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[P2–067]: MRI‐HISTOPATHOLOGY ASSOCIATIONS OF MICROBLEEDS AND MICROINFARCTS IN INTACT EX VIVO HEMISPHERES OF PATIENTS WITH CEREBRAL AMYLOID ANGIOPATHY
Author(s) -
Veluw Susanne J.,
Kouwe Andre J.,
Reijmer Yael D.,
Charidimou Andreas,
Viswanathan Anand,
Frosch Matthew P.,
Greenberg Steven M.
Publication year - 2017
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1016/j.jalz.2017.06.716
Subject(s) - cerebral amyloid angiopathy , ex vivo , pathology , neuropathology , medicine , magnetic resonance imaging , histopathology , cytoarchitecture , in vivo , radiology , biology , dementia , microbiology and biotechnology , disease
with the neuropathological diagnoses. Results: We identified four highly conserved microRNA binding-sites in the SNCA 3’UTR, and observed a neuronal-type specific expression profile for eachmicroRNA in the different isogenic iPSC-derived neurons, i.e. dopaminergic vs. cholinergic neurons. In themature cholinergic neurons, the levels of the miR-140-3p.1 and miR-223-3p were dominant. While, in mature dopaminergic neurons miR-7-5p showed the highest expression by wide-margin, followed by miR-223-3p, and miR153-3p. Furthermore, we found that the short-structural variant rs33988309-polyT was moderately and distinctively associated with DLB but not with PD. Conclusions:We suggest that neuronal type-specificmechanisms regulate SNCA gene expression and involve the contribution of both cis and trans acting factors. In support of a trans acting factor, our data showed differential expression of microRNAs in pathology-relevant cells proposing that differentmicroRNAs regulate SNCA-mRNA expression levels in a neuronal-type specific manner. As an example of a cis acting factor, we discovered a genetic variant in the SNCA-3’UTR that specifically affects DLB risk, implying that distinct genetic variability in the SNCA gene may contribute to synucleinopathies in a pathology-specific manner. fourtheen compartments: Eight biomarker compartments in brain (yellow circles) and six transit compartments from brain to CSF (white circles). Six biomarkers were measured in CSF (sAPPa, sAPPb Ab40, Ab42, Ab38 and P2-066 RESILIENCE MECHANISM OF THE AbO), indicated by the blue boxes. The drug effect of the BACE1 inhibitor (BACEiEFF) inhibitedRinb.The drug effect of theGS inhibitor (GSi EFF) inhibited Kin40,Kin40,Kin38 andKin382. As driver of biomarker responseCtarget was used, which was derived from the PKmodels of the BACE1 inhibitor (VanMaanen et al. (2016)) and GS inhibitor (unpublished), respectively. The red arrow indicates AMYLOID PRECURSOR PROTEIN (APP) PATHWAY REVEALED BY SYSTEMS PHARMACOLOGY MODELING FOLLOWINGbANDGAMMA-SECRETASE INHIBITION the homeostatic feedback on a-secretase through the action of C99. APP: Ab-precursor protein; Ab: amyloid-b-peptide; Ctarget: drug concentration target site; Kin38: Ab38 formation rate from C99; kin382: Ab38 formation rate from Ab42; Kin40: Ab40 formation rate; Kin42: Ab42 formation rate; Kout: Ab38, Ab40 and Ab42 degradation rate; Krev: Oligomer dissociation rate; KtAP: transit rate sAPPa and sAPPb from brain to CSF; Kpl: Oligomerization rate;KtAB: transit rateAb from brain to CSF;KtABO: transit rateAbO from brain to CSF;RinAPP: source of APP;Rinb: sAPPb formation rate;Rina: sAPPa formation rate; Rout: sAPPb degradation rate; Routa: sAPPa degradation rate. Eline M. T. van Maanen, Tamara J. van Steeg, Juliya Kalinina, Julie Stone, Maria S. Michener, Mary J. Savage, Matthew Kennedy, Meindert Danhof, Leiden University, Leiden, Netherlands; LAP&P Consultants, Leiden, Netherlands; Merck& Co., Inc., West Point, PA, USA; Merck Research Laboratories, Whitehouse Station, NJ, USA; Merck & Co Inc., Kenilworth, NJ, USA; Merck, West Point, PA, USA; Merck Research Laboratories, Rahway, NJ, USA; Merck & Co., Inc., Boston, MA, USA. Contact e-mail: e.vanmaanen@lapp.nl