Journal of Alzheimer’s Disease

Alzheimer’s disease (AD) is associated with impaired cerebral circulation which underscores diminished delivery of blood oxygen and nutrients to and throughout the brain. In the 3xTg-AD mouse model, we have recently found that > 10 cerebrovascular miRNAs pertaining to vascular permeability, angiogenesis, and inflammation (e.g., let-7d, miR-99a, miR-132, miR-133a, miR-151-5p, and miR-181a) track early development of AD. Further, endothelial-specific miRNAs (miR-126-3p, miR-23a/b, miR-27a) alter with onset of overall AD pathology relative to stability of smooth muscle/pericyte-specific miRNAs (miR-143, miR-145).

We tested the hypothesis that cerebrovascular miRNAs indicating AD pathology share mRNA targets that regulate key endothelial cell functions such as angiogenesis, vascular permeability, and blood flow regulation.

As detected by NanoString nCounter miRNA Expression panel for 3xTg-AD mice, 61 cerebrovascular miRNAs and respective mRNA targets were examined using Ingenuity Pathway Analysis for canonical Cardiovascular (Cardio) and Nervous System (Neuro) Signaling.

The number of targets regulated per miRNA were 21±2 and 33±3 for the Cardio and Neuro pathways respectively, whereby 14±2 targets overlap among pathways. Endothelial miRNAs primarily target members of the PDE, PDGF, SMAD, and VEGF families. Individual candidates regulated by≥4 miRNAs that best mark AD pathology presence in 3xTg-AD mice include CFL2, GRIN2B, PDGFB, SLC6A1, SMAD3, SYT3, and TNFRSF11B.

miRNAs selective for regulation of endothelial function and respective downstream mRNA targets support a molecular basis for dysregulated cerebral blood flow regulation coupled with enhanced cell growth, proliferation, and inflammation.

Extracellular vesicles (EVs) and non-coding RNAs (ncRNAs) are emerging contributors to Alzheimer’s disease (AD) pathophysiology. Differential abundance of ncRNAs carried by EVs may provide valuable insights into underlying disease mechanisms. Brain tissue-derived EVs (bdEVs) are particularly relevant, as they may offer valuable insights about the tissue of origin. However, there is limited research on diverse ncRNA species in bdEVs in AD.

This study explored whether the non-coding RNA composition of EVs isolated from post-mortem brain tissue is related to AD pathogenesis.

bdEVs from age-matched late-stage AD patients (n = 23) and controls (n = 10) that had been separated and characterized in our previous study were used for RNA extraction, small RNA sequencing, and qPCR verification.

Significant differences of non-coding RNAs between AD and controls were found, especially for miRNAs and tRNAs. AD pathology-related miRNA and tRNA differences of bdEVs partially matched expression differences in source brain tissues. AD pathology had a more prominent association than biological sex with bdEV miRNA and tRNA components in late-stage AD brains.

Our study provides further evidence that EV non-coding RNAs from human brain tissue, including but not limited to miRNAs, may be altered and contribute to AD pathogenesis.

Alzheimer’s disease (AD) is the most common form of dementia in the elderly marked by central nervous system (CNS) neuronal loss and amyloid plaques. FAM222A, encoding an amyloid plaque core protein, is an AD brain atrophy susceptibility gene that mediates amyloid-β aggregation. However, the expression interplay between FAM222A and other AD-related pathway genes is unclear.

Our goal was to study FAM222A’s whole-genome co-expression profile in multiple tissues and investigate its interplay with other AD-related genes.

We analyzed gene expression correlations in Genotype-Tissue Expression (GTEx) tissues to identify FAM222A co-expressed genes and performed functional enrichment analysis on identified genes in CNS system.

Genome-wide gene expression profiling identified 673 genes significantly correlated with FAM222A (p < 2.5×10^–6) in 48 human tissues, including 298 from 13 CNS tissues. Functional enrichment analysis revealed that FAM222A co-expressed CNS genes were enriched in multiple AD-related pathways. Gene co-expression network analysis for identified genes in each brain region predicted other disease associated genes with similar biological function. Furthermore, co-expression of 25 out of 31 AD-related pathways genes with FAM222A was replicated in brain samples from 107 aged subjects from the Aging, Dementia and TBI Study.

This gene co-expression study identified multiple AD-related genes that are associated with FAM222A, indicating that FAM222A and AD-associated genes can be active simultaneously in similar biological processes, providing evidence that supports the association of FAM222A with AD.

Alzheimer’s disease (AD) is the most common type of dementia, causing a huge socioeconomic burden. In parallel with the widespread uptake of single-cell RNA sequencing (scRNA-seq) technology, there has been a rapid accumulation of data produced by researching AD at single-cell resolution, which is more conductive to explore the neuroimmune-related mechanism of AD.

To explore the potential features of T cells in the peripheral blood and cerebrospinal fluid of AD patients.

Two datasets, GSE181279 and GSE134578, were integrated from GEO database. Seurat, Monocle, CellChat, scRepertoire, and singleR packages were mainly employed for data analysis.

Our analysis demonstrated that in peripheral blood, T cells were significantly expanded, and these expanded T cells were possessed effector function, such as CD8⁺_TEMRA, CD4⁺_TEMRA, and CD8⁺_TEM. Interestingly, CD8⁺_TEMRA and CD4⁺_TEMRA cells positioned adjacently after dimensions reduction and clustering. Notably, we identified that the expanded T cells were developed from Naïve T cells and TCM cells, and TEM cells was in the intermediate state of this developing process. Additionally, in cerebrospinal fluid of AD patients, the amplified T cells were mainly CD8⁺_TEMRA cells, and the number and strength of communication between CD4⁺_TEM, CD8⁺_TEM, and CD8⁺_TEMRA were decreased in AD patients.

Our comprehensive analyses identified the cells in cerebrospinal fluid from AD patients are expanded TEMRA or TEM cells and the TEMRA cells communicating with other immune cells is weakened, which may be an important immune feature that leads to AD.

A strong body of evidence suggests that cerebrovascular pathologies augment the onset and progression of Alzheimer’s disease (AD). One distinctive aspect of this cerebrovascular dysfunction is the degeneration of brain pericytes—often overlooked supporting cells of blood-brain barrier endothelium.

The current study investigates the influence of pericytes on gene and protein expressions in the blood-brain barrier endothelium, which is expected to facilitate the identification of pathophysiological pathways that are triggered by pericyte loss and lead to blood-brain barrier dysfunction in AD.

Bioinformatics analysis was conducted on the RNA-Seq expression counts matrix (GSE144474), which compared solo-cultured human blood-brain barrier endothelial cells against endothelial cells co-cultured with human brain pericytes in a non-contact model. We constructed a similar cell culture model to verify protein expression using western blots.

The insulin resistance and ferroptosis pathways were found to be enriched. Western blots of the insulin receptor and heme oxygenase expressions were consistent with those observed in RNA-Seq data. Additionally, we observed more than 5-fold upregulation of several genes associated with neuroprotection, including insulin-like growth factor 2 and brain-derived neurotrophic factor.

Results suggest that pericyte influence on blood-brain barrier endothelial gene expression confers protection from insulin resistance, iron accumulation, oxidative stress, and amyloid deposition. Since these are conditions associated with AD pathophysiology, they imply mechanisms by which pericyte degeneration could contribute to disease progression.

Late-onset Alzheimer’s disease (LOAD) is the most common type of dementia, but its pathogenesis remains unclear, and there is a lack of simple and convenient early diagnostic markers to predict the occurrence.

Our study aimed to identify diagnostic candidate genes to predict LOAD by machine learning methods.

Three publicly available datasets from the Gene Expression Omnibus (GEO) database containing peripheral blood gene expression data for LOAD, mild cognitive impairment (MCI), and controls (CN) were downloaded. Differential expression analysis, the least absolute shrinkage and selection operator (LASSO), and support vector machine recursive feature elimination (SVM-RFE) were used to identify LOAD diagnostic candidate genes. These candidate genes were then validated in the validation group and clinical samples, and a LOAD prediction model was established.

LASSO and SVM-RFE analyses identified 3 mitochondria-related genes (MRGs) as candidate genes, including NDUFA1, NDUFS5, and NDUFB3. In the verification of 3 MRGs, the AUC values showed that NDUFA1, NDUFS5 had better predictability. We also verified the candidate MRGs in MCI groups, the AUC values showed good performance. We then used NDUFA1, NDUFS5 and age to build a LOAD diagnostic model and AUC was 0.723. Results of qRT-PCR experiments with clinical blood samples showed that the three candidate genes were expressed significantly lower in the LOAD and MCI groups when compared to CN.

Two mitochondrial-related candidate genes, NDUFA1 and NDUFS5, were identified as diagnostic markers for LOAD and MCI. Combining these two candidate genes with age, a LOAD diagnostic prediction model was successfully constructed.

Older adults presenting with dual-decline in cognition and walking speed face a 6-fold higher risk for dementia compared with those showing no decline. We hypothesized that the metabolomics profile of dual-decliners would be unique even before they show signs of decline in cognition and gait speed.

The objective of this study was to determine if plasma metabolomics signatures can discriminate dual-decliners from no decliners, purely cognitive decliners, and purely motor decliners prior to decline.

A retrospective cross-sectional study using baseline plasma for untargeted metabolomics analyses to investigate early signals of later dual-decline status in study participants (n = 76) with convenient sampling. Dual-decline was operationalized as decline in gait speed (>10 cm/s) and cognition (>2 points decline in Montreal Cognitive Assessment score) on at least two consecutive 6-monthly assessments. The participants’ decliner status was evaluated 3 years after the blood sample was collected. Pair-wise comparison of detected compounds was completed using principal components and hierarchical clustering analyses.

Analyses did not detect any cluster separation in untargeted metabolomes across baseline groups. However, follow-up analyses of specific molecules detected 4 compounds (17-Hydroxy-12-(hydroxymethyl)-10-oxo-8 oxapentacyclomethyl hexopyranoside, Fleroxacin, Oleic acid, and 5xi-11,12-Dihydroxyabieta-8(14),9(11),12-trien-20-oic acid) were at significantly higher concentration among the dual-decliners compared to non-decliners. The pure cognitive decliner group had significantly lower concentration of six compounds (1,3-nonanediol acetate, 4-(2-carboxyethyl)-2-methoxyphenyl beta-D-glucopyranosiduronic acid, oleic acid, 2E-3-[4-(sulfo-oxy)phenyl] acrylic acid, palmitelaidic acid, and myristoleic acid) compared to the non-decliner group.

The unique metabolomics profile of dual-decliners warrants follow-up metabolomics analysis. Results may point to modifiable pathways.

Alzheimer’s disease (AD) is the most common type of dementia in the elderly. Incomplete knowledge about the pathogenesis of this disease determines the absence of medications for the treatment of AD today. Animal models can provide the necessary knowledge to understand the mechanisms of biochemical processes occurring in the body in health and disease.

To identify the most promising metabolomic predictors and biomarkers reflecting metabolic disorders in the development of AD signs.

High resolution ¹H NMR spectroscopy was used for quantitative metabolomic profiling of the hippocampus of OXYS rats, an animal model of sporadic AD, which demonstrates key characteristics of this disease. Animals were examined during several key periods: 20 days group corresponds to the “preclinical” period preceding the development of AD signs, during their manifestation (3 months), and active progression (18 months). Wistar rats of the same age were used as control.

Ranges of variation and mean concentrations were established for 59 brain metabolites. The main metabolic patterns during aging, which are involved in energy metabolism pathways and metabolic shifts of neurotransmitters, have been established. Of particular note is the significant increase of scyllo-inositol and decrease of hypotaurine in the hippocampus of OXYS rats as compared to Wistars for all studied age groups.

We suggest that the accumulation of scyllo-inositol and the reduction of hypotaurine in the brain, even at an early age, can be considered as predictors and potential biomarkers of the development of AD signs in OXYS rats and, probably, in humans.

Recent studies have identified plasma metabolites associated with cognitive decline and Alzheimer’s disease; however, little research on this topic has been conducted in Latinos, especially Puerto Ricans.

This study aims to add to the growing body of metabolomics research in Latinos to better understand and improve the health of this population.

We assessed the association between plasma metabolites and global cognition over 12 years of follow-up in 736 participants of the Boston Puerto Rican Health Study (BPRHS). Metabolites were measured with untargeted metabolomic profiling (Metabolon, Inc) at baseline. We used covariable adjusted linear mixed models (LMM) with a metabolite * time interaction term to identify metabolites (of 621 measured) associated with ∼12 years cognitive trajectory.

We observed strong inverse associations between medium-chain fatty acids, caproic acid, and the dicarboxylic acids, azelaic and sebacic acid, and global cognition. N-formylphenylalanine, a tyrosine pathway metabolite, was associated with improvement in cognitive trajectory.

The metabolites identified in this study are generally consistent with prior literature and highlight a role medium chain fatty acid and tyrosine metabolism in cognitive decline.

Alzheimer’s disease (AD) is a complicated condition involving multiple metabolic and immunologic pathophysiological processes that can occur with the hallmark pathologies of amyloid-β, tau, and neurodegeneration. Metformin, an anti-diabetes drug, targets several of these disease processes in in vitro and animal studies. However, the effects of metformin on human cerebrospinal fluid (CSF) and plasma proteins as potential biomarkers of treatment remain unexplored.

Using proteomics data from a metformin clinical trial, identify the impact of metformin on plasma and CSF proteins.

We analyzed plasma and CSF proteomics data collected previously (ClinicalTrials.gov identifier: NCT01965756, conducted between 2013 and 2015), and conduced bioinformatics analyses to compare the plasma and CSF protein levels after 8 weeks of metformin or placebo use to their baseline levels in 20 non-diabetic patients with mild cognitive impairment (MCI) and positive AD biomarkers participants.

50 proteins were significantly (unadjusted p < 0.05) altered in plasma and 26 in CSF after 8 weeks of metformin use, with 7 proteins in common (AZU1, CASP-3, CCL11, CCL20, IL32, PRTN3, and REG1A). The correlation between changes in plasma and CSF levels of these 7 proteins after metformin use relative to baseline levels was high (r = 0.98). The proteins also demonstrated temporal stability.

Our pilot study is the first to investigate the effect of metformin on plasma and CSF proteins in non-diabetic patients with MCI and positive AD biomarkers and identifies several candidate plasma biomarkers for future clinical trials after confirmatory studies.

Alzheimer’s disease (AD) is the most common form of dementia representing from 60% to 70% of the cases globally. It is a multifactorial disease that, among its many pathological characteristics, has been found to provoke the metal ion dysregulation in the brain, along with an increase in the oxidative stress. There is proof that metallic complexes formed by the amyloid-β peptide (Aβ) and extraneuronal copper can catalyze the production of reactive oxygen species, leading to an increase in oxidative stress, promoting neuronal death. Due to this interaction, bioavailable copper has become an important redox active target to consider within the search protocols of multifunctional agents for AD’s treatment.

In this study, we examined by using bioinformatics and electronic structure calculations the potential application of 44 salen-type copper chelating ligands and 12 further proposed molecules as possible multifunctional agents in the context of AD.

The candidates were evaluated by combining bioinformatic tools and electronic structure calculations, which allowed us to classify the molecules as potential antioxidants, redistributor-like compounds, and the newly proposed suppressor mechanism.

This evaluation demonstrate that salen-type ligands exhibit properties suitable for interfering in the chain of copper-induced oxidative stress reactions present in AD and potential redistributor and suppressor activity for copper ions. Finally, a novel set of plausible candidates is proposed and evaluated.

According to the evaluated criteria, a subset of 13 salen-type candidates was found to exhibit promissory pharmacological properties in the AD framework and were classified according to three plausible action mechanisms.

Recent Alzheimer’s disease (AD) discoveries are increasingly based on studies from a variety of omics technologies on large cohorts. Currently, there is no easily accessible resource for neuroscientists to browse, query, and visualize these complex datasets in a harmonized manner.

Create an online portal of public omics datasets for AD research.

We developed Alzheimer DataLENS, a web-based portal, using the R Shiny platform to query and visualize publicly available transcriptomics and genetics studies of AD on human cohorts. To ensure consistent representation of AD findings, all datasets were processed through a uniform bioinformatics pipeline.

Alzheimer DataLENS currently houses 2 single-nucleus RNA sequencing datasets, over 30 bulk RNA sequencing datasets from 19 brain regions and 3 cohorts, and 2 genome-wide association studies (GWAS). Available visualizations for single-nucleus data include bubble plots, heatmaps, and UMAP plots; for bulk expression data include box plots and heatmaps; for pathways include protein-protein interaction network plots; and for GWAS results include Manhattan plots. Alzheimer DataLENS also links to two other knowledge resources: the AD Progression Atlas and the Astrocyte Atlas.

Alzheimer DataLENS is a valuable resource for investigators to quickly and systematically explore omics datasets and is freely accessible at https://alzdatalens.partners.org.