@article {665, title = {Optimised molecular genetic diagnostics of Fanconi anaemia by whole exome sequencing and functional studies.}, journal = {J Med Genet}, volume = {57}, year = {2020}, month = {2020 04}, pages = {258-268}, abstract = {

PURPOSE: Patients with Fanconi anaemia (FA), a rare DNA repair genetic disease, exhibit chromosome fragility, bone marrow failure, malformations and cancer susceptibility. FA molecular diagnosis is challenging since FA is caused by point mutations and large deletions in 22 genes following three heritability patterns. To optimise FA patients{\textquoteright} characterisation, we developed a simplified but effective methodology based on whole exome sequencing (WES) and functional studies.

METHODS: 68 patients with FA were analysed by commercial WES services. Copy number variations were evaluated by sequencing data analysis with RStudio. To test missense variants, wt FANCA cDNA was cloned and variants were introduced by site-directed mutagenesis. Vectors were then tested for their ability to complement DNA repair defects of a FANCA-KO human cell line generated by TALEN technologies.

RESULTS: We identified 93.3\% of mutated alleles including large deletions. We determined the pathogenicity of three FANCA missense variants and demonstrated that two variants reported in mutations databases as {\textquoteright}affecting functions{\textquoteright} are SNPs. Deep analysis of sequencing data revealed patients{\textquoteright} true mutations, highlighting the importance of functional analysis. In one patient, no pathogenic variant could be identified in any of the 22 known FA genes, and in seven patients, only one deleterious variant could be identified (three patients each with FANCA and FANCD2 and one patient with FANCE mutations) CONCLUSION: WES and proper bioinformatics analysis are sufficient to effectively characterise patients with FA regardless of the rarity of their complementation group, type of mutations, mosaic condition and DNA source.

}, keywords = {Cell Line, DNA Copy Number Variations, DNA Repair, DNA-Binding Proteins, Fanconi Anemia, Fanconi Anemia Complementation Group A Protein, Female, Gene Knockout Techniques, Genetic Predisposition to Disease, Humans, Male, Mutation, Missense, Polymorphism, Single Nucleotide, whole exome sequencing}, issn = {1468-6244}, doi = {10.1136/jmedgenet-2019-106249}, author = {Bogliolo, Massimo and Pujol, Roser and Aza-Carmona, Miriam and Mu{\~n}oz-Subirana, N{\'u}ria and Rodriguez-Santiago, Benjamin and Casado, Jos{\'e} Antonio and Rio, Paula and Bauser, Christopher and Reina-Castill{\'o}n, Judith and Lopez-Sanchez, Marcos and Gonzalez-Quereda, Lidia and Gallano, Pia and Catal{\'a}, Albert and Ruiz-Llobet, Ana and Badell, Isabel and Diaz-Heredia, Cristina and Hladun, Raquel and Senent, Leonort and Argiles, Bienvenida and Bergua Burgues, Juan Miguel and Ba{\~n}ez, Fatima and Arrizabalaga, Beatriz and L{\'o}pez Almaraz, Ricardo and Lopez, Monica and Figuera, {\'A}ngela and Molin{\'e}s, Antonio and P{\'e}rez de Soto, Inmaculada and Hernando, In{\'e}s and Mu{\~n}oz, Juan Antonio and Del Rosario Marin, Maria and Balma{\~n}a, Judith and Stjepanovic, Neda and Carrasco, Estela and Cuesta, Isabel and Cosuelo, Jos{\'e} Miguel and Regueiro, Alexandra and Moraleda Jimenez, Jos{\'e} and Galera-Mi{\~n}arro, Ana Maria and Rosi{\~n}ol, Laura and Carri{\'o}, Anna and Bel{\'e}ndez-Bieler, Cristina and Escudero Soto, Antonio and Cela, Elena and de la Mata, Gregorio and Fern{\'a}ndez-Delgado, Rafael and Garcia-Pardos, Maria Carmen and S{\'a}ez-Villaverde, Raquel and Barraga{\~n}o, Marta and Portugal, Raquel and Lendinez, Francisco and Hernadez, Ines and Vagace, Jos{\'e} Manue and Tapia, Maria and Nieto, Jos{\'e} and Garcia, Marta and Gonzalez, Macarena and Vicho, Cristina and Galvez, Eva and Valiente, Alberto and Antelo, Maria Luisa and Ancliff, Phil and Garc{\'\i}a, Francisco and Dopazo, Joaquin and Sevilla, Julian and Paprotka, Tobias and P{\'e}rez-Jurado, Luis Alberto and Bueren, Juan and Surralles, Jordi} } @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 {512, title = {Discovering the hidden sub-network component in a ranked list of genes or proteins derived from genomic experiments.}, journal = {Nucleic Acids Res}, volume = {40}, year = {2012}, month = {2012 Nov 01}, pages = {e158}, abstract = {

Genomic experiments (e.g. differential gene expression, single-nucleotide polymorphism association) typically produce ranked list of genes. We present a simple but powerful approach which uses protein-protein interaction data to detect sub-networks within such ranked lists of genes or proteins. We performed an exhaustive study of network parameters that allowed us concluding that the average number of components and the average number of nodes per component are the parameters that best discriminate between real and random networks. A novel aspect that increases the efficiency of this strategy in finding sub-networks is that, in addition to direct connections, also connections mediated by intermediate nodes are considered to build up the sub-networks. The possibility of using of such intermediate nodes makes this approach more robust to noise. It also overcomes some limitations intrinsic to experimental designs based on differential expression, in which some nodes are invariant across conditions. The proposed approach can also be used for candidate disease-gene prioritization. Here, we demonstrate the usefulness of the approach by means of several case examples that include a differential expression analysis in Fanconi Anemia, a genome-wide association study of bipolar disorder and a genome-scale study of essentiality in cancer genes. An efficient and easy-to-use web interface (available at http://www.babelomics.org) based on HTML5 technologies is also provided to run the algorithm and represent the network.

}, keywords = {Bipolar Disorder, Fanconi Anemia, Gene Regulatory Networks, Genes, Neoplasm, Genome-Wide Association Study, Genomics, Humans, Protein Interaction Mapping}, issn = {1362-4962}, doi = {10.1093/nar/gks699}, author = {Garc{\'\i}a-Alonso, Luz and Alonso, Roberto and Vidal, Enrique and Amadoz, Alicia and De Maria, Alejandro and Minguez, Pablo and Medina, Ignacio and Dopazo, Joaquin} } @article {517, title = {Inferring the regulatory network behind a gene expression experiment.}, journal = {Nucleic Acids Res}, volume = {40}, year = {2012}, month = {2012 Jul}, pages = {W168-72}, abstract = {

Transcription factors (TFs) and miRNAs are the most important dynamic regulators in the control of gene expression in multicellular organisms. These regulatory elements play crucial roles in development, cell cycling and cell signaling, and they have also been associated with many diseases. The Regulatory Network Analysis Tool (RENATO) web server makes the exploration of regulatory networks easy, enabling a better understanding of functional modularity and network integrity under specific perturbations. RENATO is suitable for the analysis of the result of expression profiling experiments. The program analyses lists of genes and search for the regulators compatible with its activation or deactivation. Tests of single enrichment or gene set enrichment allow the selection of the subset of TFs or miRNAs significantly involved in the regulation of the query genes. RENATO also offers an interactive advanced graphical interface that allows exploring the regulatory network found.RENATO is available at: http://renato.bioinfo.cipf.es/.

}, keywords = {Binding Sites, Databases, Genetic, Fanconi Anemia, Gene Regulatory Networks, Internet, MicroRNAs, Software, Transcription Factors, Transcriptome}, issn = {1362-4962}, doi = {10.1093/nar/gks573}, author = {Bleda, Marta and Medina, Ignacio and Alonso, Roberto and De Maria, Alejandro and Salavert, Francisco and Dopazo, Joaquin} }