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MMAB, a novel candidate gene to be screened in the molecular diagnosis of Mevalonate Kinase Deficiency

  • Genes and Disease
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Abstract

Mevalonate kinase deficiency (MKD) is an autosomal recessive inflammatory disease. Mutations in MVK gene are associated with MKD with modest genotype–phenotype correlation. In spite of recent guidelines indicating specific MVK mutations for the more severe form or the milder one, little is known about MVK variability within and between populations. The aim of this work is to provide supplementary information about MVK variability useful in the molecular diagnosis of MKD, as well as to unravel the presence of novel genes potentially involved as involved in the clinical heterogeneity of MKD phenotype. We used a population-based approach, coupled with Combined Annotation–Dependent Depletion (CADD) score, to analyze the level of genetic variability for common and putatively deleterious MVK variants. We also performed Exome screening with the Illumina Human Exome Bead Chip on 21 MKD patients to double-check our in silico findings. Haplotype block detection in different populations revealed the existence of two blocks in MVK; interestingly, the first haploblock comprises the promoter region shared with MMAB gene. Analyses of MMAB and MVK genetic variants in 21 MKD patients strengthen our observations showing a novel scenario in which the same mutations commonly associated with MKD are found coupled with different combination of MMAB rs7134594 SNP was already described as associated with HDL cholesterol level and present in the haploblock promoter region. The rs7134594 SNP is reported as an eQTL for MVK and MMAB. Hypothesizing the presence of genetic variants modulating the complex phenotypic spectrum of MKD, we suggest that future directions in screening for MKD pathogenic variants should focus both MMAB and MVK genes.

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References

  1. Bader-Meunier B, Florkin B, Sibilia J et al (2011) Mevalonate kinase deficiency: a survey of 50 patients. Pediatrics 128:e152–e159

    Article  PubMed  Google Scholar 

  2. Haas D, Hoffmann GF (2006) Mevalonate kinase deficiencies: from mevalonic aciduria to hyperimmunoglobulinemia D syndrome. Orphanet J Rare Dis 1:1

    Article  Google Scholar 

  3. De Pieri C, Taddio A, Insalaco A et al (2014) Different presentations of mevalonate kinase deficiency: a case series. Clin Exp Rheumatol 33:437–442

    Google Scholar 

  4. Browne C, Timson DJ (2015) In silico prediction of the effects of mutations in the human mevalonate kinase gene: towards a predictive framework for mevalonate kinase deficiency. Ann Human Genet 79:451–459

    Article  CAS  Google Scholar 

  5. Stabile A, Compagnone A, Napodano S, Raffaele CG, Patti M, Rigante D (2012) Mevalonate kinase genotype in children with recurrent fevers and high serum IgD level. Rheumatol Int 33:3039–3042

    Article  PubMed  Google Scholar 

  6. Celsi F, Tommasini A, Crovella S (2014) “Hyper-IgD syndrome” or “mevalonate kinase deficiency”: an old syndrome needing a new name? Rheumatol Int 34:423–424

    Article  PubMed  Google Scholar 

  7. Moura R, Tricarico PM, Coelho AVC, Crovella S (2015) GRID2 a novel gene possibly associated with mevalonate kinase deficiency. Rheumatol Int 35:657–659

    Article  CAS  PubMed  Google Scholar 

  8. Murphy C, Murray AM, Meaney S, Gåfvels M (2007) Regulation by SREBP-2 defines a potential link between isoprenoid and adenosylcobalamin metabolism. Biochem Biophys Res Commun 355:359–364

    Article  CAS  PubMed  Google Scholar 

  9. Celsi F, Piscianz E, Romano M, Crovella S (2015) Knockdown of MVK does not lead to changes in NALP3 expression or activation. J Inflamm 12:1

    Article  Google Scholar 

  10. Consortium GP (2012) An integrated map of genetic variation from 1092 human genomes. Nature 491:56–65

    Article  Google Scholar 

  11. Wright S (1951) The genetical structure of populations. Ann Eugen 15:323–354

    Article  CAS  PubMed  Google Scholar 

  12. Benazzo A, Panziera A, Bertorelle G (2015) 4P: fast computing of population genetics statistics from large DNA polymorphism panels. Ecol Evol 5:172–175

    Article  PubMed  Google Scholar 

  13. Purcell S, Neale B, Todd-Brown K et al (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Human Genet 81:559–575

    Article  CAS  Google Scholar 

  14. Kircher M, Witten DM, Jain P, O’Roak BJ, Cooper GM, Shendure J (2014) A general framework for estimating the relative pathogenicity of human genetic variants. Nat Genet 46:310

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Gabriel SB, Schaffner SF, Nguyen H et al (2002) The structure of haplotype blocks in the human genome. Science 296:2225–2229

    Article  CAS  PubMed  Google Scholar 

  16. Foll M, Gaggiotti O (2008) A genome-scan method to identify selected loci appropriate for both dominant and codominant markers: a Bayesian perspective. Genetics 180:977–993

    Article  PubMed  PubMed Central  Google Scholar 

  17. Federici S, Sormani MP, Ozen S et al (2015) Evidence-based provisional clinical classification criteria for autoinflammatory periodic fevers. Ann Rheum Dis 74:799–805

    Article  PubMed  Google Scholar 

  18. Consortium G (2015) The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348:648–660

    Article  Google Scholar 

  19. Goldstein JI, Crenshaw A, Carey J et al (2012) zCall: a rare variant caller for array-based genotyping Genetics and population analysis. Bioinformatics 28:2543–2545

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Aulchenko YS, Ripke S, Isaacs A, Van Duijn CM (2007) GenABEL: an R library for genome-wide association analysis. Bioinformatics 23:1294–1296

    Article  CAS  PubMed  Google Scholar 

  21. Wang K, Abbott D (2008) A principal components regression approach to multilocus genetic association studies. Genet Epidemiol 32:108–118

    Article  PubMed  Google Scholar 

  22. Teslovich TM, Musunuru K, Smith AV et al (2010) Biological, clinical and population relevance of 95 loci for blood lipids. Nature 466:707–713

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Hinks A, Martin P, Thompson SD et al (2013) Autoinflammatory gene polymorphisms and susceptibility to UK juvenile idiopathic arthritis. Pediatric Rheumatol 11:1

    Article  Google Scholar 

  24. Buhaescu I, Izzedine H (2007) Mevalonate pathway: a review of clinical and therapeutical implications. Clin Biochem 40:575–584

    Article  CAS  PubMed  Google Scholar 

  25. Fogarty MP, Xiao R, Prokunina-Olsson L, Scott LJ, Mohlke KL (2010) Allelic expression imbalance at high-density lipoprotein cholesterol locus MMAB-MVK. Human Mol Genet 2010:ddq067

    Google Scholar 

  26. Leslie A, Favier, Grant S, Schulert (2016) Mevalonate kinase deficiency: current perspectives. Appl Clin Genet 9:101–110

    Article  Google Scholar 

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Acknowledgements

We are grateful to the patients for participating in this study.

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Authors and Affiliations

Authors

Contributions

MM, FC, PMT and SC designed the experimental plan and wrote the manuscript; MM performed the global variation research analysis; RRM and SC performed the exome screening; MM and RRM carried out the statistical analysis; PMT and SC revised the language grammar and style. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Sergio Crovella.

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Funding

RRM was supported by the Grant from Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (BEX-7711/15-8). The study was supported by the “Ricerca Corrente” Grants from IRCCS Burlo Garofolo RC42/11 and RC 13/08.

Conflict of interest

Massimo Mezzavilla, Ronald Rodrigues Moura, Fulvio Celsi, Paola Maura Tricarico, and Sergio Crovella declare no conflict of interest.

Ethical standards

The Ethical Committee of IRCCS Burlo Garofolo approved the research (protocol no. 185/08, 19/08/2008).

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Mezzavilla, M., Moura, R.R., Celsi, F. et al. MMAB, a novel candidate gene to be screened in the molecular diagnosis of Mevalonate Kinase Deficiency. Rheumatol Int 38, 121–127 (2018). https://doi.org/10.1007/s00296-017-3890-3

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  • DOI: https://doi.org/10.1007/s00296-017-3890-3

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