Inferring the molecular and phenotypic impact of amino acid variants with MutPred2

Structural and functional prediction, evaluation, and validation in the post-sequencing era

The surge of genome sequencing data has underlined substantial genetic variants of uncertain significance (VUS). The decryption of VUS discovered by sequencing poses a major challenge in the post-sequencing era. Although experimental assays have progressed in classifying VUS, only a tiny fraction of the human genes have been explored experimentally. Thus, it is urgently needed to generate state-of-the-art functional predictors of VUS in silico. Artificial intelligence (AI) is an invaluable tool to assist in the identification of VUS with high efficiency and accuracy. An increasing number of studies indicate that AI has brought an exciting acceleration in the interpretation of VUS, and our group has already used AI to develop protein structure-based prediction models. In this review, we provide an overview of the previous research on AI-based prediction of missense variants, and elucidate the challenges and opportunities for protein structure-based variant prediction in the post-sequencing era.

Genetic analysis of the circumsporozoite gene in Plasmodium falciparum isolates from Cameroon: Implications for efficacy and deployment of RTS,S/AS01 vaccine

Current understanding of genetic polymorphisms and natural selection in Plasmodium falciparum circumsporozoite (PfCSP), the leading malaria vaccine, is crucial for the development of next-generation vaccines, and such data is lacking in Africa. Blood samples were collected among Plasmodium-infected individuals living in four Cameroonian areas (Douala, Maroua, Mayo-Oulo, Pette). DNA samples were amplified using nested PCR protocols, sequenced, and BLASTed. Single nucleotide polymorphisms (SNPs) were analysed in each PfCSP region, and their impact on PfCSP function/structure was predicted in silico. The N-terminal region showed a limited polymorphism with four haplotypes, and three novel SNPs (N68Y, R87W, K93E) were found. Thirty-five haplotypes were identified in the central region, with several variants (e.g., NVNP and KANP). The C-terminal region was also highly diverse, with 25 haplotypes and eight novel SNPs (N290D, N308I, S312G, K317A, V344I, D356E, E357L, D359Y). Most polymorphic codon sites were mainly observed in the Th2R subregion in isolates from Douala and Pette. The codon site 321 was under episodic positive selection. One novel (E357L) and three known (K322I, G349D, D359Y) SNPs show an impact on function/structure. This study showed extensive genetic diversity with geographical patterns and evidence of the selection of Cameroonian PfCSP central and C-terminal regions.

Structural analysis of human ATE1 isoforms and their interactions with Arg-tRNAArg

Posttranslational protein arginylation has been shown as a key regulator of cellular processes in eukaryotes by affecting protein stability, function, and interaction with macromolecules. Thus, the enzyme Arginyltransferase and its targets, are of immense interest to modulate cellular processes in the normal and diseased state. While the study on the effect of this posttranslational modification in mammalian systems gained momentum in the recent times, the detail structures of human ATE1 (hATE1) enzymes has not been investigated so far. Thus, the purpose of this study was to predict the overall structure and the structure function relationship of hATE1 enzyme and its four isoforms. The structure of four ATE1 isoforms were modelled and were docked with 3'end of the Arg-tRNAArg which acts as arginine donor in the arginylation reaction, followed by MD simulation. All the isoforms showed two distinct domains. A compact domain and a somewhat flexible domain as observed in the RMSF plot. A distinct similarity in the overall structure and interacting residues were observed between hATE1-1 and X4 compared to hATE1-2 and 5. While the putative active sites of all the hATE1 isoforms were located at the same pocket, differences were observed in the active site residues across hATE1 isoforms suggesting different substrate specificity. Mining of nsSNPs showed several nsSNPs including cancer associated SNPs with deleterious consequences on hATE1 structure and function. Thus, the current study for the first time shows the structural differences in the mammalian ATE1 isoforms and their possible implications in the function of these proteins.Communicated by Ramaswamy H. Sarma.

Overcoming Resistance in Cancer Therapy: Computational Exploration of PIK3CA Mutations, Unveiling Novel Non-Toxic Inhibitors, and Molecular Insights Into Targeting PI3Kα

Phosphoinositide-3-kinases (PI3 K) are pivotal regulators of cell signaling implicated in various cancers. Particularly, mutations in the PIK3CA gene encoding the p110α catalytic subunit drive oncogenic signaling, making it an attractive therapeutic target. Our study conducted in silico exploration of 31 PIK3CA mutations across breast, endometrial, colon, and ovarian cancers, assessing their impacts on response to PI3Kα inhibitors and identifying potential non-toxic inhibitors and also elucidating their effects on protein stability and flexibility. Specifically, we observed significant alterations in the stability and flexibility of the PI3 K protein induced by these mutations. Through molecular docking analysis, we evaluated the binding interactions between the selected inhibitors and the PI3 K protein. The filtration of ligands involved calculating chemical descriptors, incorporating Veber and Lipinski rules, as well as IC50 values and toxicity predictions. This process reduced the initial dataset of 1394 ligands to 12 potential non-toxic inhibitors, and four reference inhibitors with significant biological activity in clinical trials were then chosen based on their physico-chemical properties. This analysis revealed Lig5's exceptional performance, exhibiting superior affinity and specificity compared to established reference inhibitors such as pictilisib. Lig5 formed robust binding interactions with the PI3 K protein, suggesting its potential as a highly effective therapeutic agent against PI3 K-driven cancers. Furthermore, molecular dynamics simulations provided valuable insights into Lig5's stability and its interactions with PI3 K over 100 ns. These simulations supported Lig5's potential as a versatile inhibitor capable of effectively targeting various mutational profiles of PI3 K, thereby mitigating issues related to resistance and toxicity commonly associated with current inhibitors.

Rare heterozygous variants in paediatric steroid resistant nephrotic syndrome - a population-based analysis of their significance

Genetic testing in nephrotic syndrome may identify heterozygous predicted-pathogenic variants (HPPVs) in autosomal recessive (AR) genes that are known to cause disease in the homozygous or compound heterozygous state. In such cases, it can be difficult to define the variant's true significance and questions remain about whether a second pathogenic variant has been missed during analysis or whether the variant is an incidental finding. There are now known to be over 70 genes associated with nephrotic syndrome, the majority inherited as an AR trait. Knowledge of whether such HPPVs occur with equal frequency in patients compared to the general population would assist interpretation of their significance. Exome sequencing was performed on 187 Steroid-Resistant Nephrotic Syndrome (SRNS) paediatric patients recruited to a UK rare disease registry plus originating from clinics at Evelina, London. 59 AR podocytopathy linked genes were analysed in each patient and a list of HPPVs created. We compared the frequency of detected HPPVs with a 'control' population from the gnomAD database containing exome data from approximately 50,000 individuals. A bespoke filtering process was used for both patients and controls to predict 'likely pathogenicity' of variants. In total 130 Caucasian SRNS patients were screened across 59 AR genes and 201 rare heterozygous variants were identified. 17/201 (8.5%) were assigned as 'likely pathogenic' (HPPV) using our bespoke filtering method. Comparing each gene in turn, for SRNS patients with a confirmed genetic diagnosis, in 57 of the 59 genes we found no statistically significant difference in the frequency of these HPPVs between patients and controls (In genes ARHGDIA and TP53RK, we identified a significantly higher number of HPPVs in the control population compared with the patients when filtering was performed with 'high stringency' settings only). In the SRNS patients without a genetics diagnosis confirmed, there was no statistically significant difference identified in any gene between patient and control. In children with SRNS, we propose that identification of HPPV in AR podocytopathy linked genes is not necessarily representative of pathogenicity, given that the frequency is similar to that seen in controls for the majority. Whilst this may not exclude the presence of genetic kidney disease, this type of heterozygous variant is unlikely to be causal and each result must be interpreted in its clinical context.

Structure-informed protein language models are robust predictors for variant effects

Emerging variant effect predictors, protein language models (pLMs) learn evolutionary distribution of functional sequences to capture fitness landscape. Considering that variant effects are manifested through biological contexts beyond sequence (such as structure), we first assess how much structure context is learned in sequence-only pLMs and affecting variant effect prediction. And we establish a need to inject into pLMs protein structural context purposely and controllably. We thus introduce a framework of structure-informed pLMs (SI-pLMs), by extending masked sequence denoising to cross-modality denoising for both sequence and structure. Numerical results over deep mutagenesis scanning benchmarks show that our SI-pLMs, even when using smaller models and less data, are robustly top performers against competing methods including other pLMs, which shows that introducing biological context can be more effective at capturing fitness landscape than simply using larger models or bigger data. Case studies reveal that, compared to sequence-only pLMs, SI-pLMs can be better at capturing fitness landscape because (a) learned embeddings of low/high-fitness sequences can be more separable and (b) learned amino-acid distributions of functionally and evolutionarily conserved residues can be of much lower entropy, thus much more conserved, than other residues. Our SI-pLMs are applicable to revising any sequence-only pLMs through model architecture and training objectives. They do not require structure data as model inputs for variant effect prediction and only use structures as context provider and model regularizer during training.

Using computational approaches to enhance the interpretation of missense variants in the PAX6 gene

The PAX6 gene encodes a highly-conserved transcription factor involved in eye development. Heterozygous loss-of-function variants in PAX6 can cause a range of ophthalmic disorders including aniridia. A key molecular diagnostic challenge is that many PAX6 missense changes are presently classified as variants of uncertain significance. While computational tools can be used to assess the effect of genetic alterations, the accuracy of their predictions varies. Here, we evaluated and optimised the performance of computational prediction tools in relation to PAX6 missense variants. Through inspection of publicly available resources (including HGMD, ClinVar, LOVD and gnomAD), we identified 241 PAX6 missense variants that were used for model training and evaluation. The performance of ten commonly used computational tools was assessed and a threshold optimization approach was utilized to determine optimal cut-off values. Validation studies were subsequently undertaken using PAX6 variants from a local database. AlphaMissense, SIFT4G and REVEL emerged as the best-performing predictors; the optimized thresholds of these tools were 0.967, 0.025, and 0.772, respectively. Combining the prediction from these top-three tools resulted in lower performance compared to using AlphaMissense alone. Tailoring the use of computational tools by employing optimized thresholds specific to PAX6 can enhance algorithmic performance. Our findings have implications for PAX6 variant interpretation in clinical settings.

Novel TMPRSS6 variants and their impact on iron-refractory iron deficiency anaemia in pregnancy: A North Indian genotype phenotype study

Iron-refractory iron deficiency anaemia (IRIDA) is a rare autosomal recessive disorder, distinguished by hypochromic microcytic anaemia, low transferrin levels and inappropriately elevated hepcidin (HEPC) levels. It is caused by mutations in TMPRSS6 gene. Systematic screening of 500 pregnant women with iron deficiency anaemia having moderate to severe microcytosis with no other causes of anaemia were enrolled to rule out oral iron refractoriness. It identified a final cohort of 10 (2.15% prevalence) individuals with IRIDA phenotype. Haematological and biochemical analysis revealed significant differences between iron responders and iron non-responders, with iron non-responders showing lower haemoglobin, red blood cell count, serum iron and serum ferritin levels, along with elevated HEPC (9.47 ± 2.75 ng/mL, p = 0.0009) and erythropoietin (4.58 ± 4.07 µ/mL, p = 0.0196) levels. Genetic sequencing of the TMPRSS6 gene in this final cohort identified 10 novel variants, including seven missense and three frame-shift mutations, with four missense variants showing high functional impact defining the IRIDA phenotype. Structural analysis revealed significant damage caused by two variants (p.L83R and p.S235R). This study provides valuable insights into IRIDA among pregnant women in the Indian subcontinent, unveiling its underlying causes of unresponsiveness, genetic mechanisms and prevalence. Furthermore, research collaboration is essential to validate these findings and develop effective treatments.

The first Iranian patient with You-Hoover-Fong syndrome and a review of the literature on 27 cases: expanding the genotypic and phenotypic spectrum

BACKGROUND

The ultra-rare autosomal recessive genetic disorder, You-Hoover-Fong Syndrome (YHFS), is caused by defects in the TELO2 gene and is characterized by intellectual disability, developmental delay, and ocular impairments. This study aims to contribute to a better understanding of YHFS by reviewing previous cases and introducing a novel variant in a new case.

METHODS

Whole exome sequencing (WES) was conducted on the proband to identify genetic variants, and Sanger sequencing was used to confirm variants within the family. This article presents a comprehensive collection of reported cases of YHFS, incorporating both molecular and clinical data, through an extensive literature search and analysis of English-language studies published until June 2023.

RESULTS

Using WES, a novel homozygous missense variant, c.1799A > G (p. Tyr600Cys), was identified in the TELO2 gene in a 4-year-old Iranian male patient. Novel clinical features, including choanal atresia and clubfoot, were also identified. A comprehensive literature review identified 27 patients with YHFS, with 20 variants in the TELO2 gene. Missense pathogenic variants were the most common type of pathogenic variant, and the most common features were microcephaly and intellectual impairment.

CONCLUSION

This study presents the first case of pathogenic variants in TELO2 gene in Iran, expands the genotypic and phenotypic spectrum of YHFS and contributes to the growing body of literature pertaining to YHFS. Furthermore, our findings highlight the importance of genetic testing for non-consanguineous carrier screening, as compound heterozygosity may be a significant factor in the development of YHFS. Further research is needed to clarify the molecular mechanisms underlying YHFS pathogenesis.

In-silico screening of missense nsSNPs in Delta-opioid receptor protein and their restoring tendency on MCRT interaction; focusing on dynamic nature

Delta-opioid receptor protein (OPRD1) is one of the potential targets for treating pain. The presently available opioid agonists are known to cause unnecessary side effects. To discover a novel opioid agonist, our research group has synthesized a chimeric peptide MCRT and proved its potential activity through in vivo analysis. Non-synonymous SNPs (nsSNPs) missense mutations affect the functionality and stability of proteins leading to diseases. The current research was focused on understanding the role of MCRT in restoring the binding tendency of OPRD1 nsSNPs missense mutations on dynamic nature in comparison with Deltorphin-II and morphiceptin. The deleterious effects of nsSNPs were analyzed using various bioinformatics tools for predicting structural, functional, and oncogenic influence. The shortlisted nine nsSNPs were predicted for allergic reactions, domain changes, post-translation modification, multiple sequence alignment, secondary structure, molecular dynamic simulation (MDS), and peptide docking influence. Further, the docked complex of three shortlisted deleterious nsSNPs was analyzed using an MDS study, and the highly deleterious shortlisted nsSNP A149T was further analyzed for higher trajectory analysis. MCRT restored the binding tendency influence caused by nsSNPs on the dynamics of stability, functionality, binding affinity, secondary structure, residues connection, motion, and folding of OPRD1 protein.

Functional Characterization of a Novel SLC4A4 Variant and Uniparental Isodisomy in Proximal Renal Tubular Acidosis Patient

SLC4A4 variants are the etiologies of inherited proximal renal tubular acidosis (pRTA), which results in metabolic acidosis, hypokalemia, glaucoma, band keratopathy, and cataract. This study aims to characterize SLC4A4 variant and uniparental isodisomy of chromosome 4 in a patient, and analyse the functional characterization of SLC4A4 variants. This study analyzed renal tubular acidosis disease genes by whole exome sequencing (WES). H3M2 algorithm was used to analyze the run of homozygosity region in chromosomal regions in trio-WES data. The pathogenicity analysis of variants was performed using bioinformatics tools. Additionally, protein stability was analyzed by cycloheximide chase assay. Whole-cell patch clamping was used to examine the electrophysiological properties of NBCe1-A. A novel homozygous SLC4A4 variant was identified in the patient: a missense variant c.496C > T, p. Arg166Trp (NM_003759.4). But the father was heterozygous variant carrier, and the mother did not detect the variant. The H3M2 and UPDio algorithm revealed paternal uniparental isodisomy on chromosome 4 in the patient. SIFT, Poly Phen-2, FATHMM and Mutant Taster showed that the variant might be pathogenic. The tertiary structure analysis showed that the variant could cause structural damage to NBCe1 protein. Foldx results showed that the protein stability of the variant was slightly reduced. Cycloheximide chase assay demonstrated that the variant affects protein stability. The result of electrophysiological studies showed that the variant altered Na+/HCO3- cotransport activity of protein. In conclusion, the study is the first to report a pRTA patient with Arg166Trp variant with UPiD (4) pat and analyze the function of Arg166Trp variant.

Accuracy of renovo predictions on variants reclassified over time

BACKGROUND

Interpreting the clinical consequences of genetic variants is the central problem in modern clinical genomics, for both hereditary diseases and oncology. However, clinical validation lags behind the pace of discovery, leading to distressing uncertainty for patients, physicians and researchers. This "interpretation gap" changes over time as evidence accumulates, and variants initially deemed of uncertain (VUS) significance may be subsequently reclassified in pathogenic/benign. We previously developed RENOVO, a random forest-based tool able to predict variant pathogenicity based on publicly available information from GnomAD and dbNFSP, and tested on variants that have changed their classification status over time. Here, we comprehensively evaluated the accuracy of RENOVO predictions on variants that have been reclassified over the last four years.

METHODS

we retrieved 16 retrospective instances of the ClinVar database, every 3 months since March 2020 to March 2024, and analyzed time trends of variant classifications. We identified variants that changed their status over time and compared RENOVO predictions generated in 2020 with the actual reclassifications.

RESULTS

VUS have become the most represented class in ClinVar (44.97% vs. 9.75% (likely) pathogenic and 40,33% (likely) benign). The rate of VUS reclassification is linear and slow compared to the rate of VUS reporting, exponential and currently ~ 30x faster, creating a growing divide between what can be sequenced vs. what can be interpreted. Out of 10,196 VUS variants in January 2020 that have undergone a clinically meaningful reclassification to march 2024, RENOVO correctly classified 82.6% in 2020. In addition, RENOVO correctly identified the majority of the few variants that switched clinically meaningful classes (e.g., from benign to pathogenic and vice versa). We highlight variant classes and clinically relevant genes for which RENOVO provides particularly accurate estimates. In particularly, genes characterized by large prevalence of high- or low-impact variants (e.g., POLE, NOTCH1, FANCM etc.). Suboptimal RENOVO predictions mostly concern genes validated through dedicated consortia (e.g., BRCA1/2), in which RENOVO would anyway have a limited impact.

CONCLUSIONS

Time trend analysis demonstrates that the current model of variant interpretation cannot keep up with variant discovery. Machine learning-based tools like RENOVO confirm high accuracy that can aid in clinical practice and research.

QAFI: a novel method for quantitative estimation of missense variant impact using protein-specific predictors and ensemble learning

Next-generation sequencing (NGS) has revolutionized genetic diagnostics, yet its application in precision medicine remains incomplete, despite significant advances in computational tools for variant annotation. Many variants remain unannotated, and existing tools often fail to accurately predict the range of impacts that variants have on protein function. This limitation restricts their utility in relevant applications such as predicting disease severity and onset age. In response to these challenges, a new generation of computational models is emerging, aimed at producing quantitative predictions of genetic variant impacts. However, the field is still in its early stages, and several issues need to be addressed, including improved performance and better interpretability. This study introduces QAFI, a novel methodology that integrates protein-specific regression models within an ensemble learning framework, utilizing conservation-based and structure-related features derived from AlphaFold models. Our findings indicate that QAFI significantly enhances the accuracy of quantitative predictions across various proteins. The approach has been rigorously validated through its application in the CAGI6 contest, focusing on ARSA protein variants, and further tested on a comprehensive set of clinically labeled variants, demonstrating its generalizability and robust predictive power. The straightforward nature of our models may also contribute to better interpretability of the results.

Silibinins and curcumin as promising ligands against mutant cystic fibrosis transmembrane regulator protein

Cystic Fibrosis Transmembrane Regulator (CFTR) is a significant protein that is responsible for the movement of ions across cell membranes. The cystic fibrosis (CF) occur due to the mutations in the CFTR gene as it produces the dysfunctional CFTR protein. The sequence of CFTR protein as a target structure was retrieved from UniProt and PDB database. The ligands selection was performed through virtual screening and top 3 ligands choose out of 65 ligands silibinins, curcumin, demethoxycurcumin were selected with a reference drug Trikafta (R*). According to docking, ADMET analyses, the natural ligands (Silibinins and Curcumin) displayed best binding energy, pharmacokinetic and free toxicity than other natural compounds and reference drug (R*). An MD simulation for 200 ns was also established to ensure that natural ligands (Silibinins and Curcumin) attached to the target protein favorably and dynamically, and that protein-ligand complex stability was maintained. It is concluded that silibinins and curcumins have a better capacity to decrease the effect of mutant CFTR protein through improved trafficking and the restoration of original function. In conclusion, in silico studies demonstrate the potential of silibinins and curcumin as therapeutic agents for cystic fibrosis, particularly for the D614G mutated protein. Their ability to increase CFTR function while reducing cellular stress and inflammation, together with their favorable safety profile and accessibility could make them valuable additions to cystic fibrosis treatment options. Further experimental and clinical validation will be required to fully realize their potential and include them into effective therapy regimens.

Characterization of 13 Novel Genetic Variants in Genes Associated with Epilepsy: Implications for Targeted Therapeutic Strategies

BACKGROUND

Childhood epilepsies are caused by heterogeneous underlying disorders where approximately 40% of the origins of epilepsy can be attributed to genetic factors. The application of next-generation sequencing (NGS) has revolutionized molecular diagnostics and has enabled the identification of disease-causing genes and variants in childhood epilepsies. The objective of this study was to use NGS to identify variants in patients with childhood epilepsy, to expand the variant spectrum and discover potential therapeutic targets.

METHODS

In our study, 55 children with epilepsy of unknown etiology were analyzed by combining clinical-exome and whole-exome sequencing. Novel variants were characterized using various in silico algorithms for pathogenicity and structure prediction.

RESULTS

The molecular genetic cause of epilepsy was identified in 28 patients and the overall diagnostic success rate was 50.9%. We identified variants in 22 different genes associated with epilepsy that correlate well with the described phenotype. SCN1A gene variants were found in five unrelated patients, while ALDH7A1 and KCNQ2 gene variants were found twice. In the other 19 genes, variants were found only in a single patient. This includes genes such as ASH1L, CSNK2B, RHOBTB2, and SLC13A5, which have only recently been associated with epilepsy. Almost half of diagnosed patients (46.4%) carried novel variants. Interestingly, we identified variants in ALDH7A1, KCNQ2, PNPO, SCN1A, and SCN2A resulting in gene-directed therapy decisions for 11 children from our study, including four children who all carried novel SCN1A genetic variants.

CONCLUSIONS

Described novel variants will contribute to a better understanding of the European genetic landscape, while insights into the genotype-phenotype correlation will contribute to a better understanding of childhood epilepsies worldwide. Given the expansion of molecular-based approaches, each newly identified genetic variant could become a potential therapeutic target.

Exosome theranostics: Comparative analysis of P body and exosome proteins and their mutations for clinical applications

Exosomes are lipid-bilayered vesicles, originating from early endosomes that capture cellular proteins and genetic materials to form multi-vesicular bodies. These exosomes are secreted into extracellular fluids such as cerebrospinal fluid, blood, urine, and cell culture supernatants. They play a key role in intercellular communication by carrying active molecules like lipids, cytokines, growth factors, metabolites, proteins, and RNAs. Recently, the potential of exosomal delivery for therapeutic purposes has been explored due to their low immunogenicity, nano-scale size, and ability to cross cellular barriers. This review comprehensively examines the biogenesis of exosomes, their isolation techniques, and their diverse applications in theranostics. We delve into the mechanisms and methods for loading exosomes with mRNA, miRNA, proteins, and drugs, highlighting their transformative role in delivering therapeutic payloads. Additionally, the utility of exosomes in stem cell therapy is discussed, showcasing their potential in regenerative medicine. Insights into exosome cargo using pre- or post-loading techniques are critical for exosome theranostics. We review exosome databases such as ExoCarta, Expedia, and ExoBCD, which document exosome cargo. From these databases, we identified 25 proteins common to both exosomes and P-bodies, known for mutations in the COSMIC database. Exosome databases do not integrate with mutation analysis programs; hence, we performed mutation analysis using additional databases. Accounting for the mutation status of parental cells and exosomal cargo is crucial in exosome theranostics. This review provides a comprehensive report on exosome databases, proteins common to exosomes and P-bodies, and their mutation analysis, along with the latest studies on exosome-engineered theranostics.

PHACTboost: A Phylogeny-Aware Pathogenicity Predictor for Missense Mutations via Boosting

Most algorithms that are used to predict the effects of variants rely on evolutionary conservation. However, a majority of such techniques compute evolutionary conservation by solely using the alignment of multiple sequences while overlooking the evolutionary context of substitution events. We had introduced PHACT, a scoring-based pathogenicity predictor for missense mutations that can leverage phylogenetic trees, in our previous study. By building on this foundation, we now propose PHACTboost, a gradient boosting tree-based classifier that combines PHACT scores with information from multiple sequence alignments, phylogenetic trees, and ancestral reconstruction. By learning from data, PHACTboost outperforms PHACT. Furthermore, the results of comprehensive experiments on carefully constructed sets of variants demonstrated that PHACTboost can outperform 40 prevalent pathogenicity predictors reported in the dbNSFP, including conventional tools, metapredictors, and deep learning-based approaches as well as more recent tools such as AlphaMissense, EVE, and CPT-1. The superiority of PHACTboost over these methods was particularly evident in case of hard variants for which different pathogenicity predictors offered conflicting results. We provide predictions of 215 million amino acid alterations over 20,191 proteins. PHACTboost is available at https://github.com/CompGenomeLab/PHACTboost. PHACTboost can improve our understanding of genetic diseases and facilitate more accurate diagnoses.

Evaluating predictors of kinase activity of STK11 variants identified in primary human non-small cell lung cancers

Critical evaluation of computational tools for predicting variant effects is important considering their increased use in disease diagnosis and driving molecular discoveries. In the sixth edition of the Critical Assessment of Genome Interpretation (CAGI) challenge, a dataset of 28 STK11 rare variants (27 missense, 1 single amino acid deletion), identified in primary non-small cell lung cancer biopsies, was experimentally assayed to characterize computational methods from four participating teams and five publicly available tools. Predictors demonstrated a high level of performance on key evaluation metrics, measuring correlation with the assay outputs and separating loss-of-function (LoF) variants from wildtype-like (WT-like) variants. The best participant model, 3Cnet, performed competitively with well-known tools. Unique to this challenge was that the functional data was generated with both biological and technical replicates, thus allowing the assessors to realistically establish maximum predictive performance based on experimental variability. Three out of the five publicly available tools and 3Cnet approached the performance of the assay replicates in separating LoF variants from WT-like variants. Surprisingly, REVEL, an often-used model, achieved a comparable correlation with the real-valued assay output as that seen for the experimental replicates. Performing variant interpretation by combining the new functional evidence with computational and population data evidence led to 16 new variants receiving a clinically actionable classification of likely pathogenic (LP) or likely benign (LB). Overall, the STK11 challenge highlights the utility of variant effect predictors in biomedical sciences and provides encouraging results for driving research in the field of computational genome interpretation.

Review: Cancer and neurodevelopmental disorders: multi-scale reasoning and computational guide

The connection and causality between cancer and neurodevelopmental disorders have been puzzling. How can the same cellular pathways, proteins, and mutations lead to pathologies with vastly different clinical presentations? And why do individuals with neurodevelopmental disorders, such as autism and schizophrenia, face higher chances of cancer emerging throughout their lifetime? Our broad review emphasizes the multi-scale aspect of this type of reasoning. As these examples demonstrate, rather than focusing on a specific organ system or disease, we aim at the new understanding that can be gained. Within this framework, our review calls attention to computational strategies which can be powerful in discovering connections, causalities, predicting clinical outcomes, and are vital for drug discovery. Thus, rather than centering on the clinical features, we draw on the rapidly increasing data on the molecular level, including mutations, isoforms, three-dimensional structures, and expression levels of the respective disease-associated genes. Their integrated analysis, together with chromatin states, can delineate how, despite being connected, neurodevelopmental disorders and cancer differ, and how the same mutations can lead to different clinical symptoms. Here, we seek to uncover the emerging connection between cancer, including pediatric tumors, and neurodevelopmental disorders, and the tantalizing questions that this connection raises.

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

Nonsense and missense mutations in the transcription factor PAX6 cause a wide range of eye development defects, including aniridia, microphthalmia and coloboma. To understand how changes of PAX6:DNA binding cause these phenotypes, we combined saturation mutagenesis of the paired domain of PAX6 with a yeast one-hybrid (Y1H) assay in which expression of a PAX6-GAL4 fusion gene drives antibiotic resistance. We quantified binding of more than 2700 single amino-acid variants to two DNA sequence elements. Mutations in DNA-facing residues of the N-terminal subdomain and linker region were most detrimental, as were mutations to prolines and to negatively charged residues. Many variants caused sequence-specific molecular gain-of-function effects, including variants in position 71 that increased binding to the LE9 enhancer but decreased binding to a SELEX-derived binding site. In the absence of antibiotic selection, variants that retained DNA binding slowed yeast growth, likely because such variants perturbed the yeast transcriptome. Benchmarking against known patient variants and applying ACMG/AMP guidelines to variant classification, we obtained supporting-to-moderate evidence that 977 variants are likely pathogenic and 1306 are likely benign. Our analysis shows that most pathogenic mutations in the paired domain of PAX6 can be explained simply by the effects of these mutations on PAX6:DNA association, and establishes Y1H as a generalisable assay for the interpretation of variant effects in transcription factors.

Benchmarking computational variant effect predictors by their ability to infer human traits

BACKGROUND

Computational variant effect predictors offer a scalable and increasingly reliable means of interpreting human genetic variation, but concerns of circularity and bias have limited previous methods for evaluating and comparing predictors. Population-level cohorts of genotyped and phenotyped participants that have not been used in predictor training can facilitate an unbiased benchmarking of available methods. Using a curated set of human gene-trait associations with a reported rare-variant burden association, we evaluate the correlations of 24 computational variant effect predictors with associated human traits in the UK Biobank and All of Us cohorts.

RESULTS

AlphaMissense outperformed all other predictors in inferring human traits based on rare missense variants in UK Biobank and All of Us participants. The overall rankings of computational variant effect predictors in these two cohorts showed a significant positive correlation.

CONCLUSION

We describe a method to assess computational variant effect predictors that sidesteps the limitations of previous evaluations. This approach is generalizable to future predictors and could continue to inform predictor choice for personal and clinical genetics.

Structural dynamics of clinically-reported VUS in the BARD1 ARD-BRCT region to predict the molecular basis of alterations

The functional domains of BARD1, comprise the Ankyrin Repeat Domain (ARD), C-Terminal domains (BRCTs), and a linker between ARD and the BRCTs, which are known to bind to Cleavage stimulation Factor complex-subunit of 50 kDa (CstF-50). The pathogenic mutation Q564H in the BARD1 ARD-linker-BRCT region has been reported to abrogate the binding between BARD1 and CstF-50. Intermediate penetrance variants of BARD1 are associated with the occurrence of breast cancer. Therefore, seven missense variants of unknown significance (VUS), L447V, P454L, N470S, V507M, I509T, C557S, and Q564H of BARD1, reported in the ARD domain and the linker region were evaluated via molecular dynamics (MD) simulations. The mutants revealed statistically significantly different distributions of RMSD (root mean square deviation), residuewise RMSF (root mean square fluctuation), Rg (radius of gyration), SASA (solvent accessible surface area), and COM (centre of mass)-to-COM distance between the ARD and the BRCT repeat, between the wild type and each mutant. The secondary structural composition of the mutants was slightly altered relative to that of the wild type. However, the reported in-silico based prediction require further validation using in-vitro, biophysical and structure-based approachCommunicated by Ramaswamy H. Sarma.

Characterization of a novel TFG variant causing autosomal recessive pure hereditary spastic paraplegia

OBJECTIVE

TFG mutations have previously been implicated in autosomal recessive hereditary spastic paraplegia (HSP), also known as SPG57. This study aimed to investigate the clinical and molecular features of TFG mutations in a Taiwanese HSP cohort.

METHODS

Genetic analysis of TFG was conducted in 242 unrelated Taiwanese HSP patients using a targeted resequencing panel covering the entire coding regions of TFG. Functional assays were performed using an in vitro cell model to assess the impact of TFG variants on protein function. Additionally, other representative TFG mutant proteins were examined to understand the broader implications of TFG mutations in HSP.

RESULTS

The study identified a novel homozygous TFG c.177A>C (p.(Lys59Asn)) variant in a family with adolescent-onset, pure form HSP. Functional analysis revealed that the Lys59Asn TFG variant, similar to other HSP-associated TFG mutants, exhibited a low affinity between TFG monomers and abnormal assembly of TFG homo-oligomers. These structural alterations led to aberrant intracellular distribution, compromising TFG's protein secretion function and resulting in decreased cellular viability.

INTERPRETATION

These findings confirm that the homozygous TFG c.177A>C (p.(Lys59Asn)) variant is a novel cause of SPG57. The study expands our understanding of the clinical and mutational spectrum of TFG-associated diseases, highlighting the functional defects associated with this specific TFG variant. Overall, this research contributes to the broader comprehension of the genetic and molecular mechanisms underlying HSP.

A novel target-site mutation (H146Q) outside the ubiquinone binding site of succinate dehydrogenase confers high levels of resistance to cyflumetofen and pyflubumide in Tetranychus urticae

Mitochondrial electron transfer inhibitors at complex II (METI-II), also referred to as succinate dehydrogenase inhibitors (SDHI), represent a recently developed class of acaricides encompassing cyflumetofen, cyenopyrafen, pyflubumide and cyetpyrafen. Despite their novelty, resistance has already developed in the target pest, Tetranychus urticae. In this study a new mutation, H146Q in a highly conserved region of subunit B of complex II, was identified in a T. urticae population resistant to all METI-IIs. In contrast to previously described mutations, H146Q is located outside the ubiquinone binding site of complex II. Marker-assisted backcrossing of this mutation in a susceptible genetic background validated its association with resistance to cyflumetofen and pyflubumide, but not cyenopyrafen or cyetpyrafen. Biochemical assays and the construction of inhibition curves with isolated mitochondria corroborated this selectivity. In addition, phenotypic effects of H146Q, together with the previously described H258L, were further examined via CRISPR/Cas9 gene editing. Although both mutations were successfully introduced into a susceptible T. urticae population, the H146Q gene editing event was only recovered in individuals already harboring the I260V mutation, known to confer resistance towards cyflumetofen. The combination of H146Q + I260V conferred high resistance levels to all METI-II acaricides with LC50 values over 5000 mg a.i./L for cyflumetofen and pyflubumide. Similarly, the introduction of H258L via gene editing resulted in high resistance levels to all tested acaricides, with extreme LC50 values (>5000 mg a.i./L) for cyenopyrafen and cyetpyrafen, but lower resistance levels for pyflubumide and cyflumetofen. Together, these findings indicate that different mutations result in a different cross-resistance spectrum, probably also reflecting subtle differences in the binding mode of complex II acaricides.

Computational exploration of SLC14A1 genetic variants through structure modeling, protein-ligand docking, and molecular dynamics simulation

The urea transporter UT-B1, encoded by the SLC14A1 gene, has been hypothesized to be a significant protein whose deficiency and dysfunction contribute to the pathogenesis of bladder cancer and many other diseases. Several studies reported the association of genetic alterations in the SLC14A1 (UT-B1) gene with bladder carcinogenesis, suggesting a need for thorough characterization of the UT-B1 protein's coding and non-coding variants. This study used various computational techniques to investigate the commonly occurring germ-line missense and non-coding SNPs (ncSNPs) of the SLC14A1 gene (UT-B1) for their structural, functional, and molecular implications for disease susceptibility and dysfunctionality. SLC14A1 missense variants, primarily identified from the ENSEMBL genome browser, were screened through twelve functionality prediction tools leading to two variants D280Y (predicted detrimental by maximum tools) and D280N (high global MAF) for rs1058396. Subsequently, the ConSurf and NetSurf tools revealed the D280 residue to be in a variable site and exposed on the protein surface. According to I-Mutant2.0 and MUpro, both variants are predicted to cause a significant effect on protein stability. Analysis of molecular docking anticipated these two variants to decrease the binding affinity of UT-B1 protein for the examined ligands to a significant extent. Molecular dynamics also disclosed the possible destabilization of the UT-B1 protein due to single nucleotide polymorphism compared to wild-type protein which may result in impaired protein function. Furthermore, several non-coding SNPs were estimated to affect transcription factor binding and regulation of SLC14A1 gene expression. Additionally, two ncSNPs were found to affect miRNA-based post-transcriptional regulation by creating new seed regions for miRNA binding. This comprehensive in-silico study of SLC14A1 gene variants may serve as a springboard for future large-scale investigations examining SLC14A1 polymorphisms.

Variant Impact Predictor database (VIPdb), version 2: Trends from 25 years of genetic variant impact predictors

Background

Variant interpretation is essential for identifying patients' disease-causing genetic variants amongst the millions detected in their genomes. Hundreds of Variant Impact Predictors (VIPs), also known as Variant Effect Predictors (VEPs), have been developed for this purpose, with a variety of methodologies and goals. To facilitate the exploration of available VIP options, we have created the Variant Impact Predictor database (VIPdb).

Results

The Variant Impact Predictor database (VIPdb) version 2 presents a collection of VIPs developed over the past 25 years, summarizing their characteristics, ClinGen calibrated scores, CAGI assessment results, publication details, access information, and citation patterns. We previously summarized 217 VIPs and their features in VIPdb in 2019. Building upon this foundation, we identified and categorized an additional 186 VIPs, resulting in a total of 403 VIPs in VIPdb version 2. The majority of the VIPs have the capacity to predict the impacts of single nucleotide variants and nonsynonymous variants. More VIPs tailored to predict the impacts of insertions and deletions have been developed since the 2010s. In contrast, relatively few VIPs are dedicated to the prediction of splicing, structural, synonymous, and regulatory variants. The increasing rate of citations to VIPs reflects the ongoing growth in their use, and the evolving trends in citations reveal development in the field and individual methods.

Conclusions

VIPdb version 2 summarizes 403 VIPs and their features, potentially facilitating VIP exploration for various variant interpretation applications.

Availability

VIPdb version 2 is available at https://genomeinterpretation.org/vipdb.

EDA Missense Variant in a Cat with X-Linked Hypohidrotic Ectodermal Dysplasia

Hypohidrotic ectodermal dysplasia is a developmental defect characterized by sparse or absent hair, missing or malformed teeth and defects in eccrine glands. Loss-of-function variants in the X-chromosomal EDA gene have been reported to cause hypohidrotic ectodermal dysplasia in humans, mice, dogs and cattle. We investigated a male cat exhibiting diffuse truncal alopecia with a completely absent undercoat. The cat lacked several teeth, and the remaining teeth had an abnormal conical shape. Whole-genome sequencing revealed a hemizygous missense variant in the EDA gene, XM_011291781.3:c.1042G>A or XP_011290083.1:p.(Ala348Thr). The predicted amino acid exchange is located in the C-terminal TNF signaling domain of the encoded ectodysplasin. The corresponding missense variant in the human EDA gene, p.Ala349Thr, has been reported as a recurring pathogenic variant in several human patients with X-linked hypohidrotic ectodermal dysplasia. The identified feline variant therefore represents the likely cause of the hypohidrotic ectodermal dysplasia in the investigated cat, and the genetic investigation confirmed the suspected clinical diagnosis. This is the first report of an EDA-related hypohidrotic ectodermal dysplasia in cats.

Another face of RASA1: Report of familial germline variant in RASA1 with dysmorphic features

RASopathies encompass a diverse set of disorders affecting genes that encode proteins within the RAS-MAPK pathway. RASA1 mutations are the cause of an autosomal dominant disorder called capillary malformation-arteriovenous malformation type 1 (CM-AVM1). Unlike other RASopathies, facial dysmorphism has not been described in these patients. We phenotypically delineated a large family of individuals with multifocal fast-flow capillary malformations, severe lymphatic anomalies of perinatal onset, and dysmorphic features not previously described. Sequencing studies were performed on probands and related family members, confirming the segregation of dysmorphic features in affected members of a novel heterozygous variant in RASA1 (NM_002890.3:c.2366G>A, p.(Arg789Gln)). In this work, we broaden the phenotypic spectrum of CM-AVM type 1 and propose a new RASA1 variant as likely pathogenic.

Implications of a De Novo Variant in the SOX12 Gene in a Patient with Generalized Epilepsy, Intellectual Disability, and Childhood Emotional Behavioral Disorders

SRY-box transcription factor (SOX) genes, a recently discovered gene family, play crucial roles in the regulation of neuronal stem cell proliferation and glial differentiation during nervous system development and neurogenesis. Whole exome sequencing (WES) in patients presenting with generalized epilepsy, intellectual disability, and childhood emotional behavioral disorder, uncovered a de novo variation within SOX12 gene. Notably, this gene has never been associated with neurodevelopmental disorders. No variants in known genes linked with the patient's symptoms have been detected by the WES Trio analysis. To date, any MIM phenotype number associated with intellectual developmental disorder has not been assigned for SOX12. In contrast, both SOX4 and SOX11 genes within the same C group (SoxC) of the Sox gene family have been associated with neurodevelopmental disorders. The variant identified in the patient here described was situated within the critical high-mobility group (HMG) functional site of the SOX12 protein. This domain, in the Sox protein family, is essential for DNA binding and bending, as well as being responsible for transcriptional activation or repression during the early stages of gene expression. Sequence alignment within SoxC (SOX12, SOX4 and SOX11) revealed a high conservation rate of the HMG region. The in silico predictive analysis described this novel variant as likely pathogenic. Furthermore, the mutated protein structure predictions unveiled notable changes with potential deleterious effects on the protein structure. The aim of this study is to establish a correlation between the SOX12 gene and the symptoms diagnosed in the patient.

Deciphering the etiology of undiagnosed ocular anomalies along with systemic alterations in pediatric patients through whole exome sequencing

Inherited and developmental eye diseases are quite diverse and numerous, and determining their genetic cause is challenging due to their high allelic and locus heterogeneity. New molecular approaches, such as whole exome sequencing (WES), have proven to be powerful molecular tools for addressing these cases. The present study used WES to identify the genetic etiology in ten unrelated Mexican pediatric patients with complex ocular anomalies and other systemic alterations of unknown etiology. The WES approach allowed us to identify five clinically relevant variants in the GZF1, NFIX, TRRAP, FGFR2 and PAX2 genes associated with Larsen, Malan, developmental delay with or without dysmorphic facies and autism, LADD1 and papillorenal syndromes. Mutations located in GZF1 and NFIX were classified as pathogenic, those in TRRAP and FGFR2 were classified as likely pathogenic variants, and those in PAX2 were classified as variants of unknown significance. Protein modeling of the two missense FGFR2 p.(Arg210Gln) and PAX2 p.(Met3Thr) variants showed that these changes could induce potential structural alterations in important functional regions of the proteins. Notably, four out of the five variants were not previously reported, except for the TRRAP gene. Consequently, WES enabled the identification of the genetic cause in 40% of the cases reported. All the syndromes reported herein are very rare, with phenotypes that may overlap with other genetic entities.

Predicting human and viral protein variants affecting COVID-19 susceptibility and repurposing therapeutics

The COVID-19 disease is an ongoing global health concern. Although vaccination provides some protection, people are still susceptible to re-infection. Ostensibly, certain populations or clinical groups may be more vulnerable. Factors causing these differences are unclear and whilst socioeconomic and cultural differences are likely to be important, human genetic factors could influence susceptibility. Experimental studies indicate SARS-CoV-2 uses innate immune suppression as a strategy to speed-up entry and replication into the host cell. Therefore, it is necessary to understand the impact of variants in immunity-associated human proteins on susceptibility to COVID-19. In this work, we analysed missense coding variants in several SARS-CoV-2 proteins and their human protein interactors that could enhance binding affinity to SARS-CoV-2. We curated a dataset of 19 SARS-CoV-2: human protein 3D-complexes, from the experimentally determined structures in the Protein Data Bank and models built using AlphaFold2-multimer, and analysed the impact of missense variants occurring in the protein-protein interface region. We analysed 468 missense variants from human proteins and 212 variants from SARS-CoV-2 proteins and computationally predicted their impacts on binding affinities for the human viral protein complexes. We predicted a total of 26 affinity-enhancing variants from 13 human proteins implicated in increased binding affinity to SARS-CoV-2. These include key-immunity associated genes (TOMM70, ISG15, IFIH1, IFIT2, RPS3, PALS1, NUP98, AXL, ARF6, TRIMM, TRIM25) as well as important spike receptors (KREMEN1, AXL and ACE2). We report both common (e.g., Y13N in IFIH1) and rare variants in these proteins and discuss their likely structural and functional impact, using information on known and predicted functional sites. Potential mechanisms associated with immune suppression implicated by these variants are discussed. Occurrence of certain predicted affinity-enhancing variants should be monitored as they could lead to increased susceptibility and reduced immune response to SARS-CoV-2 infection in individuals/populations carrying them. Our analyses aid in understanding the potential impact of genetic variation in immunity-associated proteins on COVID-19 susceptibility and help guide drug-repurposing strategies.

Assessing the Performance of In silico Tools and Molecular Dynamics Simulations for Predicting Pharmacogenetic Variant Impact

The ability of freely available in silico tools to predict the effect of non-synonymous single nucleotide polymorphisms (nsSNPs) in pharmacogenes on protein function is not well defined. We assessed the performance of seven sequence-based (SIFT, PolyPhen2, mutation accessor, FATHMM, PhD-SNP, MutPred2, and SNPs & Go) and five structure-based (mCSM, SDM, DDGun, CupSat, and MAESTROweb) tools in predicting the impact of 118 nsSNPs in the CYP2C19, CYP2C9, CYP2B6, CYP2D6, and DPYD genes with known function (24 normal, one increased, 42 decreased, and 51 no-function). Sequence-based tools had a higher median (IQR) positive predictive value (89% [89-94%] vs. 12% [10-15%], P < 0.001) and lower negative predictive value (30% [24-34%] vs. 90% [80-93%], P < 0.001) than structure-based tools. Accuracy did not significantly differ between sequence-based (59% [37-67%]) and structure-based (34% [23-44%]) tools (P = 0.070). Notably, the no-function CYP2C9*3 allele and decreased function CYP2C9*8 allele were predicted incorrectly as tolerated by 100% of sequenced-based tools and as stabilizing by 60% and 20% of structure-based tools, respectively. As a case study, we performed mutational analysis for the CYP2C9*1, *3 (I359L), and *8 (R150H) proteins through molecular dynamic (MD) simulations using S-warfarin as the substrate. The I359L variant increased the distance of the major metabolic site of S-warfarin to the oxy-ferryl center of CYP2C9, and I359L and R150H caused shifts in the conformation of S-warfarin to a position less favorable for metabolism. These data suggest that MD simulations may better capture the impact of nsSNPs in pharmacogenes than other tools.

Evaluation of enzyme activity predictions for variants of unknown significance in Arylsulfatase A

Continued advances in variant effect prediction are necessary to demonstrate the ability of machine learning methods to accurately determine the clinical impact of variants of unknown significance (VUS). Towards this goal, the ARSA Critical Assessment of Genome Interpretation (CAGI) challenge was designed to characterize progress by utilizing 219 experimentally assayed missense VUS in the Arylsulfatase A (ARSA) gene to assess the performance of community-submitted predictions of variant functional effects. The challenge involved 15 teams, and evaluated additional predictions from established and recently released models. Notably, a model developed by participants of a genetics and coding bootcamp, trained with standard machine-learning tools in Python, demonstrated superior performance among submissions. Furthermore, the study observed that state-of-the-art deep learning methods provided small but statistically significant improvement in predictive performance compared to less elaborate techniques. These findings underscore the utility of variant effect prediction, and the potential for models trained with modest resources to accurately classify VUS in genetic and clinical research.

SLC10A1 rs2296651 variant (S267F mutation) predicts biochemical traits, hepatitis B virus infection susceptibility and the risk of gallstone disease

Sodium taurocholate co-transporting polypeptide (NTCP), a bile acid transporter, plays a crucial role in regulating bile acid levels and influencing the risk of HBV infection. Genetic variations in the SLC10A1 gene, which encodes NTCP, affect these functions. However, the impact of SLC10A1 gene variants on the metabolic and biochemical traits remained unclear. We aimed to investigate the association of SLC10A1 gene variants with the clinical and biochemical parameters, and the risk of different HBV infection statuses and gallstone disease in the Taiwanese population. Genotyping data from 117,679 Taiwan Biobank participants were analyzed using the Axiom genome-wide CHB arrays. Regional-plot association analysis demonstrated genome-wide significant association between the SLC10A1 rs2296651 genotypes and lipid profile, gamma glutamyl transferase (γGT) level and anti-HBc-positivity. Genotype-phenotype association analyses revealed significantly lower total cholesterol, low-density lipoprotein (LDL) cholesterol and uric acid levels, a higher γGT level and a higher gallstone incidence in rare rs2296651-A allele carrier. Participants with the rs2296651 AA-genotype exhibited significantly lower rates of anti-HBc-positivity and HBsAg-positivity. Compared to those with the GG-genotype, individuals with non-GG-genotypes had reduced risks for various HBV infection statuses: the AA-genotype showed substantially lower risks, while the GA-genotype demonstrated modestly lower risks. Predictive tools also suggested that the rs2296651 variant potentially induced protein damage and pathogenic effects. In conclusion, our data revealed pleiotropic effects of the SLC10A1 rs2296651 genotypes on the levels of biochemical traits and the risk of HBV infection and gallstone disease. This confirms SLC10A1's versatility and implicates its genotypes in predicting both biochemical traits and disease susceptibility.

Critical assessment of missense variant effect predictors on disease-relevant variant data

Regular, systematic, and independent assessment of computational tools used to predict the pathogenicity of missense variants is necessary to evaluate their clinical and research utility and suggest directions for future improvement. Here, as part of the sixth edition of the Critical Assessment of Genome Interpretation (CAGI) challenge, we assess missense variant effect predictors (or variant impact predictors) on an evaluation dataset of rare missense variants from disease-relevant databases. Our assessment evaluates predictors submitted to the CAGI6 Annotate-All-Missense challenge, predictors commonly used by the clinical genetics community, and recently developed deep learning methods for variant effect prediction. To explore a variety of settings that are relevant for different clinical and research applications, we assess performance within different subsets of the evaluation data and within high-specificity and high-sensitivity regimes. We find strong performance of many predictors across multiple settings. Meta-predictors tend to outperform their constituent individual predictors; however, several individual predictors have performance similar to that of commonly used meta-predictors. The relative performance of predictors differs in high-specificity and high-sensitivity regimes, suggesting that different methods may be best suited to different use cases. We also characterize two potential sources of bias. Predictors that incorporate allele frequency as a predictive feature tend to have reduced performance when distinguishing pathogenic variants from very rare benign variants, and predictors supervised on pathogenicity labels from curated variant databases often learn label imbalances within genes. Overall, we find notable advances over the oldest and most cited missense variant effect predictors and continued improvements among the most recently developed tools, and the CAGI Annotate-All-Missense challenge (also termed the Missense Marathon) will continue to assess state-of-the-art methods as the field progresses. Together, our results help illuminate the current clinical and research utility of missense variant effect predictors and identify potential areas for future development.

APF2: an improved ensemble method for pharmacogenomic variant effect prediction

Lack of efficacy or adverse drug response are common phenomena in pharmacological therapy causing considerable morbidity and mortality. It is estimated that 20-30% of this variability in drug response stems from variations in genes encoding drug targets or factors involved in drug disposition. Leveraging such pharmacogenomic information for the preemptive identification of patients who would benefit from dose adjustments or alternative medications thus constitutes an important frontier of precision medicine. Computational methods can be used to predict the functional effects of variant of unknown significance. However, their performance on pharmacogenomic variant data has been lackluster. To overcome this limitation, we previously developed an ensemble classifier, termed APF, specifically designed for pharmacogenomic variant prediction. Here, we aimed to further improve predictions by leveraging recent key advances in the prediction of protein folding based on deep neural networks. Benchmarking of 28 variant effect predictors on 530 pharmacogenetic missense variants revealed that structural predictions using AlphaMissense were most specific, whereas APF exhibited the most balanced performance. We then developed a new tool, APF2, by optimizing algorithm parametrization of the top performing algorithms for pharmacogenomic variations and aggregating their predictions into a unified ensemble score. Importantly, APF2 provides quantitative variant effect estimates that correlate well with experimental results (R2 = 0.91, p = 0.003) and predicts the functional impact of pharmacogenomic variants with higher accuracy than previous methods, particularly for clinically relevant variations with actionable pharmacogenomic guidelines. We furthermore demonstrate better performance (92% accuracy) on an independent test set of 146 variants across 61 pharmacogenes not used for model training or validation. Application of APF2 to population-scale sequencing data from over 800,000 individuals revealed drastic ethnogeographic differences with important implications for pharmacotherapy. We thus think that APF2 holds the potential to improve the translation of genetic information into pharmacogenetic recommendations, thereby facilitating the use of Next-Generation Sequencing data for stratified medicine.

In Silico Characterization of Pathogenic Homeodomain Missense Mutations in the PITX2 Gene

Paired homologous domain transcription factor 2 (PITX2) is critically involved in ocular and cardiac development. Mutations in PITX2 are consistently reported in association with Axenfeld-Rieger syndrome, an autosomal dominant genetic disorder and atrial fibrillation, a common cardiac arrhythmia. In this study, we have mined missense mutations in PITX2 gene from NCBI-dbSNP and Ensembl databases, evaluated the pathogenicity of the missense variants in the homeodomain and C-terminal region using five in silico prediction tools SIFT, PolyPhen2, GERP, Mutation Assessor and CADD. Fifteen homeodomain mutations G42V, G42R, R45W, S49Y, R53W, E53D, E55V, R62H, P65S, R69H, G75R, R84G, R86K, R87W, R91P were found to be highly pathogenic by both SIFT, PolyPhen2 were further functionally characterized using I-Mutant 2.0, Consurf, MutPred and Project Hope. The findings of the study can be used for prioritizing mutations in the context of genetic studies.

Structure-based network analysis predicts pathogenic variants in human proteins associated with inherited retinal disease

Advances in gene sequencing technologies have accelerated the identification of genetic variants, but better tools are needed to understand which are causal of disease. This would be particularly useful in fields where gene therapy is a potential therapeutic modality for a disease-causing variant such as inherited retinal disease (IRD). Here, we apply structure-based network analysis (SBNA), which has been successfully utilized to identify variant-constrained amino acid residues in viral proteins, to identify residues that may cause IRD if subject to missense mutation. SBNA is based entirely on structural first principles and is not fit to specific outcome data, which makes it distinct from other contemporary missense prediction tools. In 4 well-studied human disease-associated proteins (BRCA1, HRAS, PTEN, and ERK2) with high-quality structural data, we find that SBNA scores correlate strongly with deep mutagenesis data. When applied to 47 IRD genes with available high-quality crystal structure data, SBNA scores reliably identified disease-causing variants according to phenotype definitions from the ClinVar database. Finally, we applied this approach to 63 patients at Massachusetts Eye and Ear (MEE) with IRD but for whom no genetic cause had been identified. Untrained models built using SBNA scores and BLOSUM62 scores for IRD-associated genes successfully predicted the pathogenicity of novel variants (AUC = 0.851), allowing us to identify likely causative disease variants in 40 IRD patients. Model performance was further augmented by incorporating orthogonal data from EVE scores (AUC = 0.927), which are based on evolutionary multiple sequence alignments. In conclusion, SBNA can used to successfully identify variants as causal of disease in human proteins and may help predict variants causative of IRD in an unbiased fashion.

The First Genetic Characterization of the SPRN Gene in Pekin Ducks (Anas platyrhynchos domesticus)

Prion diseases are fatal neurodegenerative disorders characterized by an accumulation of misfolded prion protein (PrPSc) in brain tissues. The shadow of prion protein (Sho) encoded by the shadow of prion protein gene (SPRN) is involved in prion disease progress. The interaction between Sho and PrP accelerates the PrPSc conversion rate while the SPRN gene polymorphisms have been associated with prion disease susceptibility in several species. Until now, the SPRN gene has not been investigated in ducks. We identified the duck SPRN gene sequence and investigated the genetic polymorphisms of 184 Pekin ducks. We compared the duck SPRN nucleotide sequence and the duck Sho protein amino acid sequence with those of several other species. Finally, we predicted the duck Sho protein structure and the effects of non-synonymous single nucleotide polymorphisms (SNPs) using computational programs. We were the first to report the Pekin duck SPRN gene sequence. The duck Sho protein sequence showed 100% identity compared with the chicken Sho protein sequence. We found 27 novel SNPs in the duck SPRN gene. Four amino acid substitutions were predicted to affect the hydrogen bond distribution in the duck Sho protein structure. Although MutPred2 and SNPs&GO predicted that all non-synonymous polymorphisms were neutral or benign, SIFT predicted that four variants, A22T, G49D, A68T, and M105I, were deleterious. To the best of our knowledge, this is the first report about the genetic and structural characteristics of the duck SPRN gene.

An In Silico Analysis of Genetic Variants and Structural Modeling of the Human Frataxin Protein in Friedreich's Ataxia

Friedreich's Ataxia (FRDA) stands out as the most prevalent form of hereditary ataxias, marked by progressive movement ataxia, loss of vibratory sensitivity, and skeletal deformities, severely affecting daily functioning. To date, the only medication available for treating FRDA is Omaveloxolone (Skyclarys®), recently approved by the FDA. Missense mutations within the human frataxin (FXN) gene, responsible for intracellular iron homeostasis regulation, are linked to FRDA development. These mutations induce FXN dysfunction, fostering mitochondrial iron accumulation and heightened oxidative stress, ultimately triggering neuronal cell death pathways. This study amalgamated 226 FXN genetic variants from the literature and database searches, with only 18 previously characterized. Predictive analyses revealed a notable prevalence of detrimental and destabilizing predictions for FXN mutations, predominantly impacting conserved residues crucial for protein function. Additionally, an accurate, comprehensive three-dimensional model of human FXN was constructed, serving as the basis for generating genetic variants I154F and W155R. These variants, selected for their severe clinical implications, underwent molecular dynamics (MD) simulations, unveiling flexibility and essential dynamic alterations in their N-terminal segments, encompassing FXN42, FXN56, and FXN78 domains pivotal for protein maturation. Thus, our findings indicate potential interaction profile disturbances in the FXN42, FXN56, and FXN78 domains induced by I154F and W155R mutations, aligning with the existing literature.

Genome-wide analyses reveals an association between invasive urothelial carcinoma in the Shetland sheepdog and NIPAL1

Naturally occurring canine invasive urinary carcinoma (iUC) closely resembles human muscle invasive bladder cancer in terms of histopathology, metastases, response to therapy, and low survival rate. The heterogeneous nature of the disease has led to the association of large numbers of risk loci in humans, however most are of small effect. There exists a need for new and accurate animal models of invasive bladder cancer. In dogs, distinct breeds show markedly different rates of iUC, thus presenting an opportunity to identify additional risk factors and overcome the locus heterogeneity encountered in human mapping studies. In the association study presented here, inclusive of 100 Shetland sheepdogs and 58 dogs of other breeds, we identify a homozygous protein altering point mutation within the NIPAL1 gene which increases risk by eight-fold (OR = 8.42, CI = 3.12-22.71), accounting for nearly 30% of iUC risk in the Shetland sheepdog. Inclusion of six additional loci accounts for most of the disease risk in the breed and explains nearly 75% of the phenotypes in this study. When combined with sequence data from tumors, we show that variation in the MAPK signaling pathway is an overarching cause of iUC susceptibility in dogs.

Learning peptide properties with positive examples only

Deep learning can create accurate predictive models by exploiting existing large-scale experimental data, and guide the design of molecules. However, a major barrier is the requirement of both positive and negative examples in the classical supervised learning frameworks. Notably, most peptide databases come with missing information and low number of observations on negative examples, as such sequences are hard to obtain using high-throughput screening methods. To address this challenge, we solely exploit the limited known positive examples in a semi-supervised setting, and discover peptide sequences that are likely to map to certain antimicrobial properties via positive-unlabeled learning (PU). In particular, we use the two learning strategies of adapting base classifier and reliable negative identification to build deep learning models for inferring solubility, hemolysis, binding against SHP-2, and non-fouling activity of peptides, given their sequence. We evaluate the predictive performance of our PU learning method and show that by only using the positive data, it can achieve competitive performance when compared with the classical positive-negative (PN) classification approach, where there is access to both positive and negative examples.

Predicting hotspots for disease-causing single nucleotide variants using sequences-based coevolution, network analysis, and machine learning

To enable personalized medicine, it is important yet highly challenging to accurately predict disease-causing mutations in target proteins at high throughput. Previous computational methods have been developed using evolutionary information in combination with various biochemical and structural features of protein residues to discriminate neutral vs. deleterious mutations. However, the power of these methods is often limited because they either assume known protein structures or treat residues independently without fully considering their interactions. To address the above limitations, we build upon recent progress in machine learning, network analysis, and protein language models, and develop a sequences-based variant site prediction workflow based on the protein residue contact networks: 1. We employ and integrate various methods of building protein residue networks using state-of-the-art coevolution analysis tools (RaptorX, DeepMetaPSICOV, and SPOT-Contact) powered by deep learning. 2. We use machine learning algorithms (Random Forest, Gradient Boosting, and Extreme Gradient Boosting) to optimally combine 20 network centrality scores to jointly predict key residues as hot spots for disease mutations. 3. Using a dataset of 107 proteins rich in disease mutations, we rigorously evaluate the network scores individually and collectively (via machine learning). This work supports a promising strategy of combining an ensemble of network scores based on different coevolution analysis methods (and optionally predictive scores from other methods) via machine learning to predict hotspot sites of disease mutations, which will inform downstream applications of disease diagnosis and targeted drug design.

Simulation and Computational Study of RING Domain Mutants of BRCA1 and Ube2k in AD/PD Pathophysiology

Lysine-based post-translational modification (PTM) such as acylation, acetylation, deamination, methylation, SUMOylation, and ubiquitination has proven to be a major regulator of gene expression, chromatin structure, protein stability, protein-protein interaction, protein degradation, and cellular localization. However, besides all the PTMs, ubiquitination stands as the second most common PTM after phosphorylation that is involved in the etiology of neurodegenerative diseases (NDDs) namely, Alzheimer's disease (AD) and Parkinson's disease (PD). NDDs are characterized by the accumulation of misfolded protein aggregates in the brain that lead to disease-related gene mutation and irregular protein homeostasis. The ubiquitin-proteasome system (UPS) is in charge of degrading these misfolded proteins, which involve an interplay of E1, E2, E3, and deubiquitinase enzymes. Impaired UPS has been commonly observed in NDDs and E3 ligases are the key members of the UPS, thus, dysfunction of the same can accelerate the neurodegeneration process. Therefore, the aim of this study is firstly, to find E3 ligases that are common in both AD and PD through data mining. Secondly, to study the impact of mutation on its structure and function. The study deciphered 74 E3 ligases that were common in both AD and PD. Later, 10 hub genes were calculated of which protein-protein interaction, pathway enrichment, lysine site prediction, domain, and motif analysis were performed. The results predicted BRCA1, PML, and TRIM33 as the top three putative lysine-modified E3 ligases involved in AD and PD pathogenesis. However, based on structural characterization, BRCA1 was taken further to study RING domain mutation that inferred K32Y, K32L, K32C, K45V, K45Y, and K45G as potential mutants that alter the structural and functional ability of BRCA1 to interact with Ube2k, E2-conjugating enzyme. The most probable mutant observed after molecular dynamics simulation of 50 ns is K32L. Therefore, our study concludes BRCA1, a potential E3 ligase common in AD and PD, and RING domain mutation at sites K32 and K45 possibly disturbs its interaction with its E2, Ube2k.

Molecular Dynamics Simulation of Kir6.2 Variants Reveals Potential Association with Diabetes Mellitus

Diabetes mellitus (DM) represents a problem for the healthcare system worldwide. DM has very serious complications such as blindness, kidney failure, and cardiovascular disease. In addition to the very bad socioeconomic impacts, it influences patients and their families and communities. The global costs of DM and its complications are huge and expected to rise by the year 2030. DM is caused by genetic and environmental risk factors. Genetic testing will aid in early diagnosis and identification of susceptible individuals or populations using ATP-sensitive potassium (KATP) channels present in different tissues such as the pancreas, myocardium, myocytes, and nervous tissues. The channels respond to different concentrations of blood sugar, stimulation by hormones, or ischemic conditions. In pancreatic cells, they regulate the secretion of insulin and glucagon. Mutations in the KCNJ11 gene that encodes the Kir6.2 protein (a major constituent of KATP channels) were reported to be associated with Type 2 DM, neonatal diabetes mellitus (NDM), and maturity-onset diabetes of the young (MODY). Kir6.2 harbors binding sites for ATP and phosphatidylinositol 4,5-diphosphate (PIP2). The ATP inhibits the KATP channel, while the (PIP2) activates it. A Kir6.2 mutation at tyrosine330 (Y330) was demonstrated to reduce ATP inhibition and predisposes to NDM. In this study, we examined the effect of mutations on the Kir6.2 structure using bioinformatics tools and molecular dynamic simulations (SIFT, PolyPhen, SNAP2, PANTHER, PhD&SNP, SNP&Go, I-Mutant, MuPro, MutPred, ConSurf, HOPE, and GROMACS). Our results indicated that M199R, R201H, R206H, and Y330H mutations influence Kir6.2 structure and function and therefore may cause DM. We conclude that MD simulations are useful techniques to predict the effects of mutations on protein structure. In addition, the M199R, R201H, R206H, and Y330H variant in the Kir6.2 protein may be associated with DM. These results require further verification in protein-protein interactions, Kir6.2 function, and case-control studies.

Haplotype function score improves biological interpretation and cross-ancestry polygenic prediction of human complex traits

We propose a new framework for human genetic association studies: at each locus, a deep learning model (in this study, Sei) is used to calculate the functional genomic activity score for two haplotypes per individual. This score, defined as the Haplotype Function Score (HFS), replaces the original genotype in association studies. Applying the HFS framework to 14 complex traits in the UK Biobank, we identified 3619 independent HFS-trait associations with a significance of p < 5 × 10-8. Fine-mapping revealed 2699 causal associations, corresponding to a median increase of 63 causal findings per trait compared with single-nucleotide polymorphism (SNP)-based analysis. HFS-based enrichment analysis uncovered 727 pathway-trait associations and 153 tissue-trait associations with strong biological interpretability, including 'circadian pathway-chronotype' and 'arachidonic acid-intelligence'. Lastly, we applied least absolute shrinkage and selection operator (LASSO) regression to integrate HFS prediction score with SNP-based polygenic risk scores, which showed an improvement of 16.1-39.8% in cross-ancestry polygenic prediction. We concluded that HFS is a promising strategy for understanding the genetic basis of human complex traits.

Computational Analysis of Deleterious nsSNPs in INS Gene Associated with Permanent Neonatal Diabetes Mellitus

Insulin gene mutations affect the structure of insulin and are considered a leading cause of neonatal diabetes and permanent neonatal diabetes mellitus PNDM. These mutations can affect the production and secretion of insulin, resulting in inadequate insulin levels and subsequent hyperglycemia. Early discovery or prediction of PNDM can aid in better management and treatment. The current study identified potential deleterious non-synonymous single nucleotide polymorphisms nsSNPs in the INS gene. The analysis of the nsSNPs in the INS gene was conducted using bioinformatics tools by implementing computational algorithms including SIFT, PolyPhen2, SNAP2, SNPs & GO, PhD-SNP, MutPred2, I-Mutant, MuPro, and HOPE tools to investigate the prediction of the potential association between nsSNPs in the INS gene and PNDM. Three mutations, C96Y, P52R, and C96R, were shown to potentially reduce the stability and function of the INS protein. These mutants were subjected to MDSs for structural analysis. Results suggested that these three potential pathogenic mutations may affect the stability and functionality of the insulin protein encoded by the INS gene. Therefore, these changes may influence the development of PNDM. Further researches are required to fully understand the various effects of mutations in the INS gene on insulin synthesis and function. These data can aid in genetic testing for PNDM to evaluate its risk and create treatment and prevention strategies in personalized medicine.

FiTMuSiC: leveraging structural and (co)evolutionary data for protein fitness prediction

Systematically predicting the effects of mutations on protein fitness is essential for the understanding of genetic diseases. Indeed, predictions complement experimental efforts in analyzing how variants lead to dysfunctional proteins that in turn can cause diseases. Here we present our new fitness predictor, FiTMuSiC, which leverages structural, evolutionary and coevolutionary information. We show that FiTMuSiC predicts fitness with high accuracy despite the simplicity of its underlying model: it was among the top predictors on the hydroxymethylbilane synthase (HMBS) target of the sixth round of the Critical Assessment of Genome Interpretation challenge (CAGI6) and performs as well as much more complex deep learning models such as AlphaMissense. To further demonstrate FiTMuSiC's robustness, we compared its predictions with in vitro activity data on HMBS, variant fitness data on human glucokinase (GCK), and variant deleteriousness data on HMBS and GCK. These analyses further confirm FiTMuSiC's qualities and accuracy, which compare favorably with those of other predictors. Additionally, FiTMuSiC returns two scores that separately describe the functional and structural effects of the variant, thus providing mechanistic insight into why the variant leads to fitness loss or gain. We also provide an easy-to-use webserver at https://babylone.ulb.ac.be/FiTMuSiC , which is freely available for academic use and does not require any bioinformatics expertise, which simplifies the accessibility of our tool for the entire scientific community.

Genomic evidence for human-mediated introgressive hybridization and selection in the developed breed

BACKGROUND

The pig (Sus Scrofa) is one of the oldest domesticated livestock species that has undergone extensive improvement through modern breeding. European breeds have advantages in lean meat development and highly-productive body type, whereas Asian breeds possess extraordinary fat deposition and reproductive performance. Consequently, Eurasian breeds have been extensively used to develop modern commercial breeds for fast-growing and high prolificacy. However, limited by the sequencing technology, the genome architecture of some nascent developed breeds and the human-mediated impact on their genomes are still unknown.

RESULTS

Through whole-genome analysis of 178 individuals from an Asian locally developed pig breed, Beijing Black pig, and its two ancestors from two different continents, we found the pervasive inconsistent gene trees and species trees across the genome of Beijing Black pig, which suggests its introgressive hybrid origin. Interestingly, we discovered that this developed breed has more genetic relationships with European pigs and an unexpected introgression from Asian pigs to this breed, which indicated that human-mediated introgression could form the porcine genome architecture in a completely different type compared to native introgression. We identified 554 genomic regions occupied 63.30 Mb with signals of introgression from the Asian ancestry to Beijing Black pig, and the genes in these regions enriched in pathways associated with meat quality, fertility, and disease-resistant. Additionally, a proportion of 7.77% of genomic regions were recognized as regions that have been under selection. Moreover, combined with the results of a genome-wide association study for meat quality traits in the 1537 Beijing Black pig population, two important candidate genes related to meat quality traits were identified. DNAJC6 is related to intramuscular fat content and fat deposition, and RUFY4 is related to meat pH and tenderness.

CONCLUSIONS

Our research provides insight for analyzing the origins of nascent developed breeds and genome-wide selection remaining in the developed breeds mediated by humans during modern breeding.

Elucidating the Impact of Deleterious Mutations on IGHG1 and Their Association with Huntington's Disease

Huntington's disease (HD) is a chronic, inherited neurodegenerative condition marked by chorea, dementia, and changes in personality. The primary cause of HD is a mutation characterized by the expansion of a triplet repeat (CAG) within the huntingtin gene located on chromosome 4. Despite substantial progress in elucidating the molecular and cellular mechanisms of HD, an effective treatment for this disorder is not available so far. In recent years, researchers have been interested in studying cerebrospinal fluid (CSF) as a source of biomarkers that could aid in the diagnosis and therapeutic development of this disorder. Immunoglobulin heavy constant gamma 1 (IGHG1) is one of the CSF proteins found to increase significantly in HD. Considering this, it is reasonable to study the potential involvement of deleterious mutations in IGHG1 in the pathogenesis of this disorder. In this study, we explored the potential impact of deleterious mutations on IGHG1 and their subsequent association with HD. We evaluated 126 single-point amino acid substitutions for their impact on the structure and functionality of the IGHG1 protein while exploiting multiple computational resources such as SIFT, PolyPhen-2, FATHMM, SNPs&Go mCSM, DynaMut2, MAESTROweb, PremPS, MutPred2, and PhD-SNP. The sequence- and structure-based tools highlighted 10 amino acid substitutions that were deleterious and destabilizing. Subsequently, out of these 10 mutations, eight variants (Y32C, Y32D, P34S, V39E, C83R, C83Y, V85M, and H87Q) were identified as pathogenic by disease phenotype predictors. Finally, two pathogenic variants (Y32C and P34S) were found to reduce the solubility of the protein, suggesting their propensity to form protein aggregates. These variants also exhibited higher residual frustration within the protein structure. Considering these findings, the study hypothesized that the identified variants of IGHG1 may compromise its function and potentially contribute to HD pathogenesis.