Skip to main content

Advertisement

SpringerLink
Log in

Disclosure of a putative biosignature for respiratory chain disorders through a metabolomics approach

  • Original Article
  • Published:
Metabolomics Aims and scope Submit manuscript

Abstract

The diagnosis of respiratory chain deficiencies (RCDs) is complicated and the need for a diagnostic biomarker or biosignature has been widely expressed. In this study, the metabolic profile of a selected group of 29 RCD patients, with a predominantly muscle disease phenotype, and 22 controls were investigated using targeted and untargeted analyses of three sub-sections of the human metabolome, including urinary organic acids and amino acids [measured by gas chromatography–mass spectrometry (GC–MS)], as well as acylcarnitines (measured by electrospray ionization tandem MS). Although MS technologies are highly sensitive and selective, they are restrictive by being applied only to sub-sections of the metabolome; an untargeted nuclear magnetic resonance (NMR) spectroscopy approach was therefore also included. After data reduction and pre-treatment, a biosignature comprising six organic acids (lactic, succinic, 2-hydroxyglutaric, 3-hydroxyisobutyric, 3-hydroxyisovaleric and 3-hydroxy-3-methylglutaric acids), six amino acids (alanine, glycine, glutamic acid, serine, tyrosine and α-aminoadipic acid) and creatine, was constructed from uni- and multivariate statistical analyses and verified by cross-validation. The results presented here provide the first proof-of-concept that the metabolomics approach is capable of defining a biosignature for RCDs. We postulate that the composite of organic acids ≈ amino acids > creatine > betaine > carnitines represents the basic biosignature for RCDs. Validated through a prospective study, this could offer an improved ability to assign individual patients to a group with defined RCD characteristics and improve case selection for biopsy procedures, especially in infants and children.

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

Similar content being viewed by others

References

  • Atkinson, A. J., Colburn, W. A., DeGruttola, V. G., DeMets, D. L., Downing, G. J., Hoth, D. F., et al. (2001). Biomarkers and surrogate endpoints: Preferred definitions and conceptual framework. Clinical Pharmacology & Therapeutics, 69(3), 89–95.

    Article  Google Scholar 

  • Barker, M., & Rayens, W. (2003). Partial least squares for discrimination. Journal of Chemometrics, 17(3), 166–173.

    Article  CAS  Google Scholar 

  • Bijlsma, S., Bobeldijk, I., Verheij, E. R., Ramaker, R., Kochhar, S., Macdonald, I. A., et al. (2006). Large-scale human metabolomics studies: A strategy for data (pre-) processing and validation. Analytical Chemistry, 78(2), 567–574.

    Article  PubMed  CAS  Google Scholar 

  • Brereton, R. G. (2003). Chemometrics—data analysis for the laboratory and chemical plant. Chichester: John Wiley & Sons Ltd.

    Google Scholar 

  • Calvo, S. E., & Mootha, V. K. (2010). The mitochondrial proteome and human disease. Annual Review of Genomics and Human Genetics, 11, 25–44.

    Article  PubMed  CAS  Google Scholar 

  • Chong, I. G., & Jun, C. H. (2005). Performance of some variable selection methods when multicollinearity is present. Chemometrics and Intelligent Laboratory Systems, 78(1–2), 103–112.

    Article  CAS  Google Scholar 

  • Chung, Y., Rider, L., Bell, J., Summers, R. M., Zemel, L. S., Rennebohm, R. M., et al. (2005). Muscle metabolites, detected in urine by proton spectroscopy, correlate with disease damage in juvenile idiopathic inflammatory myopathies. Arthritis Care & Research, 53(4), 565–570.

    Article  Google Scholar 

  • Ellis, S., & Steyn, H. (2003). Practical significance (effect sizes) versus or in combination with statistical significance (p-values). Management Dynamics, 12(4), 51–53.

    Google Scholar 

  • Elstner, M., & Turnbull, D. M. (2011). Transcriptome analysis in mitochondrial disease. Brain Research Bulletin,. doi:10.1016/j.brainresbull.2011.07.018.

    PubMed  Google Scholar 

  • Engelke, U. F. H., Liebrand-van Sambeek, M. L. F., de Jong, J. G., Leroy, J. G., Morava, E., Smeitink, J. A., et al. (2004). N-acetylated metabolites in urine: Proton nuclear magnetic resonance spectroscopic study on patients with inborn errors of metabolism. Clinical Chemistry, 50(1), 58–66.

    Article  PubMed  CAS  Google Scholar 

  • Engelke, U. F. H., Moolenaar, S. H., Hoenderop, S. M. G. C., van der Morava, E., Graaf, M., Heerschap, A., et al. (2007). Handbook of 1 H-NMR spectroscopy in inborn errors of metabolism: body fluid NMR spectrum and in vivo MR spectroscopy. Amsterdam: SPS Publications.

    Google Scholar 

  • Ferrara, C. T., Wang, P., Neto, E. C., Stevens, R. D., Bain, J. R., Wenner, B. R., et al. (2008). Genetic networks of liver metabolism revealed by integration of metabolic and transcriptional profiling. PLoS Genetics, 4(3), e1000034.

    Article  PubMed  Google Scholar 

  • Fluss, R., Faraggi, D., & Reiser, B. (2005). Estimation of the youden index and its associated cutoff point. Biometrical Journal, 47(4), 458–472.

    Article  PubMed  Google Scholar 

  • Frankenhaeuser, M., Lundberg, U., Von Wright, M. R., Von Wright, J., & Sedvall, G. (1986). Urinary monoamine metabolites as indices of mental stress in healthy males and females. Pharmacology Biochemistry and Behaviour, 24(6), 1521–1525.

    Article  CAS  Google Scholar 

  • Giak-Sim, K., Carpenter, K., Hammond, J., Christodoulou, J., & Wilcken, B. (2002). Quantitative fibroblast acylcarnitine profiles in mitochondrial fatty acid [beta]-oxidation defects: Phenotype/metabolite correlations. Molecular Genetics and Metabolism, 76(4), 327–334.

    Article  PubMed  Google Scholar 

  • Haas, R. H., Parikh, S., Falk, M. J., Saneto, R. P., Wolf, N. I., Darin, N., et al. (2008). The in-depth evaluation of suspected mitochondrial disease. Molecular Genetics and Metabolism, 94(1), 16–37.

    Article  PubMed  CAS  Google Scholar 

  • Hu, F. B. (2011). Metabolic profiling of diabetes: From black-box epidemiology to systems epidemiology. Clinical Chemistry, 57(9), 1224–1226.

    Article  PubMed  CAS  Google Scholar 

  • Jacobsen, M., Mattow, J., Repsilber, D., & Kaufmann, S. H. E. (2008). Novel strategies to identify biomarkers in tuberculosis. Biological Chemistry, 389(5), 487–495.

    Article  PubMed  CAS  Google Scholar 

  • Johnson, R. A., & Wichern, D. W. (1998). Applied multivariate statistical analysis (4th ed.). Englewood Cliffs, NJ: Prentice-Hall Inc.

    Google Scholar 

  • Kell, D. B. (2004). Metabolomics and systems biology: making sense of the soup. Current Opinion in Microbiology, 7(3), 296–307.

    Article  PubMed  CAS  Google Scholar 

  • Koene, S., & Smeitink, J. (2011). Mitochondrial medicine. Journal of Inherited Metabolic Disease, 34(2), 247–248.

    Article  PubMed  Google Scholar 

  • Levine, R. J., & Conn, H. O. (1967). Tyrosine metabolism in patients with liver disease. Journal of Clinical Investigation, 46(12), 2012–2020.

    Article  PubMed  CAS  Google Scholar 

  • Mancuso, M., Orsucci, D., Coppedè, F., Nesti, C., Choub, A., & Siciliano, G. (2009). Diagnostic approach to mitochondrial disorders: The need for a reliable biomarker. Current Molecular Medicine, 9(9), 1095–1107.

    Article  PubMed  CAS  Google Scholar 

  • Martín-Hernández, E., García-Silva, M. T., Vara, J., Campos, Y., Cabello, A., Muley, R., et al. (2005). Renal pathology in children with mitochondrial diseases. Pediatric Nephrology, 20(9), 1299–1305.

    Article  PubMed  Google Scholar 

  • Mels, C. M. C., van Rensburg, P. J., van der Westhuizen, F. H., Pretorius, P. J., & Erasmus, E. (2011). Increased excretion of C4-carnitine species after a therapeutic acetylsalicylic acid dose: Evidence for an inhibitory effect on short-chain fatty acid metabolism. ISRN Pharmacology,. doi:10.5402/2011/851870.

    PubMed  Google Scholar 

  • Morath, M., Okun, J., Müller, I., Sauer, S. W., Hörster, F., Hoffmann, G. F., et al. (2008). Neurodegeneration and chronic renal failure in methylmalonic aciduria—A pathophysiological approach. Journal of Inherited Metabolic Disease, 31(1), 35–43.

    Article  PubMed  CAS  Google Scholar 

  • Odièvre, M., Lombes, A., Dessemme, P., Santer, R., Brivet, M., Chevallier, B., et al. (2002). A secondary respiratory chain defect in a patient with Fanconi–Bickel syndrome. Journal of Inherited Metabolic Disease, 25(5), 379–384.

    Article  PubMed  Google Scholar 

  • Rauste-von Wright, M., & Frankenhaeuser, M. (1989). Females’ emotionality as reflected in the excretion of the dopamine metabolite HVA during mental stress. Psychological Reports, 64(3), 856–858.

    Article  PubMed  CAS  Google Scholar 

  • Reinecke, F., Smeitink, J. A. M., & van der Westhuizen, F. H. (2009). OXPHOS gene expression and control in mitochondrial disorders. Biochimicaet Biophysica Acta (BBA)—Molecular Basis of Disease, 1792(12), 1113–1121.

    Article  CAS  Google Scholar 

  • Reinecke, C. J., Koekemoer, G., van der Westhuizen, F. H., Louw, R., Lindeque, J. Z., Mienie, L. J., et al. (2012). Metabolomics of urinary organic acids in respiratory chain deficiencies in children. Metabolomics, 8(2), 264–283.

    Article  CAS  Google Scholar 

  • Shaham, O., Slate, N. G., Goldberger, O., Xu, Q., Ramanathan, A., Souza, A. L., et al. (2010). A plasma signature of human mitochondrial disease revealed through metabolic profiling of spent media from cultured muscle cells. Proceedings of the National Academy of Sciences of the USA, 107(4), 1571–1575.

    Article  PubMed  CAS  Google Scholar 

  • Smuts, I., Louw, R., Du Toit, H., Klopper, B., Mienie, L. J., & van der Westhuizen, F. H. (2010). An overview of a cohort of South African patients with mitochondrial disorders. Journal of Inherited Metabolic Disease,. doi:10.1007/s10545-009-9031-8.

    Google Scholar 

  • Suomalainen, A. (2011). Biomarkers for mitochondrial respiratory chain disorders. Journal of Inherited Metabolic Disease, 34(2), 1–6.

    Article  Google Scholar 

  • Suomalainen, A., Elo, J. M., Pietiläinen, K. H., Hakonen, A. H., Sevastianova, K., Korpela, M., et al. (2011). FGF-21 as a biomarker for muscle-manifesting mitochondrial respiratory chain deficiencies: A diagnostic study. The Lancet Neurology, 10(9), 806–818.

    Article  CAS  Google Scholar 

  • Sztajnkrycer, M. D. (2002). Valproic acid toxicity: Overview and management. Clinical Toxicology, 40(6), 789–801.

    Article  PubMed  CAS  Google Scholar 

  • Thorburn, D. (2004). Mitochondrial disorders: Prevalence, myths and advances. Journal of Inherited Metabolic Disease, 27(3), 349–362.

    Article  PubMed  CAS  Google Scholar 

  • Turnbull, D. (2011). A new biomarker for mitochondrial disease. The Lancet Neurology, 10(9), 777–778.

    Article  Google Scholar 

  • Wikoff, W. R., Pendyala, G., Siuzdak, G., & Fox, H. S. (2008). Journal of Clinical Investigation, 118(7), 2661–2669.

    Article  PubMed  CAS  Google Scholar 

  • Wolf, N. I., & Smeitink, J. A. M. (2002). Mitochondrial disorders. Neurology, 59(9), 1402–1405.

    Article  PubMed  Google Scholar 

  • Wong, L. J. C., Scaglia, F., Graham, B. H., & Craigen, W. J. (2010). Current molecular diagnostic algorithm for mitochondrial disorders. Molecular Genetics and Metabolism, 100(2), 111–117.

    Article  PubMed  CAS  Google Scholar 

Download references

Acknowledgments

We would like to thank Dr M. Duran from the Laboratory for Genetic and Metabolic Diseases, Academic Medical Centre (AMC), Amsterdam, The Netherlands, for his comments on the original manuscript. This study formed part of BioPAD Project BPP007, funded through the South African Department of Science and Technology. Additional financial support from North-West University is likewise acknowledged. S.W. Mason is a recipient of a Vrije Universiteit (VU) Amsterdam-National Research Foundation (NRF)-Desmond Tutu PhD Fellowship.

Author information

Authors and Affiliations

  1. Department of Paediatrics and Child Health, Steve Biko Academic Hospital, University of Pretoria, Pretoria, South Africa

    Izelle Smuts

  2. Centre for Human Metabonomics, North-West University, Potchefstroom Campus, Private Bag X6001, Potchefstroom, 2520, South Africa

    Francois H. van der Westhuizen, Roan Louw, Lodewyk J. Mienie, Shayne Mason & Carolus J. Reinecke

  3. Laboratory for Genetic, Endocrine and Metabolic Diseases, Department of Laboratory Medicine, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands

    Udo F. H. Engelke & Ron A. Wevers

  4. Statistical Consultation Services, North-West University, Potchefstroom Campus, Potchefstroom, South Africa

    Gerhard Koekemoer

Authors
  1. Izelle Smuts
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Francois H. van der Westhuizen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Roan Louw
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lodewyk J. Mienie
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Udo F. H. Engelke
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ron A. Wevers
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Shayne Mason
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Gerhard Koekemoer
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Carolus J. Reinecke
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Carolus J. Reinecke.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Smuts, I., van der Westhuizen, F.H., Louw, R. et al. Disclosure of a putative biosignature for respiratory chain disorders through a metabolomics approach. Metabolomics 9, 379–391 (2013). https://doi.org/10.1007/s11306-012-0455-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11306-012-0455-z

Keywords

Search

Navigation