@article {812, title = {Shared germline genomic variants in two patients with double primary gastrointestinal stromal tumours (GISTs).}, journal = {J Med Genet}, volume = {61}, year = {2024}, month = {2024 Sep 24}, pages = {927-934}, abstract = {

BACKGROUND: Gastrointestinal stromal tumours (GISTs) are prevalent mesenchymal tumours of the gastrointestinal tract, commonly exhibiting structural variations in and genes. While the mutational profiling of somatic tumours is well described, the genes behind the susceptibility to develop GIST are not yet fully discovered. This study explores the genomic landscape of two primary GIST cases, aiming to identify shared germline pathogenic variants and shed light on potential key players in tumourigenesis.

METHODS: Two patients with distinct genotypically and phenotypically GISTs underwent germline whole genome sequencing. CNV and single nucleotide variant (SNV) analyses were performed.

RESULTS: Both patients harbouring low-risk GISTs with different mutations ( and ) shared homozygous germline pathogenic deletions in both and genes. CNV analysis revealed additional shared pathogenic deletions in other genes such as . No particular pathogenic SNV shared by both patients was detected.

CONCLUSION: Our study provides new insights into germline variants that can be associated with the development of GISTs, namely, and deep deletions. Further functional validation is warranted to elucidate the precise contributions of identified germline mutations in GIST development.

}, keywords = {DNA Copy Number Variations, Female, Gastrointestinal Neoplasms, Gastrointestinal Stromal Tumors, Genetic Predisposition to Disease, Germ-Line Mutation, Humans, Male, Middle Aged, Polymorphism, Single Nucleotide, Proto-Oncogene Proteins c-kit, Receptor, Platelet-Derived Growth Factor alpha, Whole Genome Sequencing}, issn = {1468-6244}, doi = {10.1136/jmg-2024-110109}, author = {Moura, David S and L{\'o}pez L{\'o}pez, Daniel and di Lernia, Davide and Martin-Ruiz, Marta and Lopez-Alvarez, Maria and Ramos, Rafael and Merino, Jose and Dopazo, Joaquin and Lopez-Guerrero, Jose and Mondaza-Hernandez, Jose L and Romero, Pablo and Hindi, Nadia and Garcia-Foncillas, Jesus and Martin-Broto, Javier} } @article {772, title = {SigPrimedNet: A Signaling-Informed Neural Network for scRNA-seq Annotation of Known and Unknown Cell Types.}, journal = {Biology (Basel)}, volume = {12}, year = {2023}, month = {2023 Apr 10}, abstract = {

Single-cell RNA sequencing is increasing our understanding of the behavior of complex tissues or organs, by providing unprecedented details on the complex cell type landscape at the level of individual cells. Cell type definition and functional annotation are key steps to understanding the molecular processes behind the underlying cellular communication machinery. However, the exponential growth of scRNA-seq data has made the task of manually annotating cells unfeasible, due not only to an unparalleled resolution of the technology but to an ever-increasing heterogeneity of the data. Many supervised and unsupervised methods have been proposed to automatically annotate cells. Supervised approaches for cell-type annotation outperform unsupervised methods except when new (unknown) cell types are present. Here, we introduce SigPrimedNet an artificial neural network approach that leverages (i) efficient training by means of a sparsity-inducing signaling circuits-informed layer, (ii) feature representation learning through supervised training, and (iii) unknown cell-type identification by fitting an anomaly detection method on the learned representation. We show that SigPrimedNet can efficiently annotate known cell types while keeping a low false-positive rate for unseen cells across a set of publicly available datasets. In addition, the learned representation acts as a proxy for signaling circuit activity measurements, which provide useful estimations of the cell functionalities.

}, issn = {2079-7737}, doi = {10.3390/biology12040579}, author = {Gundogdu, Pelin and Alamo, Inmaculada and Nepomuceno-Chamorro, Isabel A and Dopazo, Joaquin and Loucera, Carlos} } @article {705, title = {SMN1 copy-number and sequence variant analysis from next-generation sequencing data.}, journal = {Hum Mutat}, volume = {41}, year = {2020}, month = {2020 12}, pages = {2073-2077}, abstract = {

Spinal muscular atrophy (SMA) is a severe neuromuscular autosomal recessive disorder affecting 1/10,000 live births. Most SMA patients present homozygous deletion of SMN1, while the vast majority of SMA carriers present only a single SMN1 copy. The sequence similarity between SMN1 and SMN2, and the complexity of the SMN locus makes the estimation of the SMN1 copy-number by next-generation sequencing (NGS) very difficult. Here, we present SMAca, the first python tool to detect SMA carriers and estimate the absolute SMN1 copy-number using NGS data. Moreover, SMAca takes advantage of the knowledge of certain variants specific to SMN1 duplication to also identify silent carriers. This tool has been validated with a cohort of 326 samples from the Navarra 1000 Genomes Project (NAGEN1000). SMAca was developed with a focus on execution speed and easy installation. This combination makes it especially suitable to be integrated into production NGS pipelines. Source code and documentation are available at https://www.github.com/babelomics/SMAca.

}, keywords = {Base Sequence, DNA Copy Number Variations, High-Throughput Nucleotide Sequencing, Humans, Reproducibility of Results, Software, Survival of Motor Neuron 1 Protein}, issn = {1098-1004}, doi = {10.1002/humu.24120}, author = {L{\'o}pez-L{\'o}pez, Daniel and Loucera, Carlos and Carmona, Rosario and Aquino, Virginia and Salgado, Josefa and Pasalodos, Sara and Miranda, Mar{\'\i}a and Alonso, {\'A}ngel and Dopazo, Joaquin} } @article {450, title = {Screening of CD96 and ASXL1 in 11 patients with Opitz C or Bohring-Opitz syndromes.}, journal = {Am J Med Genet A}, volume = {170A}, year = {2016}, month = {2016 Jan}, pages = {24-31}, abstract = {

Opitz C trigonocephaly (or Opitz C syndrome, OTCS) and Bohring-Opitz syndrome (BOS or C-like syndrome) are two rare genetic disorders with phenotypic overlap. The genetic causes of these diseases are not understood. However, two genes have been associated with OTCS or BOS with dominantly inherited de novo mutations. Whereas CD96 has been related to OTCS (one case) and to BOS (one case), ASXL1 has been related to BOS only (several cases). In this study we analyze CD96 and ASXL1 in a group of 11 affected individuals, including 2 sibs, 10 of them were diagnosed with OTCS, and one had a BOS phenotype. Exome sequences were available on six patients with OTCS and three parent pairs. Thus, we could analyze the CD96 and ASXL1 sequences in these patients bioinformatically. Sanger sequencing of all exons of CD96 and ASXL1 was carried out in the remaining patients. Detailed scrutiny of the sequences and assessment of variants allowed us to exclude putative pathogenic and private mutations in all but one of the patients. In this patient (with BOS) we identified a de novo mutation in ASXL1 (c.2100dupT). By nature and location within the gene, this mutation resembles those previously described in other BOS patients and we conclude that it may be responsible for the condition. Our results indicate that in 10 of 11, the disease (OTCS or BOS) cannot be explained by small changes in CD96 or ASXL1. However, the cohort is too small to make generalizations about the genetic etiology of these diseases.

}, keywords = {Adolescent, Antigens, CD, Child, Child, Preschool, Craniosynostoses, Exome, Female, High-Throughput Nucleotide Sequencing, Humans, Infant, Intellectual Disability, Male, mutation, Pedigree, Phenotype, Prognosis, Repressor Proteins}, issn = {1552-4833}, doi = {10.1002/ajmg.a.37418}, author = {Urreizti, Roser and Roca-Ayats, Neus and Trepat, Judith and Garcia-Garcia, Francisco and Alem{\'a}n, Alejandro and Orteschi, Daniela and Marangi, Giuseppe and Neri, Giovanni and Opitz, John M and Dopazo, Joaquin and Cormand, Bru and Vilageliu, Llu{\"\i}sa and Balcells, Susana and Grinberg, Daniel} } @article {445, title = {Serum metabolomic profiling facilitates the non-invasive identification of metabolic biomarkers associated with the onset and progression of non-small cell lung cancer.}, journal = {Oncotarget}, volume = {7}, year = {2016}, month = {2016 Mar 15}, pages = {12904-16}, abstract = {

Lung cancer (LC) is responsible for most cancer deaths. One of the main factors contributing to the lethality of this disease is the fact that a large proportion of patients are diagnosed at advanced stages when a clinical intervention is unlikely to succeed. In this study, we evaluated the potential of metabolomics by 1H-NMR to facilitate the identification of accurate and reliable biomarkers to support the early diagnosis and prognosis of non-small cell lung cancer (NSCLC).We found that the metabolic profile of NSCLC patients, compared with healthy individuals, is characterized by statistically significant changes in the concentration of 18 metabolites representing different amino acids, organic acids and alcohols, as well as different lipids and molecules involved in lipid metabolism. Furthermore, the analysis of the differences between the metabolic profiles of NSCLC patients at different stages of the disease revealed the existence of 17 metabolites involved in metabolic changes associated with disease progression.Our results underscore the potential of metabolomics profiling to uncover pathophysiological mechanisms that could be useful to objectively discriminate NSCLC patients from healthy individuals, as well as between different stages of the disease.

}, keywords = {Adult, Aged, Biomarkers, Tumor, Carcinoma, Non-Small-Cell Lung, Disease Progression, Female, Humans, Lung Neoplasms, Male, metabolomics, Middle Aged, Proton Magnetic Resonance Spectroscopy}, issn = {1949-2553}, doi = {10.18632/oncotarget.7354}, author = {Puchades-Carrasco, Leonor and Jantus-Lewintre, Eloisa and P{\'e}rez-Rambla, Clara and Garcia-Garcia, Francisco and Lucas, Rut and Calabuig, Silvia and Blasco, Ana and Dopazo, Joaquin and Camps, Carlos and Pineda-Lucena, Antonio} } @article {451, title = {Stress-induced activation of brown adipose tissue prevents obesity in conditions of low adaptive thermogenesis.}, journal = {Mol Metab}, volume = {5}, year = {2016}, month = {2016 Jan}, pages = {19-33}, abstract = {

BACKGROUND: Stress-associated conditions such as psychoemotional reactivity and depression have been paradoxically linked to either weight gain or weight loss. This bi-directional effect of stress is not understood at the functional level. Here we tested the hypothesis that pre-stress level of adaptive thermogenesis and brown adipose tissue (BAT) functions explain the vulnerability or resilience to stress-induced obesity.

METHODS: We used wt and triple β1,β2,β3-Adrenergic Receptors knockout (β-less) mice exposed to a model of chronic subordination stress (CSS) at either room temperature (22~{\textdegree}C) or murine thermoneutrality (30~{\textdegree}C). A combined behavioral, physiological, molecular, and immunohistochemical analysis was conducted to determine stress-induced modulation of energy balance and BAT structure and function. Immortalized brown adipocytes were used for in~vitro assays.

RESULTS: Departing from our initial observation that βARs are dispensable for cold-induced BAT browning, we demonstrated that under physiological conditions promoting low adaptive thermogenesis and BAT activity (e.g. thermoneutrality or genetic deletion of the βARs), exposure to CSS acted as a stimulus for BAT activation and thermogenesis, resulting in resistance to diet-induced obesity despite the presence of hyperphagia. Conversely, in wt mice acclimatized to room temperature, and therefore characterized by sustained BAT function, exposure to CSS increased vulnerability to obesity. Exposure to CSS enhanced the sympathetic innervation of BAT in wt acclimatized to thermoneutrality and in β-less mice. Despite increased sympathetic innervation suggesting adrenergic-mediated browning, norepinephrine did not promote browning in βARs knockout brown adipocytes, which led us to identify an alternative sympathetic/brown adipocytes purinergic pathway in the BAT. This pathway is downregulated under conditions of low adaptive thermogenesis requirements, is induced by stress, and elicits activation of UCP1 in wt and β-less brown adipocytes. Importantly, this purinergic pathway is conserved in human BAT.

CONCLUSION: Our findings demonstrate that thermogenesis and BAT function are determinant of the resilience or vulnerability to stress-induced obesity. Our data support a model in which adrenergic and purinergic pathways exert complementary/synergistic functions in BAT, thus suggesting an alternative to βARs agonists for the activation of human BAT.

}, issn = {2212-8778}, doi = {10.1016/j.molmet.2015.10.005}, author = {Razzoli, Maria and Frontini, Andrea and Gurney, Allison and Mondini, Eleonora and Cubuk, Cankut and Katz, Liora S and Cero, Cheryl and Bolan, Patrick J and Dopazo, Joaquin and Vidal-Puig, Antonio and Cinti, Saverio and Bartolomucci, Alessandro} } @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 {519, title = {SNPeffect 4.0: on-line prediction of molecular and structural effects of protein-coding variants.}, journal = {Nucleic Acids Res}, volume = {40}, year = {2012}, month = {2012 Jan}, pages = {D935-9}, abstract = {

Single nucleotide variants (SNVs) are, together with copy number variation, the primary source of variation in the human genome and are associated with phenotypic variation such as altered response to drug treatment and susceptibility to disease. Linking structural effects of non-synonymous SNVs to functional outcomes is a major issue in structural bioinformatics. The SNPeffect database (http://snpeffect.switchlab.org) uses sequence- and structure-based bioinformatics tools to predict the effect of protein-coding SNVs on the structural phenotype of proteins. It integrates aggregation prediction (TANGO), amyloid prediction (WALTZ), chaperone-binding prediction (LIMBO) and protein stability analysis (FoldX) for structural phenotyping. Additionally, SNPeffect holds information on affected catalytic sites and a number of post-translational modifications. The database contains all known human protein variants from UniProt, but users can now also submit custom protein variants for a SNPeffect analysis, including automated structure modeling. The new meta-analysis application allows plotting correlations between phenotypic features for a user-selected set of variants.

}, keywords = {Databases, Protein, Humans, Internet, Meta-Analysis as Topic, Phenotype, Polymorphism, Single Nucleotide, Protein Conformation, Proteins}, issn = {1362-4962}, doi = {10.1093/nar/gkr996}, author = {De Baets, Greet and Van Durme, Joost and Reumers, Joke and Maurer-Stroh, Sebastian and Vanhee, Peter and Dopazo, Joaquin and Schymkowitz, Joost and Rousseau, Frederic} } @article {808, title = {Selection upon Genome Architecture: Conservation of Functional Neighborhoods with Changing Genes}, journal = {PLoS Comput. Biol.}, volume = {6}, number = {10}, year = {2010}, pages = {e1000953}, doi = {doi:10.1371/journal.pcbi.1000953}, url = {http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000953}, author = {Al-Shahrour, F{\'a}tima and Minguez, Pablo and Marqu{\'e}s-Bonet, Tom{\'a}s and Gazave, Elodie and Navarro, Arcadi and Dopazo, Joaquin} } @article {576, title = {Serial Expression Analysis: a web tool for the analysis of serial gene expression data.}, journal = {Nucleic Acids Res}, volume = {38}, year = {2010}, month = {2010 Jul}, pages = {W239-45}, abstract = {

Serial transcriptomics experiments investigate the dynamics of gene expression changes associated with a quantitative variable such as time or dosage. The statistical analysis of these data implies the study of global and gene-specific expression trends, the identification of significant serial changes, the comparison of expression profiles and the assessment of transcriptional changes in terms of cellular processes. We have created the SEA (Serial Expression Analysis) suite to provide a complete web-based resource for the analysis of serial transcriptomics data. SEA offers five different algorithms based on univariate, multivariate and functional profiling strategies framed within a user-friendly interface and a project-oriented architecture to facilitate the analysis of serial gene expression data sets from different perspectives. SEA is available at sea.bioinfo.cipf.es.

}, keywords = {Algorithms, Gene Expression Profiling, Internet, Kinetics, Linear Models, Oligonucleotide Array Sequence Analysis, Software}, issn = {1362-4962}, doi = {10.1093/nar/gkq488}, author = {Nueda, Maria Jos{\'e} and Carbonell, Jos{\'e} and Medina, Ignacio and Dopazo, Joaquin and Conesa, Ana} } @article {PabloMinguez05192009, title = {SNOW, a web-based tool for the statistical analysis of protein-protein interaction networks}, journal = {Nucl. Acids Res.}, volume = {37}, year = {2009}, pages = {W109-114}, abstract = {

Understanding the structure and the dynamics of the complex intercellular network of interactions that contributes to the structure and function of a living cell is one of the main challenges of today{\textquoteright}s biology. SNOW inputs a collection of protein (or gene) identifiers and, by using the interactome as scaffold, draws the connections among them, calculates several relevant network parameters and, as a novelty among the rest of tools of its class, it estimates their statistical significance. The parameters calculated for each node are: connectivity, betweenness and clustering coefficient. It also calculates the number of components, number of bicomponents and articulation points. An interactive network viewer is also available to explore the resulting network. SNOW is available at http://snow.bioinfo.cipf.es.

}, keywords = {interactome, network, snow}, doi = {10.1093/nar/gkp402}, url = {http://nar.oxfordjournals.org/content/early/2009/05/19/nar.gkp402.full}, author = {Minguez, Pablo and Gotz, S. and Montaner, David and Fatima Al-Shahrour and Dopazo, Joaquin} } @article {586, title = {SNOW, a web-based tool for the statistical analysis of protein-protein interaction networks.}, journal = {Nucleic Acids Res}, volume = {37}, year = {2009}, month = {2009 Jul}, pages = {W109-14}, abstract = {

Understanding the structure and the dynamics of the complex intercellular network of interactions that contributes to the structure and function of a living cell is one of the main challenges of today{\textquoteright}s biology. SNOW inputs a collection of protein (or gene) identifiers and, by using the interactome as scaffold, draws the connections among them, calculates several relevant network parameters and, as a novelty among the rest of tools of its class, it estimates their statistical significance. The parameters calculated for each node are: connectivity, betweenness and clustering coefficient. It also calculates the number of components, number of bicomponents and articulation points. An interactive network viewer is also available to explore the resulting network. SNOW is available at http://snow.bioinfo.cipf.es.

}, keywords = {Computer Graphics, Data Interpretation, Statistical, Databases, Protein, Humans, Internet, Protein Interaction Mapping, Software}, issn = {1362-4962}, doi = {10.1093/nar/gkp402}, author = {Minguez, Pablo and G{\"o}tz, Stefan and Montaner, David and Al-Shahrour, F{\'a}tima and Dopazo, Joaquin} } @article {599, title = {SNP and haplotype mapping for genetic analysis in the rat.}, journal = {Nat Genet}, volume = {40}, year = {2008}, month = {2008 May}, pages = {560-6}, abstract = {

The laboratory rat is one of the most extensively studied model organisms. Inbred laboratory rat strains originated from limited Rattus norvegicus founder populations, and the inherited genetic variation provides an excellent resource for the correlation of genotype to phenotype. Here, we report a survey of genetic variation based on almost 3 million newly identified SNPs. We obtained accurate and complete genotypes for a subset of 20,238 SNPs across 167 distinct inbred rat strains, two rat recombinant inbred panels and an F2 intercross. Using 81\% of these SNPs, we constructed high-density genetic maps, creating a large dataset of fully characterized SNPs for disease gene mapping. Our data characterize the population structure and illustrate the degree of linkage disequilibrium. We provide a detailed SNP map and demonstrate its utility for mapping of quantitative trait loci. This community resource is openly available and augments the genetic tools for this workhorse of physiological studies.

}, keywords = {Animals, Chromosome Mapping, Databases, Genetic, Genome, Haplotypes, Linkage Disequilibrium, Phylogeny, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Rats, Rats, Inbred Strains, Recombination, Genetic}, issn = {1546-1718}, doi = {10.1038/ng.124}, author = {Saar, Kathrin and Beck, Alfred and Bihoreau, Marie-Th{\'e}r{\`e}se and Birney, Ewan and Brocklebank, Denise and Chen, Yuan and Cuppen, Edwin and Demonchy, Stephanie and Dopazo, Joaquin and Flicek, Paul and Foglio, Mario and Fujiyama, Asao and Gut, Ivo G and Gauguier, Dominique and Guig{\'o}, Roderic and Guryev, Victor and Heinig, Matthias and Hummel, Oliver and Jahn, Niels and Klages, Sven and Kren, Vladimir and Kube, Michael and Kuhl, Heiner and Kuramoto, Takashi and Kuroki, Yoko and Lechner, Doris and Lee, Young-Ae and Lopez-Bigas, Nuria and Lathrop, G Mark and Mashimo, Tomoji and Medina, Ignacio and Mott, Richard and Patone, Giannino and Perrier-Cornet, Jeanne-Antide and Platzer, Matthias and Pravenec, Michal and Reinhardt, Richard and Sakaki, Yoshiyuki and Schilhabel, Markus and Schulz, Herbert and Serikawa, Tadao and Shikhagaie, Medya and Tatsumoto, Shouji and Taudien, Stefan and Toyoda, Atsushi and Voigt, Birger and Zelenika, Diana and Zimdahl, Heike and Hubner, Norbert} }