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other·ophthalmology, retina, preclinical research, biomedical research, research methods·PMC10848380

The mechanistic functional landscape of retinitis pigmentosa: a machine learning-driven approach to therapeutic target discovery

Journal of Translational Medicine · 8 authors, 8 centres

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TL;DR

Researchers built a mechanistic disease map of retinitis pigmentosa by mapping RP genes onto KEGG signaling pathways and trained an explainable machine learning model to predict causal links between approved drug targets and disease functional circuits. Five computationally prioritized targets were experimentally validated in rd10 mice, a preclinical RP model.

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RP-related genes from Orphanet, OMIM, and HPO databases were mapped onto KEGG signaling pathways to construct a mechanistic disease map comprising 226 functional circuits across 40 pathways. An explainable multi-output random forest regressor was trained on normal tissue transcriptomic data to infer causal connections between DrugBank-approved drug targets and the functional circuits. The model predicted 109 targets of approved drugs with potential effects over circuits corresponding to nine disease hallmarks.

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other

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ophthalmology

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ORIGINAL PAPER

PMC10848380

The mechanistic functional landscape of retinitis pigmentosa: a machine learning-driven approach to therapeutic target discovery

Journal of Translational Medicine

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ABSTRACT

Background Retinitis pigmentosa is the prevailing genetic cause of blindness in developed nations with no effective treatments. In the pursuit of unraveling the intricate dynamics underlying this complex disease, mechanistic models emerge as a tool of proven efficiency rooted in systems biology, to elucidate the interplay between RP genes and their mechanisms. The integration of mechanistic models and drug-target interactions under the umbrella of machine learning methodologies provides a multifaceted approach that can boost the discovery of novel therapeutic targets, facilitating further drug repurposing in RP. Methods By mapping Retinitis Pigmentosa-related genes (obtained from Orphanet, OMIM and HPO databases) onto KEGG signaling pathways, a collection of signaling functional circuits encompassing Retinitis Pigmentosa molecular mechanisms was defined. Next, a mechanistic model of the so-defined disease map, where the effects of interventions can be simulated, was built. Then, an explainable multi-output random forest regressor was trained using normal tissue transcriptomic data to learn causal connections between targets of approved drugs from DrugBank and the functional circuits of the mechanistic disease map. Selected target genes involvement were validated on rd10 mice, a murine model of Retinitis Pigmentosa. Results A mechanistic functional map of Retinitis Pigmentosa was constructed resulting in 226 functional circuits belonging to 40 KEGG signaling pathways. The method predicted 109 targets of approved drugs in use with a potential effect over circuits corresponding to nine hallmarks identified. Five of those targets were selected and experimentally validated in rd10 mice: Gabre , Gabra1 (GABARα1 protein), Slc12a5 (KCC2 protein), Grin1 (NR1 protein) and Glr2a . As a result, we provide a resource to evaluate the potential impact of drug target genes in Retinitis Pigmentosa. Conclusions The possibility of building actionable disease models in combination with machine learning algorithms to learn causal drug-disease interactions opens new avenues for boosting drug discovery. Such mechanistically-based hypotheses can guide and accelerate the experimental validations prioritizing drug target candidates. In this work, a mechanistic model describing the functional disease map of Retinitis Pigmentosa was developed, identifying five promising therapeutic candidates targeted by approved drug. Further experimental validation will demonstrate the efficiency of this approach for a systematic application to other rare diseases. Supplementary Information The online version contains supplementary material available at 10.1186/s12967-024-04911-7.

CITATION EXPORT

Marina Esteban-Medina, Carlos Loucera, Kinza Rian et al.. The mechanistic functional landscape of retinitis pigmentosa: a machine learning-driven approach to therapeutic target discovery. Journal of Translational Medicine. (2024). https://doi.org/10.1186/s12967-024-04911-7

AUTHORS

8 · 8 CENTRES

Marina Esteban-Medina, Carlos Loucera, Kinza Rian, Sheyla Velasco, Lorena Olivares-González, Regina Rodrigo, Joaquin Dopazo, Maria Peña-Chilet

1Andalusian Platform for Computational Medicine, Andalusian Public Foundation Progress and Health-FPS, Seville, Spain
2grid.411109.c0000 0000 9542 1158Systems and Computational Medicine Group, Institute of Biomedicine of Seville, IBiS, University Hospital Virgen del Rocío/CSIC/University of Seville, 41013 Seville, Spain
3https://ror.org/05xr2yq54grid.418274.c0000 0004 0399 600XGroup of Pathophysiology and Therapies for Vision Disorders, Príncipe Felipe Research Center (CIPF), 46012 Valencia, Spain
4https://ror.org/00ca2c886grid.413448.e0000 0000 9314 1427Biomedical Research Networking Center in Rare Diseases (CIBERER), Health Institute Carlos III, 28029 Madrid, Spain
5https://ror.org/043nxc105grid.5338.d0000 0001 2173 938XDepartment of Physiology, University of Valencia (UV), 46100 Burjassot, Spain
6grid.440831.a0000 0004 1804 6963Department of Anatomy and Physiology, Catholic University of Valencia San Vicente Mártir, 46001 Valencia, Spain
7grid.476458.c0000 0004 0427 8560Joint Research Unit on Endocrinology, Nutrition and Clinical Dietetics UV-IIS La Fe, 46026 Valencia, Spain
8grid.84393.350000 0001 0360 9602BigData, AI, Biostatistics & Bioinformatics Platform, Health Research Institute La Fe (IISLaFe), 46026 Valencia, Spain