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Human genome resequencing projects provide an unprecedented amount of data about single-nucleotide variations occurring in protein-coding regions and often leading to observable changes in the covalent structure of gene products. For many of these variations, links to Online Mendelian Inheritance in Man (OMIM) genetic diseases are available and are reported in many databases that are collecting human variation data such as Humsavar. However, the current knowledge on the molecular mechanisms that are leading to diseases is, in many cases, still limited. For understanding the complex mechanisms behind disease insurgence, the identification of putative models, when considering the protein structure and chemico-physical features of the variations, can be useful in many contexts, including early diagnosis and prognosis. In this study, we investigate the occurrence and distribution of human disease–related variations in the context of Pfam domains. The aim of this study is the identification and characterization of Pfam domains that are statistically more likely to be associated with disease-related variations. The study takes into consideration 2,513 human protein sequences with 22,763 disease-related variations. We describe patterns of disease-related variation types in biunivocal relation with Pfam domains, which are likely to be possible markers for linking Pfam domains to OMIM diseases. Furthermore, we take advantage of the specific association between disease-related variation types and Pfam domains for clustering diseases according to the Human Disease Ontology, and we establish a relation among variation types, Pfam domains, and disease classes. We find that Pfam models are specific markers of patterns of variation types and that they can serve to bridge genes, diseases, and disease classes. Data are available as Supplementary Material for 1,670 Pfam models, including 22,763 disease-related variations associated to 3,257 OMIM diseases.

Enzymes are key proteins performing the basic functional activities in cells. In humans, enzymes can be also responsible for diseases, and the molecular mechanisms underlying the genotype to phenotype relationship are under investigation for diagnosis and medical care. Here, we focus on highlighting enzymes that are active in different metabolic pathways and become relevant hubs in protein interaction networks. We perform a statistics to derive our present knowledge on human metabolic pathways (the Kyoto Encyclopaedia of Genes and Genomes (KEGG)), and we found that activity aldehyde dehydrogenase (NAD(+)), described by Enzyme Commission number EC 1.2.1.3, and activity acetyl-CoA C-acetyltransferase (EC 2.3.1.9) are the ones most frequently involved. By associating functional activities (EC numbers) to enzyme proteins, we found the proteins most frequently involved in metabolic pathways. With our analysis, we found that these proteins are endowed with the highest numbers of interaction partners when compared to all the enzymes in the pathways and with the highest numbers of predicted interaction sites. As specific enzyme protein test cases, we focus on Alpha-Aminoadipic Semialdehyde Dehydrogenase (ALDH7A1, EC 2.3.1.9) and Acetyl-CoA acetyltransferase, cytosolic and mitochondrial (gene products of ACAT2 and ACAT1, respectively, EC 2.3.1.9). With computational approaches we show that it is possible, by starting from the enzyme structure, to highlight clues of their multiple roles in different pathways and of putative mechanisms promoting the association of genes to disease.

The Critical Assessment of Genome Interpretation (CAGI) is a global community experiment to objectively assess computational methods for predicting phenotypic impacts of genomic variation. One of the 2015-2016 competitions focused on predicting the influence of mutations on the allosteric regulation of human liver pyruvate kinase. More than 30 different researchers accessed the challenge data. However, only four groups accepted the challenge. Features used for predictions ranged from evolutionary constraints, mutant site locations relative to active and effector binding sites, and computational docking outputs. Despite the range of expertise and strategies used by predictors, the best predictions were marginally greater than random for modified allostery resulting from mutations. In contrast, several groups successfully predicted which mutations severely reduced enzymatic activity. Nonetheless, poor predictions of allostery stands in stark contrast to the impression left by more than 700 PubMed entries identified using the identifiers "computational + allosteric." This contrast highlights a specialized need for new computational tools and utilization of benchmarks that focus on allosteric regulation.

Modern sequencing technologies provide an unprecedented amount of data of single-nucleotide variations occurring in coding regions and leading to changes in the expressed protein sequences. A significant fraction of these single-residue variations is linked to disease onset and collected in public databases. In recent years, many scientific studies have been focusing on the dissection of salient features of disease-related variations from different perspectives. In this work, we complement previous analyses by updating a dataset of disease-related variations occurring in proteins with 3D structure. Within this dataset, we describe functional and structural features that can be of interest for characterizing disease-related variations, including major chemico-physical properties, the strength of association to disease of variation types, their effect on protein stability, their location on the protein structure, and their distribution in Pfam structural/functional protein models. Our results support previous findings obtained in different data sets and introduce Pfam models as possible fingerprints of patterns of disease related single-nucleotide variations.

Familial aggregation is a significant risk factor for the development of thyroid cancer and familial non-medullary thyroid cancer (FNMTC) accounts for 5-7% of all NMTC. Whole exome sequencing analysis in the family affected by FNMTC with oncocytic features where our group previously identified a predisposing locus on chromosome 19p13.2, revealed a novel heterozygous mutation (c.400G > A, NM_012335; p.Gly134Ser) in exon 5 of MYO1F, mapping to the linkage locus. In the thyroid FRTL-5 cell model stably expressing the mutant MYO1F p.Gly134Ser protein, we observed an altered mitochondrial network, with increased mitochondrial mass and a significant increase in both intracellular and extracellular reactive oxygen species, compared to cells expressing the wild-type (wt) protein or carrying the empty vector. The mutation conferred a significant advantage in colony formation, invasion and anchorage-independent growth. These data were corroborated by in vivo studies in zebrafish, since we demonstrated that the mutant MYO1F p.Gly134Ser, when overexpressed, can induce proliferation in whole vertebrate embryos, compared to the wt one. MYO1F screening in additional 192 FNMTC families identified another variant in exon 7, which leads to exon skipping, and is predicted to alter the ATP-binding domain in MYO1F. Our study identified for the first time a role for MYO1F in NMTC.

Precision medicine aims to predict a patient's disease risk and best therapeutic options by using that individual's genetic sequencing data. The Critical Assessment of Genome Interpretation (CAGI) is a community experiment consisting of genotype–phenotype prediction challenges; participants build models, undergo assessment, and share key findings. For CAGI 4, three challenges involved using exome-sequencing data: Crohn's disease, bipolar disorder, and warfarin dosing. Previous CAGI challenges included prior versions of the Crohn's disease challenge. Here, we discuss the range of techniques used for phenotype prediction as well as the methods used for assessing predictive models. Additionally, we outline some of the difficulties associated with making predictions and evaluating them. The lessons learned from the exome challenges can be applied to both research and clinical efforts to improve phenotype prediction from genotype. In addition, these challenges serve as a vehicle for sharing clinical and research exome data in a secure manner with scientists who have a broad range of expertise, contributing to a collaborative effort to advance our understanding of genotype–phenotype relationships.

Background Genetic investigations, boosted by modern sequencing techniques, allow dissecting the genetic component of different phenotypic traits. These efforts result in the compilation of lists of genes related to diseases and show that an increasing number of diseases is associated with multiple genes. Investigating functional relations among genes associated with the same disease contributes to highlighting molecular mechanisms of the pathogenesis. Results We present eDGAR, a database collecting and organizing the data on gene/disease associations as derived from OMIM, Humsavar and ClinVar. For each disease-associated gene, eDGAR collects information on its annotation. Specifically, for lists of genes, eDGAR provides information on: i) interactions retrieved from PDB, BIOGRID and STRING; ii) co-occurrence in stable and functional structural complexes; iii) shared Gene Ontology annotations; iv) shared KEGG and REACTOME pathways; v) enriched functional annotations computed with NET-GE; vi) regulatory interactions derived from TRRUST; vii) localization on chromosomes and/or co-localisation in neighboring loci. The present release of eDGAR includes 2672 diseases, related to 3658 different genes, for a total number of 5729 gene-disease associations. 71% of the genes are linked to 621 multigenic diseases and eDGAR highlights their common GO terms, KEGG/REACTOME pathways, physical and regulatory interactions. eDGAR includes a network based enrichment method for detecting statistically significant functional terms associated to groups of genes. Conclusions eDGAR offers a resource to analyze disease-gene associations. In multigenic diseases genes can share physical interactions and/or co-occurrence in the same functional processes. eDGAR is freely available at: edgar.biocomp.unibo.it Electronic supplementary material The online version of this article (doi:10.1186/s12864-017-3911-3) contains supplementary material, which is available to authorized users.

none 6 no Background: Modern genomic techniques allow to associate several Mendelian human diseases to single residue variations in different proteins. Molecular mechanisms explaining the relationship among genotype and phenotype are still under debate. Change of protein stability upon variation appears to assume a particular relevance in annotating whether a single residue substitution can or cannot be associated to a given disease. Thermodynamic properties of human proteins and of their disease related variants are lacking. In the present work, we take advantage of the available three dimensional structure of human proteins for predicting the role of disease related variations on the perturbation of protein stability. Results: We develop INPS3D, a new predictor based on protein structure for computing the effect of single residue variations on protein stability (ΔΔG), scoring at the state-of-the-art (Pearson's correlation value of the regression is equal to 0.72 with mean standard error of 1.15 kcal/mol on a blind test set comprising 351 variations in 60 proteins). We then filter 368 OMIM disease related proteins known with atomic resolution (where the three dimensional structure covers at least 70 % of the sequence) with 4717 disease related single residue variations and 685 polymorphisms without clinical consequence. We find that the effect on protein stability of disease related variations is larger than the effect of polymorphisms: in particular, by setting to |1 kcal/mol| the threshold between perturbing and not perturbing variations of the protein stability, about 44 % of disease related variations and 20 % of polymorphisms are predicted with |ΔΔG| > 1 kcal/mol, respectively. A consistent fraction of OMIM disease related variations is however predicted to promote |ΔΔG| ≤ 1 kcal/mol and we focus here on detecting features that can be associated to the thermodynamic property of the protein variant. Our analysis reveals that some 47 % of disease related variations promoting |ΔΔG| ≤ 1 are located in solvent exposed sites of the protein structure. We also find that the increase of the fraction of variations that in proteins are predicted with |ΔΔG| ≤ 1 kcal/mol, partially relates with the increasing number of the protein interacting partners, corroborating the notion that disease related, non-perturbing variations are likely to impair protein-protein interaction (70 % of the disease causing variations, with high accessible surface are indeed predicted in interacting sites). The set of OMIM surface accessible variations with |ΔΔG| ≤ 1 kcal/mol and located in interaction sites are 23 % of the total in 161 proteins. Among these, 43 proteins with some 327 disease causing variations are involved in signalling, structural biological processes, development and differentiation. Conclusions: We compute the effect of disease causing variations on protein stability with INPS3D, a new state-of-the-art tool for predicting the change in ΔΔG value associated to single residue substitution in protein structures. The analysis indicates that OMIM disease related variations in proteins promote a much larger effect on protein stability than polymorphisms non-associated to diseases. Disease related variations with a slight effect on protein stability (|ΔΔG| < 1 kcal/mol) frequently occur at the protein accessible surface suggesting that they are located in protein-protein interactions patches in putative human biological functional networks. The hypothesis is corroborated by proving that proteins with many disease related variations that slightly perturb protein stability are on average more connected in the human physical interactome (IntAct) than proteins with variations predicted with |ΔΔG| > 1 kcal/mol. Martelli, Pier Luigi; Fariselli, Piero; Savojardo, Castrense; Babbi, Giulia; Aggazio, Francesco; Casadio, Rita Martelli, Pier Luigi; Fariselli, Piero; Savojardo, Castrense; Babbi, Giulia; Aggazio, Francesco; Casadio, Rita