Biomedical subjects

Katherine A Pratte

Publications and source records attributed to Katherine A Pratte.

4 recordsLinked to original sources

PubMed2026

Proteomic Mediators of Chronic Obstructive Pulmonary Disease Phenotypes and Coronary Artery Calcification Burden in Ever Smokers.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) increases cardiovascular disease risk. Coronary artery calcification (CAC) predicts cardiovascular events and mortality in COPD. We hypothesized that plasma proteins linked to pulmonary phenotypes mediate CAC burden. METHODS: Pulmonary function, emphysema, airway wall thickening, Agatston CAC scores (inverse normal transformed), and relative abundance of 1305 plasma proteins (log-transformed) were assessed in 989 Phase 1 COPDGene (Genetic Epidemiology of COPD) participants. Proteins associated with both pulmonary phenotypes (FEV1[forced expiratory volume in 1 second]%predicted, FVC [forced vital capacity], FEV1/FVC, emphysema, airway wall thickness, wall area percentage) and CAC (false discovery rate P≤0.20) were evaluated using multivariable mediation. Model adjustments included sex, age, race, body mass index, smoking, comorbidities, and medications. Adjustment for pulmonary artery-to-aortic diameter ratio-a marker of pulmonary vascular pressure-was also explored. The95% bootstrap CIs that excluded zero were considered significant. RESULTS: FEV1%predicted (P=0.026) and FEV1/FVC (P=0.010) were associated with CAC. After adjusting for FEV1, visual emphysema, and visual airway wall thickening remained associated with CAC. Five proteins (TSP2 [thrombospondin-2], renin, MMP-7 [matrix metalloproteinase-7], ERBB1 [epidermal growth factor receptor], MIC-1 [macrophage inhibitory cytokine-1]) mediated the FEV1%predicted and CAC association. All except MIC-1 mediated FEV1/FVC and CAC. All except renin mediated quantitative airway wall thickness or wall area percentage and CAC. Additionally, α2-antiplasmin (alpha-2 antiplasmin) mediated airway wall thickness and CAC. ERBB1 mediated visual paraseptal emphysema and CAC. Pulmonary artery-to-aortic diameter ratio adjustment reduced or eliminated some mediation effects. ERBB1 remained an independent mediator across multiple phenotypes. CONCLUSIONS: Six plasma proteins mediated associations between COPD phenotypes and CAC burden. These effects were partially influenced by pulmonary artery-to-aortic diameter ratio A, suggesting shared molecular pathways linking lung dysfunction to cardiovascular risk in COPD.

Humans↗

PubMed2026

Multi-trait polygenic scores for COPD and COPD exacerbations implicate druggable proteins.

BACKGROUNDWe constructed multi-trait polygenic risk scores (PRSs) predicting chronic obstructive pulmonary disease (COPD) and exacerbations, validated their performance in diverse cohorts, and identified PRS-related proteins for potential therapeutic targeting.METHODSPRSmix+, a multi-trait PRS framework, is used to train a composite PRS (PRSmulti) in COPDGene non-Hispanic White participants (n = 6,647). Associations of PRSmulti with COPD status (GOLD 2-4 vs. GOLD 0 or ICD) and exacerbation frequency were tested in COPDGene African American (n = 2,466), ECLIPSE (n = 1,858), Mass General Brigham Biobank (n = 15,152), and All of Us (n = 118,566). Protein prediction models were applied to GWAS summary statistics from traits contributing to PRSmulti and were validated with proteomic data in COPDGene (n = 5,173) and UK Biobank (n = 5,012).RESULTSPRSmix+ selected 7 traits for PRSmulti. In multivariable models, PRSmulti was associated with COPD status (meta-analysis random effects [RE] OR 1.58 [95% CI: 1.28-1.94]) and exacerbation frequency (meta-analysis RE β 0.21 [95% CI: 0.11-0.31]), with higher effect sizes observed in smoking-enriched cohorts. PRSmulti outperformed traditional single-trait PRS in all tested cohorts. Using protein prediction models, we identified 73 proteins associated with the PRSs that were also validated with measured protein levels in COPDGene and UK Biobank. Of these proteins, 25 were linked to approved or investigational drugs. Notable targets include RAGE/sRAGE, IL1RL1, and SCARF2, all implicated in COPD pathogenesis and exacerbations.CONCLUSIONSMulti-trait PRS improves prediction of COPD and exacerbation risk. Integration with proteomic data identifies druggable protein targets, offering a promising avenue for precision medicine in COPD management.TRIAL REGISTRATIONCOPDGene: ClinicalTrials.gov NCT00608764; ECLIPSE: ClinicalTrials.gov NCT00292552.

Humans↗

PubMed2025

Proteomic discovery analysis of quantitatively assessed emphysema in the general population. The MESA Lung Study.

BACKGROUND: Pulmonary emphysema occurs frequently in older adults, often without airflow limitation. Its presence predicts symptoms, respiratory hospitalizations and deaths, and all-cause mortality. Proteomics may provide further insights into emphysema pathogenesis and inform therapeutic targets. OBJECTIVE: We performed a proteomic discovery analysis of percent emphysema on computed tomography (CT) in a population-based, multiethnic sample from the Multi-Ethnic Study of Atherosclerosis (MESA) Lung Study. Replication was performed in two chronic obstructive pulmonary disease (COPD)-based studies, the SubPopulations and InteRmediate Outcome Measures in COPD Study (SPIROMICS) and the Genetic Epidemiology of COPD (COPDGene) Study. METHODS: MESA recruited participants from the general population in 2000-02. The MESA Lung Study performed full-lung CT scans in 2010-12. Percent emphysema was defined as the percentage of lung voxels < -950 Hounsfield units. Over 7,200 plasma aptamers were measured via SomaScan. Cross-sectional linear and least absolute shrinkage and selection operator (LASSO) regression models were adjusted for demographics, anthropometrics, smoking, renal function, and scanner parameters. Statistical significance was defined as a false discovery rate p-value < 0.05. Gene Ontology (GO)/Reactome enrichment analyses were performed. LASSO-selected proteins' predictive performance was evaluated. RESULTS: Among 2,504 participants in the MESA Lung Study, mean age was 69.4 years, 1,291 had ever smoked, and median percent emphysema-like lung was 1.4%. In total, 1,234 aptamers were significantly associated with percent emphysema in the MESA Lung Study, and 35 replicated in the SPIROMICS and COPDGene Studies. Novel associations included protein family with sequence similarity (FAM) 177A1, syntenin-2, ubiquitin carboxyl-terminal hydrolase 25, and uncharacterized protein C20orf173. Previously identified emphysema-associated proteins included soluble advanced glycosylation end product-specific receptor (sRAGE), protein S100-A12, high mobility group protein B1, and roundabout homolog 2. Enrichment analyses identified 40 GO biological processes, including chemokine production and regulation and cell-cell adhesion and regulation, and two Reactome pathways, including RAGE signaling. In tenfold cross-validation, novel proteins were largely retained by LASSO (R2 = 5.4%), improved overall model performance (R2 = 24.8%), and uniquely explained greater variance in percent emphysema. CONCLUSIONS: This analysis in a general population sample identified novel and previously characterized proteins whose functional roles were validated by GO/Reactome enriched pathways, offering new insights into emphysema pathophysiology and therapeutics.

Humans↗

PubMed2025

A generalized higher-order correlation analysis framework for multi-omics network inference.

Multiple -omics (genomics, proteomics, etc.) profiles are commonly generated to gain insight into a disease or physiological system. Constructing multi-omics networks with respect to the trait(s) of interest provides an opportunity to understand relationships between molecular features but integration is challenging due to multiple data sets with high dimensionality. One approach is to use canonical correlation to integrate one or two omics types and a single trait of interest. However, these types of methods may be limited due to (1) not accounting for higher-order correlations existing among features, (2) computational inefficiency when extending to more than two omics data when using a penalty term-based sparsity method, and (3) lack of flexibility for focusing on specific correlations (e.g., omics-to-phenotype correlation versus omics-to-omics correlations). In this work, we have developed a novel multi-omics network analysis pipeline called Sparse Generalized Tensor Canonical Correlation Analysis Network Inference (SGTCCA-Net) that can effectively overcome these limitations. We also introduce an implementation to improve the summarization of networks for downstream analyses. Simulation and real-data experiments demonstrate the effectiveness of our novel method for inferring omics networks and features of interest.

Genomics↗