The mechanistic functional landscape of retinitis pigmentosa: a machine learning-driven approach to therapeutic target discovery
Por:
M. ESTEBAN-MEDINA, C. LOUCERA, K. RIAN, S. VELASCO, L. OLIVARES-GONZÁLEZ, R. RODRIGO, J. DOPAZO and M. PEÑA-CHILET
Publicada:
6 feb 2024
Resumen:
BackgroundRetinitis 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.MethodsBy 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.ResultsA 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 (GABAR alpha 1 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.ConclusionsThe 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.
Filiaciones:
M. ESTEBAN-MEDINA:
Andalusian Publ Fdn Progress & Hlth FPS, Andalusian Platform Computat Med, Seville, Spain
Univ Seville, Inst Biomed Seville, Syst & Computat Med Grp, IBiS,Univ Hosp Virgen de Rocio,CSIC, , Seville, Seville 41013, Spain
C. LOUCERA:
Andalusian Publ Fdn Progress & Hlth FPS, Andalusian Platform Computat Med, Seville, Spain
Univ Seville, Inst Biomed Seville, Syst & Computat Med Grp, IBiS,Univ Hosp Virgen de Rocio,CSIC, , Seville, Seville 41013, Spain
K. RIAN:
Andalusian Publ Fdn Progress & Hlth FPS, Andalusian Platform Computat Med, Seville, Spain
Univ Seville, Inst Biomed Seville, Syst & Computat Med Grp, IBiS,Univ Hosp Virgen de Rocio,CSIC, , Seville, Seville 41013, Spain
:
Principe Felipe Res Ctr CIPF, Grp Pathophysiol & Therapies Vis Disorders, Valencia 46012, Spain
L. OLIVARES-GONZÁLEZ:
Principe Felipe Res Ctr CIPF, Grp Pathophysiol & Therapies Vis Disorders, Valencia 46012, Spain
:
Principe Felipe Res Ctr CIPF, Grp Pathophysiol & Therapies Vis Disorders, Valencia 46012, Spain
Hlth Inst Carlos III, Biomed Res Networking Ctr Rare Dis CIBERER, Madrid 28029, Spain
Univ Valencia UV, Dept Physiol, Burjassot 46100, Spain
Catholic Univ Valencia San Vicente Martir, Dept Anat & Physiol, Valencia 46001, Spain
Nutr & Clin Dietet UV IIS La Fe, Joint Res Unit Endocrinol, Valencia 46026, Spain
:
Andalusian Publ Fdn Progress & Hlth FPS, Andalusian Platform Computat Med, Seville, Spain
Univ Seville, Inst Biomed Seville, Syst & Computat Med Grp, IBiS,Univ Hosp Virgen de Rocio,CSIC, , Seville, Seville 41013, Spain
Hlth Inst Carlos III, Biomed Res Networking Ctr Rare Dis CIBERER, Madrid 28029, Spain
M. PEÑA-CHILET:
Andalusian Publ Fdn Progress & Hlth FPS, Andalusian Platform Computat Med, Seville, Spain
Univ Seville, Inst Biomed Seville, Syst & Computat Med Grp, IBiS,Univ Hosp Virgen de Rocio,CSIC, , Seville, Seville 41013, Spain
Hlth Inst Carlos III, Biomed Res Networking Ctr Rare Dis CIBERER, Madrid 28029, Spain
Hlth Res Inst La Fe IISLaFe, BigData Biostat & Bioinformat Platform, Valencia 46026, Spain
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