Scholarly article on topic 'Leigh Map: A Novel Computational Diagnostic Resource for Mitochondrial Disease'

Leigh Map: A Novel Computational Diagnostic Resource for Mitochondrial Disease Academic research paper on "Biological sciences"

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Academic research paper on topic "Leigh Map: A Novel Computational Diagnostic Resource for Mitochondrial Disease"

Leigh Map: A Novel Computational Diagnostic Resource for Mitochondrial Disease Joyeeta Rahman1, Alberto Noronha2, Ines Thiele2, Shamima Rahman1,3*

1 Mitochondrial Research Group, Genetics and Genomic Medicine Programme UCL Great Ormond Street Institute of Child Health, London, UK

2 Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg

3 Metabolic Department, Great Ormond Street Hospital NHS Foundation Trust, London, UK

Author for correspondence:

Prof Shamima Rahman

Professor of Paediatric Metabolic Medicine

Mitochondrial Research Group, Genetics and Genomic Medicine Programme UCL Great Ormond Street Institute of Child Health 30, Guilford Street London WC1N 1EH, UK Tel: +44 20 7905 2608 Fax: +44 20 7404 6191 Email: shamima.rahman@ucl.ac.uk

Character and Word Counts:

Title: 69 Characters Running Head: 50 Characters Abstract: 100 Words Manuscript Body: 2338 Words Number of Figures and Tables: 5 Key words:

Leigh syndrome; mitochondrial disease; gene-phenotype interaction; computational network; bioinformatics

This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process which may lead to differences between this version and the Version of Record. Please cite this article as an 'Accepted Article', doi: 10.1002/ana.24835

Rahman et al Leigh Map:A Novel Diagnostic Resource

Abstract

Mitochondrial disorders are amongst the most severe metabolic disorders and are beset by genetic, biochemical, and clinical heterogeneity. Variation between individuals and poor understanding of disease pathophysiology pose significant diagnostic challenges. We present a novel interactive computational network, the Leigh Map, cataloguing >1700 gene-to-phenotype interactions in Leigh syndrome, the most common and genetically heterogeneous mitochondrial disorder. Blinded validation of the Leigh Map yielded an 80% success rate in correct identification of causative genes. We conclude that the Leigh Map is an efficacious resource that, in combination with whole-exome sequencing, can be utilized as a novel diagnostic resource for mitochondrial disease.

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Introduction

Mitochondrial disorders are among the most severe metabolic disorders wherein patients suffer from multi-systemic phenotypes, often resulting in early death1. Clinical, biochemical, and genetic heterogeneity among individuals, together with poor understanding of gene-to-£ phenotype relationships, pose significant diagnostic and therapeutic challenges for

s. In light of recent advances in next-generation sequencing technologies, whole-exome sequencing (WES) is emerging as the new global standard for the diagnosis of monogenic disorders, including mitochondrial diseases2. However, owing to genetic heterogeneity of mitochondrial disorders and ongoing discovery of novel disease genes, WES data may not provide clinicians with enough certainty for a definitive diagnosis.

With these challenges in mind, we present the Leigh Map, a novel computational gene-to-phenotype network to be used as a diagnostic resource for mitochondrial disease, using Leigh syndrome (MIM 256000), the most genetically heterogeneous and most frequent phenotype of paediatric mitochondrial disease3,4, as a prototype. Leigh syndrome is a progressive neurodegenerative disorder defined neuropathologically by spongiform basal ganglia and brainstem lesions4,5. Clinical manifestations include psychomotor retardation, with regression, and progressive neurological abnormalities related to basal ganglia and/or brainstem dysfunction, often resulting in death within two years of initial presentation4,6. However, many patients may also present with multisystemic (e.g., cardiac, hepatic, renal or haematological) phenotypes. To date there are 89 genes known to cause Leigh syndrome, the majority of which are difficult to definitively differentiate from each other, either biochemically or clinically. We hypothesized that these multisystemic features may help to distinguish different genetic subtypes of Leigh syndrome.

The Leigh Map (freely available at vmh.uni.lu/#leighmap), was built on the Molecular Interaction NEtwoRks VisuAlization (MINERVA) platform7 previously used to construct networks of Parkinson disease and human metabolism8-10. The network comprises 89 genes and 236 phenotypes, expressed in Human Phenotypic Ontology (HPO) terms11,12,

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clinician

providing sufficient phenotypic and genetic variation to test the network's diagnostic capability. The Leigh Map aims to enhance the interpretation of WES data to aid clinicians in providing faster and more accurate diagnoses for patients so that appropriate measures can be taken for optimal management. The phenotypic components of the Leigh Map can be queried to generate a list of candidate genes. In addition, the genetic components of the Leigh Map may also be queried to browse a list of all reported phenotypes associated with a particular gene defect. We propose that this functionality can be used to enhance clinical surveillance of patients with an established genetic diagnosis. Blinded validation of test cases containing clinical and biochemical, but not genetic, data demonstrated that two independent testers were able to predict the correct causative gene using this method in 80% of cases. The success of the Leigh Map demonstrates the efficacy of computational networks as diagnostic aids for mitochondrial disease (Fig 1).

Creation of the Leigh Map Systematic Literature Review

The genetic and phenotypic information gathered in this study came from an initial knowledgebase of over 900 publications, collected from PubMed (latest search November 2016) and the senior author's personal archive. To facilitate data collection from this large breadth of literature associated with Leigh syndrome, we performed systematic literature mining with QDA Miner Lite (v.1.4.2, Provalis Research ©) to generate a list of genes reported to cause Leigh syndrome or Leigh-like syndromes, and their corresponding phenotypes. Phenotypic information was standardized by manually entering each reported phenotype into phenomizer (compbio.charite.de/phenomizer)11,12, a free online resource, which catalogues thousands of standardized human phenotypes, to obtain the appropriate HPO term and number. In addition to obtaining individual Leigh syndrome genes and phenotypes, we collected information on additional parameters which will give users further insight for an informed diagnosis. Such parameters include modes of inheritance, magnetic

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resonance imaging (MRI) findings, and patient demographic information. These data were then organized into an excel file. Although we aimed to rely solely on text mining to obtain these data, some publications required manual clarification, owing to formatting errors on QDA Miner, which were especially prevalent in publications with large tables. In total, we consulted over 500 publications to create the Leigh Map. A simplified version of the gene-to-phenotype knowledgebase is provided in Tables 1 and 2.

Structure and Functionality of Leigh Map

The Leigh Map was manually assembled using CellDesigner (v.4.4)13 by incorporating phenotypic, genetic, and demographic data collected through literature mining. The map layout loosely follows mitochondrial structure. The outermost compartment represents the cytosol where it is possible to find the nucleus and the mitochondrion. Three nuclear genes, nuclear envelope protein NUP62, nuclear export protein RANBP2, and adenosine

Cleaminase ADAR, have been included in our network as genes causing a clinical and adiological phenotype closely resembling Leigh syndrome14-16. The mitochondrion is visualised in its double membrane structure and mitochondrial genes are grouped according to function and can be found in their submitochondrial location (e.g. outer membrane, matrix). To represent gene-to-phenotype associations, a submap was created for each gene, displaying all phenotypes associated with any given gene defect. Also incorporated at this stage are links to external databases (e.g., Uniprot17 and HGNC18) and modes of inheritance. This approach enables a modular overview of the map, avoiding overwhelming the user with the "hairball" effect caused by the high connectivity of the network. All submaps were integrated in the MINERVA framework7 which makes use of the Google Maps API (application programming interface), enables content query, and allows a low latency interactive navigation of the network and its submodules simply by clicking a specific gene and opening the embedded submap window available on the interface.

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Navigation through the network is similar to that of Google Maps, wherein the user can reveal increasingly specific components of information by zooming in on the different compartments (Fig. 2; Fig S1-4). Additional data (patient demographics, modes of inheritance, external annotations, etc.) can be accessed by clicking an element of the map. The corresponding data will be displayed on the left panel. The search functionality enables the query of multiple genes and phenotypes. The query results are displayed in the information panel and are also highlighted on the map. When searching for multiple phenotypes, all genes associated with each phenotype will be listed. Opening the submap for any given gene will display one or more of the highlighted phenotype elements, providing an immediate visual interpretation of the search results.

The Leigh Map provides data about 89 genes reported to cause Leigh syndrome and Leighlike syndromes, the highest number of Leigh syndrome genes that has been collated to date, as well as 236 associated phenotypes. The network consists of more than 1700 interactions, all of which can be manually queried by the user. To facilitate access, causative Leigh syndrome genes are segregated according to gene function and arranged on a simplified schematic of the mitochondrion. Genes with similar functions are grouped together in subcategories. Examples of gene categories that can be found on the Leigh Map include genes involved in oxidative phosphorylation (e.g. NDUFA1, SDHA) and genes which maintain mitochondrial DNA (e.g. POLG, SUCLA2) (Fig. 2). Expression of Leigh syndrome phenotypes in HPO terms11,12 serves to normalize the network, thereby eliminating discrepancies in clinical jargon for phenotypes for which more than one synonym exists. "Leukodystrophy", for example, can be described alternatively as "leukoencephalopathy" or "white matter changes". The use of different nomenclature varies among clinicians and in different geographical regions, therefore the use of a single HPO term (Leukodystrophy; HP: 0002415) simplifies the Leigh Map and encourages its widespread utilization (Fig. 3).

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The Efficacy of the Leigh Map as a Diagnostic Resource

Blinded validation by two non-clinical investigators using a series of anonymized test cases revealed that the Leigh Map was able to identify the correct gene for sixteen of twenty cases. The first and second authors, who both lack formal clinical expertise, acted as independent blinded testers of the network. The anonymized test cases were obtained from the senior author's clinical practice, a national mitochondrial disease clinic where patients with Leigh syndrome who have diverse clinical presentations and genetic causes are diagnosed and managed. The criteria for these test cases were patients who had a definitive genetic diagnosis for Leigh syndrome, confirmed by Sanger or whole-exome sequencing. Testers were provided with clinical vignettes and biochemical data, without genetic information. All corresponding phenotypes identified from each test case were entered into the query box of the Leigh Map, each separated by a semicolon. The search tool then generated a list of candidate genes for each phenotype in individual panels, which were then manually browsed to establish a list of candidate genes (Fig. 3). We define 'candidate genes' as those that include >50% of the queried phenotypes. Due to the immense number of phenotypes on the network, every test case generated a list of potentially causative genes. For ten cases, the Leigh Map was able to identify the correct gene as the 'top hit', i.e. the gene corresponding to the highest number of matched phenotypes. The network also predicted the correct gene for an additional six cases, which were not the top hit. In the remaining four test cases, the Leigh Map failed to produce the correct gene as one of the generated candidate genes. In all cases, the Leigh Map produced a shortlist of no more than eight candidate genes, effectively eliminating ~90% of the genes in the network. Multiple advanced search is not yet possible on this platform, so some manual deduction is required for the use of the Leigh Map

at this time. at this time.

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Rahman et al Leigh Map:A Novel Diagnostic Resource

Future Prospects

Due to its high success rate in predicting causative genes by non-clinical individuals, we conclude that the Leigh Map is an efficacious diagnostic resource that, in combination with

ata and metabolic testing, can be used by clinicians to provide patients with accurate diagnoses or to direct further biochemical investigation. Increased certainty of the genetic causes of mitochondrial disease has significant implications since it could potentially attenuate the need for invasive diagnostic procedures, namely muscle biopsy with an attendant general anaesthetic, which could pose risk to paediatric patients. It is important to iterate that we do not propose that the Leigh Map act as a substitute for WES data or other relevant functional studies, but rather is a supplement to these techniques.

The computational nature of the Leigh Map allows for the addition of novel disease genes or phenotypes with relative ease, therefore clinicians have access to a database of all current causative genes which can enhance the interpretation of WES data. Ideally, we will update both the phenotypic and genetic components of the Leigh Map concurrently with the literature and also develop a facility wherein experts can submit additional genetic or phenotypic information. This is especially beneficial within the context of mitochondrial diseases since novel genes are constantly being identified. For Leigh syndrome specifically, one third of the causative genes were identified within the last five years3.

Currently the most significant limitation of the Leigh Map is the lack of a multiple advanced search facility. While the absence of this feature does not detract from the network's accuracy, it does reduce its ease of use. Future work aims to implement this feature into the network. Furthermore, the efficacy of the Leigh Map is affected by the breadth of literature available for individual genes. SURF1, one of the earliest mitochondrial disease genes to be identified and the most common nuclear gene cause of Leigh syndrome, is the subject of numerous publications19. Thus, SURF1 is associated with more than 90 phenotypes in the Leigh Map, the largest number for any single gene. In contrast, the recently characterized complex I assembly gene C12ORF89Z0, only features in a small section of a larger

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publication and accordingly is associated only with two phenotypes on the Leigh Map, despite the fact that patients who harbour this mutation may display other phenotypes.

Expanding the current gene-to-phenotype binary of the Leigh Map is a future prospect which can further improve its usefulness as a diagnostic resource. While there are no current curative therapies for mitochondrial disease, there are numerous compounds which are aimed at symptomatic management, including anti-convulsant drugs used to manage epilepsy and cofactor and vitamin supplements, such as coenzyme Q10, thiamine, and biotin, used to treat corresponding deficiencies. The addition of drug targets (a current feature of the MINERVA platform) to the Leigh Map could potentially provide insight into the effectiveness of various agents in treating mitochondrial disease in specific genetic contexts. For example, patients with SLC19A3 mutations respond dramatically to biotin and thiamine therapy21, whilst those with HIBCH mutations may benefit from N-acetyl cysteine22. cDNA and protein mutations and annotations regarding animal models are also useful potential supplements to the Leigh Map. Leigh syndrome is a defined disorder5 wherein certain phenotypes appear rather ubiquitously, including hypotonia (91% of patients), developmental delay (82%), lactic acidosis (78%), and failure to thrive (61%). The failure to deduce the correct candidate genes for a minority of our test cases, was due to the predominant presence of these common Leigh syndrome phenotypes and a lack of discriminating phenotypes. We found more success in 'diagnosing' cases that presented with less frequently observed phenotypes such as cardiomyopathy (59%), optic atrophy (47%), or renal tubulopathy (15%). Therefore, the addition of these extra elements can be helpful in narrowing down a large list of candidate genes, thereby increasing the predictive power of the Leigh Map. An alternative approach to increase diagnostic power for common phenotypes is to incorporate a scoring system, which is a common element in other bioinformatics resources such as BLAST23. In the context of our network, we propose 'common' phenotypes be scored lower than less frequently observed phenotypes. The

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Rahman et al Leigh Map:A Novel Diagnostic Resource

addition of a scoring system would complement the more sophisticated advanced search feature, which we aim to implement in the future.

Progressive improvements in sequencing technologies and increased global cooperation have allowed for the generation of copious amounts of genetic and clinical information £ pertaining to mitochondrial disease. The Leigh Map effectively integrates these clinical and scientific data into an efficacious diagnostic resource for a genetically heterogeneous disorder, the success of which provides the basis for the construction of larger computational networks for a wider scope of mitochondrial and metabolic diseases.

Acknowledgements

S.R. is in receipt of a Great Ormond Street Hospital Children's Charity (GOSHCC) Leadership award (V1260). Funding for this study was provided by the British Inherited Metabolic Disease Group and by an ATTRACT programme grant (FNR/A12/01) from the Luxembourg National Research Fund (FNR).

Author Contributions

S.R. and I.T. were involved in the conception and design of the study. J.R. and A.N.

quired the data and created the network. J.R., A.N., I.T., and S.R. drafted the manuscript and the figures.

Conflicts of Interest

Nothing to report.

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References

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2. Wortmann, SB, Koolen, DA, Smeitink, JA, et al. Whole exome sequencing of suspected mitochondrial patients in clinical practice. J Inherit Metab Dis. 2015; 38:437-443.

3. Lake, NJ, Compton, AG, Rahman, S, et al. Leigh syndrome: One disorder, more than 75 monogenic causes. Annals of Neurology. 2016; 79:190-203.

4. Rahman, S, Blok, RB, Dahl, H-HM, et al. Leigh syndrome: Clinical features and biochemical and DNA abnormalities. Annals of Neurology. 1996; 39:343-351.

5. Leigh, D. Subacute Necrotizing Encephalomyelopathy in an infant. J Neurol Neurosurg Psychiatry. 1951; 14:216-221.

6. Sofou, K, De Coo, IFM, Isohanni, P, et al. A multicenter study on Leigh syndrome: disease course and predictors of survival. Orphanet J Rare Dis. 2014; 9:52.

7. Gawron, P, Ostaszewski, M, Satagopam, V, et al. MINERVAGÇôa platform for visualization and curation of molecular interaction networks. Npj Systems Biology And Applications. 2016; 2:16020.

8. Fujita, KA, Ostaszewski, M, Matsuoka, Y, et al. Integrating Pathways of Parkinson's Disease in a Molecular Interaction Map. Mol Neurobiol. 2014; 49:88-102.

9. Noronha A, Danfelsdottir AD, Gawron P, et al. ReconMap: An interactive visualisation of human metabolism. arXIV. 2016;1606.00042([q-bio.MN]).

10. Thiele, I, Swainston, N, Fleming, RMT, et al. A community-driven global reconstruction of human metabolism. Nat Biotech. 2013; 31:419-425.

11. Kohler, S, Schulz, MH, Krawitz, P, et al. Clinical Diagnostics in Human Genetics with Semantic Similarity Searches in Ontologies. The American Journal of Human Genetics. 2009; 85:457-464.

12. Kohler, S, Doelken, SC, Mungall, CJ, et al. The Human Phenotype Ontology project: linking molecular biology and disease through phenotype data. Nucleic Acids Research. 2014;42:D966-D974.

13. Funahashi A, Matsuoka Y, Jouraku A, et al. CellDesigner 3.5: A Versatile Modeling Tool for Biochemical Networks. Proceedings of the IEEE. 2008;96(8):1254-65.

14. Basel-Vanagaite, L, Muncher, L, Straussberg, R, et al. Mutated nup62 causes autosomal recessive infantile bilateral striatal necrosis. Ann Neurol. 2006; 60:214-222.

15. Singh, RR, Sedani, S, Lim, M, et al. RANBP2 mutation and acute necrotizing encephalopathy: 2 cases and a literature review of the expanding clinico-radiological phenotype. European Journal of Paediatric Neurology. 2015; 19:106-113.

16. Livingston, JH, Lin, JP, Dale, RC, et al. A type I interferon signature identifies bilateral striatal necrosis due to mutations in ADAR1. Journal of Medical Genetics. 2014; 51:76-82.

17. UniProt C. UniProt: a hub for protein information. Nucleic Acids Res. 2015;43(Database issue):D204-12.

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18. Gray, KA, Yates, B, Seal, RL, et al. Genenames.org: the HGNC resources in 2015. Nucleic Acids Res. 2015; 43:D1079-D1085.

19. Wedatilake, Y, Brown, RM, McFarland, R, et al. SURF1 deficiency: a multi-centre natural history study. Orphanet J Rare Dis. 2013; 8:96.

20. Floyd, B, Wilkerson, E, Veling, M, et al. Mitochondrial Protein Interaction Mapping Identifies Regulators of Respiratory Chain Function. Molecular Cell. 2016; 63:621-632.

Fassone, E, Wedatilake, Y, DeVile, C, et al. Treatable Leigh-like Encephalopathy resenting in Adolescence. BMJ Case Rep. 2013; 2013:200838.

Ferdinandusse, S, Waterham, HR, Heales, SJ, et al. HIBCH mutations can cause Leigh-like disease with combined deficiency of multiple mitochondrial respiratory chain enzymes and pyruvate dehydrogenase. Orphanet J Rare Dis. 2013; 8:188.

23. Altschul SF. A protein alignment scoring system sensitive at all evolutionary distances. J Mol Evol. 1993 Mar; 36(3):290-300.

Figure Legends

Figure 1. Conceptualization of the Leigh Map. The Leigh Map is a novel computational resource that effectively integrates a large amount of phenotypic and genetic data from the literature and synthesizes it into a comprehensive resource that has the potential to improve

diagnostic outcomes and more vigilant clinical surveillance for patients with Leigh syndrome. j

Figure 2. Schematic Layout of the Leigh Map. The Leigh Map is a novel gene-to-phenotype network which can be used as a diagnostic resource for Leigh syndrome. The layout and navigation of the Leigh Map is similar to that of Google Maps, wherein the user zooms in on components to reveal further layers of information. The outermost part of the Leigh Map is a simplified diagram of the cell (A). Clicking on a compartment (e.g. the mitochondrion) reveals categories of genes associated with Leigh syndrome (B) and zooming in on subcompartments within the mitochondrion reveals individual genes (C). Detailed information about a specific gene defect can be accessed by clicking on a gene

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(SURF1 in this example), which will display a left-hand panel that provides additional information and external annotations (D). Each gene contains a 'submodel' which can be accessed by clicking. Gene submodels display all phenotypes associated with the gene of interest (a total of 96 phenotypes in the case of SURF1 deficiency) (E). Live screenshots of the Leigh Map are provided in Supplementary Figure 1.

Figure 3. Querying the Leigh Map. All phenotypic and genetic components of the Leigh Map can be queried using the search function in the left-hand panel (A-C). The user can query a particular gene by typing in the name of the gene or any known alias into the search box. The results of the search will be displayed in the left-hand panel and the matching gene(s) will become marked on the network (A). Phenotypes can be queried in the same way. The results of a phenotype search will display all genes associated with the queried phenotype (B). Multiple phenotypes can be queried simultaneously by separating

enotypes with a semicolon. The results of a multiple phenotype search will be displayed in different tabbed panels through which the user can navigate (C). Clicking on the gene's submodel in any multiple phenotype search will display all highlighted phenotypes from the query (D).

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Table 1. Leigh Syndrome Disease Genes and Phenotypes Associated with Metabolism

Mitochondrial Dysfunction Genes (Mode of Inheritance) Example Phenotypes

OXPHOS Subunits

Complex I NDUFA1 (XL); NDUFA2, NDUFA9, NDUFA10, NDUFA12, NDUFS1, NDUFS2, NDUFS3, NDUFS4, NDUFS7, NDUFS8, NDUFV1, NDUFV2 (AR); MT-ND1, MT-ND2, MT-ND3, MT-ND4, MT-ND5, MT-MD6 (Maternal) DDwR, FTT, Hypertrichosis, HCM, LA, LD, Liver Failure, Myopathy, OA, PN, Renal Tubulopathy, SNHL, SZ

Complex II SDHA (AR) DDwR, FTT, HCM, LA, OA, Paraganglionoma, Pheochromocytoma, SZ

Complex III UQCRQ (AR) Ataxia, Dementia, DD, Dystonia, Myopathy

Complex IV COX8A, NDUFA4 (AR); MT-CO3 (Maternal) Ataxia, DD, DR, Diabetes Mellitus, LA, LD, Microcephaly, PN, SNHL, SZ

Complex V MT-ATP6 (Maternal) DDwR, FTT, HCM, LA, LD, Myopathy, OA, SZ

OXPHOS Assembly

Complex I Assembly NDUFAF2, NDUFAF4, NDUFAF5, NDUFAF6, C17ORF89, FOXRED1, NUBPL (AR) Anemia, DDwR, FTT, HCM, LA, Liver Failure, Myopathy, OA, SNHL, SZ

DDwR, LA, LD, Liver Failure, Myopathy

Complex II Assembly SDHAF1 (AR)

Complex III Assembly BCS1L, TTC19 (AR) DDwR, FTT, LD, LA, Liver Failure, Renal Tubulopathy, SNHL, SZ

Complex IV Assembly SURF1, SCO2, COXIO, COX15, PET100 (AR) DDwR, FTT, Hypertrichosis, HCM, LA, LD, Myopathy, OA, Renal Tubulopathy, SNHL, SZ

Cofactor Biosynthesis and Metabolism

CoQ10 Biosynthesis COQ9, PDSS2 (AR) DDwR, FTT, HCM, Hypotonia, Myopathy, Nephrotic Syndrome, Renal Tubulopathy, SZ

Lipoic Acid Biosynthesis LIAS, LIPT1 (AR) DDwR, Dystonia, FTT, Hypertension, LA, LD, OA, SZ

Thiamine Metabolism SLC19A3,TPK1 (AR) DDwR, Dystonia, Microcephaly, Hypoglycemia, LD, OA, SZ

Biotinidase BTD (AR) Ataxia, DR, Hypotonia, LA, Spastic Tetraplegia

Other Metabolic Dysfunction

Pyruvate Dehydrogenase Complex PDHA1 (XL); PDHX, PDHB, DLAT, DLD (AR) DD, FTT, LA, LD, Microcephaly, Myopathy, OA, PN, SZ

Amino Acid Metabolism HIBCH, ECHS1 (AR) Abnormal Plasma Acylcarnitines, DDwR, FTT, LA. LD, Microcephaly, Myopathy, OA, SZ

AD= Autosomal Dominant; AR= Autosomal Recessive; DD= Developmental Delay; DDwR= Developmental Delay with Regression; DR= Developmental Regression; LA= Lactic Acidosis; LD= Leukodystrophy; HCM= Hypertrophic Cardiomyopathy; OA= Optic Atrophy; SNHL= Sensorineural Hearing Loss; SZ= Seizures; XL= X-Linked

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Table 2. Leigh Syndrome Disease Genes and Phenotypes Associated with Other Mitochondrial Functions

Mitochondrial Dysfunction Genes (Mode of Inheritance) Example Phenotypes

Mitochondrial DNA Maintenance POLG, SUCLA2, SUCLG1, FBXL4 (AR) DDwR, FTT, HCM, LA, LD, Methylmalonic Aciduria, Myopathy, OA, Renal Tubulopathy, SZ

Mitochondria Translation GFM1, GFM2, TSFM, TRMU, MTFMT, GTPBP3, TACO1, C12ORF65, LRPPRC, EARS2, FARS2, IARS2, NARS2 (AR); MT-TI, MT-TK, MT-TL1, MT-TL2, MT-TV, MT-TW (Maternal) Anemia, DDwR, FTT, Hypoglycemia, HCM, LA, LD, OA, Renal Tubulopathy, SZ

Mitochondria Dynamics SLC25A46 (AR), DNM1L (AD) Ataxia, DDwR, FTT, Hypotonia, Microcephaly, LA, SZ

Mitochondria Import SLC25A19 (AR) DD, FTT, Hypotonia, Microcephaly, PN, SZ

Membrane Phospholipids SERAC1 (AR) 3-Methylglutaconic Aciduria, DDwR, FTT, LA, Liver Failure, OA, SNHL, SZ

Mitochondria Sulphur Dioxygenase ETHE1 (AR) DDwR, Ethylmalonic Aciduria, LA, Renal Tubulopathy, SZ

Oligomeric AAA+ ATPase CLPB (AR) DDwR, FTT, HCM, LD, OA, SZ

Apoptosis AIFM1 (AR) DDwR, HCM, Hypoglycemia, SNHL, SZ

RNA Import PNPT1 (AR) DR, Dystonia, Muscle Weakness, SNHL, SZ

RNA Specific Adenosine Deaminase ADAR (AR) DDwR, Microcephaly, SZ, Skin Hyperpigmentation

Nuclear Translocation Pathway RANBP2 (AR) Ataxia, Cognitive Impairment, Myopathy, SZ

Nuclear Pore Complex Protein NUP62 (AR) FTT, DR, OA, SZ

Manganese Transporter SLC39A8 (AR) DD, FTT, LA, SNHL, SZ

AD= Autosomal Dominant; AR= Autosomal Recessive; DD= Developmental Delay; DDwR= Developmental Delay with Regression; DR= Developmental Regression; LA= Lactic Acidosis; LD= Leukodystrophy; HCM= Hypertrophic Cardiomyopathy; OA= Optic Atrophy; SNHL= Sensorineural Hearing Loss; SZ= Seizures; XL= X-Linked

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Figure 1

Conceptualization of the Leigh Map

170x80mm (300 x 300 DPI)

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209x120mm (300 x 300 DPI)

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