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Glial Tumor Necrosis Factor Alpha (TNFα) Generates Metaplastic Inhibition of Spinal Learning

Overview of attention for article published in PLOS ONE, June 2012
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Title
Glial Tumor Necrosis Factor Alpha (TNFα) Generates Metaplastic Inhibition of Spinal Learning
Published in
PLOS ONE, June 2012
DOI 10.1371/journal.pone.0039751
Pubmed ID
Authors

J. Russell Huie, Kyle M. Baumbauer, Kuan H. Lee, Jacqueline C. Bresnahan, Michael S. Beattie, Adam R. Ferguson, James W. Grau

Abstract

Injury-induced overexpression of tumor necrosis factor alpha (TNFα) in the spinal cord can induce chronic neuroinflammation and excitotoxicity that ultimately undermines functional recovery. Here we investigate how TNFα might also act to upset spinal function by modulating spinal plasticity. Using a model of instrumental learning in the injured spinal cord, we have previously shown that peripheral intermittent stimulation can produce a plastic change in spinal plasticity (metaplasticity), resulting in the prolonged inhibition of spinal learning. We hypothesized that spinal metaplasticity may be mediated by TNFα. We found that intermittent stimulation increased protein levels in the spinal cord. Using intrathecal pharmacological manipulations, we showed TNFα to be both necessary and sufficient for the long-term inhibition of a spinal instrumental learning task. These effects were found to be dependent on glial production of TNFα and involved downstream alterations in calcium-permeable AMPA receptors. These findings suggest a crucial role for glial TNFα in undermining spinal learning, and demonstrate the therapeutic potential of inhibiting TNFα activity to rescue and restore adaptive spinal plasticity to the injured spinal cord. TNFα modulation represents a novel therapeutic target for improving rehabilitation after spinal cord injury.

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The data shown below were compiled from readership statistics for 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 34 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 21%
Professor 5 15%
Researcher 5 15%
Student > Postgraduate 4 12%
Student > Bachelor 2 6%
Other 7 21%
Unknown 4 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 10 29%
Neuroscience 7 21%
Engineering 3 9%
Nursing and Health Professions 2 6%
Mathematics 1 3%
Other 5 15%
Unknown 6 18%