Improving the clinical interpretation of missense variants in X linked genes using structural analysis.

BACKGROUND: Improving the clinical interpretation of missense variants can increase the diagnostic yield of genomic testing and lead to personalised management strategies. Currently, due to the imprecision of bioinformatic tools that aim to predict variant pathogenicity, their role in clinical guidelines remains limited. There is a clear need for more accurate prediction algorithms and this study aims to improve performance by harnessing structural biology insights. The focus of this work is missense variants in a subset of genes associated with X linked disorders. METHODS: We have developed a protein-specific variant interpreter (ProSper) that combines genetic and protein structural data. This algorithm predicts missense variant pathogenicity by applying machine learning approaches to the sequence and structural characteristics of variants. RESULTS: ProSper outperformed seven previously described tools, including meta-predictors, in correctly evaluating whether or not variants are pathogenic; this was the case for 11 of the 21 genes associated with X linked disorders that met the inclusion criteria for this study. We also determined gene-specific pathogenicity thresholds that improved the performance of VEST4, REVEL and ClinPred, the three best-performing tools out of the seven that were evaluated; this was the case in 11, 11 and 12 different genes, respectively. CONCLUSION: ProSper can form the basis of a molecule-specific prediction tool that can be implemented into diagnostic strategies. It can allow the accurate prioritisation of missense variants associated with X linked disorders, aiding precise and timely diagnosis. In addition, we demonstrate that gene-specific pathogenicity thresholds for a range of missense prioritisation tools can lead to an increase in prediction accuracy.

Insight into the stacking and the species-ordering dependences of interlayer bonding in SiC/GeC polar heterostructures.

Two-dimensional (2D) polar materials experience an in-plane charge transfer between different elements due to their electron negativities. When they form vertical heterostructures, the electrostatic force triggered by such charge transfer plays an important role in the interlayer bonding beyond van der Waals (vdW) interaction. Our comprehensive first principle study on the structural stability of the 2D SiC/GeC hybrid bilayer heterostructure has found that the electrostatic interlayer interaction can induce theπ-πorbital hybridization between adjacent layers under different stacking and out-of-plane species ordering, with strong hybridization in the cases of Si-C and C-Ge species orderings but weak hybridization in the case of the C-C ordering. In particular, the attractive electrostatic interlayer interaction in the cases of Si-C and C-Ge species orderings mainly controls the equilibrium interlayer distance and the vdW interaction makes the system attain a lower binding energy. On the contrary, the vdW interaction mostly controls the equilibrium interlayer distance in the case of the C-C species ordering and the repulsive electrostatic interlayer force has less effect. Interesting finding is that the band structure of the SiC/GeC hybrid bilayer is sensitive to the layer-layer stacking and the out-of-plane species ordering. An indirect band gap of 2.76 eV (or 2.48 eV) was found under the AA stacking with Si-C ordering (or under the AB stacking with C-C ordering). While a direct band gap of 2.00-2.88 eV was found under other stacking and species orderings, demonstrating its band gap tunable feature. Furthermore, there is a charge redistribution in the interfacial region leading to a built-in electric field. Such field will separate the photo-generated charge carriers in different layers and is expected to reduce the probability of carrier recombination, and eventually give rise to the electron tunneling between layers.

Recurrent KCNT2 missense variants affecting p.Arg190 result in a recognizable phenotype.

KCNT2 variants resulting in substitutions affecting the Arg190 residue have been shown to cause epileptic encephalopathy and a recognizable facial gestalt. We report two additional individuals with intellectual disability, dysmorphic features, hypertrichosis, macrocephaly and the same de novo KCNT2 missense variants affecting the Arg190 residue as previously described. Notably, neither patient has epilepsy. Homology modeling of these missense variants revealed that they are likely to disrupt the stabilization of a closed channel conformation of KCNT2 resulting in a constitutively open state. This is the first report of pathogenic variants in KCNT2 causing a developmental phenotype without epilepsy.

Biochemical and structural analyses suggest that plasminogen activators coevolved with their cognate protein substrates and inhibitors.

Protein sequences of members of the plasminogen activation system are present throughout the entire vertebrate phylum. This important and well-described proteolytic cascade is governed by numerous protease-substrate and protease-inhibitor interactions whose conservation is crucial to maintaining unchanged protein function throughout evolution. The pressure to preserve protein-protein interactions may lead to either co-conservation or covariation of binding interfaces. Here, we combined covariation analysis and structure-based prediction to analyze the binding interfaces of urokinase (uPA):plasminogen activator inhibitor-1 (PAI-1) and uPA:plasminogen complexes. We detected correlated variation between the S3-pocket-lining residues of uPA and the P3 residue of both PAI-1 and plasminogen. These residues are known to form numerous polar interactions in the human uPA:PAI-1 Michaelis complex. To test the effect of mutations that correlate with each other and have occurred during mammalian diversification on protein-protein interactions, we produced uPA, PAI-1, and plasminogen from human and zebrafish to represent mammalian and nonmammalian orthologs. Using single amino acid point substitutions in these proteins, we found that the binding interfaces of uPA:plasminogen and uPA:PAI-1 may have coevolved to maintain tight interactions. Moreover, we conclude that although the interaction areas between protease-substrate and protease-inhibitor are shared, the two interactions are mechanistically different. Compared with a protease cleaving its natural substrate, the interaction between a protease and its inhibitor is more complex and involves a more fine-tuned mechanism. Understanding the effects of evolution on specific protein interactions may help further pharmacological interventions of the plasminogen activation system and other proteolytic systems.

Mutations in PCYT2 disrupt etherlipid biosynthesis and cause a complex hereditary spastic paraplegia.

CTP:phosphoethanolamine cytidylyltransferase (ET), encoded by PCYT2, is the rate-limiting enzyme for phosphatidylethanolamine synthesis via the CDP-ethanolamine pathway. Phosphatidylethanolamine is one of the most abundant membrane lipids and is particularly enriched in the brain. We identified five individuals with biallelic PCYT2 variants clinically characterized by global developmental delay with regression, spastic para- or tetraparesis, epilepsy and progressive cerebral and cerebellar atrophy. Using patient fibroblasts we demonstrated that these variants are hypomorphic, result in altered but residual ET protein levels and concomitant reduced enzyme activity without affecting mRNA levels. The significantly better survival of hypomorphic CRISPR-Cas9 generated pcyt2 zebrafish knockout compared to a complete knockout, in conjunction with previously described data on the Pcyt2 mouse model, indicates that complete loss of ET function may be incompatible with life in vertebrates. Lipidomic analysis revealed profound lipid abnormalities in patient fibroblasts impacting both neutral etherlipid and etherphospholipid metabolism. Plasma lipidomics studies also identified changes in etherlipids that have the potential to be used as biomarkers for ET deficiency. In conclusion, our data establish PCYT2 as a disease gene for a new complex hereditary spastic paraplegia and confirm that etherlipid homeostasis is important for the development and function of the brain.

EVI1 carboxy-terminal phosphorylation is ATM-mediated and sustains transcriptional modulation and self-renewal via enhanced CtBP1 association.

The transcriptional regulator EVI1 has an essential role in early hematopoiesis and development. However, aberrantly high expression of EVI1 has potent oncogenic properties and confers poor prognosis and chemo-resistance in leukemia and solid tumors. To investigate to what extent EVI1 function might be regulated by post-translational modifications we carried out mass spectrometry- and antibody-based analyses and uncovered an ATM-mediated double phosphorylation of EVI1 at the carboxy-terminal S858/S860 SQS motif. In the presence of genotoxic stress EVI1-WT (SQS), but not site mutated EVI1-AQA was able to maintain transcriptional patterns and transformation potency, while under standard conditions carboxy-terminal mutation had no effect. Maintenance of hematopoietic progenitor cell clonogenic potential was profoundly impaired with EVI1-AQA compared with EVI1-WT, in particular in the presence of genotoxic stress. Exploring mechanistic events underlying these observations, we showed that after genotoxic stress EVI1-WT, but not EVI1-AQA increased its level of association with its functionally essential interaction partner CtBP1, implying a role for ATM in regulating EVI1 protein interactions via phosphorylation. This aspect of EVI1 regulation is therapeutically relevant, as chemotherapy-induced genotoxicity might detrimentally sustain EVI1 function via stress response mediated phosphorylation, and ATM-inhibition might be of specific targeted benefit in EVI1-overexpressing malignancies.

Non-muscle myosin IIB (Myh10) is required for epicardial function and coronary vessel formation during mammalian development.

The coronary vasculature is an essential vessel network providing the blood supply to the heart. Disruptions in coronary blood flow contribute to cardiac disease, a major cause of premature death worldwide. The generation of treatments for cardiovascular disease will be aided by a deeper understanding of the developmental processes that underpin coronary vessel formation. From an ENU mutagenesis screen, we have isolated a mouse mutant displaying embryonic hydrocephalus and cardiac defects (EHC). Positional cloning and candidate gene analysis revealed that the EHC phenotype results from a point mutation in a splice donor site of the Myh10 gene, which encodes NMHC IIB. Complementation testing confirmed that the Myh10 mutation causes the EHC phenotype. Characterisation of the EHC cardiac defects revealed abnormalities in myocardial development, consistent with observations from previously generated NMHC IIB null mouse lines. Analysis of the EHC mutant hearts also identified defects in the formation of the coronary vasculature. We attribute the coronary vessel abnormalities to defective epicardial cell function, as the EHC epicardium displays an abnormal cell morphology, reduced capacity to undergo epithelial-mesenchymal transition (EMT), and impaired migration of epicardial-derived cells (EPDCs) into the myocardium. Our studies on the EHC mutant demonstrate a requirement for NMHC IIB in epicardial function and coronary vessel formation, highlighting the importance of this protein in cardiac development and ultimately, embryonic survival.

Rapid functional and evolutionary changes follow gene duplication in yeast.

Duplication of genes or genomes provides the raw material for evolutionary innovation. After duplication a gene may be lost, recombine with another gene, have its function modified or be retained in an unaltered state. The fate of duplication is usually studied by comparing extant genomes and reconstructing the most likely ancestral states. Valuable as this approach is, it may miss the most rapid evolutionary events. Here, we engineered strains of Saccharomyces cerevisiae carrying tandem and non-tandem duplications of the singleton gene IFA38 to monitor (i) the fate of the duplicates in different conditions, including time scale and asymmetry of gene loss, and (ii) the changes in fitness and transcriptome of the strains immediately after duplication and after experimental evolution. We found that the duplication brings widespread transcriptional changes, but a fitness advantage is only present in fermentable media. In respiratory conditions, the yeast strains consistently lose the non-tandem IFA38 gene copy in a surprisingly short time, within only a few generations. This gene loss appears to be asymmetric and dependent on genome location, since the original IFA38 copy and the tandem duplicate are retained. Overall, this work shows for the first time that gene loss can be extremely rapid and context dependent.

Binding interface change and cryptic variation in the evolution of protein-protein interactions.

BACKGROUND: Physical interactions between proteins are essential for almost all biological functions and systems. To understand the evolution of function it is therefore important to understand the evolution of molecular interactions. Of key importance is the evolution of binding specificity, the set of interactions made by a protein, since change in specificity can lead to "rewiring" of interaction networks. Unfortunately, the interfaces through which proteins interact are complex, typically containing many amino-acid residues that collectively must contribute to binding specificity as well as binding affinity, structural integrity of the interface and solubility in the unbound state. RESULTS: In order to study the relationship between interface composition and binding specificity, we make use of paralogous pairs of yeast proteins. Immediately after duplication these paralogues will have identical sequences and protein products that make an identical set of interactions. As the sequences diverge, we can correlate amino-acid change in the interface with any change in the specificity of binding. We show that change in interface regions correlates only weakly with change in specificity, and many variants in interfaces are functionally equivalent. We show that many of the residue replacements within interfaces are silent with respect to their contribution to binding specificity. CONCLUSIONS: We conclude that such functionally-equivalent change has the potential to contribute to evolutionary plasticity in interfaces by creating cryptic variation, which in turn may provide the raw material for functional innovation and coevolution.

Toward a detailed understanding of search trajectories in fragment assembly approaches to protein structure prediction.

Energy functions, fragment libraries, and search methods constitute three key components of fragment-assembly methods for protein structure prediction, which are all crucial for their ability to generate high-accuracy predictions. All of these components are tightly coupled; efficient searching becomes more important as the quality of fragment libraries decreases. Given these relationships, there is currently a poor understanding of the strengths and weaknesses of the sampling approaches currently used in fragment-assembly techniques. Here, we determine how the performance of search techniques can be assessed in a meaningful manner, given the above problems. We describe a set of techniques that aim to reduce the impact of the energy function, and assess exploration in view of the search space defined by a given fragment library. We illustrate our approach using Rosetta and EdaFold, and show how certain features of these methods encourage or limit conformational exploration. We demonstrate that individual trajectories of Rosetta are susceptible to local minima in the energy landscape, and that this can be linked to non-uniform sampling across the protein chain. We show that EdaFold's novel approach can help balance broad exploration with locating good low-energy conformations. This occurs through two mechanisms which cannot be readily differentiated using standard performance measures: exclusion of false minima, followed by an increasingly focused search in low-energy regions of conformational space. Measures such as ours can be helpful in characterizing new fragment-based methods in terms of the quality of conformational exploration realized.

Covariation Is a Poor Measure of Molecular Coevolution.

Recent developments in the analysis of amino acid covariation are leading to breakthroughs in protein structure prediction, protein design, and prediction of the interactome. It is assumed that observed patterns of covariation are caused by molecular coevolution, where substitutions at one site affect the evolutionary forces acting at neighboring sites. Our theoretical and empirical results cast doubt on this assumption. We demonstrate that the strongest coevolutionary signal is a decrease in evolutionary rate and that unfeasibly long times are required to produce coordinated substitutions. We find that covarying substitutions are mostly found on different branches of the phylogenetic tree, indicating that they are independent events that may or may not be attributable to coevolution. These observations undermine the hypothesis that molecular coevolution is the primary cause of the covariation signal. In contrast, we find that the pairs of residues with the strongest covariation signal tend to have low evolutionary rates, and that it is this low rate that gives rise to the covariation signal. Slowly evolving residue pairs are disproportionately located in the protein's core, which explains covariation methods' ability to detect pairs of residues that are close in three dimensions. These observations lead us to propose the "coevolution paradox": The strength of coevolution required to cause coordinated changes means the evolutionary rate is so low that such changes are highly unlikely to occur. As modern covariation methods may lead to breakthroughs in structural genomics, it is critical to recognize their biases and limitations.

DupliPHY-Web: a web server for DupliPHY and DupliPHY-ML.

SUMMARY: Gene duplication and loss are important processes in the evolution of gene families. Moreover, growth of families by duplication and retention is an important mechanism by which organisms gain new functions. Therefore the ability to infer the evolutionary histories of families is an important step in understanding the evolution of function. We have recently developed DupliPHY, a software tool to infer gene family histories using parsimony and maximum likelihood. Here, we present DupliPHY-Web a web server for DupliPHY that implements additional maximum likelihood functionality and provides users an intuitive interface to run DupliPHY. AVAILABILITY AND IMPLEMENTATION: DupliPHY-Web is available at www.bioinf.manchester.ac.uk/dupliphy/ CONTACT: : ryan.ames@manchester.ac.uk SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Generating, Maintaining, and Exploiting Diversity in a Memetic Algorithm for Protein Structure Prediction.

Computational approaches to de novo protein tertiary structure prediction, including those based on the preeminent "fragment-assembly" technique, have failed to scale up fully to larger proteins (on the order of 100 residues and above). A number of limiting factors are thought to contribute to the scaling problem over and above the simple combinatorial explosion, but the key ones relate to the lack of exploration of properly diverse protein folds, and to an acute form of "deception" in the energy function, whereby low-energy conformations do not reliably equate with native structures. In this article, solutions to both of these problems are investigated through a multistage memetic algorithm incorporating the successful Rosetta method as a local search routine. We found that specialised genetic operators significantly add to structural diversity and that this translates well to reaching low energies. The use of a generalised stochastic ranking procedure for selection enables the memetic algorithm to handle and traverse deep energy wells that can be considered deceptive, which further adds to the ability of the algorithm to obtain a much-improved diversity of folds. The results should translate to a tangible improvement in the performance of protein structure prediction algorithms in blind experiments such as CASP, and potentially to a further step towards the more challenging problem of predicting the three-dimensional shape of large proteins.

Agnathia-otocephaly complex and asymmetric velopharyngeal insufficiency due to an in-frame duplication in OTX2.

Agnathia-otocephaly complex is a malformation characterized by absent/hypoplastic mandible and abnormally positioned ears. Mutations in two genes, PRRX1 and OTX2, have been described in a small number of families with this disorder. We performed clinical and genetic testing in an additional family. The proband is a healthy female with a complicated pregnancy history that includes two offspring diagnosed with agnathia-otocephaly during prenatal ultrasound scans. Exome sequencing was performed in fetal DNA from one of these two offspring revealing a heterozygous duplication in OTX2: c.271_273dupCAG, p.(Gln91dup). This change leads to the insertion of a glutamine within the OTX2 homeodomain region, and is predicted to alter this signaling molecule's ability to interact with DNA. The same variant was also identified in the proband's clinically unaffected 38-year-old husband and their 9-year-old daughter, who presented with a small mandible, normal ears and velopharyngeal insufficiency due to a short hemi-palate. This unusual presentation of OTX2-related disease suggests that OTX2 might have a role in palatal hypoplasia cases. A previously unreported OTX2 variant associated with extreme intrafamilial variability is described and the utility of exome sequencing as a tool to confirm the diagnosis of agnathia-otocephaly and to inform the reproductive decisions of affected families is highlighted.

Essentiality, protein-protein interactions and evolutionary properties are key predictors for identifying cancer-associated genes using machine learning.

The distinctive nature of cancer as a disease prompts an exploration of the special characteristics the genes implicated in cancer exhibit. The identification of cancer-associated genes and their characteristics is crucial to further our understanding of this disease and enhanced likelihood of therapeutic drug targets success. However, the rate at which cancer genes are being identified experimentally is slow. Applying predictive analysis techniques, through the building of accurate machine learning models, is potentially a useful approach in enhancing the identification rate of these genes and their characteristics. Here, we investigated gene essentiality scores and found that they tend to be higher for cancer-associated genes compared to other protein-coding human genes. We built a dataset of extended gene properties linked to essentiality and used it to train a machine-learning model; this model reached 89% accuracy and > 0.85 for the Area Under Curve (AUC). The model showed that essentiality, evolutionary-related properties, and properties arising from protein-protein interaction networks are particularly effective in predicting cancer-associated genes. We were able to use the model to identify potential candidate genes that have not been previously linked to cancer. Prioritising genes that score highly by our methods could aid scientists in their cancer genes research.

Determining the evolutionary history of gene families.

MOTIVATION: Recent large-scale studies of individuals within a population have demonstrated that there is widespread variation in copy number in many gene families. In addition, there is increasing evidence that the variation in gene copy number can give rise to substantial phenotypic effects. In some cases, these variations have been shown to be adaptive. These observations show that a full understanding of the evolution of biological function requires an understanding of gene gain and gene loss. Accurate, robust evolutionary models of gain and loss events are, therefore, required. RESULTS: We have developed weighted parsimony and maximum likelihood methods for inferring gain and loss events. To test these methods, we have used Markov models of gain and loss to simulate data with known properties. We examine three models: a simple birth-death model, a single rate model and a birth-death innovation model with parameters estimated from Drosophila genome data. We find that for all simulations maximum likelihood-based methods are very accurate for reconstructing the number of duplication events on the phylogenetic tree, and that maximum likelihood and weighted parsimony have similar accuracy for reconstructing the ancestral state. Our implementations are robust to different model parameters and provide accurate inferences of ancestral states and the number of gain and loss events. For ancestral reconstruction, we recommend weighted parsimony because it has similar accuracy to maximum likelihood, but is much faster. For inferring the number of individual gene loss or gain events, maximum likelihood is noticeably more accurate, albeit at greater computational cost. AVAILABILITY: www.bioinf.manchester.ac.uk/dupliphy CONTACT: simon.lovell@manchester.ac.uk; simon.whelan@manchester.ac.uk SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Clinical, genetic, epidemiologic, evolutionary, and functional delineation of TSPEAR-related autosomal recessive ectodermal dysplasia 14.

TSPEAR variants cause autosomal recessive ectodermal dysplasia (ARED) 14. The function of TSPEAR is unknown. The clinical features, the mutation spectrum, and the underlying mechanisms of ARED14 are poorly understood. Combining data from new and previously published individuals established that ARED14 is primarily characterized by dental anomalies such as conical tooth cusps and hypodontia, like those seen in individuals with WNT10A-related odontoonychodermal dysplasia. AlphaFold-predicted structure-based analysis showed that most of the pathogenic TSPEAR missense variants likely destabilize the ß-propeller of the protein. Analysis of 100000 Genomes Project (100KGP) data revealed multiple founder TSPEAR variants across different populations. Mutational and recombination clock analyses demonstrated that non-Finnish European founder variants likely originated around the end of the last ice age, a period of major climatic transition. Analysis of gnomAD data showed that the non-Finnish European population TSPEAR gene-carrier rate is ∼1/140, making it one of the commonest AREDs. Phylogenetic and AlphaFold structural analyses showed that TSPEAR is an ortholog of drosophila Closca, an extracellular matrix-dependent signaling regulator. We, therefore, hypothesized that TSPEAR could have a role in enamel knot, a structure that coordinates patterning of developing tooth cusps. Analysis of mouse single-cell RNA sequencing (scRNA-seq) data revealed highly restricted expression of Tspear in clusters representing enamel knots. A tspeara -/-;tspearb -/- double-knockout zebrafish model recapitulated the clinical features of ARED14 and fin regeneration abnormalities of wnt10a knockout fish, thus suggesting interaction between tspear and wnt10a. In summary, we provide insights into the role of TSPEAR in ectodermal development and the evolutionary history, epidemiology, mechanisms, and consequences of its loss of function variants.

The origins of the evolutionary signal used to predict protein-protein interactions.

BACKGROUND: The correlation of genetic distances between pairs of protein sequence alignments has been used to infer protein-protein interactions. It has been suggested that these correlations are based on the signal of co-evolution between interacting proteins. However, although mutations in different proteins associated with maintaining an interaction clearly occur (particularly in binding interfaces and neighbourhoods), many other factors contribute to correlated rates of sequence evolution. Proteins in the same genome are usually linked by shared evolutionary history and so it would be expected that there would be topological similarities in their phylogenetic trees, whether they are interacting or not. For this reason the underlying species tree is often corrected for. Moreover processes such as expression level, are known to effect evolutionary rates. However, it has been argued that the correlated rates of evolution used to predict protein interaction explicitly includes shared evolutionary history; here we test this hypothesis. RESULTS: In order to identify the evolutionary mechanisms giving rise to the correlations between interaction proteins, we use phylogenetic methods to distinguish similarities in tree topologies from similarities in genetic distances. We use a range of datasets of interacting and non-interacting proteins from Saccharomyces cerevisiae. We find that the signal of correlated evolution between interacting proteins is predominantly a result of shared evolutionary rates, rather than similarities in tree topology, independent of evolutionary divergence. CONCLUSIONS: Since interacting proteins do not have tree topologies that are more similar than the control group of non-interacting proteins, it is likely that coevolution does not contribute much to, if any, of the observed correlations.

The dual role of fragments in fragment-assembly methods for de novo protein structure prediction.

In fragment-assembly techniques for protein structure prediction, models of protein structure are assembled from fragments of known protein structures. This process is typically guided by a knowledge-based energy function and uses a heuristic optimization method. The fragments play two important roles in this process: they define the set of structural parameters available, and they also assume the role of the main variation operators that are used by the optimiser. Previous analysis has typically focused on the first of these roles. In particular, the relationship between local amino acid sequence and local protein structure has been studied by a range of authors. The correlation between the two has been shown to vary with the window length considered, and the results of these analyses have informed directly the choice of fragment length in state-of-the-art prediction techniques. Here, we focus on the second role of fragments and aim to determine the effect of fragment length from an optimization perspective. We use theoretical analyses to reveal how the size and structure of the search space changes as a function of insertion length. Furthermore, empirical analyses are used to explore additional ways in which the size of the fragment insertion influences the search both in a simulation model and for the fragment-assembly technique, Rosetta.