Comparison of predicted and actual consequences of missense mutations

Insights From Genetic Studies of Cerebral Palsy

Cohort-based whole exome and whole genome sequencing and copy number variant (CNV) studies have identified genetic etiologies for a sizable proportion of patients with cerebral palsy (CP). These findings indicate that genetic mutations collectively comprise an important cause of CP. We review findings in CP genomics and propose criteria for CP-associated genes at the level of gene discovery, research study, and clinical application. We review the published literature and report 18 genes and 5 CNVs from genomics studies with strong evidence of for the pathophysiology of CP. CP-associated genes often disrupt early brain developmental programming or predispose individuals to known environmental risk factors. We discuss the overlap of CP-associated genes with other neurodevelopmental disorders and related movement disorders. We revisit diagnostic criteria for CP and discuss how identification of genetic etiologies does not preclude CP as an appropriate diagnosis. The identification of genetic etiologies improves our understanding of the neurobiology of CP, providing opportunities to study CP pathogenesis and develop mechanism-based interventions.

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

Identifying pathogenic variants and underlying functional alterations is challenging. To this end, we introduce MutPred2, a tool that improves the prioritization of pathogenic amino acid substitutions over existing methods, generates molecular mechanisms potentially causative of disease, and returns interpretable pathogenicity score distributions on individual genomes. Whilst its prioritization performance is state-of-the-art, a distinguishing feature of MutPred2 is the probabilistic modeling of variant impact on specific aspects of protein structure and function that can serve to guide experimental studies of phenotype-altering variants. We demonstrate the utility of MutPred2 in the identification of the structural and functional mutational signatures relevant to Mendelian disorders and the prioritization of de novo mutations associated with complex neurodevelopmental disorders. We then experimentally validate the functional impact of several variants identified in patients with such disorders. We argue that mechanism-driven studies of human inherited disease have the potential to significantly accelerate the discovery of clinically actionable variants.

De novo STXBP1 mutation in a child with developmental delay and spasticity reveals a major structural alteration in the interface with syntaxin 1A

STXBP1, also known as Munc-18, is a master regulator of neurotransmitter release and synaptic function in the human brain through its direct interaction with syntaxin 1A. STXBP1 binds syntaxin 1A is an inactive conformational state. STXBP1 decreases its binding affinity to syntaxin upon phosphorylation, enabling syntaxin 1A to engage in the SNARE complex, leading to neurotransmitter release. STXBP1-related disorders are well characterized by encephalopathy with epilepsy, and a diverse range of neurological and neurodevelopmental conditions. Through exome sequencing of a child with developmental delay, hypotonia, and spasticity, we found a novel de novo insertion mutation of three nucleotides in the STXBP1 coding region, resulting in an additional arginine after position 39 (R39dup). Inconclusive results from state-of-the-art variant prediction tools mandated a structure-based approach using molecular dynamics (MD) simulations of the STXBP1-syntaxin 1A complex. Comparison of the interaction interfaces of the wild-type and the R39dup complexes revealed a reduced interaction surface area in the mutant, leading to destabilization of the protein complex. Moreover, the decrease in affinity toward syntaxin 1A is similar for the phosphorylated STXBP1 and the R39dup. We applied the same MD methodology to seven additional previously reported STXBP1 mutations and reveal that the stability of the STXBP1-syntaxin 1A interface correlates with the reported clinical phenotypes. This study provides a direct link between the outcome of a novel variant in STXBP1 and protein structure and dynamics. The structural change upon mutation drives an alteration in synaptic function.

A segregating human allele of SPO11 modeled in mice disrupts timing and amounts of meiotic recombination, causing oligospermia and a decreased ovarian reserve†

A major challenge in medical genetics is to characterize variants of unknown significance (VUS). Doing so would help delineate underlying causes of disease and the design of customized treatments. Infertility has presented an especially difficult challenge with respect to not only determining if a given patient has a genetic basis, but also to identify the causative genetic factor(s). Though genome sequencing can identify candidate variants, in silico predictions of causation are not always sufficiently reliable so as to be actionable. Thus, experimental validation is crucial. Here, we describe the phenotype of mice containing a non-synonymous (proline-to-threonine at position 306) change in Spo11, corresponding to human SNP rs185545661. SPO11 is a topoisomerase-like protein that is essential for meiosis because it induces DNA double stranded breaks (DSBs) that stimulate pairing and recombination of homologous chromosomes. Although both male and female Spo11P306T/P306T mice were fertile, they had reduced sperm and oocytes, respectively. Spermatocyte chromosomes exhibited synapsis defects (especially between the X and Y chromosomes), elevated apoptotic cells, persistent markers of DSBs, and most importantly, fewer Type 1 crossovers that causes some chromosomes to have none. Spo11P306T/- mice were sterile and made fewer meiotic DSBs than Spo11+/- animals, suggesting that the Spo11P306T allele is a hypomorph and likely is delayed in making sufficient DSBs in a timely fashion. If the consequences are recapitulated in humans, it would predict phenotypes of premature ovarian failure, reduced sperm counts, and possible increased number of aneuploid gametes. These results emphasize the importance of deep phenotyping in order to accurately assess the impact of VUSs in reproduction genes.

Functional Characterization of PALB2 Variants of Uncertain Significance: Toward Cancer Risk and Therapy Response Prediction

In recent years it has become clear that pathogenic variants in PALB2 are associated with a high risk for breast, ovarian and pancreatic cancer. However, the clinical relevance of variants of uncertain significance (VUS) in PALB2, which are increasingly identified through clinical genetic testing, is unclear. Here we review recent advances in the functional characterization of VUS in PALB2. A combination of assays has been used to assess the impact of PALB2 VUS on its function in DNA repair by homologous recombination, cell cycle regulation and the control of cellular levels of reactive oxygen species (ROS). We discuss the outcome of this comprehensive analysis of PALB2 VUS, which showed that VUS in PALB2’s Coiled-Coil (CC) domain can impair the interaction with BRCA1, whereas VUS in its WD40 domain affect PALB2 protein stability. Accordingly, the CC and WD40 domains of PALB2 represent hotspots for variants that impair PALB2 protein function. We also provide a future perspective on the high-throughput analysis of VUS in PALB2, as well as the functional characterization of variants that affect PALB2 RNA splicing. Finally, we discuss how results from these functional assays can be valuable for predicting cancer risk and responsiveness to cancer therapy, such as treatment with PARP inhibitor- or platinum-based chemotherapy.

Succession of Bifidobacterium longum Strains in Response to a Changing Early Life Nutritional Environment Reveals Dietary Substrate Adaptations

Diet-microbe interactions play a crucial role in modulation of the early life microbiota and infant health. Bifidobacterium dominates the breast-fed infant gut and may persist in individuals during transition from a milk-based to a more diversified diet. Here, we investigated adaptation of Bifidobacterium longum to the changing nutritional environment. Genomic characterization of 75 strains isolated from nine either exclusively breast- or formula-fed (pre-weaning) infants in their first 18 months revealed subspecies- and strain-specific intra-individual genomic diversity with respect to carbohydrate metabolism, which corresponded to different dietary stages. Complementary phenotypic studies indicated strain-specific differences in utilization of human milk oligosaccharides and plant carbohydrates, whereas proteomic profiling identified gene clusters involved in metabolism of selected carbohydrates. Our results indicate a strong link between infant diet and B. longum diversity and provide additional insights into possible competitive advantage mechanisms of this Bifidobacterium species and its persistence in a single host.

In Silico Structural and Biochemical Functional Analysis of a Novel CYP21A2 Pathogenic Variant

Classical congenital adrenal hyperplasia (CAH) caused by pathogenic variants in the steroid 21-hydroxylase gene (CYP21A2) is a severe life-threatening condition. We present a detailed investigation of the molecular and functional characteristics of a novel pathogenic variant in this gene. The patient, 46 XX newborn, was diagnosed with classical salt wasting CAH in the neonatal period after initially presenting with ambiguous genitalia. Multiplex ligation-dependent probe analysis demonstrated a full deletion of the paternal CYP21A2 gene, and Sanger sequencing revealed a novel de novo CYP21A2 variant c.694–696del (E232del) in the other allele. This variant resulted in the deletion of a non-conserved single amino acid, and its functional relevance was initially undetermined. We used both in silico and in vitro methods to determine the mechanistic significance of this mutation. Computational analysis relied on the solved structure of the protein (Protein-data-bank ID 4Y8W), structure prediction of the mutated protein, evolutionary analysis, and manual inspection. We predicted impaired stability and functionality of the protein due to a rotatory disposition of amino acids in positions downstream of the deletion. In vitro biochemical evaluation of enzymatic activity supported these predictions, demonstrating reduced protein levels to 22% compared to the wild-type form and decreased hydroxylase activity to 1–4%. This case demonstrates the potential of combining in-silico analysis based on evolutionary information and structure prediction with biochemical studies. This approach can be used to investigate other genetic variants to understand their potential effects.

Pathogenic and likely pathogenic variants in at least five genes account for approximately 3% of mild isolated nonsyndromic thrombocytopenia

BACKGROUND

Thrombocytopenia has a variety of different etiologies, both acquired and hereditary. Inherited thrombocytopenia may be associated with other symptoms (syndromic forms) or may be strictly isolated. To date, only about half of all the familial forms of thrombocytopenia have been accounted for in terms of well-defined genetic abnormalities. However, data are limited on the nature and frequency of the underlying causative genetic variants in individuals with mild isolated nonsyndromic thrombocytopenia.

STUDY DESIGN AND METHODS

Thirteen known or candidate genes for isolated thrombocytopenia were included in a gene panel analysis in which targeted next-generation sequencing was performed on 448 French blood donors with mild isolated nonsyndromic thrombocytopenia.

RESULTS

A total of 68 rare variants, including missense, splice site, frameshift, nonsense, and in-frame variants (all heterozygous) were identified in 11 of the 13 genes screened. Twenty-nine percent (N = 20) of the variants detected were absent from both the French Exome Project and gnomAD exome databases. Using stringent criteria and an unbiased approach, we classified seven predicted loss-of-function variants (three in ITGA2B and four in TUBB1) and four missense variants (one in GP1BA, two in ITGB3 and one in ACTN1) as being pathogenic or likely pathogenic. Altogether, they were found in 13 members (approx. 3%) of our studied cohort.

CONCLUSION

We present the results of gene panel sequencing of known and candidate thrombocytopenia genes in mild isolated nonsyndromic thrombocytopenia. Pathogenic and likely pathogenic variants in five known thrombocytopenia genes were identified, accounting for approximately 3% of individuals with the condition.

Detecting pathogenic variants in autoimmune diseases using high-throughput sequencing

Sequencing the first human genome in 2003 took 15 years and cost $2.7 billion. Advances in sequencing technologies have since decreased costs to the point where it is now feasible to resequence a whole human genome for $1000 in a single day. These advances have allowed the generation of huge volumes of high‐quality human sequence data used to construct increasingly large catalogs of both population‐level and disease‐causing variation. The existence of such databases, coupled with a high‐quality human reference genome, means we are able to interrogate and annotate all types of genetic variation and identify pathogenic variants for many diseases. Increasingly, sequencing‐based approaches are being used to elucidate the underlying genetic cause of autoimmune diseases, a group of roughly 80 polygenic diseases characterized by abnormal immune responses where healthy tissue is attacked. Although sequence data generation has become routine and affordable, significant challenges remain with no gold‐standard methodology to identify pathogenic variants currently available. This review examines the latest methodologies used to identify pathogenic variants in autoimmune diseases and considers available sequencing options and subsequent bioinformatic methodologies and strategies. The development of reliable and robust sequencing and analytic workflows to detect pathogenic variants is critical to realize the potential of precision medicine programs where patient variant information is used to inform clinical practice.

Variants in ALX4 and their association with genitourinary defects

BACKGROUND

Genitourinary anomalies occur in approximately 1% of humans, but in most cases, the cause is unknown. Aristaless-like homeobox 4 (ALX4) is an important homeodomain transcription factor. ALX4 mutations in humans and mouse have been associated with craniofacial defects and genitourinary anomalies such as cryptorchidism and epispadias.

OBJECTIVES

To investigate the presence and the functional impact of ALX4 variants in patients with genitourinary defects.

MATERIALS AND METHODS

Two separate patient cohorts were analyzed. One includes clinical exome-sequencing (ES) data from 7500 individuals. The other includes 52 ALX4 Sanger-sequenced individuals with bladder exstrophy-epispadias complex (BEEC). Dual luciferase assays were conducted to investigate the functional transcriptional impact of ALX4 variants in HeLa cells and HEK293 cells.

RESULTS

A total of 41 distinct ALX4 heterozygous missense variants were identified in the ES cohort with 15 variants present as recurrent in multiple patients. p.G369E and p.L373F were the only two present in individuals with genitourinary defects. A p.L373F heterozygous variant was also identified in one of the 52 individuals in the BEEC cohort. p.L373F and p.G369E were tested in vitro as both are considered damaging by MutationTaster, although only p.G369E was considered damaging by PolyPhen-2. p.L373F did not alter transcriptional activity in HeLa and HEK293 cells. p.G369E caused a significant 3.4- and 1.8-fold decrease in transcriptional activities relative to wild-type ALX4 in HEK293 and HeLa cells, respectively.

DISCUSSION AND CONCLUSIONS

Our study supports the idea that transcription factors like ALX4 could influence the normal development of the GU tract in humans as demonstrated in mouse models as ALX4 variant p.G369E (predicted pathogenic by multiple databases) affects ALX4 function in vitro. Variant p.L373F (predicted pathogenic by only MutationTaster) did not affect ALX4 function in vitro. Exon-sequence information and mouse genetics provide important insights into the complex mechanisms driving genitourinary defects allowing the association of transcriptional defects with congenital disorders.

A yeast-based complementation assay elucidates the functional impact of 200 missense variants in human PSAT1

Defects in serine biosynthesis resulting from loss of function mutations in PHGDH, PSAT1, and PSPH cause a set of rare, autosomal recessive diseases known as Neu-Laxova syndrome (NLS) or serine-deficiency disorders. The diseases present with a broad range of phenotypes including lethality, severe neurological manifestations, seizures, and intellectual disability. However, because L-serine supplementation, especially if started prenatally, can ameliorate and in some cases even prevent symptoms, knowledge of pathogenic variants is medically actionable. Here, we describe a functional assay that leverages the evolutionary conservation of an enzyme in the serine biosynthesis pathway, phosphoserine aminotransferase, and the ability of the human protein-coding sequence (PSAT1) to functionally replace its yeast ortholog (SER1). Results from our quantitative, yeast-based assay agree well with clinical annotations and expectations based on the disease literature. Using this assay, we have measured the functional impact of the 199 PSAT1 variants currently listed in ClinVar, gnomAD, and the literature. We anticipate that the assay could be used to comprehensively assess the functional impact of all SNP-accessible amino acid substitution mutations in PSAT1, a resource that could aid variant interpretation and identify potential NLS carriers.

BRIP1, RAD51C, and RAD51D mutations are associated with high susceptibility to ovarian cancer: mutation prevalence and precise risk estimates based on a pooled analysis of ~30,000 cases

Background

It is estimated that more than 20% of ovarian cancer cases are associated with a genetic predisposition that is only partially explained by germline mutations in the BRCA1 and BRCA2 genes. Recently, several pieces of evidence showed that mutations in three genes involved in the homologous recombination DNA repair pathway, i.e., BRIP1, RAD51C, and RAD51D, are associated with a high risk of ovarian cancer. To more precisely estimate the ovarian cancer risk attributed to BRIP1, RAD51C, and RAD51D mutations, we performed a meta-analysis based on a comparison of a total of ~ 29,400 ovarian cancer patients from 63 studies and a total of ~ 116,000 controls from the gnomAD database.

Results

The analysis allowed precise estimation of ovarian cancer risks attributed to mutations in BRIP1, RAD51C, and RAD51D, confirming that all three genes are ovarian cancer high-risk genes (odds ratio (OR) = 4.94, 95%CIs:4.07–6.00, p < 0.0001; OR = 5.59, 95%CIs:4.42–7.07, p < 0.0001; and OR = 6.94, 95%CIs:5.10–9.44, p < 0.0001, respectively). In the present report, we show, for the first time, a mutation-specific risk analysis associated with distinct, recurrent, mutations in the genes.

Conclusions

The meta-analysis provides evidence supporting the pathogenicity of BRIP1, RAD51C, and RAD51D mutations in relation to ovarian cancer. The level of ovarian cancer risk conferred by these mutations is relatively high, indicating that after BRCA1 and BRCA2, the BRIP1, RAD51C, and RAD51D genes are the most important ovarian cancer risk genes, cumulatively contributing to ~ 2% of ovarian cancer cases. The inclusion of the genes into routine diagnostic tests may influence both the prevention and the potential treatment of ovarian cancer.

Mammalian SWI/SNF Complex Genomic Alterations and Immune Checkpoint Blockade in Solid Tumors

Prior data have variably implicated the inactivation of the mammalian SWItch/Sucrose Non-Fermentable (mSWI/SNF) complex with increased tumor sensitivity to immune checkpoint inhibitors (ICI). Herein, we examined the association between mSWI/SNF variants and clinical outcomes to ICIs. We correlated somatic loss-of-function (LOF) variants in a predefined set of mSWI/SNF genes (ARID1A, ARID1B, SMARCA4, SMARCB1, PBRM1, and ARID2) with clinical outcomes in patients with cancer treated with systemic ICIs. We identified 676 patients from Dana-Farber Cancer Institute (DFCI, Boston, MA) and 848 patients from a publicly available database from Memorial Sloan Kettering Cancer Center (MSKCC, New York, NY) who met the inclusion criteria. Multivariable analyses were conducted and adjusted for available baseline factors and tumor mutational burden. Median follow-up was 19.6 (17.6-22.0) months and 28.0 (25.0-29.0) months for the DFCI and MSKCC cohorts, respectively. Seven solid tumor subtypes were examined. In the DFCI cohort, LOF variants of mSWI/SNF did not predict improved overall survival (OS), time-to-treatment failure (TTF), or disease control rate. Only patients with renal cell carcinoma with mSWI/SNF LOF showed significantly improved OS and TTF with adjusted HRs (95% confidence interval) of 0.33 (0.16-0.7) and 0.49 (0.27-0.88), respectively, and this was mostly driven by PRBM1 In the MSKCC cohort, where only OS was captured, LOF mSWI/SNF did not correlate with improved outcomes across any tumor subtype. We did not find a consistent association between mSWI/SNF LOF variants and improved clinical outcomes to ICIs, suggesting that mSWI/SNF variants should not be considered as biomarkers of response to ICIs.

Autism-associated missense genetic variants impact locomotion and neurodevelopment in Caenorhabditis elegans

Autism spectrum disorder (ASD) involves thousands of alleles in over 850 genes, but the current functional inference tools are not sufficient to predict phenotypic changes. As a result, the causal relationship of most of these genetic variants in the pathogenesis of ASD has not yet been demonstrated and an experimental method prioritizing missense alleles for further intensive analysis is crucial. For this purpose, we have designed a pipeline that uses Caenorhabditis elegans as a genetic model to screen for phenotype-changing missense alleles inferred from human ASD studies. We identified highly conserved human ASD-associated missense variants in their C. elegans orthologs, used a CRISPR/Cas9-mediated homology-directed knock-in strategy to generate missense mutants and analyzed their impact on behaviors and development via several broad-spectrum assays. All tested missense alleles were predicted to perturb protein function, but we found only 70% of them showed detectable phenotypic changes in morphology, locomotion or fecundity. Our findings indicate that certain missense variants in the C. elegans orthologs of human CACNA1D, CHD7, CHD8, CUL3, DLG4, GLRA2, NAA15, PTEN, SYNGAP1 and TPH2 impact neurodevelopment and movement functions, elevating these genes as candidates for future study into ASD. Our approach will help prioritize functionally important missense variants for detailed studies in vertebrate models and human cells.

Individualized genetic network analysis reveals new therapeutic vulnerabilities in 6,700 cancer genomes

Tumor-specific genomic alterations allow systematic identification of genetic interactions that promote tumorigenesis and tumor vulnerabilities, offering novel strategies for development of targeted therapies for individual patients. We develop an Individualized Network-based Co-Mutation (INCM) methodology by inspecting over 2.5 million nonsynonymous somatic mutations derived from 6,789 tumor exomes across 14 cancer types from The Cancer Genome Atlas. Our INCM analysis reveals a higher genetic interaction burden on the significantly mutated genes, experimentally validated cancer genes, chromosome regulatory factors, and DNA damage repair genes, as compared to human pan-cancer essential genes identified by CRISPR-Cas9 screenings on 324 cancer cell lines. We find that genes involved in the cancer type-specific genetic subnetworks identified by INCM are significantly enriched in established cancer pathways, and the INCM-inferred putative genetic interactions are correlated with patient survival. By analyzing drug pharmacogenomics profiles from the Genomics of Drug Sensitivity in Cancer database, we show that the network-predicted putative genetic interactions (e.g., BRCA2-TP53) are significantly correlated with sensitivity/resistance of multiple therapeutic agents. We experimentally validated that afatinib has the strongest cytotoxic activity on BT474 (IC₅₀ = 55.5 nM, BRCA2 and TP53 co-mutant) compared to MCF7 (IC₅₀ = 7.7 μM, both BRCA2 and TP53 wild type) and MDA-MB-231 (IC₅₀ = 7.9 μM, BRCA2 wild type but TP53 mutant). Finally, drug-target network analysis reveals several potential druggable genetic interactions by targeting tumor vulnerabilities. This study offers a powerful network-based methodology for identification of candidate therapeutic pathways that target tumor vulnerabilities and prioritization of potential pharmacogenomics biomarkers for development of personalized cancer medicine.

Recent efforts to map genetic interactions in tumor cells have suggested that tumor vulnerabilities can be exploited for development of novel targeted therapies. Tumor-specific genomic alterations derived from multi-center cancer genome projects allow identification of genetic interactions that promote tumor vulnerabilities, offering novel strategies for development of targeted cancer therapies. This study develops a novel Individualized Network-based Co-Mutation (termed INCM) methodology for quantifying the putative genetic interactions in cancer. Trained on over 2.5 million nonsynonymous somatic mutations derived from 6,789 tumor exomes across 14 cancer type, we found that genes identified in the cancer type-specific genetic subnetworks were significantly enriched in established cancer pathways. The network-predicted putative genetic interactions are correlated with patient survival. By analyzing drug pharmacogenomics profiles, we showed that the network-predicted putative genetic interactions (e.g., BRCA2-TP53) were significantly correlated with sensitivity/resistance of anticancer drugs (e.g., afatinib) and we experimentally validated it in breast cancer cell lines. Finally, drug-target network analysis reveals several potential druggable genetic interactions (e.g., PIK3CA-PTEN) by targeting tumor vulnerabilities. This study offers a generalizable network-based approach for comprehensive identification of candidate therapeutic pathways that target tumor vulnerabilities and prioritization of potential prognostic and pharmacogenomics biomarkers for development of personalized cancer medicine.

In silico prediction of the functional and structural consequences of the non-synonymous single nucleotide polymorphism A122V in bovine CXC chemokine receptor type 1

Abstract The current study aimed to assess whether the A122V causal polymorphism promotes alterations in the functional and structural proprieties of the CXC chemokine receptor type 1 protein (CXCR1) of cattle Bos taurus by in silico analyses. Two amino acid sequences of bovine CXCR1 was selected from database UniProtKB/Swiss-Prot: a) non-polymorphic sequence (A7KWG0) with alanine (A) at position 122, and b) polymorphic sequence harboring the A122V polymorphism, substituting alanine by valine (V) at same position. CXCR1 sequences were submitted as input to different Bioinformatics’ tools to examine the effects of this polymorphism on functional and structural stabilities, to predict eventual alterations in the 3-D structural modeling, and to estimate the quality and accuracy of the predictive models. The A122V polymorphism exerted tolerable and non-deleterious effects on the polymorphic CXCR1, and the predictive structural model for polymorphic CXCR1 revealed an alpha helix spatial structure typical of a receptor transmembrane polypeptide. Although higher variations in the distances between pairs of amino acid residues at target-positions are detected in the polymorphic CXCR1 protein, more than 97% of the amino acid residues in both models were located in favored and allowed conformational regions in Ramachandran plots. Evidences has supported that the A122V polymorphism in the CXCR1 protein is associated with increased clinical mastitis incidence in dairy cows. Thus, the findings described herein prove that the replacement of the alanine by valine amino acids provokes local conformational changes in the A122V-harboring CXCR1 protein, which could directly affect its post-translational folding mechanisms and biological functionality.

Assessing genomic diversity and signatures of selection in Original Braunvieh cattle using whole-genome sequencing data

Background

Autochthonous cattle breeds are an important source of genetic variation because they might carry alleles that enable them to adapt to local environment and food conditions. Original Braunvieh (OB) is a local cattle breed of Switzerland used for beef and milk production in alpine areas. Using whole-genome sequencing (WGS) data of 49 key ancestors, we characterize genomic diversity, genomic inbreeding, and signatures of selection in Swiss OB cattle at nucleotide resolution.

Results

We annotated 15,722,811 SNPs and 1,580,878 Indels including 10,738 and 2763 missense deleterious and high impact variants, respectively, that were discovered in 49 OB key ancestors. Six Mendelian trait-associated variants that were previously detected in breeds other than OB, segregated in the sequenced key ancestors including variants causal for recessive xanthinuria and albinism. The average nucleotide diversity (1.6  × 10^− 3) was higher in OB than many mainstream European cattle breeds. Accordingly, the average genomic inbreeding derived from runs of homozygosity (ROH) was relatively low (F_ROH = 0.14) in the 49 OB key ancestor animals. However, genomic inbreeding was higher in OB cattle of more recent generations (F_ROH = 0.16) due to a higher number of long (> 1 Mb) runs of homozygosity. Using two complementary approaches, composite likelihood ratio test and integrated haplotype score, we identified 95 and 162 genomic regions encompassing 136 and 157 protein-coding genes, respectively, that showed evidence (P < 0.005) of past and ongoing selection. These selection signals were enriched for quantitative trait loci related to beef traits including meat quality, feed efficiency and body weight and pathways related to blood coagulation, nervous and sensory stimulus.

Conclusions

We provide a comprehensive overview of sequence variation in Swiss OB cattle genomes. With WGS data, we observe higher genomic diversity and less inbreeding in OB than many European mainstream cattle breeds. Footprints of selection were detected in genomic regions that are possibly relevant for meat quality and adaptation to local environmental conditions. Considering that the population size is low and genomic inbreeding increased in the past generations, the implementation of optimal mating strategies seems warranted to maintain genetic diversity in the Swiss OB cattle population.

Identification of pathogenic variant enriched regions across genes and gene families

Missense variant interpretation is challenging. Essential regions for protein function are conserved among gene-family members, and genetic variants within these regions are potentially more likely to confer risk to disease. Here, we generated 2871 gene-family protein sequence alignments involving 9990 genes and performed missense variant burden analyses to identify novel essential protein regions. We mapped 2,219,811 variants from the general population into these alignments and compared their distribution with 76,153 missense variants from patients. With this gene-family approach, we identified 465 regions enriched for patient variants spanning 41,463 amino acids in 1252 genes. As a comparison, by testing the same genes individually, we identified fewer patient variant enriched regions, involving only 2639 amino acids and 215 genes. Next, we selected de novo variants from 6753 patients with neurodevelopmental disorders and 1911 unaffected siblings and observed an 8.33-fold enrichment of patient variants in our identified regions (95% C.I. = 3.90-Inf, P-value = 2.72 × 10-11). By using the complete ClinVar variant set, we found that missense variants inside the identified regions are 106-fold more likely to be classified as pathogenic in comparison to benign classification (OR = 106.15, 95% C.I = 70.66-Inf, P-value < 2.2 × 10-16). All pathogenic variant enriched regions (PERs) identified are available online through "PER viewer," a user-friendly online platform for interactive data mining, visualization, and download. In summary, our gene-family burden analysis approach identified novel PERs in protein sequences. This annotation can empower variant interpretation.

Systematic quantification of the anion transport function of pendrin (SLC26A4) and its disease-associated variants

Thanks to the advent of rapid DNA sequencing technology and its prevalence, many disease-associated genetic variants are rapidly identified in many genes from patient samples. However, the subsequent effort to experimentally validate and define their pathological roles is extremely slow. Consequently, the pathogenicity of most disease-associated genetic variants is solely speculated in silico, which is no longer deemed compelling. We developed an experimental approach to efficiently quantify the pathogenic effects of disease-associated genetic variants with a focus on SLC26A4, which is essential for normal inner ear function. Alterations of this gene are associated with both syndromic and nonsyndromic hereditary hearing loss with various degrees of severity. We established HEK293T-based stable cell lines that express pendrin missense variants in a doxycycline-dependent manner, and systematically determined their anion transport activities with high accuracy in a 96-well plate format using a high throughput plate reader. Our doxycycline dosage-dependent transport assay objectively distinguishes missense variants that indeed impair the function of pendrin from those that do not (functional variants). We also found that some of these putative missense variants disrupt normal messenger RNA splicing. Our comprehensive experimental approach helps determine the pathogenicity of each pendrin variant, which should guide future efforts to benefit patients.

Species-specific consequences of an E40K missense mutation in superoxide dismutase 1 (SOD1)

A glutamic acid to lysine (E40K) residue substitution in superoxide dismutase 1 (SOD1) is associated with canine degenerative myelopathy: the only naturally occurring large animal model of amyotrophic lateral sclerosis (ALS). The E40 residue is highly conserved across mammals, except the horse, which naturally carries the (dog mutant) K40 residue. Here we hypothesized that in vitro expression of mutant dog SOD1 would recapitulate features of human ALS (ie, SOD1 protein aggregation, reduced cell viability, perturbations in mitochondrial morphology and membrane potential, reduced ATP production, and increased superoxide ion levels); further, we hypothesized that an equivalent equine SOD1 variant would share similar perturbations in vitro, thereby explain horses' susceptibility to certain neurodegenerative diseases. As in human ALS, expression of mutant dog SOD1 was associated with statistically significant increased aggregate formation, raised superoxide levels (ROS), and altered mitochondrial morphology (increased branching (form factor)), when compared to wild-type dog SOD1-expressing cells. Similar deficits were not detected in cells expressing the equivalent horse SOD1 variant. Our data helps explain the ALS-associated cellular phenotype of dogs expressing the mutant SOD1 protein and reveals that species-specific sequence conservation does not necessarily predict pathogenicity. The work improves understanding of the etiopathogenesis of canine degenerative myelopathy.

Epigenetic signatures in overgrowth syndromes: Translational opportunities

In recent years, numerous overgrowth syndromes have been found to be caused by pathogenic DNA sequence variants in "epigenes," genes that encode proteins that function in epigenetic regulation. Epigenetic marks, including DNA methylation (DNAm), histone modifications and chromatin conformation, have emerged as a vital genome-wide regulatory mechanism that modulate the transcriptome temporally and spatially to drive normal developmental and cellular processes. Evidence suggests that epigenetic marks are layered and engage in crosstalk, in that disruptions of any one component of the epigenetic machinery impact the others. This interdependence of epigenetic marks underpins the recent identification of gene-specific DNAm signatures for a variety of disorders caused by pathogenic variants in epigenes. Here, we discuss the power of DNAm signatures with respect to furthering our understanding of disease pathophysiology, enhancing the efficacy of molecular diagnostics and identifying new targets for therapeutics of overgrowth syndromes. These findings highlight the promise of the field of epigenomics to provide unprecedented insights into disease mechanisms generating a host of opportunities to advance precision medicine.

Rare missense variants in the human cytosolic antibody receptor preserve antiviral function

The genetic basis of most human disease cannot be explained by common variants. One solution to this ‘missing heritability problem’ may be rare missense variants, which are individually scarce but collectively abundant. However, the phenotypic impact of rare variants is under-appreciated as gene function is normally studied in the context of a single ‘wild-type’ sequence. Here, we explore the impact of naturally occurring missense variants in the human population on the cytosolic antibody receptor TRIM21, using volunteer cells with variant haplotypes, CRISPR gene editing and functional reconstitution. In combination with data from a panel of computational predictors, the results suggest that protein robustness and purifying selection ensure that function is remarkably well-maintained despite coding variation.

Computational analysis of functional SNPs in Alzheimer's disease-associated endocytosis genes

Background

From genome wide association studies on Alzheimer’s disease (AD), it has been shown that many single nucleotide polymorphisms (SNPs) of genes of different pathways affect the disease risk. One of the pathways is endocytosis, and variants in these genes may affect their functions in amyloid precursor protein (APP) trafficking, amyloid-beta (Aβ) production as well as its clearance in the brain. This study uses computational methods to predict the effect of novel SNPs, including untranslated region (UTR) variants, splice site variants, synonymous SNPs (sSNPs) and non-synonymous SNPs (nsSNPs) in three endocytosis genes associated with AD, namely PICALM, SYNJ1 and SH3KBP1.

Materials and Methods

All the variants’ information was retrieved from the Ensembl genome database, and then different variation prediction analyses were performed. UTRScan was used to predict UTR variants while MaxEntScan was used to predict splice site variants. Meta-analysis by PredictSNP2 was used to predict sSNPs. Parallel prediction analyses by five different software packages including SIFT, PolyPhen-2, Mutation Assessor, I-Mutant2.0 and SNPs&GO were used to predict the effects of nsSNPs. The level of evolutionary conservation of deleterious nsSNPs was further analyzed using ConSurf server. Mutant protein structures of deleterious nsSNPs were modelled and refined using SPARKS-X and ModRefiner for structural comparison.

Results

A total of 56 deleterious variants were identified in this study, including 12 UTR variants, 18 splice site variants, eight sSNPs and 18 nsSNPs. Among these 56 deleterious variants, seven variants were also identified in the Alzheimer’s Disease Sequencing Project (ADSP), Alzheimer’s Disease Neuroimaging Initiative (ADNI) and Mount Sinai Brain Bank (MSBB) studies.

Discussion

The 56 deleterious variants were predicted to affect the regulation of gene expression, or have functional impacts on these three endocytosis genes and their gene products. The deleterious variants in these genes are expected to affect their cellular function in endocytosis and may be implicated in the pathogenesis of AD as well. The biological consequences of these deleterious variants and their potential impacts on the disease risks could be further validated experimentally and may be useful for gene-disease association study.

ENAM mutations and digenic inheritance

BACKGROUND

ENAM mutations cause autosomal dominant or recessive amelogenesis imperfecta (AI) and show a dose effect: enamel malformations are more severe or only penetrant when both ENAM alleles are defective.

METHODS

Whole exome sequences of recruited AI probands were initially screened for mutations in known AI candidate genes. Sanger sequencing was used to confirm sequence variations and their segregation with the disease phenotype. The co-occurrence of ENAM and LAMA3 mutations in one family raised the possibility of digenic inheritance. Enamel formed in Enam+/+ Ambn+/+ , Enam+/- , Ambn+/- , and Enam+/- Ambn+/- mice was characterized by dissection and backscattered scanning electron microscopy (bSEM).

RESULTS

ENAM mutations segregating with AI in five families were identified. Two novel ENAM frameshift mutations were identified. A single-nucleotide duplication (c.395dupA/p.Pro133Alafs*13) replaced amino acids 133-1142 with a 12 amino acid (ATTKAAFEAAIT*) sequence, and a single-nucleotide deletion (c.2763delT/p.Asp921Glufs*32) replaced amino acids 921-1142 with 31 amino acids (ESSPQQASYQAKETAQRRGKAKTLLEMMCPR*). Three families were heterozygous for a previously reported single-nucleotide ENAM deletion (c.588+1delG/p.Asn197Ilefs*81). One of these families also harbored a heterozygous LAMA3 mutation (c.1559G>A/p.Cys520Tyr) that cosegregated with both the AI phenotype and the ENAM mutation. In mice, Ambn+/- maxillary incisors were normal. Ambn+/- molars were also normal, except for minor surface roughness. Ambn+/- mandibular incisors were sometimes chalky and showed minor chipping. Enam+/- incisor enamel was thinner than normal with ectopic mineral deposited laterally. Enam+/- molars were sometimes chalky and rough surfaced. Enam+/- Ambn+/- enamel was thin and rough, in part due to ectopic mineralization, but also underwent accelerated attrition.

CONCLUSION

Novel ENAM mutations causing AI were identified, raising to 22 the number of ENAM variations known to cause AI. The severity of the enamel phenotype in Enam+/- Ambn+/- double heterozygous mice is caused by composite digenic effects. Digenic inheritance should be explored as a cause of AI in humans.

Whole Genome Sequencing of the Mutamouse Model Reveals Strain- and Colony-Level Variation, and Genomic Features of the Transgene Integration Site

The MutaMouse transgenic rodent model is widely used for assessing in vivo mutagenicity. Here, we report the characterization of MutaMouse's whole genome sequence and its genetic variants compared to the C57BL/6 reference genome. High coverage (>50X) next-generation sequencing (NGS) of whole genomes from multiple MutaMouse animals from the Health Canada (HC) colony showed ~5 million SNVs per genome, ~20% of which are putatively novel. Sequencing of two animals from a geographically separated colony at Covance indicated that, over the course of 23 years, each colony accumulated 47,847 (HC) and 17,677 (Covance) non-parental homozygous single nucleotide variants. We found no novel nonsense or missense mutations that impair the MutaMouse response to genotoxic agents. Pairing sequencing data with array comparative genomic hybridization (aCGH) improved the accuracy and resolution of copy number variants (CNVs) calls and identified 300 genomic regions with CNVs. We also used long-read sequence technology (PacBio) to show that the transgene integration site involved a large deletion event with multiple inversions and rearrangements near a retrotransposon. The MutaMouse genome gives important genetic context to studies using this model, offers insight on the mechanisms of structural variant formation, and contributes a framework to analyze aCGH results alongside NGS data.

Clinical sequencing: From raw data to diagnosis with lifetime value

High-throughput sequencing (HTS) has revolutionized genetics by enabling the detection of sequence variants at hitherto unprecedented large scale. Despite these advances, however, there are still remaining challenges in the complete coverage of targeted regions (genes, exome or genome) as well as in HTS data analysis and interpretation. Moreover, it is easy to get overwhelmed by the plethora of available methods and tools for HTS. Here, we review the step-by-step process from the generation of sequence data to molecular diagnosis of Mendelian diseases. Highlighting advantages and limitations, this review addresses the current state of (1) HTS technologies, considering targeted, whole-exome, and whole-genome sequencing on short- and long-read platforms; (2) read alignment, variant calling and interpretation; as well as (3) regulatory issues related to genetic counseling, reimbursement, and data storage.

What went wrong with variant effect predictor performance for the PCM1 challenge

The recent years have seen a drastic increase in the amount of available genomic sequences. Alongside this explosion, hundreds of computational tools were developed to assess the impact of observed genetic variation. Critical Assessment of Genome Interpretation (CAGI) provides a platform to evaluate the performance of these tools in experimentally relevant contexts. In the CAGI-5 challenge assessing the 38 missense variants affecting the human Pericentriolar material 1 protein (PCM1), our SNAP-based submission was the top performer, although it did worse than expected from other evaluations. Here, we compare the CAGI-5 submissions, and 24 additional commonly used variant effect predictors, to analyze the reasons for this observation. We identified per residue conservation, structural, and functional PCM1 characteristics, which may be responsible. As expected, predictors had a hard time distinguishing effect variants in nonconserved positions. They were also better able to call effect variants in a structurally rich region than in a less-structured one; in the latter, they more often correctly identified benign than effect variants. Curiously, most of the protein was predicted to be functionally robust to mutation-a feature that likely makes it a harder problem for generalized variant effect predictors.

Rare single-nucleotide variants in oculo-auriculo-vertebral spectrum (OAVS)

Background

Oculo‐auriculo‐vertebral spectrum (OAVS) is a craniofacial developmental disorder that affects structures derived from the first and second pharyngeal arches. The clinically heterogeneous phenotype involves mandibular, oral, and ear development anomalies. Etiology is complex and poorly understood. Genetic factors have been associated, evidenced by chromosomal abnormalities affecting different genomic regions and genes. However, known pathogenic single‐nucleotide variants (SNVs) have only been identified in MYT1 in a restricted number of patients. Therefore, investigations of SNVs on candidate genes may reveal other pathogenic mechanisms.

Methods

In a cohort of 73 patients, coding and untranslated regions (UTR) of 10 candidate genes (CRKL, YPEL1, MAPK1, NKX3‐2, HMX1, MYT1, OTX2, GSC, PUF60, HOXA2) were sequenced. Rare SNVs were selected and in silico predictions were performed to ascertain pathogenicity. Likely pathogenic variants were validated by Sanger sequencing and heritability was assessed when possible.

Results

Four likely pathogenic variants in heterozygous state were identified in different patients. Two SNVs were located in the 5’UTR of YPEL1; one in the 3’UTR of CRKL and one in the 3’UTR of OTX2.

Conclusion

Our work described variants in candidate genes for OAVS and supported the genetic heterogeneity of the spectrum.

CAGI5: Objective performance assessments of predictions based on the Evolutionary Action equation

Many computational approaches estimate the effect of coding variants, but their predictions often disagree with each other. These contradictions confound users and raise questions regarding reliability. Performance assessments can indicate the expected accuracy for each method and highlight advantages and limitations. The Critical Assessment of Genome Interpretation (CAGI) community aims to organize objective and systematic assessments: They challenge predictors on unpublished experimental and clinical data and assign independent assessors to evaluate the submissions. We participated in CAGI experiments as predictors, using the Evolutionary Action (EA) method to estimate the fitness effect of coding mutations. EA is untrained, uses homology information, and relies on a formal equation: The fitness effect equals the functional sensitivity to residue changes multiplied by the magnitude of the substitution. In previous CAGI experiments (between 2011 and 2016), our submissions aimed to predict the protein activity of single mutants. In 2018 (CAGI5), we also submitted predictions regarding clinical associations, folding stability, and matching genomic data with phenotype. For all these diverse challenges, we used EA to predict the fitness effect of variants, adjusted to specifically address each question. Our submissions had consistently good performance, suggesting that EA predicts reliably the effects of genetic variants.

Mutational signatures and mutable motifs in cancer genomes

Cancer is a genetic disorder, meaning that a plethora of different mutations, whether somatic or germ line, underlie the etiology of the 'Emperor of Maladies'. Point mutations, chromosomal rearrangements and copy number changes, whether they have occurred spontaneously in predisposed individuals or have been induced by intrinsic or extrinsic (environmental) mutagens, lead to the activation of oncogenes and inactivation of tumor suppressor genes, thereby promoting malignancy. This scenario has now been recognized and experimentally confirmed in a wide range of different contexts. Over the past decade, a surge in available sequencing technologies has allowed the sequencing of whole genomes from liquid malignancies and solid tumors belonging to different types and stages of cancer, giving birth to the new field of cancer genomics. One of the most striking discoveries has been that cancer genomes are highly enriched with mutations of specific kinds. It has been suggested that these mutations can be classified into 'families' based on their mutational signatures. A mutational signature may be regarded as a type of base substitution (e.g. C:G to T:A) within a particular context of neighboring nucleotide sequence (the bases upstream and/or downstream of the mutation). These mutational signatures, supplemented by mutable motifs (a wider mutational context), promise to help us to understand the nature of the mutational processes that operate during tumor evolution because they represent the footprints of interactions between DNA, mutagens and the enzymes of the repair/replication/modification pathways.

Solution structure and functional investigation of human guanylate kinase reveals allosteric networking and a crucial role for the enzyme in cancer

Human guanylate kinase (hGMPK) is the only known enzyme responsible for cellular GDP production, making it essential for cellular viability and proliferation. Moreover, hGMPK has been assigned a critical role in metabolic activation of antiviral and antineoplastic nucleoside-analog prodrugs. Given that hGMPK is indispensable for producing the nucleotide building blocks of DNA, RNA, and cGMP and that cancer cells possess elevated GTP levels, it is surprising that a detailed structural and functional characterization of hGMPK is lacking. Here, we present the first high-resolution structure of hGMPK in the apo form, determined with NMR spectroscopy. The structure revealed that hGMPK consists of three distinct regions designated as the LID, GMP-binding (GMP-BD), and CORE domains and is in an open configuration that is nucleotide binding-competent. We also demonstrate that nonsynonymous single-nucleotide variants (nsSNVs) of the hGMPK CORE domain distant from the nucleotide-binding site of this domain modulate enzymatic activity without significantly affecting hGMPK's structure. Finally, we show that knocking down the hGMPK gene in lung adenocarcinoma cell lines decreases cellular viability, proliferation, and clonogenic potential while not altering the proliferation of immortalized, noncancerous human peripheral airway cells. Taken together, our results provide an important step toward establishing hGMPK as a potential biomolecular target, from both an orthosteric (ligand-binding sites) and allosteric (location of CORE domain-located nsSNVs) standpoint.

Massively Parallel Assays and Quantitative Sequence-Function Relationships

Over the last decade, a rich variety of massively parallel assays have revolutionized our understanding of how biological sequences encode quantitative molecular phenotypes. These assays include deep mutational scanning, high-throughput SELEX, and massively parallel reporter assays. Here, we review these experimental methods and how the data they produce can be used to quantitatively model sequence-function relationships. In doing so, we touch on a diverse range of topics, including the identification of clinically relevant genomic variants, the modeling of transcription factor binding to DNA, the functional and evolutionary landscapes of proteins, and cis-regulatory mechanisms in both transcription and mRNA splicing. We further describe a unified conceptual framework and a core set of mathematical modeling strategies that studies in these diverse areas can make use of. Finally, we highlight key aspects of experimental design and mathematical modeling that are important for the results of such studies to be interpretable and reproducible.

Can Predicted Protein 3D Structures Provide Reliable Insights into whether Missense Variants Are Disease Associated?

Knowledge of protein structure can be used to predict the phenotypic consequence of a missense variant. Since structural coverage of the human proteome can be roughly tripled to over 50% of the residues if homology-predicted structures are included in addition to experimentally determined coordinates, it is important to assess the reliability of using predicted models when analyzing missense variants. Accordingly, we assess whether a missense variant is structurally damaging by using experimental and predicted structures. We considered 606 experimental structures and show that 40% of the 1965 disease-associated missense variants analyzed have a structurally damaging change in the mutant structure. Only 11% of the 2134 neutral variants are structurally damaging. Importantly, similar results are obtained when 1052 structures predicted using Phyre2 algorithm were used, even when the model shares low (<40%) sequence identity to the template. Thus, structure-based analysis of the effects of missense variants can be effectively applied to homology models. Our in-house pipeline, Missense3D, for structurally assessing missense variants was made available at http://www.sbg.bio.ic.ac.uk/~missense3d.

De novo Mutations From Whole Exome Sequencing in Neurodevelopmental and Psychiatric Disorders: From Discovery to Application

Neurodevelopmental and psychiatric disorders are a highly disabling and heterogeneous group of developmental and mental disorders, resulting from complex interactions of genetic and environmental risk factors. The nature of multifactorial traits and the presence of comorbidity and polygenicity in these disorders present challenges in both disease risk identification and clinical diagnoses. The genetic component has been firmly established, but the identification of all the causative variants remains elusive. The development of next-generation sequencing, especially whole exome sequencing (WES), has greatly enriched our knowledge of the precise genetic alterations of human diseases, including brain-related disorders. In particular, the extensive usage of WES in research studies has uncovered the important contribution of de novo mutations (DNMs) to these disorders. Trio and quad familial WES are a particularly useful approach to discover DNMs. Here, we review the major WES studies in neurodevelopmental and psychiatric disorders and summarize how genes hit by discovered DNMs are shared among different disorders. Next, we discuss different integrative approaches utilized to interrogate DNMs and to identify biological pathways that may disrupt brain development and shed light on our understanding of the genetic architecture underlying these disorders. Lastly, we discuss the current state of the transition from WES research to its routine clinical application. This review will assist researchers and clinicians in the interpretation of variants obtained from WES studies, and highlights the need to develop consensus analytical protocols and validated lists of genes appropriate for clinical laboratory analysis, in order to reach the growing demands.

A Combined in silico, in vitro and Clinical Approach to Characterize Novel Pathogenic Missense Variants in PRPF31 in Retinitis Pigmentosa

At least six different proteins of the spliceosome, including PRPF3, PRPF4, PRPF6, PRPF8, PRPF31, and SNRNP200, are mutated in autosomal dominant retinitis pigmentosa (adRP). These proteins have recently been shown to localize to the base of the connecting cilium of the retinal photoreceptor cells, elucidating this form of RP as a retinal ciliopathy. In the case of loss-of-function variants in these genes, pathogenicity can easily be ascribed. In the case of missense variants, this is more challenging. Furthermore, the exact molecular mechanism of disease in this form of RP remains poorly understood. In this paper we take advantage of the recently published cryo EM-resolved structure of the entire human spliceosome, to predict the effect of a novel missense variant in one component of the spliceosome; PRPF31, found in a patient attending the genetics eye clinic at Bristol Eye Hospital. Monoallelic variants in PRPF31 are a common cause of autosomal dominant retinitis pigmentosa (adRP) with incomplete penetrance. We use in vitro studies to confirm pathogenicity of this novel variant PRPF31 c.341T > A, p.Ile114Asn. This work demonstrates how in silico modeling of structural effects of missense variants on cryo-EM resolved protein complexes can contribute to predicting pathogenicity of novel variants, in combination with in vitro and clinical studies. It is currently a considerable challenge to assign pathogenic status to missense variants in these proteins.

Predicting mutations deleterious to function in beta-lactamase TEM1 using MM-GBSA

Missense mutations can have disastrous effects on the function of a protein. And as a result, they have been implicated in numerous diseases. However, the majority of missense variants only have a nominal impact on protein function. Thus, the ability to distinguish these two classes of missense mutations would greatly aid drug discovery efforts in target identification and validation as well as medical diagnosis. Monitoring the co-occurrence of a given missense mutation and a disease phenotype provides a pathway for classifying functionally disrupting missense mutations. But, the occurrence of a specific missense variant is often extremely rare making statistical links challenging to infer. In this study, we benchmark a physics-based approach for predicting changes in stability, MM-GBSA, and apply it to classifying mutations as functionally disrupting. A large and diverse dataset of 990 residue mutations in beta-lactamase TEM1 is used to assess performance as it is rich in both functionally disrupting mutations and functionally neutral/beneficial mutations. On this dataset, we compare the performance of MM-GBSA to alternative strategies for predicting functionally disrupting mutations. We observe that the MM-GBSA method obtains an area under the curve (AUC) of 0.75 on the entire dataset, outperforming all other predictors tested. More importantly, MM-GBSA’s performance is robust to various divisions of the dataset, speaking to the generality of the approach. Though there is one notable exception: Mutations on the surface of the protein are the mutations that are the most difficult to classify as functionally disrupting for all methods tested. This is likely due to the many mechanisms available to surface mutations to disrupt function, and thus provides a direction of focus for future studies.

Binding site comparisons for target-centered drug discovery

INTRODUCTION

The success of binding site comparisons in drug discovery is based on the recognized fact that many different proteins have similar binding sites. Indeed, binding site comparisons have found many uses in drug development and have the potential to dramatically cut the cost and shorten the time necessary for the development of new drugs. Areas covered: The authors review recent methods for comparing protein binding sites and their use in drug repurposing and polypharmacology. They examine emerging fields including the use of binding site comparisons in precision medicine, the prediction of structured water molecules, the search for targets of natural compounds, and their application in the development of protein-based drugs by loop modeling and for comparison of RNA binding sites. Expert opinion: Binding site comparisons have produced many interesting results in drug development, but relatively little work has been done on protein-protein interaction sites, which are particularly relevant in view of the success of biological drugs. Growth of protein loop modeling for modulating biological drugs is anticipated. The fusion of currently distinct methods for the comparison of RNA and protein binding sites into a single comprehensive approach could allow the search for new selective ribosomal antibiotics and initiate pharmaceutical research into other nucleoproteins.

Computational Molecular Phenotypic Analysis of PTPN22 (W620R), IL6R (D358A), and TYK2 (P1104A) Gene Mutations of Rheumatoid Arthritis

Rheumatoid arthritis (RA) is a chronic autoimmune disorder of bone joints caused by the complex interplay between several factors like body physiology, the environment with genetic background. The recent meta-analysis of GWAS has expanded the total number of RA-associated loci to more than 100, out of which approximately ∼97% (98 variants) loci are located in non-coding regions, and the other ∼3% (3 variants) are in three different non-HLA genes, i.e., TYK2 (Prp1104Ala), IL6R (Asp358Ala), and PTPN22 (Trp620Arg). However, whether these variants prompt changes in the protein phenotype with regards to its stability, structure, and interaction with other molecules, remains unknown. Thus, we selected the three clinically pathogenic variants described above, as positive controls and applied diverse computational methods to scrutinize if those mutations cause changes in the protein phenotype. Both wild type and mutant protein structures of PTPN22 (W620R), IL6R (D358A), and TYK2 (P1104A) were modeled and studied for structural deviations. Furthermore, we have also studied the secondary structure characteristics, solvent accessibility and stability, and the molecular interaction deformities caused by the amino acid substitutions. We observed that simple nucleotide predictions of SIFT, PolyPhen, CADD and FATHMM yields mixed findings in screening the RA-missense variants which showed a ≥P-value threshold of 5 × 10^-8 in genome wide association studies. However, structure-based analysis confirms that mutant structures shows subtle but significant changes at their core regions, but their functional domains seems to lose wild type like functional interaction. Our findings suggest that the multidirectional computational analysis of clinically potential RA-mutations could act as a primary screening step before undertaking functional biology assays.

New insights into the pathogenicity of non-synonymous variants through multi-level analysis

Precise classification of non-synonymous single nucleotide variants (SNVs) is a fundamental goal of clinical genetics. Next-generation sequencing technology is effective for establishing the basis of genetic diseases. However, identification of variants that are causal for genetic diseases remains a challenge. We analyzed human non-synonymous SNVs from a multilevel perspective to characterize pathogenicity. We showed that computational tools, though each having its own strength and weakness, tend to be overly dependent on the degree of conservation. For the mutations at non-degenerate sites, the amino acid sites of pathogenic substitutions show a distinct distribution in the classes of protein domains compared with the sites of benign substitutions. Overlooked disease susceptibility of genes explains in part the failures of computational tools. The more pathogenic sites observed, the more likely the gene is expressed in a high abundance or in a high tissue-specific manner, and have a high node degree of protein-protein interaction. The destroyed functions due to some false-negative mutations may arise because of a reprieve from the epigenetic repressed state which shouldn't happen in multiple biological conditions, instead of the defective protein. Our work adds more to our knowledge of non-synonymous SNVs' pathogenicity, thus will benefit the field of clinical genetics.

Protein structure aids predicting functional perturbation of missense variants in SCN5A and KCNQ1

Rare variants in the cardiac potassium channel K_V7.1 (KCNQ1) and sodium channel Na_V1.5 (SCN5A) are implicated in genetic disorders of heart rhythm, including congenital long QT and Brugada syndromes (LQTS, BrS), but also occur in reference populations. We previously reported two sets of Na_V1.5 (n = 356) and K_V7.1 (n = 144) variants with in vitro characterized channel currents gathered from the literature. Here we investigated the ability to predict commonly reported Na_V1.5 and K_V7.1 variant functional perturbations by leveraging diverse features including variant classifiers PROVEAN, PolyPhen-2, and SIFT; evolutionary rate and BLAST position specific scoring matrices (PSSM); and structure-based features including “functional densities” which is a measure of the density of pathogenic variants near the residue of interest. Structure-based functional densities were the most significant features for predicting Na_V1.5 peak current (adj. R² = 0.27) and K_V7.1 + KCNE1 half-maximal voltage of activation (adj. R² = 0.29). Additionally, use of structure-based functional density values improves loss-of-function classification of SCN5A variants with an ROC-AUC of 0.78 compared with other predictive classifiers (AUC = 0.69; two-sided DeLong test p = .01). These results suggest structural data can inform predictions of the effect of uncharacterized SCN5A and KCNQ1 variants to provide a deeper understanding of their burden on carriers.

Defective repair capacity of variant proteins of the DNA glycosylase NTHL1 for 5-hydroxyuracil, an oxidation product of cytosine

The NTHL1 gene encodes DNA glycosylase, which is involved in base excision repair, and biallelic mutations of this gene result in NTHL1-associated polyposis (NAP), a hereditary disease characterized by colorectal polyposis and multiple types of carcinomas. However, no proper functional characterization of variant NTHL1 proteins has been done so far. Herein, we report functional evaluation of variant NTHL1 proteins to aid in the accurate diagnosis of NAP. First, we investigated whether it would be appropriate to use 5-hydroxyuracil (5OHU), an oxidation product of cytosine, for the evaluation. In the supF forward mutation assay, 5OHU caused an increase of the mutation frequency in human cells, and the C→T mutation was predominant among the 5OHU-induced mutations. In addition, in DNA cleavage activity assay, 5OHU was excised by NTHL1 as well as four other DNA glycosylases (SMUG1, NEIL1, TDG, and UNG2). When human cells overexpressing the five DNA glycosylases were established, it was found that each of the five DNA glycosylases, including NTHL1, had the ability to suppress 5OHU-induced mutations. Based on the above results, we performed functional evaluation of eight NTHL1 variants using 5OHU-containing DNA substrate or shuttle plasmid. The DNA cleavage activity assay showed that the variants of NTHL1, Q90X, Y130X, R153X, and Q287X, but not R19Q, V179I, V217F, or G286S, showed defective repair activity for 5OHU and two other oxidatively damaged bases. Moreover, the supF forward mutation assay showed that the four truncated-type NTHL1 variants showed a reduced ability to suppress 5OHU-induced mutations in human cells. These results suggest that the NTHL1 variants Q90X, Y130X, R153X, and Q287X, but not R19Q, V179I, V217F, or G286S, were defective in 5OHU repair and the alleles encoding them were considered to be pathogenic for NAP.

Role of Segregation for Variant Discovery in Multiplex Families Ascertained by Probands With Left Sided Cardiovascular Malformations

Cardiovascular malformations (CVM) are common birth defects (incidence of 2-5/100 live births). Although a genetic basis is established, in most cases the cause remains unknown. Analysis of whole exome sequencing (WES) in left sided CVM case and trio series has identified large numbers of potential variants but evidence of causality has remained elusive except in a small percentage of cases. We sought to determine whether variant segregation in families would aid in novel gene discovery. The objective was to compare conventional and co-segregation approaches for WES in multiplex families. WES was performed on 52 individuals from 4 multiplex families ascertained by probands with hypoplastic left heart syndrome (HLHS). We identified rare variants with informatics support (RVIS, minor allele frequency ≤0.01 and Combined Annotation Dependent Depletion score ≥20) in probands. Non-RVIS variants did not meet these criteria. Family specific two point logarithm of the odds (LOD) scores identified co-segregating variants (C-SV) using a dominant model and 80% penetrance. In families, 702 RVIS in 668 genes were identified, but only 1 RVIS was also a C-SV (LOD ≥ 1). On the other hand, there were 109 non-RVIS variants with LOD ≥ 1. Among 110 C-SV, 97% were common (MAF > 1%). These results suggest that conventional variant identification methods focused on RVIS, miss most C-SV. For diseases such as left sided CVM, which exhibit strong familial transmission, co-segregation can identify novel candidates.

Computational Methods for the Pharmacogenetic Interpretation of Next Generation Sequencing Data

Up to half of all patients do not respond to pharmacological treatment as intended. A substantial fraction of these inter-individual differences is due to heritable factors and a growing number of associations between genetic variations and drug response phenotypes have been identified. Importantly, the rapid progress in Next Generation Sequencing technologies in recent years unveiled the true complexity of the genetic landscape in pharmacogenes with tens of thousands of rare genetic variants. As each individual was found to harbor numerous such rare variants they are anticipated to be important contributors to the genetically encoded inter-individual variability in drug effects. The fundamental challenge however is their functional interpretation due to the sheer scale of the problem that renders systematic experimental characterization of these variants currently unfeasible. Here, we review concepts and important progress in the development of computational prediction methods that allow to evaluate the effect of amino acid sequence alterations in drug metabolizing enzymes and transporters. In addition, we discuss recent advances in the interpretation of functional effects of non-coding variants, such as variations in splice sites, regulatory regions and miRNA binding sites. We anticipate that these methodologies will provide a useful toolkit to facilitate the integration of the vast extent of rare genetic variability into drug response predictions in a precision medicine framework.

CRISPR-Cas9 human gene replacement and phenomic characterization in Caenorhabditis elegans to understand the functional conservation of human genes and decipher variants of uncertain significance

Our ability to sequence genomes has vastly surpassed our ability to interpret the genetic variation we discover. This presents a major challenge in the clinical setting, where the recent application of whole-exome and whole-genome sequencing has uncovered thousands of genetic variants of uncertain significance. Here, we present a strategy for targeted human gene replacement and phenomic characterization, based on CRISPR-Cas9 genome engineering in the genetic model organism Caenorhabditis elegans, that will facilitate assessment of the functional conservation of human genes and structure-function analysis of disease-associated variants with unprecedented precision. We validate our strategy by demonstrating that direct single-copy replacement of the C. elegans ortholog (daf-18) with the critical human disease-associated gene phosphatase and tensin homolog (PTEN) is sufficient to rescue multiple phenotypic abnormalities caused by complete deletion of daf-18, including complex chemosensory and mechanosensory impairments. In addition, we used our strategy to generate animals harboring a single copy of the known pathogenic lipid phosphatase inactive PTEN variant (PTEN-G129E), and showed that our automated in vivo phenotypic assays could accurately and efficiently classify this missense variant as loss of function. The integrated nature of the human transgenes allows for analysis of both homozygous and heterozygous variants and greatly facilitates high-throughput precision medicine drug screens. By combining genome engineering with rapid and automated phenotypic characterization, our strategy streamlines the identification of novel conserved gene functions in complex sensory and learning phenotypes that can be used as in vivo functional assays to decipher variants of uncertain significance.

A putative human infertility allele of the meiotic recombinase DMC1 does not affect fertility in mice

Whole-exome or whole-genome sequencing is becoming routine in clinical situations for identifying mutations underlying presumed genetic causes of disease including infertility. While this is a powerful approach for implicating polymorphisms or de novo mutations in genes plausibly related to the phenotype, a greater challenge is to definitively prove causality. This is a crucial requisite for treatment, especially for infertility, in which validation options are limited. In this study, we created a mouse model of a putative infertility allele, DMC1M200V. DMC1 encodes a RecA homolog essential for meiotic recombination and fertility in mice. This allele was originally implicated as being responsible for the sterility of a homozygous African woman, a conclusion supported by subsequent biochemical analyses of the mutant protein and by studies of yeast with the orthologous amino acid change. Here, we found that Dmc1M200V/M200V male and female mice are fully fertile and do not exhibit any gonadal abnormalities. Detailed immunocytological analysis of meiosis revealed no defects suggestive of compromised fertility. This study serves as a cautionary tale for making conclusions about consequences of genetic variants, especially with respect to infertility, and emphasizes the importance of conducting relevant biological assays for making accurate diagnoses in the era of genomic medicine.

Comparative Genomics Approaches Accurately Predict Deleterious Variants in Plants

Recent advances in genome resequencing have led to increased interest in prediction of the functional consequences of genetic variants. Variants at phylogenetically conserved sites are of particular interest, because they are more likely than variants at phylogenetically variable sites to have deleterious effects on fitness and contribute to phenotypic variation. Numerous comparative genomic approaches have been developed to predict deleterious variants, but the approaches are nearly always assessed based on their ability to identify known disease-causing mutations in humans. Determining the accuracy of deleterious variant predictions in nonhuman species is important to understanding evolution, domestication, and potentially to improving crop quality and yield. To examine our ability to predict deleterious variants in plants we generated a curated database of 2,910 Arabidopsis thaliana mutants with known phenotypes. We evaluated seven approaches and found that while all performed well, their relative ranking differed from prior benchmarks in humans. We conclude that deleterious mutations can be reliably predicted in A. thaliana and likely other plant species, but that the relative performance of various approaches does not necessarily translate from one species to another.

Extracting Complementary Insights from Molecular Phenotypes for Prioritization of Disease-Associated Mutations

Rapid advances in next-generation sequencing technology have resulted in an explosion of whole-exome/genome sequencing data, providing an unprecedented opportunity to identify disease- and trait-associated variants in humans on a large scale. To date, the long-standing paradigm has leveraged fitness-based approximations to translate this ever-expanding sequencing data into causal insights in disease. However, while this approach robustly identifies variants under evolutionary constraint, it fails to provide molecular insights. Moreover, complex disease phenomena often violate standard assumptions of a direct organismal phenotype to overall fitness effect relationship. Here we discuss the potential of a molecular phenotype-oriented paradigm to uniquely identify candidate disease-causing mutations from the human genetic background. By providing a direct connection between single nucleotide mutations and observable organismal and cellular phenotypes associated with disease, we suggest that molecular phenotypes can readily incorporate alongside established fitness-based methodologies to provide complementary insights to the functional impact of human mutations. Lastly, we discuss how integrated approaches between molecular phenotypes and fitness-based perspectives facilitate new insights into the molecular mechanisms underlying disease-associated mutations while also providing a platform for improved interpretation of epistasis in human disease.

Structural Biology Helps Interpret Variants of Uncertain Significance in Genes Causing Endocrine and Metabolic Disorders

CONTEXT

Variants of uncertain significance (VUSs) lack sufficient evidence, in terms of statistical power or experimental studies, to allow unequivocal determination of their damaging effect. VUSs are a major burden in performing genetic analysis. Although in silico prediction tools are widely used, their specificity is low, thus urgently calling for methods for prioritizing and characterizing variants.

OBJECTIVE

To assess the frequency of VUSs in genes causing endocrine and metabolic disorders, the concordance rate of predictions from different in silico methods, and the added value of three-dimensional protein structure analysis in discerning and prioritizing damaging variants.

RESULTS

A total of 12,266 missense variants reported in 641 genes causing endocrine and metabolic disorders were analyzed. Among these, 4123 (33.7%) were VUSs, of which 2010 (48.8%) were predicted to be damaging and 1452 (35.2%) were predicted to be tolerated according to in silico tools. A total of 5383 (87.7%) of 6133 disease-causing variants and 823 (55.8%) of 1474 benign variants were correctly predicted. In silico predictions were noninformative in 5.7%, 14.4%, and 16% of damaging, benign, and VUSs, respectively. A damaging effect on 3D protein structure was present in 240 (30.9%) of predicted damaging and 40 (9.7%) of predicted tolerated VUSs (P < 0.001). An in-depth analysis of nine VUSs occurring in TSHR, LDLR, CASR, and APOE showed that they greatly affect protein stability and are therefore strong candidates for disease.

CONCLUSIONS

In our dataset, we confirmed the high sensitivity but low specificity of in silico predictions tools. 3D protein structural analysis is a compelling tool for characterizing and prioritizing VUSs and should be a part of genetic variant analysis.

A role for TLR10 in obesity and adipose tissue morphology

Toll like receptors (TLRs) are expressed in adipose tissue and promote adipose tissue inflammation during obesity. Recently, anti-inflammatory properties have been attributed to TLR10 in myeloid cells, the only member of the TLR family with inhibitory activity. In order to assess whether TLR10-induced inhibition of inflammation may be protective during the development of obesity and metabolic abnormalities we used transgenic human TLR10 mice (hTLR10tg) and wild type (WT) controls on a C57B6J background. HFD-feeding enhanced TLR10 expression in the adipose tissue, and HFD-fed hTLR10tg mice displayed reduced adipocyte size, adipose tissue weight, and a trend toward lower plasma insulin levels compared to WT mice. In humans, obese individuals with polymorphisms in the TLR10 gene displayed reduced macrophage infiltration in the adipose tissue accompanied by a trend to lower leptin levels and higher adiponectin levels in plasma. In healthy individuals with the same polymorphisms in the TLR10 gene we did not observe any difference in plasma concentrations of leptin and adiponectin. We conclude that TLR10 impacts adipose tissue morphology in obesity. Larger studies in humans are warranted to assess its potential value as therapeutic target in metabolic syndrome and type 2 diabetes.

Structural dynamics is a determinant of the functional significance of missense variants

Discrimination of clinically relevant mutations from neutral mutations is of paramount importance in precision medicine and pharmacogenomics. Our study shows that current computational predictions of pathogenicity, mostly based on analysis of sequence conservation, may be improved by considering the changes in the structural dynamics of the protein due to point mutations. We introduce and demonstrate the utility of a classifier that takes advantage of efficient evaluation of structural dynamics by elastic network models.

Accurate evaluation of the effect of point mutations on protein function is essential to assessing the genesis and prognosis of many inherited diseases and cancer types. Currently, a wealth of computational tools has been developed for pathogenicity prediction. Two major types of data are used to this aim: sequence conservation/evolution and structural properties. Here, we demonstrate in a systematic way that another determinant of the functional impact of missense variants is the protein’s structural dynamics. Measurable improvement is shown in pathogenicity prediction by taking into consideration the dynamical context and implications of the mutation. Our study suggests that the class of dynamics descriptors introduced here may be used in conjunction with existing features to not only increase the prediction accuracy of the impact of variants on biological function, but also gain insight into the physical basis of the effect of missense variants.