03598nas a2200277 4500008004100000022001400041245013400055210006900189260001600258300000800274490000700282520262900289653001202918653000902930653002502939653002402964100002702988700002003015700001603035700002003051700003103071700002003102700002003122700002403142856015403166 2024 eng d a1479-587600aThe mechanistic functional landscape of retinitis pigmentosa: a machine learning-driven approach to therapeutic target discovery.0 amechanistic functional landscape of retinitis pigmentosa a machi c2024 Feb 06 a1390 v223 a

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.

10aAnimals10aMice10aRetinitis pigmentosa10aSignal Transduction1 aEsteban-Medina, Marina1 aLoucera, Carlos1 aRian, Kinza1 aVelasco, Sheyla1 aOlivares-González, Lorena1 aRodrigo, Regina1 aDopazo, Joaquin1 aPeña-Chilet, Maria uhttp://clinbioinfosspa.es/content/mechanistic-functional-landscape-retinitis-pigmentosa-machine-learning-driven-approach-therapeutic-target-discovery02396nas a2200301 4500008004100000022001400041245012200055210006900177260001600246300001100262520135200273100002301625700002701648700002601675700001801701700002701719700001801746700002101764700002301785700002401808700003201832700002401864700002201888700002001910700001601930700002301946856012501969 2023 eng d a1474-972600amicroRNAs-mediated regulation of insulin signaling in white adipose tissue during aging: Role of caloric restriction.0 amicroRNAsmediated regulation of insulin signaling in white adipo c2023 Jul 04 ae139193 a

Caloric restriction is a non-pharmacological intervention known to ameliorate the metabolic defects associated with aging, including insulin resistance. The levels of miRNA expression may represent a predictive tool for aging-related alterations. In order to investigate the role of miRNAs underlying insulin resistance in adipose tissue during the early stages of aging, 3- and 12-month-old male animals fed ad libitum, and 12-month-old male animals fed with a 20% caloric restricted diet were used. In this work we demonstrate that specific miRNAs may contribute to the impaired insulin-stimulated glucose metabolism specifically in the subcutaneous white adipose tissue, through the regulation of target genes implicated in the insulin signaling cascade. Moreover, the expression of these miRNAs is modified by caloric restriction in middle-aged animals, in accordance with the improvement of the metabolic state. Overall, our work demonstrates that alterations in posttranscriptional gene expression because of miRNAs dysregulation might represent an endogenous mechanism by which insulin response in the subcutaneous fat depot is already affected at middle age. Importantly, caloric restriction could prevent this modulation, demonstrating that certain miRNAs could constitute potential biomarkers of age-related metabolic alterations.

1 aCorrales, Patricia1 aMartin-Taboada, Marina1 aVivas-García, Yurena1 aTorres, Lucia1 aRamirez-Jimenez, Laura1 aLopez, Yamila1 aHorrillo, Daniel1 aVila-Bedmar, Rocio1 aBarber-Cano, Eloisa1 aIzquierdo-Lahuerta, Adriana1 aPeña-Chilet, Maria1 aMartínez, Carmen1 aDopazo, Joaquin1 aRos, Manuel1 aMedina-Gomez, Gema uhttp://clinbioinfosspa.es/content/micrornas-mediated-regulation-insulin-signaling-white-adipose-tissue-during-aging-role01572nas a2200253 4500008004100000022001400041245005600055210005300111260001600164300000600180490000700186520083300193100001601026700002701042700002201069700001801091700002101109700002001130700001901150700002301169700002401192700002001216856008201236 2021 eng d a1756-038100aMechanistic modeling of the SARS-CoV-2 disease map.0 aMechanistic modeling of the SARSCoV2 disease map c2021 Jan 21 a50 v143 a

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.

1 aRian, Kinza1 aEsteban-Medina, Marina1 aHidalgo, Marta, R1 aCubuk, Cankut1 aFalco, Matias, M1 aLoucera, Carlos1 aGunyel, Devrim1 aOstaszewski, Marek1 aPeña-Chilet, Maria1 aDopazo, Joaquin uhttp://clinbioinfosspa.es/content/mechanistic-modeling-sars-cov-2-disease-map01933nas a2200277 4500008004100000022001400041245011400055210006900169260001600238490000700254520095600261100002701217700002401244700002601268700003001294700001901324700001901343700003401362700003601396700002001432700002001452700001801472700001701490700002001507856012801527 2021 eng d a2072-669400aMutational Characterization of Cutaneous Melanoma Supports Divergent Pathways Model for Melanoma Development.0 aMutational Characterization of Cutaneous Melanoma Supports Diver c2021 Oct 180 v133 a

According to the divergent pathway model, cutaneous melanoma comprises a nevogenic group with a propensity to melanocyte proliferation and another one associated with cumulative solar damage (CSD). While characterized clinically and epidemiologically, the differences in the molecular profiles between the groups have remained primarily uninvestigated. This study has used a custom gene panel and bioinformatics tools to investigate the potential molecular differences in a thoroughly characterized cohort of 119 melanoma patients belonging to nevogenic and CSD groups. We found that the nevogenic melanomas had a restricted set of mutations, with the prominently mutated gene being . The CSD melanomas, in contrast, showed mutations in a diverse group of genes that included , , , and . We thus provide evidence that nevogenic and CSD melanomas constitute different biological entities and highlight the need to explore new targeted therapies.

1 aMillán-Esteban, David1 aPeña-Chilet, Maria1 aGarcía-Casado, Zaida1 aManrique-Silva, Esperanza1 aRequena, Celia1 aBañuls, José1 aLopez-Guerrero, Jose, Antonio1 aRodríguez-Hernández, Aranzazu1 aTraves, Víctor1 aDopazo, Joaquin1 aVirós, Amaya1 aKumar, Rajiv1 aNagore, Eduardo uhttp://clinbioinfosspa.es/content/mutational-characterization-cutaneous-melanoma-supports-divergent-pathways-model-melanoma00750nas a2200157 4500008004100000245011500041210006900156260001600225490000600241100002100247700002400268700002000292700002200312700002000334856023800354 2020 eng d00aMechanistic models of signaling pathways deconvolute the glioblastoma single-cell functional landscapeAbstract0 aMechanistic models of signaling pathways deconvolute the gliobla cJan-06-20200 v21 aFalco, Matias, M1 aPeña-Chilet, Maria1 aLoucera, Carlos1 aHidalgo, Marta, R1 aDopazo, Joaquin uhttps://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.pdf02316nas a2200241 4500008004100000022001400041245014100055210006900196260001500265490000600280520146700286653001101753653004301764653001101807653000901818653001401827653002401841100001801865700001801883700002401901700002001925856012901945 2020 eng d a2073-440900aMechanistic Models of Signaling Pathways Reveal the Drug Action Mechanisms behind Gender-Specific Gene Expression for Cancer Treatments.0 aMechanistic Models of Signaling Pathways Reveal the Drug Action  c2020 06 290 v93 a

Despite the existence of differences in gene expression across numerous genes between males and females having been known for a long time, these have been mostly ignored in many studies, including drug development and its therapeutic use. In fact, the consequences of such differences over the disease mechanisms or the drug action mechanisms are completely unknown. Here we applied mechanistic mathematical models of signaling activity to reveal the ultimate functional consequences that gender-specific gene expression activities have over cell functionality and fate. Moreover, we also used the mechanistic modeling framework to simulate the drug interventions and unravel how drug action mechanisms are affected by gender-specific differential gene expression. Interestingly, some cancers have many biological processes significantly affected by these gender-specific differences (e.g., bladder or head and neck carcinomas), while others (e.g., glioblastoma or rectum cancer) are almost insensitive to them. We found that many of these gender-specific differences affect cancer-specific pathways or in physiological signaling pathways, also involved in cancer origin and development. Finally, mechanistic models have the potential to be used for finding alternative therapeutic interventions on the pathways targeted by the drug, which lead to similar results compensating the downstream consequences of gender-specific differences in gene expression.

10aFemale10aGene Expression Regulation, Neoplastic10aHumans10aMale10aNeoplasms10aSignal Transduction1 aCubuk, Cankut1 aCan, Fatma, E1 aPeña-Chilet, Maria1 aDopazo, Joaquin uhttp://clinbioinfosspa.es/content/mechanistic-models-signaling-pathways-reveal-drug-action-mechanisms-behind-gender-specific