@article {804, title = {The mechanistic functional landscape of retinitis pigmentosa: a machine learning-driven approach to therapeutic target discovery.}, journal = {J Transl Med}, volume = {22}, year = {2024}, month = {2024 Feb 06}, pages = {139}, 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 (GABARĪ±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.

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.

}, keywords = {Animals, Mice, Retinitis pigmentosa, Signal Transduction}, issn = {1479-5876}, doi = {10.1186/s12967-024-04911-7}, author = {Esteban-Medina, Marina and Loucera, Carlos and Rian, Kinza and Velasco, Sheyla and Olivares-Gonz{\'a}lez, Lorena and Rodrigo, Regina and Dopazo, Joaquin and Pe{\~n}a-Chilet, Maria} } @article {815, title = {Mutational landscape of risk variants in comorbid depression and obesity: a next-generation sequencing approach.}, journal = {Mol Psychiatry}, year = {2024}, month = {2024 May 28}, abstract = {

Major depression (MD) and obesity are complex genetic disorders that are frequently comorbid. However, the study of both diseases concurrently remains poorly addressed and therefore the underlying genetic mechanisms involved in this comorbidity remain largely unknown. Here we examine the contribution of common and rare variants to this comorbidity through a next-generation sequencing (NGS) approach. Specific genomic regions of interest in MD and obesity were sequenced in a group of 654 individuals from the PISMA-ep epidemiological study. We obtained variants across the entire frequency spectrum and assessed their association with comorbid MD and obesity, both at variant and gene levels. We identified 55 independent common variants and a burden of rare variants in 4 genes (PARK2, FGF21, HIST1H3D and RSRC1) associated with the comorbid phenotype. Follow-up analyses revealed significantly enriched gene-sets associated with biological processes and pathways involved in metabolic dysregulation, hormone signaling and cell cycle regulation. Our results suggest that, while risk variants specific to the comorbid phenotype have been identified, the genes functionally impacted by the risk variants share cell biological processes and signaling pathways with MD and obesity phenotypes separately. To the best of our knowledge, this is the first study involving a targeted sequencing approach toward the study of the comorbid MD and obesity. The framework presented here allowed a deep characterization of the genetics of the co-occurring MD and obesity, revealing insights into the mutational and functional profile that underlies this comorbidity and contributing to a better understanding of the relationship between these two disabling disorders.

}, issn = {1476-5578}, doi = {10.1038/s41380-024-02609-2}, author = {P{\'e}rez-Guti{\'e}rrez, Ana M and Carmona, Rosario and Loucera, Carlos and Cervilla, Jorge A and Guti{\'e}rrez, Blanca and Molina, Esther and L{\'o}pez-L{\'o}pez, Daniel and Perez-Florido, Javier and Zarza-Rebollo, Juan Antonio and L{\'o}pez-Isac, Elena and Dopazo, Joaquin and Martinez-Gonzalez, Luis Javier and Rivera, Margarita} } @article {711, title = {Mechanistic modeling of the SARS-CoV-2 disease map.}, journal = {BioData Min}, volume = {14}, year = {2021}, month = {2021 Jan 21}, pages = {5}, abstract = {

Here we present a web interface that implements a comprehensive mechanistic model of the SARS-CoV-2 disease map. In this framework, the detailed activity of the human signaling circuits related to the viral infection, covering from the entry and replication mechanisms to the downstream consequences as inflammation and antigenic response, can be inferred from gene expression experiments. Moreover, the effect of potential interventions, such as knock-downs, or drug effects (currently the system models the effect of more than 8000 DrugBank drugs) can be studied. This freely available tool not only provides an unprecedentedly detailed view of the mechanisms of viral invasion and the consequences in the cell but has also the potential of becoming an invaluable asset in the search for efficient antiviral treatments.

}, issn = {1756-0381}, doi = {10.1186/s13040-021-00234-1}, author = {Rian, Kinza and Esteban-Medina, Marina and Hidalgo, Marta R and Cubuk, Cankut and Falco, Matias M and Loucera, Carlos and Gunyel, Devrim and Ostaszewski, Marek and Pe{\~n}a-Chilet, Maria and Dopazo, Joaquin} } @article {702, title = {Mechanistic models of signaling pathways deconvolute the glioblastoma single-cell functional landscapeAbstract}, journal = {NAR Cancer}, volume = {2}, year = {2020}, month = {Jan-06-2020}, doi = {10.1093/narcan/zcaa011}, url = {https://academic.oup.com/narcancer/article/doi/10.1093/narcan/zcaa011/5862620http://academic.oup.com/narcancer/article-pdf/2/2/zcaa011/33428092/zcaa011.pdfhttp://academic.oup.com/narcancer/article-pdf/2/2/zcaa011/33428092/zcaa011.pdf}, author = {Falco, Matias M and Pe{\~n}a-Chilet, Maria and Loucera, Carlos and Hidalgo, Marta R and Dopazo, Joaquin} }