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Fibroblast bioenergetics to classify amyotrophic lateral sclerosis patients

Overview of attention for article published in Molecular Neurodegeneration, October 2017
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Title
Fibroblast bioenergetics to classify amyotrophic lateral sclerosis patients
Published in
Molecular Neurodegeneration, October 2017
DOI 10.1186/s13024-017-0217-5
Pubmed ID
Authors

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

Abstract

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.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 63 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 63 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 12 19%
Student > Ph. D. Student 10 16%
Researcher 9 14%
Student > Master 8 13%
Student > Doctoral Student 4 6%
Other 8 13%
Unknown 12 19%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 13 21%
Neuroscience 12 19%
Medicine and Dentistry 9 14%
Agricultural and Biological Sciences 7 11%
Computer Science 2 3%
Other 7 11%
Unknown 13 21%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 25 July 2018.
All research outputs
#15,622,905
of 20,168,546 outputs
Outputs from Molecular Neurodegeneration
#685
of 768 outputs
Outputs of similar age
#235,553
of 337,699 outputs
Outputs of similar age from Molecular Neurodegeneration
#80
of 85 outputs
Altmetric has tracked 20,168,546 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 768 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.3. This one is in the 7th percentile – i.e., 7% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 337,699 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 24th percentile – i.e., 24% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 85 others from the same source and published within six weeks on either side of this one. This one is in the 4th percentile – i.e., 4% of its contemporaries scored the same or lower than it.