@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 {736, title = {COVID19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms.}, journal = {Mol Syst Biol}, volume = {17}, year = {2021}, month = {2021 10}, pages = {e10387}, abstract = {

We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective.

}, keywords = {Antiviral Agents, Computational Biology, Computer Graphics, COVID-19, Cytokines, Data Mining, Databases, Factual, Gene Expression Regulation, Host Microbial Interactions, Humans, Immunity, Cellular, Immunity, Humoral, Immunity, Innate, Lymphocytes, Metabolic Networks and Pathways, Myeloid Cells, Protein Interaction Mapping, SARS-CoV-2, Signal Transduction, Software, Transcription Factors, Viral Proteins}, issn = {1744-4292}, doi = {10.15252/msb.202110387}, author = {Ostaszewski, Marek and Niarakis, Anna and Mazein, Alexander and Kuperstein, Inna and Phair, Robert and Orta-Resendiz, Aurelio and Singh, Vidisha and Aghamiri, Sara Sadat and Acencio, Marcio Luis and Glaab, Enrico and Ruepp, Andreas and Fobo, Gisela and Montrone, Corinna and Brauner, Barbara and Frishman, Goar and Monraz G{\'o}mez, Luis Crist{\'o}bal and Somers, Julia and Hoch, Matti and Kumar Gupta, Shailendra and Scheel, Julia and Borlinghaus, Hanna and Czauderna, Tobias and Schreiber, Falk and Montagud, Arnau and Ponce de Leon, Miguel and Funahashi, Akira and Hiki, Yusuke and Hiroi, Noriko and Yamada, Takahiro G and Dr{\"a}ger, Andreas and Renz, Alina and Naveez, Muhammad and Bocskei, Zsolt and Messina, Francesco and B{\"o}rnigen, Daniela and Fergusson, Liam and Conti, Marta and Rameil, Marius and Nakonecnij, Vanessa and Vanhoefer, Jakob and Schmiester, Leonard and Wang, Muying and Ackerman, Emily E and Shoemaker, Jason E and Zucker, Jeremy and Oxford, Kristie and Teuton, Jeremy and Kocakaya, Ebru and Summak, G{\"o}k{\c c}e Ya{\u g}mur and Hanspers, Kristina and Kutmon, Martina and Coort, Susan and Eijssen, Lars and Ehrhart, Friederike and Rex, Devasahayam Arokia Balaya and Slenter, Denise and Martens, Marvin and Pham, Nhung and Haw, Robin and Jassal, Bijay and Matthews, Lisa and Orlic-Milacic, Marija and Senff Ribeiro, Andrea and Rothfels, Karen and Shamovsky, Veronica and Stephan, Ralf and Sevilla, Cristoffer and Varusai, Thawfeek and Ravel, Jean-Marie and Fraser, Rupsha and Ortseifen, Vera and Marchesi, Silvia and Gawron, Piotr and Smula, Ewa and Heirendt, Laurent and Satagopam, Venkata and Wu, Guanming and Riutta, Anders and Golebiewski, Martin and Owen, Stuart and Goble, Carole and Hu, Xiaoming and Overall, Rupert W and Maier, Dieter and Bauch, Angela and Gyori, Benjamin M and Bachman, John A and Vega, Carlos and Grou{\`e}s, Valentin and Vazquez, Miguel and Porras, Pablo and Licata, Luana and Iannuccelli, Marta and Sacco, Francesca and Nesterova, Anastasia and Yuryev, Anton and de Waard, Anita and Turei, Denes and Luna, Augustin and Babur, Ozgun and Soliman, Sylvain and Valdeolivas, Alberto and Esteban-Medina, Marina and Pe{\~n}a-Chilet, Maria and Rian, Kinza and Helikar, Tom{\'a}{\v s} and Puniya, Bhanwar Lal and Modos, Dezso and Treveil, Agatha and Olbei, Marton and De Meulder, Bertrand and Ballereau, Stephane and Dugourd, Aur{\'e}lien and Naldi, Aur{\'e}lien and No{\"e}l, Vincent and Calzone, Laurence and Sander, Chris and Demir, Emek and Korcsmaros, Tamas and Freeman, Tom C and Aug{\'e}, Franck and Beckmann, Jacques S and Hasenauer, Jan and Wolkenhauer, Olaf and Wilighagen, Egon L and Pico, Alexander R and Evelo, Chris T and Gillespie, Marc E and Stein, Lincoln D and Hermjakob, Henning and D{\textquoteright}Eustachio, Peter and Saez-Rodriguez, Julio and Dopazo, Joaquin and Valencia, Alfonso and Kitano, Hiroaki and Barillot, Emmanuel and Auffray, Charles and Balling, Rudi and Schneider, Reinhard} } @article {713, title = {Drug repurposing for COVID-19 using machine learning and mechanistic models of signal transduction circuits related to SARS-CoV-2 infection.}, journal = {Signal Transduct Target Ther}, volume = {5}, year = {2020}, month = {2020 12 11}, pages = {290}, keywords = {Computational Chemistry, COVID-19, drug repositioning, Humans, Machine Learning, Molecular Docking Simulation, Molecular Targeted Therapy, Proteins, SARS-CoV-2, Signal Transduction}, issn = {2059-3635}, doi = {10.1038/s41392-020-00417-y}, author = {Loucera, Carlos and Esteban-Medina, Marina and Rian, Kinza and Falco, Matias M and Dopazo, Joaquin and Pe{\~n}a-Chilet, Maria} }