Fibroblast bioenergetics to classify amyotrophic lateral sclerosis patients

Csaba Konrad, Hibiki Kawamata, Kirsten G. Bredvik, Andrea J. Arreguin, Steven A. Cajamarca, Jonathan C. Hupf, John M. Ravits, Timothy M. Miller, Nicholas J. Maragakis, Chadwick M. Hales, Jonathan D. Glass, Steven Gross, Hiroshi Mitsumoto, Giovanni Manfredi

Research output: Contribution to journalArticlepeer-review

30 Scopus citations

Abstract

BACKGROUND: The objective of this study was to investigate cellular bioenergetics in primary skin fibroblasts derived from patients with amyotrophic lateral sclerosis (ALS) and to determine if they can be used as classifiers for patient stratification.

METHODS: We assembled a collection of unprecedented size of fibroblasts from patients with sporadic ALS (sALS, n = 171), primary lateral sclerosis (PLS, n = 34), ALS/PLS with C9orf72 mutations (n = 13), and healthy controls (n = 91). In search for novel ALS classifiers, we performed extensive studies of fibroblast bioenergetics, including mitochondrial membrane potential, respiration, glycolysis, and ATP content. Next, we developed a machine learning approach to determine whether fibroblast bioenergetic features could be used to stratify patients.

RESULTS: Compared to controls, sALS and PLS fibroblasts had higher average mitochondrial membrane potential, respiration, and glycolysis, suggesting that they were in a hypermetabolic state. Only membrane potential was elevated in C9Orf72 lines. ATP steady state levels did not correlate with respiration and glycolysis in sALS and PLS lines. Based on bioenergetic profiles, a support vector machine (SVM) was trained to classify sALS and PLS with 99% specificity and 70% sensitivity.

CONCLUSIONS: sALS, PLS, and C9Orf72 fibroblasts share hypermetabolic features, while presenting differences of bioenergetics. The absence of correlation between energy metabolism activation and ATP levels in sALS and PLS fibroblasts suggests that in these cells hypermetabolism is a mechanism to adapt to energy dissipation. Results from SVM support the use of metabolic characteristics of ALS fibroblasts and multivariate analysis to develop classifiers for patient stratification.

Original languageEnglish
Pages (from-to)76
Number of pages1
JournalMolecular neurodegeneration
Volume12
Issue number1
DOIs
StatePublished - Oct 24 2017

Keywords

  • ALS
  • Bioenergetics
  • Fibroblasts
  • Machine learning
  • Mitochondria
  • PLS

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