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Proteomic characterization of aging-driven changes in the mouse brain by co-expression network analysis.

Kazuya Tsumagari1,2,3,4, Yoshiaki Sato5, Hirofumi Aoyagi5

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Brain aging alters protein levels, with more extracellular proteins and fewer synaptic proteins found in aged mouse brains. These proteomic changes offer insights into brain aging and dementia risk.

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Area of Science:

  • Neuroscience
  • Proteomics
  • Aging Research

Background:

  • Brain aging is linked to functional decline and increased dementia risk, including Alzheimer's disease.
  • Understanding the molecular changes during brain aging is crucial for developing interventions.
  • Proteomic alterations are key indicators of cellular and tissue aging.

Purpose of the Study:

  • To identify and characterize age-related proteomic changes in the mouse brain.
  • To compare proteomic profiles in the cortex and hippocampus across different age groups.
  • To provide a comprehensive proteomic dataset for aging brain research.

Main Methods:

  • Quantitative proteomics was employed to analyze brain tissues (cortex and hippocampus) from mice at three ages (3, 15, and 24 months).
  • Over 7000 proteins were quantified with high reproducibility.
  • Differential protein expression analysis was performed to identify age-associated changes.

Main Results:

  • Many proteins upregulated with age were identified as extracellular proteins, including extracellular matrix and secreted proteins linked to glial cells.
  • Conversely, proteins associated with synapses, particularly the postsynaptic density, were significantly downregulated in the cortex but not the hippocampus.
  • The study identified distinct proteomic shifts in different brain regions during aging.

Conclusions:

  • Brain aging is characterized by a shift towards increased extracellular and glial-associated proteins and decreased synaptic proteins.
  • These proteomic changes, especially in synaptic components, may underlie age-related cognitive decline and dementia susceptibility.
  • The generated proteomic datasets serve as a valuable resource for future research into the molecular mechanisms of brain aging.