SORL1 Gene is Associated with the Conversion from Mild Cognitive Impairment to Alzheimer's Disease

Alzheimer Disease: Recent Updates on Apolipoprotein E and Gut Microbiome Mediation of Oxidative Stress, and Prospective Interventional Agents

Alzheimer's disease (AD) is a current public health challenge and will remain until the development of an effective intervention. However, developing an effective treatment for the disease requires a thorough understanding of its etiology, which is currently lacking. Although several studies have shown the association between oxidative damage and AD, only a few have clarified the specific mechanisms involved. Herein, we reviewed recent preclinical and clinical studies that indicated the significance of oxidative damage in AD, as well as potential antioxidants. Although several factors regulate oxidative stress in AD, we centered our investigation on apolipoprotein E and the gut microbiome. Apolipoprotein E, particularly apolipoprotein E-ε4, can impair the structural facets of the mitochondria. This, in turn, can minimize the mitochondrial functionality and result in the progressive build-up of free radicals, eventually leading to oxidative stress. Similarly, the gut microbiome can influence oxidative stress to a significant degree via its metabolite, trimethylamine N-oxide. Given the various roles of these two factors in modulating oxidative stress, we also discuss the possible relationship between them and provide future research directions.

Aquaporin-4 polymorphisms predict amyloid burden and clinical outcome in the Alzheimer's disease spectrum

Clearance of amyloid-β (Aβ) from the brain is hypothesized to be mediated by the glymphatic system through aquaporin-4 (AQP4) water channels. Genetic variation of AQP4 may impact water channel function, Aβ clearance, and clinical outcomes. We examined whether single-nucleotide polymorphisms (SNPs) of the AQP4 gene were related to Aβ neuropathology on [¹⁸F]Florbetapir PET in 100 Aβ positive late mild cognitive impairment (LMCI) or Alzheimer's disease (AD) patients and were predictive of clinical outcome in prodromal AD patients. AQP4 SNP rs72878794 was associated with decreased Aβ uptake, whereas rs151244 was associated with increased Aβ uptake, increased risk of conversion from MCI and LMCI to AD, and an increased 4-year rate of cognitive decline in LMCI. AQP4 genetic variation was associated with Aβ accumulation, disease stage progression, and cognitive decline. This variation may correspond to changes in glymphatic system functioning and brain Aβ clearance and could be a useful biomarker in predicting disease burden for those on the dementia spectrum.

The etiological effect of a new low-frequency ESR1 variant on Mild Cognitive Impairment and Alzheimer's Disease: a population-based study

Latent genetic variations of cholesterol metabolism-related genes in late-onset Alzheimer’s disease, especially, as well as in mild cognitive impairment pathogenesis are still to be studied extensively. Thus, we performed the targeted-sequencing of 12 nuclear receptor genes plus APOE which were involved in cholesterol content modulation to screen susceptible genetic variants and focused on a new risk variant ESR1 rs9340803 at 6q25.1 for both late-onset Alzheimer’s disease (OR=3.30[1.84~4.22], p<0.001) and mild cognitive impairment (OR=3.08[1.75~3.89], p<0.001). This low-frequency variant was validated in three independent cohorts totaling 854 late-onset Alzheimer’s disease cases, 1059 mild cognitive impairment cases and 1254 controls from nine provinces of China mainland. Preliminary functional study on it revealed decreased ESR1 expression in vitro. Besides, we detected higher serum Aβ1-40 concentration in participants carrying this variant (p=0.038) and lower plasma total cholesterol level in this variant carriers with late-onset Alzheimer’s disease (p=0.009). In summary, we identified a susceptible variant which might contribute to developing mild cognitive impairment at earlier stage and Alzheimer’s Disease later. Our study would provide new insight into the disease causation of late-onset Alzheimer’s disease and could be exploited therapeutically.

Circulating miR-127-3p as a Potential Biomarker for Differential Diagnosis in Frontotemporal Dementia

Given the heterogeneous nature of frontotemporal dementia (FTD), sensitive biomarkers are greatly needed for the accurate diagnosis of this neurodegenerative disorder. Circulating miRNAs have been reported as promising biomarkers for neurodegenerative disorders and processes affecting the central nervous system, especially in aging. The objective of the study was to evaluate if some circulating miRNAs linked with apoptosis (miR-29b-3p, miR-34a-5p, miR-16-5p, miR-17-5p, miR-107, miR-19b-3p, let-7b-5p, miR-26b-5p, and 127-3p) were able to distinguish between FTD patients and healthy controls. For this study, we enrolled 127 subjects, including 54 patients with FTD, 20 patients with Alzheimer's disease (AD), and 53 healthy controls. The qRT-PCR analysis showed a downregulation of miR-127-3p in FTD compared to controls, while the levels of other miRNAs remained unchanged. Then, miR-127-3p expression was also analyzed in AD patients, finding a different expression between two patient groups. A receiver operating characteristic curve was then created for miR-127-3p to discriminate FTD versus AD (AUC: 0.8986), and versus healthy controls (AUC: 0.8057). In conclusion, miR-127-3p could help to diagnose FTD and to distinguish it from AD.

HSPA12A targets the cytoplasmic domain and affects the trafficking of the Amyloid Precursor Protein receptor SorLA

SorLA and Sortilin are multifunctional receptors involved in endocytosis and intracellular sorting of different and unrelated ligands. SorLA has recently attracted much attention as a novel strong risk gene for Alzheimer’s disease, and much effort is currently being put into understanding the underlying molecular mechanism. Trafficking of SorLA and Sortilin are mediated by interacting with AP-1, AP-2, GGA 1-3 and the retromer complex. Although these cytosolic adaptor proteins all bind to both SorLA and Sortilin, a large fraction of intracellular Sortilin and SorLA are located in different subcellular vesicles. This indicates that unknown specialised adaptor proteins targeting SorLA for trafficking are yet to be discovered. We have identified HSPA12A as a new adaptor protein that, among Vps10p-D receptors, selectively binds to SorLA in an ADP/ATP dependent manner. This is the first described substrate of HSPA12A, and we demonstrate that the binding, which affects both endocytic speed and subcellular localisation of SorLA, is mediated by specific acidic residues in the cytosolic domain of SorLA. The identification of the relatively unknown HSPA12A as a SorLA specific interaction partner could lead to novel insight into the molecular mechanism of SorLA, and re-emphasises the role of heat shock proteins in neurodegenerative diseases.

Impact of late-onset Alzheimer's genetic risk factors on beta-amyloid endocytic production

The increased production of the 42 aminoacids long beta-amyloid (Aβ42) peptide has been established as a causal mechanism of the familial early onset Alzheimer's disease (AD). In contrast, the causal mechanisms of the late-onset AD (LOAD), that affects most AD patients, remain to be established. Indeed, Aβ42 accumulation has been detected more than 30 years before diagnosis. Thus, the mechanisms that control Aβ accumulation in LOAD likely go awry long before pathogenesis becomes detectable. Early on, APOE4 was identified as the biggest genetic risk factor for LOAD. However, since APOE4 is not present in all LOAD patients, genome-wide association studies of thousands of LOAD patients were undertaken to identify other genetic variants that could explain the development of LOAD. PICALM, BIN1, CD2AP, SORL1, and PLD3 are now with APOE4 among the identified genes at highest risk in LOAD that have been implicated in Aβ42 production. Recent evidence indicates that the regulation of the endocytic trafficking of the amyloid precursor protein (APP) and/or its secretases to and from sorting endosomes is determinant for Aβ42 production. Thus, here, we will review the described mechanisms, whereby these genetic risk factors can contribute to the enhanced endocytic production of Aβ42. Dissecting causal LOAD mechanisms of Aβ42 accumulation, underlying the contribution of each genetic risk factor, will be required to identify therapeutic targets for novel personalized preventive strategies.

Informatics Support for Basic Research in Biomedicine

Informatics methodologies exploit computer-assisted techniques to help biomedical researchers manage large amounts of information. In this paper, we focus on the biomedical research literature (MEDLINE). We first provide an overview of some text mining techniques that offer assistance in research by identifying biomedical entities (e.g., genes, substances, and diseases) and relations between them in text.We then discuss Semantic MEDLINE, an application that integrates PubMed document retrieval, concept and relation identification, and visualization, thus enabling a user to explore concepts and relations from within a set of retrieved citations. Semantic MEDLINE provides a roadmap through content and helps users discern patterns in large numbers of retrieved citations. We illustrate its use with an informatics method we call "discovery browsing," which provides a principled way of navigating through selected aspects of some biomedical research area. The method supports an iterative process that accommodates learning and hypothesis formation in which a user is provided with high level connections before delving into details.As a use case, we examine current developments in basic research on mechanisms of Alzheimer's disease. Out of the nearly 90 000 citations returned by the PubMed query "Alzheimer's disease," discovery browsing led us to 73 citations on sortilin and that disorder. We provide a synopsis of the basic research reported in 15 of these. There is wide-spread consensus among researchers working with a range of animal models and human cells that increased sortilin expression and decreased receptor expression are associated with amyloid beta and/or amyloid precursor protein.

Conversion Discriminative Analysis on Mild Cognitive Impairment Using Multiple Cortical Features from MR Images

Neuroimaging measurements derived from magnetic resonance imaging provide important information required for detecting changes related to the progression of mild cognitive impairment (MCI). Cortical features and changes play a crucial role in revealing unique anatomical patterns of brain regions, and further differentiate MCI patients from normal states. Four cortical features, namely, gray matter volume, cortical thickness, surface area, and mean curvature, were explored for discriminative analysis among three groups including the stable MCI (sMCI), the converted MCI (cMCI), and the normal control (NC) groups. In this study, 158 subjects (72 NC, 46 sMCI, and 40 cMCI) were selected from the Alzheimer's Disease Neuroimaging Initiative. A sparse-constrained regression model based on the l2-1-norm was introduced to reduce the feature dimensionality and retrieve essential features for the discrimination of the three groups by using a support vector machine (SVM). An optimized strategy of feature addition based on the weight of each feature was adopted for the SVM classifier in order to achieve the best classification performance. The baseline cortical features combined with the longitudinal measurements for 2 years of follow-up data yielded prominent classification results. In particular, the cortical thickness produced a classification with 98.84% accuracy, 97.5% sensitivity, and 100% specificity for the sMCI–cMCI comparison; 92.37% accuracy, 84.78% sensitivity, and 97.22% specificity for the cMCI–NC comparison; and 93.75% accuracy, 92.5% sensitivity, and 94.44% specificity for the sMCI–NC comparison. The best performances obtained by the SVM classifier using the essential features were 5–40% more than those using all of the retained features. The feasibility of the cortical features for the recognition of anatomical patterns was certified; thus, the proposed method has the potential to improve the clinical diagnosis of sub-types of MCI and predict the risk of its conversion to Alzheimer's disease.

Semiparametric Bayes conditional graphical models for imaging genetics applications

Motivated by the need for understanding neurological disorders, large-scale imaging genetic studies are being increasingly conducted. A salient objective in such studies is to identify important neuroimaging biomarkers such as the brain functional connectivity, as well as genetic biomarkers, which are predictive of disorders. However, typical approaches for estimating the group level brain functional connectivity do not account for potential variation, resulting from demographic and genetic factors, while usual methods for discovering genetic biomarkers do not factor in the influence of the brain network on the imaging phenotype. We propose a novel semiparametric Bayesian conditional graphical model for joint variable selection and graph estimation, which simultaneously estimates the brain network after accounting for heterogeneity, and infers significant genetic biomarkers. The proposed approach specifies priors on the regression coefficients, which clusters brain regions having similar activation patterns depending on covariates, leading to dimension reduction. A novel graphical prior is proposed, which encourages modularity in brain organization by specifying denser and sparse connections within and across clusters, respectively. The posterior computation proceeds via a Markov chain Monte Carlo. We apply the approach to data obtained from the Alzheimer's disease neuroimaging initiative and demonstrate numerical advantages via simulation studies.