Skip to main content
SpringerLink
Log in

SUCLG1 mutations and mitochondrial encephalomyopathy: a case study and review of the literature

  • Original Article
  • Published:
Molecular Biology Reports Aims and scope Submit manuscript

Abstract

The mitochondrial encephalomyopathies represent a clinically heterogeneous group of neurodegenerative disorders. The clinical phenotype of patients could be explained by mutations of mitochondria-related genes, notably SUCLG1 and SUCLA2. Here, we presented a 5-year-old boy with clinical features of mitochondrial encephalomyopathy from Iran. Also, a systematic review was performed to explore the involvement of SUCLG1 mutations in published mitochondrial encephalomyopathies cases. Genotyping was performed by implementing whole-exome sequencing. Moreover, quantification of the mtDNA content was performed by real-time qPCR. We identified a novel, homozygote missense variant chr2: 84676796 A > T (hg19) in the SUCLG1 gene. This mutation substitutes Cys with Ser at the 60-position of the SUCLG1 protein. Furthermore, the in-silico analysis revealed that the mutated position in the genome is well conserved in mammalians, that implies mutation in this residue would possibly result in phenotypic consequences. Here, we identified a novel, homozygote missense variant chr2: 84676796 A > T in the SUCLG1 gene. Using a range of experimental and in silico analysis, we found that the mutation might explain the observed phenotype in the family.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Data availability

The authors acknowledge that the article incorporates the data supporting the findings of the study.

References

  1. Piña-Garza JE, James KC (2019) 5 - Psychomotor retardation and regression. In: Piña-Garza JE, James KC (eds) Fenichel’s clinical pediatric neurology, 8th edn. Elsevier, Philadelphia, pp 115–149

    Google Scholar 

  2. Hirano M (2007) 86-Metabolic myopathies. In: Gilman S (ed) Neurobiology of Disease. Academic Press, Burlington, pp 947–956

    Google Scholar 

  3. Oldfors A, Tulinius M (2003) Mitochondrial encephalomyopathies. J Neuropathol Exp Neurol 62(3):217–227

    CAS  PubMed  Google Scholar 

  4. Rouzier C, Le Guedard-Mereuze S, Fragaki K, Serre V, Miro J, Tuffery-Giraud S et al (2010) The severity of phenotype linked to SUCLG1 mutations could be correlated with residual amount of SUCLG1 protein. J Med Genet 47(10):670–676

    CAS  PubMed  Google Scholar 

  5. Landsverk ML, Zhang VW, Wong LC, Andersson HC (2014) A SUCLG1 mutation in a patient with mitochondrial DNA depletion and congenital anomalies. Mol Genet Metab Rep 1:451–454

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Johnson JD, Mehus JG, Tews K, Milavetz BI, Lambeth DO (1998) Genetic evidence for the expression of ATP- and GTP-specific succinyl-CoA synthetases in multicellular eucaryotes. J Biol Chem 273(42):27580–27586

    CAS  PubMed  Google Scholar 

  7. Valayannopoulos V, Haudry C, Serre V, Barth M, Boddaert N, Arnoux JB et al (2010) New SUCLG1 patients expanding the phenotypic spectrum of this rare cause of mild methylmalonic aciduria. Mitochondrion 10(4):335–341

    CAS  PubMed  Google Scholar 

  8. Ostergaard E (2008) Disorders caused by deficiency of succinate-CoA ligase. J Inherit Metab Dis 31(2):226–229

    CAS  PubMed  Google Scholar 

  9. Wang K, Li M, Hakonarson H (2010) ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res 38(16):e164

    PubMed  PubMed Central  Google Scholar 

  10. Wheeler DL, Barrett T, Benson DA, Bryant SH, Canese K, Chetvernin V et al (2007) Database resources of the national center for biotechnology information. Nucleic Acids Res 35:D5–D12

    CAS  PubMed  Google Scholar 

  11. Lek M, Karczewski KJ, Minikel EV, Samocha KE, Banks E, Fennell T et al (2016) Analysis of protein-coding genetic variation in 60,706 humans. Nature 536(7616):285–291

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Landrum MJ, Lee JM, Riley GR, Jang W, Rubinstein WS, Church DM et al (2014) ClinVar: public archive of relationships among sequence variation and human phenotype. Nucleic Acids Res 42:D980–D985

    CAS  PubMed  Google Scholar 

  13. Berman H, Westbrook J, Feng Z, Gilliland G, Bhat T, Weissig H, et al. The protein data bank nucleic acids research, 28, 235–242. URL: www rcsb org Citation. 2000.

  14. Protein Data Bank (2019) the single global archive for 3D macromolecular structure data. Nucleic Acids Res 47(D1):D520–D528

    Google Scholar 

  15. Venselaar H, Te Beek TA, Kuipers RK, Hekkelman ML, Vriend G (2010) Protein structure analysis of mutations causing inheritable diseases: an e-Science approach with life scientist friendly interfaces. BMC Bioinforms 11:548

    Google Scholar 

  16. Rodrigues CH, Pires DE, Ascher DB (2018) DynaMut: predicting the impact of mutations on protein conformation, flexibility and stability. Nucleic Acids Res 46(W1):W350–W355

    CAS  PubMed  PubMed Central  Google Scholar 

  17. DeLano WL (2002) Pymol: an open-source molecular graphics tool. CCP Newslett Protein Crystall 40(1):82–92

    Google Scholar 

  18. Sadat R, Barca E, Masand R, Donti TR, Naini A, De Vivo DC et al (2016) Functional cellular analyses reveal energy metabolism defect and mitochondrial DNA depletion in a case of mitochondrial aconitase deficiency. Mol Genet Metab 118(1):28–34

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Donti TR, Masand R, Scott DA, Craigen WJ, Graham BH (2016) Expanding the phenotypic spectrum of Succinyl-CoA ligase deficiency through functional validation of a new SUCLG1 variant. Mol Genet Metab 119(1–2):68–74

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Fattahi Z, Beheshtian M, Mohseni M, Poustchi H, Sellars E, Nezhadi SH et al (2019) Iranome: A catalog of genomic variations in the Iranian population. Hum Mutat 40(11):1968–1984

    CAS  PubMed  Google Scholar 

  21. Shihab HA, Gough J, Cooper DN, Stenson PD, Barker GL, Edwards KJ et al (2013) Predicting the functional, molecular, and phenotypic consequences of amino acid substitutions using hidden Markov models. Hum Mutat 34(1):57–65

    CAS  PubMed  Google Scholar 

  22. Raimondi D, Tanyalcin I, Ferte J, Gazzo A, Orlando G, Lenaerts T et al (2017) DEOGEN2: prediction and interactive visualization of single amino acid variant deleteriousness in human proteins. Nucleic Acids Res 45(W1):W201–W206

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Ng PC, Henikoff S (2003) SIFT: Predicting amino acid changes that affect protein function. Nucleic Acids Res 31(13):3812–3814

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Vaser R, Adusumalli S, Leng SN, Sikic M, Ng PC (2016) SIFT missense predictions for genomes. Nat Protoc 11(1):1

    CAS  PubMed  Google Scholar 

  25. Choi Y, Chan AP (2015) PROVEAN web server: a tool to predict the functional effect of amino acid substitutions and indels. Bioinformatics (Oxford, England) 31(16):2745–2747

    CAS  Google Scholar 

  26. Sundaram L, Gao H, Padigepati SR, McRae JF, Li Y, Kosmicki JA et al (2018) Predicting the clinical impact of human mutation with deep neural networks. Nat Genet 50(8):1161–1170

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Kim S, Jhong JH, Lee J, Koo JY (2017) Meta-analytic support vector machine for integrating multiple omics data. BioData Mining 10:2

    PubMed  PubMed Central  Google Scholar 

  28. Dong C, Wei P, Jian X, Gibbs R, Boerwinkle E, Wang K et al (2015) Comparison and integration of deleteriousness prediction methods for nonsynonymous SNVs in whole exome sequencing studies. Hum Mol Genet 24(8):2125–2137

    CAS  PubMed  Google Scholar 

  29. Chun S, Fay JC (2009) Identification of deleterious mutations within three human genomes. Genome Res 19(9):1553–1561

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Guennebaud G, Jacob B, Avery P, Bachrach A, Barthelemy S (2010) Eigen v3

  31. Qi H, Chen C, Zhang H, Long JJ, Chung WK, Guan Y, et al. (2018) MVP: predicting pathogenicity of missense variants by deep learning. bioRxiv. 259390

  32. Ioannidis NM, Rothstein JH, Pejaver V, Middha S, McDonnell SK, Baheti S et al (2016) REVEL: an ensemble method for predicting the pathogenicity of rare missense variants. Am J Hum Genet 99(4):877–885

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Dai LF, Fang F, Liu ZM, Shen DM, Ding CH, Li JW et al (2019) Phenotype and genotype of twelve Chinese children with mitochondrial DNA depletion syndromes. Zhonghua er ke za zhi: Chin J Pediatr 57(3):211–216

    CAS  Google Scholar 

  34. Maalej M, Tej A, Bouguila J, Tilouche S, Majdoub S, Khabou B et al (2018) Clinical, molecular, and computational analysis in two cases with mitochondrial encephalomyopathy associated with SUCLG1 mutation in a consanguineous family. Biochem Biophys Res Commun 495(2):1730–1737

    CAS  PubMed  Google Scholar 

  35. Fang F, Liu Z, Fang H, Wu J, Shen D, Sun S et al (2017) The clinical and genetic characteristics in children with mitochondrial disease in China. Sci China Life Sci 60(7):746–757

    CAS  PubMed  Google Scholar 

  36. Carrozzo R, Verrigni D, Rasmussen M, de Coo R, Amartino H, Bianchi M et al (2016) Succinate-CoA ligase deficiency due to mutations in SUCLA2 and SUCLG1: phenotype and genotype correlations in 71 patients. J Inherit Metab Dis 39(2):243–252

    CAS  PubMed  Google Scholar 

  37. Liu Y, Li X, Wang Q, Ding Y, Song J, Yang Y (2016) Five novel SUCLG1 mutations in three Chinese patients with succinate-CoA ligase deficiency noticed by mild methylmalonic aciduria. Brain Dev 38(1):61–67

    PubMed  Google Scholar 

  38. Ostergaard E, Schwartz M, Batbayli M, Christensen E, Hjalmarson O, Kollberg G et al (2010) A novel missense mutation in SUCLG1 associated with mitochondrial DNA depletion, encephalomyopathic form, with methylmalonic aciduria. Eur J Pediatr 169(2):201–205

    PubMed  Google Scholar 

  39. Navarro-Sastre A, Tort F, Garcia-Villoria J, Pons MR, Nascimento A, Colomer J et al (2012) Mitochondrial DNA depletion syndrome: new descriptions and the use of citrate synthase as a helpful tool to better characterise the patients. Mol Genet Metab 107(3):409–415

    CAS  PubMed  Google Scholar 

  40. Van Hove JL, Saenz MS, Thomas JA, Gallagher RC, Lovell MA, Fenton LZ et al (2010) Succinyl-CoA ligase deficiency: a mitochondrial hepatoencephalomyopathy. Pediatr Res 68(2):159–164

    PubMed  PubMed Central  Google Scholar 

  41. Rivera H, Merinero B, Martinez-Pardo M, Arroyo I, Ruiz-Sala P, Bornstein B et al (2010) Marked mitochondrial DNA depletion associated with a novel SUCLG1 gene mutation resulting in lethal neonatal acidosis, multi-organ failure, and interrupted aortic arch. Mitochondrion 10(4):362–368

    CAS  PubMed  Google Scholar 

  42. Sakamoto O, Ohura T, Murayama K, Ohtake A, Harashima H, Abukawa D et al (2011) Neonatal lactic acidosis with methylmalonic aciduria due to novel mutations in the SUCLG1 gene. Pediatr Int 53(6):921–925

    CAS  PubMed  Google Scholar 

  43. Randolph LM, Jackson HA, Wang J, Shimada H, Sanchez-Lara PA, Wong DA et al (2011) Fatal infantile lactic acidosis and a novel homozygous mutation in the SUCLG1 gene: a mitochondrial DNA depletion disorder. Mol Genet Metab 102(2):149–152

    CAS  PubMed  Google Scholar 

  44. Ostergaard E, Christensen E, Kristensen E, Mogensen B, Duno M, Shoubridge EA et al (2007) Deficiency of the α subunit of succinate–coenzyme a ligase causes fatal infantile lactic acidosis with mitochondrial DNA depletion. Am J Hum Genet 81(2):383–387

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Miller C, Wang L, Ostergaard E, Dan P, Saada A (2011) The interplay between SUCLA2, SUCLG2, and mitochondrial DNA depletion. Biochem Biophys Acta 1812(5):625–629

    CAS  PubMed  Google Scholar 

Download references

Acknowledgment

The authors thank Shahid Beheshti University of Medical Sciences, for financial support.

Author information

Authors and Affiliations

  1. Department of Medical Genetics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran

    Samira Molaei Ramsheh, Tayyeb Ali Salmani & Hamid Ghaedi

  2. Department of Medical Laboratory Technology, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran

    Maryam Erfanian Omidvar

  3. RayeGen Daru Co, Tehran, Iran

    Maryam Tabasinezhad

  4. Department of Laboratory Sciences, Faculty of Paramedicine, Yasuj University of Medical Sciences, Yasuj, Iran

    Behnam Alipoor

Authors
  1. Samira Molaei Ramsheh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Maryam Erfanian Omidvar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Maryam Tabasinezhad
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Behnam Alipoor
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Tayyeb Ali Salmani
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Hamid Ghaedi
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

SMR and TAS conducted and analyzed the experiments. MEO was involved in conducting the experiments and drafting the manuscript. MT contributed to the experiment analysis and structural analysis. BA participated in analyzing the experiments and drafting the manuscript. HGH designed the study, analyzed the next-generation sequencing (NGS) data, and participated in structural analysis as well as the writing of the manuscript. All authors reviewed the manuscript and confirmed the final version.

Corresponding author

Correspondence to Hamid Ghaedi.

Ethics declarations

Conflict of interest

The authors acknowledged that there is no conflict of interest.

Ethical approval

All procedures conducted in this study involving human participants were following the ethical standards of the Ethical Committee of Shahid Beheshti University of Medical Sciences.

Informed consent

Informed consent was obtained from the patient's parents or legal representative.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Molaei Ramsheh, S., Erfanian Omidvar, M., Tabasinezhad, M. et al. SUCLG1 mutations and mitochondrial encephalomyopathy: a case study and review of the literature. Mol Biol Rep 47, 9699–9714 (2020). https://doi.org/10.1007/s11033-020-05999-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11033-020-05999-y

Keywords

Search

Navigation