Centella asiatica promotes antioxidant gene expression and mitochondrial oxidative respiration in experimental autoimmune encephalomyelitis.
Payel KunduKanon YasuharaMikah S BrandesJonathan A ZweigCody J NeffSarah HoldenKat KesslerSteven MatsumotoHalina OffnerCarin Stewart WasloArthur VandenbarkAmala SoumyanathLarry S ShermanJacob RaberNora E GrayRebbeca Irene SpainPublished in: Research square (2023)
Centella asiatica (Centella) is a traditional botanical medicine that shows promise in treating dementia based on behavioral alterations seen in animal models of aging and cognitive dysfunction. In order to determine if Centella could similarly improve cognitive function and reduce disease burden in multiple sclerosis (MS), we tested its effects in the neuroinflammatory experimental autoimmune encephalomyelitis (EAE) model of MS. In two independent experiments, C57BL/6J mice were treated following induction of EAE with either a standardized water extract of Centella (CAW) or placebo for 2 weeks. At the dosing schedule and concentrations tested, CAW did not improve behavioral performance, EAE motor disability, or degrees of demyelination. However, CAW-treated mice demonstrated increases in nuclear factor (erythroid-derived 2)-like 2 and other antioxidant response element genes, and increases in mitochondrial respiratory activity. Caw also decreased spinal cord inflammation. Our findings indicate that CAW can increase antioxidant gene expression and mitochondrial respiratory activity in mice with EAE, supporting investigation of the clinical effects of CAW in people with MS.
Keyphrases
- oxidative stress
- multiple sclerosis
- gene expression
- nuclear factor
- mass spectrometry
- high fat diet induced
- spinal cord
- anti inflammatory
- ms ms
- dna methylation
- toll like receptor
- white matter
- mild cognitive impairment
- randomized controlled trial
- clinical trial
- genome wide
- wild type
- insulin resistance
- cognitive impairment
- neuropathic pain
- transcription factor
- respiratory tract
- adipose tissue
- deep learning