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      Spectrum of germline AIRE mutations causing APS-1 and familial hypoparathyroidism


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          The autoimmune polyendocrine syndrome type 1 (APS-1) is an autosomal recessive disorder characterised by immune dysregulation and autoimmune endocrine gland destruction. APS-1 is caused by biallelic mutations affecting the autoimmune regulator ( AIRE) gene on chromosome 21q22.3, which facilitates immunological self-tolerance. The objective was to investigate >300 probands with suspected APS-1 or isolated hypoparathyroidism for AIRE abnormalities.


          Probands were assessed by DNA sequence analysis. Novel variants were characterised using 3D modelling of the AIRE protein. Restriction enzyme and microsatellite analysis were used to investigate for uniparental isodisomy.


          Biallelic AIRE mutations were identified in 35 probands with APS-1 and 5 probands with isolated hypoparathyroidism. These included a novel homozygous p.(His14Pro) mutation, predicted to disrupt the N-terminal caspase activation recruitment domain of the AIRE protein. Furthermore, an apparently homozygous AIRE mutation, p.Leu323fs, was identified in an APS-1 proband, who is the child of non-consanguineous asymptomatic parents. Microsatellite analysis revealed that the proband inherited two copies of the paternal mutant AIRE allele due to uniparental isodisomy. Hypoparathyroidism was the most common endocrine manifestation in AIRE mutation-positive probands and >45% of those harbouring AIRE mutations had at least two diseases out of the triad of candidiasis, hypoparathyroidism, and hypoadrenalism. In contrast, type 1 diabetes and hypothyroidism occurred more frequently in AIRE mutation-negative probands with suspected APS-1. Around 30% of AIRE mutation-negative probands with isolated hypoparathyroidism harboured mutations in other hypoparathyroid genes.


          This study of a large cohort referred for AIRE mutational analysis expands the spectrum of genetic abnormalities causing APS-1.

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          Highly accurate protein structure prediction with AlphaFold

          Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 1 – 4 , the structures of around 100,000 unique proteins have been determined 5 , but this represents a small fraction of the billions of known protein sequences 6 , 7 . Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’ 8 —has been an important open research problem for more than 50 years 9 . Despite recent progress 10 – 14 , existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14) 15 , demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.
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            The Human Gene Mutation Database: towards a comprehensive repository of inherited mutation data for medical research, genetic diagnosis and next-generation sequencing studies

            The Human Gene Mutation Database (HGMD®) constitutes a comprehensive collection of published germline mutations in nuclear genes that underlie, or are closely associated with human inherited disease. At the time of writing (March 2017), the database contained in excess of 203,000 different gene lesions identified in over 8000 genes manually curated from over 2600 journals. With new mutation entries currently accumulating at a rate exceeding 17,000 per annum, HGMD represents de facto the central unified gene/disease-oriented repository of heritable mutations causing human genetic disease used worldwide by researchers, clinicians, diagnostic laboratories and genetic counsellors, and is an essential tool for the annotation of next-generation sequencing data. The public version of HGMD (http://www.hgmd.org) is freely available to registered users from academic institutions and non-profit organisations whilst the subscription version (HGMD Professional) is available to academic, clinical and commercial users under license via QIAGEN Inc.
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              Positional cloning of the APECED gene.

              Autoimmune polyglandular syndrome type I (APS 1, also called APECED) is an autosomal-recessive disorder that maps to human chromosome 21q22.3 between markers D21S49 and D21S171 by linkage studies. We have isolated a novel gene from this region, AIRE (autoimmune regulator), which encodes a protein containing motifs suggestive of a transcription factor including two zinc-finger (PHD-finger) motifs, a proline-rich region and three LXXLL motifs. Two mutations, a C-->T substitution that changes the Arg 257 (CGA) to a stop codon (TGA) and an A-->G substitution that changes the Lys 83 (AAG) to a Glu codon (GAG), were found in this novel gene in Swiss and Finnish APECED patients. The Arg257stop (R257X) is the predominant mutation in Finnish APECED patients, accounting for 10/12 alleles studied. These results indicate that this gene is responsible for the pathogenesis of APECED. The identification of the gene defective in APECED should facilitate the genetic diagnosis and potential treatment of the disease and further enhance our general understanding of the mechanisms underlying autoimmune diseases.

                Author and article information

                Eur J Endocrinol
                Eur J Endocrinol
                European Journal of Endocrinology
                Bioscientifica Ltd (Bristol )
                04 May 2022
                01 July 2022
                : 187
                : 1
                : 111-122
                [1 ]Oxford Genetics Laboratories , Churchill Hospital, Oxford, UK
                [2 ]Academic Endocrine Unit , Radcliffe Department of Medicine, University of Oxford, Oxford, UK
                [3 ]Paediatric Endocrinology , Children's Hospital, John Radcliffe Hospital, Oxford, UK
                [4 ]Oxford Centre for Genomic Medicine , Nuffield Orthopaedic Centre, Oxford, UK
                [5 ]Nuffield Department of Women’s and Reproductive Health , University of Oxford, Oxford, UK
                [6 ]National Institute for Health Research Oxford Biomedical Research Centre , Oxford, UK
                Author notes
                Correspondence should be addressed to F M Hannan; Email: fadil.hannan@ 123456wrh.ox.ac.uk
                Author information
                © The authors

                This work is licensed under a Creative Commons Attribution 4.0 International License.

                : 12 September 2021
                : 04 May 2022
                Original Research

                Endocrinology & Diabetes
                Endocrinology & Diabetes


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