A genome-wide RNAi screen identifies potential drug targets in a C. elegans model of α1-antitrypsin deficiency

Abstract

α1-Antitrypsin deficiency (ATD) is a common genetic disorder that can lead to end-stage liver and lung disease. Although liver transplantation remains the only therapy currently available, manipulation of the proteostasis network (PN) by small molecule therapeutics offers great promise. To accelerate the drug-discovery process for this disease, we first developed a semi-automated high-throughput/content-genome-wide RNAi screen to identify PN modifiers affecting the accumulation of the α1-antitrypsin Z mutant (ATZ) in a Caenorhabditis elegans model of ATD. We identified 104 PN modifiers, and these genes were used in a computational strategy to identify human ortholog-ligand pairs. Based on rigorous selection criteria, we identified four FDA-approved drugs directed against four different PN targets that decreased the accumulation of ATZ in C. elegans. We also tested one of the compounds in a mammalian cell line with similar results. This methodology also proved useful in confirming drug targets in vivo, and predicting the success of combination therapy. We propose that small animal models of genetic disorders combined with genome-wide RNAi screening and computational methods can be used to rapidly, economically and strategically prime the preclinical discovery pipeline for rare and neglected diseases with limited therapeutic options.

Figure 1.

RNAi screen for sGFP::ATZ PN modifiers. (A) A schematic showing how the final list of 104 PN modifiers were obtained from 16 256 RNAi clones. (B) Summary of the genome-wide RNAi screen. Graph shows z-scores for each RNAi treatment. A positive or negative z-score indicates that the treatment either increased or decreased ATZ accumulation, respectively. (C–E) Sample fluorescence well images. Images show animals treated with an RNAi that had no effect (C), decreased (D) or increased (E) ATZ accumulation. Numbers at the bottom represent actual z-scores.

Figure 2.

Comparison of RNAi screens for modifiers of protein misfolding diseases. A Venn diagram highlighting overlapping hits from other RNAi screens using models for diseases caused by protein aggregation or misfolding. Wang et al. (26) study (A), current study (B), Nollen et al. (22) study (C) and Lejune et al. (29) study (D). The details of each screen are summarized in Supplementary Material, Table S2. Numbers within parenthesis represent the total number of hits reported. Numbers not in parentheses represent number of overlapping hits with other screens. *F48F7.1 (alg-1); ^†C14B9.7 (rpl-21), F42C5.1 (rpl-8), Y46G5.4 (phi-10); ^‡C07B5.5 (nuc-1), Y47D3B.2 (hum-5), ZC395.10.

Figure 3.

Human orthologs and drug–target interaction prediction. (A) A flow chart summarizing the in silico approach used to identify human drug–targets from the 104 C. elegans PN modifiers. (B) A Venn diagram showing the overlap between human orthologs identified by OrthoList and WormBase. (C) DAVID analysis comparing the WormBase (outer ring) and OrthoList (inner ring) assigned orthologs to the original C. elegans protein profile (middle ring). (D) Final list of targets and interacting drugs identified using STITCH and Metacore.

Figure 4.

Drug–target analysis. (A) L4 GFP::ATZ animals were treated with 100 µm of each drug for 24 h and analyzed using the ArrayScan V^TI. (B) Drug–dose response curves. The experiment was repeated three times, and a representative experiment shown. The error bars represent the SD of five replicate wells (n > 150 animals/treatment). Statistical significance was determined by using a Student's t-test. ***P < 0.001, **P < 0.01. (C) Effect of fluspirilene on steady-state levels of ATZ in a cell line model of ATZ. HeLa cells engineered to express ATZ (HTO/Z) were treated with DMSO, carbamazepine (CBZ) (positive control) or fluspirilene for 48 h. Lysates were prepared and separated into soluble and insoluble fractions. Samples were analyzed by immunoblotting with antibodies against AT (top) and GAPDH (middle). GAPDH is cytosolic marker and its absence in the insoluble fraction indicates correct fractionation. The blots were also stained with GelCode Blue (bottom) to demonstrate equal sample loading in each well.

Figure 5.

Drug–target validation. (A) Steady-state expression levels of sGFP::ATZ in the N2, age-1(hx546) and daf-16(m26) backgrounds. Data are normalized to N2;sGFP::ATZ worms. (B–D) Effect of wortmannin on steady-state levels of sGFP::ATZ. N2;sGFP::ATZ (B), sGFP::ATZ;age-1(hx546) (C) and sGFP::ATZ;daf-16(m26) (D) animals were treated with wortmannin (100 µm) for 24 h and analyzed using the ArrayscanVTI. GFP(RNAi) treatment was included as a control to show that ATZ levels could be further reduced in each line. Note wortmannin reduced the sGFP::ATZ level in the wild-type N2 but not in age-1(hx546) or daf-16(m26) mutant backgrounds. (E) Effect of various drugs on sGFP::ATZ;daf-16(m26) animals. Of the drugs known to decrease sGFP::ATZ levels in the N2 background, only wortmannin failed to reduce sGFP::ATZ in the daf-16(m26) background. (F) ATZ::GFP animals were treated with 5, 12.5 or 50 μm of fluphenazine and wortmannin, either singly or in combination. The data were normalized to the untreated DMSO control within each experiment. All experiments were repeated at least three times with n > 150 animals/treatment. Error bars represent SD (A–E) or SEM (F). Statistical significance was determined using the Student's t-test. ***P < 0.001, **P < 0.01, *P < 0.05.