Proteomic Networks and Related Genetic Variants Associated with Smoking and Chronic Obstructive Pulmonary Disease.

Background: Studies have identified individual blood biomarkers associated with chronic obstructive pulmonary disease (COPD) and related phenotypes. However, complex diseases such as COPD typically involve changes in multiple molecules with interconnections that may not be captured when considering single molecular features. Methods: Leveraging proteomic data from 3,173 COPDGene Non-Hispanic White (NHW) and African American (AA) participants, we applied sparse multiple canonical correlation network analysis (SmCCNet) to 4,776 proteins assayed on the SomaScan v4.0 platform to derive sparse networks of proteins associated with current vs. former smoking status, airflow obstruction, and emphysema quantitated from high-resolution computed tomography scans. We then used NetSHy, a dimension reduction technique leveraging network topology, to produce summary scores of each proteomic network, referred to as NetSHy scores. We next performed genome-wide association study (GWAS) to identify variants associated with the NetSHy scores, or network quantitative trait loci (nQTLs). Finally, we evaluated the replicability of the networks in an independent cohort, SPIROMICS. Results: We identified networks of 13 to 104 proteins for each phenotype and exposure in NHW and AA, and the derived NetSHy scores significantly associated with the variable of interests. Networks included known (sRAGE, ALPP, MIP1) and novel molecules (CA10, CPB1, HIS3, PXDN) and interactions involved in COPD pathogenesis. We observed 7 nQTL loci associated with NetSHy scores, 4 of which remained after conditional analysis. Networks for smoking status and emphysema, but not airflow obstruction, demonstrated a high degree of replicability across race groups and cohorts. Conclusions: In this work, we apply state-of-the-art molecular network generation and summarization approaches to proteomic data from COPDGene participants to uncover protein networks associated with COPD phenotypes. We further identify genetic associations with networks. This work discovers protein networks containing known and novel proteins and protein interactions associated with clinically relevant COPD phenotypes across race groups and cohorts.

Multi-Omic Signatures of Sarcoidosis and Progression in Bronchoalveolar Lavage Cells.

Introduction: Sarcoidosis is a heterogeneous, granulomatous disease that can prove difficult to diagnose, with no accurate biomarkers of disease progression. Therefore, we profiled and integrated the DNA methylome, mRNAs, and microRNAs to identify molecular changes associated with sarcoidosis and disease progression that might illuminate underlying mechanisms of disease and potential genomic biomarkers. Methods: Bronchoalveolar lavage cells from 64 sarcoidosis subjects and 16 healthy controls were used. DNA methylation was profiled on Illumina HumanMethylationEPIC arrays, mRNA by RNA-sequencing, and miRNAs by small RNA-sequencing. Linear models were fit to test for effect of diagnosis and phenotype, adjusting for age, sex, and smoking. We built a supervised multi-omics model using a subset of features from each dataset. Results: We identified 46,812 CpGs, 1,842 mRNAs, and 5 miRNAs associated with sarcoidosis versus controls and 1 mRNA, SEPP1 - a protein that supplies selenium to cells, associated with disease progression. Our integrated model emphasized the prominence of the PI3K/AKT1 pathway in sarcoidosis, which is important in T cell and mTOR function. Novel immune related genes and miRNAs including LYST, RGS14, SLFN12L, and hsa-miR-199b-5p, distinguished sarcoidosis from controls. Our integrated model also demonstrated differential expression/methylation of IL20RB, ABCC11, SFSWAP, AGBL4, miR-146a-3p, and miR-378b between non-progressive and progressive sarcoidosis. Conclusions: Leveraging the DNA methylome, transcriptome, and miRNA-sequencing in sarcoidosis BAL cells, we detected widespread molecular changes associated with disease, many which are involved in immune response. These molecules may serve as diagnostic/prognostic biomarkers and/or drug targets, although future testing will be required for confirmation.

Multiomic Signatures of Chronic Beryllium Disease Bronchoalveolar Lavage Cells Relate to T-Cell Function and Innate Immunity.

Chronic beryllium disease (CBD) is a Th1 granulomatous lung disease preceded by sensitization to beryllium (BeS). We profiled the methylome, transcriptome, and selected proteins in the lung to identify molecular signatures and networks associated with BeS and CBD. BAL cell DNA and RNA were profiled using microarrays from CBD (n = 30), BeS (n = 30), and control subjects (n = 12). BAL fluid proteins were measured using Olink Immune Response Panel proteins from CBD (n = 22) and BeS (n = 22) subjects. Linear models identified features associated with CBD, adjusting for covariation and batch effects. Multiomic integration methods identified correlated features between datasets. We identified 1,546 differentially expressed genes in CBD versus control subjects and 204 in CBD versus BeS. Of the 101 shared transcripts, 24 have significant cis relationships between gene expression and DNA methylation, assessed using expression quantitative trait methylation analysis, including genes not previously identified in CBD. A multiomic model of top DNA methylation and gene expression features demonstrated that the first component separated CBD from other samples and the second component separated control subjects from remaining samples. The top features on component one were enriched for T-lymphocyte function, and the top features on component two were enriched for innate immune signaling. We identified six differentially abundant proteins in CBD versus BeS, with two (SIT1 and SH2D1A) selected as important RNA features in the multiomic model. Our integrated analysis of DNA methylation, gene expression, and proteins in the lung identified multiomic signatures of CBD that differentiated it from BeS and control subjects.

Colocalization of Gene Expression and DNA Methylation with Genetic Risk Variants Supports Functional Roles of MUC5B and DSP in Idiopathic Pulmonary Fibrosis.

Rationale: Common genetic variants have been associated with idiopathic pulmonary fibrosis (IPF). Objectives: To determine functional relevance of the 10 IPF-associated common genetic variants we previously identified. Methods: We performed expression quantitative trait loci (eQTL) and methylation quantitative trait loci (mQTL) mapping, followed by co-localization of eQTL and mQTL with genetic association signals and functional validation by luciferase reporter assays. Illumina multi-ethnic genotyping arrays, mRNA sequencing, and Illumina 850k methylation arrays were performed on lung tissue of participants with IPF (234 RNA and 345 DNA samples) and non-diseased controls (188 RNA and 202 DNA samples). Measurements and Main Results: Focusing on genetic variants within 10 IPF-associated genetic loci, we identified 27 eQTLs in controls and 24 eQTLs in cases (false-discovery-rate-adjusted P < 0.05). Among these signals, we identified associations of lead variants rs35705950 with expression of MUC5B and rs2076295 with expression of DSP in both cases and controls. mQTL analysis identified CpGs in gene bodies of MUC5B (cg17589883) and DSP (cg08964675) associated with the lead variants in these two loci. We also demonstrated strong co-localization of eQTL/mQTL and genetic signal in MUC5B (rs35705950) and DSP (rs2076295). Functional validation of the mQTL in MUC5B using luciferase reporter assays demonstrates that the CpG resides within a putative internal repressor element. Conclusions: We have established a relationship of the common IPF genetic risk variants rs35705950 and rs2076295 with respective changes in MUC5B and DSP expression and methylation. These results provide additional evidence that both MUC5B and DSP are involved in the etiology of IPF.

Molecular Signatures of Idiopathic Pulmonary Fibrosis.

Molecular patterns and pathways in idiopathic pulmonary fibrosis (IPF) have been extensively investigated, but few studies have assimilated multiomic platforms to provide an integrative understanding of molecular patterns that are relevant in IPF. Herein, we combine the coding and noncoding transcriptomes, DNA methylomes, and proteomes from IPF and healthy lung tissue to identify molecules and pathways associated with this disease. RNA sequencing, Illumina MethylationEPIC array, and liquid chromatography-mass spectrometry proteomic data were collected on lung tissue from 24 subjects with IPF and 14 control subjects. Significant differential features were identified by using linear models adjusting for age and sex, inflation, and bias when appropriate. Data Integration Analysis for Biomarker Discovery Using a Latent Component Method for Omics Studies was used for integrative multiomic analysis. We identified 4,643 differentially expressed transcripts aligning to 3,439 genes, 998 differentially abundant proteins, 2,500 differentially methylated regions, and 1,269 differentially expressed long noncoding RNAs (lncRNAs) that were significant after correcting for multiple tests (false discovery rate < 0.05). Unsupervised hierarchical clustering using 20 coding mRNA, protein, methylation, and lncRNA features with the highest loadings on the top latent variable from the four data sets demonstrates perfect separation of IPF and control lungs. Our analysis confirmed previously validated molecules and pathways known to be dysregulated in disease and implicated novel molecular features as potential drivers and modifiers of disease. For example, 4 proteins, 18 differentially methylated regions, and 10 lncRNAs were found to have strong correlations (|r| > 0.8) with MMP7 (matrix metalloproteinase 7). Therefore, by using a system biology approach, we have identified novel molecular relationships in IPF.

Chronic Hypersensitivity Pneumonitis, an Interstitial Lung Disease with Distinct Molecular Signatures.

Rationale: Chronic hypersensitivity pneumonitis (CHP) is caused by an immune response to antigen inhalation and is characterized by variable histopathological and clinical features. A subset of subjects with CHP have usual interstitial pneumonia and appear to be clinically similar to subjects with idiopathic pulmonary fibrosis (IPF).Objectives: To determine the common and unique molecular features of CHP and IPF.Methods: Transcriptome analysis of lung samples from CHP (n = 82), IPF (n = 103), and unaffected controls (n = 103) was conducted. Differential gene expression was determined adjusting for sex, race, age, and smoking history and using false discovery rate to control for multiple comparisons.Measurements and Main Results: When compared with controls, we identified 413 upregulated and 317 downregulated genes in CHP and 861 upregulated and 322 downregulated genes in IPF. Concordantly upregulated or downregulated genes in CHP and IPF were related to collagen catabolic processes and epithelial development, whereas genes specific to CHP (differentially expressed in CHP when compared with control and not differentially expressed in IPF) were related to chemokine-mediated signaling and immune responsiveness. Using weighted gene coexpression network analysis, we found that among subjects with CHP, genes involved in adaptive immunity or epithelial cell development were associated with improved or reduced lung function, respectively, and that MUC5B expression was associated with epithelial cell development. MUC5B expression was also associated with lung fibrosis and honeycombing.Conclusions: Gene expression analysis of CHP and IPF identified signatures common to CHP and IPF, as well as genes uniquely expressed in CHP. Select modules of gene expression are characterized by distinct clinical and pathological features of CHP.