↓ Skip to main content

Fibroblast bioenergetics to classify amyotrophic lateral sclerosis patients

Overview of attention for article published in Molecular Neurodegeneration, October 2017
Altmetric Badge

About this Attention Score

  • Average Attention Score compared to outputs of the same age

Mentioned by

twitter
3 tweeters

Citations

dimensions_citation
36 Dimensions

Readers on

mendeley
60 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
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

The data shown below were collected from the profiles of 3 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 60 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 60 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 12 20%
Student > Ph. D. Student 10 17%
Student > Master 8 13%
Researcher 8 13%
Student > Doctoral Student 4 7%
Other 7 12%
Unknown 11 18%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 12 20%
Neuroscience 11 18%
Medicine and Dentistry 10 17%
Agricultural and Biological Sciences 6 10%
Computer Science 2 3%
Other 7 12%
Unknown 12 20%

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
#8,650,881
of 13,801,769 outputs
Outputs from Molecular Neurodegeneration
#445
of 580 outputs
Outputs of similar age
#184,070
of 316,570 outputs
Outputs of similar age from Molecular Neurodegeneration
#62
of 77 outputs
Altmetric has tracked 13,801,769 research outputs across all sources so far. This one is in the 23rd percentile – i.e., 23% of other outputs scored the same or lower than it.
So far Altmetric has tracked 580 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.6. This one is in the 14th percentile – i.e., 14% 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 316,570 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 77 others from the same source and published within six weeks on either side of this one. This one is in the 5th percentile – i.e., 5% of its contemporaries scored the same or lower than it.