@article {805, title = {Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches.}, journal = {Front Immunol}, volume = {14}, year = {2024}, month = {2023}, pages = {1282859}, abstract = {

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.

}, keywords = {Computer Simulation, COVID-19, drug repositioning, Humans, SARS-CoV-2, Systems biology}, issn = {1664-3224}, doi = {10.3389/fimmu.2023.1282859}, author = {Niarakis, Anna and Ostaszewski, Marek and Mazein, Alexander and Kuperstein, Inna and Kutmon, Martina and Gillespie, Marc E and Funahashi, Akira and Acencio, Marcio Luis and Hemedan, Ahmed and Aichem, Michael and Klein, Karsten and Czauderna, Tobias and Burtscher, Felicia and Yamada, Takahiro G and Hiki, Yusuke and Hiroi, Noriko F and Hu, Finterly and Pham, Nhung and Ehrhart, Friederike and Willighagen, Egon L and Valdeolivas, Alberto and Dugourd, Aur{\'e}lien and Messina, Francesco and Esteban-Medina, Marina and Pe{\~n}a-Chilet, Maria and Rian, Kinza and Soliman, Sylvain and Aghamiri, Sara Sadat and Puniya, Bhanwar Lal and Naldi, Aur{\'e}lien and Helikar, Tom{\'a}{\v s} and Singh, Vidisha and Fern{\'a}ndez, Marco Fari{\~n}as and Bermudez, Viviam and Tsirvouli, Eirini and Montagud, Arnau and No{\"e}l, Vincent and Ponce-de-Leon, Miguel and Maier, Dieter and Bauch, Angela and Gyori, Benjamin M and Bachman, John A and Luna, Augustin and Pi{\~n}ero, Janet and Furlong, Laura I and Balaur, Irina and Rougny, Adrien and Jarosz, Yohan and Overall, Rupert W and Phair, Robert and Perfetto, Livia and Matthews, Lisa and Rex, Devasahayam Arokia Balaya and Orlic-Milacic, Marija and Gomez, Luis Cristobal Monraz and De Meulder, Bertrand and Ravel, Jean Marie and Jassal, Bijay and Satagopam, Venkata and Wu, Guanming and Golebiewski, Martin and Gawron, Piotr and Calzone, Laurence and Beckmann, Jacques S and Evelo, Chris T and D{\textquoteright}Eustachio, Peter and Schreiber, Falk and Saez-Rodriguez, Julio and Dopazo, Joaquin and Kuiper, Martin and Valencia, Alfonso and Wolkenhauer, Olaf and Kitano, Hiroaki and Barillot, Emmanuel and Auffray, Charles and Balling, Rudi and Schneider, Reinhard} } @article {422, title = {Differential metabolic activity and discovery of therapeutic targets using summarized metabolic pathway models.}, journal = {NPJ Syst Biol Appl}, volume = {5}, year = {2019}, month = {2019}, pages = {7}, abstract = {

In 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.

}, keywords = {Computational Biology, Computer Simulation, Drug discovery, Gene Regulatory Networks, Humans, Internet, Metabolic Networks and Pathways, Models, Biological, Neoplasms, Phenotype, Software, Transcriptome}, issn = {2056-7189}, doi = {10.1038/s41540-019-0087-2}, author = {Cubuk, Cankut and Hidalgo, Marta R and Amadoz, Alicia and Rian, Kinza and Salavert, Francisco and Pujana, Miguel A and Mateo, Francesca and Herranz, Carmen and Carbonell-Caballero, Jos{\'e} and Dopazo, Joaquin} } @article {452, title = {Improving the management of Inherited Retinal Dystrophies by targeted sequencing of a population-specific gene panel.}, journal = {Sci Rep}, volume = {6}, year = {2016}, month = {2016 Apr 01}, pages = {23910}, abstract = {

Next-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.

}, keywords = {Alleles, Computer Simulation, DNA Copy Number Variations, DNA Mutational Analysis, Eye Proteins, Gene Library, Genetic Association Studies, Genetic Heterogeneity, Genetic Therapy, High-Throughput Nucleotide Sequencing, Humans, mutation, Phenotype, Retinal Dystrophies}, issn = {2045-2322}, doi = {10.1038/srep23910}, author = {Bravo-Gil, Nereida and M{\'e}ndez-Vidal, Cristina and Romero-P{\'e}rez, Laura and Gonz{\'a}lez-del Pozo, Mar{\'\i}a and Rodr{\'\i}guez-de la R{\'u}a, Enrique and Dopazo, Joaquin and Borrego, Salud and Anti{\v n}olo, Guillermo} } @article {460, title = {A Pan-Cancer Catalogue of Cancer Driver Protein Interaction Interfaces.}, journal = {PLoS Comput Biol}, volume = {11}, year = {2015}, month = {2015 Oct}, pages = {e1004518}, abstract = {

Despite 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.

}, keywords = {Animals, Base Sequence, Biomarkers, Tumor, Catalogs as Topic, Chromosome Mapping, Computer Simulation, DNA Mutational Analysis, Genetic Predisposition to Disease, Humans, Models, Genetic, Molecular Sequence Data, mutation, Neoplasm Proteins, Neoplasms, Polymorphism, Single Nucleotide, Protein Interaction Mapping, Signal Transduction}, issn = {1553-7358}, doi = {10.1371/journal.pcbi.1004518}, author = {Porta-Pardo, Eduard and Garc{\'\i}a-Alonso, Luz and Hrabe, Thomas and Dopazo, Joaquin and Godzik, Adam} } @article {580, title = {Functional assessment of time course microarray data.}, journal = {BMC Bioinformatics}, volume = {10 Suppl 6}, year = {2009}, month = {2009 Jun 16}, pages = {S9}, abstract = {

MOTIVATION: 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.

}, keywords = {Computer Simulation, Gene Expression Profiling, Oligonucleotide Array Sequence Analysis, Time Factors}, issn = {1471-2105}, doi = {10.1186/1471-2105-10-S6-S9}, author = {Nueda, Maria Jos{\'e} and Sebasti{\'a}n, Patricia and Tarazona, Sonia and Garcia-Garcia, Francisco and Dopazo, Joaquin and Ferrer, Alberto and Conesa, Ana} } @article {606, title = {From genes to functional classes in the study of biological systems.}, journal = {BMC Bioinformatics}, volume = {8}, year = {2007}, month = {2007 Apr 03}, pages = {114}, abstract = {

BACKGROUND: 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.

}, keywords = {Algorithms, Chromosome Mapping, Computer Simulation, Gene Expression Profiling, Models, Biological, Multigene Family, Signal Transduction, Software, Systems biology, User-Computer Interface}, issn = {1471-2105}, doi = {10.1186/1471-2105-8-114}, author = {Al-Shahrour, F{\'a}tima and Arbiza, Leonardo and Dopazo, Hern{\'a}n and Huerta-Cepas, Jaime and Minguez, Pablo and Montaner, David and Dopazo, Joaquin} }