Literature DB >> 26740553

Integrating population variation and protein structural analysis to improve clinical interpretation of missense variation: application to the WD40 domain.

Roman A Laskowski1, Nidhi Tyagi1, Diana Johnson2, Shelagh Joss3, Esther Kinning3, Catherine McWilliam4, Miranda Splitt5, Janet M Thornton1, Helen V Firth6, Caroline F Wright7.   

Abstract

We present a generic, multidisciplinary approach for improving our understanding of novel missense variants in recently discovered disease genes exhibiting genetic heterogeneity, by combining clinical and population genetics with protein structural analysis. Using six new de novo missense diagnoses in TBL1XR1 from the Deciphering Developmental Disorders study, together with population variation data, we show that the β-propeller structure of the ubiquitous WD40 domain provides a convincing way to discriminate between pathogenic and benign variation. Children with likely pathogenic mutations in this gene have severely delayed language development, often accompanied by intellectual disability, autism, dysmorphology and gastrointestinal problems. Amino acids affected by likely pathogenic missense mutations are either crucial for the stability of the fold, forming part of a highly conserved symmetrically repeating hydrogen-bonded tetrad, or located at the top face of the β-propeller, where 'hotspot' residues affect the binding of β-catenin to the TBLR1 protein. In contrast, those altered by population variation are significantly less likely to be spatially clustered towards the top face or to be at buried or highly conserved residues. This result is useful not only for interpreting benign and pathogenic missense variants in this gene, but also in other WD40 domains, many of which are associated with disease.
© The Author 2016. Published by Oxford University Press.

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Year:  2016        PMID: 26740553      PMCID: PMC4754046          DOI: 10.1093/hmg/ddv625

Source DB:  PubMed          Journal:  Hum Mol Genet        ISSN: 0964-6906            Impact factor:   6.150


Introduction

Understanding the impact of missense variants in known disease genes is a major challenge for the clinical application of genomics (1,2). A handful of well-known disease genes [such as CFTR (3) and TP53 (4)] have been extremely well studied over several decades through both research and clinical genetic testing, and multiple known pathogenic missense variants have been individually characterized in silico, in vitro and in vivo. However, the rate of gene discovery has grown exponentially since the completion of the human genome sequence (5): nearly 3500 suspected disease genes are currently listed in OMIM, many of which have been discovered through exome sequencing of patients with rare diseases (6) with rare nonsense or protein-truncating mutations. Many such genes are, as yet, unstudied and very sparsely populated with known pathogenic (or benign) missense variants, so most rare missense variants identified in these genes are likely to be novel. Meanwhile, massively parallel sequencing technologies are increasingly being used for clinical genetic testing in the form of multigene panels, exome sequencing and even whole-genome sequencing (7). As a result, a plethora of previously uncharacterized missense variants are being discovered regularly in known disease genes (8–10), where the consequence for protein structure, cellular processes or disease aetiology is unclear, severely compromising their clinical utility. The increasing availability of exome sequencing and whole-genome sequencing in research means that the pervasiveness of normal genetic variation is starting to become clear. A normal human genome contains three to four million variants, of which approximately 10 000 will be non-synonymous variants in coding exons predicted to cause a missense change, altering a single amino acid in the resulting protein (11,12). However, despite the fact that missense variation is extraordinarily commonplace, most genes still do not yet contain sufficient confirmed pathogenic and benign missense variants upon which to build detailed specific models to understand and accurately predict their relationship to human disease. Although numerous increasingly useful pathogenicity predictors exist (13–18), they generally have low specificity (9,19) and are based on sequence alignments that often exclude detailed knowledge of three-dimensional (3D) protein structure. However, as the same structural domain is commonly present in different proteins, encoded by different genes, and associated with different diseases, a method heavily informed by protein structure analysis is likely to yield insights across multiple genes and diseases. Sequence data on normal population variation coupled with high throughput exome/genome sequencing of patients with rare diseases offer the perfect opportunity to investigate whether there are systematic differences between pathogenic and benign missense variants at an individual gene or protein level. Here, we use novel diagnostic de novo mutations identified through the Deciphering Developmental Disorders (DDD) study (20,21) as an example to explore the application of detailed protein structure analysis to the understanding of disease. As a proof of principle, we focus here on the WD40 domain, one of the most abundant structural domains in eukaryotic genomes (22). Different WD40-containing genes have already been associated with multiple diseases (23,24), including TBL1XR1 [transducin (beta)-like 1 X-linked receptor 1], in which haploinsufficiency has recently been linked to autism spectrum disorders (25,26) and developmental delay (27–29) (OMIM no. 608628). The encoded TBL1-related protein 1 (UniProt ID Q9BZK7) is involved in a transcription signalling pathway and comprises two structural domains: an LisH domain (30) and a WD40 β-propeller domain (31). Here, we use this gene to investigate the value of integrating population variation and protein structural analysis to improve clinical interpretation of missense variation.

Results

Six children within the DDD study were found to have likely pathogenic de novo mutations in TBL1XR1, including five single nucleotide variants predicted to cause a missense change, and one 1 bp frameshift insertion predicted to result in loss of function through truncation or nonsense-mediated decay (Table 1). Two additional likely de novo missense mutations have also been published in children affected by developmental disorders (25,28), as well as a de novo 1 bp frameshift deletion (25). A number of whole gene deletions have also been described (27,29).
Table 1.

Summary of the clinical features in children with diagnostic variants in TBL1XR1

ReferencePatient IDAge (years)SexMutationHGVSClinical featuresFirst words
DDDaDECIPHER25934011MDe novo missenseENST00000430069.1c.1322A > GENSP00000405574.1:p.(His441Arg)Global developmental delay3 years
DDDaDECIPHER26121314FDe novo missenseENST00000430069.1:c.1108G > TENSP00000405574.1:p.(Asp370Tyr)Global developmental delayNon-verbal
DDDaDECIPHER2719555MDe novo missenseENST00000430069.1:c.983A > GENSP00000405574.1:p.(Asp328Gly)Global developmental delayNon-verbal
DDDaDECIPHER2733346FDe novo missenseENST00000430069.1:c.1331C > GENSP00000405574.1:p.(Pro444Arg)Global developmental delay, autism2 years
DDDaDECIPHER2807017MDe novo missenseENST00000430069.1:c.639T > AENSP00000405574.1:p.(His213Gln)Global developmental delay, autism1 year
DDDaDECIPHER2609655MDe novo frameshiftENST00000430069.1:c.800dupGENSP00000405574.1:p.(Ile269TyrfsTer8)Global developmental delay, autism2–2.5 years
Saitsu et al. (28)ClinVar 1913715FDe novo missenseENST00000430069.1:c.209G > AENSP00000405574.1p.(Gly70Asp)Developmental delay, autistic featuresNon-verbal
O'Roak et al. (25)NANot knownFDe novo missenseENST00000430069.1:c.845T > C ENSP00000405574.1:p.(Leu282Pro)Mild/moderate IQ, autismUnknown
O'Roak et al. (25)NANot knownMDe novo frameshiftENST00000430069.1:c.1190delTENSP00000405574.1:p.(Ile397SerfsTer19)AutismUnknown
Pons et al. (27)NA8FMaternally inherited gene deletion707 kb deletion (chr3:176 221 801–176 929 584)Intellectual disability, dysmorphism (also observed in mother)Delayed
Tabet et al. (29)NA6FDe novo gene deletion1.6 Mb deletion (chr3:175 507 453–177 095 072)Intellectual disability, dysmorphism2.5 years

See Supplementary Material, Table S1 for a more detailed clinical description. Variants are an notated using standard HGVS nomenclature (for simplicity, parentheses indicating missense prediction are omitted throughout the text).

aVariants deposited in DECIPHER database (https://decipher.sanger.ac.uk).

Summary of the clinical features in children with diagnostic variants in TBL1XR1 See Supplementary Material, Table S1 for a more detailed clinical description. Variants are an notated using standard HGVS nomenclature (for simplicity, parentheses indicating missense prediction are omitted throughout the text). aVariants deposited in DECIPHER database (https://decipher.sanger.ac.uk). Children with likely pathogenic mutations in TBL1XR1 have developmental delay often with autistic features (Table 1). All patients have marked expressive speech and language delay as the most consistent feature, and most have special needs requiring specialist educational assistance. In addition, most of the children identified via the DDD study have gastrointestinal disturbance or constipation. Although a number of patients have dysmorphic features, a preliminary assessment of facial photographs does not suggest an identifiable facial gestalt and growth parameters were typically within the normal range (Supplementary Material, Table S1). There are no apparent differences in either the phenotypes or severity of the children with missense mutations versus those with truncating mutations and gene deletions, potentially suggesting a common loss of function mechanism. Although TBL1XR1 is a highly constrained gene [Exome Aggregation Consortium (ExAC), Cambridge, MA, USA; http://exac.broadinstitute.org/; accessed December 2015], we were able to identify 64 unique germline population missense variants in TBL1XR1 in population controls, in which benign variants are expected to be relatively enriched and pathogenic variants relatively depleted for rare childhood onset dominant disorders with obvious phenotypes. These variants were identified using multiple databases: the ExAC (http://exac.broadinstitute.org/; accessed June 2015), dbSNP (http://www.ncbi.nlm.nih.gov/SNP/), the Exome Variant Server [NHLBI GO Exome Sequencing Project (ESP), Seattle, WA, USA; http://evs.gs.washington.edu/EVS/; accessed June 2015] and the European Variant Archive (http://www.ebi.ac.uk/eva/) (32). All five DDD missense mutations and one published likely pathogenic mutation are located within the WD40 domain of TBLR1, in addition to 33 of the population missense variants (Table 2). Interestingly, we also identified 16 likely non-pathogenic missense variants in TBL1XR1 within the DDD cohort (where the variant is in, or inherited from, an unaffected parent), all of which either lie outside the WD40 domain or have already been observed in the population.
Table 2.

All missense variants identified in TBL1XR1 overlapping the WD40 domain of TBLR1 (June 2015; see also Fig. 4)

VariationSource (allele count)Location (GRCh37)Ref/altPredicted amino acid change
PopulationExAC (1)chr3:176768368C/TGly153Glu
PopulationdbSNPchr3:176768338A/GVal163Ala
PopulationExAC (1)chr3:176768288C/TVal180Ile
PopulationExAC (1)chr3:176767892T/ASer199Cys
PopulationExAC (1)chr3:176767879G/CThr203Ser
DiagnosticDDDchr3:176767848A/THis213Gln
PopulationExAC (1)chr3:176765173C/TSer260Asn
PopulationdbSNPchr3:176765158T/CHis265Arg
DiagnosticO'Roak et al. (25)chr3:176765107A/GLeu282Pro
PopulationExAC (1)chr3:176756189T/CAsn320Ser
PopulationdbSNPchr3:176756189T/GAsn320Thr
PopulationEVAchr3:176756187T/CThr321Ala
DiagnosticDDDchr3:176756165T/CAsp328Gly
PopulationExAC (1)chr3:176756102G/TThr349Lys
PopulationExAC (2)chr3:176755930T/CThr360Ala
PopulationdnSNPchr3:176755930T/AThr360Ser
PopulationExAC (1)chr3:176755923T/GAsn362Thr
DiagnosticDDDchr3:176755900C/AAsp370Tyr
PopulationExAC (1)chr3:176752065T/CAsn391Asp
PopulationExAC (2)chr3:176752022C/TGly405Glu
PopulationExAC (5)chr3:176752016T/CAsn407Ser
PopulationdbSNPchr3:176752017T/CAsn407Asp
PopulationExAC (1)chr3:176752014T/CAsn408Asp
PopulationExAC (1)chr3:176750916A/CPhe420Cys
PopulationExAC (1)chr3:176750908T/CThr423Ala
PopulationExAC (1)chr3:176750905C/GVal424Leu
PopulationExAC (1)chr3:176750884G/CArg431Gly
PopulationdbSNPchr3:176750883C/TArg431Gln
PopulationdbSNPchr3:176750860T/CThr439Ala
PopulationdbSNPchr3:176750855T/GLys440Asn
DiagnosticDDDchr3:176750853T/CHis441Arg
DiagnosticDDDchr3:176750844G/CPro444Arg
PopulationExAC (1)chr3:176750817T/CAsp453Gly
PopulationExAC (2)chr3:176750811C/TArg455Lys
PopulationExAC (27)chr3:176744255G/AAla475Val
PopulationExAC (1)chr3:176744247G/CHis478Asp
PopulationExAC (1)chr3:176744189T/CLys497Arg
PopulationdbSNPchr3:176743294G/AArg513Trp
PopulationExAC (1)chr3:176743291T/GLys514Gln
All missense variants identified in TBL1XR1 overlapping the WD40 domain of TBLR1 (June 2015; see also Fig. 4)
Figure 4.

Z-axis location of all variants in the WD40 domain of TBLR1. (A) Graphical representation taken from the top to bottom face (PDB entry 4lg9). Likely pathogenic missense mutations are indicated in red (with new diagnoses from the DDD study completely filled), whereas population missense variants are indicated in green and other residues are indicated in blue. The backbone position of all residues is shown, based on the Z-axis location of the backbone carbonyl carbon in the crystal structure. Larger diamonds represent variants that are present multiple times across the databases, and crosses indicate the approximate interpolated location of residues that are absent from the PDB file. (B) Three-dimensional representation viewed from the side using PDB entry 4lg9, with all missense variants highlighted using-stick representation (space-filled for new DDD diagnoses). Likely pathogenic missense mutations are indicated in red, whereas population missense variants are indicated in green and the rest of the domain is represented using blue ribbons. (C) Boxplot of Z-axis location in PDF entry 4lg9 of diagnostic mutations (red), the conserved tetrads (beige), hotspot residues on the top face (grey), population variation (green) and all amino acid residues in the domain (blue) in the TBLR1 protein. P-values are not significant between the diagnostic/tetrad/top face residues or between population/all residues, but are significant between these groups (diagnostic versus population residues, P = 9×10−5).

The WD40 domain of TBLR1 has a β-propeller structure consisting of eight propeller ‘blades’, each formed by a four-stranded antiparallel β-sheet, which are joined by β-hairpins. The blades are arranged symmetrically about a central axis, like the staves of a barrel, and β-catenin binds to the ‘top’ face of the propeller to promote the transcription of Wnt target genes (33) (Fig. 1B). A number of ‘hotspot residues’ have been identified previously (31) on the top face of the domain (34), which are likely to be involved in the protein's interaction with β-catenin. In addition, the amino acid sequence of each blade of the β-propeller in most WD40 domains, including that in TBLR1, exhibits a recognizable pattern of residues known as the WD40 repeat motif, with certain residue types favoured in specific positions. The PROSITE sequence logo (35) for this motif is shown in Figure 2A, in which taller letters identify the highly conserved residues that are important for stabilization of the blade's structure. The TBLR1 protein has six complete tetrads and one incomplete tetrad that is missing the tryptophan residue (Fig. 2B). Of note in the logo are the histidine, serine/threonine, aspartic acid and tryptophan residues at motif positions 4, 22, 26 and 32, respectively. These form the Asp-His-Ser/Thr-Trp (DHSW) tetrad—a network of ‘unusually strong’ hydrogen bonds that maintains the domain's thermostability (37) (Fig. 3). The aspartic acid at motif position 26 is present in all eight blades and plays an especially important role in stabilizing the beta-hairpin structure at the top of each blade via two hydrogen bonds to the main chain nitrogen atoms of adjoining strands. An experimental study in 2010 showed that, although mutations to the tetrad residues maintained the domain's 3D structure, as evidenced by crystal structures of the mutant proteins, the stability of the proteins was severely affected (37), potentially interfering with folding or function.
Figure 1.

Structure of TBLR1. (A) Domain structure with location of diagnostic missense mutations. The five new DDD mutations are indicated in black and the two previously published mutations in grey. (B) Three-dimensional β-propeller structure of the WD40 domain from PDB entry 4lg9, top and side views. The eight propeller blades are rainbow coloured, starting with red for the N-terminus through to violet for the C-terminus.

Figure 2.

Conserved sequence elements of the WD40 motif. (A) PROSITE sequence logo for the WD40 motif, derived from a multiple sequence alignment of 6896 sequence fragments. The one-letter amino acid codes are coloured by type (blue basic, red acidic, green and purple polar and the rest black). The height of each corresponds to its frequency of occurrence in the alignment. (B) Structure-based alignment of the eight WD40 motifs in the crystal structure of TBLR1. The motifs were manually extracted from the 4lg9 PDB file and then aligned using the PDBeFold Server (36). The numbers on the left show the range of residue numbers in the sequence on that line. The one-letter amino acid codes are coloured as per the PROSITE sequence logo (A); lower-case letters correspond to residues not aligned in the 3D superposition. The numbers along the bottom roughly correspond to the sequence positions in the WD40 motif in (A). The amino acids having an orange background are those belonging to the Asp-His-Ser/Thr-Trp tetrad. The red borders identify the five amino acids involved in the DDD missense mutations: His213Gln, Asp328Gly, Asp370Tyr, His441Arg and Pro444Arg. The amino acids with the light grey backgrounds are the hotspot residues on the domain's top face, as identified by WDSPdb (31), being the ones likely to interact with β-catenin when it binds.

Figure 3.

Representation of the hydrogen-bonding network of the DHSW tetrad. Taken from the fifth WD40 motif in the 3D structure of TBLR1 (PDB entry 4lg9). (A) Schematic representation showing the four sidechains involved: Asp370, His348, Ser366 and Trp376. Hydrogen bonds are shown by the green dotted lines. (B) Three-dimensional representation showing the location and sidechains of the four tetrad residues; the rest of the domain is represented only by backbone atoms N, Cα and C. Potential hydrogen bonds are shown by the dashed lines. Note the importance of the highly conserved Asp370, which can not only hydrogen-bond to the histidine, but also to the backbone of neighbouring strands, helping hold the propeller-blade structure together.

Structure of TBLR1. (A) Domain structure with location of diagnostic missense mutations. The five new DDD mutations are indicated in black and the two previously published mutations in grey. (B) Three-dimensional β-propeller structure of the WD40 domain from PDB entry 4lg9, top and side views. The eight propeller blades are rainbow coloured, starting with red for the N-terminus through to violet for the C-terminus. Conserved sequence elements of the WD40 motif. (A) PROSITE sequence logo for the WD40 motif, derived from a multiple sequence alignment of 6896 sequence fragments. The one-letter amino acid codes are coloured by type (blue basic, red acidic, green and purple polar and the rest black). The height of each corresponds to its frequency of occurrence in the alignment. (B) Structure-based alignment of the eight WD40 motifs in the crystal structure of TBLR1. The motifs were manually extracted from the 4lg9 PDB file and then aligned using the PDBeFold Server (36). The numbers on the left show the range of residue numbers in the sequence on that line. The one-letter amino acid codes are coloured as per the PROSITE sequence logo (A); lower-case letters correspond to residues not aligned in the 3D superposition. The numbers along the bottom roughly correspond to the sequence positions in the WD40 motif in (A). The amino acids having an orange background are those belonging to the Asp-His-Ser/Thr-Trp tetrad. The red borders identify the five amino acids involved in the DDD missense mutations: His213Gln, Asp328Gly, Asp370Tyr, His441Arg and Pro444Arg. The amino acids with the light grey backgrounds are the hotspot residues on the domain's top face, as identified by WDSPdb (31), being the ones likely to interact with β-catenin when it binds. Representation of the hydrogen-bonding network of the DHSW tetrad. Taken from the fifth WD40 motif in the 3D structure of TBLR1 (PDB entry 4lg9). (A) Schematic representation showing the four sidechains involved: Asp370, His348, Ser366 and Trp376. Hydrogen bonds are shown by the green dotted lines. (B) Three-dimensional representation showing the location and sidechains of the four tetrad residues; the rest of the domain is represented only by backbone atoms N, Cα and C. Potential hydrogen bonds are shown by the dashed lines. Note the importance of the highly conserved Asp370, which can not only hydrogen-bond to the histidine, but also to the backbone of neighbouring strands, helping hold the propeller-blade structure together. The five DDD missense mutations are His213Gln, Asp328Gly, Asp370Tyr, His441Arg and Pro444Arg. The first four involve histidine and aspartic acid residues from different symmetrically repeated DHSW tetrads, at positions 4 and 26 in the WD40 motif (Fig. 2B), so that their change is likely to disrupt the stability of the protein's fold. Of particular interest is the highly conserved aspartic acid at position 26 in the WD40 motif, which can hydrogen-bond to the tetrad's histidine and also to a main-chain nitrogen on the preceding propeller blade (Thr349 in Fig. 3), and to a main-chain nitrogen two residues down (Thr372 in Fig. 3). In their native state, both are structurally stabilizing interactions, helping to hold the propeller together. The latter interaction helps maintain the beta turn that joins the two beta strands either side of the Asp. The only non-DDD likely pathogenic missense mutation identified is Leu282Pro, which is at position 21 in the WD40 motif, adjacent to a DHSW tetrad, where addition of a proline residue likely alters the packing of the strands sufficiently to alter the hydrogen bond network inside the tetrad. The fifth of the DDD mutations, Pro444Arg, occurs at position 7 in the WD40 motif (Fig. 2B). This is not a highly conserved position, although there are three proline residues at this position in TBLR1. Here, the fact that the amino acid is on the domain's top face (Fig. 4), coupled with the dramatic nature of the change, is likely to be responsible for the deleterious effect of the mutation. The mutation places a large, charged arginine at the protein–protein interface, and this potentially interferes with, or disrupts, the interaction required for the protein's function. Z-axis location of all variants in the WD40 domain of TBLR1. (A) Graphical representation taken from the top to bottom face (PDB entry 4lg9). Likely pathogenic missense mutations are indicated in red (with new diagnoses from the DDD study completely filled), whereas population missense variants are indicated in green and other residues are indicated in blue. The backbone position of all residues is shown, based on the Z-axis location of the backbone carbonyl carbon in the crystal structure. Larger diamonds represent variants that are present multiple times across the databases, and crosses indicate the approximate interpolated location of residues that are absent from the PDB file. (B) Three-dimensional representation viewed from the side using PDB entry 4lg9, with all missense variants highlighted using-stick representation (space-filled for new DDD diagnoses). Likely pathogenic missense mutations are indicated in red, whereas population missense variants are indicated in green and the rest of the domain is represented using blue ribbons. (C) Boxplot of Z-axis location in PDF entry 4lg9 of diagnostic mutations (red), the conserved tetrads (beige), hotspot residues on the top face (grey), population variation (green) and all amino acid residues in the domain (blue) in the TBLR1 protein. P-values are not significant between the diagnostic/tetrad/top face residues or between population/all residues, but are significant between these groups (diagnostic versus population residues, P = 9×10−5). To evaluate the structural impact of the missense mutations in this domain further, the six amino acids with likely pathogenic missense mutations in the WD40 domain were compared with the 29 amino acids with benign population missense variation (Table 2). The location of these amino acids along the Z-axis of the protein structure in PDB entry 4lg9 was analysed, i.e. through the middle of the β-barrel, from the top to bottom face (Fig. 4A and B), indicating that the disease-associated amino acids are clustered in 3D space and significantly different—closer to the top binding face—from those associated with presumed benign variation (P = 9 × 10−5, Fig. 4C). In addition, disease-associated amino acids were also predicted to be significantly different from those associated with benign variation using PolyPhen (15) (P = 2 × 10−8), SIFT (18) (P = 7 × 10−4), solvent-exposed surface area (38) (P = 2 × 10−7) and residue conservation (39) (P = 8 × 10−4), but did not differ significantly from the tetrad or top face hotspot residues.

Discussion

We have used the 3D structure of the WD40 domain encoded by the gene TBL1XR1 to understand and characterize the differences between likely pathogenic de novo missense mutations detected in children with severe developmental delay and presumed benign missense variation seen in population samples and the ExAC data set. Although the variants are predicted to result in missense changes, the true biological effect on the resulting protein is unknown. As has been observed previously across all proteins (40), the likely pathogenic mutations in TBLR1 are generally at more buried and conserved sites when compared with population variation. When the structure of the WD40 domain of this protein is considered in detail, there is notable clustering in 3D space, with likely pathogenic mutations more likely to be near the top face of the domain. Specifically, likely pathogenic mutations in TBLR1 all affect either the structural rigidity of the WD40 domain β-propeller, compromising the stability of the fold, or the physicochemical characteristics of the top face of the β-propeller, affecting the binding of β-catenin. The overlap of these diagnostic variants with the previously identified symmetrically repeating DHSW tetrads and top face hotspot residues (34) allows us to make strong predictions about the location of other likely pathogenic genetic variations both in TBLR1 and in other instances of this domain. The WD40 domain is one of the top 10 most abundant domains in eukaryotic genomes, although rarely present in prokaryotes (22). Its primary role appears to be in making protein–protein interactions, which it can make simultaneously with several different proteins, particularly in relation to forming and regulating protein, DNA or RNA complexes (22,41). A number of diseases are known to be associated with mutations in WD40 domains (23,24), including numerous developmental phenotypes such as lissencephaly (42), short-rib thoracic dysplasia (43) and reduced neuronal migration (44). Twenty-one proteins containing such disease-associated mutations are listed in Supplementary Material, TableS2, with their corresponding locations in the WD40 motif. As next generation sequencing of gene panels and whole exomes/genomes is increasingly applied in both research and clinical settings, more and more benign and likely pathogenic missense variants will be uncovered in known disease genes as well as in novel disease genes. Although in silico predictions alone should not be relied on as the sole basis to determine the clinical significance of missense variants in proteins, we hope that the analysis used in this study provides useful structural evidence for variant interpretation. Moreover, combining clinical and population genetics with protein structural analysis offers widely applicable in silico method for improving the clinical interpretation of novel missense variation.

Materials and Methods

The DDD study was approved by the UK Research Ethics Committee (10/H0305/83, granted by the Cambridge South REC, and GEN/284/12 granted by the Republic of Ireland REC), and appropriate informed consent was obtained from all participants. Patients meeting the recruitment criteria (neurodevelopmental disorder and/or congenital anomalies, abnormal growth parameters, dysmorphic features and unusual behavioural phenotypes) were recruited to the DDD study (www.ddduk.org) by their UK NHS and Republic of Ireland Regional Genetics Service, who also recorded clinical information and phenotypes using the Human Phenotype Ontology (45) via a secure web portal within the DECIPHER database (46). DNA samples from patients and their parents were analysed by the Wellcome Trust Sanger Institute using high-resolution microarray analysis (array-CGH and SNP-genotyping) to investigate copy number variations in the child and by exome sequencing to investigate single nucleotide polymorphisms and small insertions/deletions (indels). Putative de novo sequence variants of interest were validated in-house using either targeted Sanger sequencing or MiSeq sequencing. All genomic variants were annotated with the most severe consequence predicted by Ensembl Variant Effect Predictor (47) and their minor allele frequencies observed in diverse population samples. As has been described previously (20), likely diagnostic variants were fed back to referring clinical geneticists for validation in an accredited diagnostic laboratory and discussion with the family via patients’ record in DECIPHER, where they can be viewed in an interactive genome browser. In a data set of the first 4295 family trios (child, mother and father) with exome sequence data, we investigated genes already robustly implicated in developmental disorders with more than three de novo mutations in DDD children, where the consequence was predicted to result in different missense changes. We cross-referenced this list against the Protein Data Bank (48) to limit our analysis to genes with solved protein structures and further refined the list to those where all missense changes lay within a high-quality crystal structure from the human-derived protein. We further excluded metalloproteins and enzymes in which the missense variants clustered in the catalytic site, and here we limit our discussion to just one gene, TBL1XR1, a fairly recently identified developmental disorder gene (25–29), in which multiple likely pathogenic missense mutations were found in DDD that map onto a 3D protein domain structure. Additional causal variants in TBL1XR1 in children with autism/developmental delay were identified through ClinVar (http://www.ncbi.nlm.nih.gov/clinvar/) (49) and a search of published literature. Population variation in this gene was also investigated using the ExAC (http://exac.broadinstitute.org/; accessed June 2015), dbSNP (http://www.ncbi.nlm.nih.gov/SNP/), the Exome Variant Server (NHLBI GO ESP; http://evs.gs.washington.edu/EVS/; accessed June 2015) and the European Variant Archive (http://www.ebi.ac.uk/eva/) (32).

Supplementary Material

Supplementary Material is available at .

Funding

This work was supported by the Health Innovation Challenge Fund (grant no. HICF-1009-003), a parallel funding partnership between the Wellcome Trust and the Department of Health and the Wellcome Trust Sanger Institute (grant no. WT098051). The views expressed in this publication are those of the author(s) and not necessarily those of the Wellcome Trust or the Department of Health. The study has UK Research Ethics Committee approval (10/H0305/83, granted by the Cambridge South REC, and GEN/284/12, granted by the Republic of Ireland REC). Funding to pay the Open Access publication charges for this article was provided by the Wellcome Trust Sanger Institute.
  48 in total

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Journal:  Genes Dev       Date:  2012-06-15       Impact factor: 11.361

4.  Mutations in the gene encoding IFT dynein complex component WDR34 cause Jeune asphyxiating thoracic dystrophy.

Authors:  Miriam Schmidts; Julia Vodopiutz; Sonia Christou-Savina; Claudio R Cortés; Aideen M McInerney-Leo; Richard D Emes; Heleen H Arts; Beyhan Tüysüz; Jason D'Silva; Paul J Leo; Tom C Giles; Machteld M Oud; Jessica A Harris; Marije Koopmans; Mhairi Marshall; Nursel Elçioglu; Alma Kuechler; Detlef Bockenhauer; Anthony T Moore; Louise C Wilson; Andreas R Janecke; Matthew E Hurles; Warren Emmet; Brooke Gardiner; Berthold Streubel; Belinda Dopita; Andreas Zankl; Hülya Kayserili; Peter J Scambler; Matthew A Brown; Philip L Beales; Carol Wicking; Emma L Duncan; Hannah M Mitchison
Journal:  Am J Hum Genet       Date:  2013-10-31       Impact factor: 11.025

5.  Policy challenges of clinical genome sequencing.

Authors:  Caroline F Wright; Anna Middleton; Hilary Burton; Fiona Cunningham; Steve E Humphries; Jane Hurst; Ewan Birney; Helen V Firth
Journal:  BMJ       Date:  2013-11-22

6.  Multiplex targeted sequencing identifies recurrently mutated genes in autism spectrum disorders.

Authors:  Brian J O'Roak; Laura Vives; Wenqing Fu; Jarrett D Egertson; Ian B Stanaway; Ian G Phelps; Gemma Carvill; Akash Kumar; Choli Lee; Katy Ankenman; Jeff Munson; Joseph B Hiatt; Emily H Turner; Roie Levy; Diana R O'Day; Niklas Krumm; Bradley P Coe; Beth K Martin; Elhanan Borenstein; Deborah A Nickerson; Heather C Mefford; Dan Doherty; Joshua M Akey; Raphael Bernier; Evan E Eichler; Jay Shendure
Journal:  Science       Date:  2012-11-15       Impact factor: 47.728

7.  dbNSFP: a lightweight database of human nonsynonymous SNPs and their functional predictions.

Authors:  Xiaoming Liu; Xueqiu Jian; Eric Boerwinkle
Journal:  Hum Mutat       Date:  2011-08       Impact factor: 4.878

8.  Identifying the hotspots on the top faces of WD40-repeat proteins from their primary sequences by β-bulges and DHSW tetrads.

Authors:  Xian-Hui Wu; Yang Wang; Zhu Zhuo; Fan Jiang; Yun-Dong Wu
Journal:  PLoS One       Date:  2012-08-15       Impact factor: 3.240

9.  DbVar and DGVa: public archives for genomic structural variation.

Authors:  Ilkka Lappalainen; John Lopez; Lisa Skipper; Timothy Hefferon; J Dylan Spalding; John Garner; Chao Chen; Michael Maguire; Matt Corbett; George Zhou; Justin Paschall; Victor Ananiev; Paul Flicek; Deanna M Church
Journal:  Nucleic Acids Res       Date:  2012-11-27       Impact factor: 16.971

10.  Defining the disease liability of variants in the cystic fibrosis transmembrane conductance regulator gene.

Authors:  Patrick R Sosnay; Karen R Siklosi; Fredrick Van Goor; Kyle Kaniecki; Haihui Yu; Neeraj Sharma; Anabela S Ramalho; Margarida D Amaral; Ruslan Dorfman; Julian Zielenski; David L Masica; Rachel Karchin; Linda Millen; Philip J Thomas; George P Patrinos; Mary Corey; Michelle H Lewis; Johanna M Rommens; Carlo Castellani; Christopher M Penland; Garry R Cutting
Journal:  Nat Genet       Date:  2013-08-25       Impact factor: 38.330

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  11 in total

1.  carP, encoding a Ca2+-regulated putative phytase, is evolutionarily conserved in Pseudomonas aeruginosa and has potential as a biomarker.

Authors:  Sergio E Mares; Michelle M King; Aya Kubo; Anna A Khanov; Erika I Lutter; Noha Youssef; Marianna A Patrauchan
Journal:  Microbiology (Reading)       Date:  2021-02       Impact factor: 2.777

2.  TBL1XR1 Mutations Drive Extranodal Lymphoma by Inducing a Pro-tumorigenic Memory Fate.

Authors:  Leandro Venturutti; Matt Teater; Andrew Zhai; Amy Chadburn; Leena Babiker; Daleum Kim; Wendy Béguelin; Tak C Lee; Youngjun Kim; Christopher R Chin; William T Yewdell; Brian Raught; Jude M Phillip; Yanwen Jiang; Louis M Staudt; Michael R Green; Jayanta Chaudhuri; Olivier Elemento; Pedro Farinha; Andrew P Weng; Michael D Nissen; Christian Steidl; Ryan D Morin; David W Scott; Gilbert G Privé; Ari M Melnick
Journal:  Cell       Date:  2020-07-02       Impact factor: 41.582

Review 3.  WD40 repeat domain proteins: a novel target class?

Authors:  Matthieu Schapira; Mike Tyers; Maricel Torrent; Cheryl H Arrowsmith
Journal:  Nat Rev Drug Discov       Date:  2017-10-13       Impact factor: 84.694

4.  Structure of the MeCP2-TBLR1 complex reveals a molecular basis for Rett syndrome and related disorders.

Authors:  Valdeko Kruusvee; Matthew J Lyst; Ceitidh Taylor; Žygimantė Tarnauskaitė; Adrian P Bird; Atlanta G Cook
Journal:  Proc Natl Acad Sci U S A       Date:  2017-03-27       Impact factor: 11.205

5.  Accurate prediction of functional, structural, and stability changes in PITX2 mutations using in silico bioinformatics algorithms.

Authors:  Morteza Seifi; Michael A Walter
Journal:  PLoS One       Date:  2018-04-17       Impact factor: 3.240

6.  Nuclear Receptor Coactivators (NCOAs) and Corepressors (NCORs) in the Brain.

Authors:  Zheng Sun; Yong Xu
Journal:  Endocrinology       Date:  2020-08-01       Impact factor: 4.736

7.  Histone Deacetylase 3 Governs Perinatal Cerebral Development via Neural Stem and Progenitor Cells.

Authors:  Lin Li; Jianliang Jin; Xiang-Jiao Yang
Journal:  iScience       Date:  2019-09-14

Review 8.  Nuclear receptor corepressors in intellectual disability and autism.

Authors:  Yan Kong; Wenjun Zhou; Zheng Sun
Journal:  Mol Psychiatry       Date:  2020-02-07       Impact factor: 15.992

9.  New genes involved in Angelman syndrome-like: Expanding the genetic spectrum.

Authors:  Cinthia Aguilera; Elisabeth Gabau; Ariadna Ramirez-Mallafré; Carme Brun-Gasca; Jana Dominguez-Carral; Veronica Delgadillo; Steve Laurie; Sophia Derdak; Natàlia Padilla; Xavier de la Cruz; Núria Capdevila; Nino Spataro; Neus Baena; Miriam Guitart; Anna Ruiz
Journal:  PLoS One       Date:  2021-10-15       Impact factor: 3.240

10.  Recon3D enables a three-dimensional view of gene variation in human metabolism.

Authors:  Elizabeth Brunk; Swagatika Sahoo; Daniel C Zielinski; Ali Altunkaya; Andreas Dräger; Nathan Mih; Francesco Gatto; Avlant Nilsson; German Andres Preciat Gonzalez; Maike Kathrin Aurich; Andreas Prlić; Anand Sastry; Anna D Danielsdottir; Almut Heinken; Alberto Noronha; Peter W Rose; Stephen K Burley; Ronan M T Fleming; Jens Nielsen; Ines Thiele; Bernhard O Palsson
Journal:  Nat Biotechnol       Date:  2018-02-19       Impact factor: 54.908

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