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-discovery02754nas a2200265 4500008004100000022001400041245011700055210006900172260001600241520176800257100003002025700002102055700002002076700002302096700002302119700001902142700002602161700002602187700003302213700002302246700002002269700003602289700002202325856014102347 2024 eng d a1476-557800aMutational landscape of risk variants in comorbid depression and obesity: a next-generation sequencing approach.0 aMutational landscape of risk variants in comorbid depression and c2024 May 283 a

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.

1 aPérez-Gutiérrez, Ana, M1 aCarmona, Rosario1 aLoucera, Carlos1 aCervilla, Jorge, A1 aGutiérrez, Blanca1 aMolina, Esther1 aLópez-López, Daniel1 aPerez-Florido, Javier1 aZarza-Rebollo, Juan, Antonio1 aLópez-Isac, Elena1 aDopazo, Joaquin1 aMartinez-Gonzalez, Luis, Javier1 aRivera, Margarita uhttp://clinbioinfosspa.es/content/mutational-landscape-risk-variants-comorbid-depression-and-obesity-next-generation-sequencing-approach01572nas 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-map00750nas 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.pdf