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.

Significant Subgraph Detection in Multi-omics Networks for Disease Pathway Identification.

Chronic obstructive pulmonary disease (COPD) is one of the leading causes of death in the United States. COPD represents one of many areas of research where identifying complex pathways and networks of interacting biomarkers is an important avenue toward studying disease progression and potentially discovering cures. Recently, sparse multiple canonical correlation network analysis (SmCCNet) was developed to identify complex relationships between omics associated with a disease phenotype, such as lung function. SmCCNet uses two sets of omics datasets and an associated output phenotypes to generate a multi-omics graph, which can then be used to explore relationships between omics in the context of a disease. Detecting significant subgraphs within this multi-omics network, i.e., subgraphs which exhibit high correlation to a disease phenotype and high inter-connectivity, can help clinicians identify complex biological relationships involved in disease progression. The current approach to identifying significant subgraphs relies on hierarchical clustering, which can be used to inform clinicians about important pathways involved in the disease or phenotype of interest. The reliance on a hierarchical clustering approach can hinder subgraph quality by biasing toward finding more compact subgraphs and removing larger significant subgraphs. This study aims to introduce new significant subgraph detection techniques. In particular, we introduce two subgraph detection methods, dubbed Correlated PageRank and Correlated Louvain, by extending the Personalized PageRank Clustering and Louvain algorithms, as well as a hybrid approach combining the two proposed methods, and compare them to the hierarchical method currently in use. The proposed methods show significant improvement in the quality of the subgraphs produced when compared to the current state of the art.

SUMOylation Regulates Transcription by the Progesterone Receptor A Isoform in a Target Gene Selective Manner.

Luminal breast cancers express estrogen (ER) and progesterone (PR) receptors, and respond to endocrine therapies. However, some ER+PR+ tumors display intrinsic or acquired resistance, possibly related to PR. Two PR isoforms, PR-A and PR-B, regulate distinct gene subsets that may differentially influence tumor fate. A high PR-A:PR-B ratio is associated with poor prognosis and tamoxifen resistance. We speculate that excessive PR-A marks tumors that will relapse early. Here we address mechanisms by which PR-A regulate transcription, focusing on SUMOylation. We use receptor mutants and synthetic promoter/reporters to show that SUMOylation deficiency or the deSUMOylase SENP1 enhance transcription by PR-A, independent of the receptors' dimerization interface or DNA binding domain. De-SUMOylation exposes the agonist properties of the antiprogestin RU486. Thus, on synthetic promoters, SUMOylation functions as an independent brake on transcription by PR-A. What about PR-A SUMOylation of endogenous human breast cancer genes? To study these, we used gene expression profiling. Surprisingly, PR-A SUMOylation influences progestin target genes differentially, with some upregulated, others down-regulated, and others unaffected. Hormone-independent gene regulation is also PR-A SUMOylation dependent. Several SUMOylated genes were analyzed in clinical breast cancer database. In sum, we show that SUMOylation does not simply repress PR-A. Rather it regulates PR-A activity in a target selective manner including genes associated with poor prognosis, shortened survival, and metastasis.