@article {807, title = {HMGA1 regulates trabectedin sensitivity in advanced soft-tissue sarcoma (STS): A Spanish Group for Research on Sarcomas (GEIS) study.}, journal = {Cell Mol Life Sci}, volume = {81}, year = {2024}, month = {2024 May 17}, pages = {219}, abstract = {

HMGA1 is a structural epigenetic chromatin factor that has been associated with tumor progression and drug resistance. Here, we reported the prognostic/predictive value of HMGA1 for trabectedin in advanced soft-tissue sarcoma (STS) and the effect of inhibiting HMGA1 or the mTOR downstream pathway in trabectedin activity. The prognostic/predictive value of HMGA1 expression was assessed in a cohort of 301 STS patients at mRNA (n = 133) and protein level (n = 272), by HTG EdgeSeq transcriptomics and immunohistochemistry, respectively. The effect of HMGA1 silencing on trabectedin activity and gene expression profiling was measured in leiomyosarcoma cells. The effect of combining mTOR inhibitors with trabectedin was assessed on cell viability in vitro studies, whereas in vivo studies tested the activity of this combination. HMGA1 mRNA and protein expression were significantly associated with worse progression-free survival of trabectedin and worse overall survival in STS. HMGA1 silencing sensitized leiomyosarcoma cells for trabectedin treatment, reducing the spheroid area and increasing cell death. The downregulation of HGMA1 significantly decreased the enrichment of some specific gene sets, including the PI3K/AKT/mTOR pathway. The inhibition of mTOR, sensitized leiomyosarcoma cultures for trabectedin treatment, increasing cell death. In in vivo studies, the combination of rapamycin with trabectedin downregulated HMGA1 expression and stabilized tumor growth of 3-methylcholantrene-induced sarcoma-like models. HMGA1 is an adverse prognostic factor for trabectedin treatment in advanced STS. HMGA1 silencing increases trabectedin efficacy, in part by modulating the mTOR signaling pathway. Trabectedin plus mTOR inhibitors are active in preclinical models of sarcoma, downregulating HMGA1 expression levels and stabilizing tumor growth.

}, keywords = {Animals, Antineoplastic Agents, Alkylating, Cell Line, Tumor, Drug Resistance, Neoplasm, Female, Gene Expression Regulation, Neoplastic, HMGA1a Protein, Humans, Leiomyosarcoma, Mice, Prognosis, Sarcoma, Signal Transduction, TOR Serine-Threonine Kinases, Trabectedin, Xenograft Model Antitumor Assays}, issn = {1420-9071}, doi = {10.1007/s00018-024-05250-y}, author = {Moura, David S and Mondaza-Hernandez, Jose L and Sanchez-Bustos, Paloma and Pe{\~n}a-Chilet, Maria and Cordero-Varela, Juan A and Lopez-Alvarez, Maria and Carrillo-Garcia, Jaime and Martin-Ruiz, Marta and Romero-Gonzalez, Pablo and Renshaw-Calderon, Marta and Ramos, Rafael and Marcilla, David and Alvarez-Alegret, Ramiro and Agra-Pujol, Carolina and Izquierdo, Francisco and Ortega-Medina, Luis and Martin-Davila, Francisco and Hernandez-Leon, Carmen Nieves and Romagosa, Cleofe and Salgado, Maria Angeles Vaz and Lavernia, Javier and Bagu{\'e}, Silvia and Mayodormo-Aranda, Empar and Alvarez, Rosa and Valverde, Claudia and Martinez-Trufero, Javier and Castilla-Ramirez, Carolina and Gutierrez, Antonio and Dopazo, Joaquin and Hindi, Nadia and Garcia-Foncillas, Jesus and Martin-Broto, Javier} } @article {804, title = {The mechanistic functional landscape of retinitis pigmentosa: a machine learning-driven approach to therapeutic target discovery.}, journal = {J Transl Med}, volume = {22}, year = {2024}, month = {2024 Feb 06}, pages = {139}, abstract = {

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.

}, keywords = {Animals, Mice, Retinitis pigmentosa, Signal Transduction}, issn = {1479-5876}, doi = {10.1186/s12967-024-04911-7}, author = {Esteban-Medina, Marina and Loucera, Carlos and Rian, Kinza and Velasco, Sheyla and Olivares-Gonz{\'a}lez, Lorena and Rodrigo, Regina and Dopazo, Joaquin and Pe{\~n}a-Chilet, Maria} } @article {763, title = {Endoglin and MMP14 Contribute to Ewing Sarcoma Spreading by Modulation of Cell-Matrix Interactions.}, journal = {Int J Mol Sci}, volume = {23}, year = {2022}, month = {2022 Aug 04}, abstract = {

Endoglin (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.

}, keywords = {Bone Neoplasms, Endoglin, Humans, Matrix Metalloproteinase 14, Proteomics, Receptors, Growth Factor, Sarcoma, Ewing, Signal Transduction}, issn = {1422-0067}, doi = {10.3390/ijms23158657}, author = {Puerto-Camacho, Pilar and Diaz-Martin, Juan and Olmedo-Pelayo, Joaqu{\'\i}n and Bolado-Carrancio, Alfonso and Salguero-Aranda, Carmen and Jord{\'a}n-P{\'e}rez, Carmen and Esteban-Medina, Marina and Alamo-Alvarez, Inmaculada and Delgado-Bellido, Daniel and Lobo-Selma, Laura and Dopazo, Joaquin and Sastre, Ana and Alonso, Javier and Gr{\"u}newald, Thomas G P and Bernabeu, Carmelo and Byron, Adam and Brunton, Valerie G and Amaral, Ana Teresa and de Alava, Enrique} } @article {736, title = {COVID19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms.}, journal = {Mol Syst Biol}, volume = {17}, year = {2021}, month = {2021 10}, pages = {e10387}, abstract = {

We 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.

}, keywords = {Antiviral Agents, Computational Biology, Computer Graphics, COVID-19, Cytokines, Data Mining, Databases, Factual, Gene Expression Regulation, Host Microbial Interactions, Humans, Immunity, Cellular, Immunity, Humoral, Immunity, Innate, Lymphocytes, Metabolic Networks and Pathways, Myeloid Cells, Protein Interaction Mapping, SARS-CoV-2, Signal Transduction, Software, Transcription Factors, Viral Proteins}, issn = {1744-4292}, doi = {10.15252/msb.202110387}, author = {Ostaszewski, Marek and Niarakis, Anna and Mazein, Alexander and Kuperstein, Inna and Phair, Robert and Orta-Resendiz, Aurelio and Singh, Vidisha and Aghamiri, Sara Sadat and Acencio, Marcio Luis and Glaab, Enrico and Ruepp, Andreas and Fobo, Gisela and Montrone, Corinna and Brauner, Barbara and Frishman, Goar and Monraz G{\'o}mez, Luis Crist{\'o}bal and Somers, Julia and Hoch, Matti and Kumar Gupta, Shailendra and Scheel, Julia and Borlinghaus, Hanna and Czauderna, Tobias and Schreiber, Falk and Montagud, Arnau and Ponce de Leon, Miguel and Funahashi, Akira and Hiki, Yusuke and Hiroi, Noriko and Yamada, Takahiro G and Dr{\"a}ger, Andreas and Renz, Alina and Naveez, Muhammad and Bocskei, Zsolt and Messina, Francesco and B{\"o}rnigen, Daniela and Fergusson, Liam and Conti, Marta and Rameil, Marius and Nakonecnij, Vanessa and Vanhoefer, Jakob and Schmiester, Leonard and Wang, Muying and Ackerman, Emily E and Shoemaker, Jason E and Zucker, Jeremy and Oxford, Kristie and Teuton, Jeremy and Kocakaya, Ebru and Summak, G{\"o}k{\c c}e Ya{\u g}mur and Hanspers, Kristina and Kutmon, Martina and Coort, Susan and Eijssen, Lars and Ehrhart, Friederike and Rex, Devasahayam Arokia Balaya and Slenter, Denise and Martens, Marvin and Pham, Nhung and Haw, Robin and Jassal, Bijay and Matthews, Lisa and Orlic-Milacic, Marija and Senff Ribeiro, Andrea and Rothfels, Karen and Shamovsky, Veronica and Stephan, Ralf and Sevilla, Cristoffer and Varusai, Thawfeek and Ravel, Jean-Marie and Fraser, Rupsha and Ortseifen, Vera and Marchesi, Silvia and Gawron, Piotr and Smula, Ewa and Heirendt, Laurent and Satagopam, Venkata and Wu, Guanming and Riutta, Anders and Golebiewski, Martin and Owen, Stuart and Goble, Carole and Hu, Xiaoming and Overall, Rupert W and Maier, Dieter and Bauch, Angela and Gyori, Benjamin M and Bachman, John A and Vega, Carlos and Grou{\`e}s, Valentin and Vazquez, Miguel and Porras, Pablo and Licata, Luana and Iannuccelli, Marta and Sacco, Francesca and Nesterova, Anastasia and Yuryev, Anton and de Waard, Anita and Turei, Denes and Luna, Augustin and Babur, Ozgun and Soliman, Sylvain and Valdeolivas, Alberto and Esteban-Medina, Marina and Pe{\~n}a-Chilet, Maria and Rian, Kinza and Helikar, Tom{\'a}{\v s} and Puniya, Bhanwar Lal and Modos, Dezso and Treveil, Agatha and Olbei, Marton and De Meulder, Bertrand and Ballereau, Stephane and Dugourd, Aur{\'e}lien and Naldi, Aur{\'e}lien and No{\"e}l, Vincent and Calzone, Laurence and Sander, Chris and Demir, Emek and Korcsmaros, Tamas and Freeman, Tom C and Aug{\'e}, Franck and Beckmann, Jacques S and Hasenauer, Jan and Wolkenhauer, Olaf and Wilighagen, Egon L and Pico, Alexander R and Evelo, Chris T and Gillespie, Marc E and Stein, Lincoln D and Hermjakob, Henning and D{\textquoteright}Eustachio, Peter and Saez-Rodriguez, Julio and Dopazo, Joaquin and Valencia, Alfonso and Kitano, Hiroaki and Barillot, Emmanuel and Auffray, Charles and Balling, Rudi and Schneider, Reinhard} } @article {712, title = {A versatile workflow to integrate RNA-seq genomic and transcriptomic data into mechanistic models of signaling pathways.}, journal = {PLoS Comput Biol}, volume = {17}, year = {2021}, month = {2021 02}, pages = {e1008748}, abstract = {

MIGNON is a workflow for the analysis of RNA-Seq experiments, which not only efficiently manages the estimation of gene expression levels from raw sequencing reads, but also calls genomic variants present in the transcripts analyzed. Moreover, this is the first workflow that provides a framework for the integration of transcriptomic and genomic data based on a mechanistic model of signaling pathway activities that allows a detailed biological interpretation of the results, including a comprehensive functional profiling of cell activity. MIGNON covers the whole process, from reads to signaling circuit activity estimations, using state-of-the-art tools, it is easy to use and it is deployable in different computational environments, allowing an optimized use of the resources available.

}, keywords = {Algorithms, Cell Line, Tumor, Computational Biology, Databases, Factual, Gene Expression Profiling, Genomics, High-Throughput Nucleotide Sequencing, Humans, Models, Theoretical, mutation, RNA-seq, Signal Transduction, Software, Transcriptome, whole exome sequencing, Workflow}, issn = {1553-7358}, doi = {10.1371/journal.pcbi.1008748}, author = {Garrido-Rodriguez, Mart{\'\i}n and L{\'o}pez-L{\'o}pez, Daniel and Ortuno, Francisco M and Pe{\~n}a-Chilet, Maria and Mu{\~n}oz, Eduardo and Calzado, Marco A and Dopazo, Joaquin} } @article {713, title = {Drug repurposing for COVID-19 using machine learning and mechanistic models of signal transduction circuits related to SARS-CoV-2 infection.}, journal = {Signal Transduct Target Ther}, volume = {5}, year = {2020}, month = {2020 12 11}, pages = {290}, keywords = {Computational Chemistry, COVID-19, drug repositioning, Humans, Machine Learning, Molecular Docking Simulation, Molecular Targeted Therapy, Proteins, SARS-CoV-2, Signal Transduction}, issn = {2059-3635}, doi = {10.1038/s41392-020-00417-y}, author = {Loucera, Carlos and Esteban-Medina, Marina and Rian, Kinza and Falco, Matias M and Dopazo, Joaquin and Pe{\~n}a-Chilet, Maria} } @article {695, title = {Mechanistic Models of Signaling Pathways Reveal the Drug Action Mechanisms behind Gender-Specific Gene Expression for Cancer Treatments.}, journal = {Cells}, volume = {9}, year = {2020}, month = {2020 06 29}, abstract = {

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.

}, keywords = {Female, Gene Expression Regulation, Neoplastic, Humans, Male, Neoplasms, Signal Transduction}, issn = {2073-4409}, doi = {10.3390/cells9071579}, author = {Cubuk, Cankut and Can, Fatma E and Pe{\~n}a-Chilet, Maria and Dopazo, Joaquin} } @article {710, title = {Transcriptomic Analysis of a Diabetic Skin-Humanized Mouse Model Dissects Molecular Pathways Underlying the Delayed Wound Healing Response.}, journal = {Genes (Basel)}, volume = {12}, year = {2020}, month = {2020 12 31}, abstract = {

Defective healing leading to cutaneous ulcer formation is one of the most feared complications of diabetes due to its consequences on patients{\textquoteright} quality of life and on the healthcare system. A more in-depth analysis of the underlying molecular pathophysiology is required to develop effective healing-promoting therapies for those patients. Major architectural and functional differences with human epidermis limit extrapolation of results coming from rodents and other small mammal-healing models. Therefore, the search for reliable humanized models has become mandatory. Previously, we developed a diabetes-induced delayed humanized wound healing model that faithfully recapitulated the major histological features of such skin repair-deficient condition. Herein, we present the results of a transcriptomic and functional enrichment analysis followed by a mechanistic analysis performed in such humanized wound healing model. The deregulation of genes implicated in functions such as angiogenesis, apoptosis, and inflammatory signaling processes were evidenced, confirming published data in diabetic patients that in fact might also underlie some of the histological features previously reported in the delayed skin-humanized healing model. Altogether, these molecular findings support the utility of such preclinical model as a valuable tool to gain insight into the molecular basis of the delayed diabetic healing with potential impact in the translational medicine field.

}, keywords = {Animals, Diabetes Mellitus, Experimental, Gene Expression Profiling, Gene Expression Regulation, Gene ontology, Humans, Metabolic Networks and Pathways, Mice, Mice, Nude, Microarray Analysis, Molecular Sequence Annotation, Principal Component Analysis, Signal Transduction, Skin, Skin Transplantation, Skin Ulcer, Streptozocin, Tissue Engineering, Transcriptome, Transplantation, Heterologous, Wound Healing}, issn = {2073-4425}, doi = {10.3390/genes12010047}, author = {Le{\'o}n, Carlos and Garcia-Garcia, Francisco and Llames, Sara and Garc{\'\i}a-P{\'e}rez, Eva and Carretero, Marta and Arriba, Mar{\'\i}a Del Carmen and Dopazo, Joaquin and Del Rio, Marcela and Escamez, Maria Jos{\'e} and Mart{\'\i}nez-Santamar{\'\i}a, Luc{\'\i}a} } @article {403, title = {A comparison of mechanistic signaling pathway activity analysis methods.}, journal = {Brief Bioinform}, volume = {20}, year = {2019}, month = {2019 Sep 27}, pages = {1655-1668}, abstract = {

Understanding the aspects of cell functionality that account for disease mechanisms or drug modes of action is a main challenge for precision medicine. Classical gene-based approaches ignore the modular nature of most human traits, whereas conventional pathway enrichment approaches produce only illustrative results of limited practical utility. Recently, a family of new methods has emerged that change the focus from the whole pathways to the definition of elementary subpathways within them that have any mechanistic significance and to the study of their activities. Thus, mechanistic pathway activity (MPA) methods constitute a new paradigm that allows recoding poorly informative genomic measurements into cell activity quantitative values and relate them to phenotypes. Here we provide a review on the MPA methods available and explain their contribution to systems medicine approaches for addressing challenges in the diagnostic and treatment of complex diseases.

}, keywords = {Algorithms, Humans, Postmortem Changes, Signal Transduction, Systems biology, Transcriptome}, issn = {1477-4054}, doi = {10.1093/bib/bby040}, author = {Amadoz, Alicia and Hidalgo, Marta R and Cubuk, Cankut and Carbonell-Caballero, Jos{\'e} and Dopazo, Joaquin} } @article {610, title = {Exploring the druggable space around the Fanconi anemia pathway using machine learning and mechanistic models.}, journal = {BMC Bioinformatics}, volume = {20}, year = {2019}, month = {2019 Jul 02}, pages = {370}, abstract = {

BACKGROUND: 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.

}, keywords = {Databases, Factual, Fanconi Anemia, Genomics, Humans, Machine Learning, Phenotype, Proteins, Signal Transduction}, issn = {1471-2105}, doi = {10.1186/s12859-019-2969-0}, author = {Esteban-Medina, Marina and Pe{\~n}a-Chilet, Maria and Loucera, Carlos and Dopazo, Joaquin} } @article {404, title = {Models of cell signaling uncover molecular mechanisms of high-risk neuroblastoma and predict disease outcome.}, journal = {Biol Direct}, volume = {13}, year = {2018}, month = {2018 Aug 22}, pages = {16}, abstract = {

BACKGROUND: Despite the progress in neuroblastoma therapies the mortality of high-risk patients is still high (40-50\%) and the molecular basis of the disease remains poorly known. Recently, a mathematical model was used to demonstrate that the network regulating stress signaling by the c-Jun N-terminal kinase pathway played a crucial role in survival of patients with neuroblastoma irrespective of their MYCN amplification status. This demonstrates the enormous potential of computational models of biological modules for the discovery of underlying molecular mechanisms of diseases.

RESULTS: Since signaling is known to be highly relevant in cancer, we have used a computational model of the whole cell signaling network to understand the molecular determinants of bad prognostic in neuroblastoma. Our model produced a comprehensive view of the molecular mechanisms of neuroblastoma tumorigenesis and progression.

CONCLUSION: We have also shown how the activity of signaling circuits can be considered a reliable model-based prognostic biomarker.

REVIEWERS: This article was reviewed by Tim Beissbarth, Wenzhong Xiao and Joanna Polanska. For the full reviews, please go to the Reviewers{\textquoteright} comments section.

}, keywords = {Computational Biology, Gene Expression Regulation, Neoplastic, Humans, JNK Mitogen-Activated Protein Kinases, Models, Theoretical, Neuroblastoma, Signal Transduction}, issn = {1745-6150}, doi = {10.1186/s13062-018-0219-4}, author = {Hidalgo, Marta R and Amadoz, Alicia and Cubuk, Cankut and Carbonell-Caballero, Jos{\'e} and Dopazo, Joaquin} } @article {434, title = {High throughput estimation of functional cell activities reveals disease mechanisms and predicts relevant clinical outcomes.}, journal = {Oncotarget}, volume = {8}, year = {2017}, month = {2017 Jan 17}, pages = {5160-5178}, abstract = {

Understanding the aspects of the cell functionality that account for disease or drug action mechanisms is a main challenge for precision medicine. Here we propose a new method that models cell signaling using biological knowledge on signal transduction. The method recodes individual gene expression values (and/or gene mutations) into accurate measurements of changes in the activity of signaling circuits, which ultimately constitute high-throughput estimations of cell functionalities caused by gene activity within the pathway. Moreover, such estimations can be obtained either at cohort-level, in case/control comparisons, or personalized for individual patients. The accuracy of the method is demonstrated in an extensive analysis involving 5640 patients from 12 different cancer types. Circuit activity measurements not only have a high diagnostic value but also can be related to relevant disease outcomes such as survival, and can be used to assess therapeutic interventions.

}, keywords = {Computational Biology, gene expression, Gene Regulatory Networks, Humans, mutation, Neoplasms, Precision Medicine, Sequence Analysis, RNA, Signal Transduction}, issn = {1949-2553}, doi = {10.18632/oncotarget.14107}, author = {Hidalgo, Marta R and Cubuk, Cankut and Amadoz, Alicia and Salavert, Francisco and Carbonell-Caballero, Jos{\'e} and Dopazo, Joaquin} } @article {458, title = {Deregulation of key signaling pathways involved in oocyte maturation in FMR1 premutation carriers with Fragile X-associated primary ovarian insufficiency.}, journal = {Gene}, volume = {571}, year = {2015}, month = {2015 Oct 15}, pages = {52-7}, abstract = {

FMR1 premutation female carriers are at risk for Fragile X-associated primary ovarian insufficiency (FXPOI). Insights from knock-in mouse model have recently demonstrated that FXPOI is due to an increased rate of follicle depletion or an impaired development of the growing follicles. Molecular mechanisms responsible for this reduced viability are still unknown. In an attempt to provide new data on the mechanisms that lead to FXPOI, we report the first investigation involving transcription profiling of total blood from FMR1 premutation female carriers with and without FXPOI. A total of 16 unrelated female individuals (6 FMR1 premutated females with FXPOI; 6 FMR1 premutated females without FXPOI; and 4 no-FXPOI females) were studied by whole human genome oligonucleotide microarray (Agilent Technologies). Fold change analysis did not show any genes with significant differential gene expression. However, functional profiling by gene set analysis showed large number of statistically significant deregulated GO annotations as well as numerous KEGG pathways in FXPOI females. These results suggest that the impairment of fertility in these females might be due to a generalized deregulation of key signaling pathways involved in oocyte maturation. In particular, the vasoendotelial growth factor signaling, the inositol phosphate metabolism, the cell cycle, and the MAPK signaling pathways were found to be down-regulated in FXPOI females. Furthermore, a high statistical enrichment of biological processes involved in cell death and survival were found deregulated among FXPOI females. Our results provide new strategic approaches to further investigate the molecular mechanisms and potential therapeutic targets for FXPOI not focused in a single gene but rather in the set of genes involved in these pathways.

}, keywords = {Adult, Aged, Female, Fragile X Mental Retardation Protein, Fragile X Syndrome, Gene Expression Profiling, Gene Expression Regulation, Developmental, Gene ontology, Genome-Wide Association Study, Heterozygote, Humans, Middle Aged, Models, Genetic, mutation, Oligonucleotide Array Sequence Analysis, Oocytes, Primary Ovarian Insufficiency, Signal Transduction}, issn = {1879-0038}, doi = {10.1016/j.gene.2015.06.039}, author = {Alvarez-Mora, M I and Rodriguez-Revenga, L and Madrigal, I and Garc{\'\i}a-Garc{\'\i}a, F and Duran, M and Dopazo, J and Estivill, X and Mil{\`a}, M} } @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 {474, title = {Using activation status of signaling pathways as mechanism-based biomarkers to predict drug sensitivity.}, journal = {Sci Rep}, volume = {5}, year = {2015}, month = {2015 Dec 18}, pages = {18494}, abstract = {

Many complex traits, as drug response, are associated with changes in biological pathways rather than being caused by single gene alterations. Here, a predictive framework is presented in which gene expression data are recoded into activity statuses of signal transduction circuits (sub-pathways within signaling pathways that connect receptor proteins to final effector proteins that trigger cell actions). Such activity values are used as features by a prediction algorithm which can efficiently predict a continuous variable such as the IC50 value. The main advantage of this prediction method is that the features selected by the predictor, the signaling circuits, are themselves rich-informative, mechanism-based biomarkers which provide insight into or drug molecular mechanisms of action (MoA).

}, keywords = {Algorithms, Antineoplastic Agents, biomarkers, Cell Line, Tumor, Cell Survival, gene expression, Humans, Lethal Dose 50, Neoplasms, Phosphorylation, Proteins, Signal Transduction}, issn = {2045-2322}, doi = {10.1038/srep18494}, author = {Amadoz, Alicia and Sebasti{\'a}n-Leon, Patricia and Vidal, Enrique and Salavert, Francisco and Dopazo, Joaquin} } @article {483, title = {Sequencing and functional analysis of the genome of a nematode egg-parasitic fungus, Pochonia chlamydosporia.}, journal = {Fungal Genet Biol}, volume = {65}, year = {2014}, month = {2014 Apr}, pages = {69-80}, abstract = {

Pochonia chlamydosporia is a worldwide-distributed soil fungus with a great capacity to infect and destroy the eggs and kill females of plant-parasitic nematodes. Additionally, it has the ability to colonize endophytically roots of economically-important crop plants, thereby promoting their growth and eliciting plant defenses. This multitrophic behavior makes P. chlamydosporia a potentially useful tool for sustainable agriculture approaches. We sequenced and assembled \~{}41 Mb of P. chlamydosporia genomic DNA and predicted 12,122 gene models, of which many were homologous to genes of fungal pathogens of invertebrates and fungal plant pathogens. Predicted genes (65\%) were functionally annotated according to Gene Ontology, and 16\% of them found to share homology with genes in the Pathogen Host Interactions (PHI) database. The genome of this fungus is highly enriched in genes encoding hydrolytic enzymes, such as proteases, glycoside hydrolases and carbohydrate esterases. We used RNA-Seq technology in order to identify the genes expressed during endophytic behavior of P. chlamydosporia when colonizing barley roots. Functional annotation of these genes showed that hydrolytic enzymes and transporters are expressed during endophytism. This structural and functional analysis of the P. chlamydosporia genome provides a starting point for understanding the molecular mechanisms involved in the multitrophic lifestyle of this fungus. The genomic information provided here should also prove useful for enhancing the capabilities of this fungus as a biocontrol agent of plant-parasitic nematodes and as a plant growth-promoting organism.

}, keywords = {Animals, Ascomycota, Female, Gene Expression Regulation, Fungal, Gene ontology, Genome, Fungal, Hordeum, Host-Pathogen Interactions, Nematoda, Ovum, Phylogeny, Plant Roots, Sequence Analysis, DNA, Signal Transduction, Transcriptome}, issn = {1096-0937}, doi = {10.1016/j.fgb.2014.02.002}, author = {Larriba, Eduardo and Jaime, Mar{\'\i}a D L A and Carbonell-Caballero, Jos{\'e} and Conesa, Ana and Dopazo, Joaquin and Nislow, Corey and Mart{\'\i}n-Nieto, Jos{\'e} and Lopez-Llorca, Luis Vicente} } @article {500, title = {Inferring the functional effect of gene expression changes in signaling pathways.}, journal = {Nucleic Acids Res}, volume = {41}, year = {2013}, month = {2013 Jul}, pages = {W213-7}, abstract = {

Signaling pathways constitute a valuable source of information that allows interpreting the way in which alterations in gene activities affect to particular cell functionalities. There are web tools available that allow viewing and editing pathways, as well as representing experimental data on them. However, few methods aimed to identify the signaling circuits, within a pathway, associated to the biological problem studied exist and none of them provide a convenient graphical web interface. We present PATHiWAYS, a web-based signaling pathway visualization system that infers changes in signaling that affect cell functionality from the measurements of gene expression values in typical expression microarray case-control experiments. A simple probabilistic model of the pathway is used to estimate the probabilities for signal transmission from any receptor to any final effector molecule (taking into account the pathway topology) using for this the individual probabilities of gene product presence/absence inferred from gene expression values. Significant changes in these probabilities allow linking different cell functionalities triggered by the pathway to the biological problem studied. PATHiWAYS is available at: http://pathiways.babelomics.org/.

}, keywords = {Animals, Humans, Internet, Mice, Models, Statistical, Receptors, Cell Surface, Signal Transduction, Software, Transcriptome}, issn = {1362-4962}, doi = {10.1093/nar/gkt451}, author = {Sebasti{\'a}n-Leon, Patricia and Carbonell, Jos{\'e} and Salavert, Francisco and S{\'a}nchez, Rub{\'e}n and Medina, Ignacio and Dopazo, Joaquin} } @article {516, title = {IL1β induces mesenchymal stem cells migration and leucocyte chemotaxis through NF-κB.}, journal = {Stem Cell Rev Rep}, volume = {8}, year = {2012}, month = {2012 Sep}, pages = {905-16}, abstract = {

Mesenchymal stem cells are often transplanted into inflammatory environments where they are able to survive and modulate host immune responses through a poorly understood mechanism. In this paper we analyzed the responses of MSC to IL-1β: a representative inflammatory mediator. Microarray analysis of MSC treated with IL-1β revealed that this cytokine activateds a set of genes related to biological processes such as cell survival, cell migration, cell adhesion, chemokine production, induction of angiogenesis and modulation of the immune response. Further more detailed analysis by real-time PCR and functional assays revealed that IL-1β mainly increaseds the production of chemokines such as CCL5, CCL20, CXCL1, CXCL3, CXCL5, CXCL6, CXCL10, CXCL11 and CX(3)CL1, interleukins IL-6, IL-8, IL23A, IL32, Toll-like receptors TLR2, TLR4, CLDN1, metalloproteins MMP1 and MMP3, growth factors CSF2 and TNF-α, together with adhesion molecules ICAM1 and ICAM4. Functional analysis of MSC proliferation, migration and adhesion to extracellular matrix components revealed that IL-1β did not affect proliferation but also served to induce the secretion of trophic factors and adhesion to ECM components such as collagen and laminin. IL-1β treatment enhanced the ability of MSC to recruit monocytes and granulocytes in vitro. Blockade of NF-κβ transcription factor activation with IκB kinase beta (IKKβ) shRNA impaired MSC migration, adhesion and leucocyte recruitment, induced by IL-1β demonstrating that NF-κB pathway is an important downstream regulator of these responses. These findings are relevant to understanding the biological responses of MSC to inflammatory environments.

}, keywords = {Cell Adhesion, Cell Movement, Cell Proliferation, Chemokines, Chemotaxis, Leukocyte, Collagen, Fibronectins, Gene Expression Profiling, Gene Knockdown Techniques, HEK293 Cells, Humans, I-kappa B Kinase, Inflammation Mediators, Intercellular Signaling Peptides and Proteins, Interleukin-1beta, Laminin, Leukocytes, Mesenchymal Stem Cells, NF-kappa B, Oligonucleotide Array Sequence Analysis, RNA Interference, Signal Transduction}, issn = {2629-3277}, doi = {10.1007/s12015-012-9364-9}, author = {Carrero, Rub{\'e}n and Cerrada, Inmaculada and Lled{\'o}, Elisa and Dopazo, Joaquin and Garcia-Garcia, Francisco and Rubio, Mari-Paz and Trigueros, C{\'e}sar and Dorronsoro, Akaitz and Ruiz-Sauri, Amparo and Montero, Jos{\'e} Anastasio and Sep{\'u}lveda, Pilar} } @article {531, title = {Early peroxisome proliferator-activated receptor gamma regulated genes involved in expansion of pancreatic beta cell mass.}, journal = {BMC Med Genomics}, volume = {4}, year = {2011}, month = {2011 Dec 30}, pages = {86}, abstract = {

BACKGROUND: The progression towards type 2 diabetes depends on the allostatic response of pancreatic beta cells to synthesise and secrete enough insulin to compensate for insulin resistance. The endocrine pancreas is a plastic tissue able to expand or regress in response to the requirements imposed by physiological and pathophysiological states associated to insulin resistance such as pregnancy, obesity or ageing, but the mechanisms mediating beta cell mass expansion in these scenarios are not well defined. We have recently shown that ob/ob mice with genetic ablation of PPARγ2, a mouse model known as the POKO mouse failed to expand its beta cell mass. This phenotype contrasted with the appropriate expansion of the beta cell mass observed in their obese littermate ob/ob mice. Thus, comparison of these models islets particularly at early ages could provide some new insights on early PPARγ dependent transcriptional responses involved in the process of beta cell mass expansion

RESULTS: Here we have investigated PPARγ dependent transcriptional responses occurring during the early stages of beta cell adaptation to insulin resistance in wild type, ob/ob, PPARγ2 KO and POKO mice. We have identified genes known to regulate both the rate of proliferation and the survival signals of beta cells. Moreover we have also identified new pathways induced in ob/ob islets that remained unchanged in POKO islets, suggesting an important role for PPARγ in maintenance/activation of mechanisms essential for the continued function of the beta cell.

CONCLUSIONS: Our data suggest that the expansion of beta cell mass observed in ob/ob islets is associated with the activation of an immune response that fails to occur in POKO islets. We have also indentified other PPARγ dependent differentially regulated pathways including cholesterol biosynthesis, apoptosis through TGF-β signaling and decreased oxidative phosphorylation.

}, keywords = {Animals, Cell Proliferation, Cell Survival, Cholesterol, Down-Regulation, Female, Gene Expression Regulation, Gene Knockout Techniques, Insulin Resistance, Insulin-Secreting Cells, Mice, obesity, Oxidation-Reduction, Phosphorylation, PPAR gamma, Signal Transduction, Transcription, Genetic, Transforming Growth Factor beta}, issn = {1755-8794}, doi = {10.1186/1755-8794-4-86}, author = {Vivas, Yurena and Martinez-Garcia, Cristina and Izquierdo, Adriana and Garcia-Garcia, Francisco and Callejas, Sergio and Velasco, Ismael and Campbell, Mark and Ros, Manuel and Dopazo, Ana and Dopazo, Joaquin and Vidal-Puig, Antonio and Medina-Gomez, Gema} } @article {532, title = {Early transcriptional defense responses in Arabidopsis cell suspension culture under high-light conditions.}, journal = {Plant Physiol}, volume = {156}, year = {2011}, month = {2011 Jul}, pages = {1439-56}, abstract = {

The early transcriptional defense responses and reactive oxygen species (ROS) production in Arabidopsis (Arabidopsis thaliana) cell suspension culture (ACSC), containing functional chloroplasts, were examined at high light (HL). The transcriptional analysis revealed that most of the ROS markers identified among the 449 transcripts with significant differential expression were transcripts specifically up-regulated by singlet oxygen ((1)O(2)). On the contrary, minimal correlation was established with transcripts specifically up-regulated by superoxide radical or hydrogen peroxide. The transcriptional analysis was supported by fluorescence microscopy experiments. The incubation of ACSC with the (1)O(2) sensor green reagent and 2{\textquoteright},7{\textquoteright}-dichlorofluorescein diacetate showed that the 30-min-HL-treated cultures emitted fluorescence that corresponded with the production of (1)O(2) but not of hydrogen peroxide. Furthermore, the in vivo photodamage of the D1 protein of photosystem II indicated that the photogeneration of (1)O(2) took place within the photosystem II reaction center. Functional enrichment analyses identified transcripts that are key components of the ROS signaling transduction pathway in plants as well as others encoding transcription factors that regulate both ROS scavenging and water deficit stress. A meta-analysis examining the transcriptional profiles of mutants and hormone treatments in Arabidopsis showed a high correlation between ACSC at HL and the fluorescent mutant family of Arabidopsis, a producer of (1)O(2) in plastids. Intriguingly, a high correlation was also observed with ABA deficient1 and more axillary growth4, two mutants with defects in the biosynthesis pathways of two key (apo)carotenoid-derived plant hormones (i.e. abscisic acid and strigolactones, respectively). ACSC has proven to be a valuable system for studying early transcriptional responses to HL stress.

}, keywords = {Arabidopsis, Blotting, Western, Cell Culture Techniques, Cells, Cultured, Chloroplasts, Cluster Analysis, Gene Expression Profiling, Gene Expression Regulation, Plant, Hydrogen Peroxide, Light, mutation, Oligonucleotide Array Sequence Analysis, Photosystem II Protein Complex, Plant Growth Regulators, Reproducibility of Results, Reverse Transcriptase Polymerase Chain Reaction, RNA, Messenger, Signal Transduction, Stress, Physiological, Transcription, Genetic}, issn = {1532-2548}, doi = {10.1104/pp.111.177766}, author = {Gonz{\'a}lez-P{\'e}rez, Sergio and Guti{\'e}rrez, Jorge and Garcia-Garcia, Francisco and Osuna, Daniel and Dopazo, Joaquin and Lorenzo, Oscar and Revuelta, Jos{\'e} L and Arellano, Juan B} } @article {535, title = {Large-scale transcriptional profiling and functional assays reveal important roles for Rho-GTPase signalling and SCL during haematopoietic differentiation of human embryonic stem cells.}, journal = {Hum Mol Genet}, volume = {20}, year = {2011}, month = {2011 Dec 15}, pages = {4932-46}, abstract = {

Understanding the transcriptional cues that direct differentiation of human embryonic stem cells (hESCs) and human-induced pluripotent stem cells to defined and functional cell types is essential for future clinical applications. In this study, we have compared transcriptional profiles of haematopoietic progenitors derived from hESCs at various developmental stages of a feeder- and serum-free differentiation method and show that the largest transcriptional changes occur during the first 4 days of differentiation. Data mining on the basis of molecular function revealed Rho-GTPase signalling as a key regulator of differentiation. Inhibition of this pathway resulted in a significant reduction in the numbers of emerging haematopoietic progenitors throughout the differentiation window, thereby uncovering a previously unappreciated role for Rho-GTPase signalling during human haematopoietic development. Our analysis indicated that SCL was the 11th most upregulated transcript during the first 4 days of the hESC differentiation process. Overexpression of SCL in hESCs promoted differentiation to meso-endodermal lineages, the emergence of haematopoietic and erythro-megakaryocytic progenitors and accelerated erythroid differentiation. Importantly, intrasplenic transplantation of SCL-overexpressing hESC-derived haematopoietic cells enhanced recovery from induced acute anaemia without significant cell engraftment, suggesting a paracrine-mediated effect.

}, keywords = {Acute Disease, Anemia, Hemolytic, Animals, Basic Helix-Loop-Helix Transcription Factors, Cell Differentiation, Cell Line, Cell Lineage, Cluster Analysis, Embryonic Stem Cells, Erythroid Cells, Flow Cytometry, Gene Expression Profiling, Hematopoietic Stem Cells, Humans, Mice, Myeloid Cells, Paracrine Communication, Proto-Oncogene Proteins, Reverse Transcriptase Polymerase Chain Reaction, rho GTP-Binding Proteins, Signal Transduction, Stem Cell Transplantation, T-Cell Acute Lymphocytic Leukemia Protein 1, Transcriptome}, issn = {1460-2083}, doi = {10.1093/hmg/ddr431}, author = {Yung, Sun and Ledran, Maria and Moreno-Gimeno, Inmaculada and Conesa, Ana and Montaner, David and Dopazo, Joaquin and Dimmick, Ian and Slater, Nicholas J and Marenah, Lamin and Real, Pedro J and Paraskevopoulou, Iliana and Bisbal, Viviana and Burks, Deborah and Santibanez-Koref, Mauro and Moreno, Ruben and Mountford, Joanne and Menendez, Pablo and Armstrong, Lyle and Lako, Majlinda} } @article {597, title = {Molecular profiling related to poor prognosis in thyroid carcinoma. Combining gene expression data and biological information.}, journal = {Oncogene}, volume = {27}, year = {2008}, month = {2008 Mar 06}, pages = {1554-61}, abstract = {

Undifferentiated and poorly differentiated thyroid tumors are responsible for more than half of thyroid cancer patient deaths in spite of their low incidence. Conventional treatments do not obtain substantial benefits, and the lack of alternative approaches limits patient survival. Additionally, the absence of prognostic markers for well-differentiated tumors complicates patient-specific treatments and favors the progression of recurrent forms. In order to recognize the molecular basis involved in tumor dedifferentiation and identify potential markers for thyroid cancer prognosis prediction, we analysed the expression profile of 44 thyroid primary tumors with different degrees of dedifferentiation and aggressiveness using cDNA microarrays. Transcriptome comparison of dedifferentiated and well-differentiated thyroid tumors identified 1031 genes with >2-fold difference in absolute values and false discovery rate of <0.15. According to known molecular interaction and reaction networks, the products of these genes were mainly clustered in the MAPkinase signaling pathway, the TGF-beta signaling pathway, focal adhesion and cell motility, activation of actin polymerization and cell cycle. An exhaustive search in several databases allowed us to identify various members of the matrix metalloproteinase, melanoma antigen A and collagen gene families within the upregulated gene set. We also identified a prognosis classifier comprising just 30 transcripts with an overall accuracy of 95\%. These findings may clarify the molecular mechanisms involved in thyroid tumor dedifferentiation and provide a potential prognosis predictor as well as targets for new therapies.

}, keywords = {Adenoma, Adolescent, Adult, Aged, Biomarkers, Tumor, Carcinoma, Carcinoma, Papillary, Cell Differentiation, Female, Gene Expression Profiling, Gene Expression Regulation, Neoplastic, Humans, Male, Middle Aged, Oligonucleotide Array Sequence Analysis, Prognosis, Reverse Transcriptase Polymerase Chain Reaction, RNA, Neoplasm, Signal Transduction, Thyroid Neoplasms}, issn = {1476-5594}, doi = {10.1038/sj.onc.1210792}, author = {Montero-Conde, C and Mart{\'\i}n-Campos, J M and Lerma, E and Gimenez, G and Mart{\'\i}nez-Guitarte, J L and Combal{\'\i}a, N and Montaner, D and Mat{\'\i}as-Guiu, X and Dopazo, J and de Leiva, A and Robledo, M and Mauricio, D} } @article {604, title = {Evidence for systems-level molecular mechanisms of tumorigenesis.}, journal = {BMC Genomics}, volume = {8}, year = {2007}, month = {2007 Jun 20}, pages = {185}, abstract = {

BACKGROUND: Cancer arises from the consecutive acquisition of genetic alterations. Increasing evidence suggests that as a consequence of these alterations, molecular interactions are reprogrammed in the context of highly connected and regulated cellular networks. Coordinated reprogramming would allow the cell to acquire the capabilities for malignant growth.

RESULTS: Here, we determine the coordinated function of cancer gene products (i.e., proteins encoded by differentially expressed genes in tumors relative to healthy tissue counterparts, hereafter referred to as "CGPs") defined as their topological properties and organization in the interactome network. We show that CGPs are central to information exchange and propagation and that they are specifically organized to promote tumorigenesis. Centrality is identified by both local (degree) and global (betweenness and closeness) measures, and systematically appears in down-regulated CGPs. Up-regulated CGPs do not consistently exhibit centrality, but both types of cancer products determine the overall integrity of the network structure. In addition to centrality, down-regulated CGPs show topological association that correlates with common biological processes and pathways involved in tumorigenesis.

CONCLUSION: Given the current limited coverage of the human interactome, this study proposes that tumorigenesis takes place in a specific and organized way at the molecular systems-level and suggests a model that comprises the precise down-regulation of groups of topologically-associated proteins involved in particular functions, orchestrated with the up-regulation of specific proteins.

}, keywords = {Cell Transformation, Neoplastic, Gene Expression Profiling, Gene Expression Regulation, Neoplastic, Humans, Male, Models, Biological, Models, Genetic, Models, Statistical, Neoplasm Proteins, Neoplasms, Prostatic Neoplasms, Protein Interaction Mapping, RNA, Messenger, Signal Transduction, Systems biology}, issn = {1471-2164}, doi = {10.1186/1471-2164-8-185}, author = {Hern{\'a}ndez, Pilar and Huerta-Cepas, Jaime and Montaner, David and Al-Shahrour, F{\'a}tima and Valls, Joan and G{\'o}mez, Laia and Capell{\`a}, Gabriel and Dopazo, Joaquin and Pujana, Miguel Angel} } @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} }