Harnessing Big Data to Advance Treatment and Understanding of Pulmonary Hypertension

Precision Medicine for Pulmonary Vascular Disease: The Future Is Now (2023 Grover Conference Series)

Pulmonary vascular disease is not a single condition; rather it can accompany a variety of pathologies that impact the pulmonary vasculature. Applying precision medicine strategies to better phenotype, diagnose, monitor, and treat pulmonary vascular disease is increasingly possible with the growing accessibility of powerful clinical and research tools. Nevertheless, challenges exist in implementing these tools to optimal effect. The 2023 Grover Conference Series reviewed the research landscape to summarize the current state of the art and provide a better understanding of the application of precision medicine to managing pulmonary vascular disease. In particular, the following aspects were discussed: (1) Clinical phenotypes, (2) genetics, (3) epigenetics, (4) biomarker discovery, (5) application of precision biology to clinical trials, (6) the right ventricle (RV), and (7) integrating precision medicine to clinical care. The present review summarizes the content of these discussions and the prospects for the future.

Inhibition of DYRK1A attenuates vascular remodeling in pulmonary arterial hypertension via suppressing STAT3/Pim-1/NFAT pathway

Pulmonary arterial hypertension (PAH) is characterized by progressive vascular remodeling caused by the excessive proliferation and survival of pulmonary artery smooth muscle cells (PASMCs). Dual-specificity tyrosine regulated kinase 1A (DYRK1A) is a pleiotropic kinase involved in the regulation of multiple biological functions, including cell proliferation and survival. However, the role and underlying mechanisms of DYRK1A in PAH pathogenesis remain unclear. We found that DYRK1A was upregulated in PASMCs in response to hypoxia, both in vivo and in vitro. Inhibition of DYRK1A by harmine significantly attenuated hypoxia-induced pulmonary hypertension and pulmonary artery remodeling. Mechanistically, we found that DYRK1A promoted pulmonary arterial remodeling by enhancing the proliferation and survival of PASMCs through activating the STAT3/Pim-1/NFAT pathway, because STAT3 gain-of-function via adeno-associated virus serotype 2 (AAV2) carrying the constitutively active form of STAT3 (STAT3C) nearly abolished the protective effect of harmine on PAH. Collectively, our results reveal a significant role for DYRK1A in pulmonary arterial remodeling and suggest it as a drug target with translational potential for the treatment of PAH.

Disease Network-Based Approaches to Study Comorbidity in Heart Failure: Current State and Future Perspectives

PURPOSE OF REVIEW

Heart failure (HF) is often accompanied by a constellation of comorbidities, leading to diverse patient presentations and clinical trajectories. While traditional methods have provided valuable insights into our understanding of HF, network medicine approaches seek to leverage these complex relationships by analyzing disease at a systems level. This review introduces the concepts of network medicine and explores the use of comorbidity networks to study HF and heart disease.

RECENT FINDINGS

Comorbidity networks are used to understand disease trajectories, predict outcomes, and uncover potential molecular mechanisms through identification of genes and pathways relevant to comorbidity. These networks have shown the importance of non-cardiovascular comorbidities to the clinical journey of patients with HF. However, the community should be aware of important limitations in developing and implementing these methods. Network approaches hold promise for unraveling the impact of comorbidities in the complex presentation and genetics of HF. Methods that consider comorbidity presence and timing have the potential to help optimize management strategies and identify pathophysiological mechanisms.

Exploring IRGs as a Biomarker of Pulmonary Hypertension Using Multiple Machine Learning Algorithms

BACKGROUND

Pulmonary arterial hypertension (PAH) is a severe disease with poor prognosis and high mortality, lacking simple and sensitive diagnostic biomarkers in clinical practice. This study aims to identify novel diagnostic biomarkers for PAH using genomics research.

METHODS

We conducted a comprehensive analysis of a large transcriptome dataset, including PAH and inflammatory response genes (IRGs), integrated with 113 machine learning models to assess diagnostic potential. We developed a clinical diagnostic model based on hub genes, evaluating their effectiveness through calibration curves, clinical decision curves, and ROC curves. An animal model of PAH was also established to validate hub gene expression patterns.

RESULTS

Among the 113 machine learning algorithms, the Lasso + LDA model achieved the highest AUC of 0.741. Differential expression profiles of hub genes CTGF, DDR2, FGFR2, MYH10, and YAP1 were observed between the PAH and normal control groups. A diagnostic model utilizing these hub genes was developed, showing high accuracy with an AUC of 0.87. MYH10 demonstrated the most favorable diagnostic performance with an AUC of 0.8. Animal experiments confirmed the differential expression of CTGF, DDR2, FGFR2, MYH10, and YAP1 between the PAH and control groups (p < 0.05); Conclusions: We successfully established a diagnostic model for PAH using IRGs, demonstrating excellent diagnostic performance. CTGF, DDR2, FGFR2, MYH10, and YAP1 may serve as novel molecular diagnostic markers for PAH.

Right Ventricular Pressure Waveform Analysis-Clinical Relevance and Future Directions

Continuous measurement of pressure in the right atrium and pulmonary artery has commonly been used to monitor right ventricular function in critically ill and surgical patients. This approach is largely based upon the assumption that right atrial and pulmonary arterial pressures provide accurate surrogates for diastolic filling and peak right ventricular pressures, respectively. However, due to both technical and physiologic factors, this assumption is not always true. Accordingly, recent studies have begun to emphasize the potential clinical value of also measuring right ventricular pressure at the bedside. This has highlighted both past and emerging research demonstrating the utility of analyzing not only the amplitude of right ventricular pressure but also the shape of the pressure waveform. This brief review summarizes data demonstrating that combining conventional measurements of right ventricular pressure with variables derived from waveform shape allows for more comprehensive and ideally continuous bedside assessment of right ventricular function, particularly when combined with stroke volume measurement or 3D echocardiography, and discusses the potential use of right ventricular pressure analysis in computational models for evaluating cardiac function.

MOSGAT: Uniting Specificity-Aware GATs and Cross Modal-Attention to Integrate Multi-Omics Data for Disease Diagnosis

With the advancement of sequencing methodologies, the acquisition of vast amounts of multi-omics data presents a significant opportunity for comprehending the intricate biological mechanisms underlying diseases and achieving precise diagnosis and treatment for complex disorders. However, as diverse omics data are integrated, extracting sample-specific features within each omics modality and exploring potential correlations among different modalities while avoiding mutual interference becomes a critical challenge in multi-omics data integration research. In the context of this study, we proposed a framework that unites specificity-aware GATs and cross-modal attention to integrate different omics data (MOSGAT). To be specific, we devise Graph Attention Networks (GATs) tailored for each omics modality data to perform feature extraction on samples. Additionally, an adaptive confidence attention weighting technique is incorporated to enhance the confidence in the extracted features. Finally, a cross-modal attention mechanism was devised based on multi-head self-attention, thoroughly uncovering potential correlations between different omics data. Extensive experiments were conducted on four publicly available medical datasets, highlighting the superiority of the proposed framework when compared to state-of-the-art methodologies, particularly in the realm of classification tasks. The experimental results underscore MOSGAT's effectiveness in extracting features and exploring potential inter-omics associations.

Phenotypes in pulmonary hypertension

The clinical classification of pulmonary hypertension (PH) has guided diagnosis and treatment of patients with PH for several decades. Discoveries relating to underlying mechanisms, pathobiology and responses to treatments for PH have informed the evolution in this clinical classification to describe the heterogeneity in PH phenotypes. In more recent years, advances in imaging, computational science and multi-omic approaches have yielded new insights into potential phenotypes and sub-phenotypes within the existing clinical classification. Identification of novel phenotypes in pulmonary arterial hypertension (PAH) with unique molecular profiles, for example, could lead to new precision therapies. Recent phenotyping studies have also identified groups of patients with PAH that more closely resemble patients with left heart disease (group 2 PH) and lung disease (group 3 PH), which has important prognostic and therapeutic implications. Within group 2 and group 3 PH, novel phenotypes have emerged that reflect a persistent and severe pulmonary vasculopathy that is associated with worse prognosis but still distinct from PAH. In group 4 PH (chronic thromboembolic pulmonary disease) and sarcoidosis (group 5 PH), the current approach to patient phenotyping integrates clinical, haemodynamic and imaging characteristics to guide treatment but applications of multi-omic approaches to sub-phenotyping in these areas are sparse. The next iterations of the PH clinical classification are likely to reflect several emerging PH phenotypes and improve the next generation of prognostication tools and clinical trial design, and improve treatment selection in clinical practice.

Harnessing machine learning potential for personalised drug design and overcoming drug resistance

Drug resistance in cancer treatment presents a significant challenge, necessitating innovative approaches to improve therapeutic efficacy. Integrating machine learning (ML) in cancer research is promising as ML algorithms outrival in analysing complex datasets, identifying patterns, and predicting treatment outcomes. Leveraging diverse data sources such as genomic profiles, clinical records, and drug response assays, ML uncovers molecular mechanisms of drug resistance, enabling personalised treatment, maximising efficacy and minimising adverse effects. Various ML algorithms contribute to the drug discovery process - Random Forest and Decision Trees predict drug-target interactions and aid in virtual screening, and SVM classify leads on bioactivity data. Neural Networks model QSAR to optimise lead compounds and K-means clustering group compounds with similar chemical properties aiding compound selection. Gaussian Processes predict drug responses, Bayesian Networks infer causal relationships, Autoencoders generate novel compounds, and Genetic Algorithms optimise molecular structures. These algorithms collectively enhance efficiency and success rates in drug design endeavours, from lead identification to optimisation and are cost-effective, empowering clinicians with real-time treatment monitoring and improving patient outcomes. This review highlights the immense potential of ML in revolutionising cancer care through effective drug design to reduce drug resistance, and we have also discussed various limitations and research gaps to understand better.

Unraveling the genetic landscape of pulmonary arterial hypertension in Indian patients: A transcriptome study

BACKGROUND

Pulmonary hypertension (PH) is the abnormal elevation of pressure in the pulmonary vascular system, with various underlying causes. A specific type of PH is pulmonary arterial hypertension (PAH), a severe condition characterized by high pulmonary arterial pressure resulting from structural changes in distal pulmonary vessels, altered arterial tone, and inflammation. This leads to right ventricular hypertrophy and heart failure. The molecular mechanisms behind PAH are not well understood. This manuscript aims to elucidate these mechanisms using the genetic tool, aiding in diagnosis and treatment selection.

METHOD

In our present study, we have obtained blood samples from both patients with pulmonary arterial hypertension (PAH) and healthy individuals. We conducted a comparative transcriptome analysis to identify genes that are either upregulated or downregulated in PAH patients when compared to the control group. Subsequently, we carried out a validation study focusing on the log2-fold downregulated genes in PAH, employing Quantitative Real-Time PCR for confirmation. Additionally, we quantified the proteins encoded by the validated genes using the ELISA technique.

RESULTS

The results of the transcriptome analysis revealed that 97 genes were significantly upregulated, and 6 genes were significantly downregulated. Among these, we chose to focus on and validate only four of the downregulated genes, as they were directly or indirectly associated with the hypertension pathway. We also conducted validation studies for the proteins encoded by these genes, and the results were consistent with those obtained in the transcriptome analysis.

CONCLUSION

In conclusion, the findings of this study indicate that the four validated genes identified in the context of PAH can be further explored as potential targets for both diagnostic and therapeutic applications.

Understanding pulmonary hypertension: the need for an integrative metabolomics and transcriptomics approach

Pulmonary hypertension (PH), characterised by mean pulmonary arterial pressure (mPAP) >20 mm Hg at rest, is a complex pathophysiological disorder associated with multiple clinical conditions. The high prevalence of the disease along with increased mortality and morbidity makes it a global health burden. Despite major advances in understanding the disease pathophysiology, much of the underlying complex molecular mechanism remains to be elucidated. Lack of a robust diagnostic test and specific therapeutic targets also poses major challenges. This review provides a comprehensive update on the dysregulated pathways and promising candidate markers identified in PH patients using the transcriptomics and metabolomics approach. The review also highlights the need of using an integrative multi-omics approach for obtaining insight into the disease at a molecular level. The integrative multi-omics/pan-omics approach envisaged to help in bridging the gap from genotype to phenotype is outlined. Finally, the challenges commonly encountered while conducting omics-driven studies are also discussed.

[The importance of data in Pulmonary Arterial Hypertension: from international registries to Machine Learning]

Real-world registries have been critical to building the scientific knowledge of rare diseases, including Pulmonary Arterial Hypertension (PAH). In the past 4 decades, a considerable number of registries on this condition have allowed to improve the pathology and its subgroupś definition, to advance in the understanding of its pathophysiology, to elaborate prognostic scales and to check the transferability of the results from clinical trials to clinical practice. However, in a moment where a huge amount of data from multiple sources is available, they are not always taken into account by the registries. For that reason, Machine Learning (ML) offer a unique opportunity to manage all these data and, finally, to obtain tools that may help to get an earlier diagnose, to help to deduce the prognosis and, in the end, to advance in Personalized Medicine. Thus, we present a narrative revision with the aims of, in one hand, summing up the aspects in which data extraction is important in rare diseases -focusing on the knowledge gained from PAH real-world registries- and, on the other hand, describing some of the achievements and the potential use of the ML techniques on PAH.

Predictive Models to Assess Risk of Persistent Opioid Use, Opioid Use Disorder, and Overdose

BACKGROUND

This systematic review summarizes the development, accuracy, quality, and clinical utility of predictive models to assess the risk of opioid use disorder (OUD), persistent opioid use, and opioid overdose.

METHODS

In accordance with Preferred Reporting Items for a Systematic Review and Meta-analysis guidelines, 8 electronic databases were searched for studies on predictive models and OUD, overdose, or persistent use in adults until June 25, 2023. Study selection and data extraction were completed independently by 2 reviewers. Risk of bias of included studies was assessed independently by 2 reviewers using the Prediction model Risk of Bias ASsessment Tool (PROBAST).

RESULTS

The literature search yielded 3130 reports; after removing 199 duplicates, excluding 2685 studies after abstract review, and excluding 204 studies after full-text review, the final sample consisted of 41 studies that developed more than 160 predictive models. Primary outcomes included opioid overdose (31.6% of studies), OUD (41.4%), and persistent opioid use (17%). The most common modeling approach was regression modeling, and the most common predictors included age, sex, mental health diagnosis history, and substance use disorder history. Most studies reported model performance via the c statistic, ranging from 0.507 to 0.959; gradient boosting tree models and neural network models performed well in the context of their own study. One study deployed a model in real time. Risk of bias was predominantly high; concerns regarding applicability were predominantly low.

CONCLUSIONS

Models to predict opioid-related risks are developed using diverse data sources and predictors, with a wide and heterogenous range of accuracy metrics. There is a need for further research to improve their accuracy and implementation.

A perfectly imperfect engine: Utilizing the digital twin paradigm in pulmonary hypertension

Pulmonary hypertension (PH) is a severe medical condition with a number of treatment options, the majority of which are introduced without consideration of the underlying mechanisms driving it within an individual and thus a lack of tailored approach to treatment. The one exception is a patient presenting with apparent pulmonary arterial hypertension and shown to have vaso-responsive disease, whose clinical course and prognosis is significantly improved by high dose calcium channel blockers. PH is however characterized by a relative abundance of available data from patient cohorts, ranging from molecular data characterizing gene and protein expression in different tissues to physiological data at the organ level and clinical information. Integrating available data with mechanistic information at the different scales into computational models suggests an approach to a more personalized treatment of the disease using model-based optimization of interventions for individual patients. That is, constructing digital twins of the disease, customized to a patient, promises to be a key technology for personalized medicine, with the aim of optimizing use of existing treatments and developing novel interventions, such as new drugs. This article presents a perspective on this approach in the context of a review of existing computational models for different aspects of the disease, and it lays out a roadmap for a path to realizing it.

Mendelian Randomization Study With Clinical Follow-Up Links Metabolites to Risk and Severity of Pulmonary Arterial Hypertension

BACKGROUND

Pulmonary arterial hypertension (PAH) exhibits phenotypic heterogeneity and variable response to therapy. The metabolome has been implicated in the pathogenesis of PAH, but previous works have lacked power to implicate specific metabolites. Mendelian randomization (MR) is a method for causal inference between exposures and outcomes.

METHODS AND RESULTS

Using genome-wide association study summary statistics, we implemented MR analysis to test for potential causal relationships between serum concentration of 575 metabolites and PAH. Five metabolites were causally associated with the risk of PAH after multiple testing correction. Next, we measured serum concentration of candidate metabolites in an independent clinical cohort of 449 patients with PAH to check whether metabolite concentrations are correlated with markers of disease severity. Of the 5 candidates nominated by our MR work, serine was negatively associated and homostachydrine was positively associated with clinical severity of PAH via direct measurement in this independent clinical cohort. Finally we used conditional and orthogonal approaches to explore the biology underlying our lead metabolites. Rare variant burden testing was carried out using whole exome sequencing data from 578 PAH cases and 361 675 controls. Multivariable MR is an extension of MR that uses a single set of instrumental single-nucleotide polymorphisms to measure multiple exposures; multivariable MR is used to determine interdependence between the effects of different exposures on a single outcome. Rare variant analysis demonstrated that loss-of-function mutations within activating transcription factor 4, a transcription factor responsible for upregulation of serine synthesis under conditions of serine starvation, are associated with higher risk for PAH. Homostachydrine is a xenobiotic metabolite that is structurally related to l-proline betaine, which has previously been linked to modulation of inflammation and tissue remodeling in PAH. Our multivariable MR analysis suggests that the effect of l-proline betaine is actually mediated indirectly via homostachydrine.

CONCLUSIONS

Our data present a method for study of the metabolome in the context of PAH, and suggests several candidates for further evaluation and translational research.

Extrapulmonary manifestations of pulmonary arterial hypertension

INTRODUCTION

Extrapulmonary manifestations of pulmonary arterial hypertension (PAH) may play a critical pathobiological role and a deeper understanding will advance insight into mechanisms and novel therapeutic targets. This manuscript reviews our understanding of extrapulmonary manifestations of PAH.

AREAS COVERED

A group of experts was assembled and a complimentary PubMed search performed (October 2023 - March 2024). Inflammation is observed throughout the central nervous system and attempts at manipulation are an encouraging step toward novel therapeutics. Retinal vascular imaging holds promise as a noninvasive method of detecting early disease and monitoring treatment responses. PAH patients have gut flora alterations and dysbiosis likely plays a role in systemic inflammation. Despite inconsistent observations, the roles of obesity, insulin resistance and dysregulated metabolism may be illuminated by deep phenotyping of body composition. Skeletal muscle dysfunction is perpetuated by metabolic dysfunction, inflammation, and hypoperfusion, but exercise training shows benefit. Renal, hepatic, and bone marrow abnormalities are observed in PAH and may represent both end-organ damage and disease modifiers.

EXPERT OPINION

Insights into systemic manifestations of PAH will illuminate disease mechanisms and novel therapeutic targets. Additional study is needed to understand whether extrapulmonary manifestations are a cause or effect of PAH and how manipulation may affect outcomes.

Artificial Intelligence in Heart Failure: Friend or Foe?

In recent times, there have been notable changes in cardiovascular medicine, propelled by the swift advancements in artificial intelligence (AI). The present work provides an overview of the current applications and challenges of AI in the field of heart failure. It emphasizes the "garbage in, garbage out" issue, where AI systems can produce inaccurate results with skewed data. The discussion covers issues in heart failure diagnostic algorithms, particularly discrepancies between existing models. Concerns about the reliance on the left ventricular ejection fraction (LVEF) for classification and treatment are highlighted, showcasing differences in current scientific perceptions. This review also delves into challenges in implementing AI, including variable considerations and biases in training data. It underscores the limitations of current AI models in real-world scenarios and the difficulty in interpreting their predictions, contributing to limited physician trust in AI-based models. The overarching suggestion is that AI can be a valuable tool in clinicians' hands for treating heart failure patients, as far as existing medical inaccuracies have been addressed before integrating AI into these frameworks.

Real-world evidence to advance knowledge in pulmonary hypertension: Status, challenges, and opportunities. A consensus statement from the Pulmonary Vascular Research Institute's Innovative Drug Development Initiative's Real-world Evidence Working Group

This manuscript on real-world evidence (RWE) in pulmonary hypertension (PH) incorporates the broad experience of members of the Pulmonary Vascular Research Institute's Innovative Drug Development Initiative Real-World Evidence Working Group. We aim to strengthen the research community's understanding of RWE in PH to facilitate clinical research advances and ultimately improve patient care. Herein, we review real-world data (RWD) sources, discuss challenges and opportunities when using RWD sources to study PH populations, and identify resources needed to support the generation of meaningful RWE for the global PH community.

Health Care Disparities in Pulmonary Arterial Hypertension

The current approach for the management of pulmonary arterial hypertension (PAH) relies on data gathered from clinical trials and large registries. However, there is concern that minorities including Black, Indigenous, and People of Color are underrepresented in these trials and registries, making current data not generalizable to these groups of patients. Hence, it is important to discuss the significance of race/ethnicity and socioeconomic factors in patients with PAH. Here, we review the current knowledge on health care disparities in PAH. We also propose future steps in the global task of assuring justice and equality in access to pulmonary hypertension health care.

Multiomic Investigations into Lung Health and Disease

Diseases of the lung account for more than 5 million deaths worldwide and are a healthcare burden. Improving clinical outcomes, including mortality and quality of life, involves a holistic understanding of the disease, which can be provided by the integration of lung multi-omics data. An enhanced understanding of comprehensive multiomic datasets provides opportunities to leverage those datasets to inform the treatment and prevention of lung diseases by classifying severity, prognostication, and discovery of biomarkers. The main objective of this review is to summarize the use of multiomics investigations in lung disease, including multiomics integration and the use of machine learning computational methods. This review also discusses lung disease models, including animal models, organoids, and single-cell lines, to study multiomics in lung health and disease. We provide examples of lung diseases where multi-omics investigations have provided deeper insight into etiopathogenesis and have resulted in improved preventative and therapeutic interventions.

Emerging pharmacotherapies for the treatment of pulmonary arterial hypertension

INTRODUCTION

Pulmonary arterial hypertension (PAH) is a progressive and life-threatening disease. Approved treatment options currently primarily target abnormal cell signaling pathways involved in vasoconstriction and proliferation, such as those mediated by prostacyclin, cyclic guanosine monophosphate, and endothelin.

AREAS COVERED

Recent advancements have led to new applications and modes of delivery of currently approved PAH medications. At the same time, novel drugs targeting specific molecular pathways involved in PAH pathogenesis have been developed and are being investigated in clinical trials. This review summarizes investigational drug trials for PAH gathered from a comprehensive search using PubMed and ClinicalTrials.gov between 2003 and 2023. It includes both currently approved medications studied at different doses or new administration forms and experimental drugs that have not yet been approved.

EXPERT OPINION

Approved treatments for PAH target imbalances in pulmonary vasoactive pathways that work primarily on enhancing pulmonary vasodilation with less salient effects on pulmonary vascular remodeling. The advent of more locally acting inhaled medications offers additional therapeutic options that may improve the ease of drug delivery and reduce adverse systemic effects. The more recent emphasis on developing and applying therapeutics that directly impact the aberrant signaling pathways implicated in PAH appears more likely to advance the treatment of this devastating disease.

Pursuing functional biomarkers in complex disease: Focus on pulmonary arterial hypertension

A major gap in diagnosis, classification, risk stratification, and prediction of therapeutic response exists in pulmonary arterial hypertension (PAH), driven in part by a lack of functional biomarkers that are also disease-specific. In this regard, leveraging big data-omics analyses using innovative approaches that integrate network medicine and machine learning correlated with clinically useful indices or risk stratification scores is an approach well-positioned to advance PAH precision medicine. For example, machine learning applied to a panel of 48 cytokines, chemokines, and growth factors could prognosticate PAH patients with immune-dominant subphenotypes at elevated or low-risk for mortality. Here, we discuss strengths and weaknesses of the most current studies evaluating omics-derived biomarkers in PAH. Progress in this field is offset by studies with small sample size, pervasive limitations in bioinformatics, and lack of standardized methods for data processing and interpretation. Future success in this field, in turn, is likely to hinge on mechanistic validation of data outputs in order to couple functional biomarker data with target-specific therapeutics in clinical practice.

Multiomics Network Medicine Approaches to Precision Medicine and Therapeutics in Cardiovascular Diseases

Cardiovascular diseases (CVD) are the leading cause of death worldwide and display complex phenotypic heterogeneity caused by many convergent processes, including interactions between genetic variation and environmental factors. Despite the identification of a large number of associated genes and genetic loci, the precise mechanisms by which these genes systematically influence the phenotypic heterogeneity of CVD are not well understood. In addition to DNA sequence, understanding the molecular mechanisms of CVD requires data from other omics levels, including the epigenome, the transcriptome, the proteome, as well as the metabolome. Recent advances in multiomics technologies have opened new precision medicine opportunities beyond genomics that can guide precise diagnosis and personalized treatment. At the same time, network medicine has emerged as an interdisciplinary field that integrates systems biology and network science to focus on the interactions among biological components in health and disease, providing an unbiased framework through which to integrate systematically these multiomics data. In this review, we briefly present such multiomics technologies, including bulk omics and single-cell omics technologies, and discuss how they can contribute to precision medicine. We then highlight network medicine-based integration of multiomics data for precision medicine and therapeutics in CVD. We also include a discussion of current challenges, potential limitations, and future directions in the study of CVD using multiomics network medicine approaches.

PARM1 Drives Smooth Muscle Cell Proliferation in Pulmonary Arterial Hypertension via AKT/FOXO3A Axis

Pulmonary arterial hypertension (PAH) is a group of severe, progressive, and debilitating diseases with limited therapeutic options. This study aimed to explore novel therapeutic targets in PAH through bioinformatics and experiments. Weighted gene co-expression network analysis (WGCNA) was applied to detect gene modules related to PAH, based on the GSE15197, GSE113439, and GSE117261. GSE53408 was applied as validation set. Subsequently, the validated most differentially regulated hub gene was selected for further ex vivo and in vitro assays. PARM1, TSHZ2, and CCDC80 were analyzed as potential intervention targets for PAH. Consistently with the bioinformatic results, our ex vivo and in vitro data indicated that PARM1 expression increased significantly in the lung tissue and/or pulmonary artery of the MCT-induced PAH rats and hypoxia-induced PAH mice in comparison with the respective controls. Besides, a similar expression pattern of PARM1 was found in the hypoxia- and PDGF--treated isolated rat primary pulmonary arterial smooth muscle cells (PASMCs). In addition, hypoxia/PDGF--induced PARM1 protein expression could promote the elevation of phosphorylation of AKT, phosphorylation of FOXO3A and PCNA, and finally the proliferation of PASMCs in vitro, whereas PARM1 siRNA treatment inhibited it. Mechanistically, PARM1 promoted PAH via AKT/FOXO3A/PCNA signaling pathway-induced PASMC proliferation.

Circ-Ntrk2 acts as a miR-296-5p sponge to activate the TGF-β1/p38 MAPK pathway and promote pulmonary hypertension and vascular remodelling

BACKGROUND

Circular RNAs (circRNAs), a novel class of non-coding RNAs, play an important regulatory role in pulmonary arterial hypertension (PAH); however, the specific mechanism is rarely studied. In this study, we aimed to discover functional circRNAs and investigate their effects and mechanisms in hypoxia-induced pulmonary vascular remodelling, a core pathological change in PAH.

METHODS

RNA sequencing was used to illustrate the expression profile of circRNAs in hypoxic PAH. Bioinformatics, Sanger sequencing, and quantitative real-time PCR were used to identify the ring-forming characteristics of RNA and analyse its expression. Then, we established a hypoxia-induced PAH mouse model to evaluate circRNA function in PAH by echocardiography and hemodynamic measurements. Moreover, microRNA target gene database screening, fluorescence in situ hybridisation, luciferase reporter gene detection, and western blotting were used to explore the mechanism of circRNAs.

RESULTS

RNA sequencing identified 432 differentially expressed circRNAs in mouse hypoxic lung tissues. Our results indicated that circ-Ntrk2 is a stable cytoplasmic circRNA derived from Ntrk2 mRNA and frequently upregulated in hypoxic lung tissue. We further found that circ-Ntrk2 sponges miR-296-5p and miR-296-5p can bind to the 3'-untranslated region of transforming growth factor-β1 (TGF-β1) mRNA, thereby attenuating TGF-β1 translation. Through gene knockout or exogenous expression, we demonstrated that circ-Ntrk2 could promote PAH and vascular remodelling. Moreover, we verified that miR-296-5p overexpression alleviated pulmonary vascular remodelling and improved PAH through the TGF-β1/p38 MAPK pathway.

CONCLUSIONS

We identified a new circRNA (circ-Ntrk2) and explored its function and mechanism in PAH, thereby establishing potential targets for the diagnosis and treatment of PAH. Furthermore, our study contributes to the understanding of circRNA in relation to PAH.

Proline and glucose metabolic reprogramming supports vascular endothelial and medial biomass in pulmonary arterial hypertension

In pulmonary arterial hypertension (PAH), inflammation promotes a fibroproliferative pulmonary vasculopathy. Reductionist studies emphasizing single biochemical reactions suggest a shift toward glycolytic metabolism in PAH; however, key questions remain regarding the metabolic profile of specific cell types within PAH vascular lesions in vivo. We used RNA-Seq to profile the transcriptome of pulmonary artery endothelial cells (PAECs) freshly isolated from an inflammatory vascular injury model of PAH ex vivo, and these data were integrated with information from human gene ontology pathways. Network medicine was then used to map all aa and glucose pathways to the consolidated human interactome, which includes data on 233,957 physical protein-protein interactions. Glucose and proline pathways were significantly close to the human PAH disease module, suggesting that these pathways are functionally relevant to PAH pathobiology. To test this observation in vivo, we used multi-isotope imaging mass spectrometry to map and quantify utilization of glucose and proline in the PAH pulmonary vasculature at subcellular resolution. Our findings suggest that elevated glucose and proline avidity underlie increased biomass in PAECs and the media of fibrosed PAH pulmonary arterioles. Overall, these data show that anabolic utilization of glucose and proline are fundamental to the vascular pathology of PAH.

Tensions in Taxonomies: Current Understanding and Future Directions in the Pathobiologic Basis and Treatment of Group 1 and Group 3 Pulmonary Hypertension

In the over 100 years since the recognition of pulmonary hypertension (PH), immense progress and significant achievements have been made with regard to understanding the pathophysiology of the disease and its treatment. These advances have been mostly in idiopathic pulmonary arterial hypertension (IPAH), which was classified as Group 1 Pulmonary Hypertension (PH) at the Second World Symposia on PH in 1998. However, the pathobiology of PH due to chronic lung disease, classified as Group 3 PH, remains poorly understood and its treatments thus remain limited. We review the history of the classification of the five groups of PH and aim to provide a state-of-the-art review of the understanding of the pathogenesis of Group 1 PH and Group 3 PH including insights gained from novel high-throughput omics technologies that have revealed heterogeneities within these categories as well as similarities between them. Leveraging the substantial gains made in understanding the genomics, epigenomics, proteomics, and metabolomics of PAH to understand the full spectrum of the complex, heterogeneous disease of PH is needed. Multimodal omics data as well as supervised and unbiased machine learning approaches after careful consideration of the powerful advantages as well as of the limitations and pitfalls of these technologies could lead to earlier diagnosis, more precise risk stratification, better predictions of disease response, new sub-phenotype groupings within types of PH, and identification of shared pathways between PAH and other types of PH that could lead to new treatment targets. © 2023 American Physiological Society. Compr Physiol 13:4295-4319, 2023.

Integrating epigenetics and metabolomics to advance treatments for pulmonary arterial hypertension

Pulmonary arterial hypertension (PAH) is a devastating vascular disease with multiple etiologies. Emerging evidence supports a fundamental role for epigenetic machinery and metabolism in the initiation and progression of PAH. Here, we summarize emerging epigenetic mechanisms that have been identified as contributors to PAH evolution, specifically, DNA methylation, histone modifications, and microRNAs. Furthermore, the interplay between epigenetics with metabolism is explored while new crosstalk targets to be investigated in PAH are proposed that highlight multi-omics strategies including integrated epigenomics and metabolomics. Therapeutic opportunities and challenges associated with epigenetics and metabolomics in PAH are examined, highlighting the role that epigenetics and metabolomics have in facilitating early detection, personalized dietary plans, and advanced drug therapy for PAH.