A proactive genotype-to-patient-phenotype map for cystathionine beta-synthase

Comprehensive analysis of the functional impact of single nucleotide variants of human CHEK2

Loss of function mutations in the checkpoint kinase gene CHEK2 are associated with increased risk of breast and other cancers. Most of the 3,188 unique amino acid changes that can result from non-synonymous single nucleotide variants (SNVs) of CHEK2, however, have not been tested for their impact on the function of the CHEK2-enocded protein (CHK2). One successful approach to testing the function of variants has been to test for their ability to complement mutations in the yeast ortholog of CHEK2, RAD53. This approach has been used to provide functional information on over 100 CHEK2 SNVs and the results align with functional assays in human cells and known pathogenicity. Here we tested all but two of the 4,887 possible SNVs in the CHEK2 open reading frame for their ability to complement RAD53 mutants using a high throughput technique of deep mutational scanning (DMS). Among the non-synonymous changes, 770 were damaging to protein function while 2,417 were tolerated. The results correlate well with previous structure and function data and provide a first or additional functional assay for all the variants of uncertain significance identified in clinical databases. Combined, this approach can be used to help predict the pathogenicity of CHEK2 variants of uncertain significance that are found in susceptibility screening and could be applied to other cancer risk genes.

Scalable approaches for generating, validating and incorporating data from high-throughput functional assays to improve clinical variant classification

As the adoption and scope of genetic testing continue to expand, interpreting the clinical significance of DNA sequence variants at scale remains a formidable challenge, with a high proportion classified as variants of uncertain significance (VUSs). Genetic testing laboratories have historically relied, in part, on functional data from academic literature to support variant classification. High-throughput functional assays or multiplex assays of variant effect (MAVEs), designed to assess the effects of DNA variants on protein stability and function, represent an important and increasingly available source of evidence for variant classification, but their potential is just beginning to be realized in clinical lab settings. Here, we describe a framework for generating, validating and incorporating data from MAVEs into a semi-quantitative variant classification method applied to clinical genetic testing. Using single-cell gene expression measurements, cellular evidence models were built to assess the effects of DNA variation in 44 genes of clinical interest. This framework was also applied to models for an additional 22 genes with previously published MAVE datasets. In total, modeling data was incorporated from 24 genes into our variant classification method. These data contributed evidence for classifying 4043 observed variants in over 57,000 individuals. Genetic testing laboratories are uniquely positioned to generate, analyze, validate, and incorporate evidence from high-throughput functional data and ultimately enable the use of these data to provide definitive clinical variant classifications for more patients.

A probabilistic graphical model for estimating selection coefficient of missense variants from human population sequence data

Accurately predicting the effect of missense variants is a central problem in interpretation of genomic variation. Commonly used computational methods does not capture the quantitative impact on fitness in populations. We developed MisFit to estimate missense fitness effect using biobank-scale human population genome data. MisFit jointly models the effect at molecular level ( d ) and population level (selection coefficient, s ), assuming that in the same gene, missense variants with similar d have similar s . MisFit is a probabilistic graphical model that integrates deep neural network components and population genetics models efficiently with inductive bias based on biological causality of variant effect. We trained it by maximizing probability of observed allele counts in 236,017 European individuals. We show that s is informative in predicting frequency across ancestries and consistent with the fraction of de novo mutations given s . Finally, MisFit outperforms previous methods in prioritizing missense variants in individuals with neurodevelopmental disorders.

ProteinGym: Large-Scale Benchmarks for Protein Design and Fitness Prediction

Predicting the effects of mutations in proteins is critical to many applications, from understanding genetic disease to designing novel proteins that can address our most pressing challenges in climate, agriculture and healthcare. Despite a surge in machine learning-based protein models to tackle these questions, an assessment of their respective benefits is challenging due to the use of distinct, often contrived, experimental datasets, and the variable performance of models across different protein families. Addressing these challenges requires scale. To that end we introduce ProteinGym, a large-scale and holistic set of benchmarks specifically designed for protein fitness prediction and design. It encompasses both a broad collection of over 250 standardized deep mutational scanning assays, spanning millions of mutated sequences, as well as curated clinical datasets providing high-quality expert annotations about mutation effects. We devise a robust evaluation framework that combines metrics for both fitness prediction and design, factors in known limitations of the underlying experimental methods, and covers both zero-shot and supervised settings. We report the performance of a diverse set of over 70 high-performing models from various subfields (eg., alignment-based, inverse folding) into a unified benchmark suite. We open source the corresponding codebase, datasets, MSAs, structures, model predictions and develop a user-friendly website that facilitates data access and analysis.

Systematically testing human HMBS missense variants to reveal mechanism and pathogenic variation

Defects in hydroxymethylbilane synthase (HMBS) can cause acute intermittent porphyria (AIP), an acute neurological disease. Although sequencing-based diagnosis can be definitive, ∼⅓ of clinical HMBS variants are missense variants, and most clinically reported HMBS missense variants are designated as "variants of uncertain significance" (VUSs). Using saturation mutagenesis, en masse selection, and sequencing, we applied a multiplexed validated assay to both the erythroid-specific and ubiquitous isoforms of HMBS, obtaining confident functional impact scores for >84% of all possible amino acid substitutions. The resulting variant effect maps generally agreed with biochemical expectations and provide further evidence that HMBS can function as a monomer. Additionally, the maps implicated specific residues as having roles in active site dynamics, which was further supported by molecular dynamics simulations. Most importantly, these maps can help discriminate pathogenic from benign HMBS variants, proactively providing evidence even for yet-to-be-observed clinical missense variants.

Accurate proteome-wide missense variant effect prediction with AlphaMissense

The vast majority of missense variants observed in the human genome are of unknown clinical significance. We present AlphaMissense, an adaptation of AlphaFold fine-tuned on human and primate variant population frequency databases to predict missense variant pathogenicity. By combining structural context and evolutionary conservation, our model achieves state-of-the-art results across a wide range of genetic and experimental benchmarks, all without explicitly training on such data. The average pathogenicity score of genes is also predictive for their cell essentiality, capable of identifying short essential genes that existing statistical approaches are underpowered to detect. As a resource to the community, we provide a database of predictions for all possible human single amino acid substitutions and classify 89% of missense variants as either likely benign or likely pathogenic.

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.

Genome-scale mapping of DNA damage suppressors through phenotypic CRISPR-Cas9 screens

To maintain genome integrity, cells must accurately duplicate their genome and repair DNA lesions when they occur. To uncover genes that suppress DNA damage in human cells, we undertook flow-cytometry-based CRISPR-Cas9 screens that monitored DNA damage. We identified 160 genes whose mutation caused spontaneous DNA damage, a list enriched in essential genes, highlighting the importance of genomic integrity for cellular fitness. We also identified 227 genes whose mutation caused DNA damage in replication-perturbed cells. Among the genes characterized, we discovered that deoxyribose-phosphate aldolase DERA suppresses DNA damage caused by cytarabine (Ara-C) and that GNB1L, a gene implicated in 22q11.2 syndrome, promotes biogenesis of ATR and related phosphatidylinositol 3-kinase-related kinases (PIKKs). These results implicate defective PIKK biogenesis as a cause of some phenotypes associated with 22q11.2 syndrome. The phenotypic mapping of genes that suppress DNA damage therefore provides a rich resource to probe the cellular pathways that influence genome maintenance.

Discovering functionally important sites in proteins

Proteins play important roles in biology, biotechnology and pharmacology, and missense variants are a common cause of disease. Discovering functionally important sites in proteins is a central but difficult problem because of the lack of large, systematic data sets. Sequence conservation can highlight residues that are functionally important but is often convoluted with a signal for preserving structural stability. We here present a machine learning method to predict functional sites by combining statistical models for protein sequences with biophysical models of stability. We train the model using multiplexed experimental data on variant effects and validate it broadly. We show how the model can be used to discover active sites, as well as regulatory and binding sites. We illustrate the utility of the model by prospective prediction and subsequent experimental validation on the functional consequences of missense variants in HPRT1 which may cause Lesch-Nyhan syndrome, and pinpoint the molecular mechanisms by which they cause disease.

Marfan Syndrome: Enhanced Diagnostic Tools and Follow-up Management Strategies

Marfan syndrome (MFS) is a rare inherited autosomic disorder, which encompasses a variety of systemic manifestations caused by mutations in the Fibrillin-1 encoding gene (FBN1). Cardinal clinical phenotypes of MFS are highly variable in terms of severity, and commonly involve cardiovascular, ocular, and musculoskeletal systems with a wide range of manifestations, such as ascending aorta aneurysms and dissection, mitral valve prolapse, ectopia lentis and long bone overgrowth, respectively. Of note, an accurate and prompt diagnosis is pivotal in order to provide the best treatment to the patients as early as possible. To date, the diagnosis of the syndrome has relied upon a systemic score calculation as well as DNA mutation identification. The aim of this review is to summarize the latest MFS evidence regarding the definition, differences and similarities with other connective tissue pathologies with severe systemic phenotypes (e.g., Autosomal dominant Weill-Marchesani syndrome, Loeys-Dietz syndrome, Ehlers-Danlos syndrome) and clinical assessment. In this regard, the management of MFS requires a multidisciplinary team in order to accurately control the evolution of the most severe and potentially life-threatening complications. Based on recent findings in the literature and our clinical experience, we propose a multidisciplinary approach involving specialists in different clinical fields (i.e., cardiologists, surgeons, ophthalmologists, orthopedics, pneumologists, neurologists, endocrinologists, geneticists, and psychologists) to comprehensively characterize, treat, and manage MFS patients with a personalized medicine approach.

A comprehensive map of human glucokinase variant activity

BACKGROUND

Glucokinase (GCK) regulates insulin secretion to maintain appropriate blood glucose levels. Sequence variants can alter GCK activity to cause hyperinsulinemic hypoglycemia or hyperglycemia associated with GCK-maturity-onset diabetes of the young (GCK-MODY), collectively affecting up to 10 million people worldwide. Patients with GCK-MODY are frequently misdiagnosed and treated unnecessarily. Genetic testing can prevent this but is hampered by the challenge of interpreting novel missense variants.

RESULT

Here, we exploit a multiplexed yeast complementation assay to measure both hyper- and hypoactive GCK variation, capturing 97% of all possible missense and nonsense variants. Activity scores correlate with in vitro catalytic efficiency, fasting glucose levels in carriers of GCK variants and with evolutionary conservation. Hypoactive variants are concentrated at buried positions, near the active site, and at a region of known importance for GCK conformational dynamics. Some hyperactive variants shift the conformational equilibrium towards the active state through a relative destabilization of the inactive conformation.

CONCLUSION

Our comprehensive assessment of GCK variant activity promises to facilitate variant interpretation and diagnosis, expand our mechanistic understanding of hyperactive variants, and inform development of therapeutics targeting GCK.

satmut_utils: a simulation and variant calling package for multiplexed assays of variant effect

The impact of millions of individual genetic variants on molecular phenotypes in coding sequences remains unknown. Multiplexed assays of variant effect (MAVEs) are scalable methods to annotate relevant variants, but existing software lacks standardization, requires cumbersome configuration, and does not scale to large targets. We present satmut_utils as a flexible solution for simulation and variant quantification. We then benchmark MAVE software using simulated and real MAVE data. We finally determine mRNA abundance for thousands of cystathionine beta-synthase variants using two experimental methods. The satmut_utils package enables high-performance analysis of MAVEs and reveals the capability of variants to alter mRNA abundance.

Systematically testing human HMBS missense variants to reveal mechanism and pathogenic variation

Defects in hydroxymethylbilane synthase (HMBS) can cause Acute Intermittent Porphyria (AIP), an acute neurological disease. Although sequencing-based diagnosis can be definitive, ~⅓ of clinical HMBS variants are missense variants, and most clinically-reported HMBS missense variants are designated as "variants of uncertain significance" (VUS). Using saturation mutagenesis, en masse selection, and sequencing, we applied a multiplexed validated assay to both the erythroid-specific and ubiquitous isoforms of HMBS, obtaining confident functional impact scores for >84% of all possible amino-acid substitutions. The resulting variant effect maps generally agreed with biochemical expectation. However, the maps showed variants at the dimerization interface to be unexpectedly well tolerated, and suggested residue roles in active site dynamics that were supported by molecular dynamics simulations. Most importantly, these HMBS variant effect maps can help discriminate pathogenic from benign variants, proactively providing evidence even for yet-to-be-observed clinical missense variants.

Recent therapeutic approaches to cystathionine beta-synthase-deficient homocystinuria

Cystathionine beta-synthase (CBS)-deficient homocystinuria (HCU) is the most common inborn error of sulfur amino acid metabolism. The pyridoxine non-responsive form of the disease manifests itself by massively increasing plasma and tissue concentrations of homocysteine, a toxic intermediate of methionine metabolism that is thought to be the major cause of clinical complications including skeletal deformities, connective tissue defects, thromboembolism and cognitive impairment. The current standard of care involves significant dietary interventions that, despite being effective, often adversely affect quality of life of HCU patients, leading to poor adherence to therapy and inadequate biochemical control with clinical complications. In recent years, the unmet need for better therapeutic options has resulted in development of novel enzyme and gene therapies and exploration of pharmacological approaches to rescue CBS folding defects caused by missense pathogenic mutations. Here, we review scientific evidence and current state of affairs in development of recent approaches to treat HCU.

Current and Novel Therapeutical Approaches of Classical Homocystinuria in Childhood With Special Focus on Enzyme Replacement Therapy, Liver-Directed Therapy and Gene Therapy

Classical homocystinuria is a hereditary defect of the enzyme cystathionine beta synthase, which is produced in the liver. If this enzyme fails, the synthesis pathway of cysteine from methionine is interrupted, leading to the accumulation of homocysteine in the blood plasma and homocysteine in the urine. After birth, the children are unremarkable except for the characteristic laboratory findings. Symptoms rarely appear before the second year of life. The most common symptom is a prolapse of the crystalline lens. This finding is seen in 70% of untreated 10-year-old affected individuals. As the earliest symptom, psychomotor retardation occurs in the majority of patients already during the first two years of life. Limiting factors in terms of life expectancy are thromboembolism, peripheral arterial disease, myocardial infarction, and stroke. These symptoms are due to the damage to the vessels caused by the elevated amino acid levels. About 30% suffer a thromboembolic event by the age of 20, about half by the age of 30. This review focus on present and new therapeutical approaches like the role of enzyme replacement with presentation of different novel targets in research like pegtibatinase, pegtarviliase, CDX-6512, erymethionase, chaperones, proteasome inhibitors and probiotic treatment with SYNB 1353. Furthermore, we analyze the role of liver-directed therapy with three dimensional (3D) bioprinting, liver bioengineering of liver organoids in vitro and liver transplantation. The role of different gene therapy options to treat and cure this extremely rare disease in childhood will be discussed.

Elevated homocysteine levels: What inborn errors of metabolism might we be missing?

Elevated total plasma homocysteine (hyperhomocysteinemia) is a marker of cardiovascular, thrombotic, and neuropsychological disease. It has multiple causes, including the common nutritional vitamin B12 or folate deficiency. However, some rare but treatable, inborn errors of metabolism (IEM) characterized by hyperhomocysteinemia can be missed due to variable presentations and the lack of awareness. The aim of this study is to identify undiagnosed IEM in adults with significantly elevated homocysteine using key existing clinical data points, then IEM specific treatment can be offered to improve outcome. We conducted a retrospective study with data mining and chart review of patients with plasma total homocysteine >30 μmol/L over a two-year period. We offer biochemical and genetic testing to patients with significant hyperhomocysteinemia without a clear explanation to diagnose IEM. We identified 22 subjects with significant hyperhomocysteinemia but no clear explanation. Subsequently, we offered genetic testing to seven patients and diagnosed one patient with classic homocystinuria due to cystathionine beta-synthase deficiency. With treatment, she lowered her plasma homocysteine and improved her health. This study stresses the importance of a thorough investigation of hyperhomocysteinemia in adults to identify rare but treatable IEM. We propose a metabolic evaluation algorithm for elevated homocysteine levels.

Integrating deep mutational scanning and low-throughput mutagenesis data to predict the impact of amino acid variants

BACKGROUND

Evaluating the impact of amino acid variants has been a critical challenge for studying protein function and interpreting genomic data. High-throughput experimental methods like deep mutational scanning (DMS) can measure the effect of large numbers of variants in a target protein, but because DMS studies have not been performed on all proteins, researchers also model DMS data computationally to estimate variant impacts by predictors.

RESULTS

In this study, we extended a linear regression-based predictor to explore whether incorporating data from alanine scanning (AS), a widely used low-throughput mutagenesis method, would improve prediction results. To evaluate our model, we collected 146 AS datasets, mapping to 54 DMS datasets across 22 distinct proteins.

CONCLUSIONS

We show that improved model performance depends on the compatibility of the DMS and AS assays, and the scale of improvement is closely related to the correlation between DMS and AS results.

Scalable Functional Assays for the Interpretation of Human Genetic Variation

Scalable sequence-function studies have enabled the systematic analysis and cataloging of hundreds of thousands of coding and noncoding genetic variants in the human genome. This has improved clinical variant interpretation and provided insights into the molecular, biophysical, and cellular effects of genetic variants at an astonishing scale and resolution across the spectrum of allele frequencies. In this review, we explore current applications and prospects for the field and outline the principles underlying scalable functional assay design, with a focus on the study of single-nucleotide coding and noncoding variants.

Humanized yeast to model human biology, disease and evolution

For decades, budding yeast, a single-cellular eukaryote, has provided remarkable insights into human biology. Yeast and humans share several thousand genes despite morphological and cellular differences and over a billion years of separate evolution. These genes encode critical cellular processes, the failure of which in humans results in disease. Although recent developments in genome engineering of mammalian cells permit genetic assays in human cell lines, there is still a need to develop biological reagents to study human disease variants in a high-throughput manner. Many protein-coding human genes can successfully substitute for their yeast equivalents and sustain yeast growth, thus opening up doors for developing direct assays of human gene function in a tractable system referred to as 'humanized yeast'. Humanized yeast permits the discovery of new human biology by measuring human protein activity in a simplified organismal context. This Review summarizes recent developments showing how humanized yeast can directly assay human gene function and explore variant effects at scale. Thus, by extending the 'awesome power of yeast genetics' to study human biology, humanizing yeast reinforces the high relevance of evolutionarily distant model organisms to explore human gene evolution, function and disease.

Computational and experimental methods for classifying variants of unknown clinical significance

The increase in sequencing capacity, reduction in costs, and national and international coordinated efforts have led to the widespread introduction of next-generation sequencing (NGS) technologies in patient care. More generally, human genetics and genomic medicine are gaining importance for more and more patients. Some communities are already discussing the prospect of sequencing each individual's genome at time of birth. Together with digital health records, this shall enable individualized treatments and preventive measures, so-called precision medicine. A central step in this process is the identification of disease causal mutations or variant combinations that make us more susceptible for diseases. Although various technological advances have improved the identification of genetic alterations, the interpretation and ranking of the identified variants remains a major challenge. Based on our knowledge of molecular processes or previously identified disease variants, we can identify potentially functional genetic variants and, using different lines of evidence, we are sometimes able to demonstrate their pathogenicity directly. However, the vast majority of variants are classified as variants of uncertain clinical significance (VUSs) with not enough experimental evidence to determine their pathogenicity. In these cases, computational methods may be used to improve the prioritization and an increasing toolbox of experimental methods is emerging that can be used to assay the molecular effects of VUSs. Here, we discuss how computational and experimental methods can be used to create catalogs of variant effects for a variety of molecular and cellular phenotypes. We discuss the prospects of integrating large-scale functional data with machine learning and clinical knowledge for the development of accurate pathogenicity predictions for clinical applications.

Machine Learning Models for Accurate Prioritization of Variants of Uncertain Significance

The growing use of next generation sequencing technologies on genetic diagnosis has produced an exponential increase in the number of Variants of Uncertain Significance (VUS). In this manuscript we compare three machine learning methods to classify VUS as Pathogenic or No pathogenic, implementing a Random Forest (RF), a Support Vector Machine (SVM), and a Multilayer Perceptron (MLP). To train the models, we extracted high quality variants from ClinVar that were previously classified as VUS. For each variant, we retrieved 9 conservation scores, the loss of function tool and allele frequencies. For the RF and SVM models, hyperparameters were tuned using cross validation with a grid search. The three models were tested on a non-overlapping set of variants that had been classified as VUS any time along the last three years but had been reclassified in august 2020. The three models yielded superior accuracy on this set compared to the benchmarked tools. The RF based model yielded the best performance across different variant types and was used to create VusPrize, an open source software tool for prioritization of variants of uncertain significance. We believe that our model can improve the process of genetic diagnosis in research and clinical settings. This article is protected by copyright. All rights reserved.

Predicting and interpreting large-scale mutagenesis data using analyses of protein stability and conservation

Understanding and predicting the functional consequences of single amino acid changes is central in many areas of protein science. Here, we collect and analyze experimental measurements of effects of >150,000 variants in 29 proteins. We use biophysical calculations to predict changes in stability for each variant and assess them in light of sequence conservation. We find that the sequence analyses give more accurate prediction of variant effects than predictions of stability and that about half of the variants that show loss of function do so due to stability effects. We construct a machine learning model to predict variant effects from protein structure and sequence alignments and show how the two sources of information support one another and enable mechanistic interpretations. Together, our results show how one can leverage large-scale experimental assessments of variant effects to gain deeper and general insights into the mechanisms that cause loss of function.

Improved pathogenicity prediction for rare human missense variants

The success of personalized genomic medicine depends on our ability to assess the pathogenicity of rare human variants, including the important class of missense variation. There are many challenges in training accurate computational systems, e.g., in finding the balance between quantity, quality, and bias in the variant sets used as training examples and avoiding predictive features that can accentuate the effects of bias. Here, we describe VARITY, which judiciously exploits a larger reservoir of training examples with uncertain accuracy and representativity. To limit circularity and bias, VARITY excludes features informed by variant annotation and protein identity. To provide a rationale for each prediction, we quantified the contribution of features and feature combinations to the pathogenicity inference of each variant. VARITY outperformed all previous computational methods evaluated, identifying at least 10% more pathogenic variants at thresholds achieving high (90% precision) stringency.

Linking genome variants to disease: scalable approaches to test the functional impact of human mutations

The application of genomics to medicine has accelerated the discovery of mutations underlying disease and has enhanced our knowledge of the molecular underpinnings of diverse pathologies. As the amount of human genetic material queried via sequencing has grown exponentially in recent years, so too has the number of rare variants observed. Despite progress, our ability to distinguish which rare variants have clinical significance remains limited. Over the last decade, however, powerful experimental approaches have emerged to characterize variant effects orders of magnitude faster than before. Fueled by improved DNA synthesis and sequencing and, more recently, by CRISPR/Cas9 genome editing, multiplex functional assays provide a means of generating variant effect data in wide-ranging experimental systems. Here, I review recent applications of multiplex assays that link human variants to disease phenotypes and I describe emerging strategies that will enhance their clinical utility in coming years.

Integrative analysis of hub genes and key pathway in two subtypes of diffuse large B-cell lymphoma by bioinformatics and basic experiments

Background

The germinal center B‐cell (GCB) and activated B‐cell (ABC) subtypes of diffuse large B‐cell lymphoma (DLBCL) have a significant difference in prognosis. This study aimed to identify potential hub genes, and key pathways involved in them.

Methods

Databases including Gene Expression Omnibus (GEO), Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and STRING were accessed to obtain potential crucial genes and key pathways associated with the GCB and ABC. Then qRT‐PCR and Western blot experiments were performed to verify the most clinically significant gene and pathway.

Results

Three cohort datasets from the GEO database were analyzed, including 195 GCB and 169 ABC samples. We identified 1113 differentially expressed genes (DEGs) between the GCB and ABC subtypes. The DEGs were mainly enriched in biological processes (BP). The KEGG analysis showed enrichment in cell cycle and Wnt signaling pathways. We selected the top 10 genes using the STRING database and Cytoscape software. We used 5 calculation methods of the cytoHubba plugin, and found 3 central genes (IL‐10, CD44, CCND2). CCND2 was significantly related to the prognosis of DLBCL patients. Besides, our experimental results demonstrated a significantly higher expression of CCND2 in the ABC‐type cell line than in the GCB‐type; it was proportional to the expression of key proteins in the Wnt signaling pathway.

Conclusion

CCND2 overexpression and Wnt pathway activation might be the main reasons for the poor prognosis of ABC‐DLBCL.

Shifting landscapes of human MTHFR missense-variant effects

Most rare clinical missense variants cannot currently be classified as pathogenic or benign. Deficiency in human 5,10-methylenetetrahydrofolate reductase (MTHFR), the most common inherited disorder of folate metabolism, is caused primarily by rare missense variants. Further complicating variant interpretation, variant impacts often depend on environment. An important example of this phenomenon is the MTHFR variant p.Ala222Val (c.665C>T), which is carried by half of all humans and has a phenotypic impact that depends on dietary folate. Here we describe the results of 98,336 variant functional-impact assays, covering nearly all possible MTHFR amino acid substitutions in four folinate environments, each in the presence and absence of p.Ala222Val. The resulting atlas of MTHFR variant effects reveals many complex dependencies on both folinate and p.Ala222Val. MTHFR atlas scores can distinguish pathogenic from benign variants and, among individuals with severe MTHFR deficiency, correlate with age of disease onset. Providing a powerful tool for understanding structure-function relationships, the atlas suggests a role for a disordered loop in retaining cofactor at the active site and identifies variants that enable escape of inhibition by S-adenosylmethionine. Thus, a model based on eight MTHFR variant effect maps illustrates how shifting landscapes of environment- and genetic-background-dependent missense variation can inform our clinical, structural, and functional understanding of MTHFR deficiency.

Cystathionine β-synthase deficiency in the E-HOD registry-part I: pyridoxine responsiveness as a determinant of biochemical and clinical phenotype at diagnosis

Cystathionine β-synthase (CBS) deficiency has a wide clinical spectrum, ranging from neurodevelopmental problems, lens dislocation and marfanoid features in early childhood to adult onset disease with predominantly thromboembolic complications. We have analysed clinical and laboratory data at the time of diagnosis in 328 patients with CBS deficiency from the E-HOD (European network and registry for Homocystinurias and methylation Defects) registry. We developed comprehensive criteria to classify patients into four groups of pyridoxine responsivity: non-responders (NR), partial, full and extreme responders (PR, FR and ER, respectively). All groups showed overlapping concentrations of plasma total homocysteine while pyridoxine responsiveness inversely correlated with plasma/serum methionine concentrations. The FR and ER groups had a later age of onset and diagnosis and a longer diagnostic delay than NR and PR patients. Lens dislocation was common in all groups except ER but the age of dislocation increased with increasing responsiveness. Developmental delay was commonest in the NR group while no ER patient had cognitive impairment. Thromboembolism was the commonest presenting feature in ER patients, whereas it was least likely at presentation in the NR group. This probably is due to the differences in ages at presentation: all groups had a similar number of thromboembolic events per 1000 patient-years. Clinical severity of CBS deficiency depends on the degree of pyridoxine responsiveness. Therefore, a standardised pyridoxine-responsiveness test in newly diagnosed patients and a critical review of previous assessments is indispensable to ensure adequate therapy and to prevent or reduce long-term complications.

Prioritizing genes for systematic variant effect mapping

MOTIVATION

When rare missense variants are clinically interpreted as to their pathogenicity, most are classified as variants of uncertain significance (VUS). Although functional assays can provide strong evidence for variant classification, such results are generally unavailable. Multiplexed assays of variant effect can generate experimental 'variant effect maps' that score nearly all possible missense variants in selected protein targets for their impact on protein function. However, these efforts have not always prioritized proteins for which variant effect maps would have the greatest impact on clinical variant interpretation.

RESULTS

Here, we mined databases of clinically interpreted variants and applied three strategies, each building on the previous, to prioritize genes for systematic functional testing of missense variation. The strategies ranked genes (i) by the number of unique missense VUS that had been reported to ClinVar; (ii) by movability- and reappearance-weighted impact scores, to give extra weight to reappearing, movable VUS and (iii) by difficulty-adjusted impact scores, to account for the more resource-intensive nature of generating variant effect maps for longer genes. Our results could be used to guide systematic functional testing of missense variation toward greater impact on clinical variant interpretation.

AVAILABILITY AND IMPLEMENTATION

Source code available at: https://github.com/rothlab/mave-gene-prioritization.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

ELASPIC2 (EL2): Combining Contextualized Language Models and Graph Neural Networks to Predict Effects of Mutations

The ELASPIC web server allows users to evaluate the effect of mutations on protein folding and protein-protein interaction on a proteome-wide scale. It uses homology models of proteins and protein-protein interactions, which have been precalculated for several proteomes, and machine learning models, which integrate structural information with sequence conservation scores, in order to make its predictions. Since the original publication of the ELASPIC web server, several advances have motivated a revisiting of the problem of mutation effect prediction. First, progress in neural network architectures and self-supervised pre-trained has resulted in models which provide more informative embeddings of protein sequence and structure than those used by the original version of ELASPIC. Second, the amount of training data has increased several-fold, largely driven by advances in deep mutation scanning and other multiplexed assays of variant effect. Here, we describe two machine learning models which leverage the recent advances in order to achieve superior accuracy in predicting the effect of mutation on protein folding and protein-protein interaction. The models incorporate features generated using pre-trained transformer- and graph convolution-based neural networks, and are trained to optimize a ranking objective function, which permits the use of heterogeneous training data. The outputs from the new models have been incorporated into the ELASPIC web server, available at http://elaspic.kimlab.org.

Massively parallel functional testing of MSH2 missense variants conferring Lynch syndrome risk

The lack of functional evidence for the majority of missense variants limits their clinical interpretability and poses a key barrier to the broad utility of carrier screening. In Lynch syndrome (LS), one of the most highly prevalent cancer syndromes, nearly 90% of clinically observed missense variants are deemed “variants of uncertain significance” (VUS). To systematically resolve their functional status, we performed a massively parallel screen in human cells to identify loss-of-function missense variants in the key DNA mismatch repair factor MSH2. The resulting functional effect map is substantially complete, covering 94% of the 17,746 possible variants, and is highly concordant (96%) with existing functional data and expert clinicians’ interpretations. The large majority (89%) of missense variants were functionally neutral, perhaps unexpectedly in light of its evolutionary conservation. These data provide ready-to-use functional evidence to resolve the ∼1,300 extant missense VUSs in MSH2 and may facilitate the prospective classification of newly discovered variants in the clinic.

MaveQuest: a web resource for planning experimental tests of human variant effects

Summary

Fully realizing the promise of personalized medicine will require rapid and accurate classification of pathogenic human variation. Multiplexed assays of variant effect (MAVEs) can experimentally test nearly all possible variants in selected gene targets. Planning a MAVE study involves identifying target genes with clinical impact, and identifying scalable functional assays for that target. Here, we describe MaveQuest, a web-based resource enabling systematic variant effect mapping studies by identifying potential functional assays, disease phenotypes and clinical relevance for nearly all human protein-coding genes.

Availability and implementation

MaveQuest service: https://mavequest.varianteffect.org/. MaveQuest source code: https://github.com/kvnkuang/mavequest-front-end/.

Supplementary information

Supplementary data are available at Bioinformatics online.

Lessons Learned from Inherited Metabolic Disorders of Sulfur-Containing Amino Acids Metabolism

The metabolism of sulfur-containing amino acids (SAAs) requires an orchestrated interplay among several dozen enzymes and transporters, and an adequate dietary intake of methionine (Met), cysteine (Cys), and B vitamins. Known human genetic disorders are due to defects in Met demethylation, homocysteine (Hcy) remethylation, or cobalamin and folate metabolism, in Hcy transsulfuration, and Cys and hydrogen sulfide (H2S) catabolism. These disorders may manifest between the newborn period and late adulthood by a combination of neuropsychiatric abnormalities, thromboembolism, megaloblastic anemia, hepatopathy, myopathy, and bone and connective tissue abnormalities. Biochemical features include metabolite deficiencies (e.g. Met, S-adenosylmethionine (AdoMet), intermediates in 1-carbon metabolism, Cys, or glutathione) and/or their accumulation (e.g. S-adenosylhomocysteine, Hcy, H2S, or sulfite). Treatment should be started as early as possible and may include a low-protein/low-Met diet with Cys-enriched amino acid supplements, pharmacological doses of B vitamins, betaine to stimulate Hcy remethylation, the provision of N-acetylcysteine or AdoMet, or experimental approaches such as liver transplantation or enzyme replacement therapy. In several disorders, patients are exposed to long-term markedly elevated Met concentrations. Although these conditions may inform on Met toxicity, interpretation is difficult due to the presence of additional metabolic changes. Two disorders seem to exhibit Met-associated toxicity in the brain. An increased risk of demyelination in patients with Met adenosyltransferase I/III (MATI/III) deficiency due to biallelic mutations in the MATIA gene has been attributed to very high blood Met concentrations (typically >800 μmol/L) and possibly also to decreased liver AdoMet synthesis. An excessively high Met concentration in some patients with cystathionine β-synthase deficiency has been associated with encephalopathy and brain edema, and direct toxicity of Met has been postulated. In summary, studies in patients with various disorders of SAA metabolism showed complex metabolic changes with distant cellular consequences, most of which are not attributable to direct Met toxicity.

Systematic Humanization of the Yeast Cytoskeleton Discerns Functionally Replaceable from Divergent Human Genes

Many gene families have been expanded by gene duplications along the human lineage, relative to ancestral opisthokonts, but the extent to which the duplicated genes function similarly is understudied. Here, we focused on structural cytoskeletal genes involved in critical cellular processes, including chromosome segregation, macromolecular transport, and cell shape maintenance. To determine functional redundancy and divergence of duplicated human genes, we systematically humanized the yeast actin, myosin, tubulin, and septin genes, testing ∼81% of human cytoskeletal genes across seven gene families for their ability to complement a growth defect induced by inactivation or deletion of the corresponding yeast ortholog. In five of seven families-all but α-tubulin and light myosin, we found at least one human gene capable of complementing loss of the yeast gene. Despite rescuing growth defects, we observed differential abilities of human genes to rescue cell morphology, meiosis, and mating defects. By comparing phenotypes of humanized strains with deletion phenotypes of their interaction partners, we identify instances of human genes in the actin and septin families capable of carrying out essential functions, but failing to fully complement the cytoskeletal roles of their yeast orthologs, thus leading to abnormal cell morphologies. Overall, we show that duplicated human cytoskeletal genes appear to have diverged such that only a few human genes within each family are capable of replacing the essential roles of their yeast orthologs. The resulting yeast strains with humanized cytoskeletal components now provide surrogate platforms to characterize human genes in simplified eukaryotic contexts.