Alzheimer s Research & TherapyNeurosciencePeer-reviewed journal article
Increased cerebrospinal fluid and plasma apoE glycosylation is associated with reduced levels of Alzheimer's disease biomarkers
Dobrin Nedelkov, Zoe E. Tsokolas, Matheus Scarpatto RodriguesiD, Isabel J. SibleiD, S. Duke HaniD, Bilal E. KermaniD, Michael RentelniD, Wendy Jean MackiD, Tharick Ali PascoaliD, Hussein N. YassineiD, the Alzheimer’s Disease Neuroimaging Initiative
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Abstract
The apolipoprotein E (APOE) {varepsilon}4 allele is the strongest genetic risk factor for Alzheimers disease (AD). ApoE is glycosylated with an O-linked Core-1 sialylated glycan at several sites, yet the impact and function of this glycosylation on AD biomarkers remains unclear. We examined apoE glycosylation in a cohort of cerebrospinal fluid (CSF, n=181) and plasma (n= 178) samples from the Alzheimers Disease Neuroimaging Initiative (ADNI) stratified into 4 groups: cognitively normal (CN), Mild Cognitive Impairment (MCI), progressors and non-progressors based on delayed word recall performance over 4 years. We observed decreasing glycosylation from apoE2 > apoE3 > apoE4 in CSF, and in plasma (apoE3 > apoE4). ApoE glycosylation was reduced in the MCI compared with CN groups, and in progressors compared to non-progressors. In CSF, higher apoE glycosylation associated cross-sectionally with lower total tau (t-tau), p-tau181, and with higher A{beta}1-42. Similar associations of apoE glycosylation with higher A{beta}1-42 were observed in plasma. In CSF, greater apoE4 glycosylation was associated with lower t-tau and p-tau181. Over a 6-year period, higher baseline levels of CSF apoE glycosylation predicted lower rates of increase in CSF t-tau and p-tau181 and lower rates of decrease in CSF A{beta}1-42. These results indicate strong associations of apoE glycosylation with biomarkers of AD pathology independent of apoE genotype, warranting a deeper understanding of the functional role of apoE glycosylation on AD tau pathology.
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- approach: statistical model
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