scholarly journals Butyrylcholinesterase Protein Ends in the Pathogenesis of Alzheimer’s Disease—Could BCHE Genotyping Be Helpful in Alzheimer’s Therapy?

Biomolecules ◽  
2019 ◽  
Vol 9 (10) ◽  
pp. 592 ◽  
Author(s):  
Jacek Jasiecki ◽  
Bartosz Wasąg
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
At Risk ◽  
Enzyme Activity ◽  
Late Onset ◽  
Past Century ◽  
Nucleotide Polymorphisms ◽  
Amino Acid Residues ◽  
Patients At Risk ◽  
K Variant

Late-onset Alzheimer’s disease (AD) is clinically characterized by a progressive decline of memory and other cognitive functions leading to the loss of the ability to perform everyday activities. Only a few drugs have been approved to treat AD dementia over the past century since the first AD patient was diagnosed. Drugs increasing the availability of neurotransmitters at synapses in the brain are used clinically in the treatment of AD dementia, and cholinesterase inhibitors (ChEIs) are the mainstay of the therapy. A detrimental effect on cognitive function has been reported in patients with pharmacological inhibition of acetylcholinesterase (AChE) by ChEIs and reduced butyrylcholinesterase (BChE) activity due to the single nucleotide polymorphisms. The BChE K-variant (rs1803274), the most common genetic variant of the BCHE gene, was thought to reduce enzyme activity reflecting the lower clinical response to rivastigmine in AD patients. During ChEIs therapy, patients carrying reduced-activity BChE do not present such improved attention like patients with the wild-type enzyme. On the other hand, alterations in the BCHE gene causing enzyme activity reduction may delay AD onset in patients at risk by preserving the level of cortical acetylcholine (ACh). Based on our previous results, we conclude that SNPs localized outside of the coding sequence, in 5’UTR (rs1126680) and/or intron 2 (rs55781031) of the BCHE gene, but not solely K-variant alteration (p.A539T) itself, are responsible for reduced enzyme activity. Therefore, we suspect that not BChE-K itself, but these coexisting SNPs (rs1126680 and rs55781031), could be associated with deleterious changes in cognitive decline in patients treated with ChEIs. Based on the results, we suggest that SNPs (rs1126680) and/or (rs55781031) genotyping should be performed to identify subjects at risk for lowered efficacy ChEIs therapy, and such patients should be treated with a lower rivastigmine dosage. Finally, our sequence analysis of the N-terminal end of N-BChE revealed evolutionarily conserved amino acid residues that can be involved in disulfide bond formation and anchoring of N-BChE in the cell membrane.

Association of butyrylcholinesterase K variant with cholinesterase-positive neuritic plaques in the temporal cortex in late-onset Alzheimer's disease

2000 ◽  
Vol 106 (4) ◽  
pp. 447-452 ◽  
Author(s):  
Donald J. Lehmann ◽  
Zsuzsanna Nagy ◽  
Suzanne Litchfield ◽  
Mario Cortina Borja ◽  
A. David Smith
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Late Onset ◽  
Temporal Cortex ◽  
Neuritic Plaques ◽  
K Variant

Identification of Biological Pathways to Alzheimer's Disease Using Polygenic Scores

2017 ◽  
Vol 41 (S1) ◽  
pp. S166-S167
Author(s):  
J. Harrison ◽  
E. Baker ◽  
L. Hubbard ◽  
D. Linden ◽  
J. Williams ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Protein Folding ◽  
Late Onset ◽  
Cell Lineage ◽  
Biological Pathways ◽  
Risk Scores ◽  
Nucleotide Polymorphisms ◽  
Discovery Sample ◽  
Polygenic Scores

IntroductionSingle nucleotide polymorphisms (SNPs) contribute small increases in risk for late-onset Alzheimer's disease (LOAD). LOAD SNPs cluster around genes with similar biological functions (pathways). Polygenic risk scores (PRS) aggregate the effect of SNPs genome-wide. However, this approach has not been widely used for SNPs within specific pathways.ObjectivesWe investigated whether pathway-specific PRS were significant predictors of LOAD case/control status.MethodsWe mapped SNPs to genes within 8 pathways implicated in LOAD. For our polygenic analysis, the discovery sample comprised 13,831 LOAD cases and 29,877 controls. LOAD risk alleles for SNPs in our 8 pathways were identified at a P-value threshold of 0.5. Pathway-specific PRS were calculated in a target sample of 3332 cases and 9832 controls. The genetic data were pruned with R2 > 0.2 while retaining the SNPs most significantly associated with AD. We tested whether pathway-specific PRS were associated with LOAD using logistic regression, adjusting for age, sex, country, and principal components. We report the proportion of variance in liability explained by each pathway.ResultsThe most strongly associated pathways were the immune response (NSNPs = 9304, = 5.63 × 10−19, R2 = 0.04) and hemostasis (NSNPs = 7832, P = 5.47 × 10−7, R2 = 0.015). Regulation of endocytosis, hematopoietic cell lineage, cholesterol transport, clathrin and protein folding were also significantly associated but accounted for less than 1% of the variance. With APOE excluded, all pathways remained significant except proteasome-ubiquitin activity and protein folding.ConclusionsGenetic risk for LOAD can be split into contributions from different biological pathways. These offer a means to explore disease mechanisms and to stratify patients.Disclosure of interestThe authors have not supplied their declaration of competing interest.

scholarly journals FDG and Amyloid PET in Cognitively Normal Individuals at Risk for Late-Onset Alzheimer’s Disease

2014 ◽  
Vol 04 (02) ◽  
pp. 15-26 ◽  
Author(s):  
John Murray ◽  
Wai H. Tsui ◽  
Yi Li ◽  
Pauline McHugh ◽  
Schantel Williams ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
At Risk ◽  
Late Onset ◽  
Amyloid Pet ◽  
Cognitively Normal ◽  
Normal Individuals

scholarly journals Cell type-specific histone acetylation profiling of Alzheimer’s Disease subjects and integration with genetics

2020 ◽  
Author(s):  
Easwaran Ramamurthy ◽  
Gwyneth Welch ◽  
Jemmie Cheng ◽  
Yixin Yuan ◽  
Laura Gunsalus ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Late Onset ◽  
Cell Types ◽  
Brain Cell ◽  
Nucleotide Polymorphisms ◽  
Risk Genes ◽  
Genome Wide ◽  
Dorsolateral Prefrontal ◽  
Genomic Regions

We profile genome-wide histone 3 lysine 27 acetylation (H3K27ac) of 3 major brain cell types from hippocampus and dorsolateral prefrontal cortex (dlPFC) of subjects with and without Alzheimer’s Disease (AD). We confirm that single nucleotide polymorphisms (SNPs) associated with late onset AD (LOAD) prefer to reside in the microglial histone acetylome, which varies most strongly with age. We observe acetylation differences associated with AD pathology at 3,598 peaks, predominantly in an oligodendrocyte-enriched population. Strikingly, these differences occur at the promoters of known early onset AD (EOAD) risk genes (APP, PSEN1, PSEN2, BACE1), late onset AD (LOAD) risk genes (BIN1, PICALM, CLU, ADAM10, ADAMTS4, SORL1 and FERMT2), and putative enhancers annotated to other genes associated with AD pathology (MAPT). More broadly, acetylation differences in the oligodendrocyte-enriched population occur near genes in pathways for central nervous system myelination and oxidative phosphorylation. In most cases, these promoter acetylation differences are associated with differences in transcription in oligodendrocytes. Overall, we reveal deregulation of known and novel pathways in AD and highlight genomic regions as therapeutic targets in oligodendrocytes of hippocampus and dlPFC.

scholarly journals Identifying Genetic Interactions Associated with Late-Onset Alzheimer’s Disease

2013 ◽  
Author(s):  
Charalampos S Floudas ◽  
Nara Um ◽  
M. Ilyas Kamboh ◽  
Michael M Barmada ◽  
Shyam Visweswaran
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Late Onset ◽  
Association Studies ◽  
Univariate Analysis ◽  
Genetic Interactions ◽  
Supporting Evidence ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Genome Wide

Background Identifying genetic interactions in data obtained from genome-wide association studies (GWASs) can help in understanding the genetic basis of complex diseases. The large number of single nucleotide polymorphisms (SNPs) in GWASs however makes the identification of genetic interactions computationally challenging. We developed the Bayesian Combinatorial Method (BCM) that can identify pairs of SNPs that in combination have high statistical association with disease. Results We applied BCM to two late-onset Alzheimer’s disease (LOAD) GWAS datasets to identify SNP-SNP interactions between a set of known SNP associations and the dataset SNPs. For evaluation we compared our results with those from logistic regression, as implemented in PLINK. Gene Ontology analysis of genes from the top 200 dataset SNPs for both GWAS datasets showed overrepresentation of LOAD-related terms. Four genes were common to both datasets: APOE and APOC1, which have well established associations with LOAD, and CAMK1D and FBXL13, not previously linked to LOAD but having evidence of involvement in LOAD. Supporting evidence was also found for additional genes from the top 30 dataset SNPs. Conclusion BCM performed well in identifying several SNPs having evidence of involvement in the pathogenesis of LOAD that would not have been identified by univariate analysis due to small main effect. These results provide support for applying BCM to identify potential genetic variants such as SNPs from high dimensional GWAS datasets.

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