Cluster analysis identifies novel real-world lung disease-pulmonary hypertension subphenotypes: implications for treatment response.

Background: Clinical trials repurposing pulmonary arterial hypertension (PAH) therapies to patients with lung disease- or hypoxia-pulmonary hypertension (PH) (classified as World Health Organization Group 3 PH) have failed to show a consistent benefit. However, Group 3 PH clinical heterogeneity suggests robust phenotyping may inform detection of treatment-responsive subgroups. We hypothesised that cluster analysis would identify subphenotypes with differential responses to oral PAH therapy. Methods: Two k-means analyses were performed on a national cohort of US veterans with Group 3 PH; an inclusive model (I) of all treated patients (n=196) and a haemodynamic model (H) limited to patients with right heart catheterisations (n=112). The primary outcome was organ failure or all-cause mortality by cluster. An exploratory analysis evaluated within-cluster treatment effects. Results: Three distinct clusters of Group 3 PH patients were identified. In the inclusive model (C1I n=43, 21.9%; C2I n=102, 52.0%; C3I n=51, 26.0%), lung disease and spirometry drove cluster assignment. By contrast, in the haemodynamic model (C1H n=44, 39.3%; C2H n=43, 38.4%; C3H n=25, 22.3%), right heart catheterisation data surpassed the importance of lung disease and spirometry. In the haemodynamic model, compared to C3H, C1H experienced the greatest hazard for respiratory failure or death (HR 6.1, 95% CI 3.2-11.8). In an exploratory analysis, cluster determined treatment response (p=0.006). Conclusions regarding within-cluster treatment responses were limited by significant differences between select variables in the treated and untreated groups. Conclusions: Cluster analysis identifies novel real-world subphenotypes of Group 3 PH patients with distinct clinical trajectories. Future studies may consider this methodological approach to identify subgroups of heterogeneous patients that may be responsive to existing pulmonary vasodilatory therapies.

Drugst.One - a plug-and-play solution for online systems medicine and network-based drug repurposing.

In recent decades, the development of new drugs has become increasingly expensive and inefficient, and the molecular mechanisms of most pharmaceuticals remain poorly understood. In response, computational systems and network medicine tools have emerged to identify potential drug repurposing candidates. However, these tools often require complex installation and lack intuitive visual network mining capabilities. To tackle these challenges, we introduce Drugst.One, a platform that assists specialized computational medicine tools in becoming user-friendly, web-based utilities for drug repurposing. With just three lines of code, Drugst.One turns any systems biology software into an interactive web tool for modeling and analyzing complex protein-drug-disease networks. Demonstrating its broad adaptability, Drugst.One has been successfully integrated with 21 computational systems medicine tools. Available at https://drugst.one, Drugst.One has significant potential for streamlining the drug discovery process, allowing researchers to focus on essential aspects of pharmaceutical treatment research.

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.

[Difference of lipid-lowering efficacy of "Xinjianqu" before and after fermentation and its mechanism based on LKB1-AMPK pathway and 16S rDNA sequencing technology].

On the basis of establishing the prescription of Xinjianqu and clarifying the increase of the lipid-lowering active ingredients of Xinjianqu by fermentation, this paper further compared the differences in the lipid-lowering effects of Xinjianqu before and after fermentation, and studied the mechanism of Xinjianqu in the treatment of hyperlipidemia. Seventy SD rats were randomly divided into seven groups, including normal group, model group, positive drug simvastatin group(0.02 g·kg~(-1)), and low-dose and high-dose Xinjianqu groups before and after fermentation(1.6 g·kg~(-1) and 8 g·kg~(-1)), with ten rats in each group. Rats in each group were given high-fat diet continuously for six weeks to establish the model of hyperlipidemia(HLP). After successful modeling, the rats were given high-fat diet and gavaged by the corresponding drugs for six weeks, once a day, to compare the effects of Xinjianqu on the body mass, liver coefficient, and small intestine propulsion rate of rats with HLP before and after fermentation. The effects of Xinjianqu before and after fermentation on total cholesterol(TC), triacylglyceride(TG), high-density lipoprotein cholesterol(HDL-C), low-density lipoprotein cholesterol(LDL-C), alanine aminotransferase(ALT), aspartate aminotransferase(AST), blood urea nitrogen(BUN), creatinine(Cr), motilin(MTL), gastrin(GAS), and the Na~+-K~+-ATPase levels were determined by enzyme-linked immunosorbent assay(ELISA). The effects of Xinjianqu on liver morphology of rats with HLP were investigated by hematoxylin-eosin(HE) staining and oil red O fat staining. The effects of Xinjianqu on the protein expression of adenosine 5'-monophosphate(AMP)-activated protein kinase(AMPK), phosphorylated AMPK(p-AMPK), liver kinase B1(LKB1), and 3-hydroxy-3-methylglutarate monoacyl coenzyme A reductase(HMGCR) in liver tissues were investigated by immunohistochemistry. The effects of Xinjianqu on the regulation of intestinal flora structure of rats with HLP were studied based on 16S rDNA high-throughput sequencing technology. The results showed that compared with those in the normal group, rats in the model group had significantly higher body mass and liver coefficient(P<0.01), significantly lower small intestine propulsion rate(P<0.01), significantly higher serum levels of TC, TG, LDL-C, ALT, AST, BUN, Cr, and AQP2(P<0.01), and significantly lower serum levels of HDL-C, MTL, GAS, Na~+-K~+-ATP levels(P<0.01). The protein expression of AMPK, p-AMPK, and LKB1 in the livers of rats in the model group was significantly decreased(P<0.01), and that of HMGCR was significantly increased(P<0.01). In addition, the observed＿otus, Shannon, and Chao1 indices were significantly decreased(P<0.05 or P<0.01) in rat fecal flora in the model group. Besides, in the model group, the relative abundance of Firmicutes was reduced, while that of Verrucomicrobia and Proteobacteria was increased, and the relative abundance of beneficial genera such as Ligilactobacillus and Lachnospiraceae＿NK4A136＿group was reduced. Compared with the model group, all Xinjianqu groups regulated the body mass, liver coefficient, and small intestine index of rats with HLP(P<0.05 or P<0.01), reduced the serum levels of TC, TG, LDL-C, ALT, AST, BUN, Cr, and AQP2, increased the serum levels of HDL-C, MTL, GAS, and Na~+-K~+-ATP, improved the liver morphology, and increased the protein expression gray value of AMPK, p-AMPK, and LKB1 in the liver of rats with HLP and decreased that of LKB1. Xinjianqu groups could regulate the intestinal flora structure of rats with HLP, increased observed＿otus, Shannon, Chao1 indices, and increased the relative abundance of Firmicutes, Ligilactobacillus(genus), Lachnospiraceae＿NK4A136＿group(genus). Besides, the high-dose Xinjianqu-fermented group had significant effects on body mass, liver coefficient, small intestine propulsion rate, and serum index levels of rats with HLP(P<0.01), and the effects were better than those of Xinjianqu groups before fermentation. The above results show that Xinjianqu can improve the blood lipid level, liver and kidney function, and gastrointestinal motility of rats with HLP, and the improvement effect of Xinjianqu on hyperlipidemia is significantly enhanced by fermentation. The mechanism may be related to AMPK, p-AMPK, LKB1, and HMGCR protein in the LKB1-AMPK pathway and the regulation of intestinal flora structure.

Elevated Pulmonary Arterial Compliance Is Associated with Survival in Pulmonary Hypertension: Results from a Novel Network Medicine Analysis.

Rationale: Predictors of adverse outcome in pulmonary hypertension (PH) are well established; however, data that inform survival are lacking. Objectives: We aim to identify clinical markers and therapeutic targets that inform the survival in PH. Methods: We included data from patients with elevated mean pulmonary artery pressure (mPAP) diagnosed by right heart catheterization in the U.S. Veterans Affairs system (October 1, 2006-September 30, 2018). Network medicine framework was used to subgroup patients when considering an N of 79 variables per patient. The results informed outcome analyses in the discovery cohort and a sex-balanced validation right heart catheterization cohort from Vanderbilt University (September 24, 1998-December 20, 2013). Measurements and Main Results: From an N of 4,737 complete case patients with mPAP of 19-24 mm Hg, there were 21 distinct subgroups (network modules) (all-cause mortality range = 15.9-61.2% per module). Pulmonary arterial compliance (PAC) drove patient assignment to modules characterized by increased survival. When modeled continuously in patients with mPAP ⩾19 mm Hg (N = 37,744; age, 67.2 yr [range = 61.7-73.8 yr]; 96.7% male; median follow-up time, 1,236 d [range = 570-1,971 d]), the adjusted all-cause mortality hazard ratio was <1.0 beginning at PAC ⩾3.0 ml/mm Hg and decreased progressively to ∼7 ml/mm Hg. A protective association between PAC ⩾3.0 ml/mm Hg and mortality was also observed in the validation cohort (N = 1,514; age, 60.2 yr [range = 49.2-69.1 yr]; 48.0% male; median follow-up time, 2,485 d [range = 671-3,580 d]). The association was strongest in patients with precapillary PH at the time of catheterization, in whom 41% (95% confidence interval, 0.55-0.62; P < 0.001) and 49% (95% confidence interval, 0.38-0.69; P < 0.001) improvements in survival were observed for PAC ⩾3.0 versus <3.0 ml/mm Hg in the discovery and validation cohorts, respectively. Conclusions: These data identify elevated PAC as an important parameter associated with survival in PH. Prospective studies are warranted that consider PAC ⩾3.0 ml/mm Hg as a therapeutic target to achieve through proven interventions.

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.

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.

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.

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.

[Investigating erythrocyte hemolysis assay use for proinflammatory potential prediction of silica particles].

OBJECTIVES: Crystalline silica, which is a causative agent of silicosis (an occupational disease), is manufactured in a variety of products (particles) with different particle characteristics, such as size and surface properties. In Japan, the products are currently uniformly controlled as crystalline silica, which is a substance subject to labeling and notification requirements. However, since the toxicity of silica particles reportedly varies depending on its characteristics, businesses are encouraged to conduct appropriate risk assessments for each product to prevent silicosis. Recently, silica particles have been reported to induce lysosomal membrane damage, leading to the activation of proinflammatory factors. An indirect method to evaluate lysosomal membrane damage known as the erythrocyte hemolysis assay, in which the erythrocyte membrane is assumed to be the lysosomal membrane, was performed. This study aimed to examine the possibility of constructing a screening system for proinflammatory potential prediction of silica particles based on their erythrocyte hemolytic activity. METHODS: Hemolysis assays were performed on the silica particles with different sizes, crystallinity, and surface functional groups using the erythrocytes from a healthy volunteer. Additionally, the hemolytic activity of other element particles was compared with that of the silica particles, and 27 types of commercially available crystalline silica particle products underwent screening trials. RESULTS: The hemolytic activity of silica particles was higher in crystalline than that in amorphous and increased with the decreasing size. The hemolytic reaction was particular to silica particles and rarely occurred in particles of other elements. Moreover, the hemolytic activity was significantly suppressed if the silica particles surface was modified with metal ions (Fe3+, Al3+). The hemolytic activities of the crystalline silica products used industrially significantly differed. CONCLUSIONS: This study revealed that particle properties, such as size, crystallinity, and surface functional groups, affect the hemolytic activity of silica particles. Particularly, the surface functional groups (silanol groups) that are unique to silica particles were considered to be strongly involved in hemolytic activities. Since grading the commercially available crystalline silica particle products based on the hemolytic rate was possible, hemolytic activity was suggested to be an evaluation index for predicting the proinflammatory potential of silica particles.

What Causes Hypertrophic Cardiomyopathy?

Hypertrophic cardiomyopathy (HCM) is a global and relatively common cause of patient morbidity and mortality and is among the first reported monogenic cardiac diseases. For 30 years, the basic etiology of HCM has been attributed largely to variants in individual genes encoding cardiac sarcomere proteins, with the implication that HCM is fundamentally a genetic disease. However, data from clinical and network medicine analyses, as well as contemporary genetic studies show that single gene variants do not fully explain the broad and diverse HCM clinical spectrum. These transformative advances place a new focus on possible novel interactions between acquired disease determinants and genetic context to produce complex HCM phenotypes, also offering a measure of caution against overemphasizing monogenics as the principal cause of this disease. These new perspectives in which HCM is not a uniformly genetic disease but likely explained by multifactorial etiology will also unavoidably impact how HCM is viewed by patients and families in the clinical practicing community going forward, including relevance to genetic counseling and access to healthcare insurance and psychosocial wellness.

Pulmonary endothelial NEDD9 and the prothrombotic pathophenotype of acute respiratory distress syndrome due to SARS-CoV-2 infection.

The pathobiology of in situ pulmonary thrombosis in acute respiratory distress syndrome (ARDS) due to severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection is incompletely characterized. In human pulmonary artery endothelial cells (HPAECs), hypoxia increases neural precursor cell expressed, developmentally downregulated 9 (NEDD9) and induces expression of a prothrombotic NEDD9 peptide (N9P) on the extracellular plasma membrane surface. We hypothesized that the SARS-CoV-2-ARDS pathophenotype involves increased pulmonary endothelial N9P. Paraffin-embedded autopsy lung specimens were acquired from patients with SARS-CoV-2-​​​​​​ARDS (n = 13), ARDS from other causes (n = 10), and organ donor controls (n = 5). Immunofluorescence characterized the expression of N9P, fibrin, and transcription factor 12 (TCF12), a putative binding target of SARS-CoV-2 and known transcriptional regulator of NEDD9. We performed RNA-sequencing on normal HPAECs treated with normoxia or hypoxia (0.2% O2) for 24 h. Immunoprecipitation-liquid chromatography-mass spectrometry (IP-LC-MS) profiled protein-protein interactions involving N9P relevant to thrombus stabilization. Hypoxia increased TCF12 messenger RNA significantly compared to normoxia in HPAECs in vitro (+1.19-fold, p = 0.001; false discovery rate = 0.005), and pulmonary endothelial TCF12 expression was increased threefold in SARS-CoV-2-ARDS versus donor control lungs (p < 0.001). Compared to donor controls, pulmonary endothelial N9P-fibrin colocalization was increased in situ in non-SARS-CoV-2-ARDS and SARS-CoV-2-ARDS decedents (3.7 ± 1.2 vs. 10.3 ± 3.2 and 21.8 ± 4.0 arb. units, p < 0.001). However, total pulmonary endothelial N9P was increased significantly only in SARS-CoV-2-ARDS versus donor controls (15 ± 4.2 vs. 6.3 ± 0.9 arb. units, p < 0.001). In HPAEC plasma membrane isolates, IP-LC-MS identified a novel protein-protein interaction between NEDD9 and the ß3-subunit of the αvß3-integrin, which regulates fibrin anchoring to endothelial cells. In conclusion, lethal SARS-CoV-2-ARDS is associated with increased pulmonary endothelial N9P expression and N9P-fibrin colocalization in situ. Further investigation is needed to determine the pathogenetic and potential therapeutic relevance of N9P to the thrombotic pathophenotype of SARS-CoV-2-ARDS.

Comprehensive network medicine-based drug repositioning via integration of therapeutic efficacy and side effects.

Despite advances in modern medicine that led to improvements in cardiovascular outcomes, cardiovascular disease (CVD) remains the leading cause of mortality and morbidity globally. Thus, there is an urgent need for new approaches to improve CVD drug treatments. As the development time and cost of drug discovery to clinical application are excessive, alternate strategies for drug development are warranted. Among these are included computational approaches based on omics data for drug repositioning, which have attracted increasing attention. In this work, we developed an adjusted similarity measure implemented by the algorithm SAveRUNNER to reposition drugs for cardiovascular diseases while, at the same time, considering the side effects of drug candidates. We analyzed nine cardiovascular disorders and two side effects. We formulated both disease disorders and side effects as network modules in the human interactome, and considered those drug candidates that are proximal to disease modules but far from side-effects modules as ideal. Our method provides a list of drug candidates for cardiovascular diseases that are unlikely to produce common, adverse side-effects. This approach incorporating side effects is applicable to other diseases, as well.

2-mercaptobenzothiazole generates γ-H2AX via CYP2E1-dependent production of reactive oxygen species in urothelial cells.

Ortho (o)-toluidine is a widely known carcinogenic substance associated with cancers of the human bladder. A study on British chemical factory workers exposed to 2-mercaptobenzothiazole, phenyl-ß-naphthylamine, aniline, and o-toluidine demonstrated the crucial roles of o-toluidine, 2-mercaptobenzothiazole, and phenyl-ß-naphthylamine in the development of bladder cancer. As genotoxic events are crucial steps in the initiation of cancer, in the present study, we aimed to examine the genotoxic potential of the four chemicals using phosphorylated histone H2AX (γ-H2AX), which is a sensitive and reliable marker of DNA damage, in cultured human urothelial cells. Of the four chemicals, 2-mercaptobenzothiazole was a particularly potent DNA-damaging agent. Moreover, mechanistic studies revealed that γ-H2AX generation by 2-mercaptobenzothiazole was mainly associated with the generation of reactive oxygen species via cytochrome P450 2E1-mediated metabolism. The findings of this study may provide information that is important for the assessment of risks associated with chemicals as well as the interpretation of epidemiological studies investigating occupational bladder cancer.

hDirect-MAP: projection-free single-cell modeling of response to checkpoint immunotherapy.

There is a lack of robust generalizable predictive biomarkers of response to immune checkpoint blockade in multiple types of cancer. We develop hDirect-MAP, an algorithm that maps T cells into a shared high-dimensional (HD) expression space of diverse T cell functional signatures in which cells group by the common T cell phenotypes rather than dimensional reduced features or a distorted view of these features. Using projection-free single-cell modeling, hDirect-MAP first removed a large group of cells that did not contribute to response and then clearly distinguished T cells into response-specific subpopulations that were defined by critical T cell functional markers of strong differential expression patterns. We found that these grouped cells cannot be distinguished by dimensional-reduction algorithms but are blended by diluted expression patterns. Moreover, these identified response-specific T cell subpopulations enabled a generalizable prediction by their HD metrics. Tested using five single-cell RNA-seq or mass cytometry datasets from basal cell carcinoma, squamous cell carcinoma and melanoma, hDirect-MAP demonstrated common response-specific T cell phenotypes that defined a generalizable and accurate predictive biomarker.

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.

NEDD9 Is a Novel and Modifiable Mediator of Platelet-Endothelial Adhesion in the Pulmonary Circulation.

Rationale: Data on the molecular mechanisms that regulate platelet-pulmonary endothelial adhesion under conditions of hypoxia are lacking, but may have important therapeutic implications. Objectives: To identify a hypoxia-sensitive, modifiable mediator of platelet-pulmonary artery endothelial cell adhesion and thrombotic remodeling. Methods: Network medicine was used to profile protein-protein interactions in hypoxia-treated human pulmonary artery endothelial cells. Data from liquid chromatography-mass spectrometry and microscale thermophoresis informed the development of a novel antibody (Ab) to inhibit platelet-endothelial adhesion, which was tested in cells from patients with chronic thromboembolic pulmonary hypertension (CTEPH) and three animal models in vivo. Measurements and Main Results: The protein NEDD9 was identified in the hypoxia thrombosome network in silico. Compared with normoxia, hypoxia (0.2% O2) for 24 hours increased HIF-1α (hypoxia-inducible factor-1α)-dependent NEDD9 upregulation in vitro. Increased NEDD9 was localized to the plasma-membrane surface of cells from control donors and patients with CTEPH. In endarterectomy specimens, NEDD9 colocalized with the platelet surface adhesion molecule P-selectin. Our custom-made anti-NEDD9 Ab targeted the NEDD9-P-selectin interaction and inhibited the adhesion of activated platelets to pulmonary artery endothelial cells from control donors in vitro and from patients with CTEPH ex vivo. Compared with control mice, platelet-pulmonary endothelial aggregates and pulmonary hypertension induced by ADP were decreased in NEDD9-/- mice or wild-type mice treated with the anti-NEDD9 Ab, which also decreased chronic pulmonary thromboembolic remodeling in vivo. Conclusions: The NEDD9-P-selectin protein-protein interaction is a modifiable target with which to inhibit platelet-pulmonary endothelial adhesion and thromboembolic vascular remodeling, with potential therapeutic implications for patients with disorders of increased hypoxia signaling pathways, including CTEPH.