04620nas a2201141 4500008004100000022001400041245011000055210006900165260000900234300001200243490000700255520126600262653002401528653001301552653002301565653001101588653001501599653002001614100001901634700002301653700002201676700002101698700002001719700002301739700002101762700002601783700001901809700002001828700001901848700002201867700002301889700002401912700001701936700002101953700001701974700001601991700002402007700002502031700002502056700002302081700002302104700002702127700002402154700001602178700002102194700002602215700002502241700002102266700002102287700001902308700003202327700002102359700002202380700002002402700001902422700002602441700001802467700001802485700002302503700002102526700001902547700001902566700002202585700001802607700001902625700001802644700002302662700001802685700002002703700001902723700003302742700002602775700002702801700002502828700002302853700001802876700002302894700001702917700002402934700001802958700002202976700002502998700002003023700002303043700002003066700002603086700002003112700001903132700002203151700002203173700002003195700002303215700002103238700001803259700002403277710003503301856014203336 2024 eng d a1664-322400aDrug-target identification in COVID-19 disease mechanisms using computational systems biology approaches.0 aDrugtarget identification in COVID19 disease mechanisms using co c2023 a12828590 v143 a
INTRODUCTION: The COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing.
METHODS: Extensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework can link biomolecules from omics data analysis and computational modelling to dysregulated pathways in a cell-, tissue- or patient-specific manner. Drug repurposing using text mining and AI-assisted analysis identified potential drugs, chemicals and microRNAs that could target the identified key factors.
RESULTS: Results revealed drugs already tested for anti-COVID-19 efficacy, providing a mechanistic context for their mode of action, and drugs already in clinical trials for treating other diseases, never tested against COVID-19.
DISCUSSION: The key advance is that the proposed framework is versatile and expandable, offering a significant upgrade in the arsenal for virus-host interactions and other complex pathologies.
10aComputer Simulation10aCOVID-1910adrug repositioning10aHumans10aSARS-CoV-210aSystems biology1 aNiarakis, Anna1 aOstaszewski, Marek1 aMazein, Alexander1 aKuperstein, Inna1 aKutmon, Martina1 aGillespie, Marc, E1 aFunahashi, Akira1 aAcencio, Marcio, Luis1 aHemedan, Ahmed1 aAichem, Michael1 aKlein, Karsten1 aCzauderna, Tobias1 aBurtscher, Felicia1 aYamada, Takahiro, G1 aHiki, Yusuke1 aHiroi, Noriko, F1 aHu, Finterly1 aPham, Nhung1 aEhrhart, Friederike1 aWillighagen, Egon, L1 aValdeolivas, Alberto1 aDugourd, Aurélien1 aMessina, Francesco1 aEsteban-Medina, Marina1 aPeña-Chilet, Maria1 aRian, Kinza1 aSoliman, Sylvain1 aAghamiri, Sara, Sadat1 aPuniya, Bhanwar, Lal1 aNaldi, Aurélien1 aHelikar, Tomáš1 aSingh, Vidisha1 aFernández, Marco, Fariñas1 aBermudez, Viviam1 aTsirvouli, Eirini1 aMontagud, Arnau1 aNoël, Vincent1 aPonce-de-Leon, Miguel1 aMaier, Dieter1 aBauch, Angela1 aGyori, Benjamin, M1 aBachman, John, A1 aLuna, Augustin1 aPiñero, Janet1 aFurlong, Laura, I1 aBalaur, Irina1 aRougny, Adrien1 aJarosz, Yohan1 aOverall, Rupert, W1 aPhair, Robert1 aPerfetto, Livia1 aMatthews, Lisa1 aRex, Devasahayam, Arokia Bal1 aOrlic-Milacic, Marija1 aGomez, Luis, Cristobal1 aDe Meulder, Bertrand1 aRavel, Jean, Marie1 aJassal, Bijay1 aSatagopam, Venkata1 aWu, Guanming1 aGolebiewski, Martin1 aGawron, Piotr1 aCalzone, Laurence1 aBeckmann, Jacques, S1 aEvelo, Chris, T1 aD'Eustachio, Peter1 aSchreiber, Falk1 aSaez-Rodriguez, Julio1 aDopazo, Joaquin1 aKuiper, Martin1 aValencia, Alfonso1 aWolkenhauer, Olaf1 aKitano, Hiroaki1 aBarillot, Emmanuel1 aAuffray, Charles1 aBalling, Rudi1 aSchneider, Reinhard1 aCOVID-19 Disease Map Community uhttp://clinbioinfosspa.es/content/drug-target-identification-covid-19-disease-mechanisms-using-computational-systems-biology-approaches-003598nas 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 aBACKGROUND: 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-discovery02334nas a2200229 4500008004100000022001400041245013100055210006900186260001600255300000800271490000700279520147800286100002001764700002101784700002701805700002301832700003101855700002101886700002401907700002001931856015301951 2023 eng d a1743-422X00aReal-world evidence with a retrospective cohort of 15,968 COVID-19 hospitalized patients suggests 21 new effective treatments.0 aRealworld evidence with a retrospective cohort of 15968 COVID19 c2023 Oct 06 a2260 v203 aPURPOSE: Despite the extensive vaccination campaigns in many countries, COVID-19 is still a major worldwide health problem because of its associated morbidity and mortality. Therefore, finding efficient treatments as fast as possible is a pressing need. Drug repurposing constitutes a convenient alternative when the need for new drugs in an unexpected medical scenario is urgent, as is the case with COVID-19.
METHODS: Using data from a central registry of electronic health records (the Andalusian Population Health Database), the effect of prior consumption of drugs for other indications previous to the hospitalization with respect to patient outcomes, including survival and lymphocyte progression, was studied on a retrospective cohort of 15,968 individuals, comprising all COVID-19 patients hospitalized in Andalusia between January and November 2020.
RESULTS: Covariate-adjusted hazard ratios and analysis of lymphocyte progression curves support a significant association between consumption of 21 different drugs and better patient survival. Contrarily, one drug, furosemide, displayed a significant increase in patient mortality.
CONCLUSIONS: In this study we have taken advantage of the availability of a regional clinical database to study the effect of drugs, which patients were taking for other indications, on their survival. The large size of the database allowed us to control covariates effectively.
1 aLoucera, Carlos1 aCarmona, Rosario1 aEsteban-Medina, Marina1 aBostelmann, Gerrit1 aMuñoyerro-Muñiz, Dolores1 aVillegas, Román1 aPeña-Chilet, Maria1 aDopazo, Joaquin uhttp://clinbioinfosspa.es/content/real-world-evidence-retrospective-cohort-15968-covid-19-hospitalized-patients-suggests-21-new-effective-treatments03070nas a2200325 4500008004100000022001400041245009700055210006900152260000900221300001200230490000600242520201700248100001802265700001802283700001902301700002402320700002702344700003202371700002202403700001502425700002202440700002202462700002202484700002602506700002402532700002002556700002202576700002302598856012302621 2023 eng d a2673-764700aVisualization of automatically combined disease maps and pathway diagrams for rare diseases.0 aVisualization of automatically combined disease maps and pathway c2023 a11015050 v33 aInvestigation of molecular mechanisms of human disorders, especially rare diseases, require exploration of various knowledge repositories for building precise hypotheses and complex data interpretation. Recently, increasingly more resources offer diagrammatic representation of such mechanisms, including disease-dedicated schematics in pathway databases and disease maps. However, collection of knowledge across them is challenging, especially for research projects with limited manpower. In this article we present an automated workflow for construction of maps of molecular mechanisms for rare diseases. The workflow requires a standardized definition of a disease using Orphanet or HPO identifiers to collect relevant genes and variants, and to assemble a functional, visual repository of related mechanisms, including data overlays. The diagrams composing the final map are unified to a common systems biology format from CellDesigner SBML, GPML and SBML+layout+render. The constructed resource contains disease-relevant genes and variants as data overlays for immediate visual exploration, including embedded genetic variant browser and protein structure viewer. We demonstrate the functionality of our workflow on two examples of rare diseases: Kawasaki disease and retinitis pigmentosa. Two maps are constructed based on their corresponding identifiers. Moreover, for the retinitis pigmentosa use-case, we include a list of differentially expressed genes to demonstrate how to tailor the workflow using omics datasets. In summary, our work allows for an ad-hoc construction of molecular diagrams combined from different sources, preserving their layout and graphical style, but integrating them into a single resource. This allows to reduce time consuming tasks of prototyping of a molecular disease map, enabling visual exploration, hypothesis building, data visualization and further refinement. The code of the workflow is open and accessible at https://gitlab.lcsb.uni.lu/minerva/automap/.
1 aGawron, Piotr1 aHoksza, David1 aPiñero, Janet1 aPeña-Chilet, Maria1 aEsteban-Medina, Marina1 aFernandez-Rueda, Jose, Luis1 aColonna, Vincenza1 aSmula, Ewa1 aHeirendt, Laurent1 aAncien, François1 aGrouès, Valentin1 aSatagopam, Venkata, P1 aSchneider, Reinhard1 aDopazo, Joaquin1 aFurlong, Laura, I1 aOstaszewski, Marek uhttp://clinbioinfosspa.es/content/visualization-automatically-combined-disease-maps-and-pathway-diagrams-rare-diseases02967nas a2200445 4500008004100000022001400041245010400055210006900159260001600228490000700244520152100251653001901772653001301791653001101804653003201815653001501847653002901862653001901891653002401910100002601934700002201960700002801982700003002010700002802040700002702068700002702095700003002122700002802152700002202180700002002202700001602222700001902238700002802257700002202285700001602307700002402323700002402347700002202371856012802393 2022 eng d a1422-006700aEndoglin and MMP14 Contribute to Ewing Sarcoma Spreading by Modulation of Cell-Matrix Interactions.0 aEndoglin and MMP14 Contribute to Ewing Sarcoma Spreading by Modu c2022 Aug 040 v233 aEndoglin (ENG) is a mesenchymal stem cell (MSC) marker typically expressed by active endothelium. This transmembrane glycoprotein is shed by matrix metalloproteinase 14 (MMP14). Our previous work demonstrated potent preclinical activity of first-in-class anti-ENG antibody-drug conjugates as a nascent strategy to eradicate Ewing sarcoma (ES), a devastating rare bone/soft tissue cancer with a putative MSC origin. We also defined a correlation between ENG and MMP14 expression in ES. Herein, we show that ENG expression is significantly associated with a dismal prognosis in a large cohort of ES patients. Moreover, both ENG/MMP14 are frequently expressed in primary ES tumors and metastasis. To deepen in their functional relevance in ES, we conducted transcriptomic and proteomic profiling of in vitro ES models that unveiled a key role of ENG and MMP14 in cell mechano-transduction. Migration and adhesion assays confirmed that loss of ENG disrupts actin filament assembly and filopodia formation, with a concomitant effect on cell spreading. Furthermore, we observed that ENG regulates cell-matrix interaction through activation of focal adhesion signaling and protein kinase C expression. In turn, loss of MMP14 contributed to a more adhesive phenotype of ES cells by modulating the transcriptional extracellular matrix dynamics. Overall, these results suggest that ENG and MMP14 exert a significant role in mediating correct spreading machinery of ES cells, impacting the aggressiveness of the disease.
10aBone Neoplasms10aEndoglin10aHumans10aMatrix Metalloproteinase 1410aProteomics10aReceptors, Growth Factor10aSarcoma, Ewing10aSignal Transduction1 aPuerto-Camacho, Pilar1 aDiaz-Martin, Juan1 aOlmedo-Pelayo, Joaquín1 aBolado-Carrancio, Alfonso1 aSalguero-Aranda, Carmen1 aJordán-Pérez, Carmen1 aEsteban-Medina, Marina1 aAlamo-Alvarez, Inmaculada1 aDelgado-Bellido, Daniel1 aLobo-Selma, Laura1 aDopazo, Joaquin1 aSastre, Ana1 aAlonso, Javier1 aGrünewald, Thomas, G P1 aBernabeu, Carmelo1 aByron, Adam1 aBrunton, Valerie, G1 aAmaral, Ana, Teresa1 ade Alava, Enrique uhttp://clinbioinfosspa.es/content/endoglin-and-mmp14-contribute-ewing-sarcoma-spreading-modulation-cell-matrix-interactions02438nas a2200241 4500008004100000022001400041245012800055210006900183260001600252490000700268520156100275100003101836700002001867700001901887700002701906700001901933700002001952700002901972700002402001700002402025700002002049856012702069 2022 eng d a2076-392100aAn SPM-Enriched Marine Oil Supplement Shifted Microglia Polarization toward M2, Ameliorating Retinal Degeneration in Mice.0 aSPMEnriched Marine Oil Supplement Shifted Microglia Polarization c2022 Dec 300 v123 aRetinitis pigmentosa (RP) is the most common inherited retinal dystrophy causing progressive vision loss. It is accompanied by chronic and sustained inflammation, including M1 microglia activation. This study evaluated the effect of an essential fatty acid (EFA) supplement containing specialized pro-resolving mediators (SPMs), on retinal degeneration and microglia activation in mice, a model of RP, as well as on LPS-stimulated BV2 cells. The EFA supplement was orally administered to mice from postnatal day (P)9 to P18. At P18, the electrical activity of the retina was examined by electroretinography (ERG) and innate behavior in response to light were measured. Retinal degeneration was studied via histology including the TUNEL assay and microglia immunolabeling. Microglia polarization (M1/M2) was assessed by flow cytometry, qPCR, ELISA and histology. Redox status was analyzed by measuring antioxidant enzymes and markers of oxidative damage. Interestingly, the EFA supplement ameliorated retinal dysfunction and degeneration by improving ERG recording and sensitivity to light, and reducing photoreceptor cell loss. The EFA supplement reduced inflammation and microglia activation attenuating M1 markers as well as inducing a shift to the M2 phenotype in mouse retinas and LPS-stimulated BV2 cells. It also reduced oxidative stress markers of lipid peroxidation and carbonylation. These findings could open up new therapeutic opportunities based on resolving inflammation with oral supplementation with SPMs such as the EFA supplement.
1 aOlivares-González, Lorena1 aVelasco, Sheyla1 aGallego, Idoia1 aEsteban-Medina, Marina1 aPuras, Gustavo1 aLoucera, Carlos1 aMartínez-Romero, Alicia1 aPeña-Chilet, Maria1 aPedraz, José, Luis1 aRodrigo, Regina uhttp://clinbioinfosspa.es/content/spm-enriched-marine-oil-supplement-shifted-microglia-polarization-toward-m2-ameliorating07188nas a2202077 4500008004100000022001400041245010000055210006900155260001200224300001100236490000700247520130900254653002101563653002601584653002201610653001301632653001401645653001601659653002301675653003101698653003201729653001101761653002301772653002201795653002101817653001601838653003601854653001801890653003201908653001501940653002401955653001301979653002601992653001902018100002302037700001902060700002202079700002102101700001802122700002702140700001902167700002602186700002602212700001802238700001902256700001702275700002202292700002102314700001902335700002902354700001802383700001602401700002902417700001802446700002302464700002202487700002002509700002002529700002702549700002102576700001702597700001802614700002402632700002102656700001602677700002102693700001902714700002302733700002302756700002002779700001702799700001902816700002402835700002102859700002402880700001702904700002302921700002402944700001902968700002002987700001903007700001903026700002903045700002303074700002003097700001703117700001803134700002403152700003303176700002003209700002003229700001603249700001503265700001803280700001903298700002603317700002703343700002003370700002403390700001803414700002403432700002203456700002203478700001903500700002003519700002103539700001803560700001503578700002203593700002303615700001703638700001903655700002403674700001703698700001803715700001703733700002303750700001803773700001803791700002303809700002103832700001703853700002203870700002003892700001803912700001803930700002303948700002103971700002503992700001804017700002004035700001704055700001904072700001704091700002104108700002504129700002704154700002404181700001604205700002104221700002504242700001704267700002004284700001804304700002504322700002404347700002304371700002104394700001904415700002204434700001804456700001604474700002204490700002004512700001804532700002504550700001904575700002204594700002404616700002304640700002004663700002304683700002204706700002304728700002304751700002604774700002004800700002204820700002004842700002304862700002104885700001804906700002404924710003504948856012704983 2021 eng d a1744-429200aCOVID19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms.0 aCOVID19 Disease Map a computational knowledge repository of viru c2021 10 ae103870 v173 aWe 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.
10aAntiviral Agents10aComputational Biology10aComputer Graphics10aCOVID-1910aCytokines10aData Mining10aDatabases, Factual10aGene Expression Regulation10aHost Microbial Interactions10aHumans10aImmunity, Cellular10aImmunity, Humoral10aImmunity, Innate10aLymphocytes10aMetabolic Networks and Pathways10aMyeloid Cells10aProtein Interaction Mapping10aSARS-CoV-210aSignal Transduction10aSoftware10aTranscription Factors10aViral Proteins1 aOstaszewski, Marek1 aNiarakis, Anna1 aMazein, Alexander1 aKuperstein, Inna1 aPhair, Robert1 aOrta-Resendiz, Aurelio1 aSingh, Vidisha1 aAghamiri, Sara, Sadat1 aAcencio, Marcio, Luis1 aGlaab, Enrico1 aRuepp, Andreas1 aFobo, Gisela1 aMontrone, Corinna1 aBrauner, Barbara1 aFrishman, Goar1 aGómez, Luis, Cristóbal1 aSomers, Julia1 aHoch, Matti1 aGupta, Shailendra, Kumar1 aScheel, Julia1 aBorlinghaus, Hanna1 aCzauderna, Tobias1 aSchreiber, Falk1 aMontagud, Arnau1 ade Leon, Miguel, Ponce1 aFunahashi, Akira1 aHiki, Yusuke1 aHiroi, Noriko1 aYamada, Takahiro, G1 aDräger, Andreas1 aRenz, Alina1 aNaveez, Muhammad1 aBocskei, Zsolt1 aMessina, Francesco1 aBörnigen, Daniela1 aFergusson, Liam1 aConti, Marta1 aRameil, Marius1 aNakonecnij, Vanessa1 aVanhoefer, Jakob1 aSchmiester, Leonard1 aWang, Muying1 aAckerman, Emily, E1 aShoemaker, Jason, E1 aZucker, Jeremy1 aOxford, Kristie1 aTeuton, Jeremy1 aKocakaya, Ebru1 aSummak, Gökçe, Yağmur1 aHanspers, Kristina1 aKutmon, Martina1 aCoort, Susan1 aEijssen, Lars1 aEhrhart, Friederike1 aRex, Devasahayam, Arokia Bal1 aSlenter, Denise1 aMartens, Marvin1 aPham, Nhung1 aHaw, Robin1 aJassal, Bijay1 aMatthews, Lisa1 aOrlic-Milacic, Marija1 aRibeiro, Andrea, Senff1 aRothfels, Karen1 aShamovsky, Veronica1 aStephan, Ralf1 aSevilla, Cristoffer1 aVarusai, Thawfeek1 aRavel, Jean-Marie1 aFraser, Rupsha1 aOrtseifen, Vera1 aMarchesi, Silvia1 aGawron, Piotr1 aSmula, Ewa1 aHeirendt, Laurent1 aSatagopam, Venkata1 aWu, Guanming1 aRiutta, Anders1 aGolebiewski, Martin1 aOwen, Stuart1 aGoble, Carole1 aHu, Xiaoming1 aOverall, Rupert, W1 aMaier, Dieter1 aBauch, Angela1 aGyori, Benjamin, M1 aBachman, John, A1 aVega, Carlos1 aGrouès, Valentin1 aVazquez, Miguel1 aPorras, Pablo1 aLicata, Luana1 aIannuccelli, Marta1 aSacco, Francesca1 aNesterova, Anastasia1 aYuryev, Anton1 ade Waard, Anita1 aTurei, Denes1 aLuna, Augustin1 aBabur, Ozgun1 aSoliman, Sylvain1 aValdeolivas, Alberto1 aEsteban-Medina, Marina1 aPeña-Chilet, Maria1 aRian, Kinza1 aHelikar, Tomáš1 aPuniya, Bhanwar, Lal1 aModos, Dezso1 aTreveil, Agatha1 aOlbei, Marton1 aDe Meulder, Bertrand1 aBallereau, Stephane1 aDugourd, Aurélien1 aNaldi, Aurélien1 aNoël, Vincent1 aCalzone, Laurence1 aSander, Chris1 aDemir, Emek1 aKorcsmaros, Tamas1 aFreeman, Tom, C1 aAugé, Franck1 aBeckmann, Jacques, S1 aHasenauer, Jan1 aWolkenhauer, Olaf1 aWilighagen, Egon, L1 aPico, Alexander, R1 aEvelo, Chris, T1 aGillespie, Marc, E1 aStein, Lincoln, D1 aHermjakob, Henning1 aD'Eustachio, Peter1 aSaez-Rodriguez, Julio1 aDopazo, Joaquin1 aValencia, Alfonso1 aKitano, Hiroaki1 aBarillot, Emmanuel1 aAuffray, Charles1 aBalling, Rudi1 aSchneider, Reinhard1 aCOVID-19 Disease Map Community uhttp://clinbioinfosspa.es/content/covid19-disease-map-computational-knowledge-repository-virus-host-interaction-mechanisms00806nas a2200229 4500008004100000022001300041245014700054210006900201260001600270300001600286490000700302100001600309700002300325700001800348700002200366700002000388700002700408700003000435700002400465700002000489856006700509 2021 eng d a2001037000aGenome-scale mechanistic modeling of signaling pathways made easy: A bioconductor/cytoscape/web server framework for the analysis of omic data0 aGenomescale mechanistic modeling of signaling pathways made easy cJan-01-2021 a2968 - 29780 v191 aRian, Kinza1 aHidalgo, Marta, R.1 aCubuk, Cankut1 aFalco, Matias, M.1 aLoucera, Carlos1 aEsteban-Medina, Marina1 aAlamo-Alvarez, Inmaculada1 aPeña-Chilet, Maria1 aDopazo, Joaquin uhttps://linkinghub.elsevier.com/retrieve/pii/S200103702100203801572nas 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 aHere 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-map02756nas a2200385 4500008004100000022001400041245015900055210006900214260001500283300001000298490000700308520150900315653001601824653001301840653001101853653001101864653002601875653000901901653002601910653001001936653002201946653001401968100002001982700002402002700002702026700003102053700002102084700002602105700002902131700001802160700002002178700002002198700002802218856012402246 2021 eng d a2045-232200aReal world evidence of calcifediol or vitamin D prescription and mortality rate of COVID-19 in a retrospective cohort of hospitalized Andalusian patients.0 aReal world evidence of calcifediol or vitamin D prescription and c2021 12 03 a233800 v113 aCOVID-19 is a major worldwide health problem because of acute respiratory distress syndrome, and mortality. Several lines of evidence have suggested a relationship between the vitamin D endocrine system and severity of COVID-19. We present a survival study on a retrospective cohort of 15,968 patients, comprising all COVID-19 patients hospitalized in Andalusia between January and November 2020. Based on a central registry of electronic health records (the Andalusian Population Health Database, BPS), prescription of vitamin D or its metabolites within 15-30 days before hospitalization were recorded. The effect of prescription of vitamin D (metabolites) for other indication previous to the hospitalization was studied with respect to patient survival. Kaplan-Meier survival curves and hazard ratios support an association between prescription of these metabolites and patient survival. Such association was stronger for calcifediol (Hazard Ratio, HR = 0.67, with 95% confidence interval, CI, of [0.50-0.91]) than for cholecalciferol (HR = 0.75, with 95% CI of [0.61-0.91]), when prescribed 15 days prior hospitalization. Although the relation is maintained, there is a general decrease of this effect when a longer period of 30 days prior hospitalization is considered (calcifediol HR = 0.73, with 95% CI [0.57-0.95] and cholecalciferol HR = 0.88, with 95% CI [0.75, 1.03]), suggesting that association was stronger when the prescription was closer to the hospitalization.
10aCalcifediol10aCOVID-1910aFemale10aHumans10aKaplan-Meier Estimate10aMale10aRetrospective Studies10aSpain10aSurvival Analysis10aVitamin D1 aLoucera, Carlos1 aPeña-Chilet, Maria1 aEsteban-Medina, Marina1 aMuñoyerro-Muñiz, Dolores1 aVillegas, Román1 aLópez-Miranda, José1 aRodríguez-Baño, Jesús1 aTúnez, Isaac1 aBouillon, Roger1 aDopazo, Joaquin1 aGomez, Jose, Manuel Que uhttp://clinbioinfosspa.es/content/real-world-evidence-calcifediol-or-vitamin-d-prescription-and-mortality-rate-covid-1901082nas a2200313 4500008004100000022001400041245014500055210006900200260001500269300000800284490000600292653002800298653001300326653002300339653001100362653002100373653003300394653003100427653001300458653001500471653002400486100002000510700002700530700001600557700002100573700002000594700002400614856013000638 2020 eng d a2059-363500aDrug repurposing for COVID-19 using machine learning and mechanistic models of signal transduction circuits related to SARS-CoV-2 infection.0 aDrug repurposing for COVID19 using machine learning and mechanis c2020 12 11 a2900 v510aComputational Chemistry10aCOVID-1910adrug repositioning10aHumans10aMachine Learning10aMolecular Docking Simulation10aMolecular Targeted Therapy10aProteins10aSARS-CoV-210aSignal Transduction1 aLoucera, Carlos1 aEsteban-Medina, Marina1 aRian, Kinza1 aFalco, Matias, M1 aDopazo, Joaquin1 aPeña-Chilet, Maria uhttp://clinbioinfosspa.es/content/drug-repurposing-covid-19-using-machine-learning-and-mechanistic-models-signal-transduction01957nas a2200277 4500008004100000022001400041245011500055210006900170260001600239300000800255490000700263520105900270653002301329653001901352653001301371653001101384653002101395653001401416653001301430653002401443100002701467700002401494700002001518700002001538856012101558 2019 eng d a1471-210500aExploring the druggable space around the Fanconi anemia pathway using machine learning and mechanistic models.0 aExploring the druggable space around the Fanconi anemia pathway c2019 Jul 02 a3700 v203 aBACKGROUND: In spite of the abundance of genomic data, predictive models that describe phenotypes as a function of gene expression or mutations are difficult to obtain because they are affected by the curse of dimensionality, given the disbalance between samples and candidate genes. And this is especially dramatic in scenarios in which the availability of samples is difficult, such as the case of rare diseases.
RESULTS: The application of multi-output regression machine learning methodologies to predict the potential effect of external proteins over the signaling circuits that trigger Fanconi anemia related cell functionalities, inferred with a mechanistic model, allowed us to detect over 20 potential therapeutic targets.
CONCLUSIONS: The use of artificial intelligence methods for the prediction of potentially causal relationships between proteins of interest and cell activities related with disease-related phenotypes opens promising avenues for the systematic search of new targets in rare diseases.
10aDatabases, Factual10aFanconi Anemia10aGenomics10aHumans10aMachine Learning10aPhenotype10aProteins10aSignal Transduction1 aEsteban-Medina, Marina1 aPeña-Chilet, Maria1 aLoucera, Carlos1 aDopazo, Joaquin uhttp://clinbioinfosspa.es/content/exploring-druggable-space-around-fanconi-anemia-pathway-using-machine-learning-and00765nas a2200181 4500008004100000245009000041210006900131260001600200490000600216100002400222700002700246700002200273700001600295700002300311700002000334700002000354856020900374 2019 eng d00aUsing mechanistic models for the clinical interpretation of complex genomic variation0 aUsing mechanistic models for the clinical interpretation of comp cJan-12-20190 v91 aPeña-Chilet, Maria1 aEsteban-Medina, Marina1 aFalco, Matias, M.1 aRian, Kinza1 aHidalgo, Marta, R.1 aLoucera, Carlos1 aDopazo, Joaquin uhttp://www.nature.com/articles/s41598-019-55454-7http://www.nature.com/articles/s41598-019-55454-7.pdfhttp://www.nature.com/articles/s41598-019-55454-7.pdfhttp://www.nature.com/articles/s41598-019-55454-7