Curating genomic disease-gene relationships with Gene2Phenotype (G2P).

Genetically determined disorders are highly heterogenous in clinical presentation and underlying molecular mechanism. The evidence underpinning these conditions in the peer-reviewed literature requires robust critical evaluation for diagnostic use. Here, we present a structured curation process for Gene2Phenotype (G2P).

Understanding the heterogeneous performance of variant effect predictors across human protein-coding genes.

Variant effect predictors (VEPs) are computational tools developed to assess the impacts of genetic mutations, often in terms of likely pathogenicity, employing diverse algorithms and training data. Here, we investigate the performance of 35 VEPs in the discrimination between pathogenic and putatively benign missense variants across 963 human protein-coding genes. We observe considerable gene-level heterogeneity as measured by the widely used area under the receiver operating characteristic curve (AUROC) metric.

Protein structural context of cancer mutations reveals molecular mechanisms and candidate driver genes.

Advances in protein structure determination and modeling allow us to study the structural context of human genetic variants on an unprecedented scale. Here, we analyze millions of cancer-associated missense mutations based on their structural locations and predicted perturbative effects. By considering the collective properties of mutations at the level of individual proteins, we identify distinct patterns associated with tumor suppressors and oncogenes.

Structural insight into the function of human peptidyl arginine deiminase 6.

Peptidyl arginine deiminase 6 (PADI6 or PAD6) is vital for early embryonic development in mice and humans, yet its function remains elusive. PADI6 is less conserved than other PADIs and it is currently unknown whether it has a catalytic function. Here we show that human PADI6 dimerises like hPADIs 2-4, however, does not bind Ca and is inactive in assays against standard PADI substrates.

Proteome-scale prediction of molecular mechanisms underlying dominant genetic diseases.

Many dominant genetic disorders result from protein-altering mutations, acting primarily through dominant-negative (DN), gain-of-function (GOF), and loss-of-function (LOF) mechanisms. Deciphering the mechanisms by which dominant diseases exert their effects is often experimentally challenging and resource intensive, but is essential for developing appropriate therapeutic approaches. Diseases that arise via a LOF mechanism are more amenable to be treated by conventional gene therapy, whereas DN and GOF mechanisms may require gene editing or targeting by small molecules.

Whole genome sequencing enhances molecular diagnosis of primary ciliary dyskinesia.

Background: Primary ciliary dyskinesia (PCD) is a genetic disorder affecting motile cilia. Most cases are inherited recessively, due to variants in >50 genes that result in abnormal or absent motile cilia. This leads to chronic upper and lower airway disease, subfertility, and laterality defects.

Deep mutational scanning quantifies DNA binding and predicts clinical outcomes of PAX6 variants.

Nonsense and missense mutations in the transcription factor PAX6 cause a wide range of eye development defects, including aniridia, microphthalmia and coloboma. To understand how changes of PAX6:DNA binding cause these phenotypes, we combined saturation mutagenesis of the paired domain of PAX6 with a yeast one-hybrid (Y1H) assay in which expression of a PAX6-GAL4 fusion gene drives antibiotic resistance. We quantified binding of more than 2700 single amino-acid variants to two DNA sequence elements.

Guidelines for releasing a variant effect predictor.

Computational methods for assessing the likely impacts of mutations, known as variant effect predictors (VEPs), are widely used in the assessment and interpretation of human genetic variation, as well as in other applications like protein engineering. Many different VEPs have been released to date, and there is tremendous variability in their underlying algorithms and outputs, and in the ways in which the methodologies and predictions are shared. This leads to considerable challenges for end users in knowing which VEPs to use and how to use them.

CDK4/6 inhibitor-mediated cell overgrowth triggers osmotic and replication stress to promote senescence.

Abnormal increases in cell size are associated with senescence and cell cycle exit. The mechanisms by which overgrowth primes cells to withdraw from the cell cycle remain unknown. We address this question using CDK4/6 inhibitors, which arrest cells in G0/G1 and are licensed to treat advanced HR+/HER2- breast cancer.

Predicting pathogenic protein variants.

Machine-learning algorithm uses structure prediction to spot disease-causing mutations.

Updated benchmarking of variant effect predictors using deep mutational scanning.

The assessment of variant effect predictor (VEP) performance is fraught with biases introduced by benchmarking against clinical observations. In this study, building on our previous work, we use independently generated measurements of protein function from deep mutational scanning (DMS) experiments for 26 human proteins to benchmark 55 different VEPs, while introducing minimal data circularity. Many top-performing VEPs are unsupervised methods including EVE, DeepSequence and ESM-1v, a protein language model that ranked first overall.

Buffering of genetic dominance by allele-specific protein complex assembly.

Protein complex assembly often occurs while subunits are being translated, resulting in complexes whose subunits were translated from the same mRNA in an allele-specific manner. It has thus been hypothesized that such cotranslational assembly may counter the assembly-mediated dominant-negative effect, whereby co-assembly of mutant and wild-type subunits "poisons" complex activity. Here, we show that cotranslationally assembling subunits are much less likely to be associated with autosomal dominant relative to recessive disorders, and that subunits with dominant-negative disease mutations are significantly depleted in cotranslational assembly compared to those associated with loss-of-function mutations.

Correspondence between functional scores from deep mutational scans and predicted effects on protein stability.

Many methodologically diverse computational methods have been applied to the growing challenge of predicting and interpreting the effects of protein variants. As many pathogenic mutations have a perturbing effect on protein stability or intermolecular interactions, one highly interpretable approach is to use protein structural information to model the physical impacts of variants and predict their likely effects on protein stability and interactions. Previous efforts have assessed the accuracy of stability predictors in reproducing thermodynamically accurate values and evaluated their ability to distinguish between known pathogenic and benign mutations.

Hallmarks and evolutionary drivers of cotranslational protein complex assembly.

Recent discoveries have highlighted the prevalence of cotranslational assembly in proteomes, revealing a range of mechanisms that enables the assembly of protein complex subunits on the ribosome. Structural analyses have uncovered emergent properties that may inherently control whether a subunit undergoes cotranslational assembly. However, the evolutionary paths that have yielded such complexes over an extended timescale remain largely unclear.

Type I Interferonopathy due to a Homozygous Loss-of-Inhibitory Function Mutation in STAT2.

Purpose: STAT2 is both an effector and negative regulator of type I interferon (IFN-I) signalling. We describe the characterization of a novel homozygous missense STAT2 substitution in a patient with a type I interferonopathy.

Methods: Whole-genome sequencing (WGS) was used to identify the genetic basis of disease in a patient with features of enhanced IFN-I signalling.

Expanding the neurodevelopmental phenotype associated with HK1 de novo heterozygous missense variants.

Neurodevelopmental disorder with visual defects and brain anomalies (NEDVIBA) is a recently described genetic condition caused by de novo missense HK1 variants. Phenotypic data is currently limited; only seven patients have been published to date. This descriptive case series of a further four patients with de novo missense HK1 variants, alongside integration of phenotypic data with the reported cases, aims to improve our understanding of the associated phenotype.