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Recent work has revealed an important role for rare, incompletely penetrant inherited coding variants in neurodevelopmental disorders (NDDs). Additionally, we have previously shown that common variants contribute to risk for rare NDDs. Here, we investigate whether common variants exert their effects by modifying gene expression, using multi-cis-expression quantitative trait loci (cis-eQTL) prediction models. We first performed a transcriptome-wide association study for NDDs using 6,987 probands from the Deciphering Developmental Disorders (DDD) study and 9,720 controls, and found one gene,RAB2A, that passed multiple testing correction (p = 6.7×10−7). We then investigated whethercis-eQTLs modify the penetrance of putatively damaging, rare coding variants inherited by NDD probands from their unaffected parents in a set of 1,700 trios. We found no evidence that unaffected parents transmitting putatively damaging coding variants had higher genetically-predicted expression of the variant-harboring gene than their child. In probands carrying putatively damaging variants in constrained genes, the genetically-predicted expression of these genes in blood was lower than in controls (p = 2.7×10−3). However, results for proband-control comparisons were inconsistent across different sets of genes, variant filters and tissues. We find limited evidence that commoncis-eQTLs modify penetrance of rare coding variants in a large cohort of NDD probands.

OBJECTIVE To determine the incremental yield of exome sequencing (ES) over chromosomal microarray analysis (CMA) or karyotyping in prenatally diagnosed non-immune hydrops fetalis (NIHF). METHODS A prospective cohort study (comprising an extended group of the Prenatal Assessment of Genomes and Exomes (PAGE) study) was performed which included 28 cases of prenatally diagnosed NIHF undergoing trio ES following negative CMA or karyotyping. These cases were combined with data from a systematic review of the literature. MEDLINE, EMBASE, CINAHL and ClinicalTrials.gov databases were searched electronically (January 2000 to October 2020) for studies reporting on the incremental yield of ES over CMA or karyotyping in fetuses with prenatally detected NIHF. Inclusion criteria for the systematic review were: (i) at least two cases of NIHF undergoing sequencing; (ii) testing initiated based on prenatal ultrasound-based phenotype; and (iii) negative CMA or karyotyping result. The incremental diagnostic yield of ES was assessed in: (i) all cases of NIHF; (ii) isolated NIHF; (iii) NIHF associated with an additional fetal structural anomaly; and (iv) NIHF according to severity (i.e. two vs three or more cavities affected). RESULTS In the extended PAGE study cohort, the additional diagnostic yield of ES over CMA or karyotyping was 25.0% (7/28) in all NIHF cases, 21.4% (3/14) in those with isolated NIHF and 28.6% (4/14) in those with non-isolated NIHF. In the meta-analysis, the pooled incremental yield based on 21 studies (306 cases) was 29% (95% CI, 24-34%; P

OBJECTIVE To evaluate the utility of prenatal exome sequencing (ES) for isolated increased nuchal translucency (NT) and to investigate factors that increase diagnostic yield. DESIGN Retrospective analysis of data from two prospective cohort studies. SETTING Fetal medicine centres in the UK and USA. POPULATION Fetuses with increased NT ≥3.5 mm at 11-14 weeks of gestation recruited to the Prenatal Assessment of Genomes and Exomes (PAGE) and Columbia fetal whole exome sequencing studies (n = 213). METHODS We grouped cases based on (1) the presence of additional structural abnormalities at presentation in the first trimester or later in pregnancy, and (2) NT measurement at presentation. We compared diagnostic rates between groups using Fisher exact test. MAIN OUTCOME MEASURES Detection of diagnostic genetic variants considered to have caused the observed fetal structural anomaly. RESULTS Diagnostic variants were detected in 12 (22.2%) of 54 fetuses presenting with non-isolated increased NT, 12 (32.4%) of 37 fetuses with isolated increased NT in the first trimester and additional abnormalities later in pregnancy, and 2 (1.8%) of 111 fetuses with isolated increased NT in the first trimester and no other abnormalities on subsequent scans. Diagnostic rate also increased with increasing size of NT. CONCLUSIONS The diagnostic yield of prenatal ES is low for fetuses with isolated increased NT but significantly higher where there are additional structural anomalies. Prenatal ES may not be appropriate for truly isolated increased NT but timely, careful ultrasound scanning to identify other anomalies emerging later can direct testing to focus where there is a higher likelihood of diagnosis.

BackgroundPediatric disorders include a range of highly genetically heterogeneous conditions that are amenable to genome-wide diagnostic approaches. Finding a molecular diagnosis is challenging but can have profound lifelong benefits.MethodsThe Deciphering Developmental Disorders (DDD) study recruited >33,500 individuals from families with severe, likely monogenic developmental disorders from 24 regional genetics services around the UK and Ireland. We collected detailed standardised phenotype data and performed whole-exome sequencing and microarray analysis to investigate novel genetic causes. We developed an augmented variant analysis and re-analysis pipeline to maximise sensitivity and specificity, and communicated candidate variants to clinical teams for validation and diagnostic interpretation. We performed multiple regression analyses to evaluate factors affecting the probability of being diagnosed.ResultsWe reported approximately one candidate variant per parent-offspring trio and 2.5 variants per singleton proband, including both sequence and structural variants. Using clinical and computational approaches to variant classification, we have achieved a diagnosis in at least 34% (4507 probands), of whom 67% have a pathogenicde novomutation. Being recruited as a parent-offspring trio had the largest impact on the chance of being diagnosed (OR=4.70). Probands who were extremely premature (OR=0.39), hadin uteroexposure to antiepileptic medications (OR=0.44), or whose mothers had diabetes (OR=0.52) were less likely to be diagnosed, as were those of African ancestry (OR=0.51).ConclusionsOptimising diagnosis and discovery in highly penetrant genomic disease depends upon ongoing and novel scientific analyses, ethical recruitment and feedback policies, and collaborative clinical-research partnerships.

OBJECTIVE To determine the incremental yield of antenatal exome sequencing (ES) over chromosomal microarray analysis (CMA) or conventional karyotyping in prenatally diagnosed congenital heart disease (CHD). METHODS A prospective cohort study of 197 trios undergoing ES following CMA or karyotyping owing to CHD identified prenatally and a systematic review of the literature were performed. MEDLINE, EMBASE, CINAHL and ClinicalTrials.gov (January 2000 to October 2019) databases were searched electronically for studies reporting on the diagnostic yield of ES in prenatally diagnosed CHD. Selected studies included those with more than three cases, with initiation of testing based upon prenatal phenotype only and that included cases in which CMA or karyotyping was negative. The incremental diagnostic yield of ES was assessed in: (1) all cases of CHD; (2) isolated CHD; (3) CHD associated with extracardiac anomaly (ECA); and (4) CHD according to phenotypic subgroup. RESULTS In our cohort, ES had an additional diagnostic yield in all CHD, isolated CHD and CHD associated with ECA of 12.7% (25/197), 11.5% (14/122) and 14.7% (11/75), respectively (P = 0.81). The corresponding pooled incremental yields from 18 studies (encompassing 636 CHD cases) included in the systematic review were 21% (95% CI, 15-27%), 11% (95% CI, 7-15%) and 37% (95% CI, 18-56%), respectively. The results did not differ significantly when subanalysis was limited to studies including more than 20 cases, except for CHD associated with ECA, in which the incremental yield was greater (49% (95% CI, 17-80%)). In cases of CHD associated with ECA in the primary analysis, the most common extracardiac anomalies associated with a pathogenic variant were those affecting the genitourinary system (23/52 (44.2%)). The greatest incremental yield was in cardiac shunt lesions (41% (95% CI, 19-63%)), followed by right-sided lesions (26% (95% CI, 9-43%)). In the majority (68/96 (70.8%)) of instances, pathogenic variants occurred de novo and in autosomal dominant (monoallelic) disease genes. The most common (19/96 (19.8%)) monogenic syndrome identified was Kabuki syndrome. CONCLUSIONS There is an apparent incremental yield of prenatal ES in CHD. While the greatest yield is in CHD associated with ECA, consideration could also be given to performing ES in the presence of an isolated cardiac abnormality. A policy of routine application of ES would require the adoption of robust bioinformatic, clinical and ethical pathways. Copyright © 2020 ISUOG. Published by John Wiley & Sons Ltd.

Purpose Automated variant filtering is an essential part of diagnostic genome-wide sequencing but may generate false negative results. We sought to investigate whether some previously identified pathogenic variants may be being routinely excluded by standard variant filtering pipelines. Methods We evaluated variants that were previously classified as pathogenic or likely pathogenic in ClinVar in known developmental disorder genes using exome sequence data from the Deciphering Developmental Disorders (DDD) study. Results Of these ClinVar pathogenic variants, 3.6% were identified among 13,462 DDD probands, and 1134/1352 (83.9%) had already been independently communicated to clinicians using DDD variant filtering pipelines as plausibly pathogenic. The remaining 218 variants failed consequence, inheritance, or other automated variant filters. Following clinical review of these additional variants, we were able to identify 112 variants in 107 (0.8%) DDD probands as potential diagnoses. Conclusion Lower minor allele frequency (1 star) are good predictors of a previously identified variant being plausibly diagnostic for developmental disorders. However, around half of previously identified pathogenic variants excluded by automated variant filtering did not appear to be disease-causing, underlining the continued need for clinical evaluation of candidate variants as part of the diagnostic process.

PurposeStructural mosaicism has been previously implicated in developmental disorders. We aim to identify rare mosaic chromosomal alterations (MCAs) in probands with severe undiagnosed developmental disorders.MethodsWe identified MCAs in SNP array data from 12,530 probands in the Deciphering Developmental Disorders (DDD) study using MoChA.ResultsWe found 61 MCAs in 57 probands, many of these were tissue specific. In 23/26 (88.5%) cases for which the MCA was detected in saliva where blood was also available for analysis, the MCA could not be detected in blood. The MCAs included 20 polysomies, comprising either one arm of a chromosome or a whole chromosome, for which we were able to show the timing of the error (25% mitosis, 40% meiosis I, 35% meiosis II). Only 2/57 (3.5%) of the probands in whom we found MCAs had another likely genetic diagnosis identified by whole exome sequencing, despite an overall diagnostic yield of ∼40% across the cohort.ConclusionOur results show that identification of MCAs provides candidate diagnoses for previously undiagnosed patients with developmental disorders, potentially explaining ∼0.45% of cases in the DDD study. Nearly 90% of these MCAs would have remained undetected by analysing DNA from blood and no other tissue.

Structural variation (SV) describes a broad class of genetic variation greater than 50 bp in size. SVs can cause a wide range of genetic diseases and are prevalent in rare developmental disorders (DDs). Individuals presenting with DDs are often referred for diagnostic testing with chromosomal microarrays (CMAs) to identify large copy-number variants (CNVs) and/or with single-gene, gene-panel, or exome sequencing (ES) to identify single-nucleotide variants, small insertions/deletions, and CNVs. However, individuals with pathogenic SVs undetectable by conventional analysis often remain undiagnosed. Consequently, we have developed the tool InDelible, which interrogates short-read sequencing data for split-read clusters characteristic of SV breakpoints. We applied InDelible to 13,438 probands with severe DDs recruited as part of the Deciphering Developmental Disorders (DDD) study and discovered 63 rare, damaging variants in genes previously associated with DDs missed by standard SNV, indel, or CNV discovery approaches. Clinical review of these 63 variants determined that about half (30/63) were plausibly pathogenic. InDelible was particularly effective at ascertaining variants between 21 and 500 bp in size and increased the total number of potentially pathogenic variants identified by DDD in this size range by 42.9%. Of particular interest were seven confirmed de novo variants in MECP2, which represent 35.0% of all de novo protein-truncating variants in MECP2 among DDD study participants. InDelible provides a framework for the discovery of pathogenic SVs that are most likely missed by standard analytical workflows and has the potential to improve the diagnostic yield of ES across a broad range of genetic diseases. ispartof: AMERICAN JOURNAL OF HUMAN GENETICS vol:108 issue:11 pages:2186-2194 ispartof: location:United States status: published

SummaryStructural Variation (SV) describes a broad class of genetic variation greater than 50bps in size. SVs can cause a wide range of genetic diseases and are prevalent in rare developmental disorders (DD). Patients presenting with DD are often referred for diagnostic testing with chromosomal microarrays (CMA) to identify large copy-number variants (CNVs) and/or with single gene, gene-panel, or exome sequencing (ES) to identify single nucleotide variants, small insertions/deletions, and CNVs. However, patients with pathogenic SVs undetectable by conventional analysis often remain undiagnosed. Consequently, we have developed the novel tool ‘InDelible’, which interrogates short-read sequencing data for split-read clusters characteristic of SV breakpoints. We applied InDelible to 13,438 probands with severe DD recruited as part of the Deciphering Developmental Disorders (DDD) study and discovered 64 rare, damaging variants in genes previously associated with DD missed by standard SNV, InDel or CNV discovery approaches. Clinical review of these 64 variants determined that about half (30/64) were plausibly pathogenic. InDelible was particularly effective at ascertaining variants between 21-500 bps in size, and increased the total number of potentially pathogenic variants identified by DDD in this size range by 42.3%. Of particular interest were seven confirmed de novo variants in MECP2 which represent 35.0% of all de novo protein truncating variants in MECP2 among DDD patients. InDelible provides a framework for the discovery of pathogenic SVs that are likely missed by standard analytical workflows and has the potential to improve the diagnostic yield of ES across a broad range of genetic diseases.

SummaryDe novo mutations (DNMs) in protein-coding genes are a well-established cause of developmental disorders (DD). However, known DD-associated genes only account for a minority of the observed excess of such DNMs. To identify novel DD-associated genes, we integrated healthcare and research exome sequences on 31,058 DD parent-offspring trios, and developed a simulation-based statistical test to identify gene-specific enrichments of DNMs. We identified 285 significantly DD-associated genes, including 28 not previously robustly associated with DDs. Despite detecting more DD-associated genes than in any previous study, much of the excess of DNMs of protein-coding genes remains unaccounted for. Modelling suggests that over 1,000 novel DD-associated genes await discovery, many of which are likely to be less penetrant than the currently known genes. Research access to clinical diagnostic datasets will be critical for completing the map of dominant DDs.

Background Fetal structural anomalies, which are detected by ultrasonography, have a range of genetic causes, including chromosomal aneuploidy, copy number variations (CNVs; which are detectable by chromosomal microarrays), and pathogenic sequence variants in developmental genes. Testing for aneuploidy and CNVs is routine during the investigation of fetal structural anomalies, but there is little information on the clinical usefulness of genome-wide next-generation sequencing in the prenatal setting. We therefore aimed to evaluate the proportion of fetuses with structural abnormalities that had identifiable variants in genes associated with developmental disorders when assessed with whole-exome sequencing (WES). Methods In this prospective cohort study, two groups in Birmingham and London recruited patients from 34 fetal medicine units in England and Scotland. We used whole-exome sequencing (WES) to evaluate the presence of genetic variants in developmental disorder genes (diagnostic genetic variants) in a cohort of fetuses with structural anomalies and samples from their parents, after exclusion of aneuploidy and large CNVs. Women were eligible for inclusion if they were undergoing invasive testing for identified nuchal translucency or structural anomalies in their fetus, as detected by ultrasound after 11 weeks of gestation. The partners of these women also had to consent to participate. Sequencing results were interpreted with a targeted virtual gene panel for developmental disorders that comprised 1628 genes. Genetic results related to fetal structural anomaly phenotypes were then validated and reported postnatally. The primary endpoint, which was assessed in all fetuses, was the detection of diagnostic genetic variants considered to have caused the fetal developmental anomaly. Findings The cohort was recruited between Oct 22, 2014, and June 29, 2017, and clinical data were collected until March 31, 2018. After exclusion of fetuses with aneuploidy and CNVs, 610 fetuses with structural anomalies and 1202 matched parental samples (analysed as 596 fetus-parental trios, including two sets of twins, and 14 fetus-parent dyads) were analysed by WES. After bioinformatic filtering and prioritisation according to allele frequency and effect on protein and inheritance pattern, 321 genetic variants (representing 255 potential diagnoses) were selected as potentially pathogenic genetic variants (diagnostic genetic variants), and these variants were reviewed by a multidisciplinary clinical review panel. A diagnostic genetic variant was identified in 52 (8·5%; 95% CI 6·4–11·0) of 610 fetuses assessed and an additional 24 (3·9%) fetuses had a variant of uncertain significance that had potential clinical usefulness. Detection of diagnostic genetic variants enabled us to distinguish between syndromic and non-syndromic fetal anomalies (eg, congenital heart disease only vs a syndrome with congenital heart disease and learning disability). Diagnostic genetic variants were present in 22 (15·4%) of 143 fetuses with multisystem anomalies (ie, more than one fetal structural anomaly), nine (11·1%) of 81 fetuses with cardiac anomalies, and ten (15·4%) of 65 fetuses with skeletal anomalies; these phenotypes were most commonly associated with diagnostic variants. However, diagnostic genetic variants were least common in fetuses with isolated increased nuchal translucency (≥4·0 mm) in the first trimester (in three [3·2%] of 93 fetuses). Interpretation WES facilitates genetic diagnosis of fetal structural anomalies, which enables more accurate predictions of fetal prognosis and risk of recurrence in future pregnancies. However, the overall detection of diagnostic genetic variants in a prospectively ascertained cohort with a broad range of fetal structural anomalies is lower than that suggested by previous smaller-scale studies of fewer phenotypes. WES improved the identification of genetic disorders in fetuses with structural abnormalities; however, before clinical implementation, careful consideration should be given to case selection to maximise clinical usefulness. Funding UK Department of Health and Social Care and The Wellcome Trust.

Approximately 2% of de novo single nucleotide variants (SNVs) appear as part of clustered mutations that create multinucleotide variants (MNVs). MNVs are an important source of genomic variability as they are more likely to alter an encoded protein than a SNV, which has important implications in disease as well as evolution. Previous studies of MNVs have focused on their mutational origins and have not systematically evaluated their functional impact and contribution to disease. We identified 69,940 MNVs and 106 de novo MNVs in 6,688 exome sequenced parent-offspring trios from the Deciphering Developmental Disorders Study comprising families with severe developmental disorders. We replicated the previously described MNV mutational signatures associated with DNA polymerase zeta, an error-prone translesion polymerase, and the APOBEC family of DNA deaminases. We found that most MNVs within a single codon create a missense change that could not have been created by a SNV. MNV-induced missense changes were, on average, more physico-chemically divergent, more depleted in highly constrained genes (pLI>=0.9) and were under stronger purifying selection compared to SNV-induced missense changes. We found that de novo MNVs were significantly enriched in genes previously associated with developmental disorders in affected children. This demonstrates that MNVs can be more damaging than SNVs even when both induce missense changes and are an important variant type to consider in relation to human disease.

The GO annotation dataset provided by the UniProt Consortium (GOA: http://www.ebi.ac.uk/GOA) is a comprehensive set of evidenced-based associations between terms from the Gene Ontology resource and UniProtKB proteins. Currently supplying over 100 million annotations to 11 million proteins in more than 360,000 taxa, this resource has increased 2-fold over the last 2 years and has benefited from a wealth of checks to improve annotation correctness and consistency as well as now supplying a greater information content enabled by GO Consortium annotation format developments. Detailed, manual GO annotations obtained from the curation of peer-reviewed papers are directly contributed by all UniProt curators and supplemented with manual and electronic annotations from 36 model organism and domain-focused scientific resources. The inclusion of high-quality, automatic annotation predictions ensures the UniProt GO annotation dataset supplies functional information to a wide range of proteins, including those from poorly characterized, non-model organism species. UniProt GO annotations are freely available in a range of formats accessible by both file downloads and web-based views. In addition, the introduction of a new, normalized file format in 2010 has made for easier handling of the complete UniProt-GOA data set.

The Gene Ontology (GO) (http://www.geneontology.org) is a community bioinformatics resource that represents gene product function through the use of structured, controlled vocabularies. The number of GO annotations of gene products has increased due to curation efforts among GO Consortium (GOC) groups, including focused literature-based annotation and ortholog-based functional inference. The GO ontologies continue to expand and improve as a result of targeted ontology development, including the introduction of computable logical definitions and development of new tools for the streamlined addition of terms to the ontology. The GOC continues to support its user community through the use of e-mail lists, social media and web-based resources.

International audience; In the last two years the Pfam database (http://pfam.xfam.org) has undergone a substantial reorganisation to reduce the effort involved in making a release, thereby permitting more frequent releases. Arguably the most significant of these changes is that Pfam is now primarily based on the UniProtKB reference proteomes, with the counts of matched sequences and species reported on the website restricted to this smaller set. Building families on reference proteomes sequences brings greater stability, which decreases the amount of manual curation required to maintain them. It also reduces the number of sequences displayed on the website, whilst still providing access to many important model organisms. Matches to the full UniProtKB database are, however, still available and Pfam annotations for individual UniProtKB sequences can still be retrieved. Some Pfam entries (1.6%) which have no matches to reference proteomes remain; we are working with UniProt to see if sequences from them can be incorporated into reference proteomes. Pfam-B, the automatically-generated supplement to Pfam, has been removed. The current release (Pfam 29.0) includes 16 295 entries and 559 clans. The facility to view the relationship between families within a clan has been improved by the introduction of a new tool.

The primary task of the Rfam database is to collate experimentally validated noncoding RNA (ncRNA) sequences from the published literature and facilitate the prediction and annotation of new homologues in novel nucleotide sequences. We group homologous ncRNA sequences into "families" and related families are further grouped into "clans." We collate and manually curate data cross-references for these families from other databases and external resources. Our Web site offers researchers a simple interface to Rfam and provides tools with which to annotate their own sequences using our covariance models (CMs), through our tools for searching, browsing, and downloading information on Rfam families. In this chapter, we will work through examples of annotating a query sequence, collating family information, and searching for data.

The primary task of the Rfam database is to collate experimentally validated noncoding RNA (ncRNA) sequences from the published literature and facilitate the prediction and annotation of new homologues in novel nucleotide sequences. We group homologous ncRNA sequences into "families" and related families are further grouped into "clans." We collate and manually curate data cross-references for these families from other databases and external resources. Our Web site offers researchers a simple interface to Rfam and provides tools with which to annotate their own sequences using our covariance models (CMs), through our tools for searching, browsing, and downloading information on Rfam families. In this chapter, we will work through examples of annotating a query sequence, collating family information, and searching for data.

Background Gut microbiome metagenomics has revealed many protein families and domains found largely or exclusively in that environment. Proteins containing the GxGYxYP domain are over-represented in the gut microbiota, and are found in Polysaccharide Utilization Loci in the gut symbiont Bacteroides thetaiotaomicron, suggesting their involvement in polysaccharide metabolism, but little else is known of the function of this domain. Results Genomic context and domain architecture analyses support a role for the GxGYxYP domain in carbohydrate metabolism. Sparse occurrences in eukaryotes are the result of lateral gene transfer. The structure of the GxGYxYP domain-containing protein encoded by the BT2193 locus reveals two structural domains, the first composed of three divergent repeats with no recognisable homology to previously solved structures, the second a more familiar seven-stranded β/α barrel. Structure-based analyses including conservation mapping localise a presumed functional site to a cleft between the two domains of BT2193. Matching to a catalytic site template from a GH9 cellulase and other analyses point to a putative catalytic triad composed of Glu272, Asp331 and Asp333. Conclusions We suggest that GxGYxYP-containing proteins constitute a novel glycoside hydrolase family of as yet unknown specificity.

Background Bacteroides spp. form a significant part of our gut microbiome and are well known for optimized metabolism of diverse polysaccharides. Initial analysis of the archetypal Bacteroides thetaiotaomicron genome identified 172 glycosyl hydrolases and a large number of uncharacterized proteins associated with polysaccharide metabolism. Results BT_1012 from Bacteroides thetaiotaomicron VPI-5482 is a protein of unknown function and a member of a large protein family consisting entirely of uncharacterized proteins. Initial sequence analysis predicted that this protein has two domains, one on the N- and one on the C-terminal. A PSI-BLAST search found over 150 full length and over 90 half size homologs consisting only of the N-terminal domain. The experimentally determined three-dimensional structure of the BT_1012 protein confirms its two-domain architecture and structural analysis of both domains suggests their specific functions. The N-terminal domain is a putative catalytic domain with significant similarity to known glycoside hydrolases, the C-terminal domain has a beta-sandwich fold typically found in C-terminal domains of other glycosyl hydrolases, however these domains are typically involved in substrate binding. We describe the structure of the BT_1012 protein and discuss its sequence-structure relationship and their possible functional implications. Conclusions Structural and sequence analyses of the BT_1012 protein identifies it as a glycosyl hydrolase, expanding an already impressive catalog of enzymes involved in polysaccharide metabolism in Bacteroides spp. Based on this we have renamed the Pfam families representing the two domains found in the BT_1012 protein, PF13204 and PF12904, as putative glycoside hydrolase and glycoside hydrolase-associated C-terminal domain respectively.

The recycling of photosynthetically fixed carbon in plant cell walls is a key microbial process. In anaerobes, the degradation is carried out by a high molecular weight multifunctional complex termed the cellulosome. This consists of a number of independent enzyme components, each of which contains a conserved dockerin domain, which functions to bind the enzyme to a cohesin domain within the protein scaffoldin protein. Here we describe the first three-dimensional structure of a fungal dockerin, the N-terminal dockerin of Cel45A from the anaerobic fungus Piromyces equi. The structure contains a novel fold of 42 residues. The ligand binding site consists of residues Trp 35, Tyr 8 and Asp 23, which are conserved in all fungal dockerins. The binding site is on the opposite side of the N- and C-termini of the molecule, implying that tandem dockerin domains, seen in the majority of anaerobic fungal plant cell wall degrading enzymes, could present multiple simultaneous binding sites and, therefore, permit tailoring of binding to catalytic demands.

Enzyme-linked immunosorbant assays (ELISAs) were developed for the detection of antibodies against the major envelope glycoprotein (G(L)) of equine arteritis virus (EAV). A 6-Histidine tagged recombinant protein expressing the complete G(L) ectodomain (G(L)-6His), a glutathione-S-transferase recombinant protein expressing amino acids 55-98 of G(L) (G(L)-GST) and an ovalbumin-conjugated synthetic peptide representing amino acids 81-106 of G(L) (G(L)-OVA) were used as diagnostic antigens. An ELISA procedure was developed and optimised for each antigen. The G(L)-OVA and G(L)-6His assays showed the greatest specificity while the G(L)-GST assay was slightly more sensitive that the G(L)-OVA and G(L)-6His assays; results based on the analysis of 50 virus neutralisation positive and 50 virus neutralisation negative sera. The G(L)-OVA ELISA was selected for further evaluation since it was simpler to use than ELISAs based on recombinant antigens and did not suffer from background reactivity. The final sensitivity and specificity of the G(L)-OVA ELISA were 96.75 and 95.6%, respectively, results based on the analysis of 400 virus neutralisation positive and 400 virus neutralisation negative sera. It also detected EAV antibody (100% efficiency) in seropositive shedding stallions and, in ponies infected experimentally with the UK93 isolate of EAV, the appearance of virus neutralising antibodies and G(L)-OVA ELISA-specific immunoglobulins coincided.

Two endoglucanase cDNAs, designated cel5A and cel45A, were isolated from a cDNA library of the anaerobic fungus Piromyces equi. Sequence analysis revealed that cel5A has an open reading frame of 5142 bp and encodes a 1714 amino acid modular enzyme, Cel5A, with a molecular mass of 194847 Da. Cel5A consists of four catalytic domains homologous to family-5 glycosyl hydrolases, two C-terminal dockerins and one N-terminal dockerin. This is the first report of a complete gene containing tandem repeats of family-5 catalytic domains. The cDNA cel45A has an open reading frame of 1233 bp and encodes a 410 amino acid modular enzyme, Cel45A, with a molecular mass of 44380 Da. The catalytic domain, located at the C terminus, is homologous to the family-45 glycosyl hydrolases. Cel45A is the first family-45 enzyme to be described in an anaerobe. The presence of dockerins at the N and C termini of Cel5A and at the N terminus of Cel45A implies that both enzymes are part of the high-molecular-mass cellulose-degrading complex produced by Piromyces equi. The catalytic domain nearest the C terminus of Cel5A and the catalytic domain of Cel45A were hyperexpressed as thioredoxin fusion proteins, Trx-Cel5A′ and Trx-Cel45A′, and subjected to biochemical analysis. Trx-Cel5A′ has a broad substrate range, showing activity against carboxymethylcellulose, acid-swollen cellulose, barley β-glucan, lichenin, carob galactomannan,p-nitrophenyl β-D-cellobiopyranoside and xylan. Trx-Cel45A′ is active against carboxymethylcellulose, acid-swollen cellulose and the mixed linkage glucans, barley β-glucan and lichenin.

Pfam, available via servers in the UK (http://pfam.sanger.ac.uk/) and the USA (http://pfam.janelia.org/), is a widely used database of protein families, containing 14 831 manually curated entries in the current release, version 27.0. Since the last update article 2 years ago, we have generated 1182 new families and maintained sequence coverage of the UniProt Knowledgebase (UniProtKB) at nearly 80%, despite a 50% increase in the size of the underlying sequence database. Since our 2012 article describing Pfam, we have also undertaken a comprehensive review of the features that are provided by Pfam over and above the basic family data. For each feature, we determined the relevance, computational burden, usage statistics and the functionality of the feature in a website context. As a consequence of this review, we have removed some features, enhanced others and developed new ones to meet the changing demands of computational biology. Here, we describe the changes to Pfam content. Notably, we now provide family alignments based on four different representative proteome sequence data sets and a new interactive DNA search interface. We also discuss the mapping between Pfam and known 3D structures.