Cigarette smoking induces complex signaling disruptions that contribute to diseases such as COPD and lung cancer, yet the molecular mechanisms underlying these effects remain incompletely understood. To address this gap, we analyzed peripheral blood from 3190 COPDGene participants using LIONESS and PUMA and constructed miRNA-mRNA regulatory networks associated with smoking status. Comparing networks for active versus former smokers uncovered a striking shift in regulatory architecture: active smokers exhibited elevated miRNA targeting of the mitochondrial complex I protein NDUFA12. This finding was validated in lung tissue expression data from the Lung Genomics Research Consortium (LGRC), where we observed that ever-smokers showed consistent dysregulation of Ndufa12-targeting miRNAs compared to never-smokers. This allowed us to identify a set of smoking-defined circulating and tissue-associated miRNAs. To investigate the specific cellular compartment, we analyzed cell-type deconvoluted expression data from COPDGene blood and LTRC (Lung Tissue Research Consortium) lung tissue, as well as lung transcriptomics data from cigarette smoke-exposed mice, and identified the monocyte/macrophage compartment as a principal site of NDUFA12/Ndufa12 expression. Human THP-1 macrophages treated with cigarette smoke extract demonstrated selective inhibition of NDUFA12 by network-defined miRNAs. These distinct, NDUFA12-targeting, smoking-associated miRNA signatures, revealed through network analysis, describe new smoking-mitochondrial interactions that may serve as novel targets for therapeutic intervention.

There are significant sex differences in cancer incidence, yet the underlying regulatory mechanisms in normal tissues remain poorly understood. We studied 8,279 gene regulatory networks across 29 non-cancerous tissues and compared network centrality by sex. Cancer genes were differentially targeted by transcription factors in males and females, with an overrepresentation on the X chromosome, particularly among X-inactivation escapees, and key signaling pathways such as WNT, NOTCH, and p53. We observed higher targeting of cancer-related pathways in females for tissues that have higher tumor incidence in females (breast, lung, and thyroid) and higher targeting in males for tissues with increased tumor incidence in males (stomach, colon, and liver), a pattern replicated in independent lung data. Sex-biased transcription factors were enriched for sex hormone response elements. These findings suggest that sex-biased transcriptional programs in normal tissues contribute to sex differences in cancer incidence and should be considered in cancer prevention strategies.

Chronic obstructive pulmonary disease (COPD) is a debilitating and progressive lung disease that affects millions of people worldwide. There is a continuing clinical need to characterize COPD at the molecular level to be able to identify the multi-omic biomarkers of its pathogenesis and to enable more accurate diagnoses and more effective treatment. We used Multi-Omics Factor Analysis (MOFA) to jointly analyze genomic, blood transcriptomic, and plasma proteomic data collected from 1,872 participants in the Genetic Epidemiology of COPD study who had moderate to very severe COPD. Five latent factors identified by MOFA were associated with COPD-related lung function, chest computed tomography (CT) imaging, and blood count phenotypes, as well as all-cause mortality. The top genetic, transcriptomic and proteomic contributors to these latent factors were also individually associated with COPD-related outcomes. Moreover, factor loadings and expression levels of top omic drivers helped distinguish between patient subgroups. Quantitative trait loci analysis of a latent factor that was jointly driven by transcriptomics and proteomics revealed potential common genetic control of gene expression and protein abundance. Polygenic risk scores derived from a genomics-driven latent factor were associated with chest CT imaging and lung function phenotypes, and these associations were replicated in an independent COPD cohort. Together, our results suggest the potential of integrative omic approaches to identify the major axes of heterogeneity in COPD and uncover the multi-omic interplay between the contributors to each axis.

Genetic variants near Hedgehog interacting protein (HHIP) have been consistently associated with increased risk for chronic obstructive pulmonary disease (COPD), the third leading cause of death worldwide. However, HHIP's role in COPD pathogenesis remains elusive. Canonically, HHIP is a negative regulator of the Hedgehog pathway and downstream GLI1 and GLI2 activation. The Hedgehog pathway plays an important role in wound healing, specifically in activating transcription factors that drive the epithelial-mesenchymal transition (EMT), which in its intermediate state (partial EMT) is necessary for the collective movement of cells closing a wound. Herein, we use a systems biology approach to propose a mechanism to explain HHIP's role in faulty epithelial wound healing, which could contribute to the development of emphysema, a key feature of COPD. Using two different Boolean models, we show dysfunctional HHIP results in a lack of negative feedback on GLI, triggering a full EMT, where cells become mesenchymal and do not properly close the wound. We validate these Boolean models with experimental evidence gathered from published scientific literature. Finally, we show evidence supporting our hypothesis in single-cell and single-nucleus RNA-Seq data from different COPD cohorts and Hhip heterozygous knockout mice. Overall, our analyses suggest that aberrant wound healing due to dysfunctional HHIP, combined with chronic epithelial damage through cigarette smoke exposure, may be a primary cause of COPD-associated emphysema.

Analysis of biological networks can provide unprecedented insights into the mechanisms underlying disease. Although many methods have been developed to estimate biological networks, these approaches typically use multiple experimental samples to estimate a single 'aggregate' network, which fails to capture population-level heterogeneity. Recently, several methods have been developed that overcome this limitation by inferring networks for individual samples, i.e. single-sample networks. However, each approach for inferring single-sample networks has been formulated differently, making it challenging to compare them. To address this issue, we re-cast the mathematics of several single-sample network methods using common variables. We then systematically explore the parameters, caveats, and underlying assumptions made by each method and examine how these differences impact single-sample network prediction. Our analyses point to a critical trade-off that occurs when trying to simultaneously predict network edges that are both shared across samples as well as edges that are specific to a given sample. For example, the mathematics of both SWEET and BONOBO includes a scale factor that drives the weights of edges in the predicted single-sample networks toward a background network. The result is that, although networks predicted by these methods tend to have the highest accuracy, this often comes at the cost of very low specificity, an important caveat since the primary goal of sample-specific network modeling is to obtain networks that are specific to each input sample. In contrast, SSN estimates the most specific but least accurate networks, while LIONESS straddles these domains, with an accuracy almost as high as SWEET and BONOBO and a specificity almost as high as SSN. Overall, our analyses highlight some of the broader challenges in this emerging field. However, they also point to important methodological synergies, providing an opportunity to create a common framework that can be used to improve single-sample network inference.

Chronic Obstructive Pulmonary Disease (COPD) is a complex, heterogeneous disease. Traditional subtyping methods generally focus on either the clinical manifestations or the molecular endotypes of the disease, leading to classifications that only partially reflect disease heterogeneity. Here, we introduce a variational autoencoder-based subtyping pipeline that jointly embeds clinical and gene expression data into a single subject-level representation. We evaluate the framework in the COPDGene study, a large study of current and former smoking individuals with and without COPD. Prediction experiments show that the embeddings have predictive accuracy comparable to or better than other unsupervised embedding approaches. Using trajectory learning approaches, we identify five well-separated subtypes with distinct clinical phenotypes, expression signatures, and longitudinal outcomes. Finally, we show that our findings generalize to an external validation cohort. Overall, our approach enables a transition from isolated phenotypic or molecular subtyping toward an integrated and clinically meaningful understanding of COPD heterogeneity.

The response to injury involves a complex series of events, including cellular migration and proliferation, inflammatory processes, and tissue remodeling. The oral mucosa exhibits a more regenerative repair response that resolves with minimal scarring when compared to skin. To investigate mechanisms driving this differential healing response, we integrated gene expression data from adult human palate and skin wounds over seven days with transcription factor binding data and protein-protein interaction data to estimate sample-specific gene regulatory networks. Comparative analysis between unwounded palate and skin networks revealed tissue-specific transcription factor targeting and gene set co-expression. Upon injury, global network changes between tissues were divergent, indicating distinct gene regulatory programs. Gene regulatory analyses revealed that the acute response to injury is characterized by transcription factor mediated gene repression, followed by tissue-specific gene activation at later healing stages. Notably, a subset of palate-specific transcription factors previously linked to regeneration in model organisms correlated with gene targeting and expression during acute injury. Specifically, BATF3 promoted cell migration and re-epithelialization after injury, but not proliferation. In summary, these findings highlight the temporal dynamics of transcription factors in wound response between palate and skin and provide insights into the gene regulatory mechanisms governing regenerative and non-regenerative healing.

RATIONALE: Chronic Obstructive Pulmonary Disease (COPD) exacerbations, manifesting as episodes of respiratory symptoms worsening, are a major cause of COPD morbidity and mortality. While severe COPD patients are prone to exacerbations, there is marked heterogeneity in exacerbation frequency, duration, severity, and response to treatment. Understanding the molecular mechanisms behind this heterogeneity could inform the development of targeted therapies. OBJECTIVES: To identify COPD exacerbation endotypes – i.e. individuals with shared biology – within individuals with severe COPD (GOLD spirometry grade 3-4) using individual-level networks. METHODS: We use a gene regulatory network (GRN) modeling approach to dissect heterogeneity among participants in the COPDGene study with severe COPD (GOLD 3-4). Generally, GRNs model molecular interactions regulating gene expression as networks of genes and transcription factors (TF). We reconstruct individual GRNs for each study subject by applying the PANDA and LIONESS algorithms to predicted TF-binding and whole blood RNA-Seq data from the COPDGene 5-year follow-up visit. These GRNs are composed of weighted edges representing subject-specific TF-gene interactions. For each TF-gene pair, we perform linear regression of subjects’ edge weights against annual exacerbation rates, adjusting for confounders, and select TF-gene edges with significant associations (FDR<0.1). We cluster COPD subjects by their weights in this significant edge subset and compare spirometry, imaging, and molecular traits across clusters. Marker regulatory edges—edges with weight distributions differing significantly in a given cluster compared to others—are identified for each group. Finally, we perform Gene Set Enrichment Analysis. RESULTS: We include 418 GOLD 3-4 COPD participants with individual-level regulatory networks. Clustering analysis identifies two endotypes with similar exacerbation rates and quantitative emphysema but different clinical and molecular characteristics. One endotype (low airway group) exhibits significantly lower (p-val<0.1) CT-assessed wall area percentage and Pi10, lower FEV1/FVC ratio, and higher peripheral blood lymphocyte percentage. Marker nodes of this cluster are associated (FDR<0.1) with the Cell Adhesion Molecules, Antigen Processing and Presentation, and Asthma KEGG pathways. Conversely, another cluster (high airway group) displays significantly higher (p-val<0.1) airway wall area percentage, Pi10, and neutrophil percentage, and lower lymphocyte percentage. Enriched pathways (FDR<0.1) in its marker nodes include the Cytokine-cytokine Receptor Interaction, WNT signaling, and Purine Metabolism KEGG pathways. CONCLUSION: By reconstructing the individual GRNs of severe COPD subjects, our study identifies two endotypes with distinct physiological, clinical, and molecular patterns. These results shed light on the molecular processes underlying COPD exacerbation risk, generating testable predictions that could improve the drug-target selection process.

Micro-ribonucleic acids (miRNAs) are key post-transcriptional regulators of the immune system and may play a role in Chronic Obstructive Pulmonary Disease (COPD). In this paper, we constructed subject-specific miRNA-mRNA regulatory networks using bulk and deconvoluted whole blood RNA-sequencing, whole blood miRNA-sequencing, and B-cell receptor-sequencing data from up to 570 miRNAs, 11,859 mRNAs, and 3,190 participants in the COPDGene study. Analysis of whole blood networks revealed two subnetworks of miRNA-mRNA interactions significantly (FDR<0.05) associated with changes in FEV 1 / FVC . We found that miRNAs (and mRNAs) in the network-identified groups had distinct expression patterns, with miRNAs (and mRNAs) in one group having overall higher expression in COPD (decreasing FEV 1 / FVC ) and miRNAs (and mRNAs) in the other group having overall higher expression in controls (increasing FEV 1 / FVC ). In addition, miRNAs (and mRNAs) within the same group were positively correlated, while those in different groups were negatively correlated, indicating distinct functional roles for these miRNAs (and mRNAs) as a function of increased COPD severity. Network analysis also identified PAX5, a transcription factor master regulator of B-cell development, as the main mRNA network hub. Using ChIP-seq data in lymphoblastoid cells, we identified a PAX5 binding site overlapping with a COPD genome-wide association signal in the promoter region of ADAM19. We also found a loss of co-expression between PAX5 and ADAM19 in COPD subjects. Furthermore, in B-cell deconvoluted data, PAX5 was differentially co-expressed with genes associated with B-cell activation and differentiation, revealing a possible mechanism for the regulation of the immune response in COPD. Finally, in B-cell receptor sequencing data, PAX5 and the identified mRNA subnetworks were negatively associated (FDR<0.05) with immunoglobulin class switching, and positively associated with IgM and IgD counts. In conclusion, PAX5 is a known regulator of B-cell identity. B cells are recognized as key players in chronic inflammation and immune dysregulation in COPD. Our work suggests that PAX5 plays a mediating role both in ADAM19 regulation and in miRNA regulation of early B cells in COPD.

BackgroundLung adenocarcinoma shows distinct differences between males and females in incidence, prognosis, and treatment response, suggesting unique molecular mechanisms that remain underexplored. This study aims to identify sex-specific molecular signatures and therapeutic targets in lung adenocarcinoma using multi-omics approaches to inform personalized treatment strategies.MethodsWe conducted an integrative analysis of transcriptomic and proteomic data from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) and The Cancer Genome Atlas (TCGA) datasets, comparing male and female lung adenocarcinoma profiles. Transcription factor activity was assessed using TIGER on gene expression data, while kinase activity was evaluated with PTM-SEA on proteomic data. These results were combined to build a kinase-transcription factor signaling network. Potential sex-specific drugs were identified using the PRISM drug screening database.ResultsThe analysis revealed significant sex-based differences in transcription factor and kinase activity. Notably, NR3C1, AR, and AURKA exhibited sex-biased expression and activity. The constructed signaling network highlighted druggable pathways linked to cancer-related processes, with distinct profiles in males and females. PRISM screening identified glucocorticoid receptor agonists and aurora kinase inhibitors as promising sex-specific therapeutic candidates.ConclusionsOur findings underscore the importance of considering sex differences in lung adenocarcinoma molecular profiles. The integration of transcriptomic and proteomic data reveals sex-specific pathways and potential therapies, paving the way for personalized treatment approaches tailored to male and female patients.

Sex differences appear in healthy and pathological conditions and may influence sex-specific therapeutic responses. Understanding such differences is a key activity for developing precision medicine strategies. This study investigates sex differences in gene expression across 40 human tissues by applying a Differential Causal Network (DCN) analysis using data from the Genotype-Tissue Expression project. We identified sex-based DCNs that highlight distinct molecular mechanisms influencing both health and disease in men and women. For example, in pancreas tissue, genes associated with immune system show significant differences in their regulatory patterns between sexes, demonstrating a possible different response to diseases such as diabetes mellitus and cancer. Our findings provide valuable information on the biological underpinnings of sex differences, offering potential pathways for the development of precision medicine strategies.

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.

Chronic obstructive pulmonary disease (COPD) often develops at an earlier age in women than in men, with worse respiratory symptoms despite lower smoking exposure. However, most preventive and therapeutic strategies ignore biological sex differences in COPD. Our goal was to better understand sex-specific gene regulatory processes in lung tissue and the molecular basis for sex differences in COPD onset and severity. We analyzed lung tissue gene expression and DNA methylation data from 747 individuals in the Lung Tissue Research Consortium and 85 individuals in an independent dataset. We identified sex differences in COPD-associated gene regulation using gene regulatory networks. We used linear regression to test for sex-biased associations of methylation with lung function, emphysema, smoking, and age. Analyzing gene regulatory networks in the control group, we identified that genes involved in the extracellular matrix (ECM) have higher transcriptional factor targeting in female subjects than in male subjects. However, this pattern is reversed in COPD, with men showing stronger regulatory targeting of ECM-related genes than women. Smoking exposure, age, lung function, and emphysema were all associated with sex-specific differential methylation of ECM-related genes. We identified sex-based gene regulatory patterns of ECM-related genes associated with lung function and emphysema. Multiple factors, including epigenetics, smoking, aging, and cell heterogeneity, influence sex-specific gene regulation in COPD. Our findings underscore the importance of considering sex as a key factor in disease susceptibility and severity.

The rising incidence of lung cancer among individuals without a history of smoking highlights the need to explore biological mechanisms underlying this phenomenon. Our study aims to identify gene regulatory mechanisms that drive lung cancer risk among never-smokers by analyzing how gene regulatory networks differ between individuals with and without lung cancer, depending on their smoking history. We used RNA-Seq data from the Lung Tissue Research Consortium (LTRC) collected via TOPMed, comprising non-cancerous lung tissue samples from 344 individuals with non-small cell carcinoma and 329 lung tissue samples from individuals without cancer. Across all, 18% reported no history of smoking. We ran differential gene expression analysis by voom, adjusting for age, sex, COPD status, and examining statistical interactions between smoking (never/former) and cancer status. We analyzed sample-specific transcription factor-gene regulatory networks generated by PANDA-LIONESS. We used linear regression to evaluate the association between gene targeting score (measured by gene indegree) and the interaction between smoking and cancer status. We ran gene set enrichment analysis with genes ranked by the corresponding smoking by cancer interaction coefficients. Differential expression analysis of lung tissues from individuals with and without cancer showed that genes overexpressed in cancer are enriched for canonical cancer pathways, such as p53, MAPK, and WNT signaling pathways (FDR<0.05). We found a significant interaction between cancer and smoking status, indicating these cancer-related pathways had higher enrichment in former-smokers compared to never smokers. Gene regulatory network analysis indicated that these differential expression patterns are possibly driven by increased transcriptional targeting of these cancer-related pathways. Among never-smokers, individuals with cancer showed higher targeting of metabolic pathways (e.g., fructose and mannose, glutathione, phenylalanine, histidine, arginine, and proline metabolism) compared to lung tissue from individuals without cancer (FDR<0.05), highlighting a possible role of metabolic pathways in tumor development, uniquely among never-smokers. A negative correlation between metabolic pathway targeting scores and age was observed exclusively in individuals with cancer without a history of smoking (Mean Pearson R=-0.24). Our findings reveal transcriptional targeting differences in lung cancer by smoking history. Among individuals without a history of smoking, the increased targeting of metabolic pathways in cancer, which is pronounced in younger compared to older individuals, may contribute to the higher risk of early-onset lung cancer among never-smokers, offering new avenues for understanding and addressing the disease in populations without a history of smoking. Camila Lopes-Ramos, Enakshi Saha, Jeong Yun, Craig Hersh, Edwin Silverman, Dawn DeMeo, John Quackenbush, Kimberly Glass. Lung cancer gene regulatory networks reflect smoking history and age-related metabolic pathway alterations [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 7478.

Lung adenocarcinoma (LUAD) exhibits differences between the sexes in incidence, prognosis, and therapy, suggesting underexplored molecular mechanisms. We conducted an integrative multi-omics analysis using the Clinical Proteomic Tumor Analysis Consortium (CPTAC) and The Cancer Genome Atlas (TCGA) datasets to contrast transcriptomes and proteomes between sexes. We used TIGER to analyze TCGA-LUAD expression data and found sex-biased activity of transcription factors (TFs); we used PTM-SEA with CPTAC-LUAD proteomics data and found sex-biased kinase activity. We combined these to construct a kinase-TF signaling network and discovered druggable pathways linked to cancer-related processes. We also found significant sex biases in clinically relevant TFs and kinases, including NR3C1, AR, and AURKA. Using the PRISM drug screening database, we identified potential sex-specific drugs, such as glucocorticoid receptor agonists and aurora kinase inhibitors. Our findings emphasize the importance of considering sex and using multi-omics network methods to discover personalized cancer therapies.

PPARγ is the pharmacological target of thiazolidinediones (TZDs), potent insulin sensitizers that prevent metabolic disease morbidity but are accompanied by side effects such as weight gain, in part due to non-physiological transcriptional agonism. Using high throughput genome engineering, we targeted nonsense mutations to every exon of PPARG, finding an ATG in Exon 2 (chr3:12381414, CCDS2609 c.A403) that functions as an alternative translational start site. This downstream translation initiation site gives rise to a PPARγ protein isoform (M135), preferentially generated from alleles containing nonsense mutations upstream of c.A403. PPARγ M135 retains the DNA and ligand binding domains of full-length PPARγ but lacks the N-terminal AF-1 domain. Despite being truncated, PPARγ M135 shows increased transactivation of target genes, but only in the presence of agonists. Accordingly, human missense mutations disrupting AF-1 domain function actually increase agonist-induced cellular PPARγ activity compared to wild-type (WT), and carriers of these AF-1 disrupting variants are protected from metabolic syndrome. Thus, we propose the existence of PPARγ M135 as a fully functional, alternatively translated isoform that may be therapeutically generated to treat insulin resistance-related disorders.

Network biology, an interdisciplinary field at the intersection of computational and biological sciences, is critical for deepening understanding of cellular functioning and disease. While the field has existed for about two decades now, it is still relatively young. There have been rapid changes to it and new computational challenges have arisen. This is caused by many factors, including increasing data complexity, such as multiple types of data becoming available at different levels of biological organization, as well as growing data size. This means that the research directions in the field need to evolve as well. Hence, a workshop on Future Directions in Network Biology was organized and held at the University of Notre Dame in 2022, which brought together active researchers in various computational and in particular algorithmic aspects of network biology to identify pressing challenges in this field. Topics that were discussed during the workshop include: inference and comparison of biological networks, multimodal data integration and heterogeneous networks, higher-order network analysis, machine learning on networks, and network-based personalized medicine. Video recordings of the workshop presentations are publicly available on YouTube. For even broader impact of the workshop, this paper, co-authored mostly by the workshop participants, summarizes the discussion from the workshop. As such, it is expected to help shape short- and long-term vision for future computational and algorithmic research in network biology.

Modelling the regulatory mechanisms that determine cell fate, response to external perturbation, and disease state depends on measuring many factors, a task made more difficult by the plasticity of the epigenome. Scanning the genome for the sequence patterns defined by Position Weight Matrices (PWM) can be used to estimate transcription factor (TF) binding locations. However, this approach does not incorporate information regarding the epigenetic context necessary for TF binding. CpG methylation is an epigenetic mark influenced by environmental factors that is commonly assayed in human cohort studies. We developed a framework to score inferred TF binding locations using methylation data. We intersected motif locations identified using PWMs with methylation information captured in both whole-genome bisulfite sequencing and Illumina EPIC array data for six cell lines, scored motif locations based on these data, and compared with experimental data characterizing TF binding (ChIP-seq). We found that for most TFs, binding prediction improves using methylation-based scoring compared to standard PWM-scores. We also illustrate that our approach can be generalized to infer TF binding when methylation information is only proximally available, i.e. measured for nearby CpGs that do not directly overlap with a motif location. Overall, our approach provides a framework for inferring context-specific TF binding using methylation data. Importantly, the availability of DNA methylation data in existing patient populations provides an opportunity to use our approach to understand the impact of methylation on gene regulatory processes in the context of human disease.

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.

The versatility of cellular response arises from the communication, or crosstalk, of signaling pathways in a complex network of signaling and transcriptional regulatory interactions. Understanding the various mechanisms underlying crosstalk on a global scale requires untargeted computational approaches. We present a network-based statistical approach, MuXTalk, that uses high-dimensional edges called multilinks to model the unique ways in which signaling and regulatory interactions can interface. We demonstrate that the signaling-regulatory interface is located primarily in the intermediary region between signaling pathways where crosstalk occurs, and that multilinks can differentiate between distinct signaling-transcriptional mechanisms. Using statistically over-represented multilinks as proxies of crosstalk, we infer crosstalk among 60 signaling pathways, expanding currently available crosstalk databases by more than five-fold. MuXTalk surpasses existing methods in terms of model performance metrics, identifies additions to manual curation efforts, and pinpoints potential mediators of crosstalk. Moreover, it accommodates the inherent context-dependence of crosstalk, allowing future applications to cell type- and disease-specific crosstalk.