Optimizing genomic medicine in epilepsy through a gene-customized approach to missense variant interpretation

Genetic architecture of telomere length in 462,666 UK Biobank whole-genome sequences

Telomeres protect chromosome ends from damage and their length is linked with human disease and aging. We developed a joint telomere length metric, combining quantitative PCR and whole-genome sequencing measurements from 462,666 UK Biobank participants. This metric increased SNP heritability, suggesting that it better captures genetic regulation of telomere length. Exome-wide rare-variant and gene-level collapsing association studies identified 64 variants and 30 genes significantly associated with telomere length, including allelic series in ACD and RTEL1. Notably, 16% of these genes are known drivers of clonal hematopoiesis-an age-related somatic mosaicism associated with myeloid cancers and several nonmalignant diseases. Somatic variant analyses revealed gene-specific associations with telomere length, including lengthened telomeres in individuals with large SRSF2-mutant clones, compared with shortened telomeres in individuals with clonal expansions driven by other genes. Collectively, our findings demonstrate the impact of rare variants on telomere length, with larger effects observed among genes also associated with clonal hematopoiesis.

MTR3D-AF2: Expanding the coverage of spatially derived missense tolerance scores across the human proteome using AlphaFold2

The missense tolerance ratio (MTR) was developed as a novel approach to assess the deleteriousness of variants. Its three-dimensional successor, MTR3D, was demonstrated powerful at discriminating pathogenic from benign variants. However, its reliance on experimental structures and homologs limited its coverage of the proteome. We have now utilized AlphaFold2 models to develop MTR3D-AF2, which covers 89.31% of proteins and 85.39% of residues across the human proteome. This work has improved MTR3D's ability to distinguish clinically established pathogenic from benign variants. MTR3D-AF2 is freely available as an interactive web server at https://biosig.lab.uq.edu.au/mtr3daf2/.

Genetic constraint at single amino acid resolution in protein domains improves missense variant prioritisation and gene discovery

BACKGROUND

One of the major hurdles in clinical genetics is interpreting the clinical consequences associated with germline missense variants in humans. Recent significant advances have leveraged natural variation observed in large-scale human populations to uncover genes or genomic regions that show a depletion of natural variation, indicative of selection pressure. We refer to this as "genetic constraint". Although existing genetic constraint metrics have been demonstrated to be successful in prioritising genes or genomic regions associated with diseases, their spatial resolution is limited in distinguishing pathogenic variants from benign variants within genes.

METHODS

We aim to identify missense variants that are significantly depleted in the general human population. Given the size of currently available human populations with exome or genome sequencing data, it is not possible to directly detect depletion of individual missense variants, since the average expected number of observations of a variant at most positions is less than one. We instead focus on protein domains, grouping homologous variants with similar functional impacts to examine the depletion of natural variations within these comparable sets. To accomplish this, we develop the Homologous Missense Constraint (HMC) score. We utilise the Genome Aggregation Database (gnomAD) 125 K exome sequencing data and evaluate genetic constraint at quasi amino-acid resolution by combining signals across protein homologues.

RESULTS

We identify one million possible missense variants under strong negative selection within protein domains. Though our approach annotates only protein domains, it nonetheless allows us to assess 22% of the exome confidently. It precisely distinguishes pathogenic variants from benign variants for both early-onset and adult-onset disorders. It outperforms existing constraint metrics and pathogenicity meta-predictors in prioritising de novo mutations from probands with developmental disorders (DD). It is also methodologically independent of these, adding power to predict variant pathogenicity when used in combination. We demonstrate utility for gene discovery by identifying seven genes newly significantly associated with DD that could act through an altered-function mechanism.

CONCLUSIONS

Grouping variants of comparable functional impacts is effective in evaluating their genetic constraint. HMC is a novel and accurate predictor of missense consequence for improved variant interpretation.

Inter-BRCT linker is probably the most intolerant region of the BRCA1 BRCT domain

Pathogenic mutations in BRCA1 are associated with an increased risk of hereditary breast, ovarian, and some other cancers; however, the clinical significance of many mutations in this gene remains unknown (Variants of Unknown Significance/VUS). Since mutations in intolerant regions of a protein lead to dysfunction and pathogenicity, identifying these regions helps to predict the clinical importance of VUSs. This study aimed to identify intolerant regions of BRCA1 and understand the possible root of this susceptibility. Intolerant regions appear to carry more pathogenic mutations than expected due to their lower tolerance to missense variations. Therefore, we hypothesized that among the BRCA1 regions, the higher the mutation density, the greater the intolerance. Thus, pathogenic mutation density and regional intolerance scores were calculated to identify BRCA1-intolerant regions. To investigate the pathogenic mechanisms of missense-intolerant regions in BRCA1, transcription activation (TA) experiments and molecular dynamics (MD) simulations were also performed. The results showed that the RING domain, followed by the BRCT domain, has the highest density of pathogenic mutations. In the BRCT domain, a higher density of pathogenic mutations was observed in the inter-BRCT linker. Additionally, scores generated by Missense Tolerance Ratio-3D (MTR3D) and the Missense Tolerance Ratio consensus (MTRX) showed that the inter-BRCT linker is more intolerant than other regions of the BRCT domain. The MD results showed that mutations in the inter-BRCT linker led to cancer susceptibility, likely due to disruption of the interaction between BRCA1 and phosphopeptides. TA laboratory assays further supported the importance of the inter-BRCT linker.Communicated by Ramaswamy H. Sarma.

A deep catalogue of protein-coding variation in 983,578 individuals

Rare coding variants that substantially affect function provide insights into the biology of a gene1-3. However, ascertaining the frequency of such variants requires large sample sizes4-8. Here we present a catalogue of human protein-coding variation, derived from exome sequencing of 983,578 individuals across diverse populations. In total, 23% of the Regeneron Genetics Center Million Exome (RGC-ME) data come from individuals of African, East Asian, Indigenous American, Middle Eastern and South Asian ancestry. The catalogue includes more than 10.4 million missense and 1.1 million predicted loss-of-function (pLOF) variants. We identify individuals with rare biallelic pLOF variants in 4,848 genes, 1,751 of which have not been previously reported. From precise quantitative estimates of selection against heterozygous loss of function (LOF), we identify 3,988 LOF-intolerant genes, including 86 that were previously assessed as tolerant and 1,153 that lack established disease annotation. We also define regions of missense depletion at high resolution. Notably, 1,482 genes have regions that are depleted of missense variants despite being tolerant of pLOF variants. Finally, we estimate that 3% of individuals have a clinically actionable genetic variant, and that 11,773 variants reported in ClinVar with unknown significance are likely to be deleterious cryptic splice sites. To facilitate variant interpretation and genetics-informed precision medicine, we make this resource of coding variation from the RGC-ME dataset publicly accessible through a variant allele frequency browser.

The landscape of regional missense mutational intolerance quantified from 125,748 exomes

Missense variants can have a range of functional impacts depending on factors such as the specific amino acid substitution and location within the gene. To interpret their deleteriousness, studies have sought to identify regions within genes that are specifically intolerant of missense variation 1-12 . Here, we leverage the patterns of rare missense variation in 125,748 individuals in the Genome Aggregation Database (gnomAD) 13 against a null mutational model to identify transcripts that display regional differences in missense constraint. Missense-depleted regions are enriched for ClinVar 14 pathogenic variants, de novo missense variants from individuals with neurodevelopmental disorders (NDDs) 15,16 , and complex trait heritability. Following ClinGen calibration recommendations for the ACMG/AMP guidelines, we establish that regions with less than 20% of their expected missense variation achieve moderate support for pathogenicity. We create a missense deleteriousness metric (MPC) that incorporates regional constraint and outperforms other deleteriousness scores at stratifying case and control de novo missense variation, with a strong enrichment in NDDs. These results provide additional tools to aid in missense variant interpretation.

Gain-of-function and loss-of-function variants in GRIA3 lead to distinct neurodevelopmental phenotypes

AMPA (α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid) receptors (AMPARs) mediate fast excitatory neurotransmission in the brain. AMPARs form by homo- or heteromeric assembly of subunits encoded by the GRIA1-GRIA4 genes, of which only GRIA3 is X-chromosomal. Increasing numbers of GRIA3 missense variants are reported in patients with neurodevelopmental disorders (NDD), but only a few have been examined functionally. Here, we evaluated the impact on AMPAR function of one frameshift and 43 rare missense GRIA3 variants identified in patients with NDD by electrophysiological assays. Thirty-one variants alter receptor function and show loss-of-function or gain-of-function properties, whereas 13 appeared neutral. We collected detailed clinical data from 25 patients (from 23 families) harbouring 17 of these variants. All patients had global developmental impairment, mostly moderate (9/25) or severe (12/25). Twelve patients had seizures, including focal motor (6/12), unknown onset motor (4/12), focal impaired awareness (1/12), (atypical) absence (2/12), myoclonic (5/12) and generalized tonic-clonic (1/12) or atonic (1/12) seizures. The epilepsy syndrome was classified as developmental and epileptic encephalopathy in eight patients, developmental encephalopathy without seizures in 13 patients, and intellectual disability with epilepsy in four patients. Limb muscular hypotonia was reported in 13/25, and hypertonia in 10/25. Movement disorders were reported in 14/25, with hyperekplexia or non-epileptic erratic myoclonus being the most prevalent feature (8/25). Correlating receptor functional phenotype with clinical features revealed clinical features for GRIA3-associated NDDs and distinct NDD phenotypes for loss-of-function and gain-of-function variants. Gain-of-function variants were associated with more severe outcomes: patients were younger at the time of seizure onset (median age: 1 month), hypertonic and more often had movement disorders, including hyperekplexia. Patients with loss-of-function variants were older at the time of seizure onset (median age: 16 months), hypotonic and had sleeping disturbances. Loss-of-function and gain-of-function variants were disease-causing in both sexes but affected males often carried de novo or hemizygous loss-of-function variants inherited from healthy mothers, whereas affected females had mostly de novo heterozygous gain-of-function variants.

Unravelling the Genetic Landscape of Hemiplegic Migraine: Exploring Innovative Strategies and Emerging Approaches

Migraine is a severe, debilitating neurovascular disorder. Hemiplegic migraine (HM) is a rare and debilitating neurological condition with a strong genetic basis. Sequencing technologies have improved the diagnosis and our understanding of the molecular pathophysiology of HM. Linkage analysis and sequencing studies in HM families have identified pathogenic variants in ion channels and related genes, including CACNA1A, ATP1A2, and SCN1A, that cause HM. However, approximately 75% of HM patients are negative for these mutations, indicating there are other genes involved in disease causation. In this review, we explored our current understanding of the genetics of HM. The evidence presented herein summarises the current knowledge of the genetics of HM, which can be expanded further to explain the remaining heritability of this debilitating condition. Innovative bioinformatics and computational strategies to cover the entire genetic spectrum of HM are also discussed in this review.

MLe-KCNQ2: An Artificial Intelligence Model for the Prognosis of Missense KCNQ2 Gene Variants

Despite the increasing availability of genomic data and enhanced data analysis procedures, predicting the severity of associated diseases remains elusive in the absence of clinical descriptors. To address this challenge, we have focused on the KV7.2 voltage-gated potassium channel gene (KCNQ2), known for its link to developmental delays and various epilepsies, including self-limited benign familial neonatal epilepsy and epileptic encephalopathy. Genome-wide tools often exhibit a tendency to overestimate deleterious mutations, frequently overlooking tolerated variants, and lack the capacity to discriminate variant severity. This study introduces a novel approach by evaluating multiple machine learning (ML) protocols and descriptors. The combination of genomic information with a novel Variant Frequency Index (VFI) builds a robust foundation for constructing reliable gene-specific ML models. The ensemble model, MLe-KCNQ2, formed through logistic regression, support vector machine, random forest and gradient boosting algorithms, achieves specificity and sensitivity values surpassing 0.95 (AUC-ROC > 0.98). The ensemble MLe-KCNQ2 model also categorizes pathogenic mutations as benign or severe, with an area under the receiver operating characteristic curve (AUC-ROC) above 0.67. This study not only presents a transferable methodology for accurately classifying KCNQ2 missense variants, but also provides valuable insights for clinical counseling and aids in the determination of variant severity. The research context emphasizes the necessity of precise variant classification, especially for genes like KCNQ2, contributing to the broader understanding of gene-specific challenges in the field of genomic research. The MLe-KCNQ2 model stands as a promising tool for enhancing clinical decision making and prognosis in the realm of KCNQ2-related pathologies.

Clinical features, functional consequences, and rescue pharmacology of missense GRID1 and GRID2 human variants

GRID1 and GRID2 encode the enigmatic GluD1 and GluD2 proteins, which form tetrameric receptors that play important roles in synapse organization and development of the central nervous system. Variation in these genes has been implicated in neurodevelopmental phenotypes. We evaluated GRID1 and GRID2 human variants from the literature, ClinVar, and clinical laboratories and found that many of these variants reside in intolerant domains, including the amino terminal domain of both GRID1 and GRID2. Other conserved regions, such as the M3 transmembrane domain, show different intolerance between GRID1 and GRID2. We introduced these variants into GluD1 and GluD2 cDNA and performed electrophysiological and biochemical assays to investigate the mechanisms of dysfunction of GRID1/2 variants. One variant in the GRID1 distal amino terminal domain resides at a position predicted to interact with Cbln2/Cbln4, and the variant disrupts complex formation between GluD1 and Cbln2, which could perturb its role in synapse organization. We also discovered that, like the lurcher mutation (GluD2-A654T), other rare variants in the GRID2 M3 domain create constitutively active receptors that share similar pathogenic phenotypes. We also found that the SCHEMA schizophrenia M3 variant GluD1-A650T produced constitutively active receptors. We tested a variety of compounds for their ability to inhibit constitutive currents of GluD receptor variants and found that pentamidine potently inhibited GluD2-T649A constitutive channels (IC50 50 nM). These results identify regions of intolerance to variation in the GRID genes, illustrate the functional consequences of GRID1 and GRID2 variants, and suggest how these receptors function normally and in disease.

A Frameshift Variant of GluN2A Identified in an Epilepsy Patient Results in NMDA Receptor Mistargeting

N-methyl-D-aspartate receptors (NMDARs) are crucial for neuronal development and synaptic plasticity. Dysfunction of NMDARs is associated with multiple neurodevelopmental disorders, including epilepsy, autism spectrum disorder, and intellectual disability. Understanding the impact of genetic variants of NMDAR subunits can shed light on the mechanisms of disease. Here, we characterized the functional implications of a de novo mutation of the GluN2A subunit (P1199Rfs*32) resulting in the truncation of the C-terminal domain. The variant was identified in a male patient with epileptic encephalopathy, multiple seizure types, severe aphasia, and neurobehavioral changes. Given the known role of the CTD in NMDAR trafficking, we examined changes in receptor localization and abundance at the postsynaptic membrane using a combination of molecular assays in heterologous cells and rat primary neuronal cultures. We observed that the GluN2A P1199Rfs*32-containing receptors traffic efficiently to the postsynaptic membrane but have increased extra-synaptic expression relative to WT GluN2A-containing NMDARs. Using in silico predictions, we hypothesized that the mutant would lose all PDZ interactions, except for the recycling protein Scribble1. Indeed, we observed impaired binding to the scaffolding protein postsynaptic protein-95 (PSD-95); however, we found the mutant interacts with Scribble1, which facilitates the recycling of both the mutant and the WT GluN2A. Finally, we found that neurons expressing GluN2A P1199Rfs*32 have fewer synapses and decreased spine density, indicating compromised synaptic transmission in these neurons. Overall, our data show that GluN2A P1199Rfs*32 is a loss-of-function variant with altered membrane localization in neurons and provide mechanistic insight into disease etiology.

Characterizing and predicting ccRCC-causing missense mutations in Von Hippel-Lindau disease

BACKGROUND

Mutations within the Von Hippel-Lindau (VHL) tumor suppressor gene are known to cause VHL disease, which is characterized by the formation of cysts and tumors in multiple organs of the body, particularly clear cell renal cell carcinoma (ccRCC). A major challenge in clinical practice is determining tumor risk from a given mutation in the VHL gene. Previous efforts have been hindered by limited available clinical data and technological constraints.

METHODS

To overcome this, we initially manually curated the largest set of clinically validated VHL mutations to date, enabling a robust assessment of existing predictive tools on an independent test set. Additionally, we comprehensively characterized the effects of mutations within VHL using in silico biophysical tools describing changes in protein stability, dynamics and affinity to binding partners to provide insights into the structure-phenotype relationship. These descriptive properties were used as molecular features for the construction of a machine learning model, designed to predict the risk of ccRCC development as a result of a VHL missense mutation.

RESULTS

Analysis of our model showed an accuracy of 0.81 in the identification of ccRCC-causing missense mutations, and a Matthew's Correlation Coefficient of 0.44 on a non-redundant blind test, a significant improvement in comparison to the previous available approaches.

CONCLUSION

This work highlights the power of using protein 3D structure to fully explore the range of molecular and functional consequences of genomic variants. We believe this optimized model will better enable its clinical implementation and assist guiding patient risk stratification and management.

A deep catalog of protein-coding variation in 985,830 individuals

Coding variants that have significant impact on function can provide insights into the biology of a gene but are typically rare in the population. Identifying and ascertaining the frequency of such rare variants requires very large sample sizes. Here, we present the largest catalog of human protein-coding variation to date, derived from exome sequencing of 985,830 individuals of diverse ancestry to serve as a rich resource for studying rare coding variants. Individuals of African, Admixed American, East Asian, Middle Eastern, and South Asian ancestry account for 20% of this Exome dataset. Our catalog of variants includes approximately 10.5 million missense (54% novel) and 1.1 million predicted loss-of-function (pLOF) variants (65% novel, 53% observed only once). We identified individuals with rare homozygous pLOF variants in 4,874 genes, and for 1,838 of these this work is the first to document at least one pLOF homozygote. Additional insights from the RGC-ME dataset include 1) improved estimates of selection against heterozygous loss-of-function and identification of 3,459 genes intolerant to loss-of-function, 83 of which were previously assessed as tolerant to loss-of-function and 1,241 that lack disease annotations; 2) identification of regions depleted of missense variation in 457 genes that are tolerant to loss-of-function; 3) functional interpretation for 10,708 variants of unknown or conflicting significance reported in ClinVar as cryptic splice sites using splicing score thresholds based on empirical variant deleteriousness scores derived from RGC-ME; and 4) an observation that approximately 3% of sequenced individuals carry a clinically actionable genetic variant in the ACMG SF 3.1 list of genes. We make this important resource of coding variation available to the public through a variant allele frequency browser. We anticipate that this report and the RGC-ME dataset will serve as a valuable reference for understanding rare coding variation and help advance precision medicine efforts.

Rare variant associations with plasma protein levels in the UK Biobank

Integrating human genomics and proteomics can help elucidate disease mechanisms, identify clinical biomarkers and discover drug targets1-4. Because previous proteogenomic studies have focused on common variation via genome-wide association studies, the contribution of rare variants to the plasma proteome remains largely unknown. Here we identify associations between rare protein-coding variants and 2,923 plasma protein abundances measured in 49,736 UK Biobank individuals. Our variant-level exome-wide association study identified 5,433 rare genotype-protein associations, of which 81% were undetected in a previous genome-wide association study of the same cohort5. We then looked at aggregate signals using gene-level collapsing analysis, which revealed 1,962 gene-protein associations. Of the 691 gene-level signals from protein-truncating variants, 99.4% were associated with decreased protein levels. STAB1 and STAB2, encoding scavenger receptors involved in plasma protein clearance, emerged as pleiotropic loci, with 77 and 41 protein associations, respectively. We demonstrate the utility of our publicly accessible resource through several applications. These include detailing an allelic series in NLRC4, identifying potential biomarkers for a fatty liver disease-associated variant in HSD17B13 and bolstering phenome-wide association studies by integrating protein quantitative trait loci with protein-truncating variants in collapsing analyses. Finally, we uncover distinct proteomic consequences of clonal haematopoiesis (CH), including an association between TET2-CH and increased FLT3 levels. Our results highlight a considerable role for rare variation in plasma protein abundance and the value of proteogenomics in therapeutic discovery.

Identifying Innate Resistance Hotspots for SARS-CoV-2 Antivirals Using In Silico Protein Techniques

The development and approval of antivirals against SARS-CoV-2 has further equipped clinicians with treatment strategies against the COVID-19 pandemic, reducing deaths post-infection. Extensive clinical use of antivirals, however, can impart additional selective pressure, leading to the emergence of antiviral resistance. While we have previously characterized possible effects of circulating SARS-CoV-2 missense mutations on proteome function and stability, their direct effects on the novel antivirals remains unexplored. To address this, we have computationally calculated the consequences of mutations in the antiviral targets: RNA-dependent RNA polymerase and main protease, on target stability and interactions with their antiviral, nucleic acids, and other proteins. By analyzing circulating variants prior to antiviral approval, this work highlighted the inherent resistance potential of different genome regions. Namely, within the main protease binding site, missense mutations imparted a lower fitness cost, while the opposite was noted for the RNA-dependent RNA polymerase binding site. This suggests that resistance to nirmatrelvir/ritonavir combination treatment is more likely to occur and proliferate than that to molnupiravir. These insights are crucial both clinically in drug stewardship, and preclinically in the identification of less mutable targets for novel therapeutic design.

ATP6V0C variants impair V-ATPase function causing a neurodevelopmental disorder often associated with epilepsy

The vacuolar H+-ATPase is an enzymatic complex that functions in an ATP-dependent manner to pump protons across membranes and acidify organelles, thereby creating the proton/pH gradient required for membrane trafficking by several different types of transporters. We describe heterozygous point variants in ATP6V0C, encoding the c-subunit in the membrane bound integral domain of the vacuolar H+-ATPase, in 27 patients with neurodevelopmental abnormalities with or without epilepsy. Corpus callosum hypoplasia and cardiac abnormalities were also present in some patients. In silico modelling suggested that the patient variants interfere with the interactions between the ATP6V0C and ATP6V0A subunits during ATP hydrolysis. Consistent with decreased vacuolar H+-ATPase activity, functional analyses conducted in Saccharomyces cerevisiae revealed reduced LysoSensor fluorescence and reduced growth in media containing varying concentrations of CaCl2. Knockdown of ATP6V0C in Drosophila resulted in increased duration of seizure-like behaviour, and the expression of selected patient variants in Caenorhabditis elegans led to reduced growth, motor dysfunction and reduced lifespan. In summary, this study establishes ATP6V0C as an important disease gene, describes the clinical features of the associated neurodevelopmental disorder and provides insight into disease mechanisms.

Insights from Spatial Measures of Intolerance to Identifying Pathogenic Variants in Developmental and Epileptic Encephalopathies

Developmental and epileptic encephalopathies (DEEs) are a group of epilepsies with early onset and severe symptoms that sometimes lead to death. Although previous work successfully discovered several genes implicated in disease outcomes, it remains challenging to identify causative mutations within these genes from the background variation present in all individuals due to disease heterogeneity. Nevertheless, our ability to detect possible pathogenic variants has continued to improve as in silico predictors of deleteriousness have advanced. We investigate their use in prioritising likely pathogenic variants in epileptic encephalopathy patients’ whole exome sequences. We showed that the inclusion of structure-based predictors of intolerance improved upon previous attempts to demonstrate enrichment within epilepsy genes.

Effects of protein-coding variants on blood metabolite measurements and clinical biomarkers in the UK Biobank

Genome-wide association studies (GWASs) have established the contribution of common and low-frequency variants to metabolic blood measurements in the UK Biobank (UKB). To complement existing GWAS findings, we assessed the contribution of rare protein-coding variants in relation to 355 metabolic blood measurements-including 325 predominantly lipid-related nuclear magnetic resonance (NMR)-derived blood metabolite measurements (Nightingale Health Plc) and 30 clinical blood biomarkers-using 412,393 exome sequences from four genetically diverse ancestries in the UKB. Gene-level collapsing analyses were conducted to evaluate a diverse range of rare-variant architectures for the metabolic blood measurements. Altogether, we identified significant associations (p < 1 × 10^-8) for 205 distinct genes that involved 1,968 significant relationships for the Nightingale blood metabolite measurements and 331 for the clinical blood biomarkers. These include associations for rare non-synonymous variants in PLIN1 and CREB3L3 with lipid metabolite measurements and SYT7 with creatinine, among others, which may not only provide insights into novel biology but also deepen our understanding of established disease mechanisms. Of the study-wide significant clinical biomarker associations, 40% were not previously detected on analyzing coding variants in a GWAS in the same cohort, reinforcing the importance of studying rare variation to fully understand the genetic architecture of metabolic blood measurements.

Delineation of functionally essential protein regions for 242 neurodevelopmental genes

Neurodevelopmental disorders (NDDs), including severe paediatric epilepsy, autism and intellectual disabilities are heterogeneous conditions in which clinical genetic testing can often identify a pathogenic variant. For many of them, genetic therapies will be tested in this or the coming years in clinical trials. In contrast to first-generation symptomatic treatments, the new disease-modifying precision medicines require a genetic test-informed diagnosis before a patient can be enrolled in a clinical trial. However, even in 2022, most identified genetic variants in NDD genes are 'variants of uncertain significance'. To safely enrol patients in precision medicine clinical trials, it is important to increase our knowledge about which regions in NDD-associated proteins can 'tolerate' missense variants and which ones are 'essential' and will cause a NDD when mutated. In addition, knowledge about functionally indispensable regions in the 3D structure context of proteins can also provide insights into the molecular mechanisms of disease variants. We developed a novel consensus approach that overlays evolutionary, and population based genomic scores to identify 3D essential sites (Essential3D) on protein structures. After extensive benchmarking of AlphaFold predicted and experimentally solved protein structures, we generated the currently largest expert curated protein structure set for 242 NDDs and identified 14 377 Essential3D sites across 189 gene disorders associated proteins. We demonstrate that the consensus annotation of Essential3D sites improves prioritization of disease mutations over single annotations. The identified Essential3D sites were enriched for functional features such as intermembrane regions or active sites and discovered key inter-molecule interactions in protein complexes that were otherwise not annotated. Using the currently largest autism, developmental disorders, and epilepsies exome sequencing studies including >360 000 NDD patients and population controls, we found that missense variants at Essential3D sites are 8-fold enriched in patients. In summary, we developed a comprehensive protein structure set for 242 NDDs and identified 14 377 Essential3D sites in these. All data are available at https://es-ndd.broadinstitute.org for interactive visual inspection to enhance variant interpretation and development of mechanistic hypotheses for 242 NDDs genes. The provided resources will enhance clinical variant interpretation and in silico drug target development for NDD-associated genes and encoded proteins.

Using human genetics to improve safety assessment of therapeutics

Human genetics research has discovered thousands of proteins associated with complex and rare diseases. Genome-wide association studies (GWAS) and studies of Mendelian disease have resulted in an increased understanding of the role of gene function and regulation in human conditions. Although the application of human genetics has been explored primarily as a method to identify potential drug targets and support their relevance to disease in humans, there is increasing interest in using genetic data to identify potential safety liabilities of modulating a given target. Human genetic variants can be used as a model to anticipate the effect of lifelong modulation of therapeutic targets and identify the potential risk for on-target adverse events. This approach is particularly useful for non-clinical safety evaluation of novel therapeutics that lack pharmacologically relevant animal models and can contribute to the intrinsic safety profile of a drug target. This Review illustrates applications of human genetics to safety studies during drug discovery and development, including assessing the potential for on- and off-target associated adverse events, carcinogenicity risk assessment, and guiding translational safety study designs and monitoring strategies. A summary of available human genetic resources and recommended best practices is provided. The challenges and future perspectives of translating human genetic information to identify risks for potential drug effects in preclinical and clinical development are discussed.

Functional Assessment of Cancer-Linked Mutations in Sensitive Regions of Regulators of G Protein Signaling Predicted by Three-Dimensional Missense Tolerance Ratio Analysis

Regulators of G protein signaling (RGS) proteins modulate G protein-coupled receptor (GPCR) signaling by acting as negative regulators of G proteins. Genetic variants in RGS proteins are associated with many diseases, including cancers, although the impact of these mutations on protein function is uncertain. Here we analyze the RGS domains of 15 RGS protein family members using a novel bioinformatic tool that measures the missense tolerance ratio (MTR) using a three-dimensional (3D) structure (3DMTR). Subsequent permutation analysis can define the protein regions that are most significantly intolerant (P < 0.05) in each dataset. We further focused on RGS14, RGS10, and RGS4. RGS14 exhibited seven significantly tolerant and seven significantly intolerant residues, RGS10 had six intolerant residues, and RGS4 had eight tolerant and six intolerant residues. Intolerant and tolerant-control residues that overlap with pathogenic cancer mutations reported in the COSMIC cancer database were selected to define the functional phenotype. Using complimentary cellular and biochemical approaches, proteins were tested for effects on GPCR-Gα activation, Gα binding properties, and downstream cAMP levels. Identified intolerant residues with reported cancer-linked mutations RGS14-R173C/H and RGS4-K125Q/E126K, and tolerant RGS14-S127P and RGS10-S64T resulted in a loss-of-function phenotype in GPCR-G protein signaling activity. In downstream cAMP measurement, tolerant RGS14-D137Y and RGS10-S64T and intolerant RGS10-K89M resulted in change of function phenotypes. These findings show that 3DMTR identified intolerant residues that overlap with cancer-linked mutations cause phenotypic changes that negatively impact GPCR-G protein signaling and suggests that 3DMTR is a potentially useful bioinformatics tool for predicting functionally important protein residues. SIGNIFICANCE STATEMENT: Human genetic variant/mutation information has expanded rapidly in recent years, including cancer-linked mutations in regulator of G protein signaling (RGS) proteins. However, experimental testing of the impact of this vast catalogue of mutations on protein function is not feasible. We used the novel bioinformatics tool three-dimensional missense tolerance ratio (3DMTR) to define regions of genetic intolerance in RGS proteins and prioritize which cancer-linked mutants to test. We found that 3DMTR more accurately classifies loss-of-function mutations in RGS proteins than other databases thereby offering a valuable new research tool.

LMNA Co-Regulated Gene Expression as a Suitable Readout after Precise Gene Correction

LMNA-related muscular dystrophy is an autosomal-dominant progressive disorder caused by mutations in LMNA. LMNA missense mutations are becoming correctable with CRISPR/Cas9-derived tools. Evaluating the functional recovery of LMNA after gene editing bears challenges as there is no reported direct loss of function of lamin A/C proteins in patient-derived cells. The proteins encoded by LMNA are lamins A/C, important ubiquitous nuclear envelope proteins but absent in pluripotent stem cells. We induced lamin A/C expression in induced pluripotent stem cells (iPSCs) of two patients with LMNA-related muscular dystrophy, NM_170707.4 (LMNA): c.1366A > G, p.(Asn456Asp) and c.1494G > T, p.(Trp498Cys), using a short three-day, serum-induced differentiation protocol and analyzed expression profiles of co-regulated genes, examples being COL1A2 and S100A6. We then performed precise gene editing of LMNA c.1366A > G using the near-PAMless (PAM: protospacer-adjacent motif) cytosine base editor. We show that the mutation can be repaired to 100% efficiency in individual iPSC clones. The fast differentiation protocol provided a functional readout and demonstrated increased lamin A/C expression as well as normalized expression of co-regulated genes. Collectively, our findings demonstrate the power of CRISPR/Cas9-mediated gene correction and effective outcome measures in a disease with, so far, little perspective on therapies.

Human genetics uncovers MAP3K15 as an obesity-independent therapeutic target for diabetes

We performed collapsing analyses on 454,796 UK Biobank (UKB) exomes to detect gene-level associations with diabetes. Recessive carriers of nonsynonymous variants in MAP3K15 were 30% less likely to develop diabetes (P = 5.7 × 10-10) and had lower glycosylated hemoglobin (β = -0.14 SD units, P = 1.1 × 10-24). These associations were independent of body mass index, suggesting protection against insulin resistance even in the setting of obesity. We replicated these findings in 96,811 Admixed Americans in the Mexico City Prospective Study (P < 0.05)Moreover, the protective effect of MAP3K15 variants was stronger in individuals who did not carry the Latino-enriched SLC16A11 risk haplotype (P = 6.0 × 10-4). Separately, we identified a Finnish-enriched MAP3K15 protein-truncating variant associated with decreased odds of both type 1 and type 2 diabetes (P < 0.05) in FinnGen. No adverse phenotypes were associated with protein-truncating MAP3K15 variants in the UKB, supporting this gene as a therapeutic target for diabetes.

Compendium of proteins containing segments that exhibit zero-tolerance to amino acid variation in humans

Genetic missense tolerance ratio (MTR) analysis systematically evaluates all possible segments in a given protein-encoding transcript found in the human population. This method scores each segment for the number of observed missense variants versus the number of silent mutations in that same segment. An MTR score of 0 indicates that no missense mutations are observed within a given segment. This is indicative of evolutionary purifying selection, which excludes mutations in that segment from the general human population. Here, we conducted MTR analysis on each of the roughly 20,000 protein-encoding human genes. It was seen that there are 257 genes with at least one 31-residue encoding segment with MTR = 0 (1.3% of all human genes). The proteins encoded by these 257 genes were tabulated along with information regarding the sequence location of each intolerant segment, the likely function of the protein, and so forth. The most functionally-enriched family among these proteins is a collection of several dozen proteins that are directly involved in RNA splicing. Some of the other proteins with zero-tolerance segments have thus far escaped significant characterization. Indeed, while a number of these proteins have previously been genetically linked to human disorders, many have not. We hypothesize that this compendium of human proteins with zero-tolerance segments can be used to complement disease mutation data as a pointer to genes and proteins that are associated with interesting and underexplored human biology.

Cancer-driving mutations are enriched in genic regions intolerant to germline variation

Large reference datasets of protein-coding variation in human populations have allowed us to determine which genes and genic subregions are intolerant to germline genetic variation. There is also a growing number of genes implicated in severe Mendelian diseases that overlap with genes implicated in cancer. We hypothesized that cancer-driving mutations might be enriched in genic subregions that are depleted of germline variation relative to somatic variation. We introduce a new metric, OncMTR (oncology missense tolerance ratio), which uses 125,748 exomes in the Genome Aggregation Database (gnomAD) to identify these genic subregions. We demonstrate that OncMTR can significantly predict driver mutations implicated in hematologic malignancies. Divergent OncMTR regions were enriched for cancer-relevant protein domains, and overlaying OncMTR scores on protein structures identified functionally important protein residues. Last, we performed a rare variant, gene-based collapsing analysis on an independent set of 394,694 exomes from the UK Biobank and find that OncMTR markedly improves genetic signals for hematologic malignancies.

Clinical significance of genetic variation in hypertrophic cardiomyopathy: comparison of computational tools to prioritize missense variants

Hypertrophic cardiomyopathy (HCM) is a common heart disease associated with sudden cardiac death. Early diagnosis is critical to identify patients who may benefit from implantable cardioverter defibrillator therapy. Although genetic testing is an integral part of the clinical evaluation and management of patients with HCM and their families, in many cases the genetic analysis fails to identify a disease-causing mutation. This is in part due to difficulties in classifying newly detected rare genetic variants as well as variants-of-unknown-significance (VUS). Multiple computational algorithms have been developed to predict the potential pathogenicity of genetic variants, but their relative performance in HCM has not been comprehensively assessed. Here, we compared the performance of 39 currently available prediction tools in distinguishing between high-confidence HCM-causing missense variants and benign variants, and we developed an easy-to-use-tool to perform variant prediction benchmarks based on annotated VCF files (VETA). Our results show that tool performance increases after HCM-specific calibration of thresholds. After excluding potential biases due to circularity type I issues, we identified ClinPred, MISTIC, FATHMM, MPC and MetaLR as the five best performer tools in discriminating HCM-associated variants. We propose combining these tools in order to prioritize unknown HCM missense variants that should be closely followed-up in the clinic.

Allosteric inhibition of PPM1D serine/threonine phosphatase via an altered conformational state

PPM1D encodes a serine/threonine phosphatase that regulates numerous pathways including the DNA damage response and p53. Activating mutations and amplification of PPM1D are found across numerous cancer types. GSK2830371 is a potent and selective allosteric inhibitor of PPM1D, but its mechanism of binding and inhibition of catalytic activity are unknown. Here we use computational, biochemical and functional genetic studies to elucidate the molecular basis of GSK2830371 activity. These data confirm that GSK2830371 binds an allosteric site of PPM1D with high affinity. By further incorporating data from hydrogen deuterium exchange mass spectrometry and sedimentation velocity analytical ultracentrifugation, we demonstrate that PPM1D exists in an equilibrium between two conformations that are defined by the movement of the flap domain, which is required for substrate recognition. A hinge region was identified that is critical for switching between the two conformations and was directly implicated in the high-affinity binding of GSK2830371 to PPM1D. We propose that the two conformations represent active and inactive forms of the protein reflected by the position of the flap, and that binding of GSK2830371 shifts the equilibrium to the inactive form. Finally, we found that C-terminal truncating mutations proximal to residue 400 result in destabilization of the protein via loss of a stabilizing N- and C-terminal interaction, consistent with the observation from human genetic data that nearly all PPM1D mutations in cancer are truncating and occur distal to residue 400. Taken together, our findings elucidate the mechanism by which binding of a small molecule to an allosteric site of PPM1D inhibits its activity and provides insights into the biology of PPM1D.

The impact of rare germline variants on human somatic mutation processes

Somatic mutations are an inevitable component of ageing and the most important cause of cancer. The rates and types of somatic mutation vary across individuals, but relatively few inherited influences on mutation processes are known. We perform a gene-based rare variant association study with diverse mutational processes, using human cancer genomes from over 11,000 individuals of European ancestry. By combining burden and variance tests, we identify 207 associations involving 15 somatic mutational phenotypes and 42 genes that replicated in an independent data set at a false discovery rate of 1%. We associate rare inherited deleterious variants in genes such as MSH3, EXO1, SETD2, and MTOR with two phenotypically different forms of DNA mismatch repair deficiency, and variants in genes such as EXO1, PAXIP1, RIF1, and WRN with deficiency in homologous recombination repair. In addition, we identify associations with other mutational processes, such as APEX1 with APOBEC-signature mutagenesis. Many of the genes interact with each other and with known mutator genes within cellular sub-networks. Considered collectively, damaging variants in the identified genes are prevalent in the population. We suggest that rare germline variation in diverse genes commonly impacts mutational processes in somatic cells.

Bioinformatics Approaches to Predict Mutation Effects in the Binding Site of the Proangiogenic Molecule CD93

The transmembrane glycoprotein CD93 has been identified as a potential new target to inhibit tumor angiogenesis. Recently, Multimerin-2 (MMRN2), a pan-endothelial extracellular matrix protein, has been identified as a ligand for CD93, but the interaction mechanism between these two proteins is yet to be studied. In this article, we aim to investigate the structural and functional effects of induced mutations on the binding domain of CD93 to MMRN2. Starting from experimental data, we assessed how specific mutations in the C-type lectin-like domain (CTLD) affect the binding interaction profile. We described a four-step workflow in order to predict the effects of variations on the inter-residue interaction network at the PPI, based on evolutionary information, complex network metrics, and energetic affinity. We showed that the application of computational approaches, combined with experimental data, allowed us to gain more in-depth molecular insights into the CD93–MMRN2 interaction, offering a platform for developing innovative therapeutics able to target these molecules and block their interaction. This comprehensive molecular insight might prove useful in drug design in cancer therapy.

Towards generalizable predictions for G protein-coupled receptor variant expression

Missense mutations that compromise the plasma membrane expression (PME) of integral membrane proteins are the root cause of numerous genetic diseases. Differentiation of this class of mutations from those that specifically modify the activity of the folded protein has proven useful for the development and targeting of precision therapeutics. Nevertheless, it remains challenging to predict the effects of mutations on the stability and/ or expression of membrane proteins. In this work, we utilize deep mutational scanning data to train a series of artificial neural networks to predict the PME of transmembrane domain variants of G protein-coupled receptors from structural and/ or evolutionary features. We show that our best-performing network, which we term the PME predictor, can recapitulate mutagenic trends within rhodopsin and can differentiate pathogenic transmembrane domain variants that cause it to misfold from those that compromise its signaling. This network also generates statistically significant predictions for the relative PME of transmembrane domain variants for another class A G protein-coupled receptor (β₂ adrenergic receptor) but not for an unrelated voltage-gated potassium channel (KCNQ1). Notably, our analyses of these networks suggest structural features alone are generally sufficient to recapitulate the observed mutagenic trends. Moreover, our findings imply that networks trained in this manner may be generalizable to proteins that share a common fold. Implications of our findings for the design of mechanistically specific genetic predictors are discussed.

The 3D mutational constraint on amino acid sites in the human proteome

Quantification of the tolerance of protein sites to genetic variation has become a cornerstone of variant interpretation. We hypothesize that the constraint on missense variation at individual amino acid sites is largely shaped by direct interactions with 3D neighboring sites. To quantify this constraint, we introduce a framework called COntact Set MISsense tolerance (or COSMIS) and comprehensively map the landscape of 3D mutational constraint on 6.1 million amino acid sites covering 16,533 human proteins. We show that 3D mutational constraint is pervasive and that the level of constraint is strongly associated with disease relevance both at the site and the protein level. We demonstrate that COSMIS performs significantly better at variant interpretation tasks than other population-based constraint metrics while also providing structural insight into the functional roles of constrained sites. We anticipate that COSMIS will facilitate the interpretation of protein-coding variation in evolution and prioritization of sites for mechanistic investigation.

Identification and functional evaluation of GRIA1 missense and truncation variants in individuals with ID: An emerging neurodevelopmental syndrome

GRIA1 encodes the GluA1 subunit of α-amino-3-hydroxy-5-methyl-4-isoxazole propionate (AMPA) receptors, which are ligand-gated ion channels that act as excitatory receptors for the neurotransmitter L-glutamate (Glu). AMPA receptors (AMPARs) are homo- or heteromeric protein complexes with four subunits, each encoded by different genes, GRIA1 to GRIA4. Although GluA1-containing AMPARs have a crucial role in brain function, the human phenotype associated with deleterious GRIA1 sequence variants has not been established. Subjects with de novo missense and nonsense GRIA1 variants were identified through international collaboration. Detailed phenotypic and genetic assessments of the subjects were carried out and the pathogenicity of the variants was evaluated in vitro to characterize changes in AMPAR function and expression. In addition, two Xenopus gria1 CRISPR-Cas9 F₀ models were established to characterize the in vivo consequences. Seven unrelated individuals with rare GRIA1 variants were identified. One individual carried a homozygous nonsense variant (p.Arg377Ter), and six had heterozygous missense variations (p.Arg345Gln, p.Ala636Thr, p.Ile627Thr, and p.Gly745Asp), of which the p.Ala636Thr variant was recurrent in three individuals. The cohort revealed subjects to have a recurrent neurodevelopmental disorder mostly affecting cognition and speech. Functional evaluation of major GluA1-containing AMPAR subtypes carrying the GRIA1 variant mutations showed that three of the four missense variants profoundly perturb receptor function. The homozygous stop-gain variant completely destroys the expression of GluA1-containing AMPARs. The Xenopus gria1 models show transient motor deficits, an intermittent seizure phenotype, and a significant impairment to working memory in mutants. These data support a developmental disorder caused by both heterozygous and homozygous variants in GRIA1 affecting AMPAR function.

Spectrum of Phenotypic, Genetic, and Functional Characteristics in Patients With Epilepsy With KCNC2 Pathogenic Variants

BACKGROUND AND OBJECTIVES

KCNC2 encodes Kv3.2, a member of the Shaw-related (Kv3) voltage-gated potassium channel subfamily, which is important for sustained high-frequency firing and optimized energy efficiency of action potentials in the brain. The objective of this study was to analyze the clinical phenotype, genetic background, and biophysical function of disease-associated Kv3.2 variants.

METHODS

Individuals with KCNC2 variants detected by exome sequencing were selected for clinical, further genetic, and functional analysis. Cases were referred through clinical and research collaborations. Selected de novo variants were examined electrophysiologically in Xenopus laevis oocytes.

RESULTS

We identified novel KCNC2 variants in 18 patients with various forms of epilepsy, including genetic generalized epilepsy (GGE), developmental and epileptic encephalopathy (DEE) including early-onset absence epilepsy, focal epilepsy, and myoclonic-atonic epilepsy. Of the 18 variants, 10 were de novo and 8 were classified as modifying variants. Eight drug-responsive patients became seizure-free using valproic acid as monotherapy or in combination, including severe DEE cases. Functional analysis of 4 variants demonstrated gain of function in 3 severely affected DEE cases and loss of function in 1 case with a milder phenotype (GGE) as the underlying pathomechanisms.

DISCUSSION

These findings implicate KCNC2 as a novel causative gene for epilepsy and emphasize the critical role of KV3.2 in the regulation of brain excitability.

Integration of Protein Structure and Population-Scale DNA Sequence Data for Disease Gene Discovery and Variant Interpretation

The experimental and computational techniques for capturing information about protein structures and genetic variation within the human genome have advanced dramatically in the past 20 years, generating extensive new data resources. In this review, we discuss these advances, along with new approaches for determining the impact a genetic variant has on protein function. We focus on the potential of new methods that integrate human genetic variation into protein structures to discover relationships to disease, including the discovery of mutational hotspots in cancer-related proteins, the localization of protein-altering variants within protein regions for common complex diseases, and the assessment of variants of unknown significance for Mendelian traits. We expect that approaches that integrate these data sources will play increasingly important roles in disease gene discovery and variant interpretation. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 5 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Systematic evaluation of computational tools to predict the effects of mutations on protein stability in the absence of experimental structures

Changes in protein sequence can have dramatic effects on how proteins fold, their stability and dynamics. Over the last 20 years, pioneering methods have been developed to try to estimate the effects of missense mutations on protein stability, leveraging growing availability of protein 3D structures. These, however, have been developed and validated using experimentally derived structures and biophysical measurements. A large proportion of protein structures remain to be experimentally elucidated and, while many studies have based their conclusions on predictions made using homology models, there has been no systematic evaluation of the reliability of these tools in the absence of experimental structural data. We have, therefore, systematically investigated the performance and robustness of ten widely used structural methods when presented with homology models built using templates at a range of sequence identity levels (from 15% to 95%) and contrasted performance with sequence-based tools, as a baseline. We found there is indeed performance deterioration on homology models built using templates with sequence identity below 40%, where sequence-based tools might become preferable. This was most marked for mutations in solvent exposed residues and stabilizing mutations. As structure prediction tools improve, the reliability of these predictors is expected to follow, however we strongly suggest that these factors should be taken into consideration when interpreting results from structure-based predictors of mutation effects on protein stability.

Association of ultra-rare coding variants with genetic generalized epilepsy: A case-control whole exome sequencing study

OBJECTIVE

We aimed to identify genes associated with genetic generalized epilepsy (GGE) by combining large cohorts enriched with individuals with a positive family history. Secondarily, we set out to compare the association of genes independently with familial and sporadic GGE.

METHODS

We performed a case-control whole exome sequencing study in unrelated individuals of European descent diagnosed with GGE (previously recruited and sequenced through multiple international collaborations) and ancestry-matched controls. The association of ultra-rare variants (URVs; in 18 834 protein-coding genes) with epilepsy was examined in 1928 individuals with GGE (vs. 8578 controls), then separately in 945 individuals with familial GGE (vs. 8626 controls), and finally in 1005 individuals with sporadic GGE (vs. 8621 controls). We additionally examined the association of URVs with familial and sporadic GGE in two gene sets important for inhibitory signaling (19 genes encoding γ-aminobutyric acid type A [GABAA ] receptors, 113 genes representing the GABAergic pathway).

RESULTS

GABRG2 was associated with GGE (p = 1.8 × 10-5 ), approaching study-wide significance in familial GGE (p = 3.0 × 10-6 ), whereas no gene approached a significant association with sporadic GGE. Deleterious URVs in the most intolerant subgenic regions in genes encoding GABAA receptors were associated with familial GGE (odds ratio [OR] = 3.9, 95% confidence interval [CI] = 1.9-7.8, false discovery rate [FDR]-adjusted p = .0024), whereas their association with sporadic GGE had marginally lower odds (OR = 3.1, 95% CI = 1.3-6.7, FDR-adjusted p = .022). URVs in GABAergic pathway genes were associated with familial GGE (OR = 1.8, 95% CI = 1.3-2.5, FDR-adjusted p = .0024) but not with sporadic GGE (OR = 1.3, 95% CI = .9-1.9, FDR-adjusted p = .19).

SIGNIFICANCE

URVs in GABRG2 are likely an important risk factor for familial GGE. The association of gene sets of GABAergic signaling with familial GGE is more prominent than with sporadic GGE.

Rare germline heterozygous missense variants in BRCA1-associated protein 1, BAP1, cause a syndromic neurodevelopmental disorder

Nuclear deubiquitinase BAP1 (BRCA1-associated protein 1) is a core component of multiprotein complexes that promote transcription by reversing the ubiquitination of histone 2A (H2A). BAP1 is a tumor suppressor whose germline loss-of-function variants predispose to cancer. To our knowledge, there are very rare examples of different germline variants in the same gene causing either a neurodevelopmental disorder (NDD) or a tumor predisposition syndrome. Here, we report a series of 11 de novo germline heterozygous missense BAP1 variants associated with a rare syndromic NDD. Functional analysis showed that most of the variants cannot rescue the consequences of BAP1 inactivation, suggesting a loss-of-function mechanism. In T cells isolated from two affected children, H2A deubiquitination was impaired. In matching peripheral blood mononuclear cells, histone H3 K27 acetylation ChIP-seq indicated that these BAP1 variants induced genome-wide chromatin state alterations, with enrichment for regulatory regions surrounding genes of the ubiquitin-proteasome system (UPS). Altogether, these results define a clinical syndrome caused by rare germline missense BAP1 variants that alter chromatin remodeling through abnormal histone ubiquitination and lead to transcriptional dysregulation of developmental genes.

OUP accepted manuscript

Epilepsy is one of the most frequent neurological diseases, with focal epilepsy accounting for the largest number of cases. The genetic alterations involved in focal epilepsy are far from being fully elucidated.

Here, we show that defective lipid signalling caused by heterozygous ultra-rare variants in PIK3C2B, encoding for the class II phosphatidylinositol 3-kinase PI3K-C2β, underlie focal epilepsy in humans. We demonstrate that patients’ variants act as loss-of-function alleles, leading to impaired synthesis of the rare signalling lipid phosphatidylinositol 3,4-bisphosphate, resulting in mTORC1 hyperactivation. In vivo, mutant Pik3c2b alleles caused dose-dependent neuronal hyperexcitability and increased seizure susceptibility, indicating haploinsufficiency as a key driver of disease. Moreover, acute mTORC1 inhibition in mutant mice prevented experimentally induced seizures, providing a potential therapeutic option for a selective group of patients with focal epilepsy.

Our findings reveal an unexpected role for class II PI3K-mediated lipid signalling in regulating mTORC1-dependent neuronal excitability in mice and humans.

HGDiscovery: An online tool providing functional and phenotypic information on novel variants of homogentisate 1,2- dioxigenase

Alkaptonuria (AKU), a rare genetic disorder, is characterized by the accumulation of homogentisic acid (HGA) in the body. Affected individuals lack functional levels of an enzyme required to breakdown HGA. Mutations in the homogentisate 1,2-dioxygenase (HGD) gene cause AKU and they are responsible for deficient levels of functional HGD, which, in turn, leads to excess levels of HGA. Although HGA is rapidly cleared from the body by the kidneys, in the long term it starts accumulating in various tissues, especially cartilage. Over time (rarely before adulthood), it eventually changes the color of affected tissue to slate blue or black. Here we report a comprehensive mutation analysis of 111 pathogenic and 190 non-pathogenic HGD missense mutations using protein structural information. Using our comprehensive suite of graph-based signature methods, mCSM complemented with sequence-based tools, we studied the functional and molecular consequences of each mutation on protein stability, interaction and evolutionary conservation. The scores generated from the structure and sequence-based tools were used to train a supervised machine learning algorithm with 89% accuracy. The empirical classifier was used to generate the variant phenotype for novel HGD missense mutations. All this information is deployed as a user friendly freely available web server called HGDiscovery (https://biosig.lab.uq.edu.au/hgdiscovery/).

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Functional and phenotypic consequences of HGD non-synonymous variations.

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Biophysical, structural and evolutionary analysis of novel and known clinical variants.

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Pathogenic mutations affected protein stability and conformational flexibility.

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Pathogenic mutations associated with deleterious scores for sequence-based features.

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HGDiscovery (http://biosig.unimelb.edu.au/hgdiscovery/) – webserver.

A Folding Insulator Defines Cryptic Domains in Tropomyosin

Multidomain proteins are the product of evolutionary selection for diversity of function through concatenation and repurposing of existing modular units of structures. In structures of proteins with multiple domains, components are often globular units stitched together with flexible linkers. Multidomain proteins often fold as multiple distinct order-disorder transitions. However, the relationship between structure and folding is not always straightforward. Tropomyosin binds to actin in muscle and cytoskeletal filaments. The structure is that of a continuous ɑ-helix lacking domain boundaries, but unfolding shows distinct transitions suggesting at least three possible domains do exist. To explore how domains might occur in a continuous structure, we used Lifson-Roig helix-coil models with sequence domains of varying helical nucleation propensities. Of these models, ones with a central folding insulator, separating folding of N- and C-terminal domains, are most consistent with experimental folding studies. The positions of domain boundaries are identified by hydrogen-deuterium exchange mass spectrometry. The presence of structurally cryptic folding domains in tropomyosin could relate to its evolution and explain the uneven distribution of deleterious mutations that lead to various cardiomyopathies.

A domain damage index to prioritizing the pathogenicity of missense variants

Prioritizing causal variants is one major challenge for the clinical application of sequencing data. Prompted by the observation that 74.3% of missense pathogenic variants locate in protein domains, we developed an approach named domain damage index (DDI). DDI identifies protein domains depleted of rare missense variations in the general population, which can be further used as a metric to prioritize variants. DDI is significantly correlated with phylogenetic conservation, variant-level metrics, and reported pathogenicity. DDI achieved great performance for distinguishing pathogenic variants from benign ones in three benchmark datasets. The combination of DDI with the other two best approaches improved the performance of each individual method considerably, suggesting DDI provides a powerful and complementary way of variant prioritization.

De novo variants in CACNA1E found in patients with intellectual disability, developmental regression and social cognition deficit but no seizures

BACKGROUND

De novo variants in the voltage-gated calcium channel subunit α1 E gene (CACNA1E) have been described as causative of epileptic encephalopathy with contractures, macrocephaly and dyskinesias.

METHODS

Following the observation of an index patient with developmental delay and autism spectrum disorder (ASD) without seizures who had a de novo deleterious CACNA1E variant, we screened GeneMatcher for other individuals with CACNA1E variants and neurodevelopmental phenotypes without epilepsy. The spectrum of pathogenic CACNA1E variants was compared to the mutational landscape of variants in the gnomAD control population database.

RESULTS

We identified seven unrelated individuals with intellectual disability, developmental regression and ASD-like behavioral profile, and notably without epilepsy, who had de novo heterozygous putatively pathogenic variants in CACNA1E. Age of onset of clinical manifestation, presence or absence of regression and degree of severity were variable, and no clear-cut genotype-phenotype association could be recognized. The analysis of disease-associated variants and their comparison to benign variants from the control population allowed for the identification of regions in the CACNA1E protein that seem to be intolerant to substitutions and thus more likely to harbor pathogenic variants. As in a few reported cases with CACNA1E variants and epilepsy, one patient showed a positive clinical behavioral response to topiramate, a specific calcium channel modulator.

LIMITATIONS

The significance of our study is limited by the absence of functional experiments of the effect of identified variants, the small sample size and the lack of systematic ASD assessment in all participants. Moreover, topiramate was given to one patient only and for a short period of time.

CONCLUSIONS

Our results indicate that CACNA1E variants may result in neurodevelopmental disorders without epilepsy and expand the mutational and phenotypic spectrum of this gene. CACNA1E deserves to be included in gene panels for non-specific developmental disorders, including ASD, and not limited to patients with seizures, to improve diagnostic recognition and explore the possible efficacy of topiramate.

AHDC1 missense mutations in Xia-Gibbs syndrome

Xia-Gibbs syndrome (XGS; MIM: 615829) is a phenotypically heterogeneous neurodevelopmental disorder (NDD) caused by newly arising mutations in the AT-Hook DNA-Binding Motif-Containing 1 (AHDC1) gene that are predicted to lead to truncated AHDC1 protein synthesis. More than 270 individuals have been diagnosed with XGS worldwide. Despite the absence of an independent assay for AHDC1 protein function to corroborate potential functional consequences of rare variant genetic findings, there are also reports of individuals with XGS-like trait manifestations who have de novo missense AHDC1 mutations and who have been provided a molecular diagnosis of the disorder. To investigate a potential contribution of missense mutations to XGS, we mapped the missense mutations from 10 such individuals to the AHDC1 conserved protein domain structure and detailed the observed phenotypes. Five newly identified individuals were ascertained from a local XGS Registry, and an additional five were taken from external reports or databases, including one publication. Where clinical data were available, individuals with missense mutations all displayed phenotypes consistent with those observed in individuals with AHDC1 truncating mutations, including delayed motor milestones, intellectual disability (ID), hypotonia, and speech delay. A subset of the 10 reported missense mutations cluster in two regions of the AHDC1 protein with known conserved domains, likely representing functional motifs. Variants outside the clustered regions score lower for computational prediction of their likely damaging effects. Overall, de novo missense variants in AHDC1 are likely diagnostic of XGS when in silico analysis of their position relative to conserved regions is considered together with disease trait manifestations.

End-Truncated LAMB1 Causes a Hippocampal Memory Defect and a Leukoencephalopathy

OBJECTIVE

The majority of patients with a familial cerebral small vessel disease (CSVD) referred for molecular screening do not show pathogenic variants in known genes. In this study, we aimed to identify novel CSVD causal genes.

METHODS

We performed a gene-based collapsing test of rare protein-truncating variants identified in exome data of 258 unrelated CSVD patients of an ethnically matched control cohort and of 2 publicly available large-scale databases, gnomAD and TOPMed. Western blotting was used to investigate the functional consequences of variants. Clinical and magnetic resonance imaging features of mutated patients were characterized.

RESULTS

We showed that LAMB1 truncating variants escaping nonsense-mediated messenger RNA decay are strongly overrepresented in CSVD patients, reaching genome-wide significance (p < 5 × 10^-8 ). Using 2 antibodies recognizing the N- and C-terminal parts of LAMB1, we showed that truncated forms of LAMB1 are expressed in the endogenous fibroblasts of patients and trapped in the cytosol. These variants are associated with a novel phenotype characterized by the association of a hippocampal type episodic memory defect and a diffuse vascular leukoencephalopathy.

INTERPRETATION

These findings are important for diagnosis and clinical care, to avoid unnecessary and sometimes invasive investigations, and also from a mechanistic point of view to understand the role of extracellular matrix proteins in neuronal homeostasis. ANN NEUROL 2021;90:962-975.

Structure, Function, and Pharmacology of Glutamate Receptor Ion Channels

Many physiologic effects of l-glutamate, the major excitatory neurotransmitter in the mammalian central nervous system, are mediated via signaling by ionotropic glutamate receptors (iGluRs). These ligand-gated ion channels are critical to brain function and are centrally implicated in numerous psychiatric and neurologic disorders. There are different classes of iGluRs with a variety of receptor subtypes in each class that play distinct roles in neuronal functions. The diversity in iGluR subtypes, with their unique functional properties and physiologic roles, has motivated a large number of studies. Our understanding of receptor subtypes has advanced considerably since the first iGluR subunit gene was cloned in 1989, and the research focus has expanded to encompass facets of biology that have been recently discovered and to exploit experimental paradigms made possible by technological advances. Here, we review insights from more than 3 decades of iGluR studies with an emphasis on the progress that has occurred in the past decade. We cover structure, function, pharmacology, roles in neurophysiology, and therapeutic implications for all classes of receptors assembled from the subunits encoded by the 18 ionotropic glutamate receptor genes. SIGNIFICANCE STATEMENT: Glutamate receptors play important roles in virtually all aspects of brain function and are either involved in mediating some clinical features of neurological disease or represent a therapeutic target for treatment. Therefore, understanding the structure, function, and pharmacology of this class of receptors will advance our understanding of many aspects of brain function at molecular, cellular, and system levels and provide new opportunities to treat patients.

Distinct gene-set burden patterns underlie common generalized and focal epilepsies

Background

Analyses of few gene-sets in epilepsy showed a potential to unravel key disease associations. We set out to investigate the burden of ultra-rare variants (URVs) in a comprehensive range of biologically informed gene-sets presumed to be implicated in epileptogenesis.

Methods

The burden of 12 URV types in 92 gene-sets was compared between cases and controls using whole exome sequencing data from individuals of European descent with developmental and epileptic encephalopathies (DEE, n = 1,003), genetic generalized epilepsy (GGE, n = 3,064), or non-acquired focal epilepsy (NAFE, n = 3,522), collected by the Epi25 Collaborative, compared to 3,962 ancestry-matched controls.

Findings

Missense URVs in highly constrained regions were enriched in neuron-specific and developmental genes, whereas genes not expressed in brain were not affected. GGE featured a higher burden in gene-sets derived from inhibitory vs. excitatory neurons or associated receptors, whereas the opposite was found for NAFE, and DEE featured a burden in both. Top-ranked susceptibility genes from recent genome-wide association studies (GWAS) and gene-sets derived from generalized vs. focal epilepsies revealed specific enrichment patterns of URVs in GGE vs. NAFE.

Interpretation

Missense URVs affecting highly constrained sites differentially impact genes expressed in inhibitory vs. excitatory pathways in generalized vs. focal epilepsies. The excess of URVs in top-ranked GWAS risk-genes suggests a convergence of rare deleterious and common risk-variants in the pathogenesis of generalized and focal epilepsies.

Funding

DFG Research Unit FOR-2715 (Germany), FNR (Luxembourg), NHGRI (US), NHLBI (US), DAAD (Germany).

Structure-guided machine learning prediction of drug resistance mutations in Abelson 1 kinase

Kinases play crucial roles in cellular signalling and biological processes with their dysregulation associated with diseases, including cancers. Kinase inhibitors, most notably those targeting ABeLson 1 (ABL1) kinase in chronic myeloid leukemia, have had a significant impact on cancer survival, yet emergence of resistance mutations can reduce their effectiveness, leading to therapeutic failure. Limited effort, however, has been devoted to developing tools to accurately identify ABL1 resistance mutations, as well as providing insights into their molecular mechanisms. Here we investigated the structural basis of ABL1 mutations modulating binding affinity of eight FDA-approved drugs. We found mutations impair affinity of type I and type II inhibitors differently and used this insight to developed a novel web-based diagnostic tool, SUSPECT-ABL, to pre-emptively predict resistance profiles and binding free-energy changes (ΔΔG) of all possible ABL1 mutations against inhibitors with different binding modes. Resistance mutations in ABL1 were successfully identified, achieving a Matthew's Correlation Coefficient of up to 0.73 and the resulting change in ligand binding affinity with a Pearson's correlation of up to 0.77, with performances consistent across non-redundant blind tests. Through an in silico saturation mutagenesis, our tool has identified possibly emerging resistance mutations, which offers opportunities for in vivo experimental validation. We believe SUSPECT-ABL will be an important tool not just for improving precision medicine efforts, but for facilitating the development of next-generation inhibitors that are less prone to resistance. We have made our tool freely available at http://biosig.unimelb.edu.au/suspect_abl/.

Pathogenic MAST3 Variants in the STK Domain Are Associated with Epilepsy

OBJECTIVE

The MAST family of microtubule-associated serine-threonine kinases (STKs) have distinct expression patterns in the developing and mature human and mouse brain. To date, only MAST1 has been conclusively associated with neurological disease, with de novo variants in individuals with a neurodevelopmental disorder, including a mega corpus callosum.

METHODS

Using exome sequencing, we identify MAST3 missense variants in individuals with epilepsy. We also assess the effect of these variants on the ability of MAST3 to phosphorylate the target gene product ARPP-16 in HEK293T cells.

RESULTS

We identify de novo missense variants in the STK domain in 11 individuals, including 2 recurrent variants p.G510S (n = 5) and p.G515S (n = 3). All 11 individuals had developmental and epileptic encephalopathy, with 8 having normal development prior to seizure onset at <2 years of age. All patients developed multiple seizure types, 9 of 11 patients had seizures triggered by fever and 9 of 11 patients had drug-resistant seizures. In vitro analysis of HEK293T cells transfected with MAST3 cDNA carrying a subset of these patient-specific missense variants demonstrated variable but generally lower expression, with concomitant increased phosphorylation of the MAST3 target, ARPP-16, compared to wild-type. These findings suggest the patient-specific variants may confer MAST3 gain-of-function. Moreover, single-nuclei RNA sequencing and immunohistochemistry shows that MAST3 expression is restricted to excitatory neurons in the cortex late in prenatal development and postnatally.

INTERPRETATION

In summary, we describe MAST3 as a novel epilepsy-associated gene with a potential gain-of-function pathogenic mechanism that may be primarily restricted to excitatory neurons in the cortex. ANN NEUROL 2021;90:274-284.

Exome sequencing in obsessive-compulsive disorder reveals a burden of rare damaging coding variants

Obsessive-compulsive disorder (OCD) affects 1-2% of the population, and, as with other complex neuropsychiatric disorders, it is thought that rare variation contributes to its genetic risk. In this study, we performed exome sequencing in the largest OCD cohort to date (1,313 total cases, consisting of 587 trios, 41 quartets and 644 singletons of affected individuals) and describe contributions to disease risk from rare damaging coding variants. In case-control analyses (n = 1,263/11,580), the most significant single-gene result was observed in SLITRK5 (odds ratio (OR) = 8.8, 95% confidence interval 3.4-22.5, P = 2.3 × 10-6). Across the exome, there was an excess of loss of function (LoF) variation specifically within genes that are LoF-intolerant (OR = 1.33, P = 0.01). In an analysis of trios, we observed an excess of de novo missense predicted damaging variants relative to controls (OR = 1.22, P = 0.02), alongside an excess of de novo LoF mutations in LoF-intolerant genes (OR = 2.55, P = 7.33 × 10-3). These data support a contribution of rare coding variants to OCD genetic risk.