@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 {719, title = {Taxonomic variations in the gut microbiome of gout patients with and without tophi might have a functional impact on urate metabolism.}, journal = {Mol Med}, volume = {27}, year = {2021}, month = {2021 05 24}, pages = {50}, abstract = {

OBJECTIVE: To evaluate the taxonomic composition of the gut microbiome in gout patients with and without tophi formation, and predict bacterial functions that might have an impact on urate metabolism.

METHODS: Hypervariable V3-V4 regions of the bacterial 16S rRNA gene from fecal samples of gout patients with and without tophi (n = 33 and n = 25, respectively) were sequenced and compared to fecal samples from 53 healthy controls. We explored predictive functional profiles using bioinformatics in order to identify differences in taxonomy and metabolic pathways.

RESULTS: We identified a microbiome characterized by the lowest richness and a higher abundance of Phascolarctobacterium, Bacteroides, Akkermansia, and Ruminococcus_gnavus_group genera in patients with gout without tophi when compared to controls. The Proteobacteria phylum and the Escherichia-Shigella genus were more abundant in patients with tophaceous gout than in controls. Fold change analysis detected nine genera enriched in healthy controls compared to gout groups (Bifidobacterium, Butyricicoccus, Oscillobacter, Ruminococcaceae_UCG_010, Lachnospiraceae_ND2007_group, Haemophilus, Ruminococcus_1, Clostridium_sensu_stricto_1, and Ruminococcaceae_UGC_013). We found that the core microbiota of both gout groups shared Bacteroides caccae, Bacteroides stercoris ATCC 43183, and Bacteroides coprocola DSM 17136. These bacteria might perform functions linked to one-carbon metabolism, nucleotide binding, amino acid biosynthesis, and purine biosynthesis. Finally, we observed differences in key bacterial enzymes involved in urate synthesis, degradation, and elimination.

CONCLUSION: Our findings revealed that taxonomic variations in the gut microbiome of gout patients with and without tophi might have a functional impact on urate metabolism.

}, keywords = {Biodiversity, Computational Biology, Dysbiosis, Gastrointestinal Microbiome, Gout, Humans, Metagenome, metagenomics, Protein Interaction Mapping, Protein Interaction Maps, Uric Acid}, issn = {1528-3658}, doi = {10.1186/s10020-021-00311-5}, author = {M{\'e}ndez-Salazar, Eder Orlando and V{\'a}zquez-Mellado, Janitzia and Casimiro-Soriguer, Carlos S and Dopazo, Joaquin and Cubuk, Cankut and Zamudio-Cuevas, Yessica and Francisco-Balderas, Adriana and Mart{\'\i}nez-Flores, Karina and Fern{\'a}ndez-Torres, Javier and Lozada-P{\'e}rez, Carlos and Pineda, Carlos and S{\'a}nchez-Gonz{\'a}lez, Austreberto and Silveira, Luis H and Burguete-Garc{\'\i}a, Ana I and Orbe-Orihuela, Citlalli and Lagunas-Mart{\'\i}nez, Alfredo and Vazquez-Gomez, Alonso and L{\'o}pez-Reyes, Alberto and Palacios-Gonz{\'a}lez, Berenice and Mart{\'\i}nez-Nava, Gabriela Ang{\'e}lica} } @article {437, title = {Identification of the Photoreceptor Transcriptional Co-Repressor SAMD11 as Novel Cause of Autosomal Recessive Retinitis Pigmentosa.}, journal = {Sci Rep}, volume = {6}, year = {2016}, month = {2016 Oct 13}, pages = {35370}, abstract = {

Retinitis pigmentosa (RP), the most frequent form of inherited retinal dystrophy is characterized by progressive photoreceptor degeneration. Many genes have been implicated in RP development, but several others remain to be identified. Using a combination of homozygosity mapping, whole-exome and targeted next-generation sequencing, we found a novel homozygous nonsense mutation in SAMD11 in five individuals diagnosed with adult-onset RP from two unrelated consanguineous Spanish families. SAMD11 is ortholog to the mouse major retinal SAM domain (mr-s) protein that is implicated in CRX-mediated transcriptional regulation in the retina. Accordingly, protein-protein network analysis revealed a significant interaction of SAMD11 with CRX. Immunoblotting analysis confirmed strong expression of SAMD11 in human retina. Immunolocalization studies revealed SAMD11 was detected in the three nuclear layers of the human retina and interestingly differential expression between cone and rod photoreceptors was observed. Our study strongly implicates SAMD11 as novel cause of RP playing an important role in the pathogenesis of human degeneration of photoreceptors.

}, keywords = {Aged, Animals, Co-Repressor Proteins, Codon, Nonsense, Cohort Studies, Comparative Genomic Hybridization, Consanguinity, DNA Mutational Analysis, Exome, Eye Proteins, Female, Gene Expression Regulation, Genes, Recessive, Homeodomain Proteins, Homozygote, Humans, Male, Mice, Middle Aged, Polymorphism, Single Nucleotide, Protein Interaction Mapping, Retina, Retinal Dystrophies, Retinal Rod Photoreceptor Cells, Retinitis pigmentosa, Spain, Trans-Activators, Transcription Factors}, issn = {2045-2322}, doi = {10.1038/srep35370}, author = {Corton, M and Avila-Fern{\'a}ndez, A and Campello, L and S{\'a}nchez, M and Benavides, B and L{\'o}pez-Molina, M I and Fern{\'a}ndez-S{\'a}nchez, L and S{\'a}nchez-Alcudia, R and da Silva, L R J and Reyes, N and Mart{\'\i}n-Garrido, E and Zurita, O and Fern{\'a}ndez-San Jos{\'e}, P and P{\'e}rez-Carro, R and Garc{\'\i}a-Garc{\'\i}a, F and Dopazo, J and Garc{\'\i}a-Sandoval, B and Cuenca, N and Ayuso, C} } @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 {468, title = {PTMcode v2: a resource for functional associations of post-translational modifications within and between proteins.}, journal = {Nucleic Acids Res}, volume = {43}, year = {2015}, month = {2015 Jan}, pages = {D494-502}, abstract = {

The post-translational regulation of proteins is mainly driven by two molecular events, their modification by several types of moieties and their interaction with other proteins. These two processes are interdependent and together are responsible for the function of the protein in a particular cell state. Several databases focus on the prediction and compilation of protein-protein interactions (PPIs) and no less on the collection and analysis of protein post-translational modifications (PTMs), however, there are no resources that concentrate on describing the regulatory role of PTMs in PPIs. We developed several methods based on residue co-evolution and proximity to predict the functional associations of pairs of PTMs that we apply to modifications in the same protein and between two interacting proteins. In order to make data available for understudied organisms, PTMcode v2 (http://ptmcode.embl.de) includes a new strategy to propagate PTMs from validated modified sites through orthologous proteins. The second release of PTMcode covers 19 eukaryotic species from which we collected more than 300,000 experimentally verified PTMs (>1,300,000 propagated) of 69 types extracting the post-translational regulation of >100,000 proteins and >100,000 interactions. In total, we report 8 million associations of PTMs regulating single proteins and over 9.4 million interplays tuning PPIs.

}, keywords = {Databases, Protein, Internet, Protein Interaction Mapping, Protein Processing, Post-Translational}, issn = {1362-4962}, doi = {10.1093/nar/gku1081}, author = {Minguez, Pablo and Letunic, Ivica and Parca, Luca and Garc{\'\i}a-Alonso, Luz and Dopazo, Joaquin and Huerta-Cepas, Jaime and Bork, Peer} } @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 {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 {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} }