Prospective functional classification of all possible missense variants in PPARG

Multiplex and multimodal mapping of variant effects in secreted proteins via MultiSTEP

Despite widespread advances in DNA sequencing, the functional consequences of most genetic variants remain poorly understood. Multiplexed assays of variant effect can measure the function of variants at scale but cannot readily be applied to the ~10% of human genes encoding secreted proteins. Here we develop a flexible, scalable human cell surface display method, multiplexed surface tethering of extracellular proteins (MultiSTEP), to study the consequences of missense variation in coagulation factor IX (FIX), a serine protease in which genetic variation can cause hemophilia B. We combine MultiSTEP with a panel of antibodies to detect FIX secretion and post-translational modification (PTM), measuring 44,816 variant effects for 436 synonymous variants and 8,528 of the 8,759 possible F9 missense variants. Almost half of missense variants impact secretion, PTM or both. We also identify functional constraints on secretion within the signal peptide and for nearly all gain or loss of cysteine variants. Secretion scores correlate strongly with FIX levels in hemophilia B and reveal that loss-of-secretion variants are more often associated with severe disease. Integration of the secretion and PTM scores enables reclassification of 63.1% of F9 variants of uncertain significance in the My Life, Our Future hemophilia genotyping project. Lastly, we show that MultiSTEP can be applied to other secreted proteins, thus demonstrating that MultiSTEP is a multiplexed, multimodal and generalizable method for systematically assessing variant effects in secreted proteins at scale.

Variant effect predictor correlation with functional assays is reflective of clinical classification performance

BACKGROUND

Understanding the relationship between protein sequence and function is crucial for accurate classification of missense variants. Variant effect predictors (VEPs) play a vital role in deciphering this complex relationship, yet evaluating their performance remains challenging for several reasons, including data circularity, where the same or related data is used for training and assessment. High-throughput experimental strategies like deep mutational scanning (DMS) offer a promising solution.

RESULTS

In this study, we extend upon our previous benchmarking approach, assessing the performance of 97 VEPs using missense DMS measurements from 36 different human proteins. In addition, a new pairwise, VEP-centric approach mitigates the impact of missing predictions on overall performance comparison. We observe a strong correspondence between VEP performance in DMS-based benchmarks and clinical variant classification, especially for predictors that have not been directly trained on human clinical variants.

CONCLUSIONS

Our results suggest that comparing VEP performance against diverse functional assays represents a reliable strategy for assessing their relative performance in clinical variant classification. However, major challenges in clinical interpretation of VEP scores persist, highlighting the need for further research to fully leverage computational predictors for genetic diagnosis. We also address practical considerations for end users in terms of choice of methodology.

An Alternatively Translated Isoform of PPARG Suggests AF-1 Domain Inhibition as an Insulin Sensitization Target

ARTICLE HIGHLIGHTS

Genetic screens were performed across PPARG to study how disruptive mutations across the full coding sequence affect function. An alternative translational start site in PPARG generates a truncated isoform, peroxisome proliferator-activated receptor γ (PPARγ) M135, which lacks the N-terminal activation function 1 (AF-1) domain and shows increased agonist-induced transactivation of target genes. In human carriers of rare PPARG variants, AF-1 domain-disrupting genetic variants increase agonist-induced PPARγ activity and decrease metabolic syndrome severity. Targeting the AF-1 domain is a potential therapeutic strategy for insulin sensitization.

Multiplex, multimodal mapping of variant effects in secreted proteins

Despite widespread advances in DNA sequencing, the functional consequences of most genetic variants remain poorly understood. Multiplexed Assays of Variant Effect (MAVEs) can measure the function of variants at scale, and are beginning to address this problem. However, MAVEs cannot readily be applied to the ~10% of human genes encoding secreted proteins. We developed a flexible, scalable human cell surface display method, Multiplexed Surface Tethering of Extracellular Proteins (MultiSTEP), to measure secreted protein variant effects. We used MultiSTEP to study the consequences of missense variation in coagulation factor IX (FIX), a serine protease where genetic variation can cause hemophilia B. We combined MultiSTEP with a panel of antibodies to detect FIX secretion and post-translational modification, measuring a total of 44,816 effects for 436 synonymous variants and 8,528 of the 8,759 possible missense variants. 49.6% of possible F9 missense variants impacted secretion, post-translational modification, or both. We also identified functional constraints on secretion within the signal peptide and for nearly all variants that caused gain or loss of cysteine. Secretion scores correlated strongly with FIX levels in hemophilia B and revealed that loss of secretion variants are particularly likely to cause severe disease. Integration of the secretion and post-translational modification scores enabled reclassification of 63.1% of F9 variants of uncertain significance in the My Life, Our Future hemophilia genotyping project. Lastly, we showed that MultiSTEP can be applied to a wide variety of secreted proteins. Thus, MultiSTEP is a multiplexed, multimodal, and generalizable method for systematically assessing variant effects in secreted proteins at scale.

MaveDB 2024: a curated community database with over seven million variant effects from multiplexed functional assays

Multiplexed assays of variant effect (MAVEs) are a critical tool for researchers and clinicians to understand genetic variants. Here we describe the 2024 update to MaveDB ( https://www.mavedb.org/ ) with four key improvements to the MAVE community's database of record: more available data including over 7 million variant effect measurements, an improved data model supporting assays such as saturation genome editing, new built-in exploration and visualization tools, and powerful APIs for data federation and streamlined submission and access. Together these changes support MaveDB's role as a hub for the analysis and dissemination of MAVEs now and into the future.

Rare variant associations with birth weight identify genes involved in adipose tissue regulation, placental function and insulin-like growth factor signalling

Investigating the genetic factors influencing human birth weight may lead to biological insights into fetal growth and long-term health. We report analyses of rare variants that impact birth weight when carried by either fetus or mother, using whole exome sequencing data in up to 234,675 participants. Rare protein-truncating and deleterious missense variants are collapsed to perform gene burden tests. We identify 9 genes; 5 with fetal-only effects on birth weight, 1 with maternal-only effects, 3 with both, and observe directionally concordant associations in an independent sample. Four of the genes were previously implicated by GWAS of birth weight. IGF1R and PAPPA2 (fetal and maternal-acting) have known roles in insulin-like growth factor bioavailability and signalling. PPARG, INHBE and ACVR1C (fetal-acting) are involved in adipose tissue regulation, and the latter two also show associations with favourable adiposity patterns in adults. We highlight the dual role of PPARG (fetal-acting) in adipocyte differentiation and placental angiogenesis. NOS3 (fetal and maternal-acting), NRK (fetal), and ADAMTS8 (maternal-acting) have been implicated in placental function and hypertension. To conclude, our analysis of rare coding variants identifies regulators of fetal adipose tissue and fetoplacental angiogenesis as determinants of birth weight, and further evidence for the role of insulin-like growth factors.

Peroxisome proliferator-activated receptor gamma mutation in familial partial lipodystrophy type three: A case report and review of literature

BACKGROUND

Familial partial lipodystrophy disease (FPLD) is a collection of rare genetic diseases featuring partial loss of adipose tissue. However, metabolic difficulties, such as severe insulin resistance, diabetes, hypertriglyceridemia, and hypertension frequently occur alongside adipose tissue loss, making it susceptible to misdiagnosis and delaying effective treatment. Numerous genes are implicated in the occurrence of FPLD, and genetic testing has been for conditions linked to single gene mutation related to FPLD. Reviewing recent reports, treatment of the disease is limited to preventing and improving complications in patients.

CASE SUMMARY

In 2017, a 31-year-old woman with diabetes, hypertension and hypertriglyceridemia was hospitalized. We identified a mutation in her peroxisome proliferator-activated receptor gamma (PPARG) gene, Y151C (p.Tyr151Cys), which results in a nucleotide substitution residue 452 in the DNA-binding domain (DBD) of PPARG. The unaffected family member did not carry this mutation. Pioglitazone, a PPARG agonist, improved the patient's responsiveness to hypoglycemic and antihypertensive therapy. After one year of treatment in our hospital, the fasting blood glucose and glycosylated hemoglobin of the patient were close to normal.

CONCLUSION

We report a rare PPARG mutation, Y151C, which is located in the DBD of PPARG and leads to FPLD, and the preferred agent is PPARG agonists. We then summarized clinical phenotypic characteristics of FPLD3 caused by PPARG gene mutations, and clarified the relationship between different mutations of PPARG gene and the clinical manifestations of this type of FPLD. Additionally, current treatments for FPLD caused by PPARG mutations are reviewed.

Saturation mutagenesis-reinforced functional assays for disease-related genes

Interpretation of disease-causing genetic variants remains a challenge in human genetics. Current costs and complexity of deep mutational scanning methods are obstacles for achieving genome-wide resolution of variants in disease-related genes. Our framework, saturation mutagenesis-reinforced functional assays (SMuRF), offers simple and cost-effective saturation mutagenesis paired with streamlined functional assays to enhance the interpretation of unresolved variants. Applying SMuRF to neuromuscular disease genes FKRP and LARGE1, we generated functional scores for all possible coding single-nucleotide variants, which aid in resolving clinically reported variants of uncertain significance. SMuRF also demonstrates utility in predicting disease severity, resolving critical structural regions, and providing training datasets for the development of computational predictors. Overall, our approach enables variant-to-function insights for disease genes in a cost-effective manner that can be broadly implemented by standard research laboratories.

A cohort analysis of familial partial lipodystrophy from two Mediterranean countries

AIM

To assess the disease burden of familial partial lipodystrophy (FPLD) caused by LMNA (FPLD2) and PPARG (FPLD3) variants to augment the knowledge of these rare disorders characterized by selective fat loss and metabolic complications.

MATERIALS AND METHODS

An observational longitudinal study, including 157 patients (FPLD2: 139 patients, mean age 46 ± 17 years, 70% women; FPLD3: 18 patients, mean age: 44 ± 17 years, 78% women) from 66 independent families in two countries (83 from Turkey and 74 from Spain), was conducted.

RESULTS

Patients were diagnosed at a mean age of 39 ± 19 years, 20 ± 16 years after the first clinical signs appeared. Men reported symptoms later than women. Symptom onset was earlier in FPLD2. Fat loss was less prominent in FPLD3. In total, 92 subjects (59%) had diabetes (age at diagnosis: 34 ± 1 years). Retinopathy was more commonly detected in FPLD3 (P < .05). Severe hypertriglyceridaemia was more frequent among patients with FPLD3 (44% vs. 17%, P = .01). Hepatic steatosis was detected in 100 subjects (66%) (age at diagnosis: 36 ± 2 years). Coronary artery disease developed in 26 patients (17%) and 17 (11%) suffered from a myocardial infarction. Turkish patients had a lower body mass index, a higher prevalence of hepatic steatosis, greater triglyceride levels and a tendency towards a higher prevalence of coronary artery disease. A total of 17 patients died, with a mean time to death of 75 ± 3 years, which was shorter in the Turkish cohort (68 ± 2 vs. 83 ± 4 years, P = .01). Cardiovascular events were a major cause of death.

CONCLUSIONS

Our analysis highlights severe organ complications in patients with FPLD, showing differences between genotypes and Mediterranean countries. FPLD3 presents a milder phenotype than FPLD2, but with comparable or even greater severity of metabolic disturbances.

Damaging mutations in liver X receptor-α are hepatotoxic and implicate cholesterol sensing in liver health

Liver X receptor-α (LXRα) regulates cellular cholesterol abundance and potently activates hepatic lipogenesis. Here we show that at least 1 in 450 people in the UK Biobank carry functionally impaired mutations in LXRα, which is associated with biochemical evidence of hepatic dysfunction. On a western diet, male and female mice homozygous for a dominant negative mutation in LXRα have elevated liver cholesterol, diffuse cholesterol crystal accumulation and develop severe hepatitis and fibrosis, despite reduced liver triglyceride and no steatosis. This phenotype does not occur on low-cholesterol diets and can be prevented by hepatocyte-specific overexpression of LXRα. LXRα knockout mice exhibit a milder phenotype with regional variation in cholesterol crystal deposition and inflammation inversely correlating with steatosis. In summary, LXRα is necessary for the maintenance of hepatocyte health, likely due to regulation of cellular cholesterol content. The inverse association between steatosis and both inflammation and cholesterol crystallization may represent a protective action of hepatic lipogenesis in the context of excess hepatic cholesterol.

Familial partial lipodystrophy resulting from loss-of-function PPARγ pathogenic variants: phenotypic, clinical, and genetic features

The PPARG gene encodes a member of a nuclear receptor superfamily known as peroxisome proliferator-activated gamma (PPARγ). PPARγ plays an essential role in adipogenesis, stimulating the differentiation of preadipocytes into adipocytes. Loss-of-function pathogenic variants in PPARG reduce the activity of the PPARγ receptor and can lead to severe metabolic consequences associated with familial partial lipodystrophy type 3 (FPLD3). This review focuses on recent scientific data related to FPLD3, including the role of PPARγ in adipose tissue metabolism and the phenotypic and clinical consequences of loss-of-function variants in the PPARG gene. The clinical features of 41 PPARG pathogenic variants associated with FPLD3 patients were reviewed, highlighting the genetic and clinical heterogeneity observed among 91 patients. Most of them were female, and the average age at the onset and diagnosis of lipoatrophy was 21 years and 33 years, respectively. Considering the metabolic profile, hypertriglyceridemia (91.9% of cases), diabetes (77%), hypertension (59.5%), polycystic ovary syndrome (58.2% of women), and metabolic-dysfunction-associated fatty liver disease (87,5%). We also discuss the current treatment for FPLD3. This review provides new data concerning the genetic and clinical heterogeneity in FPLD3 and highlights the importance of further understanding the genetics of this rare disease.

Sequencing methods, functional characterization, prevalence, and penetrance of rare coding mutations in panels of monogenic obesity genes from the leptin-melanocortin pathway: A systematic review

The recent development of next-generation sequencing (NGS) technologies has led to an increase of mutation screening reports of monogenic obesity genes in diverse experimental designs. However, no study to date has summarized their findings. Two reviewers independently conducted a systematic review of MEDLINE, Embase, and Web of Science Core Collection databases from inception to September 2022 to identify monogenic non-syndromic obesity gene screening studies. Of 1051 identified references, 31 were eligible after title and abstract screening and 28 after full-text reading and risk of bias and quality assessment. Most studies (82%) used NGS methods. The number of genes screened varied from 2 to 12 genes from the leptin-melanocortin pathway. While all the included studies used in silico tools to assess the functional status of mutations, only 2 performed in vitro tests. The prevalence of carriers of pathogenic/likely pathogenic monogenic mutations is 13.24% on average (heterozygous: 12.31%; homozygous/heterozygous composite: 0.93%). As no study reported the penetrance of pathogenic mutations on obesity, we estimated that homozygous carriers exhibited a complete penetrance (100%) and heterozygous carriers a variable penetrance (3-100%). The review provides an exhaustive description of sequencing methods, functional characterization, prevalence, and penetrance of rare coding mutations in monogenic non-syndromic obesity 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.

Case report: two novel PPARG pathogenic variants associated with type 3 familial partial lipodystrophy in Brazil

INTRODUCTION AND AIM

Type 3 Familial Partial Lipodystrophy (FPLD3) is a rare metabolic disease related to pathogenic PPARG gene variants. FPLD3 is characterized by a loss of fatty tissue in the upper and lower limbs, hips, and face. FPLD3 pathophysiology is usually associated with metabolic comorbidities such as type 2 diabetes, insulin resistance, hypertriglyceridemia, and liver dysfunction. Here, we clinically and molecularly characterized FPLD3 patients harboring novel PPARG pathogenic variants.

MATERIALS AND METHODS

Lipodystrophy-suspected patients were recruited by clinicians from an Endocrinology Reference Center. Clinical evaluation was performed, biological samples were collected for biochemical analysis, and DNA sequencing was performed to define the pathogenic variants associated with the lipodystrophic phenotype found in our clinically diagnosed FPLD subjects. Bioinformatics predictions were conducted to characterize the novel mutated PPARγ proteins.

RESULTS

We clinically described FPLD patients harboring two novel heterozygous PPARG variants in Brazil. Case 1 had the c.533T > C variant, which promotes the substitution of leucine to proline in position 178 (p.Leu178Pro), and cases 2 and 3 had the c.641 C > T variant, which results in the substitution of proline to leucine in the position 214 (p.Pro214Leu) at the PPARγ2 protein. These variants result in substantial conformational changes in the PPARγ2 protein.

CONCLUSION

Two novel PPARG pathogenic variants related to FPLD3 were identified in a Brazilian FPLD cohort. These data will provide new epidemiologic data concerning FPLD3 and help understand the genotype-phenotype relationships related to the PPARG gene.

Deep Mutational Scanning in Disease-related Genes with Saturation Mutagenesis-Reinforced Functional Assays (SMuRF)

Interpretation of disease-causing genetic variants remains a challenge in human genetics. Current costs and complexity of deep mutational scanning methods hamper crowd-sourcing approaches toward genome-wide resolution of variants in disease-related genes. Our framework, Saturation Mutagenesis-Reinforced Functional assays (SMuRF), addresses these issues by offering simple and cost-effective saturation mutagenesis, as well as streamlining functional assays to enhance the interpretation of unresolved variants. Applying SMuRF to neuromuscular disease genes FKRP and LARGE1, we generated functional scores for all possible coding single nucleotide variants, which aid in resolving clinically reported variants of uncertain significance. SMuRF also demonstrates utility in predicting disease severity, resolving critical structural regions, and providing training datasets for the development of computational predictors. Our approach opens new directions for enabling variant-to-function insights for disease genes in a manner that is broadly useful for crowd-sourcing implementation across standard research laboratories.

Integrated multiplexed assays of variant effect reveal determinants of catechol-O-methyltransferase gene expression

Multiplexed assays of variant effect are powerful methods to profile the consequences of rare variants on gene expression and organismal fitness. Yet, few studies have integrated several multiplexed assays to map variant effects on gene expression in coding sequences. Here, we pioneered a multiplexed assay based on polysome profiling to measure variant effects on translation at scale, uncovering single-nucleotide variants that increase or decrease ribosome load. By combining high-throughput ribosome load data with multiplexed mRNA and protein abundance readouts, we mapped the cis-regulatory landscape of thousands of catechol-O-methyltransferase (COMT) variants from RNA to protein and found numerous coding variants that alter COMT expression. Finally, we trained machine learning models to map signatures of variant effects on COMT gene expression and uncovered both directional and divergent impacts across expression layers. Our analyses reveal expression phenotypes for thousands of variants in COMT and highlight variant effects on both single and multiple layers of expression. Our findings prompt future studies that integrate several multiplexed assays for the readout of gene expression.

Rare variant associations with birth weight identify genes involved in adipose tissue regulation, placental function and insulin-like growth factor signalling

Investigating the genetic factors influencing human birth weight may lead to biological insights into fetal growth and long-term health. Genome-wide association studies of birth weight have highlighted associated variants in more than 200 regions of the genome, but the causal genes are mostly unknown. Rare genetic variants with robust evidence of association are more likely to point to causal genes, but to date, only a few rare variants are known to influence birth weight. We aimed to identify genes that harbour rare variants that impact birth weight when carried by either the fetus or the mother, by analysing whole exome sequence data in UK Biobank participants. We annotated rare (minor allele frequency <0.1%) protein-truncating or high impact missense variants on whole exome sequence data in up to 234,675 participants with data on their own birth weight (fetal variants), and up to 181,883 mothers who reported the birth weight of their first child (maternal variants). Variants within each gene were collapsed to perform gene burden tests and for each associated gene, we compared the observed fetal and maternal effects. We identified 8 genes with evidence of rare fetal variant effects on birth weight, of which 2 also showed maternal effects. One additional gene showed evidence of maternal effects only. We observed 10/11 directionally concordant associations in an independent sample of up to 45,622 individuals (sign test P=0.01). Of the genes identified, IGF1R and PAPPA2 (fetal and maternal-acting) have known roles in insulin-like growth factor bioavailability and signalling. PPARG, INHBE and ACVR1C (all fetal-acting) have known roles in adipose tissue regulation and rare variants in the latter two also showed associations with favourable adiposity patterns in adults. We highlight the dual role of PPARG in both adipocyte differentiation and placental angiogenesis. NOS3, NRK, and ADAMTS8 (fetal and maternal-acting) have been implicated in both placental function and hypertension. Analysis of rare coding variants has identified regulators of fetal adipose tissue and fetoplacental angiogenesis as determinants of birth weight, as well as further evidence for the role of insulin-like growth factors.

Rheostats, toggles, and neutrals, Oh my! A new framework for understanding how amino acid changes modulate protein function

Advances in personalized medicine and protein engineering require accurately predicting outcomes of amino acid substitutions. Many algorithms correctly predict that evolutionarily-conserved positions show "toggle" substitution phenotypes, which is defined when a few substitutions at that position retain function. In contrast, predictions often fail for substitutions at the less-studied "rheostat" positions, which are defined when different amino acid substitutions at a position sample at least half of the possible functional range. This review describes efforts to understand the impact and significance of rheostat positions: (1) They have been observed in globular soluble, integral membrane, and intrinsically disordered proteins; within single proteins, their prevalence can be up to 40%. (2) Substitutions at rheostat positions can have biological consequences and ∼10% of substitutions gain function. (3) Although both rheostat and "neutral" (defined when all substitutions exhibit wild-type function) positions are nonconserved, the two classes have different evolutionary signatures. (4) Some rheostat positions have pleiotropic effects on function, simultaneously modulating multiple parameters (e.g., altering both affinity and allosteric coupling). (5) In structural studies, substitutions at rheostat positions appear to cause only local perturbations; the overall conformations appear unchanged. (6) Measured functional changes show promising correlations with predicted changes in protein dynamics; the emergent properties of predicted, dynamically coupled amino acid networks might explain some of the complex functional outcomes observed when substituting rheostat positions. Overall, rheostat positions provide unique opportunities for using single substitutions to tune protein function. Future studies of these positions will yield important insights into the protein sequence/function relationship.

The intrinsically disordered transcriptional activation domain of CIITA is functionally tuneable by single substitutions: An exception or a new paradigm?

During protein evolution, some amino acid substitutions modulate protein function ("tuneability"). In most proteins, the tuneable range is wide and can be sampled by a set of protein variants that each contains multiple amino acid substitutions. In other proteins, the full tuneable range can be accessed by a set of variants that each contains a single substitution. Indeed, in some globular proteins, the full tuneable range can be accessed by the set of site-saturating substitutions at an individual "rheostat" position. However, in proteins with intrinsically disordered regions (IDRs), most functional studies-which would also detect tuneability-used multiple substitutions or small deletions. In disordered transcriptional activation domains (ADs), studies with multiple substitutions led to the "acidic exposure" model, which does not anticipate the existence of rheostat positions. In the few studies that did assess effects of single substitutions on AD function, results were mixed: the ADs of two full-length transcription factors did not show tuneability, whereas a fragment of a third AD was tuneable by single substitutions. In this study, we tested tuneability in the AD of full-length human class II transactivator (CIITA). Sequence analyses and experiments showed that CIITA's AD is an IDR. Functional assays of singly-substituted AD variants showed that CIITA's function was highly tuneable, with outcomes not predicted by the acidic exposure model. Four tested positions showed rheostat behavior for transcriptional activation. Thus, tuneability of different IDRs can vary widely. Future studies are needed to illuminate the biophysical features that govern whether an IDR is tuneable by single substitutions.

SIGMA leverages protein structural information to predict the pathogenicity of missense variants

Leveraging protein structural information to evaluate pathogenicity has been hindered by the scarcity of experimentally determined 3D protein. With the aid of AlphaFold2 predictions, we developed the structure-informed genetic missense mutation assessor (SIGMA) to predict missense variant pathogenicity. In comparison with existing predictors across labeled variant datasets and experimental datasets, SIGMA demonstrates superior performance in predicting missense variant pathogenicity (AUC = 0.933). We found that the relative solvent accessibility of the mutated residue contributed greatly to the predictive ability of SIGMA. We further explored combining SIGMA with other top-tier predictors to create SIGMA+, proving highly effective for variant pathogenicity prediction (AUC = 0.966). To facilitate the application of SIGMA, we pre-computed SIGMA scores for over 48 million possible missense variants across 3,454 disease-associated genes and developed an interactive online platform (https://www.sigma-pred.org/). Overall, by leveraging protein structure information, SIGMA offers an accurate structure-based approach to evaluating the pathogenicity of missense variants.

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.

MmisAT and MmisP: an efficient and accurate suite of variant analysis toolkit for primary mitochondrial diseases

Recent advances in next-generation sequencing (NGS) technology have greatly accelerated the need for efficient annotation to accurately interpret clinically relevant genetic variants in human diseases. Therefore, it is crucial to develop appropriate analytical tools to improve the interpretation of disease variants. Given the unique genetic characteristics of mitochondria, including haplogroup, heteroplasmy, and maternal inheritance, we developed a suite of variant analysis toolkits specifically designed for primary mitochondrial diseases: the Mitochondrial Missense Variant Annotation Tool (MmisAT) and the Mitochondrial Missense Variant Pathogenicity Predictor (MmisP). MmisAT can handle protein-coding variants from both nuclear DNA and mtDNA and generate 349 annotation types across six categories. It processes 4.78 million variant data in 76 min, making it a valuable resource for clinical and research applications. Additionally, MmisP provides pathogenicity scores to predict the pathogenicity of genetic variations in mitochondrial disease. It has been validated using cross-validation and external datasets and demonstrated higher overall discriminant accuracy with a receiver operating characteristic (ROC) curve area under the curve (AUC) of 0.94, outperforming existing pathogenicity predictors. In conclusion, the MmisAT is an efficient tool that greatly facilitates the process of variant annotation, expanding the scope of variant annotation information. Furthermore, the development of MmisP provides valuable insights into the creation of disease-specific, phenotype-specific, and even gene-specific predictors of pathogenicity, further advancing our understanding of specific fields.

Integrated multiplexed assays of variant effect reveal cis-regulatory determinants of catechol-O-methyltransferase gene expression

Multiplexed assays of variant effect are powerful methods to profile the consequences of rare variants on gene expression and organismal fitness. Yet, few studies have integrated several multiplexed assays to map variant effects on gene expression in coding sequences. Here, we pioneered a multiplexed assay based on polysome profiling to measure variant effects on translation at scale, uncovering single-nucleotide variants that increase and decrease ribosome load. By combining high-throughput ribosome load data with multiplexed mRNA and protein abundance readouts, we mapped the cis-regulatory landscape of thousands of catechol-O-methyltransferase (COMT) variants from RNA to protein and found numerous coding variants that alter COMT expression. Finally, we trained machine learning models to map signatures of variant effects on COMT gene expression and uncovered both directional and divergent impacts across expression layers. Our analyses reveal expression phenotypes for thousands of variants in COMT and highlight variant effects on both single and multiple layers of expression. Our findings prompt future studies that integrate several multiplexed assays for the readout of gene expression.

Genotype-stratified treatment for monogenic insulin resistance: a systematic review

BACKGROUND

Monogenic insulin resistance (IR) includes lipodystrophy and disorders of insulin signalling. We sought to assess the effects of interventions in monogenic IR, stratified by genetic aetiology.

METHODS

Systematic review using PubMed, MEDLINE and Embase (1 January 1987 to 23 June 2021). Studies reporting individual-level effects of pharmacologic and/or surgical interventions in monogenic IR were eligible. Individual data were extracted and duplicates were removed. Outcomes were analysed for each gene and intervention, and in aggregate for partial, generalised and all lipodystrophy.

RESULTS

10 non-randomised experimental studies, 8 case series, and 23 case reports meet inclusion criteria, all rated as having moderate or serious risk of bias. Metreleptin use is associated with the lowering of triglycerides and haemoglobin A1c (HbA1c) in all lipodystrophy (n = 111), partial (n = 71) and generalised lipodystrophy (n = 41), and in LMNA, PPARG, AGPAT2 or BSCL2 subgroups (n = 72,13,21 and 21 respectively). Body Mass Index (BMI) is lowered in partial and generalised lipodystrophy, and in LMNA or BSCL2, but not PPARG or AGPAT2 subgroups. Thiazolidinediones are associated with improved HbA1c and triglycerides in all lipodystrophy (n = 13), improved HbA1c in PPARG (n = 5), and improved triglycerides in LMNA (n = 7). In INSR-related IR, rhIGF-1, alone or with IGFBP3, is associated with improved HbA1c (n = 17). The small size or absence of other genotype-treatment combinations preclude firm conclusions.

CONCLUSIONS

The evidence guiding genotype-specific treatment of monogenic IR is of low to very low quality. Metreleptin and Thiazolidinediones appear to improve metabolic markers in lipodystrophy, and rhIGF-1 appears to lower HbA1c in INSR-related IR. For other interventions, there is insufficient evidence to assess efficacy and risks in aggregated lipodystrophy or genetic subgroups.

The use of precision diagnostics for monogenic diabetes: a systematic review and expert opinion

BACKGROUND

Monogenic diabetes presents opportunities for precision medicine but is underdiagnosed. This review systematically assessed the evidence for (1) clinical criteria and (2) methods for genetic testing for monogenic diabetes, summarized resources for (3) considering a gene or (4) variant as causal for monogenic diabetes, provided expert recommendations for (5) reporting of results; and reviewed (6) next steps after monogenic diabetes diagnosis and (7) challenges in precision medicine field.

METHODS

Pubmed and Embase databases were searched (1990-2022) using inclusion/exclusion criteria for studies that sequenced one or more monogenic diabetes genes in at least 100 probands (Question 1), evaluated a non-obsolete genetic testing method to diagnose monogenic diabetes (Question 2). The risk of bias was assessed using the revised QUADAS-2 tool. Existing guidelines were summarized for questions 3-5, and review of studies for questions 6-7, supplemented by expert recommendations. Results were summarized in tables and informed recommendations for clinical practice.

RESULTS

There are 100, 32, 36, and 14 studies included for questions 1, 2, 6, and 7 respectively. On this basis, four recommendations for who to test and five on how to test for monogenic diabetes are provided. Existing guidelines for variant curation and gene-disease validity curation are summarized. Reporting by gene names is recommended as an alternative to the term MODY. Key steps after making a genetic diagnosis and major gaps in our current knowledge are highlighted.

CONCLUSIONS

We provide a synthesis of current evidence and expert opinion on how to use precision diagnostics to identify individuals with monogenic diabetes.

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.

PFAS Exposures and the Human Metabolome: A Systematic Review of Epidemiological Studies

Purpose of Review

There is a growing interest in understanding the health effects of exposure to per- and polyfluoroalkyl substances (PFAS) through the study of the human metabolome. In this systematic review, we aimed to identify consistent findings between PFAS and metabolomic signatures. We conducted a search matching specific keywords that was independently reviewed by two authors on two databases (EMBASE and PubMed) from their inception through July 19, 2022 following PRISMA guidelines.

Recent Findings

We identified a total of 28 eligible observational studies that evaluated the associations between 31 different PFAS exposures and metabolomics in humans. The most common exposure evaluated was legacy long-chain PFAS. Population sample sizes ranged from 40 to 1,105 participants at different stages across the lifespan. A total of 19 studies used a non-targeted metabolomics approach, 7 used targeted approaches, and 2 included both. The majority of studies were cross-sectional (n = 25), including four with prospective analyses of PFAS measured prior to metabolomics.

Summary

Most frequently reported associations across studies were observed between PFAS and amino acids, fatty acids, glycerophospholipids, glycerolipids, phosphosphingolipids, bile acids, ceramides, purines, and acylcarnitines. Corresponding metabolic pathways were also altered, including lipid, amino acid, carbohydrate, nucleotide, energy metabolism, glycan biosynthesis and metabolism, and metabolism of cofactors and vitamins. We found consistent evidence across studies indicating PFAS-induced alterations in lipid and amino acid metabolites, which may be involved in energy and cell membrane disruption.

Analysis of the genetic contribution to thoracic aortic aneurysm or dissection in a prospective cohort of patients with familial and sporadic cases in East China

BACKGROUND

Thoracic aortic aneurysm or dissections (TAADs) represent a group of life-threatening diseases. Genetic aetiology can affect the age of onset, clinical phenotype, and timing of intervention. We conducted a prospective trial to determine the prevalence of pathogenic variants in TAAD patients and to elucidate the traits related to harbouring the pathogenic variants. One hundred and one unrelated TAAD patients underwent genetic sequencing and analysis for 23 TAAD-associated genes using a targeted PCR and next-generation sequencing-based panel.

RESULTS

A total of 47 variants were identified in 52 TAAD patients (51.5%), including 5 pathogenic, 1 likely pathogenic and 41 variants of uncertain significance. The pathogenic or likely pathogenic (P/LP) variants in 4 disease-causing genes were carried by 1 patient with familial and 5 patients with sporadic TAAD (5.9%). In addition to harbouring one variant causing familial TAAD, the FBN1 gene harboured half of the P/LP variants causing sporadic TAAD. Individuals with an age of onset less than 50 years or normotension had a significantly increased genetic risk.

CONCLUSIONS

TAAD patients with a younger age at diagnosis or normotension were more likely to carry a P/LP variant; thus, routine genetic testing will be beneficial to a better prognosis through genetically personalized care prior to acute rupture or dissection.

Human gain-of-function variants in HNF1A confer protection from diabetes but independently increase hepatic secretion of atherogenic lipoproteins

Loss-of-function mutations in hepatocyte nuclear factor 1A (HNF1A) are known to cause rare forms of diabetes and alter hepatic physiology through unclear mechanisms. In the general population, 1:100 individuals carry a rare, protein-coding HNF1A variant, most of unknown functional consequence. To characterize the full allelic series, we performed deep mutational scanning of 11,970 protein-coding HNF1A variants in human hepatocytes and clinical correlation with 553,246 exome-sequenced individuals. Surprisingly, we found that ∼1:5 rare protein-coding HNF1A variants in the general population cause molecular gain of function (GOF), increasing the transcriptional activity of HNF1A by up to 50% and conferring protection from type 2 diabetes (odds ratio [OR] = 0.77, p = 0.007). Increased hepatic expression of HNF1A promoted a pro-atherogenic serum profile mediated in part by enhanced transcription of risk genes including ANGPTL3 and PCSK9. In summary, ∼1:300 individuals carry a GOF variant in HNF1A that protects carriers from diabetes but enhances hepatic secretion of atherogenic lipoproteins.

A systematic review on machine learning approaches in the diagnosis and prognosis of rare genetic diseases

BACKGROUND

The diagnosis of rare genetic diseases is often challenging due to the complexity of the genetic underpinnings of these conditions and the limited availability of diagnostic tools. Machine learning (ML) algorithms have the potential to improve the accuracy and speed of diagnosis by analyzing large amounts of genomic data and identifying complex multiallelic patterns that may be associated with specific diseases. In this systematic review, we aimed to identify the methodological trends and the ML application areas in rare genetic diseases.

METHODS

We performed a systematic review of the literature following the PRISMA guidelines to search studies that used ML approaches to enhance the diagnosis of rare genetic diseases. Studies that used DNA-based sequencing data and a variety of ML algorithms were included, summarized, and analyzed using bibliometric methods, visualization tools, and a feature co-occurrence analysis.

FINDINGS

Our search identified 22 studies that met the inclusion criteria. We found that exome sequencing was the most frequently used sequencing technology (59%), and rare neoplastic diseases were the most prevalent disease scenario (59%). In rare neoplasms, the most frequent applications of ML models were the differential diagnosis or stratification of patients (38.5%) and the identification of somatic mutations (30.8%). In other rare diseases, the most frequent goals were the prioritization of rare variants or genes (55.5%) and the identification of biallelic or digenic inheritance (33.3%). The most employed method was the random forest algorithm (54.5%). In addition, the features of the datasets needed for training these algorithms were distinctive depending on the goal pursued, including the mutational load in each gene for the differential diagnosis of patients, or the combination of genotype features and sequence-derived features (such as GC-content) for the identification of somatic mutations.

CONCLUSIONS

ML algorithms based on sequencing data are mainly used for the diagnosis of rare neoplastic diseases, with random forest being the most common approach. We identified key features in the datasets used for training these ML models according to the objective pursued. These features can support the development of future ML models in the diagnosis of rare genetic diseases.

Quantitative determination of SLC2A1 variant functional effects in GLUT1 deficiency syndrome

OBJECTIVE

The goal of this study is to demonstrate the utility of a growth assay to quantify the functional impact of single nucleotide variants (SNVs) in SLC2A1, the gene responsible for Glut1DS.

METHODS

The functional impact of 40 SNVs in SLC2A1 was quantitatively determined in HAP1 cells in which SLC2A1 is required for growth. Donor libraries were introduced into the endogenous SLC2A1 gene in HAP1-Lig4KO cells using CRISPR/Cas9. Cell populations were harvested and sequenced to quantify the effect of variants on growth and generate a functional score. Quantitative functional scores were compared to 3-OMG uptake, SLC2A1 cell surface expression, CADD score, and clinical data, including CSF/blood glucose ratio.

RESULTS

Nonsense variants (N = 3) were reduced in cell culture over time resulting in negative scores (mean score: -1.15 ± 0.17), whereas synonymous variants (N = 10) were not depleted (mean score: 0.25 ± 0.12) (P < 2e-16). Missense variants (N = 27) yielded a range of functional scores including slightly negative scores, supporting a partial function and intermediate phenotype. Several variants with normal results on either cell surface expression (p.N34S and p.W65R) or 3-OMG uptake (p.W65R) had negative functional scores. There is a moderate but significant correlation between our functional scores and CADD scores.

INTERPRETATION

Cell growth is useful to quantitatively determine the functional effects of SLC2A1 variants. Nonsense variants were reliably distinguished from benign variants in this in vitro functional assay. For facilitating early diagnosis and therapeutic intervention, future work is needed to determine the functional effect of every possible variant in SLC2A1.

Systematic review of genotype-stratified treatment for monogenic insulin resistance

Objective

To assess the effects of pharmacologic and/or surgical interventions in monogenic insulin resistance (IR), stratified by genetic aetiology.

Design

Systematic review.

Data sources

PubMed, MEDLINE and Embase, from 1 January 1987 to 23 June 2021.

Review methods

Studies reporting individual-level effects of pharmacologic and/or surgical interventions in monogenic IR were eligible. Individual subject data were extracted and duplicate data removed. Outcomes were analyzed for each affected gene and intervention, and in aggregate for partial, generalised and all lipodystrophy.

Results

10 non-randomised experimental studies, 8 case series, and 21 single case reports met inclusion criteria, all rated as having moderate or serious risk of bias. Metreleptin was associated with lower triglycerides and hemoglobin A1c in aggregated lipodystrophy (n=111), in partial lipodystrophy (n=71) and generalised lipodystrophy (n=41)), and in LMNA , PPARG , AGPAT2 or BSCL2 subgroups (n=72,13,21 and 21 respectively). Body Mass Index (BMI) was lower after treatment in partial and generalised lipodystrophy overall, and in LMNA or BSCL2 , but not PPARG or AGPAT2 subgroups. Thiazolidinedione use was associated with improved hemoglobin A1c and triglycerides in aggregated lipodystrophy (n=13), improved hemoglobin A1c only in the PPARG subgroup (n=5), and improved triglycerides only in the LMNA subgroup (n=7). In INSR -related IR, use of rhIGF-1, alone or with IGFBP3, was associated with improved hemoglobin A1c (n=15). The small size or absence of all other genotype-treatment combinations precluded firm conclusions.

Conclusions

The evidence guiding genotype-specific treatment of monogenic IR is of low to very low quality. Metreleptin and Thiazolidinediones appear to have beneficial metabolic effects in lipodystrophy, and rhIGF-1 appears to lower hemoglobin A1c in INSR-related IR. For other interventions there is insufficient evidence to assess efficacy and risks either in aggregated lipodystrophy or in genetic subgroups. There is a pressing need to improve the evidence base for management of monogenic IR.

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.

A Systematic Review of the use of Precision Diagnostics in Monogenic Diabetes

Monogenic forms of diabetes present opportunities for precision medicine as identification of the underlying genetic cause has implications for treatment and prognosis. However, genetic testing remains inconsistent across countries and health providers, often resulting in both missed diagnosis and misclassification of diabetes type. One of the barriers to deploying genetic testing is uncertainty over whom to test as the clinical features for monogenic diabetes overlap with those for both type 1 and type 2 diabetes. In this review, we perform a systematic evaluation of the evidence for the clinical and biochemical criteria used to guide selection of individuals with diabetes for genetic testing and review the evidence for the optimal methods for variant detection in genes involved in monogenic diabetes. In parallel we revisit the current clinical guidelines for genetic testing for monogenic diabetes and provide expert opinion on the interpretation and reporting of genetic tests. We provide a series of recommendations for the field informed by our systematic review, synthesizing evidence, and expert opinion. Finally, we identify major challenges for the field and highlight areas for future research and investment to support wider implementation of precision diagnostics for monogenic diabetes.

Molecular Mechanisms and Clinical Phenotypes of GJB2 Missense Variants

The GJB2 gene is the most common gene responsible for hearing loss (HL) worldwide, and missense variants are the most abundant type. GJB2 pathogenic missense variants cause nonsyndromic HL (autosomal recessive and dominant) and syndromic HL combined with skin diseases. However, the mechanism by which these different missense variants cause the different phenotypes is unknown. Over 2/3 of the GJB2 missense variants have yet to be functionally studied and are currently classified as variants of uncertain significance (VUS). Based on these functionally determined missense variants, we reviewed the clinical phenotypes and investigated the molecular mechanisms that affected hemichannel and gap junction functions, including connexin biosynthesis, trafficking, oligomerization into connexons, permeability, and interactions between other coexpressed connexins. We predict that all possible GJB2 missense variants will be described in the future by deep mutational scanning technology and optimizing computational models. Therefore, the mechanisms by which different missense variants cause different phenotypes will be fully elucidated.

Perinatal, metabolic, and reproductive features in PPARG-related lipodystrophy

OBJECTIVE

The adipogenic PPARG-encoded PPARγ nuclear receptor also displays essential placental functions. We evaluated the metabolic, reproductive, and perinatal features of patients with PPARG-related lipodystrophy.

METHODS

Current and retrospective data were collected in patients referred to a National Rare Diseases Reference Centre.

RESULTS

26 patients from 15 unrelated families were studied (18 women, median age 43 years). They carried monoallelic PPARG variants except a homozygous patient with congenital generalized lipodystrophy. Among heterozygous patients aged 16 or more (n = 24), 92% had diabetes, 96% partial lipodystrophy (median age at diagnosis 24 and 37 years), 78% hypertriglyceridaemia, 71% liver steatosis, and 58% hypertension. The mean BMI was 26 ± 5.0 kg/m2. Women (n = 16) were frequently affected by acute pancreatitis (n = 6) and/or polycystic ovary syndrome (n = 12). Eleven women obtained one or several pregnancies, all complicated by diabetes (n = 8), hypertension (n = 4), and/or hypertriglyceridaemia (n = 10). We analysed perinatal data of patients according to the presence (n = 8) or absence (n = 9) of a maternal dysmetabolic environment. The median gestational age at birth was low in both groups (37 and 36 weeks of amenorrhea, respectively). As expected, the birth weight was higher in patients exposed to a foetal dysmetabolic environment of maternal origin. In contrast, 85.7% of non-exposed patients, in whom the variant is, or is very likely to be, paternally-inherited, were small for gestational age.

CONCLUSIONS

Lipodystrophy-related PPARG variants induce early metabolic complications. Our results suggest that placental expression of PPARG pathogenic variants carried by affected foetuses could impair prenatal growth and parturition. This justifies careful pregnancy monitoring in affected families.

Hacking hematopoiesis - emerging tools for examining variant effects

Hematopoiesis is a continuous process of blood and immune cell production. It is orchestrated by thousands of gene products that respond to extracellular signals by guiding cell fate decisions to meet the needs of the organism. Although much of our knowledge of this process comes from work in model systems, we have learned a great deal from studies on human genetic variation. Considerable insight has emerged from studies on presumed monogenic blood disorders, which continue to provide key insights into the mechanisms critical for hematopoiesis. Furthermore, the emergence of large-scale biobanks and cohorts has uncovered thousands of genomic loci associated with blood cell traits and diseases. Some of these blood cell trait-associated loci act as modifiers of what were once thought to be monogenic blood diseases. However, most of these loci await functional validation. Here, we discuss the validation bottleneck and emerging methods to more effectively connect variant to function. In particular, we highlight recent innovations in genome editing, which have paved the path forward for high-throughput functional assessment of loci. Finally, we discuss existing barriers to progress, including challenges in manipulating the genomes of primary hematopoietic cells.

Deep mutational scanning: A versatile tool in systematically mapping genotypes to phenotypes

Unveiling how genetic variations lead to phenotypic variations is one of the key questions in evolutionary biology, genetics, and biomedical research. Deep mutational scanning (DMS) technology has allowed the mapping of tens of thousands of genetic variations to phenotypic variations efficiently and economically. Since its first systematic introduction about a decade ago, we have witnessed the use of deep mutational scanning in many research areas leading to scientific breakthroughs. Also, the methods in each step of deep mutational scanning have become much more versatile thanks to the oligo-synthesizing technology, high-throughput phenotyping methods and deep sequencing technology. However, each specific possible step of deep mutational scanning has its pros and cons, and some limitations still await further technological development. Here, we discuss recent scientific accomplishments achieved through the deep mutational scanning and describe widely used methods in each step of deep mutational scanning. We also compare these different methods and analyze their advantages and disadvantages, providing insight into how to design a deep mutational scanning study that best suits the aims of the readers' projects.

Disabling Uncompetitive Inhibition of Oncogenic IDH Mutations Drives Acquired Resistance

Mutations in IDH genes occur frequently in acute myeloid leukemia (AML) and other human cancers to generate the oncometabolite R-2HG. Allosteric inhibition of mutant IDH suppresses R-2HG production in a subset of patients with AML; however, acquired resistance emerges as a new challenge, and the underlying mechanisms remain incompletely understood. Here we establish isogenic leukemia cells containing common IDH oncogenic mutations by CRISPR base editing. By mutational scanning of IDH single amino acid variants in base-edited cells, we describe a repertoire of IDH second-site mutations responsible for therapy resistance through disabling uncompetitive enzyme inhibition. Recurrent mutations at NADPH binding sites within IDH heterodimers act in cis or trans to prevent the formation of stable enzyme-inhibitor complexes, restore R-2HG production in the presence of inhibitors, and drive therapy resistance in IDH-mutant AML cells and patients. We therefore uncover a new class of pathogenic mutations and mechanisms for acquired resistance to targeted cancer therapies.

SIGNIFICANCE

Comprehensive scanning of IDH single amino acid variants in base-edited leukemia cells uncovers recurrent mutations conferring resistance to IDH inhibition through disabling NADPH-dependent uncompetitive inhibition. Together with targeted sequencing, structural, and functional studies, we identify a new class of pathogenic mutations and mechanisms for acquired resistance to IDH-targeting cancer therapies. This article is highlighted in the In This Issue feature, p. 1.

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.

Insights on variant analysis in silico tools for pathogenicity prediction

Molecular biology is currently a fast-advancing science. Sequencing techniques are getting cheaper, but the interpretation of genetic variants requires expertise and computational power, therefore is still a challenge. Next-generation sequencing releases thousands of variants and to classify them, researchers propose protocols with several parameters. Here we present a review of several in silico pathogenicity prediction tools involved in the variant prioritization/classification process used by some international protocols for variant analysis and studies evaluating their efficiency.

PPARγ lipodystrophy mutants reveal intermolecular interactions required for enhancer activation

Peroxisome proliferator-activated receptor γ (PPARγ) is the master regulator of adipocyte differentiation, and mutations that interfere with its function cause lipodystrophy. PPARγ is a highly modular protein, and structural studies indicate that PPARγ domains engage in several intra- and inter-molecular interactions. How these interactions modulate PPARγ's ability to activate target genes in a cellular context is currently poorly understood. Here we take advantage of two previously uncharacterized lipodystrophy mutations, R212Q and E379K, that are predicted to interfere with the interaction of the hinge of PPARγ with DNA and with the interaction of PPARγ ligand binding domain (LBD) with the DNA-binding domain (DBD) of the retinoid X receptor, respectively. Using biochemical and genome-wide approaches we show that these mutations impair PPARγ function on an overlapping subset of target enhancers. The hinge region-DNA interaction appears mostly important for binding and remodelling of target enhancers in inaccessible chromatin, whereas the PPARγ-LBD:RXR-DBD interface stabilizes the PPARγ:RXR:DNA ternary complex. Our data demonstrate how in-depth analyses of lipodystrophy mutants can unravel molecular mechanisms of PPARγ function.

Defining the sensitivity landscape of EGFR variants to tyrosine kinase inhibitors

Tyrosine kinase inhibitor (TKI) is a standard treatment for patients with NSCLC harboring constitutively active epidermal growth factor receptor (EGFR) mutations. However, most rare EGFR mutations lack treatment regimens except for the well-studied ones. We constructed two EGFR variant libraries containing substitutions, deletions, or insertions using the saturation mutagenesis method. All the variants were located in the EGFR mutation hotspot (exons 18-21). The sensitivity of these variants to afatinib, erlotinib, gefitinib, icotinib, and osimertinib was systematically studied by determining their enrichment in massively parallel cytotoxicity assays using an endogenous EGFR-depleted cell line. A total of 3914 and 70,475 variants were detected in the constructed EGFR Substitution-Deletion (Sub-Del) and exon 20 Insertion (Ins) libraries. Of the 3914 Sub-Del variants, 221 proliferated fast in the control assay and were sensitive to EGFR-TKIs. For the 70,475 Ins variants, insertions at amino acid positions 770-774 were highly enriched in all 5 TKI cytotoxicity assays. Moreover, the top 5% of the enriched insertion variants included a glycine or serine insertion at high frequency. We present a comprehensive reference for the sensitivity of EGFR variants to five commonly used TKIs. The approach used here should be applicable to other genes and targeted drugs. BACKGROUND: Tyrosine kinase inhibitors (TKIs) therapy is a standard treatment for patients with advanced non-small-cell lung carcinoma (NSCLC) when activating epidermal growth factor receptor (EGFR) mutations are detected. However, except for the well-studied EGFR mutations, most EGFR mutations lack treatment regimens. TRANSLATIONAL SIGNIFICANCE: The results demonstrated that patients with rare EGFR mutations were most likely to benefit from osimertinib therapy compared to afatinib, erlotinib, gefitinib, or icotinib therapy. This study provides a case of deep mutational scanning that simultaneously assayed substitution, deletion, and insertion variants. This approach is applicable for other oncogenes and targeted drugs.

Achievements, prospects and challenges in precision care for monogenic insulin-deficient and insulin-resistant diabetes

Integration of genomic and other data has begun to stratify type 2 diabetes in prognostically meaningful ways, but this has yet to impact on mainstream diabetes practice. The subgroup of diabetes caused by single gene defects thus provides the best example to date of the vision of 'precision diabetes'. Monogenic diabetes may be divided into primary pancreatic beta cell failure, and primary insulin resistance. In both groups, clear examples of genotype-selective responses to therapy have been advanced. The benign trajectory of diabetes due to pathogenic GCK mutations, and the sulfonylurea-hyperresponsiveness conferred by activating KCNJ11 or ABCC8 mutations, or loss-of-function HNF1A or HNF4A mutations, often decisively guide clinical management. In monogenic insulin-resistant diabetes, subcutaneous leptin therapy is beneficial in some severe lipodystrophy. Increasing evidence also supports use of 'obesity therapies' in lipodystrophic people even without obesity. In beta cell diabetes the main challenge is now implementation of the precision diabetes vision at scale. In monogenic insulin-resistant diabetes genotype-specific benefits are proven in far fewer patients to date, although further genotype-targeted therapies are being evaluated. The conceptual paradigm established by the insulin-resistant subgroup with 'adipose failure' may have a wider influence on precision therapy for common type 2 diabetes, however. For all forms of monogenic diabetes, population-wide genome sequencing is currently forcing reappraisal of the importance assigned to pathogenic mutations when gene sequencing is uncoupled from prior suspicion of monogenic diabetes.

Phenotypic Differences Among Familial Partial Lipodystrophy Due to LMNA or PPARG Variants

Context

Despite several reports of familial partial lipodystrophy (FPLD) type 2 (FPLD2) due to heterozygous LMNA variants and FPLD3 due to PPARG variants, the phenotypic differences among them remain unclear.

Objective

To compare the body fat distribution, metabolic parameters, and prevalence of metabolic complications between FPLD3 and FPLD2.

Methods

A retrospective, cross-sectional comparison of patients from 2 tertiary referral centers-UT Southwestern Medical Center and the National Institute of Diabetes and Digestive and Kidney Diseases. A total of 196 females and 59 males with FPLD2 (age 2-86 years) and 28 females and 4 males with FPLD3 (age 9-72 years) were included. The main outcome measures were skinfold thickness, regional body fat by dual-energy X-ray absorptiometry (DXA), metabolic variables, and prevalence of diabetes mellitus and hypertriglyceridemia.

Results

Compared with subjects with FPLD2, subjects with FPLD3 had significantly increased prevalence of hypertriglyceridemia (66% vs 84%) and diabetes (44% vs 72%); and had higher median fasting serum triglycerides (208 vs 255 mg/dL), and mean hemoglobin A1c (6.4% vs 7.5%). Compared with subjects with FPLD2, subjects with FPLD3 also had significantly higher mean upper limb fat (21% vs 27%) and lower limb fat (16% vs 21%) on DXA and increased median skinfold thickness at the anterior thigh (5.8 vs 11.3 mm), calf (4 vs 6 mm), triceps (5.5 vs 7.5 mm), and biceps (4.3 vs 6.8 mm).

Conclusion

Compared with subjects with FPLD2, subjects with FPLD3 have milder lipodystrophy but develop more severe metabolic complications, suggesting that the remaining adipose tissue in subjects with FPLD3 may be dysfunctional or those with mild metabolic disease are underrecognized.

Intragenic compensation through the lens of deep mutational scanning

A significant fraction of mutations in proteins are deleterious and result in adverse consequences for protein function, stability, or interaction with other molecules. Intragenic compensation is a specific case of positive epistasis when a neutral missense mutation cancels effect of a deleterious mutation in the same protein. Permissive compensatory mutations facilitate protein evolution, since without them all sequences would be extremely conserved. Understanding compensatory mechanisms is an important scientific challenge at the intersection of protein biophysics and evolution. In human genetics, intragenic compensatory interactions are important since they may result in variable penetrance of pathogenic mutations or fixation of pathogenic human alleles in orthologous proteins from related species. The latter phenomenon complicates computational and clinical inference of an allele's pathogenicity. Deep mutational scanning is a relatively new technique that enables experimental studies of functional effects of thousands of mutations in proteins. We review the important aspects of the field and discuss existing limitations of current datasets. We reviewed ten published DMS datasets with quantified functional effects of single and double mutations and described rates and patterns of intragenic compensation in eight of them.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12551-022-01005-w.

Embeddings from protein language models predict conservation and variant effects

The emergence of SARS-CoV-2 variants stressed the demand for tools allowing to interpret the effect of single amino acid variants (SAVs) on protein function. While Deep Mutational Scanning (DMS) sets continue to expand our understanding of the mutational landscape of single proteins, the results continue to challenge analyses. Protein Language Models (pLMs) use the latest deep learning (DL) algorithms to leverage growing databases of protein sequences. These methods learn to predict missing or masked amino acids from the context of entire sequence regions. Here, we used pLM representations (embeddings) to predict sequence conservation and SAV effects without multiple sequence alignments (MSAs). Embeddings alone predicted residue conservation almost as accurately from single sequences as ConSeq using MSAs (two-state Matthews Correlation Coefficient-MCC-for ProtT5 embeddings of 0.596 ± 0.006 vs. 0.608 ± 0.006 for ConSeq). Inputting the conservation prediction along with BLOSUM62 substitution scores and pLM mask reconstruction probabilities into a simplistic logistic regression (LR) ensemble for Variant Effect Score Prediction without Alignments (VESPA) predicted SAV effect magnitude without any optimization on DMS data. Comparing predictions for a standard set of 39 DMS experiments to other methods (incl. ESM-1v, DeepSequence, and GEMME) revealed our approach as competitive with the state-of-the-art (SOTA) methods using MSA input. No method outperformed all others, neither consistently nor statistically significantly, independently of the performance measure applied (Spearman and Pearson correlation). Finally, we investigated binary effect predictions on DMS experiments for four human proteins. Overall, embedding-based methods have become competitive with methods relying on MSAs for SAV effect prediction at a fraction of the costs in computing/energy. Our method predicted SAV effects for the entire human proteome (~ 20 k proteins) within 40 min on one Nvidia Quadro RTX 8000. All methods and data sets are freely available for local and online execution through bioembeddings.com, https://github.com/Rostlab/VESPA , and PredictProtein.

Multiancestry exome sequencing reveals INHBE mutations associated with favorable fat distribution and protection from diabetes

Body fat distribution is a major, heritable risk factor for cardiometabolic disease, independent of overall adiposity. Using exome-sequencing in 618,375 individuals (including 160,058 non-Europeans) from the UK, Sweden and Mexico, we identify 16 genes associated with fat distribution at exome-wide significance. We show 6-fold larger effect for fat-distribution associated rare coding variants compared with fine-mapped common alleles, enrichment for genes expressed in adipose tissue and causal genes for partial lipodystrophies, and evidence of sex-dimorphism. We describe an association with favorable fat distribution (p = 1.8 × 10-09), favorable metabolic profile and protection from type 2 diabetes (~28% lower odds; p = 0.004) for heterozygous protein-truncating mutations in INHBE, which encodes a circulating growth factor of the activin family, highly and specifically expressed in hepatocytes. Our results suggest that inhibin βE is a liver-expressed negative regulator of adipose storage whose blockade may be beneficial in fat distribution-associated metabolic disease.