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T-cell metabolism in autoimmune disease

Overview of attention for article published in Arthritis Research & Therapy, February 2015
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  • Good Attention Score compared to outputs of the same age (75th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (64th percentile)

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9 X users

Citations

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121 Dimensions

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203 Mendeley
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Title
T-cell metabolism in autoimmune disease
Published in
Arthritis Research & Therapy, February 2015
DOI 10.1186/s13075-015-0542-4
Pubmed ID
Authors

Zhen Yang, Eric L Matteson, Jörg J Goronzy, Cornelia M Weyand

Abstract

Cancer cells have long been known to fuel their pathogenic growth habits by sustaining a high glycolytic flux, first described almost 90 years ago as the so-called Warburg effect. Immune cells utilize a similar strategy to generate the energy carriers and metabolic intermediates they need to produce biomass and inflammatory mediators. Resting lymphocytes generate energy through oxidative phosphorylation and breakdown of fatty acids, and upon activation rapidly switch to aerobic glycolysis and low tricarboxylic acid flux. T cells in patients with rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE) have a disease-specific metabolic signature that may explain, at least in part, why they are dysfunctional. RA T cells are characterized by low adenosine triphosphate and lactate levels and increased availability of the cellular reductant NADPH. This anti-Warburg effect results from insufficient activity of the glycolytic enzyme phosphofructokinase and differentiates the metabolic status in RA T cells from those in cancer cells. Excess production of reactive oxygen species and a defect in lipid metabolism characterizes metabolic conditions in SLE T cells. Owing to increased production of the glycosphingolipids lactosylceramide, globotriaosylceramide and monosialotetrahexosylganglioside, SLE T cells change membrane raft formation and fail to phosphorylate pERK, yet hyperproliferate. Borrowing from cancer metabolomics, the metabolic modifications occurring in autoimmune disease are probably heterogeneous and context dependent. Variations of glucose, amino acid and lipid metabolism in different disease states may provide opportunities to develop biomarkers and exploit metabolic pathways as therapeutic targets.

X Demographics

X Demographics

The data shown below were collected from the profiles of 9 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 203 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 <1%
Bulgaria 1 <1%
Portugal 1 <1%
India 1 <1%
United States 1 <1%
Unknown 197 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 45 22%
Researcher 33 16%
Student > Master 31 15%
Student > Bachelor 20 10%
Other 15 7%
Other 32 16%
Unknown 27 13%
Readers by discipline Count As %
Immunology and Microbiology 44 22%
Medicine and Dentistry 39 19%
Agricultural and Biological Sciences 33 16%
Biochemistry, Genetics and Molecular Biology 26 13%
Nursing and Health Professions 5 2%
Other 18 9%
Unknown 38 19%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 02 February 2016.
All research outputs
#6,875,065
of 25,371,288 outputs
Outputs from Arthritis Research & Therapy
#1,440
of 3,380 outputs
Outputs of similar age
#88,295
of 367,173 outputs
Outputs of similar age from Arthritis Research & Therapy
#25
of 71 outputs
Altmetric has tracked 25,371,288 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 3,380 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.2. This one has gotten more attention than average, scoring higher than 57% of its peers.
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 367,173 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 75% of its contemporaries.
We're also able to compare this research output to 71 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 64% of its contemporaries.