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      Mutation in the MICOS subunit gene APOO (MIC26) associated with an X-linked recessive mitochondrial myopathy, lactic acidosis, cognitive impairment and autistic features

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          Abstract

          Background

          Mitochondria provide ATP through the process of oxidative phosphorylation, physically located in the inner mitochondrial membrane (IMM). The mitochondrial contact site and organising system (MICOS) complex is known as the ’mitoskeleton’ due to its role in maintaining IMM architecture. APOO encodes MIC26, a component of MICOS, whose exact function in its maintenance or assembly has still not been completely elucidated.

          Methods

          We have studied a family in which the most affected subject presented progressive developmental delay, lactic acidosis, muscle weakness, hypotonia, weight loss, gastrointestinal and body temperature dysautonomia, repetitive infections, cognitive impairment and autistic behaviour. Other family members showed variable phenotype presentation. Whole exome sequencing was used to screen for pathological variants. Patient-derived skin fibroblasts were used to confirm the pathogenicity of the variant found in APOO. Knockout models in Drosophila melanogaster and Saccharomyces cerevisiae were employed to validate MIC26 involvement in MICOS assembly and mitochondrial function.

          Results

          A likely pathogenic c.350T>C transition was found in APOO predicting an I117T substitution in MIC26. The mutation caused impaired processing of the protein during import and faulty insertion into the IMM. This was associated with altered MICOS assembly and cristae junction disruption. The corresponding mutation in MIC26 or complete loss was associated with mitochondrial structural and functional deficiencies in yeast and D. melanogaster models.

          Conclusion

          This is the first case of pathogenic mutation in APOO, causing altered MICOS assembly and neuromuscular impairment. MIC26 is involved in the assembly or stability of MICOS in humans, yeast and flies.

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          Most cited references52

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          A global reference for human genetic variation

          The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies.
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            Analysis of protein-coding genetic variation in 60,706 humans

            Summary Large-scale reference data sets of human genetic variation are critical for the medical and functional interpretation of DNA sequence changes. We describe the aggregation and analysis of high-quality exome (protein-coding region) sequence data for 60,706 individuals of diverse ethnicities generated as part of the Exome Aggregation Consortium (ExAC). This catalogue of human genetic diversity contains an average of one variant every eight bases of the exome, and provides direct evidence for the presence of widespread mutational recurrence. We have used this catalogue to calculate objective metrics of pathogenicity for sequence variants, and to identify genes subject to strong selection against various classes of mutation; identifying 3,230 genes with near-complete depletion of truncating variants with 72% having no currently established human disease phenotype. Finally, we demonstrate that these data can be used for the efficient filtering of candidate disease-causing variants, and for the discovery of human “knockout” variants in protein-coding genes.
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              Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes.

              We describe and validate a new membrane protein topology prediction method, TMHMM, based on a hidden Markov model. We present a detailed analysis of TMHMM's performance, and show that it correctly predicts 97-98 % of the transmembrane helices. Additionally, TMHMM can discriminate between soluble and membrane proteins with both specificity and sensitivity better than 99 %, although the accuracy drops when signal peptides are present. This high degree of accuracy allowed us to predict reliably integral membrane proteins in a large collection of genomes. Based on these predictions, we estimate that 20-30 % of all genes in most genomes encode membrane proteins, which is in agreement with previous estimates. We further discovered that proteins with N(in)-C(in) topologies are strongly preferred in all examined organisms, except Caenorhabditis elegans, where the large number of 7TM receptors increases the counts for N(out)-C(in) topologies. We discuss the possible relevance of this finding for our understanding of membrane protein assembly mechanisms. A TMHMM prediction service is available at http://www.cbs.dtu.dk/services/TMHMM/. Copyright 2001 Academic Press.
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                Author and article information

                Journal
                2985087R
                J Med Genet
                J Med Genet
                Journal of medical genetics
                0022-2593
                1468-6244
                04 December 2020
                01 March 2021
                21 May 2020
                01 March 2021
                : 58
                : 3
                : 155-167
                Affiliations
                [1 ]Medical research council, Mitochondrial Biology Unit, cambridge, cambridgeshire, UK
                [2 ]Department of genetics, Federal University of Parana, curitiba, Paraná, Brazil
                [3 ]Department of Biology, University of Padova, Padova, Veneto, italy
                [4 ]Neuropediatric Division, hospital Pequeno Principe, curitiba, Paraná, Brazil
                [5 ]Department of chemistry, University of Parma, Parma, emilia-romagna, italy
                [6 ]Medical research council, laboratory of Molecular Biology, cambridge, UK
                [7 ]Department of neurosciences, University of Padova, Padova, Veneto, italy
                Author notes
                Correspondence to Professor Massimo Zeviani, Mitochondrial Biology Unit, Medical Research Council, Cambridge, CB2 0XY, UK; massimo.zeviani@ 123456unipd.it
                Author information
                http://orcid.org/0000-0001-7933-860X
                http://orcid.org/0000-0003-4480-2286
                http://orcid.org/0000-0001-9005-6726
                http://orcid.org/0000-0002-8280-7849
                Article
                EMS107715
                10.1136/jmedgenet-2020-106861
                7116790
                32439808
                701b0798-26c5-47ca-ab58-80b7727e381a

                This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: https://creativecommons.org/licenses/by-nc/4.0/.

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                Genetics
                Genetics

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