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-002819nas a2200397 4500008004100000022001400041245011600055210006900171260000900240300000600249490000600255520157600261653002601837653002401863653001901887653002901906653001101935653001301946653003601959653002301995653001402018653001402032653001302046653001802059100001802077700002202095700001902117700001602136700002402152700002202176700002102198700002002219700003102239700002002270856013102290 2019 eng d a2056-718900aDifferential metabolic activity and discovery of therapeutic targets using summarized metabolic pathway models.0 aDifferential metabolic activity and discovery of therapeutic tar c2019 a70 v53 aIn spite of the increasing availability of genomic and transcriptomic data, there is still a gap between the detection of perturbations in gene expression and the understanding of their contribution to the molecular mechanisms that ultimately account for the phenotype studied. Alterations in the metabolism are behind the initiation and progression of many diseases, including cancer. The wealth of available knowledge on metabolic processes can therefore be used to derive mechanistic models that link gene expression perturbations to changes in metabolic activity that provide relevant clues on molecular mechanisms of disease and drug modes of action (MoA). In particular, pathway modules, which recapitulate the main aspects of metabolism, are especially suitable for this type of modeling. We present Metabolizer, a web-based application that offers an intuitive, easy-to-use interactive interface to analyze differences in pathway metabolic module activities that can also be used for class prediction and in silico prediction of knock-out (KO) effects. Moreover, Metabolizer can automatically predict the optimal KO intervention for restoring a diseased phenotype. We provide different types of validations of some of the predictions made by Metabolizer. Metabolizer is a web tool that allows understanding molecular mechanisms of disease or the MoA of drugs within the context of the metabolism by using gene expression measurements. In addition, this tool automatically suggests potential therapeutic targets for individualized therapeutic interventions.
10aComputational Biology10aComputer Simulation10aDrug discovery10aGene Regulatory Networks10aHumans10aInternet10aMetabolic Networks and Pathways10aModels, Biological10aNeoplasms10aPhenotype10aSoftware10aTranscriptome1 aCubuk, Cankut1 aHidalgo, Marta, R1 aAmadoz, Alicia1 aRian, Kinza1 aSalavert, Francisco1 aPujana, Miguel, A1 aMateo, Francesca1 aHerranz, Carmen1 aCarbonell-Caballero, José1 aDopazo, Joaquin uhttp://clinbioinfosspa.es/content/differential-metabolic-activity-and-discovery-therapeutic-targets-using-summarized-metabolic02756nas a2200397 4500008004100000022001400041245012200055210006900177260001600246300001000262490000600272520143100278653001201709653002401721653003101745653002801776653001701804653001701821653003201838653002601870653002001896653004201916653001101958653001301969653001401982653002401996100002302020700002802043700002502071700003302096700003602129700002002165700001902185700002502204856012902229 2016 eng d a2045-232200aImproving the management of Inherited Retinal Dystrophies by targeted sequencing of a population-specific gene panel.0 aImproving the management of Inherited Retinal Dystrophies by tar c2016 Apr 01 a239100 v63 aNext-generation sequencing (NGS) has overcome important limitations to the molecular diagnosis of Inherited Retinal Dystrophies (IRD) such as the high clinical and genetic heterogeneity and the overlapping phenotypes. The purpose of this study was the identification of the genetic defect in 32 Spanish families with different forms of IRD. With that aim, we implemented a custom NGS panel comprising 64 IRD-associated genes in our population, and three disease-associated intronic regions. A total of 37 pathogenic mutations (14 novels) were found in 73% of IRD patients ranging from 50% for autosomal dominant cases, 75% for syndromic cases, 83% for autosomal recessive cases, and 100% for X-linked cases. Additionally, unexpected phenotype-genotype correlations were found in 6 probands, which led to the refinement of their clinical diagnoses. Furthermore, intra- and interfamilial phenotypic variability was observed in two cases. Moreover, two cases unsuccessfully analysed by exome sequencing were resolved by applying this panel. Our results demonstrate that this hypothesis-free approach based on frequently mutated, population-specific loci is highly cost-efficient for the routine diagnosis of this heterogeneous condition and allows the unbiased analysis of a miscellaneous cohort. The molecular information found here has aid clinical diagnosis and has improved genetic counselling and patient management.
10aAlleles10aComputer Simulation10aDNA Copy Number Variations10aDNA Mutational Analysis10aEye Proteins10aGene Library10aGenetic Association Studies10aGenetic Heterogeneity10aGenetic Therapy10aHigh-Throughput Nucleotide Sequencing10aHumans10amutation10aPhenotype10aRetinal Dystrophies1 aBravo-Gil, Nereida1 aMéndez-Vidal, Cristina1 aRomero-Pérez, Laura1 adel Pozo, María, González-1 ade la Rúa, Enrique, Rodríguez1 aDopazo, Joaquin1 aBorrego, Salud1 aAntiňolo, Guillermo uhttp://clinbioinfosspa.es/content/improving-management-inherited-retinal-dystrophies-targeted-sequencing-population-specific02385nas a2200397 4500008004100000022001400041245007600055210006900131260001300200300001300213490000700226520116000233653001201393653001801405653002201423653002201445653002301467653002401490653002801514653003801542653001101580653002001591653002801611653001301639653002201652653001401674653003601688653003201724653002401756100002401780700002401804700001801828700002001846700001701866856010401883 2015 eng d a1553-735800aA Pan-Cancer Catalogue of Cancer Driver Protein Interaction Interfaces.0 aPanCancer Catalogue of Cancer Driver Protein Interaction Interfa c2015 Oct ae10045180 v113 aDespite their importance in maintaining the integrity of all cellular pathways, the role of mutations on protein-protein interaction (PPI) interfaces as cancer drivers has not been systematically studied. Here we analyzed the mutation patterns of the PPI interfaces from 10,028 proteins in a pan-cancer cohort of 5,989 tumors from 23 projects of The Cancer Genome Atlas (TCGA) to find interfaces enriched in somatic missense mutations. To that end we use e-Driver, an algorithm to analyze the mutation distribution of specific protein functional regions. We identified 103 PPI interfaces enriched in somatic cancer mutations. 32 of these interfaces are found in proteins coded by known cancer driver genes. The remaining 71 interfaces are found in proteins that have not been previously identified as cancer drivers even that, in most cases, there is an extensive literature suggesting they play an important role in cancer. Finally, we integrate these findings with clinical information to show how tumors apparently driven by the same gene have different behaviors, including patient outcomes, depending on which specific interfaces are mutated.
10aAnimals10aBase Sequence10aBiomarkers, Tumor10aCatalogs as Topic10aChromosome Mapping10aComputer Simulation10aDNA Mutational Analysis10aGenetic Predisposition to Disease10aHumans10aModels, Genetic10aMolecular Sequence Data10amutation10aNeoplasm Proteins10aNeoplasms10aPolymorphism, Single Nucleotide10aProtein Interaction Mapping10aSignal Transduction1 aPorta-Pardo, Eduard1 aGarcía-Alonso, Luz1 aHrabe, Thomas1 aDopazo, Joaquin1 aGodzik, Adam uhttp://clinbioinfosspa.es/content/pan-cancer-catalogue-cancer-driver-protein-interaction-interfaces02713nas a2200265 4500008004100000022001400041245005800055210005700113260001600170300000700186490001500193520188200208653002402090653003002114653004402144653001702188100002402205700002502229700002002254700002902274700002002303700002002323700001602343856008802359 2009 eng d a1471-210500aFunctional assessment of time course microarray data.0 aFunctional assessment of time course microarray data c2009 Jun 16 aS90 v10 Suppl 63 aMOTIVATION: Time-course microarray experiments study the progress of gene expression along time across one or several experimental conditions. Most developed analysis methods focus on the clustering or the differential expression analysis of genes and do not integrate functional information. The assessment of the functional aspects of time-course transcriptomics data requires the use of approaches that exploit the activation dynamics of the functional categories to where genes are annotated.
METHODS: We present three novel methodologies for the functional assessment of time-course microarray data. i) maSigFun derives from the maSigPro method, a regression-based strategy to model time-dependent expression patterns and identify genes with differences across series. maSigFun fits a regression model for groups of genes labeled by a functional class and selects those categories which have a significant model. ii) PCA-maSigFun fits a PCA model of each functional class-defined expression matrix to extract orthogonal patterns of expression change, which are then assessed for their fit to a time-dependent regression model. iii) ASCA-functional uses the ASCA model to rank genes according to their correlation to principal time expression patterns and assess functional enrichment on a GSA fashion. We used simulated and experimental datasets to study these novel approaches. Results were compared to alternative methodologies.
RESULTS: Synthetic and experimental data showed that the different methods are able to capture different aspects of the relationship between genes, functions and co-expression that are biologically meaningful. The methods should not be considered as competitive but they provide different insights into the molecular and functional dynamic events taking place within the biological system under study.
10aComputer Simulation10aGene Expression Profiling10aOligonucleotide Array Sequence Analysis10aTime Factors1 aNueda, Maria, José1 aSebastián, Patricia1 aTarazona, Sonia1 aGarcia-Garcia, Francisco1 aDopazo, Joaquin1 aFerrer, Alberto1 aConesa, Ana uhttp://clinbioinfosspa.es/content/functional-assessment-time-course-microarray-data02514nas a2200337 4500008004100000022001400041245007300055210006900128260001600197300000800213490000600221520148900227653001501716653002301731653002401754653003001778653002301808653002101831653002401852653001301876653002001889653002801909100002501937700002101962700002001983700002402003700001902027700002002046700002002066856009002086 2007 eng d a1471-210500aFrom genes to functional classes in the study of biological systems.0 aFrom genes to functional classes in the study of biological syst c2007 Apr 03 a1140 v83 aBACKGROUND: With the popularization of high-throughput techniques, the need for procedures that help in the biological interpretation of results has increased enormously. Recently, new procedures inspired in systems biology criteria have started to be developed.
RESULTS: Here we present FatiScan, a web-based program which implements a threshold-independent test for the functional interpretation of large-scale experiments that does not depend on the pre-selection of genes based on the multiple application of independent tests to each gene. The test implemented aims to directly test the behaviour of blocks of functionally related genes, instead of focusing on single genes. In addition, the test does not depend on the type of the data used for obtaining significance values, and consequently different types of biologically informative terms (gene ontology, pathways, functional motifs, transcription factor binding sites or regulatory sites from CisRed) can be applied to different classes of genome-scale studies. We exemplify its application in microarray gene expression, evolution and interactomics.
CONCLUSION: Methods for gene set enrichment which, in addition, are independent from the original data and experimental design constitute a promising alternative for the functional profiling of genome-scale experiments. A web server that performs the test described and other similar ones can be found at: http://www.babelomics.org.
10aAlgorithms10aChromosome Mapping10aComputer Simulation10aGene Expression Profiling10aModels, Biological10aMultigene Family10aSignal Transduction10aSoftware10aSystems biology10aUser-Computer Interface1 aAl-Shahrour, Fátima1 aArbiza, Leonardo1 aDopazo, Hernán1 aHuerta-Cepas, Jaime1 aMinguez, Pablo1 aMontaner, David1 aDopazo, Joaquin uhttp://clinbioinfosspa.es/content/genes-functional-classes-study-biological-systems-0