Well-known Alzheimer's Disease Research Articles

Unraveling APOE4's Role in Alzheimer's Disease: Pathologies and Therapeutic Strategies.

Alzheimer's disease (AD), the most common kind of dementia worldwide, is characterized by elevated levels of the amyloid-β (Aβ) peptide and hyperphosphorylated tau protein in the neurons. The complexity of AD makes the development of treatments infamously challenging. Apolipoprotein E (APOE) genes's ɛ4 allele is one of the main genetic risk factors for AD. While the APOE gene's ɛ4 allele considerably increases the chance of developing AD, the ɛ2 allele is protective compared to the prevalent ɛ3 variant. It is fiercely discussed how APOE affects the development and course of disease since it has a variety of activities that influence both neuronal and non-neuronal cells. ApoE4 contributes to the formation of tau tangles, deposition of Aβ, neuroinflammation, and other processes. Four decades of research have provided a significant understanding of the structure of APOE and how this may affect the neuropathology and pathogenesis of AD. APOE is a crucial lipid transporter essential for the growth of the central nervous system (CNS), upkeep, and repair. The mechanisms by which APOE contributes to the pathophysiology of AD are still up for discussion, though. Evidence suggests that APOE affects the brain's clearance and deposition of Aβ. Additionally, APOE has Aβ-independent pathways in AD, which has led to the identification of new functions for APOE, including mitochondrial dysfunction. This study summarizes important studies that describe how APOE4 affects well-known AD pathologies, including tau pathology, Aβ, neuroinflammation, and dysfunction of neural networks. This study also envisions some of the therapeutic approaches being used to target APOE4 in the hopes of preventing or treating AD.

ApoE Gene Polymorphism and Clinical, Biochemical, and Sociodemographic Characteristics of Alzheimer's Disease Patients From Northern and Southern Regions of Kazakhstan.

Alzheimer's disease (AD) is the most frequent cause of dementia in seniors and is also one of the critical social issues of modern healthcare. Since AD is considered a multifactorial disease, the significance of particular risk factors in different ethnic populations is constantly reevaluated. The study group consisted of 181 patients with AD, and the control group included 244 healthy seniors comparable in sex and age to the dementia group. Our study compared clinical data, blood biochemical parameters, various sociodemographic characteristics, and ApoE gene polymorphism in patients diagnosed with AD from Kazakhstan's north (Astana city) and south (Almaty city) regions. In our cohort, significant dementia-associated variables included smoking, clinically significant depression, dyslipidemia, impaired glucose metabolism, insulin resistance, and liver dysfunction. Notably, AD patients had higher HDL levels, lower ALT levels, and higher total bilirubin and AST/ALT ratios. The ApoE ɛ4 genotype, a well-known AD risk factor, was more prevalent in the northern AD group. Additionally, participants from Astana city had a higher incidence of strokes, potentially linked to elevated LDL levels, while Almaty city residents exhibited a higher prevalence of clinically severe depression. These findings underscore the importance of considering bio-geographic and environmental factors in AD research. The study's outcomes may aid in further research and the development of personalized approaches for managing and treating AD in distinct geographical regions.

Transcriptome and proteome profiling reveals TREM2-dependent and -independent glial response and metabolic perturbation in an Alzheimer’s mouse model

Elucidating the intricate molecular mechanisms of Alzheimer's disease (AD) requires a multidimensional analysis incorporating various omics data. In this study, we employed transcriptome and proteome profiling of AppNL-G-F, a human APP knock-in model of amyloidosis, at the early and mid-stages of amyloid-beta (Aβ) pathology to delineate the impacts of Aβ deposition on brain cells. By contrasting AppNL-G-F mice with TREM2 (Triggering receptor expressed on myeloid cells 2) knockout models, our study further investigates the role of TREM2, a well-known AD risk gene, in influencing microglial responses to Aβ pathology. Our results highlight altered microglial states as a central feature of Aβ pathology, characterized by the significant upregulation of microglia-specific genes related to immune responses such as complement system and antigen presentation, and catabolic pathways such as phagosome formation and lysosome biogenesis. The absence of TREM2 markedly diminishes the induction of these genes, impairs Aβ clearance, and exacerbates dystrophic neurite formation. Importantly, TREM2 is required for the microglial engagement with Aβ plaques and the formation of compact Aβ plaque cores. Furthermore, this study reveals substantial disruptions in energy metabolism and protein synthesis, signaling a shift from anabolism to catabolism in response to Aβ deposition. This metabolic alteration, coupled with a decrease in synaptic protein abundance, occurs independently of TREM2, suggesting the direct effects of Aβ deposition on synaptic integrity and plasticity. In summary, our findings demonstrate altered microglial states and metabolic disruption following Aβ deposition, offering mechanistic insights into Aβ pathology and highlighting the potential of targeting these pathways in AD therapy.

Disrupted mitochondrial response to nutrients is a presymptomatic event in the cortex of the APPSAA knock-in mouse model of Alzheimer's disease.

Reduced brain energy metabolism, mammalian target of rapamycin (mTOR) dysregulation, and extracellular amyloid beta (Aβ) oligomer (xcAβO) buildup are some well-known Alzheimer's disease (AD) features; how they promote neurodegeneration is poorly understood. We previously reported that xcAβOs inhibit nutrient-induced mitochondrial activity (NiMA) in cultured neurons. We now report NiMA disruption in vivo. Brain energy metabolism and oxygen consumption were recorded in heterozygous amyloid precursor protein knock-in (APPSAA) mice using two-photon fluorescence lifetime imaging and multiparametric photoacoustic microscopy. NiMA is inhibited in APPSAA mice before other defects are detected in these Aβ-producing animals that do not overexpress APP or contain foreign DNA inserts into genomic DNA. Glycogen synthase kinase 3 (GSK3β) signals through mTORC1 to regulate NiMA independently of mitochondrial biogenesis. Inhibition of GSK3β with TWS119 stimulates NiMA in cultured human neurons, and mitochondrial activity and oxygen consumption in APPSAA mice. NiMA disruption in vivo occurs before plaques, neuroinflammation, and cognitive decline in APPSAA mice, and may represent an early stage in human AD. Amyloid beta blocks communication between lysosomes and mitochondria in vivo. Nutrient-induced mitochondrial activity (NiMA) is disrupted long before the appearance of Alzheimer's disease (AD) histopathology in heterozygous amyloid precursor protein knock-in (APPSAA/+) mice. NiMA is disrupted long before learning and memory deficits in APPSAA/+ mice. Pharmacological interventions can rescue AD-related NiMA disruption in vivo.

Real-time detection of Tau-381 protein using liquid crystal-based sensors for Alzheimer's disease diagnosis

Tau is a protein found in the central nervous system (CNS) and is involved in stabilizing microtubules in axons. Given the link between Tau levels in the body and Alzheimer's disease (AD), there is a demand for straightforward and precise strategies to detect Tau in body fluids. In this study, we report liquid crystal (LC)-based sensors for the real-time detection of Tau protein, a well-known AD biomarker. The sensor uses a detection method based on the orientation change of the LC because of the competitive biomolecular interaction between Tau and Tau aptamers with the cationic polymer poly-L-lysine (PLL). Tau and its aptamers form stable complexes through electrostatic interactions. Owing to the consumption of the aptamer, the positively charged PLL fails to interact with the aptamer but binds to the negatively charged 1.2-dioleoyl-sn-glycero-3-phospho-(1′-rac-glycerol) sodium salt (DOPG). The PLL and DOPG complex alters the orientation of the LC to ensure a planar anchoring of the 4-cyano-4′-pentylbiphenyl (5CB)/aqueous interface; this anchoring intensifies with increasing Tau concentration, thus enabling the observation of a bright optical image. Our LC-based sensor demonstrated a low detection limit of 2.77 pg/mL in phosphate buffered saline (PBS) and 10.86 pg/mL and 19.31 pg/mL in human serum and plasma, respectively. Moreover, it is anticipated to be suitable for point-of-care diagnosis of AD because it does not require specialized analytical equipment and only requires microliters of sample.

Proteome-wide association studies using summary pQTL data of three tissues identified 30 risk genes of Alzheimer's disease dementia.

Proteome-wide association study (PWAS) integrating proteomic data with genome-wide association study (GWAS) summary data is a powerful tool for studying Alzheimer's disease (AD) dementia. Existing PWAS analyses of AD often rely on the availability of individual-level proteomic and genetic data of a reference panel. Leveraging summary protein quantitative trait loci (pQTL) reference data of multiple AD-relevant tissues is expected to improve PWAS findings of AD dementia. We conducted PWAS of AD dementia by integrating publicly available summary pQTL data of brain, cerebrospinal fluid (CSF), and plasma tissues, with the latest GWAS summary data of AD dementia. For each target protein per tissue, we employed our recently published OTTERS tool to obtain omnibus PWAS p-value, to test whether the genetically regulated protein abundance in the corresponding tissue is associated with AD dementia. Protein-protein interactions and enriched pathways of identified significant PWAS risk genes were analyzed by STRING. The potential causal effects of these PWAS risk genes were assessed by probabilistic Mendelian randomization analyses. We identified 30 unique significant PWAS risk genes for AD dementia, including 11 for brain, 9 for CSF, and 16 for plasma tissues. Four of these were shared by at least two tissues, and gene MAPK3 was found in all three tissues. We found that 11 of these PWAS risk genes were associated with AD or AD pathological hall marks as shown in GWAS Catalog; 18 of these were detected by transcriptome-wide association studies (TWAS); and 25 of these, including 8 out of 9 novel genes, were interconnected within a protein-protein interaction network involving the well-known AD risk gene APOE. Especially, these PWAS risk genes were enriched in immune response, glial cell proliferation, and high-density lipoprotein particle clearance pathways. Mediated causal effects were validated for 13 PWAS risk genes (43.3%). Our findings provide novel insights into the genetic mechanisms of AD dementia in brain, CSF, and plasma tissues, and targets for developing therapeutic interventions. We also demonstrated the effectiveness of integrating summary pQTL and GWAS data for mapping risk genes of complex human diseases.

Omnibus proteome-wide association study identifies 43 risk genes for Alzheimer disease dementia

Transcriptome-wide association study (TWAS) tools have been applied to conduct proteome-wide association studies (PWASs) by integrating proteomics data with genome-wide association study (GWAS) summary data. The genetic effects of PWAS-identified significant genes are potentially mediated through genetically regulated protein abundance, thus informing the underlying disease mechanisms better than GWAS loci. However, existing TWAS/PWAS tools are limited by considering only one statistical model. We propose an omnibus PWAS pipeline to account for multiple statistical models and demonstrate improved performance by simulation and application studies of Alzheimer disease (AD) dementia. We employ the Aggregated Cauchy Association Test to derive omnibus PWAS (PWAS-O) p values from PWAS p values obtained by three existing tools assuming complementary statistical models-TIGAR, PrediXcan, and FUSION. Our simulation studies demonstrated improved power, with well-calibrated type I error, for PWAS-O over all three individual tools. We applied PWAS-O to studying AD dementia with reference proteomic data profiled from dorsolateral prefrontal cortex of postmortem brains from individuals of European ancestry. We identified 43 risk genes, including 5 not identified by previous studies, which are interconnected through a protein-protein interaction network that includes the well-known AD risk genes TOMM40, APOC1, and APOC2. We also validated causal genetic effects mediated through the proteome for 27 (63%) PWAS-O risk genes, providing insights into the underlying biological mechanisms of AD dementia and highlighting promising targets for therapeutic development. PWAS-O can be easily applied to studying other complex diseases.

An alternative method of SNP inclusion to develop a generalized polygenic risk score analysis across Alzheimer's disease cohorts.

Polygenic risk scores (PRSs) have great clinical potential for detecting late-onset diseases such as Alzheimer's disease (AD), allowing the identification of those most at risk years before the symptoms present. Although many studies use various and complicated machine learning algorithms to determine the best discriminatory values for PRSs, few studies look at the commonality of the Single Nucleotide Polymorphisms (SNPs) utilized in these models. This investigation focussed on identifying SNPs that tag blocks of linkage disequilibrium across the genome, allowing for a generalized PRS model across cohorts and genotyping panels. PRS modeling was conducted on five AD development cohorts, with the best discriminatory models exploring for a commonality of linkage disequilibrium clumps. Clumps that contributed to the discrimination of cases from controls that occurred in multiple cohorts were used to create a generalized model of PRS, which was then tested in the five development cohorts and three further AD cohorts. The model developed provided a discriminability accuracy average of over 70% in multiple AD cohorts and included variants of several well-known AD risk genes. A key element of devising a polygenic risk score that can be used in the clinical setting is one that has consistency in the SNPs that are used to calculate the score; this study demonstrates that using a model based on commonality of association findings rather than meta-analyses may prove useful.

Meta-GWAS of amyloid burden endophenotype combining PET and CSF results.

It is well known that Alzheimer's disease (AD) has a strong genetic component. Even though it is a highly heritable disease, a big fraction of AD heritability remains to be elucidated. Genetic analysis of endophenotypes tightly linked to the disease might help to identify or confirm AD genetic risk factors. GWAS results Amyloid β (Aβ42) in cerebrospinal fluid (CSF) or positron emission tomography (PET) measures were combined in a single meta-analysis. Six independent GWAS were run using a generalized linear model with four principal components and status as a covariate (PLINK software). The datasets had data for two different amyloid endophenotypes: CSF-Aβ42 levels from ADNI, Marqués de Valdecilla Hospital (Santander) and Fundació ACE (N= 1051); or PET-SUVR (amyloid global standardized uptake ratio) from ADNI and Fundació ACE (N= 607). GWAS results were meta-analysed using the sample size weighted method (METAL software). To reduce false positive findings, only genetic signals with opposite effect direction in PET and CSF endophenotypes were considered. Finally, we tried to replicate previously known genes for AD (de Rojas et al., MedRxiv 2019). After selecting SNPs with opposite effect on CSF and PET associations (N= 325051), we only observed genome wide significance in APOE locus (rs429358, P-value= 2.202×10-48) in the meta-GWAS. We also observed several suggestive signals in chromosomes 3, 5 and 6 (rs72977014, rs13164192 and rs4960198); further studies are needed to add evidence of AD association. Finally, relative to known AD genes we were able to replicate CR1 (rs4844610, P-value= 0.045) signal. By combining data from both β-amyloid endophenotypes (CSF and PET) we were able to replicate some well-known AD genes indicating that this approach could be a useful strategy to identify novel variants increasing the knowledge of AD genetic architecture.

Hyper-graph based sparse canonical correlation analysis for the diagnosis of Alzheimer's disease from multi-dimensional genomic data.

The effective and accurate diagnosis of Alzheimer's disease (AD), especially in the early stage (i.e., mild cognitive impairment (MCI)) remains a big challenge in AD research. So far, multiple biomarkers have been associated with AD diagnosis and progression. However, most of the existing research only utilized single modality data for diagnostic biomarker identification, which did not take the advantages of multi-modal data that provide comprehensive and complementary information at multiple levels into consideration. In this paper, we integrate multi-modal genomic data from postmortem AD brains (i.e., mRNA, miRNA and epigenomic data) and propose a hyper-graph based sparse canonical correlation analysis (HGSCCA) method to extract the most correlated multi-modal biomarkers associated with AD and MCI. Specifically, our model utilizes the sparse canonical correlation analysis framework (SCCA), which aims at finding the best linear projections for each input modality so that the strongest correlation within the selected features of multi-dimensional genomic data can be captured. In addition, with the consideration of high-order relationships among different subjects, we also introduce a hyper-graph-based regularization term that will lead to the selection of more discriminative biomarkers. To evaluate the effectiveness of the proposed method, we conduct the experiments on the well-known AD cohort study, The Religious Orders Study and Memory and Aging Project (ROSMAP) dataset, and the results show that our method can not only identify meaningful biomarkers for the diagnosis AD disease, but also achieve superior classification performance than the comparing methods.

Genome-wide Network-assisted Association and Enrichment Study of Amyloid Imaging Phenotype in Alzheimer's Disease.

The etiology of Alzheimer's disease remains poorly understood at the mechanistic level, and genome-wide network-based genetics have the potential to provide new insights into the disease mechanisms. The study aimed to explore the collective effects of multiple genetic association signals on an AV-45 PET measure, which is a well-known Alzheimer's disease biomarker, by employing a network assisted strategy. First, we took advantage of a dense module search algorithm to identify modules enriched by genetic association signals in a protein-protein interaction network. Next, we performed statistical evaluation to the modules identified by dense module search, including a normalization process to adjust the topological bias in the network, a replication test to ensure the modules were not found randomly , and a permutation test to evaluate unbiased associations between the modules and amyloid imaging phenotype. Finally, topological analysis, module similarity tests and functional enrichment analysis were performed for the identified modules. We identified 24 consensus modules enriched by robust genetic signals in a genome-wide association analysis. The results not only validated several previously reported AD genes (APOE, APP, TOMM40, DDAH1, PARK2, ATP5C1, PVRL2, ELAVL1, ACTN1 and NRF1), but also nominated a few novel genes (ABL1, ABLIM2) that have not been studied in Alzheimer's disease but have shown associations with other neurodegenerative diseases. The identified genes, consensus modules and enriched pathways may provide important clues to future research on the neurobiology of Alzheimer's disease and suggest potential therapeutic targets.

Analytical Strategy to Prioritize Alzheimer's Disease Candidate Genes in Gene Regulatory Networks Using Public Expression Data.

Alzheimer’s disease (AD) progressively destroys cognitive abilities in the aging population with tremendous effects on memory. Despite recent progress in understanding the underlying mechanisms, high drug attrition rates have put a question mark behind our knowledge about its etiology. Re-evaluation of past studies could help us to elucidate molecular-level details of this disease. Several methods to infer such networks exist, but most of them do not elaborate on context specificity and completeness of the generated networks, missing out on lesser-known candidates. In this study, we present a novel strategy that corroborates common mechanistic patterns across large scale AD gene expression studies and further prioritizes potential biomarker candidates. To infer gene regulatory networks (GRNs), we applied an optimized version of the BC3Net algorithm, named BC3Net10, capable of deriving robust and coherent patterns. In principle, this approach initially leverages the power of literature knowledge to extract AD specific genes for generating viable networks. Our findings suggest that AD GRNs show significant enrichment for key signaling mechanisms involved in neurotransmission. Among the prioritized genes, well-known AD genes were prominent in synaptic transmission, implicated in cognitive deficits. Moreover, less intensive studied AD candidates (STX2, HLA-F, HLA-C, RAB11FIP4, ARAP3, AP2A2, ATP2B4, ITPR2, and ATP2A3) are also involved in neurotransmission, providing new insights into the underlying mechanism. To our knowledge, this is the first study to generate knowledge-instructed GRNs that demonstrates an effective way of combining literature-based knowledge and data-driven analysis to identify lesser known candidates embedded in stable and robust functional patterns across disparate datasets.

Tetramethylpyrazine reverses intracerebroventricular streptozotocin-induced memory deficits by inhibiting GSK-3β.

Brain dysfunction, especially cognitive impairment, is one of the main complications in Alzheimer's disease (AD), which threatens the health of 46.8 million people worldwide. At present, the pathogenesis of cognitive dysfunction is only partially understood, and effective therapies for memory loss in AD remain elusive. Tetramethylpyrazine (TMP) is one of the major bioactive compounds purified from Chuanxiong, a Chinese herb used for the treatment of neurovascular and cardiovascular diseases. The neuroprotective properties of TMP are evident in some neurodegenerative diseases, including Parkinson's disease. However, whether TMP plays a neuroprotective role in AD is still unknown. Here, we report that 2-week treatment with TMP rescued both short-term and long-term fear memory impairment induced by intracerebroventricular injection of streptozotocin in a well-known AD rat model. Administration of TMP also restored spatial learning and memory retention abilities in streptozotocin-injected rats. Furthermore, TMP inhibited the activity of GSK-3β, an important kinase that mediates hippocampal synaptic and memory disorders in diabetes mellitus. Finally, we found that TMP treatment restored the function of cholinergic neurons. Our data suggest that dietary uptake of TMP can provide protection against memory loss in AD, and the inhibition of GSK-3β may play an important role in this protective effect.

STRUCTURAL CORRELATES OF ALZHEIMER’S DISEASE AND AGING IN DOWN SYNDROME: AN MRI STUDY

Down syndrome (DS) has been proposed as a genetic form of Alzheimer's Disease (AD). However, few studies had assessed the structural correlates of AD and aging in DS. Our objective was to evaluate the brain regions vulnerable to AD and aging in DS and the possible modulating effects of amyloid deposition. Subjects were recruited from the Down Alzheimer Barcelona Neuroimaging Initiative (DABNI), a longitudinal cohort to study AD in DS. 68 subjects underwent a 3 Tesla structural MRI and a lumbar puncture (mean age: 41.32, 34.1% female). DS subjects were classified into a cognitive stable group (cs-DS, N=52) and an AD group (n=16). The AD group was subdivided into prodomal Alzheimer (p-DS, N=10) and AD dementia (d-DS, N=6) subgroups. CSF Aß1–42 levels were measured by ELISA. Subjects were classified as Aß+ or Aß- (cutoff <550pg/ml). Cortical thickness (CTh) and normalized hippocampal volume (HV) were obtained using Freesurfer v5.1. We computed the mean CTh in a well-known AD signature map (Dickerson 2009). We performed group comparissons and correlation analyses. We also performed an interaction analysis between age and CSF Aß1–42 to show the regions with an amyloid by age interaction on CTh. The AD group had extensive regions of cortical atrophy in posterior temporoparietal areas with a relative preservation of frontal areas when compared with s-DS. There was a significant gradient of atrophy in the AD signature and HV across groups (d-DS>p-DS>cs-DS). Aging negatively correlated CTh in cs-DS in regions of the frontal cortex. This correlation was also significant for HV but not for the AD signature. CSF Aß1–42 did not correlate with CTh and there were no differences in the Aß+ vs Aß- comparison in cs-DS. However, we found a significant interaction between CSF Aß1–42 levels and age on brain structure in posterior regions.

NFATc2 Modulates Microglial Activation in the AβPP/PS1 Mouse Model of Alzheimer's Disease.

Alzheimer's disease (AD) brains are characterized by fibrillar amyloid-β (Aβ) peptide containing plaques and associated reactive microglia. The proinflammatory phenotype of the microglia suggests that they may negatively affect disease course and contribute to behavioral decline. This hypothesis predicts that attenuating microglial activation may provide benefit against disease. Prior work from our laboratory and others has characterized a role for the transcription factor, nuclear factor of activated T cells (NFAT), in regulating microglial phenotype in response to different stimuli, including Aβ peptide. We observed that the NFATc2 isoform was the most highly expressed in murine microglia cultures, and inhibition or deletion of NFATc2 was sufficient to attenuate the ability of the microglia to secrete cytokines. In order to determine whether the NFATc2 isoform, in particular, was a valid immunomodulatory target in vivo, we crossed an NFATc2-/- line to a well-known AD mouse model, an AβPP/PS1 mouse line. As expected, the AβPP/PS1 x NFATc2-/- mice had attenuated cytokine levels compared to AβPP/PS1 mice as well as reduced microgliosis and astrogliosis with no effect on plaque load. Although some species differences in relative isoform expression may exist between murine and human microglia, it appears that microglial NFAT activity is a viable target for modulating the proinflammatory changes that occur during AD.

Identifying Multimodal Intermediate Phenotypes Between Genetic Risk Factors and Disease Status in Alzheimer’s Disease

Neuroimaging genetics has attracted growing attention and interest, which is thought to be a powerful strategy to examine the influence of genetic variants (i.e., single nucleotide polymorphisms (SNPs)) on structures or functions of human brain. In recent studies, univariate or multivariate regression analysis methods are typically used to capture the effective associations between genetic variants and quantitative traits (QTs) such as brain imaging phenotypes. The identified imaging QTs, although associated with certain genetic markers, may not be all disease specific. A useful, but underexplored, scenario could be to discover only those QTs associated with both genetic markers and disease status for revealing the chain from genotype to phenotype to symptom. In addition, multimodal brain imaging phenotypes are extracted from different perspectives and imaging markers consistently showing up in multimodalities may provide more insights for mechanistic understanding of diseases (i.e., Alzheimer's disease (AD)). In this work, we propose a general framework to exploit multi-modal brain imaging phenotypes as intermediate traits that bridge genetic risk factors and multi-class disease status. We applied our proposed method to explore the relation between the well-known AD risk SNP APOE rs429358 and three baseline brain imaging modalities (i.e., structural magnetic resonance imaging (MRI), fluorodeoxyglucose positron emission tomography (FDG-PET) and F-18 florbetapir PET scans amyloid imaging (AV45)) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The empirical results demonstrate that our proposed method not only helps improve the performances of imaging genetic associations, but also discovers robust and consistent regions of interests (ROIs) across multi-modalities to guide the disease-induced interpretation.

A cognitive psychometric model for the psychodiagnostic assessment of memory-related deficits.

Clinical tests used for psychodiagnostic purposes, such as the well-known Alzheimer's Disease Assessment Scale: Cognitive subscale (ADAS-Cog), include a free-recall task. The free-recall task taps into latent cognitive processes associated with learning and memory components of human cognition, any of which might be impaired with the progression of Alzheimer's disease (AD). A Hidden Markov model of free recall is developed to measure latent cognitive processes used during the free-recall task. In return, these cognitive measurements give us insight into the degree to which normal cognitive functions are differentially impaired by medical conditions, such as AD and related disorders. The model is used to analyze the free-recall data obtained from healthy elderly participants, participants diagnosed as having mild cognitive impairment, and participants diagnosed with early AD. The model is specified hierarchically to handle item differences because of the serial position curve in free recall, as well as within-group individual differences in participants' recall abilities. Bayesian hierarchical inference is used to estimate the model. The model analysis suggests that the impaired patients have the following: (1) long-term memory encoding deficits, (2) short-term memory (STM) retrieval deficits for all but very short time intervals, (3) poorer transfer into long-term memory for items successfully retrieved from STM, and (4) poorer retention of items encoded into long-term memory after longer delays. Yet, impaired patients appear to have no deficit in immediate recall of encoded words in long-term memory or for very short time intervals in STM.

Marine Natural Products as Acetylcholinesterase Inhibitor: Comparative Quantum Mechanics and Molecular Docking Study

Alzheimer's disease (AD) is the most common form of dementia which affects the elderly population throughout the world. The inhibition of acetylcholinesterase (AChE) has appeared as one of the most promising strategies for the AD treatment. In this study, the density functional theory and molecular docking studies have been carried out on seven halogenated sesquiterpenes derived from the Persian Gulf sea hare, Aplysia dactylomela, to reveal their electronic, structural and chemical properties. Moreover, influences of these properties on their AChE-inhibition properties have been investigated theoretically. The results indicate that these compounds have several interactions with important residues of AChE active sites. Three of the investigated molecules correlate better to well-known AD drugs such as huperzine A, galanthamine and donepezil which represent possible AChE inhibitors against Alzheimer disease. In conclusion, the information obtained from this theoretical study may aid in the discovery of new potential AChE inhibitors with marine origin.

Evidence of Recessive Alzheimer Disease Loci in a Caribbean Hispanic Data Set

IMPORTANCE The search for novel Alzheimer disease (AD) genes or pathologic mutations within known AD loci is ongoing. The development of array technologies has helped to identify rare recessive mutations among long runs of homozygosity (ROHs), in which both parental alleles are identical. Caribbean Hispanics are known to have an elevated risk for AD and tend to have large families with evidence of inbreeding. OBJECTIVE To test the hypothesis that the late-onset AD in a Caribbean Hispanic population might be explained in part by the homozygosity of unknown loci that could harbor recessive AD risk haplotypes or pathologic mutations. DESIGN We used genome-wide array data to identify ROHs (&gt;1 megabase) and conducted global burden and locus-specific ROH analyses. SETTING A whole-genome case-control ROH study. PARTICIPANTS A Caribbean Hispanic data set of 547 unrelated cases (48.8% with familial AD) and 542 controls collected from a population known to have a 3-fold higher risk of AD vs non-Hispanics in the same community. Based on a Structure program analysis, our data set consisted of African Hispanic (207 cases and 192 controls) and European Hispanic (329 cases and 326 controls) participants. EXPOSURE Alzheimer disease risk genes. MAIN OUTCOMES AND MEASURES We calculated the total and mean lengths of the ROHs per sample. Global burden measurements among autosomal chromosomes were investigated in cases vs controls. Pools of overlapping ROH segments (consensus regions) were identified, and the case to control ratio was calculated for each consensus region. We formulated the tested hypothesis before data collection. RESULTS In total, we identified 17 137 autosomal regions with ROHs. The mean length of the ROH per person was significantly greater in cases vs controls (P = .0039), and this association was stronger with familial AD (P = .0005). Among the European Hispanics, a consensus region at the EXOC4 locus was significantly associated with AD even after correction for multiple testing (empirical P value 1 [EMP1], .0001; EMP2, .002; 21 AD cases vs 2 controls). Among the African Hispanic subset, the most significant but nominal association was observed for CTNNA3, a well-known AD gene candidate (EMP1, .002; 10 AD cases vs 0 controls). CONCLUSIONS AND RELEVANCE Our results show that ROHs could significantly contribute to the etiology of AD. Future studies would require the analysis of larger, relatively inbred data sets that might reveal novel recessive AD genes. The next step is to conduct sequencing of top significant loci in a subset of samples with overlapping ROHs.

PP2A Ligand ITH12246 Protects against Memory Impairment and Focal Cerebral Ischemia in Mice

ITH12246 (ethyl 5-amino-2-methyl-6,7,8,9-tetrahydrobenzo[b][1,8]naphthyridine-3-carboxylate) is a 1,8-naphthyridine described to feature an interesting neuroprotective profile in in vitro models of Alzheimer's disease. These effects were proposed to be due in part to a regulatory action on protein phosphatase 2A inhibition, as it prevented binding of its inhibitor okadaic acid. We decided to investigate the pharmacological properties of ITH12246, evaluating its ability to counteract the memory impairment evoked by scopolamine, a muscarinic antagonist described to promote memory loss, as well as to reduce the infarct volume in mice suffering phototrombosis. Prior to conducting these experiments, we confirmed its in vitro neuroprotective activity against both oxidative stress and Ca(2+) overload-derived excitotoxicity, using SH-SY5Y neuroblastoma cells and rat hippocampal slices. Using a predictive model of blood-brain barrier crossing, it seems that the passage of ITH12246 is not hindered. Its potential hepatotoxicity was observed only at very high concentrations, from 0.1 mM. ITH12246, at the concentration of 10 mg/kg i.p., was able to improve the memory index of mice treated with scopolamine, from 0.22 to 0.35, in a similar fashion to the well-known Alzheimer's disease drug galantamine 2.5 mg/kg. On the other hand, ITH12246, at the concentration of 2.5 mg/kg, reduced the phototrombosis-triggered infarct volume by 67%. In the same experimental conditions, 15 mg/kg melatonin, used as control standard, reduced the infarct volume by 30%. All of these findings allow us to consider ITH12246 as a new potential drug for the treatment of neurodegenerative diseases, which would act as a multifactorial neuroprotectant.