Protein Carbonylation of an Amino Acid Residue of the Na/K‐ATPase α1 Subunit Determines Na/K‐ATPase Signaling and Sodium Transport in Renal Proximal Tubular Cells

Background We have demonstrated that cardiotonic steroids, such as ouabain, signaling through the Na/K‐ATPase, regulate sodium reabsorption in the renal proximal tubule. By direct carbonylation modification of the Pro222 residue in the actuator (A) domain of pig Na/K‐ATPase α1 subunit, reactive oxygen species are required for ouabain‐stimulated Na/K‐ATPase/c‐Src signaling and subsequent regulation of active transepithelial 22Na+ transport. In the present study we sought to determine the functional role of Pro222 carbonylation in Na/K‐ATPase signaling and sodium handling. Methods and Results Stable pig α1 knockdown LLC‐PK1‐originated PY‐17 cells were rescued by expressing wild‐type rat α1 and rat α1 with a single mutation of Pro224 (corresponding to pig Pro222) to Ala. This mutation does not affect ouabain‐induced inhibition of Na/K‐ATPase activity, but abolishes the effects of ouabain on Na/K‐ATPase/c‐Src signaling, protein carbonylation, Na/K‐ATPase endocytosis, and active transepithelial 22Na+ transport. Conclusions Direct carbonylation modification of Pro224 in the rat α1 subunit determines ouabain‐mediated Na/K‐ATPase signal transduction and subsequent regulation of renal proximal tubule sodium transport

Strategies to Identify Common Low Effect COPD Risk Variants

Rationale: Chronic Obstructive Pulmonary Disease (COPD) is a common progressive lung disease. Risk for COPD is determined by an intersection between cigarette smoke inhalation and genetic predisposition. As with other disease risks determined by complex genetics, most variants thus far identified to be associated with COPD risk are common (variant allele frequency ≥0.05) and typically have low effect. Thus, after conclusion of multiple COPD Genome Wide Association Studies (GWAS) comprising thousands of subjects, most variants discovered account for less than 1% and the total accounts for less than 10% of variation in risk. Similarly sized GWAS that employ stringent control for false discovery do not have sufficient power to identify the common very low effect variants hypothesized to account for remaining variation in risk. Because larger GWAS are limited by economics and logistics, there is a need to develop strategies that increase the power to identify common very low effect COPD risk variants. There is mounting evidence that risk SNPs are located within regions responsible for cis-regulation of transcription. Our hypothesis is that false discovery will be reduced by restricting analysis to variants that a) are in putative cis-regulatory regions of high prior likelihood gene pathways, b) replicate for COPD association across multiple GWAS, and c) are experimentally confirmed to have cis-regulatory function. Methods: COPD GWAS datasets phs000335 (Lung Health Study [LHS]) and phs000765 (COPDgene) were downloaded from the database of Genotypes and Phenotypes (dbGaP). Each dataset comprised subjects with primarily European ancestry. We restricted these datasets to putative cis-regulatory variants within 900 genes in pathways that have high prior likelihood for role in COPD causation based on literature, then assessed for replication across GWAS after sub-phenotyping as chronic bronchitis (CB) or emphysema (E). Results: After meta-analysis, 19 previously unreported SNPs, including nine CB-associated and ten E-associated, had p value ≤ 0.0005. Rather than controlling for false discovery by Bonferroni adjustment, we are now assessing these SNP sets for lung tissue cis-regulatory function in the GTEx eQTL expression database and by transcript allelic-imbalance analysis in NBEC. Conclusion: We describe a novel approach to reduce false discovery and increase power to discover COPD risk variants in existing GWAS datasets that function by affecting cis-regulation. The set of SNPs enriched for replication are confirmed to have cis-regulatory function in NBEC will be assessed for validation in an independent completed GWAS cohorts available through dbGAP