Details

Autor(en) / Beteiligte

• Graham, Stewart F
• Rey, Nolwen L
• Yilmaz, Ali
• Kumar, Praveen
• Madaj, Zachary
• Maddens, Michael
• Bahado-Singh, Ray O
• Becker, Katelyn
• Schulz, Emily
• Meyerdirk, Lindsay K
• Steiner, Jennifer A
• Ma, Jiyan
• Brundin, Patrik

Titel

Biochemical Profiling of the Brain and Blood Metabolome in a Mouse Model of Prodromal Parkinson’s Disease Reveals Distinct Metabolic Profiles

Ist Teil von

• Journal of proteome research, 2018-07, Vol.17 (7), p.2460-2469

Ort / Verlag

United States: American Chemical Society

Erscheinungsjahr

2018

Link zum Volltext

Quelle

MEDLINE

Beschreibungen/Notizen

• Parkinson’s disease is the second most common neurodegenerative disease. In the vast majority of cases the origin is not genetic and the cause is not well understood, although progressive accumulation of α-synuclein aggregates appears central to the pathogenesis. Currently, treatments that slow disease progression are lacking, and there are no robust biomarkers that can facilitate the development of such treatments or act as aids in early diagnosis. Therefore, we have defined metabolomic changes in the brain and serum in an animal model of prodromal Parkinson’s disease. We biochemically profiled the brain tissue and serum in a mouse model with progressive synucleinopathy propagation in the brain triggered by unilateral injection of preformed α-synuclein fibrils in the olfactory bulb. In total, we accurately identified and quantified 71 metabolites in the brain and 182 in serum using 1H NMR and targeted mass spectrometry, respectively. Using multivariate analysis, we accurately identified which metabolites explain the most variation between cases and controls. Using pathway enrichment analysis, we highlight significantly perturbed biochemical pathways in the brain and correlate these with the progression of the disease. Furthermore, we identified the top six discriminatory metabolites and were able to develop a model capable of identifying animals with the pathology from healthy controls with high accuracy (AUC (95% CI) = 0.861 (0.755–0.968)). Our study highlights the utility of metabolomics in identifying elements of Parkinson’s disease pathogenesis and for the development of early diagnostic biomarkers of the disease.