Fibroblast bioenergetics to classify amyotrophic lateral sclerosis patients.
Fibroblast bioenergetics to classify amyotrophic lateral sclerosis patients.
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DOI：
10.1186/s13024-017-0217-5
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发表时间：
2017-10-24
影响因子：
15.1
通讯作者：
Manfredi G
中科院分区：
文献类型：
--
作者：
Konrad C;Kawamata H;Bredvik KG;Arreguin AJ;Cajamarca SA;Hupf JC;Ravits JM;Miller TM;Maragakis NJ;Hales CM;Glass JD;Gross S;Mitsumoto H;Manfredi G
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. 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. 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. 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. The online version of this article (10.1186/s13024-017-0217-5) contains supplementary material, which is available to authorized users.
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影响因子：
2.7
作者：
Chen Y;Liu XH;Wu JJ;Ren HM;Wang J;Ding ZT;Jiang YP
通讯作者：
Jiang YP
影响因子：
7.1
作者：
Paré B;Touzel-Deschênes L;Lamontagne R;Lamarre MS;Scott FD;Khuong HT;Dion PA;Bouchard JP;Gould P;Rouleau GA;Dupré N;Berthod F;Gros-Louis F
通讯作者：
Gros-Louis F
影响因子：
4.1
作者：
Pare, Bastien;Gros-Louis, Francois
通讯作者：
Gros-Louis, Francois
DOI：
10.3109/21678421.2016.1167913
发表时间：
2016-07-01
影响因子：
2.8
作者：
Oeckl, Patrick;Jardel, Claude;Otto, Markus
通讯作者：
Otto, Markus
影响因子：
3.5
作者：
Oketa, Y.;Higashida, K.;Ono, S.
通讯作者：
Ono, S.