Characterization of pathogenic germline mutations in human Protein Kinases

Background: Protein Kinases are a superfamily of proteins involved in crucial cellular processes such as cell cycle regulation and signal transduction. Accordingly, they play an important role in cancer biology. To contribute to the study of the relation between kinases and disease we compared pathogenic mutations to neutral mutations as an extension to our previous analysis of cancer somatic mutations. First, we analyzed native and mutant proteins in terms of amino acid composition. Secondly, mutations were characterized according to their potential structural effects and finally, we assessed the location of the different classes of polymorphisms with respect to kinase-relevant positions in terms of subfamily specificity, conservation, accessibility and functional sites.Results: Pathogenic Protein Kinase mutations perturb essential aspects of protein function, including disruption of substrate binding and/or effector recognition at family-specific positions. Interestingly these mutations in Protein Kinases display a tendency to avoid structurally relevant positions, what represents a significant difference with respect to the average distribution of pathogenic mutations in other protein families.Conclusions: Disease-associated mutations display sound differences with respect to neutral mutations: several amino acids are specific of each mutation type, different structural properties characterize each class and the distribution of pathogenic mutations within the consensus structure of the Protein Kinase domain is substantially different to that for non-pathogenic mutations. This preferential distribution confirms previous observations about the functional and structural distribution of the controversial cancer driver and passenger somatic mutations and their use as a proxy for the study of the involvement of somatic mutations in cancer development. © 2011 Izarzugaza et al; licensee BioMed Central Ltd

Structural impact of SNPs

The simplest form of mutation is a single DNA base change, frequently referred to as a “single nucleotide polymorphism” (SNP). Strictly, this term should only be applied to single base changes that are observed in at least 1% of a “normal” population. However, it is frequently used to refer to any single base mutation and is used in that context here. Many SNPs occur in noncoding regions of DNA, where they may affect transcription, mRNA splicing, or mRNA stability. When a single base change occurs in an exon, it will fall into one of three classes: (1) a “synonymous” mutation which does not change the amino acid sequence of the resultant protein (although this may still affect expression, splicing, or mRNA stability), (2) a “nonsense” mutation resulting in premature termination of the protein sequence, or (3) a “non-synonymous” (or “missense”) mutation (an nsSNP) resulting in a single amino acid change. At the protein level, an nsSNP results in a “single amino acid..

Structural analysis of single amino acid polymorphisms

Understanding genetic variation is the basis for prevention and diagnosis of inherited disease. In the ‘next generation sequencing’ era with rapidly accumulating variation data, the focus has shifted from population-level analyses to individuals. This thesis is centred on the problem of gathering, storing and analysing mutation data to understand and predict the effects single amino acid mutations will have on protein structure and function. I present analysis of a subset of mutations and a new predictive method implemented to expand the coverage of the structural effects by our pipeline. I characterised a subset of pathogenic mutations: ‘compensated pathogenic deviations’. These are mutations which cause disease in humans, but the mutant residues are found as native residues in other species. During evolution, they are presumed to spread through populations by co-evolving with another, neutralising mutation. When compared with uncompensated mutations, they often cause milder structural disruptions, prefer less conserved structural environments and are often found on the protein surface. I describe the development of a new analysis to test the effects of mutations by predicting residues involved in protein-protein interfaces where the structure of the complex is unknown. Two machine learning methods (multilayer perceptrons and, in particular, random forests) show an improvement over previously published protein-protein interface predictors. This new method further increases the ability of the SAAPdb analysis pipeline to show the effects of mutations on protein structure and function. Furthermore, it is a template for building prediction-based structural analysis methods for the pipeline, where available structural data are insufficient. In summary this thesis examines mutations from both an evolutionary and a disease perspective. In addition, a novel method for predicting protein interaction regions is developed thus expanding the existing pipeline and furthering our ability to understand mutations and use them in a predictive context

An integrated approach to the interpretation of Single Amino Acid Polymorphisms within the framework of CATH and Gene3D

Background: The phenotypic effects of sequence variations in protein-coding regions come about primarily via their effects on the resulting structures, for example by disrupting active sites or affecting structural stability. In order better to understand the mechanisms behind known mutant phenotypes, and predict the effects of novel variations, biologists need tools to gauge the impacts of DNA mutations in terms of their structural manifestation. Although many mutations occur within domains whose structure has been solved, many more occur within genes whose protein products have not been structurally characterized.Results: Here we present 3DSim (3D Structural Implication of Mutations), a database and web application facilitating the localization and visualization of single amino acid polymorphisms (SAAPs) mapped to protein structures even where the structure of the protein of interest is unknown. The server displays information on 6514 point mutations, 4865 of them known to be associated with disease. These polymorphisms are drawn from SAAPdb, which aggregates data from various sources including dbSNP and several pathogenic mutation databases. While the SAAPdb interface displays mutations on known structures, 3DSim projects mutations onto known sequence domains in Gene3D. This resource contains sequences annotated with domains predicted to belong to structural families in the CATH database. Mappings between domain sequences in Gene3D and known structures in CATH are obtained using a MUSCLE alignment. 1210 three-dimensional structures corresponding to CATH structural domains are currently included in 3DSim; these domains are distributed across 396 CATH superfamilies, and provide a comprehensive overview of the distribution of mutations in structural space.Conclusion: The server is publicly available at http://3DSim.bioinfo.cnio.es/. In addition, the database containing the mapping between SAAPdb, Gene3D and CATH is available on request and most of the functionality is available through programmatic web service access

IntPred: a structure-based predictor of protein-protein interaction sites

Motivation Protein–protein interactions are vital for protein function with the average protein having between three and ten interacting partners. Knowledge of precise protein–protein interfaces comes from crystal structures deposited in the Protein Data Bank (PDB), but only 50% of structures in the PDB are complexes. There is therefore a need to predict protein–protein interfaces in silico and various methods for this purpose. Here we explore the use of a predictor based on structural features and which exploits random forest machine learning, comparing its performance with a number of popular established methods. Results On an independent test set of obligate and transient complexes, our IntPred predictor performs well (MCC = 0.370, ACC = 0.811, SPEC = 0.916, SENS = 0.411) and compares favourably with other methods. Overall, IntPred ranks second of six methods tested with SPPIDER having slightly better overall performance (MCC = 0.410, ACC = 0.759, SPEC = 0.783, SENS = 0.676), but considerably worse specificity than IntPred. As with SPPIDER, using an independent test set of obligate complexes enhanced performance (MCC = 0.381) while performance is somewhat reduced on a dataset of transient complexes (MCC = 0.303). The trade-off between sensitivity and specificity compared with SPPIDER suggests that the choice of the appropriate tool is application-dependent

Malignant struma ovarii harboring a unique NRAS mutation: case report and review of the literature

Struma ovarii (SO), a rare tumor containing at least 50% of thyroid tissue, represents approximately 5% of all ovarian teratomas; its malignant transformation rate is reported to occur in up to 10% of cases and metastases occur in about 5-6% of them. We describe a 36-year old woman who underwent laparoscopic left annessectomy two years earlier because of an ovarian cyst. Follow-up imaging revealed a right adnexal mass, ascitis and peritoneal nodes that were diagnosed as comprising a malignant SO with peritoneal secondary localizations at histopathology performed after intervention. Restaging with F-18-FDG-PE T/CT scan, abdominal CT and ultrasonography showed abnormalities in the perihepatic region and presacral space and left hypochondrium localizations. The patient underwent thyroidectomy, hepatic nodulectomy and cytoreductive peritonectomy: histopathological examination did not show any malignant disease in the thyroid and confirmed the presence of peritoneal localizations due to malignant SO; molecular analysis detected NRAS Q61K mutation in exon 3, whereas no mutations were identified on the BRAF gene. The patient underwent radioiodine treatment: serum Tg was decreased at first follow-up after three months of I-131-therapy. We believe that our case raises some interesting considerations. First, pathologists should be aware of this entity and should check for the presence of point mutations suggesting an aggressive disease behavior, which could be beneficial for an optimal therapeutic approach. Second, although most of the knowledge in this field comes from case reports, efforts should be made to standardize the management of patients affected by malignant SO, including use of practice guidelines

The P4G-Getting to Zero Coalition Partnership: Finding and supporting opportunities to decarbonise shipping in Indonesia, Mexico and South Africa

The International Maritime Organization has committed to reducing greenhouse gases emissions from international shipping by at least 50% by 2050 compared to 2008 levels. To reach that goal, a shift towards new low- and zero-carbon fuels -such as hydrogen and ammonia- is urgently needed, along with the deployment of safe and reliable zero-emission vessels, technologies and infrastructure. With shipping being a potential significant demand driver for these new fuels, it can act as a trigger and catalyst for the broader energy transition, benefiting other sectors of the economy. The P4G-Getting to Zero (GtZ) Coalition Partnership is a two-year project that focuses on shipping decarbonisation business and development opportunities in Indonesia, Mexico and South Africa. To the project’s core is the priority of bringing forward the national voices, priorities and policies around climate change, energy transition, job generation and air pollution. To that end, the P4G-GtZ team detected and engaged with key national and international stakeholders that can provide the current countries’ landscape, diagnostic and synergies around shipping opportunities. Apart from generating a networking space for the different key stakeholders, the project delivered detailed shipping activity maps coupled with energy studies that throw light at which low/zero-carbon fuel offers better feasibility to decarbonise shipping taking into account policy, job generation and international competition. The poster introduces the P4G-GtZ Coalition Partnership and the progress done so far while highlighting key findings around shipping decarbonisation, hydrogen-based fuels potential and energy transitions

Characterization of pathogenic germline mutations in human Protein Kinases

Background Protein Kinases are a superfamily of proteins involved in crucial cellular processes such as cell cycle regulation and signal transduction. Accordingly, they play an important role in cancer biology. To contribute to the study of the relation between kinases and disease we compared pathogenic mutations to neutral mutations as an extension to our previous analysis of cancer somatic mutations. First, we analyzed native and mutant proteins in terms of amino acid composition. Secondly, mutations were characterized according to their potential structural effects and finally, we assessed the location of the different classes of polymorphisms with respect to kinase-relevant positions in terms of subfamily specificity, conservation, accessibility and functional sites.<p></p> Results Pathogenic Protein Kinase mutations perturb essential aspects of protein function, including disruption of substrate binding and/or effector recognition at family-specific positions. Interestingly these mutations in Protein Kinases display a tendency to avoid structurally relevant positions, what represents a significant difference with respect to the average distribution of pathogenic mutations in other protein families.<p></p> Conclusions Disease-associated mutations display sound differences with respect to neutral mutations: several amino acids are specific of each mutation type, different structural properties characterize each class and the distribution of pathogenic mutations within the consensus structure of the Protein Kinase domain is substantially different to that for non-pathogenic mutations. This preferential distribution confirms previous observations about the functional and structural distribution of the controversial cancer driver and passenger somatic mutations and their use as a proxy for the study of the involvement of somatic mutations in cancer development.<p></p&gt

An integrated approach to the interpretation of Single Amino Acid Polymorphisms within the framework of CATH and Gene3D

Background The phenotypic effects of sequence variations in protein-coding regions come about primarily via their effects on the resulting structures, for example by disrupting active sites or affecting structural stability. In order better to understand the mechanisms behind known mutant phenotypes, and predict the effects of novel variations, biologists need tools to gauge the impacts of DNA mutations in terms of their structural manifestation. Although many mutations occur within domains whose structure has been solved, many more occur within genes whose protein products have not been structurally characterized.<p></p> Results Here we present 3DSim (3D Structural Implication of Mutations), a database and web application facilitating the localization and visualization of single amino acid polymorphisms (SAAPs) mapped to protein structures even where the structure of the protein of interest is unknown. The server displays information on 6514 point mutations, 4865 of them known to be associated with disease. These polymorphisms are drawn from SAAPdb, which aggregates data from various sources including dbSNP and several pathogenic mutation databases. While the SAAPdb interface displays mutations on known structures, 3DSim projects mutations onto known sequence domains in Gene3D. This resource contains sequences annotated with domains predicted to belong to structural families in the CATH database. Mappings between domain sequences in Gene3D and known structures in CATH are obtained using a MUSCLE alignment. 1210 three-dimensional structures corresponding to CATH structural domains are currently included in 3DSim; these domains are distributed across 396 CATH superfamilies, and provide a comprehensive overview of the distribution of mutations in structural space.<p></p> Conclusion The server is publicly available at http://3DSim.bioinfo.cnio.es/ webcite. In addition, the database containing the mapping between SAAPdb, Gene3D and CATH is available on request and most of the functionality is available through programmatic web service access.<p></p&gt

Rheumatology training experience across Europe : Analysis of core competences

Publisher Copyright: © 2016 The Author(s). Copyright: Copyright 2019 Elsevier B.V., All rights reserved.Background: The aim of this project was to analyze and compare the educational experience in rheumatology specialty training programs across European countries, with a focus on self-reported ability. Method: An electronic survey was designed to assess the training experience in terms of self-reported ability, existence of formal education, number of patients managed and assessments performed during rheumatology training in 21 core competences including managing specific diseases, generic competences and procedures. The target population consisted of rheumatology trainees and recently certified rheumatologists across Europe. The relationship between the country of training and the self-reported ability or training methods for each competence was analyzed through linear or logistic regression, as appropriate. Results: In total 1079 questionnaires from 41 countries were gathered. Self-reported ability was high for most competences, range 7.5-9.4 (0-10 scale) for clinical competences, 5.8-9.0 for technical procedures and 7.8-8.9 for generic competences. Competences with lower self-reported ability included managing patients with vasculitis, identifying crystals and performing an ultrasound. Between 53 and 91 % of the trainees received formal education and between 7 and 61 % of the trainees reported limited practical experience (managing ≤10 patients) in each competence. Evaluation of each competence was reported by 29-60 % of the respondents. In adjusted multivariable analysis, the country of training was associated with significant differences in self-reported ability for all individual competences. Conclusion: Even though self-reported ability is generally high, there are significant differences amongst European countries, including differences in the learning structure and assessment of competences. This suggests that educational outcomes may also differ. Efforts to promote European harmonization in rheumatology training should be encouraged and supported.publishersversionPeer reviewe