Additionally, compared to FEM-only inverse method with errors greater than 50%, our DNN-FEM inverse approach significantly improved the parameter identification accuracy and reduced the errors to less than 1%.

Introduction: Pulmonary hypertension (PH) is a fatal disease without a cure, in which endothelial dysfunction drives pathologic remodeling of the pulmonary vasculature. Individuals with Smith-Lemli-Opitz syndrome (SLOS) develop PH, but the underlying mechanisms remain undefined. SLOS is an autosomal recessive disorder of cholesterol synthesis, resulting from loss-of-function mutations in 7-dehydrocholesterol reductase (DHCR7)-the terminal enzyme in the de novo synthesis of cholesterol- that leads to accumulation of 7-dehydrocholesterol (7-DHC) and cytotoxic oxysterol species. The role of DHCR7 in PH, however, is unknown. Hypothesis: Elevated cytotoxic oxysterols resulting from genetic or acquired DHCR7 deficiency promotes endothelial dysfunction and the development of PH. Methods & Results: DHCR7 mRNA and protein expression was decreased in pulmonary artery endothelial cells (ECs) in cellular (P=0.0089), rodent, and human PH models, mirroring DHCR7 reduction in SLOS. Both genetic loss of DHCR7 and hypoxia promoted a global shutdown of de novo cholesterol synthesis, resulting in an accumulation of cholesterol intermediates and increased derivative oxysterols. Specifically, 7β-hydroxycholesterol (7β-HC) was elevated in DHCR7-deficient ECs (P<0.0001), and 7β-HC drove endothelial apoptosis (P<0.0001) via downregulation of insulin-like growth factor 1 (IGF1) in ECs (fold change=0.101 ± 0.034, FDR=6.65E-11). Conversely, overexpression of DHCR7 upregulated IGF1 expression (P<0.0007), and IGF1 delivery reversed apoptosis (P<0.0001). Correspondingly, ECs derived from SLOS patient stem cells with DHCR7 mutations (p.T93M/c.964-1G>C) exhibited IGF1-dependent apoptosis. Finally, EC-specific DHCR7 deletion in chronically hypoxic mice induced more severe PH in vivo (right ventricular systolic pressure 36.4 mmHg vs. 30.8 mmHg, P<0.0130). Conclusions: Resulting either from hypoxic or genetic causes, DHCR7 deficiency promoted EC apoptosis and PH via 7β-HC accumulation and subsequent IGF1 downregulation. Our work is the first to define an oxysterol-dependent mechanism underlying the link between genetic and acquired DHCR7 deficiency and PH.