Identification of Key Regulatory Genes and Pathways in Prefrontal Cortex of Alzheimer's Disease

Genome-Wide DNA Methylation in Early-Onset-Dementia Patients Brain Tissue and Lymphoblastoid Cell Lines

Epigenetics, a potential underlying pathogenic mechanism of neurodegenerative diseases, has been in the scope of several studies performed so far. However, there is a gap in regard to analyzing different forms of early-onset dementia and the use of Lymphoblastoid cell lines (LCLs). We performed a genome-wide DNA methylation analysis on sixty-four samples (from the prefrontal cortex and LCLs) including those taken from patients with early-onset forms of Alzheimer's disease (AD) and frontotemporal dementia (FTD) and healthy controls. A beta regression model and adjusted p-values were used to obtain differentially methylated positions (DMPs) via pairwise comparisons. A correlation analysis of DMP levels with Clariom D array gene expression data from the same cohort was also performed. The results showed hypermethylation as the most frequent finding in both tissues studied in the patient groups. Biological significance analysis revealed common pathways altered in AD and FTD patients, affecting neuron development, metabolism, signal transduction, and immune system pathways. These alterations were also found in LCL samples, suggesting the epigenetic changes might not be limited to the central nervous system. In the brain, CpG methylation presented an inverse correlation with gene expression, while in LCLs, we observed mainly a positive correlation. This study enhances our understanding of the biological pathways that are associated with neurodegeneration, describes differential methylation patterns, and suggests LCLs are a potential cell model for studying neurodegenerative diseases in earlier clinical phases than brain tissue.

Proteomic characterization of hUC-MSC extracellular vesicles and evaluation of its therapeutic potential to treat Alzheimer's disease

In recent years, human umbilical cord mesenchymal stem cell (hUC-MSC) extracellular vesicles (EVs) have been used as a cell replacement therapy and have been shown to effectively overcome some of the disadvantages of cell therapy. However, the specific mechanism of action of EVs is still unclear, and there is no appropriate system for characterizing the differences in the molecular active substances of EVs produced by cells in different physiological states. We used a data-independent acquisition (DIA) quantitative proteomics method to identify and quantify the protein composition of two generations EVs from three different donors and analysed the function and possible mechanism of action of the proteins in EVs of hUC-MSCs via bioinformatics. By comparative proteomic analysis, we characterized the different passages EVs. Furthermore, we found that adaptor-related protein complex 2 subunit alpha 1 (AP2A1) and adaptor-related protein complex 2 subunit beta 1 (AP2B1) in hUC-MSC-derived EVs may play a significant role in the treatment of Alzheimer's disease (AD) by regulating the synaptic vesicle cycle signalling pathway. Our work provides a direction for batch-to-batch quality control of hUC-MSC-derived EVs and their application in AD treatment.

A review and analysis of key biomarkers in Alzheimer's disease

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that affects over 50 million elderly individuals worldwide. Although the pathogenesis of AD is not fully understood, based on current research, researchers are able to identify potential biomarker genes and proteins that may serve as effective targets against AD. This article aims to present a comprehensive overview of recent advances in AD biomarker identification, with highlights on the use of various algorithms, the exploration of relevant biological processes, and the investigation of shared biomarkers with co-occurring diseases. Additionally, this article includes a statistical analysis of key genes reported in the research literature, and identifies the intersection with AD-related gene sets from databases such as AlzGen, GeneCard, and DisGeNet. For these gene sets, besides enrichment analysis, protein-protein interaction (PPI) networks utilized to identify central genes among the overlapping genes. Enrichment analysis, protein interaction network analysis, and tissue-specific connectedness analysis based on GTEx database performed on multiple groups of overlapping genes. Our work has laid the foundation for a better understanding of the molecular mechanisms of AD and more accurate identification of key AD markers.

STW-MD: a novel spatio-temporal weighting and multi-step decision tree method for considering spatial heterogeneity in brain gene expression data

Gene expression during brain development or abnormal development is a biological process that is highly dynamic in spatio and temporal. Previous studies have mainly focused on individual brain regions or a certain developmental stage. Our motivation is to address this gap by incorporating spatio-temporal information to gain a more complete understanding of brain development or abnormal brain development, such as Alzheimer's disease (AD), and to identify potential determinants of response. In this study, we propose a novel two-step framework based on spatial-temporal information weighting and multi-step decision trees. This framework can effectively exploit the spatial similarity and temporal dependence between different stages and different brain regions, and facilitate differential gene analysis in brain regions with high heterogeneity. We focus on two datasets: the AD dataset, which includes gene expression data from early, middle and late stages, and the brain development dataset, spanning fetal development to adulthood. Our findings highlight the advantages of the proposed framework in discovering gene classes and elucidating their impact on brain development and AD progression across diverse brain regions and stages. These findings align with existing studies and provide insights into the processes of normal and abnormal brain development.

Persistent diazinon induced neurotoxicity: The effect on inhibitory avoidance memory performance, amyloid precursor proteins, and TNF-α levels in the prefrontal cortex of rats

INTRODUCTION

Organophosphate pesticides (Ops) like diazinon (DZN) have well-known neurotoxic effects and low-level chronic exposure has been linked to detrimental neurobehavioral impairments and memory deficits. However, it's not entirely clear how DZN-induced biological changes, particularly in the prefrontal cortex (PFC) contribute to these effects. The purpose of this study is to investigate the impact of DZN exposure on inhibitory avoidance (IA) memory function, amyloid precursor expression (APP), and proinflammatory tumor necrosis factor-α (TNF-α) levels in the rat cortex.

MATERIALS AND METHODS

Rats were divided into 4 groups and recived 2 mg/kg DZN for 5-days or 12-weeks and two control groups recived the same volume of vehicle. IA memory was assesed using the shuttle box apparatus. Rats were sacrificed and the prefrontal cortex PFC were removed. Real-time PCR and Western blotting were used to messure TNF-α, and amyloid protein precursors gene expression and protein levels.

RESULTS

Our findings indicated that DZN caused body weight loss and a notable decline in performance on the IA memory. Additionally, 5-days exposure increased APP and APLP2 protein levels in the PFC, while 12-weeks exposure decreased these levels. Furthermore, expression of APP and APLP2 gens were decreased in PFC. TNF-α levels increased as a result of 5-days exposure to DZN, but these levels dropped to normal after 12-weeks administration, and this observation was significant.

CONCLUSION

Taken together, exposure to low doses of DZN leads to disturbances in IA memory performance and also alternations in amyloid beta precursors that can be related to increased risk of Alzheimer's disease.

Gene Co-Expression Analysis of Multiple Brain Tissues Reveals Correlation of FAM222A Expression with Multiple Alzheimer's Disease-Related Genes

Background

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.

Objective

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

Methods

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.

Results

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.

Conclusion

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.

Identification of region-specific splicing QTLs in human hippocampal tissue and its distinctive role in brain disorders

Alternative splicing (AS) regulation has an essential role in complex diseases. However, the AS profiles in the hippocampal (HIPPO) region of human brain are underexplored. Here, we investigated cis-acting sQTLs of HIPPO region in 264 samples and identified thousands of significant sQTLs. By enrichment analysis and functional characterization of these sQTLs, we found that the HIPPO sQTLs were enriched among histone-marked regions, transcription factors binding sites, RNA binding proteins sites, and brain disorders-associated loci. Comparative analyses with the dorsolateral prefrontal cortex revealed the importance of AS regulation in HIPPO (rg = 0.87). Furthermore, we performed a transcriptome-wide association study of Alzheimer's disease and identified 16 significant genes whose genetically regulated splicing levels may have a causal role in Alzheimer. Overall, our study improves our knowledge of the transcriptome gene regulation in the HIPPO region and provides novel insights into elucidating the pathogenesis of potential genes associated with brain disorders.

Systematic analysis of cuproptosis-related genes in immunological characterization and predictive drugs in Alzheimer's disease

Objectives

This study aimed to make a systematic analysis of cuproptosis-related genes (CRGs) in immunological characterization and predictive drugs in Alzheimer's disease (AD) through bioinformatics and biological experiments.

Methods

The molecular clusters related to CRGs and associated immune cell infiltrations in AD were investigated. The diagnostic models were constructed for AD and different AD subtypes. Moreover, drug prediction and molecular docking were also performed. Subsequently, a molecular dynamics (MD) simulation was conducted to further verify the findings. Finally, RT-qPCR validation was performed.

Results

The characterization of 12 AD-related CRGs was evaluated in AD, and a diagnostic model for AD showed a satisfying discrimination power based on five CRGs by LASSO regression analysis. The dysregulated CRGs and activated immune responses partially differed between patients with AD and healthy subjects. Furthermore, two molecular subtypes (clusters A and B) with different immune infiltration characteristics in AD were identified. Similarly, a diagnostic model for different AD subtypes was built with nine CRGs, which achieved a good performance. Molecular docking revealed the optimum conformation of CHEMBL261454 and its target gene CSNK1D, which was further validated by MD simulation. The RT-qPCR results were consistent with those of the comprehensive analysis.

Conclusion

This study systematically elucidated the complex relationship between cuproptosis and AD, providing novel molecular targets for treatment and diagnosis biomarkers of AD.

CEGA: a method for inferring natural selection by comparative population genomic analysis across species

We developed maximum likelihood method for detecting positive selection or balancing selection using multilocus or genomic polymorphism and divergence data from two species. The method is especially useful for investigating natural selection in noncoding regions. Simulations demonstrate that the method outperforms existing methods in detecting both positive and balancing selection. We apply the method to population genomic data from human and chimpanzee. The list of genes identified under selection in the noncoding regions is prominently enriched in pathways related to the brain and nervous system. Therefore, our method will serve as a useful tool for comparative population genomic analysis.

Influence of N6-methyladenosine (m6A) modification on cell phenotype in Alzheimer's disease

OBJECTIVE

Recent research has suggested that m6A modification takes on critical significance to Neurodegeneration. As indicated by the genome-wide map of m6A mRNA, genes in Alzheimer's disease model achieved significant m6A methylation. This study aimed to investigate the hub gene and pathway of m6A modification in the pathogenesis of AD. Moreover, possible brain regions with higher gene expression levels and compounds exerting potential therapeutic effects were identified. Thus, this study can provide a novel idea to explore the treatment of AD.

METHODS

Differential expression genes (DEGs) of GSE5281 and GSE48350 from the Gene Expression Omnibus (GEO) database were screened using the Limma package. Next, the enrichment analysis was conducted on the screened DEGs. Moreover, the functional annotation was given for N6-methyladenosine (m6A) modification gene. The protein-protein interaction network (PPI) analysis and the visualization analysis were conducted using STRING and Cytoscape. The hub gene was identified using CytoHubba. The expression levels of Hub genes in different regions of brain tissue were analyzed based on Human Protein Atlas (HPA) database and Bgee database. Subsequently, the candidate drugs targeting hub genes were screened using cMAP.

RESULTS

A total of 42 m6A modified genes were identified in AD (20 up-regulated and 22 down-regulated genes). The above-described genes played a certain role in biological processes (e.g., retinoic acid, DNA damage response and cysteine-type endopeptidase activity), cellular components (e.g., mitochondrial protein complex), and molecular functions (e.g., RNA methyltransferase activity and ubiquitin protein ligase). KEGG results suggested that the above-mentioned genes were primarily involved in the Hippo signaling pathway of neurodegeneration disease. A total of 10 hub genes were screened using the protein-protein interaction network, and the expression of hub genes in different regions of human brain was studied. Furthermore, 10 compounds with potential therapeutic effects on AD were predicted.

CONCLUSION

This study revealed the potential role of the m6A modification gene in Alzheimer's disease through the bioinformatics analysis. The biological changes may be correlated with retinoic acid, DNA damage response and cysteine-type endopeptidase activity, which may occur through Hippo signaling pathway. The hub genes (SOX2, KLF4, ITGB4, CD44, MSX1, YAP1, AQP1, EGR2, YWHAZ and TFAP2C) and potential drugs may provide novel research directions for future prognosis and precise treatment.

LncRNA NKILA Exacerbates Alzheimer's Disease Progression by Regulating the FOXA1-Mediated Transcription of TNFAIP1

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases in the world, which seriously affects AD patients' life quality. Recently, long non-coding RNAs (lncRNAs) have been reported to play a key role in AD pathogenesis, however, the specific mechanism remains unclear. Herein, we aimed to investigate the role of lncRNA NKILA in AD. The learning and memory performance of rats from streptozotocin (STZ)-treated or other treated groups were tested by Morris water maze test. Relative levels of genes and proteins were measured using RT-qPCR and Western blotting. Mitochondrial membrane potential was tested by JC-1 staining. Levels of ROS, SOD, MDA, GSH-Px, and LDH were measured using corresponding commercial kits. Apoptosis was evaluated by TUNEL staining or Flow cytometry assay. RNA Immunoprecipitation (RIP), RNA pulldown, Chromatin immunoprecipitation (ChIP), and dual-luciferase reporter assays were utilized to test the interaction between indicated molecules. STZ treatment caused learning and memory impairment in rats and oxidative stress damage in SH-SY5Y cells. LncRNA NKILA was found to be elevated in the hippocampal tissues of rats and SH-SY5Y cells after STZ exposure. Knockdown of lncRNA NKILA alleviated STZ-induced neuronal damage. Furthermore, lncRNA NKILA could bind to ELAVL1, which regulate the stability of FOXA1 mRNA. Moreover, TNFAIP1 transcription process was controlled by FOXA1, which targeted the promoter of TNFAIP1. In vivo results demonstrated that lncRNA NKILA accelerated STZ-induced neuronal damage and oxidative stress by FOXA1/TNFAIP1 axis. Our findings indicated that knockdown of lncRNA NKILA inhibited the neuronal damage and oxidative stress induced by STZ through the FOXA1/TNFAIP1 axis, thereby alleviating the development of AD, revealing a promising therapeutic axis for AD treatment.

CSF-Targeted Proteomics Indicate Amyloid-Beta Ratios in Patients with Alzheimer's Dementia Spectrum

Background

According to recent studies, amyloid-β (Aβ) isoforms as cerebrospinal fluid (CSF) biomarkers have remarkable predictive value for cognitive decline in the early stages of Alzheimer's disease (AD). Herein, we aimed to investigate the correlations between several targeted proteomics in CSF samples with Aβ ratios and cognitive scores in patients in AD spectrum to search for potential early diagnostic utility.

Methods

A total of 719 participants were found eligible for inclusion. Patients were then categorized into cognitively normal (CN), mild cognitive impairment (MCI), and AD and underwent an assessment of Aβ and proteomics. Clinical Dementia Rating (CDR), Alzheimer's Disease Assessment Scale (ADAS), and Mini Mental State Exam (MMSE) were used for further cognitive assessment. The Aβ42, Aβ42/Aβ40, and Aβ42/38 ratios were considered as means of comparison to identify those peptides corresponding significantly to these established biomarkers and cognitive scores. The diagnostic utility of the IASNTQSR, VAELEDEK, VVSSIEQK, GDSVVYGLR, EPVAGDAVPGPK, and QETLPSK was assessed.

Results

All investigated peptides corresponded significantly to Aβ42 in controls. In those with MCI, VAELEDEK and EPVAGDAVPGPK were significantly correlated with Aβ42 (p value < 0.001). Additionally, IASNTQSR, VVSSIEQK, GDSVVYGLR, and QETLPSK were significantly correlated with Aβ42/Aβ40 and Aβ42/38 (p value < 0.001) in this group. This group of peptides similarly corresponded to Aβ ratios in those with AD. Eventually, IASNTQSR, VAELEDEK, and VVSSIEQK were significantly associated with CDR, ADAS-11, and ADAS-13, particularly in MCI group.

Conclusion

Our research suggests potential early diagnostic and prognostic utilities for certain peptides extracted from CSF-targeted proteomics research. The ethical approval of ADNI is available at ClinicalTrials.gov with Identifier: NCT00106899.

CSF peptides from VGF and other markers enhance prediction of MCI to AD progression using the ATN framework

The amyloid beta, tau, neurodegenerative markers framework has been proposed to serve as a system to classify and combine biomarkers for Alzheimer's Disease (AD). Although cerebrospinal (CSF) fluid AT (amyloid beta and tau)-based biomarkers have a well-established track record to distinguish AD from control subjects and to predict conversion from mild cognitive impairment (MCI) to AD, there is not an established non-tau based neurodegenerative ("N") marker from CSF. Here, we examine the ability of several candidate peptides in the CSF to serve as "N" markers to both classify disease state and predict MCI to AD conversion. We observed that although many putative N markers involved in synaptic processing and neuroinflammation were able to, when examined in isolation, distinguish MCI converters from non-converters, a derivative from VGF, when combined with AT markers, most strongly enhanced prediction of MCI to AD conversion. Low CSF VGF levels were also predictive of MCI to dementia conversion in the setting of normal AT markers, suggesting that it may serve as a very early predictor of dementia conversion. Other markers derived from neuronal pentraxin 2, GAP-43 and a 14-3-3 protein were also able to enhance MCI to AD prediction when used as a marker of neurodegeneration, but VGF had the highest predictive capacity. Thus, we propose that low levels of VGF in CSF may serve as "N" in the amyloid beta, tau, neurodegenerative markers framework to enhance the prediction of MCI to AD conversion.

Transcriptomic Analyses of Exercise Training in Alzheimer's Disease Cerebral Cortex

BACKGROUND

Research reported exercise could reduce Alzheimer's disease (AD) symptoms in human and animals. However, the molecular mechanism of exercise training via transcriptomic analysis was unclear especially in AD in the cortex area.

OBJECTIVE

Investigate potential significant pathways in the cortex area that were affected by exercise during AD.

METHODS

RNA-seq analysis, differential expressed genes, functional enrichment analysis, and GSOAP clustering analysis were performed in the isolated cerebral cortex from eight 3xTg AD mice (12 weeks old) randomly and equally divided into control (AD) and exercise training (AD-EX) group. Swimming exercise training in AD-EX group was conducted 30 min/day for 1 month.

RESULTS

There were 412 genes significant differentially expressed in AD-EX group compared to AD group. Top 10 upregulated genes in AD-EX group against AD group mostly correlated with neuroinflammation, while top 10 downregulated genes mostly had connection with vascularization, membrane transport, learning memory, and chemokine signal. Pathway analysis revealed the upregulated interferon alpha beta signaling in AD-EX had association with cytokines delivery in microglia cells compared to AD and top 10 upregulated genes involved in interferon alpha beta were Usp18, Isg15, Mx1, Mx2, Stat1, Oas1a, and Irf9; The downregulated extracellular matrix organization in AD-EX had correlation with Aβ and neuron cells interaction and Vtn was one of the top 10 downregulated genes involved in this pathway.

CONCLUSION

Exercise training influenced 3xTg mice cortex through interferon alpha beta signaling upregulation and extracellular matrix organization downregulation based on transcriptomics analysis.

Ginsenoside Rd protects transgenic Caenorhabditis elegans from β-amyloid toxicity by activating oxidative resistant

Alzheimer's disease (AD) is a serious public health issue but few drugs are currently available for the disease, and these only target the symptoms. It is well established that oxidative stress plays a crucial role in AD, and there is compelling evidence linking oxidative stress to β-amyloid (Aβ). An exciting source of potential new AD therapeutic medication possibilities is medicinal plants. Ginsenoside Rd (GS-Rd) is one of the main bioactive substances in ginseng extracts. In our study, we used a network pharmacology analysis to identify overlapping GS-Rd (therapeutic) and AD (disease)-relevant protein targets, gene ontology (GO) and bio-process annotation, and the KEGG pathway analysis data predicted that GS-Rd impacts multiple targets and pathways, such as the MAPK signal pathway and the JAT-STAT3 signaling pathway. We then assessed the role of GS-Rd in C. elegans and found that GS-Rd prolongs lifespan, improves resistance to heat stress, delays physical paralysis and increases oxidative stress responses. Overall, these results suggest that GS-Rd protects against the toxicity of Aβ. The RNA-seq analysis revealed that GS-Rd achieves its effects by regulating gene expressions like daf-16 and skn-1, as well as by participating in many AD-related pathways like the MAPK signaling pathway. In addition, in CL4176 worms, GS-Rd decreased reactive oxygen species (ROS) levels and increased SOD activity. Additional research with transgenic worms showed that GS-Rd aided in the movement of DAF-16 from the cytoplasm to the nucleus. Taken together, the results indicate that GS-Rd significantly reduces Aβ aggregation by targeting the MAPK signal pathway, induces nuclear translocation of DAF-16 to activate downstream signaling pathways and increases resistance to oxidative stress in C. elegans to protect against Aβ-induced toxicity.

Accelerated aging-related transcriptome alterations in neurovascular unit cells in the brain of Alzheimer’s disease

Alzheimer’s disease (AD) is the most common cause of dementia with no effective therapies. Aging is a dominant risk factor for AD. The neurovascular unit (NVU) plays an important role in maintaining homeostasis of the brain microenvironment. The accelerated aging of NVU cells may directly impair NVU function and contribute to AD pathogenesis. However, the expression patterns of aging-related genes (AGs) in NVU cells of AD remain unclear. In this study, we performed single-nucleus transcriptome analysis of 61,768 nuclei from prefrontal cortical samples of patients with AD and normal control (NC) subjects. Eight main cell types were identified, including astrocytes, microglia, excitatory neurons, inhibitory neurons, oligodendrocytes, oligodendrocyte precursor cells, pericytes, and endothelial cells. Transcriptomic analysis identified the expression patterns of AGs in NVU cells of AD. Gene set enrichment analysis confirmed the key aging-associated cellular pathways enriched in microglia and oligodendrocytes. These aging-related transcriptomic changes in NVU were cross-validated using bulk transcriptome data. The least absolute shrinkage and selection operator regression method was used to select the crucial AGs most associated with AD: IGF1R, MXI1, RB1, PPARA, NFE2L2, STAT5B, FOS, PRKCD, YWHAZ, HTT, MAPK9, HSPA9, SDHC, PRKDC, and PDPK1. This 15-gene model performed well in discriminating AD from NC samples. Among them, IGF1R, MXI1, PPARA, YWHAZ, and MAPK9 strongly correlated with pathologic progression in AD, were identified as critical regulators of AD. Although most AGs showed similar trends of expression changes in different types of NVU cells in AD, certain AGs were expressed in a cell-specific manner. Our comprehensive analysis of brain NVU from patients with AD reveals previously unknown molecular changes associated with aging that may underlie the functional dysregulation of NVU, providing important insights for exploring potential cell-specific therapeutic targets to restore brain homeostasis in AD.

Identification of risk genes for Alzheimer's disease by gene embedding

Most disease-gene association methods do not account for gene-gene interactions, even though these play a crucial role in complex, polygenic diseases like Alzheimer's disease (AD). To discover new genes whose interactions may contribute to pathology, we introduce GeneEMBED. This approach compares the functional perturbations induced in gene interaction network neighborhoods by coding variants from disease versus healthy subjects. In two independent AD cohorts of 5,169 exomes and 969 genomes, GeneEMBED identified novel candidates. These genes were differentially expressed in post mortem AD brains and modulated neurological phenotypes in mice. Four that were differentially overexpressed and modified neurodegeneration in vivo are PLEC, UTRN, TP53, and POLD1. Notably, TP53 and POLD1 are involved in DNA break repair and inhibited by approved drugs. While these data show proof of concept in AD, GeneEMBED is a general approach that should be broadly applicable to identify genes relevant to risk mechanisms and therapy of other complex diseases.

Characterization of Early Alzheimer's Disease-Like Pathological Alterations in Non-Human Primates with Aging: A Pilot Study

BACKGROUND

Sporadic or late onset Alzheimer's disease (LOAD) is a multifactorial neurodegenerative disease with aging the most known risk factor. Non-human primates (NHPs) may serve as an excellent model to study LOAD because of their close similarity to humans in many aspects including neuroanatomy and neurodevelopment. Recent studies reveal AD-like pathology in old NHPs.

OBJECTIVE

In this pilot study, we took advantage of brain samples from 6 Cynomolgus macaques that were divided into two groups: middle aged (average age 14.81 years) and older (average age 19.33 years). We investigated whether AD-like brain pathologies are present in the NHPs.

METHODS

We used immunohistochemical method to examine brain Aβ pathology and neuron density. We applied biochemical assays to measure tau phosphorylation and multiple signaling pathways indicated in AD. We performed electron microscopy experiments to study alterations of postsynaptic density and mitochondrial morphology in the brain of NHPs.

RESULTS

We found multiple AD-like pathological alteration in the prefrontal cortex (but not in the hippocampus) of the older NHPs including tau hyperphosphorylation, increased activity of AMP-activated protein kinase (AMPK), decreased expression of protein phosphatase 2A (PP2A), impairments in mitochondrial morphology, and postsynaptic densities formation.

CONCLUSION

These findings may provide insights into the factors contributing to the development of LOAD, particularly during the early stage transitioning from middle to old age. Future endeavors are warranted to elucidate mechanisms underlying the regional (and perhaps cellular) vulnerability with aging and the functional correlation of such pathological changes in NHPs.

Tangled quest of post-COVID-19 infection-caused neuropathology and what 3P nano-bio-medicine can solve?

COVID-19-caused neurological problems are the important post-CoV-2 infection complications, which are recorded in ~ 40% of critically ill COVID-19 patients. Neurodegeneration (ND) is one of the most serious complications. It is necessary to understand its molecular mechanism(s), define research gaps to direct research to, hopefully, design new treatment modalities, for predictive diagnosis, patient stratification, targeted prevention, prognostic assessment, and personalized medical services for this type of complication. Individualized nano-bio-medicine combines nano-medicine (NM) with clinical and molecular biomarkers based on omics data to improve during- and post-illness management or post-infection prognosis, in addition to personalized dosage profiling and drug selection for maximum treatment efficacy, safety with least side-effects. This review will enumerate proteins, receptors, and enzymes involved in CoV-2 entrance into the central nervous system (CNS) via the blood–brain barrier (BBB), and list the repercussions after that entry, ranging from neuroinflammation to neurological symptoms disruption mechanism. Moreover, molecular mechanisms that mediate the host effect or viral detrimental effect on the host are discussed here, including autophagy, non-coding RNAs, inflammasome, and other molecular mechanisms of CoV-2 infection neuro-affection that are defined here as hallmarks of neuropathology related to COVID-19 infection. Thus, a couple of questions are raised; for example, “What are the hallmarks of neurodegeneration during COVID-19 infection?” and “Are epigenetics promising solution against post-COVID-19 neurodegeneration?” In addition, nano-formulas might be a better novel treatment for COVID-19 neurological complications, which raises one more question, “What are the challenges of nano-bio-based nanocarriers pre- or post-COVID-19 infection?” especially in the light of omics-based changes/challenges, research, and clinical practice in the framework of predictive preventive personalized medicine (PPPM / 3P medicine).

A transcriptome-wide association study of Alzheimer's disease using prediction models of relevant tissues identifies novel candidate susceptibility genes

BACKGROUND

Genome-wide association studies (GWAS) have identified over 56 susceptibility loci associated with Alzheimer's disease (AD), but the genes responsible for these associations remain largely unknown.

METHODS

We performed a large transcriptome-wide association study (TWAS) leveraging modified UTMOST (Unified Test for MOlecular SignaTures) prediction models of ten brain tissues that are potentially related to AD to discover novel AD genetic loci and putative target genes in 71,880 (proxy) cases and 383,378 (proxy) controls of European ancestry.

RESULTS

We identified 53 genes with predicted expression associations with AD risk at Bonferroni correction threshold (P value < 3.38 × 10^-6). Based on fine-mapping analyses, 21 genes at nine loci showed strong support for being causal.

CONCLUSIONS

Our study provides new insights into the etiology and underlying genetic architecture of AD.

Identification of key genes and crucial pathways for major depressive disorder using peripheral blood samples and chronic unpredictable mild stress rat models

BACKGROUND

Accurate diagnosis of major depressive disorder (MDD) remains difficult, and one of the key challenges in diagnosing MDD is the lack of reliable diagnostic biomarkers. The objective of this study was to explore gene networks and identify potential biomarkers for MDD.

METHODS

In the present study, we performed a comprehensive analysis of the mRNA expression profiles using blood samples of four patients with MDD and four controls by RNA sequencing. Differentially expressed genes (DEGs) were screened, and functional and pathway enrichment analyses were performed using the Database for Annotation, Visualization, and Integrated Discovery. All DEGs were inputted to the STRING database to build a PPI network, and the top 10 hub genes were screened using the cytoHubba plugin of the Cytoscape software. The relative expression of 10 key genes was identified by quantitative real-time polymerase chain reaction (qRT-PCR) of blood samples from 50 MDD patients and 50 controls. Plasma levels of SQSTM1 and TNFα were measured using an enzyme-linked immunosorbent assay in blood samples of 44 MDD patients and 44 controls. A sucrose preference test was used to evaluate depression-like behavior in chronic unpredictable mild stress (CUMS) model rats. Immunofluorescence assay and western blotting were performed to study the expression of proteins in the brain samples of CUMS model rats.

RESULTS

We identified 247 DEGs that were closely associated with MDD. Gene ontology analyses suggested that the DEGs were mainly enriched in negative regulation of transcription by RNA polymerase II promoter, cytoplasm, and protein binding. Moreover, Kyoto Encyclopedia of Genes and Genomes pathway analysis suggested that the DEGs were significantly enriched in the MAPK signaling pathway. Ten hub genes were screened through the PPI network, and qRT-PCR assay revealed that one and six genes were downregulated and upregulated, respectively; however, SMARCA2, PPP3CB, and RAB5C were not detected. Pathway enrichment analysis for the 10 genes showed that the mTOR signaling pathway was also enriched. A strong positive correlation was observed between SQSTM1 and TNFα protein levels in patients with MDD. LC3 II and SQSTM1 protein levels were increased in the CUMS rat model; however, p-mTOR protein levels were decreased. The sucrose preference values decreased in the CUMS rat model.

CONCLUSIONS

We identified 247 DEGs and constructed an MDD-specific network; thereafter, 10 hub genes were selected for further analysis. Our results provide novel insights into the pathogenesis of MDD. Moreover, SQSTM1, which is related to autophagy and inflammatory reactions, may play a key role in MDD. SQSTM1 may be used as a promising therapeutic target in MDD; additionally, more molecular mechanisms have been suggested that should be focused on in future in vivo and in vitro studies.

Differentially expressed genes in Alzheimer's disease highlighting the roles of microglia genes including OLR1 and astrocyte gene CDK2AP1

BACKGROUND

Alzheimer's disease (AD) is associated with abnormal tau and amyloid-β accumulation in the brain, leading to neurofibrillary tangles, neuropil threads and extracellular amyloid-β plaques. Treatment is limited to symptom management, a disease-modifying therapy is not available. To advance search of therapy approaches, there is a continued need to identify targets for disease intervention both by confirming existing hypotheses and generating new hypotheses.

METHODS

We conducted a mRNA-seq study to identify genes associated with AD in post-mortem brain samples from the superior temporal gyrus (STG, n ​= ​76), and inferior frontal gyrus (IFG, n ​= ​65) brain regions. Differentially expressed genes (DEGs) were identified correcting for gender and surrogate variables to capture hidden variation not accounted for by pre-planned covariates. The results from this study were compared with the transcriptome studies from the Accelerated Medicine Partnership - Alzheimer's Disease (AMP-AD) initiative. Over-representation and gene set enrichment analysis (GSEA) was used to identify disease-associated pathways. Protein-protein interaction (PPI) and weighted gene co-expression network analysis (WGCNA) analyses were carried out and co-expressed gene modules and their hub genes were identified and associated with additional phenotypic traits of interest.

RESULTS

Several hundred mRNAs were differentially expressed between AD cases and cognitively normal controls in the STG, while no and few transcripts met the same criteria (adjusted p less than 0.05 and fold change greater than 1.2) in the IFG. The findings were consistent at the gene set level with two out of three cohorts from AMP-AD. PPI analysis suggested that the DEGs were enriched in protein-protein interactions than expected by random chance. Over-representation and GSEA analysis suggested genes playing roles in neuroinflammation, amyloid-β, autophagy and trafficking being important for the AD disease process. At the gene level, 10 genes from the STG that were consistently differentially expressed in this study and in the MSBB study (one of the three cohorts within the AMP-AD initiative) were enriched in microglial genes (TREM2, C3AR1, ITGAX, OLR1, CD74, and HLA-DRA), but also included genes with a broader cell type expression pattern such as CDK2AP1. Among the DEGs with supporting evidence from an independent study, CDK2AP1 (most abundantly expressed in astrocyte) was the transcript with strongest association with antemortem cognitive measure (last Mini-Mental State Examination score) and neurofibril tangle burden but also associated with amyloid plaque burden, while OLR1 was the transcript with strongest association with amyloid plaque burden. GSEA and over-representation analyses revealed gene sets related to immune processes including neutrophil degranulation, interleukin 10 signaling, and interferon gamma signaling, complement and coagulation cascades, phosphatidylinositol signaling system, phagosome and neurotransmitter receptors and postsynaptic signal transmission were enriched from this study and replicated in an independent study.

CONCLUSION

This study identified differential gene sets, common with two out of three AMP-AD cohorts (ROSMAP and MSBB) and highlights microglia and astrocyte as the key cell-types with DGEs associated with AD clinical diagnosis, and/or antemortem cognitive measure as well as neuropathological indices. Future meta-analysis and causal inferential analysis will be helpful in pinpointing the most relevant pathways and genes to intervene.

Genomic sequence analysis of lung infections using artificial intelligence technique

Attributable to the modernization of Artificial Intelligence (AI) procedures in healthcare services, various developments including Support Vector Machine (SVM), and profound learning. For example, Convolutional Neural systems (CNN) have prevalently engaged in a significant job of various classificational investigation in lung malignant growth, and different infections. In this paper, Parallel based SVM (P-SVM) and IoT has been utilized to examine the ideal order of lung infections caused by genomic sequence. The proposed method develops a new methodology to locate the ideal characterization of lung sicknesses and determine its growth in its early stages, to control the growth and prevent lung sickness. Further, in the investigation, the P-SVM calculation has been created for arranging high-dimensional distinctive lung ailment datasets. The data used in the assessment has been fetched from real-time data through cloud and IoT. The acquired outcome demonstrates that the developed P-SVM calculation has 83% higher accuracy and 88% precision in characterization with ideal informational collections when contrasted with other learning methods.

SARS-CoV-2 dysregulation of PTBP1 and YWHAE/Z gene expression: A primer of neurodegeneration

SARS-CoV-2 neurotropism has been increasingly recognized by its imaging and syndromic manifestations in the literature. The purpose of this report is to explore the limited yet salient current evidence that SARS-CoV-2′s host genomic targets PTBP1 and the 14-3-3 protein isoform encoding genes YWHAE and YWHAZ may be hold the key to understanding how neurotropism triggers neurodegeneration and how it may contribute to the onset of neurodegenerative disease. Considering that PTBP1 silencing in particular has recently been shown to reverse clinical parkinsonism and induce neurogenesis, as well as the known interactions of PTBP1 and YWHAE/Z with coronaviruses – most notably 14-3-3 and SARS-CoV, recent studies reinvigorate the infectious etiology hypotheses on major neurodegenerative disease such as AD and iPD. Considering that human coronaviruses with definite neurotropism have been shown to achieve long-term latency within the mammalian CNS as a result of specific accommodating mutations, the corroboration of genomic-level evidence with neuroimaging has vast potential implications for neurodegenerative disease.

Retinal Image Analysis for Ocular Disease Prediction Using Rule Mining Algorithms

Medical image processing is now gaining a significant momentum in clinical situation to undertake diagnosis of different anatomical defects. However, with regard to eye diseases, there is no such well-defined image processing technique in medical image analysis. The scope of this study is to automate computer analysis of ocular disease-related retinal images, which may ease the job of ophthalmologists to rule out the diseased condition. In this present work, eye images are subjected for developing a reliable tool for processing the eye retinal fundus images. The primary objective is to effectively probe retinal image data for providing a holistic approach in automatic fundus disease detection and screening to help clinicians in addition with a developed reliable image processing technique combined with a rule-based clustering method for automatic analysis of fundus images in a reduced time frame. More than 400 eye images available in online are examined. The images were preprocessed by grayscale conversion, retinal segmentation, ROI and crop ROI, image resizing, and extraction in RGB channels. Then these images were segmented by NRR from RGB channels, centroids of rows and columns, and NRR to binary image conversion. Then extraction of features like cup to disc area, optic cup area, and NRR calculations prior to measuring ISNT. A unique algorithm named as EARMAM was introduced for the prediction of diseased image from healthy eye image pool is envisaged in this paper. The functional significance of the EARMAM algorithm was compared with other common classification algorithm of current practice such as SVM, naïve Bayes, random forest, and SMO. The results of confusion matrix have shown that there was 93% prediction accuracy which was higher than the predictive values of other algorithms. The above results are discussed with future improvement and application in clinical field.