scholarly journals Dysregulated expression levels of APH1B in peripheral blood are associated with brain atrophy and amyloid-β deposition in Alzheimer’s disease

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Young Ho Park ◽  
Jung-Min Pyun ◽  
Angela Hodges ◽  
Jae-Won Jang ◽  
Paula J. Bice ◽  
...  
Keyword(s):  
Gene Expression ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Large Scale ◽  
Target Genes ◽  
Linear Regression Analysis ◽  
Amyloid Β ◽  
Imaging Biomarkers ◽  
Eqtl Analysis ◽  
Expression Levels

Abstract Background The interaction between the brain and periphery might play a crucial role in the development of Alzheimer’s disease (AD). Methods Using blood transcriptomic profile data from two independent AD cohorts, we performed expression quantitative trait locus (cis-eQTL) analysis of 29 significant genetic loci from a recent large-scale genome-wide association study to investigate the effects of the AD genetic variants on gene expression levels and identify their potential target genes. We then performed differential gene expression analysis of identified AD target genes and linear regression analysis to evaluate the association of differentially expressed genes with neuroimaging biomarkers. Results A cis-eQTL analysis identified and replicated significant associations in seven genes (APH1B, BIN1, FCER1G, GATS, MS4A6A, RABEP1, TRIM4). APH1B expression levels in the blood increased in AD and were associated with entorhinal cortical thickness and global cortical amyloid-β deposition. Conclusion An integrative analysis of genetics, blood-based transcriptomic profiles, and imaging biomarkers suggests that APH1B expression levels in the blood might play a role in the pathogenesis of AD.

scholarly journals Comparison of Amyloid β and Tau Spread Models in Alzheimer’s Disease

2018 ◽  
Vol 29 (10) ◽  
pp. 4291-4302 ◽  
Author(s):  
Hang-Rai Kim ◽  
Peter Lee ◽  
Sang Won Seo ◽  
Jee Hoon Roh ◽  
Minyoung Oh ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Large Scale ◽  
Amyloid Β ◽  
Brain Network ◽  
Amyloid Positron Emission Tomography ◽  
Local Spread ◽  
Positron Emission ◽  
Spread Model ◽  
Graph Theoretical Approach

Abstract Tau and amyloid β (Aβ), 2 key pathogenic proteins in Alzheimer’s disease (AD), reportedly spread throughout the brain as the disease progresses. Models of how these pathogenic proteins spread from affected to unaffected areas had been proposed based on the observation that these proteins could transmit to other regions either through neural fibers (transneuronal spread model) or through extracellular space (local spread model). In this study, we modeled the spread of tau and Aβ using a graph theoretical approach based on resting-state functional magnetic resonance imaging. We tested whether these models predict the distribution of tau and Aβ in the brains of AD spectrum patients. To assess the models’ performance, we calculated spatial correlation between the model-predicted map and the actual map from tau and amyloid positron emission tomography. The transneuronal spread model predicted the distribution of tau and Aβ deposition with significantly higher accuracy than the local spread model. Compared with tau, the local spread model also predicted a comparable portion of Aβ deposition. These findings provide evidence of transneuronal spread of AD pathogenic proteins in a large-scale brain network and furthermore suggest different contributions of spread models for tau and Aβ in AD.

scholarly journals Molecular and Imaging Biomarkers in Alzheimer’s Disease: A Focus on Recent Insights

2020 ◽  
Vol 10 (3) ◽  
pp. 61 ◽  
Author(s):  
Chiara Villa ◽  
Marialuisa Lavitrano ◽  
Elena Salvatore ◽  
Romina Combi
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Molecular Mechanisms ◽  
Biological Fluids ◽  
Imaging Techniques ◽  
Amyloid Β ◽  
The Elderly ◽  
Diagnostic Methods ◽  
Imaging Biomarkers ◽  
Attractive Alternative

Alzheimer’s disease (AD) is the most common neurodegenerative disease among the elderly, affecting millions of people worldwide and clinically characterized by a progressive and irreversible cognitive decline. The rapid increase in the incidence of AD highlights the need for an easy, efficient and accurate diagnosis of the disease in its initial stages in order to halt or delay the progression. The currently used diagnostic methods rely on measures of amyloid-β (Aβ), phosphorylated (p-tau) and total tau (t-tau) protein levels in the cerebrospinal fluid (CSF) aided by advanced neuroimaging techniques like positron emission tomography (PET) and magnetic resonance imaging (MRI). However, the invasiveness of these procedures and the high cost restrict their utilization. Hence, biomarkers from biological fluids obtained using non-invasive methods and novel neuroimaging approaches provide an attractive alternative for the early diagnosis of AD. Such biomarkers may also be helpful for better understanding of the molecular mechanisms underlying the disease, allowing differential diagnosis or at least prolonging the pre-symptomatic stage in patients suffering from AD. Herein, we discuss the advantages and limits of the conventional biomarkers as well as recent promising candidates from alternative body fluids and new imaging techniques.

scholarly journals PLASMA BIOMARKER FOR ALZHEIMER’S DISEASE: ARE WE READY NOW FOR CLINICAL PRACTICE AND DRUG TRIALS?

2018 ◽  
pp. 1-2
Author(s):  
A. Nakamura
Keyword(s):  
Mass Spectrometry ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Clinical Utility ◽  
Large Scale ◽  
Amyloid Β ◽  
Imaging Biomarker ◽  
Validation Dataset ◽  
Data Sets ◽  
Detailed Kinetics

To facilitate disease-modifying clinical trials for Alzheimer’s Disease (AD), a blood-based amyloid-β (Aβ) biomarker, which can accurately detect an early pathological signature of AD at prodromal or preclinical stages, has been strongly desired, because it is simpler, less invasive and less costly compared to PET or lumbar puncture. Despite plasma Aβ biomarkers having been extensively investigated, most studies failed to demonstrate clinical utility (1, 2), and at the end of 2016, there was a rather pessimistic mood that this objective might be impossible to realize (3). However, since the latter half of 2017, the situation appears to have changed dramatically, in that several groups have reported potential clinical utility of plasma Aβ biomarkers using different methodologies (4-7). Especially, immunoprecipitation followed by mass spectrometry (IP-MS) assays have shown promising converging evidence. In 2014, we, the National Center for Geriatrics and Gerontology (NCGG) and Koichi Tanaka Mass Spectrometry Research Laboratory at Shimadzu Corporation (Shimadzu), reported that the plasma ratio of Aβ1-42 to a novel APP669-711 fragment (APP669–711/Aβ 1–42) as determined by IP-MS could discriminate high Aβ (Aβ+) individuals from low Aβ (Aβ-) individuals (classified using PiB-PET) with more than 90% accuracy (n=62) (8). In 2017, the Washington University group analyzed detailed kinetics of plasma Aβs, and reported that Aβ42/Aβ40 as measured by IP-MS could distinguish Aβ+ and Aβ- individuals with 88.7% areas under the curve value (n=41) (5). Then very recently, we, in collaboration with the Australian Imaging, Biomarker and Lifestyle Study of Aging (AIBL), have demonstrated that plasma biomarkers, APP669-711/Aβ1-42, Aβ1-40/Aβ1-42, and their composites (composite biomarker), as generated by improved IP-MS methodology performs very well in larger independent datasets: a discovery dataset (NCGG, n=121) and a validation dataset (AIBL, n=252 which includes n=111 PiB-PET and 141 with other ligands) both of which included individuals with normal cognition, MCI and AD. Particularly, the composite biomarker showed very high AUCs in both datasets (discovery 96.7%, n=121, and validation 94.1%, n=111) with accuracy c.a. 90% when using PiB-PET as standard of truth. The findings of the study were considered to be robust, reproducible and reliable because biomarker performance was validated in a blinded manner using independent data sets (Japan and Australia) and involved an established large-scale multicenter cohort (AIBL).

scholarly journals Enhancer variants associated with Alzheimer’s disease affect gene expression via chromatin looping

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Masataka Kikuchi ◽  
Norikazu Hara ◽  
Mai Hasegawa ◽  
Akinori Miyashita ◽  
Ryozo Kuwano ◽  
...  
Keyword(s):  
Gene Expression ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Regulatory Elements ◽  
Genome Wide Association Studies ◽  
Expression Levels ◽  
Chromatin Loops ◽  
Coding Regions ◽  
Affect Gene Expression ◽  
Gene Expression Levels

Abstract Background Genome-wide association studies (GWASs) have identified single-nucleotide polymorphisms (SNPs) that may be genetic factors underlying Alzheimer’s disease (AD). However, how these AD-associated SNPs (AD SNPs) contribute to the pathogenesis of this disease is poorly understood because most of them are located in non-coding regions, such as introns and intergenic regions. Previous studies reported that some disease-associated SNPs affect regulatory elements including enhancers. We hypothesized that non-coding AD SNPs are located in enhancers and affect gene expression levels via chromatin loops. Methods To characterize AD SNPs within non-coding regions, we extracted 406 AD SNPs with GWAS p-values of less than 1.00 × 10− 6 from the GWAS catalog database. Of these, we selected 392 SNPs within non-coding regions. Next, we checked whether those non-coding AD SNPs were located in enhancers that typically regulate gene expression levels using publicly available data for enhancers that were predicted in 127 human tissues or cell types. We sought expression quantitative trait locus (eQTL) genes affected by non-coding AD SNPs within enhancers because enhancers are regulatory elements that influence the gene expression levels. To elucidate how the non-coding AD SNPs within enhancers affect the gene expression levels, we identified chromatin-chromatin interactions by Hi-C experiments. Results We report the following findings: (1) nearly 30% of non-coding AD SNPs are located in enhancers; (2) eQTL genes affected by non-coding AD SNPs within enhancers are associated with amyloid beta clearance, synaptic transmission, and immune responses; (3) 95% of the AD SNPs located in enhancers co-localize with their eQTL genes in topologically associating domains suggesting that regulation may occur through chromatin higher-order structures; (4) rs1476679 spatially contacts the promoters of eQTL genes via CTCF-CTCF interactions; (5) the effect of other AD SNPs such as rs7364180 is likely to be, at least in part, indirect through regulation of transcription factors that in turn regulate AD associated genes. Conclusion Our results suggest that non-coding AD SNPs may affect the function of enhancers thereby influencing the expression levels of surrounding or distant genes via chromatin loops. This result may explain how some non-coding AD SNPs contribute to AD pathogenesis.

scholarly journals RGS2 expression predicts amyloid-β sensitivity, MCI and Alzheimer’s disease: genome-wide transcriptomic profiling and bioinformatics data mining

2016 ◽  
Vol 6 (10) ◽  
pp. e909-e909 ◽  
Author(s):  
A Hadar ◽  
E Milanesi ◽  
A Squassina ◽  
P Niola ◽  
C Chillotti ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Data Mining ◽  
Alzheimer's Disease ◽  
G Protein ◽  
Amyloid Β ◽  
Healthy Individuals ◽  
Transcriptomic Profiling ◽  
Expression Levels ◽  
Genome Wide ◽  
Lower Expression

Abstract Alzheimer's disease (AD) is the most frequent cause of dementia. Misfolded protein pathological hallmarks of AD are brain deposits of amyloid-β (Aβ) plaques and phosphorylated tau neurofibrillary tangles. However, doubts about the role of Aβ in AD pathology have been raised as Aβ is a common component of extracellular brain deposits found, also by in vivo imaging, in non-demented aged individuals. It has been suggested that some individuals are more prone to Aβ neurotoxicity and hence more likely to develop AD when aging brains start accumulating Aβ plaques. Here, we applied genome-wide transcriptomic profiling of lymphoblastoid cells lines (LCLs) from healthy individuals and AD patients for identifying genes that predict sensitivity to Aβ. Real-time PCR validation identified 3.78-fold lower expression of RGS2 (regulator of G-protein signaling 2; P=0.0085) in LCLs from healthy individuals exhibiting high vs low Aβ sensitivity. Furthermore, RGS2 showed 3.3-fold lower expression (P=0.0008) in AD LCLs compared with controls. Notably, RGS2 expression in AD LCLs correlated with the patients’ cognitive function. Lower RGS2 expression levels were also discovered in published expression data sets from postmortem AD brain tissues as well as in mild cognitive impairment and AD blood samples compared with controls. In conclusion, Aβ sensitivity phenotyping followed by transcriptomic profiling and published patient data mining identified reduced peripheral and brain expression levels of RGS2, a key regulator of G-protein-coupled receptor signaling and neuronal plasticity. RGS2 is suggested as a novel AD biomarker (alongside other genes) toward early AD detection and future disease modifying therapeutics.

scholarly journals Association of MAPT haplotypes with Alzheimer’s disease risk and MAPT brain gene expression levels

2014 ◽  
Vol 6 (4) ◽  
pp. 39 ◽  
Author(s):  
Mariet Allen ◽  
Michaela Kachadoorian ◽  
Zachary Quicksall ◽  
Fanggeng Zou ◽  
High Chai ◽  
...  
Keyword(s):  
Gene Expression ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Disease Risk ◽  
Expression Levels ◽  
Brain Gene Expression ◽  
Gene Expression Levels

scholarly journals The solution of large-scale Minimum Cost SAT Problem as a tool for data analysis in bioinformatics

2016 ◽  
Author(s):  
Giovanni Felici ◽  
Daniele Ferone ◽  
Paola Festa ◽  
Antonio Napoletano ◽  
Tommaso Pastore
Keyword(s):  
Gene Expression ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Feature Selection ◽  
Large Scale ◽  
Large Body ◽  
Minimum Cost ◽  
Iterative Solution ◽  
Gene Expression Measurement ◽  
Species Classification

Data mining is one of the main activities in bioinformatics, specifically to extract knowledge from massive data sets related with gene expression measurement, CNV, DNA strings, and others. A long array of methods are used to perform such task, ranging from the more established parametric statistical analysis to non parametric techniques, to classification methods that have been developed in knowledge engineering and artificial intelligence. In this paper, we consider a method for extracting logic formulas from data that relies on a large body of literature in integer and logic optimization, originally presented in [1], that has been largely and successfully applied to different problems in bioinformatics ([2], [3], [4], [5], [6]). Such method is based on the iterative solution of Minimum Cost SAT Problems and is able to extract logic formulas in DNF form that possess interesting features for their interpretation. While leaving the discussion of the main features and motivations of this approach to the related literature, in this talk we focus on the problem of solving efficiently very large scale instances of this well known logic programming problem and propose a new GRASP approach that, being able to exploit the specific structure of the problem, largely outperforms other established solvers for the same problem. References [1] G. Felici, K. Truemper. A Minsat Approach for Learning in Logic Domains, INFORMS Journal on Computing 14(1): 20-36 (2002). [2] P. Bertolazzi, G. Felici, E. Weitschek. Learning to classify species with barcodes, BMC Bioinformatics, 10:1-12 (2009). [3] M. Arisi, R. D’Onofrio, A. Brandi, S. Felsani, G. Capsoni, G. Drovandi, G. Felici, E. Weitschek, P. Bertolazzi, A. Cattaneo. Gene Expression Biomarkers in the Brain of a Mouse Model for Alzheimer’s Disease: Mining of Microarray Data by Logic Classification and Feature Selection. Journal of Alzheimer's Disease, 24(4) 721-738 (2011). [4] E. Weitschek, A. Lo Presti, G. Drovandi, G. Felici, M. Ciccozzi, M. Ciotti, P. Bertolazzi. Human polyomaviruses identification by logic mining techniques. BMC Virology Journal, 9:58 (2012). [5] E. Weitschek, G. Fiscon, G. Felici. Supervised DNA Barcodes species classification: analysis, comparisons and results, BMC BioData Mining, 7:4 (2014). [6] P. Bertolazzi, G. Felici, P. Festa, G. Fiscon, E. Weitschek. Integer Programming models for Feature Selection: new extensions and a randomized solution algorithm, European Journal of Operational Research, 250-389–399, 250 (2016).

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