Prioritized polycystic kidney disease drug targets and repurposing candidates from pre-cystic and cystic mouse model gene expression reversion
Wilk, E. J.; Howton, T. C.; Fisher, J. L.; Oza, V. H.; Brownlee, R. T.; McPherson, K. C.; Cleary, H. L.; Yoder, B. K.; George, J. F.; Mrug, M.; Lasseigne, B. N.
Show abstract
Autosomal dominant polycystic kidney disease (ADPKD) is one of the most prevalent monogenic human diseases. It is mostly caused by pathogenic variants in PKD1 or PKD2 genes that encode interacting transmembrane proteins polycystin-1 (PC1) and polycystin-2 (PC2). Among many pathogenic processes described in ADPKD, those associated with cAMP signaling, inflammation, and metabolic reprogramming appear to regulate the disease manifestations. Tolvaptan, a vasopressin receptor-2 antagonist that regulates cAMP pathway, is the only FDA-approved ADPKD therapeutic. Tolvaptan reduces renal cyst growth and kidney function loss, but it is not tolerated by many patients and is associated with idiosyncratic liver toxicity. Therefore, additional therapeutic options for ADPKD treatment are needed. As drug repurposing of FDA-approved drug candidates can significantly decrease the time and cost associated with traditional drug discovery, we used the computational approach signature reversion to detect inversely related drug response gene expression signatures from the Library of Integrated Network-Based Cellular Signatures (LINCS) database and identified compounds predicted to reverse disease-associated transcriptomic signatures in three publicly available Pkd2 kidney transcriptomic data sets of mouse ADPKD models. We focused on a pre-cystic model for signature reversion, as it was less impacted by confounding secondary disease mechanisms in ADPKD, and then compared the resulting candidates target differential expression in the two cystic mouse models. We further prioritized these drug candidates based on their known mechanism of action, FDA status, targets, and by functional enrichment analysis. With this in-silico approach, we prioritized 29 unique drug targets differentially expressed in Pkd2 ADPKD cystic models and 16 prioritized drug repurposing candidates that target them, including bromocriptine and mirtazapine, which can be further tested in-vitro and in-vivo. Collectively, these indicate drug targets and repurposing candidates that may effectively treat pre-cystic as well as cystic ADPKD. O_FIG O_LINKSMALLFIG WIDTH=110 HEIGHT=200 SRC="FIGDIR/small/518863v2_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@18af02org.highwire.dtl.DTLVardef@89d45corg.highwire.dtl.DTLVardef@d4fffdorg.highwire.dtl.DTLVardef@1f244b3_HPS_FORMAT_FIGEXP M_FIG Graphical abstract of the study created with Biorender.com. C_FIG
Matching journals
The top 12 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Genetic Association and Transferability for Urinary Albumin-Creatinine Ratio as a Marker of Kidney Disease in four Sub-Saharan African Populations and non-continental Individuals of African Ancestry 90%
- GSTM1 copy number is not associated with risk of kidney failure in a large cohort 90%
- Genome-Wide Association Study Finds Multiple Loci Associated with Intraocular Pressure in HS Rats 90%
Similar papers in this journal
- Rapid response to the Alpha-1 Adrenergic Agent Phenylephrine in the Perioperative Period is Impacted by Genomics and Ancestry 89%
- Implementation of Pre-emptive Pharmacogenomics Testing in Outpatient Clinics in Asia (IMPT study) 89%
- Leveraging Genetic Correlations to Prioritize Drug Groups for Repurposing in Type 2 Diabetes 87%
Similar papers in this journal
- Temporal and sex-dependent gene expression patterns in a renal ischemia-reperfusion injury and recovery pig model 92%
- The p21 dependent G2 arrest of the cell cycle in epithelial tubular cells links to the early stage of renal fibrosis 92%
- Creation of X-linked Alport Syndrome Rat Model with Col4a5 Deficiency 91%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.