The mechanistic functional landscape of retinitis pigmentosa: a machine learning-driven approach to therapeutic target discovery
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 bio...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2024-02-01
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| Series: | Journal of Translational Medicine |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12967-024-04911-7 |
| _version_ | 1797273449127739392 |
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| author | Marina Esteban-Medina Carlos Loucera Kinza Rian Sheyla Velasco Lorena Olivares-González Regina Rodrigo Joaquin Dopazo Maria Peña-Chilet |
| author_facet | Marina Esteban-Medina Carlos Loucera Kinza Rian Sheyla Velasco Lorena Olivares-González Regina Rodrigo Joaquin Dopazo Maria Peña-Chilet |
| author_sort | Marina Esteban-Medina |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-07T14:43:26Z |
| format | Article |
| id | doaj.art-29bdd87afef049caa57e40dc554563ba |
| institution | Directory Open Access Journal |
| issn | 1479-5876 |
| language | English |
| last_indexed | 2024-03-07T14:43:26Z |
| publishDate | 2024-02-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Translational Medicine |
| spelling | doaj.art-29bdd87afef049caa57e40dc554563ba2024-03-05T20:06:51ZengBMCJournal of Translational Medicine1479-58762024-02-0122112410.1186/s12967-024-04911-7The mechanistic functional landscape of retinitis pigmentosa: a machine learning-driven approach to therapeutic target discoveryMarina Esteban-Medina0Carlos Loucera1Kinza Rian2Sheyla Velasco3Lorena Olivares-González4Regina Rodrigo5Joaquin Dopazo6Maria Peña-Chilet7Andalusian Platform for Computational Medicine, Andalusian Public Foundation Progress and Health-FPSAndalusian Platform for Computational Medicine, Andalusian Public Foundation Progress and Health-FPSAndalusian Platform for Computational Medicine, Andalusian Public Foundation Progress and Health-FPSGroup of Pathophysiology and Therapies for Vision Disorders, Príncipe Felipe Research Center (CIPF)Group of Pathophysiology and Therapies for Vision Disorders, Príncipe Felipe Research Center (CIPF)Group of Pathophysiology and Therapies for Vision Disorders, Príncipe Felipe Research Center (CIPF)Andalusian Platform for Computational Medicine, Andalusian Public Foundation Progress and Health-FPSAndalusian Platform for Computational Medicine, Andalusian Public Foundation Progress and Health-FPSAbstract 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.https://doi.org/10.1186/s12967-024-04911-7Retinitis pigmentosaRare diseasesDrug-repurposingDisease mapsGABAergic neurotransmissionSystems medicine |
| spellingShingle | Marina Esteban-Medina Carlos Loucera Kinza Rian Sheyla Velasco Lorena Olivares-González Regina Rodrigo Joaquin Dopazo Maria Peña-Chilet The mechanistic functional landscape of retinitis pigmentosa: a machine learning-driven approach to therapeutic target discovery Journal of Translational Medicine Retinitis pigmentosa Rare diseases Drug-repurposing Disease maps GABAergic neurotransmission Systems medicine |
| title | The mechanistic functional landscape of retinitis pigmentosa: a machine learning-driven approach to therapeutic target discovery |
| title_full | The mechanistic functional landscape of retinitis pigmentosa: a machine learning-driven approach to therapeutic target discovery |
| title_fullStr | The mechanistic functional landscape of retinitis pigmentosa: a machine learning-driven approach to therapeutic target discovery |
| title_full_unstemmed | The mechanistic functional landscape of retinitis pigmentosa: a machine learning-driven approach to therapeutic target discovery |
| title_short | The mechanistic functional landscape of retinitis pigmentosa: a machine learning-driven approach to therapeutic target discovery |
| title_sort | mechanistic functional landscape of retinitis pigmentosa a machine learning driven approach to therapeutic target discovery |
| topic | Retinitis pigmentosa Rare diseases Drug-repurposing Disease maps GABAergic neurotransmission Systems medicine |
| url | https://doi.org/10.1186/s12967-024-04911-7 |
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