Integrative genomic meta-analysis reveals novel molecular insights into cystic fibrosis and ΔF508-CFTR rescue

Abstract

Cystic fibrosis (CF), caused by mutations to CFTR, leads to severe and progressive lung disease. The most common mutant, ΔF508-CFTR, undergoes proteasomal degradation, extinguishing its anion channel function. Numerous in vitro interventions have been identified to partially rescue ΔF508-CFTR function yet remain poorly understood. Improved understanding of both the altered state of CF cells and the mechanisms of existing rescue strategies could reveal novel therapeutic strategies. Toward this aim, we measured transcriptional profiles of established temperature, genetic, and chemical interventions that rescue ΔF508-CFTR and also re-analyzed public datasets characterizing transcription in human CF vs. non-CF samples from airway and whole blood. Meta-analysis yielded a core disease signature and two core rescue signatures. To interpret these through the lens of prior knowledge, we compiled a “CFTR Gene Set Library” from literature. The core disease signature revealed remarkably strong connections to genes with established effects on CFTR trafficking and function and suggested novel roles of EGR1 and SGK1 in the disease state. Our data also revealed an unexpected mechanistic link between several genetic rescue interventions and the unfolded protein response. Finally, we found that C18, an analog of the CFTR corrector compound Lumacaftor, induces almost no transcriptional perturbation despite its rescue activity.

Integrative chemogenomic analysis identifies small molecules that partially rescue ΔF508‐CFTR for cystic fibrosis

“…The connections between the UPR and CFTR rescue are wellrecognized, 12,[35][36][37] and the activation of UPR is perhaps not that surprising given the prominence of UPR in both the RNAi and low temperature signatures, as discussed in ref. 19. Regarding TNFα signaling, there is recent evidence that modulation of this pathway can positively affect ΔF508-CFTR trafficking and function.…”

“…Second, the CF transcriptional signatures that we use are derived from metaanalyses of multiple studies, and hence are expected to be of higher quality than signatures derived from individual, smaller-scale studies. 19 Third, we develop an integrative scoring approach to rank small molecules for predicted efficacy using multiple CF-relevant queries, including both pathways and transcriptional signatures and show that our integrative approach outperforms comparable approaches based on a single data source. Finally, we present an approach to computationally validate our scoring strategy using previously identified small molecules as a validation set.…”

“…Similar approaches have been reported (e.g., applying connectivity mapping in the context of signaling pathways to identify candidate small molecules to treat idiopathic pulmonary fibrosis 16,17 or integrating multiple disease signatures characterizing lung cancer to prioritize and study small molecules with common mechanisms). 18 The pipeline described herein builds on our recent work 19 by integrating three core gene expression signatures associated with CF disease and in vitro rescue along with expert-curated pathways 20 relevant to CFTR biosynthesis and trafficking. This work is not the first to apply a connectivity mapping strategy to identify novel CFTR correctors, and, importantly, prior work has provided evidence to support such a strategy.…”

“…First, we cast a wider net by querying signatures from 13,000 molecules from the LINCS L1000 dataset in addition to the 1309 from CMap used in past studies. Second, the CF transcriptional signatures that we use are derived from meta‐analyses of multiple studies, and hence are expected to be of higher quality than signatures derived from individual, smaller‐scale studies 19 . Third, we develop an integrative scoring approach to rank small molecules for predicted efficacy using multiple CF‐relevant queries, including both pathways and transcriptional signatures and show that our integrative approach outperforms comparable approaches based on a single data source.…”

“…CHEMOGENOMIC ANALYSIS OF ΔF508-CFTR RESCUE F I G U R E 1 (a) Integrative small molecule prioritization pipeline for CF therapeutic discovery. Molecules were prioritized using an aggregate score derived from three signatures from a prior meta-analysis 19 (and using three numbers of DEGs for each signature, denoted by K i for i in 1-3), in combination with nine pathways related to CFTR processing and trafficking (denoted by P i for i in 1-9). Scores for each cell-specific signature are first collapsed across the 18 CF query gene sets, and then collapsed across cell lines tested per small molecule.…”