EXPRESS: Stratified Distributional Analysis - a Novel Perspective on RT Distributions.

Response times and their distributions serve as a powerful lens into cognitive processes. We present a novel statistical methodology called Stratified Distributional Analysis (SDA) to quantitatively assess how key determinants of response times (word frequency and length) shape their distributions. Taking advantage of the availability of millions of lexical decision response times in the English Lexicon Project and the British Lexicon Project, we made important advances into the theoretical issue of linking response times and word frequency by analysing RT distributions as a function of word frequency and word length. We tested these distributions against the lognormal, Wald, and Gamma distributions and three measures of word occurrence (word form frequencies obtained from subtitles and contextual diversity as operationalized as discourse contextual diversity and user contextual diversity). We found that the RT distributions were best described by a lnorm distribution across both megastudies when word occurrence was quantified by a contextual diversity measure. The link between the lnorm distribution and its generative process highlights the power of SDA in elucidating mechanisms that govern the generation of RTs through the fitting of probability distributions. Using a hierarchical Bayesian framework, SDA yielded posterior distributions for the distributional parameters at the single-participant level, enabling probabilistic predictions of response times as a function of word frequency and word length, which has the potential to serve as a diagnostic tool to uncover idiosyncratic features of word processing. Crucially, while we applied our parsimonious methodology to lexical decision response times, it is applicable to a variety of tasks such as word-naming and eye-tracking data.

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 Ca2+ and is inactive in in vitro assays against standard PADI substrates. By determining the crystal structure of hPADI6, we show that hPADI6 is structured in the absence of Ca2+ where hPADI2 and hPADI4 are not, and the Ca-binding sites are not conserved. Moreover, we show that whilst the key catalytic aspartic acid and histidine residues are structurally conserved, the cysteine is displaced far from the active site centre and the hPADI6 active site pocket appears closed through a unique evolved mechanism in hPADI6, not present in the other PADIs. Taken together, these findings provide insight into how the function of hPADI6 may differ from the other PADIs based on its structure and provides a resource for characterising the damaging effect of clinically significant PADI6 variants.

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. Given overlapping clinical features and genetic heterogeneity, diagnosis can be difficult and often occurs late. Of those tested an estimated 30% of genetically screened PCD patients still lack a molecular diagnosis. A molecular diagnosis allows for appropriate clinical management including prediction of phenotypic features correlated to genotype. Here, we aimed to identify how readily a genetic diagnosis could be made using whole genome sequencing (WGS) to facilitate identification of pathogenic variants in known genes as well as novel PCD candidate genes. METHODS: WGS was used to screen for pathogenic variants in eight patients with PCD. RESULTS: 7/8 cases had homozygous or biallelic variants in DNAH5, DNAAF4 or DNAH11 classified as pathogenic or likely pathogenic. Three identified variants were deletions, ranging from 3 to 13 kb, for which WGS identified precise breakpoints, permitting confirmation by Sanger sequencing. WGS yielded identification of a de novo variant in a novel PCD gene TUBB4B. CONCLUSION: Here, WGS uplifted genetic diagnosis of PCD by identifying structural variants and novel modes of inheritance in new candidate genes. WGS could be an important component of the PCD diagnostic toolkit, increasing molecular diagnostic yield from current (70%) levels, and enhancing our understanding of fundamental biology of motile cilia and variants in the noncoding genome.

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. Moreover, pathogenic missense mutations that act via DN and GOF mechanisms are more difficult to identify than those that act via LOF using nearly all currently available variant effect predictors. Here, we introduce a tripartite statistical model made up of support vector machine binary classifiers trained to predict whether human protein coding genes are likely to be associated with DN, GOF, or LOF molecular disease mechanisms. We test the utility of the predictions by examining biologically and clinically meaningful properties known to be associated with the mechanisms. Our results strongly support that the models are able to generalise on unseen data and offer insight into the functional attributes of proteins associated with different mechanisms. We hope that our predictions will serve as a springboard for researchers studying novel variants and those of uncertain clinical significance, guiding variant interpretation strategies and experimental characterisation. Predictions for the human UniProt reference proteome are available at https://osf.io/z4dcp/.

ALDH1A3-acetaldehyde metabolism potentiates transcriptional heterogeneity in melanoma.

Cancer cellular heterogeneity and therapy resistance arise substantially from metabolic and transcriptional adaptations, but how these are interconnected is poorly understood. Here, we show that, in melanoma, the cancer stem cell marker aldehyde dehydrogenase 1A3 (ALDH1A3) forms an enzymatic partnership with acetyl-coenzyme A (CoA) synthetase 2 (ACSS2) in the nucleus to couple high glucose metabolic flux with acetyl-histone H3 modification of neural crest (NC) lineage and glucose metabolism genes. Importantly, we show that acetaldehyde is a metabolite source for acetyl-histone H3 modification in an ALDH1A3-dependent manner, providing a physiologic function for this highly volatile and toxic metabolite. In a zebrafish melanoma residual disease model, an ALDH1-high subpopulation emerges following BRAF inhibitor treatment, and targeting these with an ALDH1 suicide inhibitor, nifuroxazide, delays or prevents BRAF inhibitor drug-resistant relapse. Our work reveals that the ALDH1A3-ACSS2 couple directly coordinates nuclear acetaldehyde-acetyl-CoA metabolism with specific chromatin-based gene regulation and represents a potential therapeutic vulnerability in melanoma.

Gain-of-function human UNC93B1 variants cause systemic lupus erythematosus and chilblain lupus.

UNC93B1 is a transmembrane domain protein mediating the signaling of endosomal Toll-like receptors (TLRs). We report five families harboring rare missense substitutions (I317M, G325C, L330R, R466S, and R525P) in UNC93B1 causing systemic lupus erythematosus (SLE) or chilblain lupus (CBL) as either autosomal dominant or autosomal recessive traits. As for a D34A mutation causing murine lupus, we recorded a gain of TLR7 and, to a lesser extent, TLR8 activity with the I317M (in vitro) and G325C (in vitro and ex vivo) variants in the context of SLE. Contrastingly, in three families segregating CBL, the L330R, R466S, and R525P variants were isomorphic with respect to TLR7 activity in vitro and, for R525P, ex vivo. Rather, these variants demonstrated a gain of TLR8 activity. We observed enhanced interaction of the G325C, L330R, and R466S variants with TLR8, but not the R525P substitution, indicating different disease mechanisms. Overall, these observations suggest that UNC93B1 mutations cause monogenic SLE or CBL due to differentially enhanced TLR7 and TLR8 signaling.

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. Mutations in DNA-facing residues of the N-terminal subdomain and linker region were most detrimental, as were mutations to prolines and to negatively charged residues. Many variants caused sequence-specific molecular gain-of-function effects, including variants in position 71 that increased binding to the LE9 enhancer but decreased binding to a SELEX-derived binding site. In the absence of antibiotic selection, variants that retained DNA binding slowed yeast growth, likely because such variants perturbed the yeast transcriptome. Benchmarking against known patient variants and applying ACMG/AMP guidelines to variant classification, we obtained supporting-to-moderate evidence that 977 variants are likely pathogenic and 1306 are likely benign. Our analysis shows that most pathogenic mutations in the paired domain of PAX6 can be explained simply by the effects of these mutations on PAX6:DNA association, and establishes Y1H as a generalisable assay for the interpretation of variant effects in transcription factors.

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. Here, to address these issues, we provide guidelines and recommendations for the release of novel VEPs. Emphasising open-source availability, transparent methodologies, clear variant effect score interpretations, standardised scales, accessible predictions, and rigorous training data disclosure, we aim to improve the usability and interpretability of VEPs, and promote their integration into analysis and evaluation pipelines. We also provide a large, categorised list of currently available VEPs, aiming to facilitate the discovery and encourage the usage of novel methods within the scientific community.

Interface Gain-of-Function Mutations in TLR7 Cause Systemic and Neuro-inflammatory Disease.

TLR7 recognizes pathogen-derived single-stranded RNA (ssRNA), a function integral to the innate immune response to viral infection. Notably, TLR7 can also recognize self-derived ssRNA, with gain-of-function mutations in human TLR7 recently identified to cause both early-onset systemic lupus erythematosus (SLE) and neuromyelitis optica. Here, we describe two novel mutations in TLR7, F507S and L528I. While the L528I substitution arose de novo, the F507S mutation was present in three individuals from the same family, including a severely affected male, notably given that the TLR7 gene is situated on the X chromosome and that all other cases so far described have been female. The observation of mutations at residues 507 and 528 of TLR7 indicates the importance of the TLR7 dimerization interface in maintaining immune homeostasis, where we predict that altered homo-dimerization enhances TLR7 signaling. Finally, while mutations in TLR7 can result in SLE-like disease, our data suggest a broader phenotypic spectrum associated with TLR7 gain-of-function, including significant neurological involvement.

Expansion of the neurodevelopmental phenotype of individuals with EEF1A2 variants and genotype-phenotype study.

Translation elongation factor eEF1A2 constitutes the alpha subunit of the elongation factor-1 complex, responsible for the enzymatic binding of aminoacyl-tRNA to the ribosome. Since 2012, 21 pathogenic missense variants affecting EEF1A2 have been described in 42 individuals with a severe neurodevelopmental phenotype including epileptic encephalopathy and moderate to profound intellectual disability (ID), with neurological regression in some patients. Through international collaborative call, we collected 26 patients with EEF1A2 variants and compared them to the literature. Our cohort shows a significantly milder phenotype. 83% of the patients are walking (vs. 29% in the literature), and 84% of the patients have language skills (vs. 15%). Three of our patients do not have ID. Epilepsy is present in 63% (vs. 93%). Neurological examination shows a less severe phenotype with significantly less hypotonia (58% vs. 96%), and pyramidal signs (24% vs. 68%). Cognitive regression was noted in 4% (vs. 56% in the literature). Among individuals over 10 years, 56% disclosed neurocognitive regression, with a mean age of onset at 2 years. We describe 8 novel missense variants of EEF1A2. Modeling of the different amino-acid sites shows that the variants associated with a severe phenotype, and the majority of those associated with a moderate phenotype, cluster within the switch II region of the protein and thus may affect GTP exchange. In contrast, variants associated with milder phenotypes may impact secondary functions such as actin binding. We report the largest cohort of individuals with EEF1A2 variants thus far, allowing us to expand the phenotype spectrum and reveal genotype-phenotype correlations.

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. We demonstrate that CDK4/6-inhibited cells overgrow during G0/G1, causing p38/p53/p21-dependent cell cycle withdrawal. Cell cycle withdrawal is triggered by biphasic p21 induction. The first p21 wave is caused by osmotic stress, leading to p38- and size-dependent accumulation of p21. CDK4/6 inhibitor washout results in some cells entering S-phase. Overgrown cells experience replication stress, resulting in a second p21 wave that promotes cell cycle withdrawal from G2 or the subsequent G1. We propose that the levels of p21 integrate signals from overgrowth-triggered stresses to determine cell fate. This model explains how hypertrophy can drive senescence and why CDK4/6 inhibitors have long-lasting effects in patients.

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. However, the strong performance of recent supervised VEPs, in particular VARITY, shows that developers are taking data circularity and bias issues seriously. We also assess the performance of DMS and unsupervised VEPs for discriminating between known pathogenic and putatively benign missense variants. Our findings are mixed, demonstrating that some DMS datasets perform exceptionally at variant classification, while others are poor. Notably, we observe a striking correlation between VEP agreement with DMS data and performance in identifying clinically relevant variants, strongly supporting the validity of our rankings and the utility of DMS for independent benchmarking.

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. Here, we take an alternate approach, and explore how well stability predictor scores correlate with functional impacts derived from deep mutational scanning (DMS) experiments. In this work, we compare the predictions of 9 protein stability-based tools against mutant protein fitness values from 49 independent DMS datasets, covering 170,940 unique single amino acid variants. We find that FoldX and Rosetta show the strongest correlations with DMS-based functional scores, similar to their previous top performance in distinguishing between pathogenic and benign variants. For both methods, performance is considerably improved when considering intermolecular interactions from protein complex structures, when available. Furthermore, using these two predictors, we derive a "Foldetta" consensus score, which improves upon the performance of both, and manages to match dedicated variant effect predictors in reflecting variant functional impacts. Finally, we also highlight that predicted stability effects show consistently higher correlations with certain DMS experimental phenotypes, particularly those based upon protein abundance, and, in certain cases, can significantly outcompete sequence-based variant effect prediction methodologies for predicting functional scores from DMS experiments.

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. In this review, we reflect on historical experiments that contributed to the field, including breakthroughs that have made possible the proteome-wide detection of cotranslational assembly, and the technical challenges yet to be overcome. We introduce a simple framework that encapsulates the hallmarks of cotranslational assembly and discuss how results from new experiments are shaping our view of the mechanistic, structural and evolutionary factors driving the phenomenon.

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. We also find that complexes with known dominant-negative effects tend to expose their interfaces late during translation, lessening the likelihood of cotranslational assembly. Finally, by combining complex properties with other features, we trained a computational model for predicting proteins likely to be associated with non-loss-of-function disease mechanisms, which we believe will be of considerable utility for protein variant interpretation.

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. After stable lentiviral reconstitution of STAT2-null human fibrosarcoma U6A cells with STAT2 wild type or p.(A219V), we performed quantitative polymerase chain reaction, western blotting, immunofluorescence, and co-immunoprecipitation to functionally characterize the p.(A219V) variant. RESULTS: WGS identified a rare homozygous single nucleotide transition in STAT2 (c.656C > T), resulting in a p.(A219V) substitution, in a patient displaying developmental delay, intracranial calcification, and up-regulation of interferon-stimulated gene (ISG) expression in blood. In vitro studies revealed that the STAT2 p.(A219V) variant retained the ability to transduce an IFN-I stimulus. Notably, STAT2 p.(A219V) failed to support receptor desensitization, resulting in sustained STAT2 phosphorylation and ISG up-regulation. Mechanistically, STAT2 p.(A219V) showed defective binding to ubiquitin specific protease 18 (USP18), providing a possible explanation for the chronic IFN-I pathway activation seen in the patient. CONCLUSION: Our data indicate an impaired negative regulatory role of STAT2 p.(A219V) in IFN-I signalling and that mutations in STAT2 resulting in a type I interferonopathy state are not limited to the previously reported R148 residue. Indeed, structural modelling highlights at least 3 further residues critical to mediating a STAT2-USP18 interaction, in which mutations might be expected to result in defective negative feedback regulation of IFN-I signalling.