Network module-based drug repositioning for pulmonary arterial hypertension

HHIP protein interactions in lung cells provide insight into COPD pathogenesis

Chronic obstructive pulmonary disease (COPD) is the third leading cause of death worldwide. The primary causes of COPD are environmental, including cigarette smoking; however, genetic susceptibility also contributes to COPD risk. Genome-Wide Association Studies (GWASes) have revealed more than 80 genetic loci associated with COPD, leading to the identification of multiple COPD GWAS genes. However, the biological relationships between the identified COPD susceptibility genes are largely unknown. Genes associated with a complex disease are often in close network proximity, i.e. their protein products often interact directly with each other and/or similar proteins. In this study, we use affinity purification mass spectrometry (AP-MS) to identify protein interactions with HHIP, a well-established COPD GWAS gene which is part of the sonic hedgehog pathway, in two disease-relevant lung cell lines (IMR90 and 16HBE). To better understand the network neighborhood of HHIP, its proximity to the protein products of other COPD GWAS genes, and its functional role in COPD pathogenesis, we create HUBRIS, a protein-protein interaction network compiled from 8 publicly available databases. We identified both common and cell type-specific protein-protein interactors of HHIP. We find that our newly identified interactions shorten the network distance between HHIP and the protein products of several COPD GWAS genes, including DSP, MFAP2, TET2, and FBLN5. These new shorter paths include proteins that are encoded by genes involved in extracellular matrix and tissue organization. We found and validated interactions to proteins that provide new insights into COPD pathobiology, including CAVIN1 (IMR90) and TP53 (16HBE). The newly discovered HHIP interactions with CAVIN1 and TP53 implicate HHIP in response to oxidative stress.

Exploring common mechanisms of adverse drug reactions and disease phenotypes through network-based analysis

The need for a deeper understanding of adverse drug reaction (ADR) mechanisms is vital for improving drug safety and repurposing. This study introduces Drug Adverse Reaction Mechanism Explainer (DREAMER), a network-based framework that uses a comprehensive knowledge graph to uncover molecular mechanisms underlying ADRs and disease phenotypes. By examining shared phenotypes of drugs and diseases and their effects on protein-protein interaction networks, DREAMER identifies proteins linked to ADR mechanisms. Applied to 649 ADRs, DREAMER identified molecular mechanisms for 67 ADRs, including ventricular arrhythmia and metabolic acidosis, and emphasized pathways like GABAergic signaling and coagulation proteins in personality disorders and intracranial hemorrhage. We further demonstrate the application of DREAMER in drug repurposing and propose sotalol, ranolazine, and diltiazem as candidate drugs to be repurposed for cardiac arrest. In summary, DREAMER effectively detects molecular mechanisms underlying phenotypes, emphasizing the importance of network-based analyses with integrative data for enhancing drug safety and accelerating the discovery of novel therapeutic strategies.

Non-canonical lysosomal lipolysis drives mobilization of adipose tissue energy stores with fasting

Physiological adaptations to fasting enable humans to survive for prolonged periods without food and involve molecular pathways that may drive life-prolonging effects of dietary restriction in model organisms. Mobilization of fatty acids and glycerol from adipocyte lipid stores by canonical neutral lipases, including the rate limiting adipose triglyceride lipase (Pnpla2/ATGL), is critical to the adaptive fasting response. Here we discovered an alternative mechanism of lipolysis in adipocytes involving a lysosomal program. We functionally tested lysosomal lipolysis with pharmacological and genetic approaches in mice and in murine and human adipocyte and adipose tissue explant culture, establishing dependency on lysosomal acid lipase (LIPA/LAL) and the microphthalmia/transcription factor E (MiT/TFE) family. Our study establishes a model whereby the canonical pathway is critical for rapid lipolytic responses to adrenergic stimuli operative in the acute stage of fasting, while the alternative lysosomal pathway dominates with prolonged fasting.

Polygenic and transcriptional risk scores identify chronic obstructive pulmonary disease subtypes in the COPDGene and ECLIPSE cohort studies

BACKGROUND

Genetic variants and gene expression predict risk of chronic obstructive pulmonary disease (COPD), but their effect on COPD heterogeneity is unclear. We aimed to define high-risk COPD subtypes using genetics (polygenic risk score, PRS) and blood gene expression (transcriptional risk score, TRS) and assess differences in clinical and molecular characteristics.

METHODS

We defined high-risk groups based on PRS and TRS quantiles by maximising differences in protein biomarkers in a COPDGene training set and identified these groups in COPDGene and ECLIPSE test sets. We tested multivariable associations of subgroups with clinical outcomes and compared protein-protein interaction networks and drug repurposing analyses between high-risk groups.

FINDINGS

We examined two high-risk omics-defined groups in non-overlapping test sets (n = 1133 NHW COPDGene, n = 299 African American (AA) COPDGene, n = 468 ECLIPSE). We defined "high activity" (low PRS, high TRS) and "severe risk" (high PRS, high TRS) subgroups. Participants in both subgroups had lower body-mass index (BMI), lower lung function, and alterations in metabolic, growth, and immune signalling processes compared to a low-risk (low PRS, low TRS) subgroup. "High activity" but not "severe risk" participants had greater prospective FEV1 decline (COPDGene: -51 mL/year; ECLIPSE: -40 mL/year) and proteomic profiles were enriched in gene sets perturbed by treatment with 5-lipoxygenase inhibitors and angiotensin-converting enzyme (ACE) inhibitors.

INTERPRETATION

Concomitant use of polygenic and transcriptional risk scores identified clinical and molecular heterogeneity amongst high-risk individuals. Proteomic and drug repurposing analysis identified subtype-specific enrichment for therapies and suggest prior drug repurposing failures may be explained by patient selection.

FUNDING

National Institutes of Health.

Target repositioning using multi-layer networks and machine learning: The case of prostate cancer

The discovery of novel therapeutic targets, defined as proteins which drugs can interact with to induce therapeutic benefits, typically represent the first and most important step of drug discovery. One solution for target discovery is target repositioning, a strategy which relies on the repurposing of known targets for new diseases, leading to new treatments, less side effects and potential drug synergies. Biological networks have emerged as powerful tools for integrating heterogeneous data and facilitating the prediction of biological or therapeutic properties. Consequently, they are widely employed to predict new therapeutic targets by characterizing potential candidates, often based on their interactions within a Protein-Protein Interaction (PPI) network, and their proximity to genes associated with the disease. However, over-reliance on PPI networks and the assumption that potential targets are necessarily near known genes can introduce biases that may limit the effectiveness of these methods. This study addresses these limitations in two ways. First, by exploiting a multi-layer network which incorporates additional information such as gene regulation, metabolite interactions, metabolic pathways, and several disease signatures such as Differentially Expressed Genes, mutated genes, Copy Number Alteration, and structural variants. Second, by extracting relevant features from the network using several approaches including proximity to disease-associated genes, but also unbiased approaches such as propagation-based methods, topological metrics, and module detection algorithms. Using prostate cancer as a case study, the best features were identified and utilized to train machine learning algorithms to predict 5 novel promising therapeutic targets for prostate cancer: IGF2R, C5AR, RAB7, SETD2 and NPBWR1.

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.

Disease gene prioritization with quantum walks

MOTIVATION

Disease gene prioritization methods assign scores to genes or proteins according to their likely relevance for a given disease based on a provided set of seed genes. This scoring can be used to find new biologically relevant genes or proteins for many diseases. Although methods based on classical random walks have proven to yield competitive results, quantum walk methods have not been explored to this end.

RESULTS

We propose a new algorithm for disease gene prioritization based on continuous-time quantum walks using the adjacency matrix of a protein-protein interaction (PPI) network. We demonstrate the success of our proposed quantum walk method by comparing it to several well-known gene prioritization methods on three disease sets, across seven different PPI networks. In order to compare these methods, we use cross-validation and examine the mean reciprocal ranks of recall and average precision values. We further validate our method by performing an enrichment analysis of the predicted genes for coronary artery disease.

AVAILABILITY AND IMPLEMENTATION

The data and code for the methods can be accessed at https://github.com/markgolds/qdgp.

Polygenic and transcriptional risk scores identify chronic obstructive pulmonary disease subtypes

Rationale

Genetic variants and gene expression predict risk of chronic obstructive pulmonary disease (COPD), but their effect on COPD heterogeneity is unclear.

Objectives

Define high-risk COPD subtypes using both genetics (polygenic risk score, PRS) and blood gene expression (transcriptional risk score, TRS) and assess differences in clinical and molecular characteristics.

Methods

We defined high-risk groups based on PRS and TRS quantiles by maximizing differences in protein biomarkers in a COPDGene training set and identified these groups in COPDGene and ECLIPSE test sets. We tested multivariable associations of subgroups with clinical outcomes and compared protein-protein interaction networks and drug repurposing analyses between high-risk groups.

Measurements and Main Results

We examined two high-risk omics-defined groups in non-overlapping test sets (n=1,133 NHW COPDGene, n=299 African American (AA) COPDGene, n=468 ECLIPSE). We defined "High activity" (low PRS/high TRS) and "severe risk" (high PRS/high TRS) subgroups. Participants in both subgroups had lower body-mass index (BMI), lower lung function, and alterations in metabolic, growth, and immune signaling processes compared to a low-risk (low PRS, low TRS) reference subgroup. "High activity" but not "severe risk" participants had greater prospective FEV 1 decline (COPDGene: -51 mL/year; ECLIPSE: - 40 mL/year) and their proteomic profiles were enriched in gene sets perturbed by treatment with 5-lipoxygenase inhibitors and angiotensin-converting enzyme (ACE) inhibitors.

Conclusions

Concomitant use of polygenic and transcriptional risk scores identified clinical and molecular heterogeneity amongst high-risk individuals. Proteomic and drug repurposing analysis identified subtype-specific enrichment for therapies and suggest prior drug repurposing failures may be explained by patient selection.

HHIP protein interactions in lung cells provide insight into COPD pathogenesis

Chronic obstructive pulmonary disease (COPD) is the third leading cause of death worldwide. The primary causes of COPD are environmental, including cigarette smoking; however, genetic susceptibility also contributes to COPD risk. Genome-Wide Association Studies (GWASes) have revealed more than 80 genetic loci associated with COPD, leading to the identification of multiple COPD GWAS genes. However, the biological relationships between the identified COPD susceptibility genes are largely unknown. Genes associated with a complex disease are often in close network proximity, i.e. their protein products often interact directly with each other and/or similar proteins. In this study, we use affinity purification mass spectrometry (AP-MS) to identify protein interactions with HHIP , a well-established COPD GWAS gene which is part of the sonic hedgehog pathway, in two disease-relevant lung cell lines (IMR90 and 16HBE). To better understand the network neighborhood of HHIP , its proximity to the protein products of other COPD GWAS genes, and its functional role in COPD pathogenesis, we create HUBRIS, a protein-protein interaction network compiled from 8 publicly available databases. We identified both common and cell type-specific protein-protein interactors of HHIP. We find that our newly identified interactions shorten the network distance between HHIP and the protein products of several COPD GWAS genes, including DSP, MFAP2, TET2 , and FBLN5 . These new shorter paths include proteins that are encoded by genes involved in extracellular matrix and tissue organization. We found and validated interactions to proteins that provide new insights into COPD pathobiology, including CAVIN1 (IMR90) and TP53 (16HBE). The newly discovered HHIP interactions with CAVIN1 and TP53 implicate HHIP in response to oxidative stress.

Machine learning-based integration of network features and chemical structure of compounds for SARS-CoV-2 drug effect analysis

High drug development costs and the limited number of new annual drug approvals increase the need for innovative approaches for drug effect prediction. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause of coronavirus disease 2019 (COVID-19), led to a global pandemic with high morbidity and mortality. Although effective preventive measures exist, there are few effective treatments for hospitalized patients with SARS-CoV-2 infection. Drug repurposing and drug effect prediction are promising strategies that could shorten development time and reduce costs compared with de novo drug discovery. In this work, we present a machine learning framework to integrate a variety of target network features and physicochemical properties of compounds, and analyze their influence on the therapeutic effects for SARS-CoV-2 infection and on host cell cytotoxic effects. Random forest models trained on compounds with known experimental effects on SARS-CoV-2 infection and subsequent feature importance analysis based on Shapley values provided insights into the determinants of drug efficacy and cytotoxicity, which can be incorporated into novel drug discovery approaches. Given the complexity of molecular mechanisms of drug action and limited sample sizes, our models achieve a reasonable mean area under the receiver operating characteristic curve (ROC-AUC) of 0.73 on an unseen validation set. To our knowledge, this is the first work to incorporate a combination of network and physicochemical features of compounds into a machine learning model to predict drug effects on SARS-CoV-2 infection. Our systems pharmacology-based machine learning framework can be used to classify other existing drugs for SARS-CoV-2 infection and can easily be adapted to drug effect prediction for future viral outbreaks.

Persistent homology analysis of type 2 diabetes genome-wide association studies in protein-protein interaction networks

Genome-wide association studies (GWAS) involving increasing sample sizes have identified hundreds of genetic variants associated with complex diseases, such as type 2 diabetes (T2D); however, it is unclear how GWAS hits form unique topological structures in protein-protein interaction (PPI) networks. Using persistent homology, this study explores the evolution and persistence of the topological features of T2D GWAS hits in the PPI network with increasing p-value thresholds. We define an n-dimensional persistent disease module as a higher-order generalization of the largest connected component (LCC). The 0-dimensional persistent T2D disease module is the LCC of the T2D GWAS hits, which is significantly detected in the PPI network (196 nodes and 235 edges, P< 0.05). In the 1-dimensional homology group analysis, all 18 1-dimensional holes (loops) of the T2D GWAS hits persist over all p-value thresholds. The 1-dimensional persistent T2D disease module comprising these 18 persistent 1-dimensional holes is significantly larger than that expected by chance (59 nodes and 83 edges, P< 0.001), indicating a significant topological structure in the PPI network. Our computational topology framework potentially possesses broad applicability to other complex phenotypes in identifying topological features that play an important role in disease pathobiology.

Repurposing Drugs for the Treatment of COVID-19 and Its Cardiovascular Manifestations

COVID-19 is an infectious disease caused by SARS-CoV-2 leading to the ongoing global pandemic. Infected patients developed a range of respiratory symptoms, including respiratory failure, as well as other extrapulmonary complications. Multiple comorbidities, including hypertension, diabetes, cardiovascular diseases, and chronic kidney diseases, are associated with the severity and increased mortality of COVID-19. SARS-CoV-2 infection also causes a range of cardiovascular complications, including myocarditis, myocardial injury, heart failure, arrhythmias, acute coronary syndrome, and venous thromboembolism. Although a variety of methods have been developed and many clinical trials have been launched for drug repositioning for COVID-19, treatments that consider cardiovascular manifestations and cardiovascular disease comorbidities specifically are limited. In this review, we summarize recent advances in drug repositioning for COVID-19, including experimental drug repositioning, high-throughput drug screening, omics data-based, and network medicine-based computational drug repositioning, with particular attention on those drug treatments that consider cardiovascular manifestations of COVID-19. We discuss prospective opportunities and potential methods for repurposing drugs to treat cardiovascular complications of COVID-19.

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.

Uncovering common pathobiological processes between COVID-19 and pulmonary arterial hypertension by integrating Omics data

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which led to the current pandemic. Many factors, including age and comorbidities, influence the severity and mortality of COVID-19. SARS-CoV-2 infection can cause pulmonary vascular dysfunction. The COVID-19 case-fatality rate in patients with pulmonary arterial hypertension (PAH) is higher in comparison with the general population. In this study, we aimed to identify pathobiological processes common to COVID-19 and PAH by utilizing the human protein-protein interactome and whole-genome transcription data from peripheral blood mononuclear cells (PBMCs) and from lung tissue. We found that there are significantly more interactions between SARS-CoV-2 targets and PAH disease proteins than expected by chance, suggesting that the PAH disease module is in the neighborhood of SARS-CoV-2 targets in the human interactome. In addition, SARS-CoV-2 infection-induced changes in gene expression significantly overlap with PAH-induced gene expression changes in both tissues, indicating SARS-CoV-2 and PAH may share common transcriptional regulators. We identified many upregulated genes and downregulated genes common to COVID-19 and PAH. Interestingly, we observed different co-regulation patterns and dysfunctional signaling pathways in PBMCs versus lung tissue. Endophenotype enrichment analysis revealed that genes regulating fibrosis, inflammation, hypoxia, oxidative stress, immune response, and thromboembolism are significantly enriched in the COVID-19-PAH co-expression modules. We examined the network proximity of the targets of repositioned drugs for COVID-19 to the co-expression modules in PBMCs and lung tissue, and identified 42 drugs that can be potentially used for COVID-19 patients with PAH as a comorbidity. The uncovered common pathobiological pathways are crucial for discovering therapeutic targets and designing tailored treatments for COVID-19 patients who also have PAH.

Association Between Circulating CD4+ T Cell Methylation Signatures of Network-Oriented SOCS3 Gene and Hemodynamics in Patients Suffering Pulmonary Arterial Hypertension

Pathogenic DNA methylation changes may be involved in pulmonary arterial hypertension (PAH) onset and its progression, but there is no data on potential associations with patient-derived hemodynamic parameters. The reduced representation bisulfite sequencing (RRBS) platform identified N = 631 differentially methylated CpG sites which annotated to N = 408 genes (DMGs) in circulating CD4⁺ T cells isolated from PAH patients vs. healthy controls (CTRLs). A promoter-restricted network analysis established the PAH subnetwork that included 5 hub DMGs (SOCS3, GNAS, ITGAL, NCOR2, NFIC) and 5 non-hub DMGs (NR4A2, GRM2, PGK1, STMN1, LIMS2). The functional analysis revealed that the SOCS3 gene was the most recurrent among the top ten significant pathways enriching the PAH subnetwork, including the growth hormone receptor and the interleukin-6 signaling. Correlation analysis showed that the promoter methylation levels of each network-oriented DMG were associated individually with hemodynamic parameters. In particular, SOCS3 hypomethylation was negatively associated with right atrial pressure (RAP) and positively associated with cardiac index (CI) (|r|≥ 0.6). A significant upregulation of the SOCS3, ITGAL, NFIC, NCOR2, and PGK1 mRNA levels (qRT-PCR) in peripheral blood mononuclear cells from PAH patients vs. CTRLs was found (P ≤ 0.05). By immunoblotting, a significant upregulation of the SOCS3 protein was confirmed in PAH patients vs. CTRLs (P < 0.01). This is the first network-oriented study which integrates circulating CD4⁺ T cell DNA methylation signatures, hemodynamic parameters, and validation experiments in PAH patients at first diagnosis or early follow-up. Our data suggests that SOCS3 gene might be involved in PAH pathogenesis and serve as potential prognostic biomarker.

Harnessing Big Data to Advance Treatment and Understanding of Pulmonary Hypertension

Pulmonary hypertension is a complex disease with multiple causes, corresponding to phenotypic heterogeneity and variable therapeutic responses. Advancing understanding of pulmonary hypertension pathogenesis is likely to hinge on integrated methods that leverage data from health records, imaging, novel molecular -omics profiling, and other modalities. In this review, we summarize key data sets generated thus far in the field and describe analytical methods that hold promise for deciphering the molecular mechanisms that underpin pulmonary vascular remodeling, including machine learning, network medicine, and functional genetics. We also detail how genetic and subphenotyping approaches enable earlier diagnosis, refined prognostication, and optimized treatment prediction. We propose strategies that identify functionally important molecular pathways, bolstered by findings across multi-omics platforms, which are well-positioned to individualize drug therapy selection and advance precision medicine in this highly morbid disease.

Cell-to-Cell Crosstalk: A New Insight into Pulmonary Hypertension

Pulmonary hypertension (PH) is a disease with high pulmonary arterial pressure, pulmonary vasoconstriction, pulmonary vascular remodeling, and microthrombosis in complex plexiform lesions, but it has been unclear of the exact mechanism of PH. A new understanding of the pathogenesis of PH is occurred and focused on the role of crosstalk between the cells on pulmonary vessels and pulmonary alveoli. It was found that the crosstalks among the endothelial cells, smooth muscle cells, fibroblasts, pericytes, alveolar epithelial cells, and macrophages play important roles in cell proliferation, migration, inflammation, and so on. Therefore, the heterogeneity of multiple pulmonary blood vessels and alveolar cells and tracking the transmitters of cell communication could be conducive to the further insights into the pathogenesis of PH to discover the potential therapeutic targets for PH.

Network module-based drug repositioning for pulmonary arterial hypertension

Pulmonary arterial hypertension (PAH) is a progressive disorder characterized by pulmonary vascular remodeling leading to increased pulmonary vascular resistance and pulmonary arterial pressure. PAH is a highly morbid cardiopulmonary disease adversely affecting lifespan and quality of life. Despite increased awareness and advances of medical therapies in recent decades, long-term prognosis and survival remain poor for patients with PAH. Novel therapies that can target the underlying pathobiology of PAH and reverse pulmonary vascular remodeling are clearly needed. In this study, we develop a network module-based framework to examine potential drug repositioning for PAH. The rationale for this approach is that in order to have therapeutic effects, the targets of potential drugs must be significantly proximate to the disease module of interest in the human protein-protein interactome. Based on 15 existing drugs for treating PAH, our framework integrates drug-drug interactions, drug-drug chemical similarity, drug targets, and PAH disease proteins into the human interactome, and prioritizes candidate drugs for PAH. We identified 53 drugs that could potentially be repurposed for PAH. Many of these candidates have strong literature support. Compared to black-box-like machine learning models, network module-based drug repositioning can provide mechanistic insights into how repositioned drugs can target the underlying pathobiological mechanisms of PAH.