CFTR Lifecycle Map-A Systems Medicine Model of CFTR Maturation to Predict Possible Active Compound Combinations

A Proteomic Survey of the Cystic Fibrosis Transmembrane Conductance Regulator Surfaceome

The aim of this review article is to collate recent contributions of proteomic studies to cystic fibrosis transmembrane conductance regulator (CFTR) biology. We summarize advances from these studies and create an accessible resource for future CFTR proteomic efforts. We focus our attention on the CFTR interaction network at the cell surface, thus generating a CFTR 'surfaceome'. We review the main findings about CFTR interactions and highlight several functional categories amongst these that could lead to the discovery of potential biomarkers and drug targets for CF.

Changes in cystic fibrosis transmembrane conductance regulator protein expression prior to and during elexacaftor-tezacaftor-ivacaftor therapy

Background: Defects in expression, maturation or function of the epithelial membrane glycoprotein CFTR are causative for the progressive disease cystic fibrosis. Recently, molecular therapeutics that improve CFTR maturation and functional defects have been approved. We aimed to verify whether we could detect an improvement of CFTR protein expression and maturation by triple therapy with elexacaftor-tezacaftor-ivacaftor (ELX/TEZ/IVA). Methods: Rectal suction biopsies of 21 p.Phe508del homozygous or compound heterozygous CF patients obtained pre- and during treatment with ELX/TEZ/IVA were analyzed by CFTR Western blot that was optimized to distinguish CFTR glycoisoforms. Findings: CFTR western immunoblot analysis revealed that-compared to baseline-the levels of CFTR protein increased by at least twofold in eight out of 12 patients upon treatment with ELX/TEZ/IVA compared to baseline (p < 0.02). However, polydispersity of the mutant CFTR protein was lower than that of the fully glycosylated wild type CFTR Golgi isoform, indicating an incompletely glycosylated p.Phe508el CFTR protein isoform C* in patients with CF which persists after ELX/TEZ/IVA treatment. Interpretation: Treatment with ELX/TEZ/IVA increased protein expression by facilitating the posttranslational processing of mutant CFTR but apparently did not succeed in generating the polydisperse spectrum of N-linked oligosaccharides that is characteristic for the wild type CFTR band C glycoisoform. Our results caution that the lower amounts or immature glycosylation of the C* glycoisoform observed in patients' biomaterial might not translate to fully restored function of mutant CFTR necessary for long-term provision of clinical benefit.

Complementary Dual Approach for In Silico Target Identification of Potential Pharmaceutical Compounds in Cystic Fibrosis

Cystic fibrosis is a genetic disease caused by mutation of the CFTR gene, which encodes a chloride and bicarbonate transporter in epithelial cells. Due to the vast range of geno- and phenotypes, it is difficult to find causative treatments; however, small-molecule therapeutics have been clinically approved in the last decade. Still, the search for novel therapeutics is ongoing, and thousands of compounds are being tested in different assays, often leaving their mechanism of action unknown. Here, we bring together a CFTR-specific compound database (CandActCFTR) and systems biology model (CFTR Lifecycle Map) to identify the targets of the most promising compounds. We use a dual inverse screening approach, where we employ target- and ligand-based methods to suggest targets of 309 active compounds in the database amongst 90 protein targets from the systems biology model. Overall, we identified 1038 potential target–compound pairings and were able to suggest targets for all 309 active compounds in the database.

Integrating Text Mining into the Curation of Disease Maps

An adequate visualization form is required to gain an overview and ultimately understand the complex and diverse biological mechanisms of diseases. Recently, disease maps have been introduced for this purpose. A disease map is defined as a systems biological map or model that combines metabolic, signaling, and physiological pathways to create a comprehensive overview of known disease mechanisms. With the increase in publications describing biological interactions, efforts in creating and curating comprehensive disease maps is growing accordingly. Therefore, new computational approaches are needed to reduce the time that manual curation takes. Test mining algorithms can be used to analyse the natural language of scientific publications. These types of algorithms can take humanly readable text passages and convert them into a more ordered, machine-usable data structure. To support the creation of disease maps by text mining, we developed an interactive, user-friendly disease map viewer. The disease map viewer displays text mining results in a systems biology map, where the user can review them and either validate or reject identified interactions. Ultimately, the viewer brings together the time-saving advantages of text mining with the accuracy of manual data curation.

Proximity Profiling of the CFTR Interaction Landscape in Response to Orkambi

Deletion of phenylalanine 508 (∆F508) of the Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) anion channel protein is the leading cause of Cystic Fibrosis (CF). Here, we report the analysis of CFTR and ∆F508-CFTR interactomes using BioID (proximity-dependent biotin identification), a technique that can also detect transient associations. We identified 474 high-confidence CFTR proximity-interactors, 57 of which have been previously validated, with the remainder representing novel interaction space. The ∆F508 interactome, comprising 626 proximity-interactors was markedly different from its wild type counterpart, with numerous alterations in protein associations categorized in membrane trafficking and cellular stress functions. Furthermore, analysis of the ∆F508 interactome in cells treated with Orkambi identified several interactions that were altered as a result of this drug therapy. We examined two candidate CFTR proximity interactors, VAPB and NOS1AP, in functional assays designed to assess surface delivery and overall chloride efflux. VAPB depletion impacted both CFTR surface delivery and chloride efflux, whereas NOS1AP depletion only affected the latter. The wild type and ∆F508-CFTR interactomes represent rich datasets that could be further mined to reveal additional candidates for the functional rescue of ∆F508-CFTR.

Mapping Compound Databases to Disease Maps-A MINERVA Plugin for CandActBase

The MINERVA platform is currently the most widely used platform for visualizing and providing access to disease maps. Disease maps are systems biological maps of molecular interactions relevant in a certain disease context, where they can be used to support drug discovery. For this purpose, we extended MINERVA's own drug and chemical search using the MINERVA plugin starter kit. We developed a plugin to provide a linkage between disease maps in MINERVA and application-specific databases of candidate therapeutics. The plugin has three main functionalities; one shows all the targets of all the compounds in the database, the second is a compound-based search to highlight targets of specific compounds, and the third can be used to find compounds that affect a certain target. As a use case, we applied the plugin to link a disease map and compound database we previously established in the context of cystic fibrosis and, herein, point out possible issues and difficulties. The plugin is publicly available on GitLab; the use-case application to cystic fibrosis, connecting disease maps and the compound database CandActCFTR, is available online.