Blood genome-wide transcriptional profiles reflect broad molecular impairments and strong blood-brain links in Alzheimer's disease

Multiple Transcriptomic Analyses Explore Potential Synaptic Biomarker Rabphilin-3A for Alzheimer's Disease

Alzheimer's disease (AD) is a prevalent neurodegenerative disorder afflicting the elderly population worldwide. The identification of potential gene candidates for AD holds promises for diagnostic biomarkers and therapeutic targets. Employing a comprehensive strategy, this study integrated transcriptomic data from diverse data sources, including microarray and single-cell datasets from blood and tissue samples, enabling a detailed exploration of gene expression dynamics. Through this thorough investigation, 19 notable candidate genes were found with consistent expression changes across both blood and tissue datasets, suggesting their potential as biomarkers for AD. In addition, single cell sequencing analysis further highlighted their specific expression in excitatory and inhibitory neurons, the primary functional units in the brain, underscoring their relevance to AD pathology. Moreover, the functional enrichment analysis revealed that three of the candidate genes were downregulated in synaptic signaling pathway. Further validation experiments significantly showed reduced levels of rabphilin-3A (RPH3A) in 3xTg-AD model mice, implying its role in disease pathogenesis. Given its role in neurotransmitter exocytosis and synaptic function, further investigation into RPH3A and its interactions with neurotrophic proteins may provide valuable insights into the complex molecular mechanisms underlying synaptic dysfunction in AD.

Integration of single-cell sequencing with machine learning and Mendelian randomization analysis identifies the NAP1L1 gene as a predictive biomarker for Alzheimer's disease

Background

The most effective approach to managing Alzheimer's disease (AD) lies in identifying reliable biomarkers for AD to forecast the disease in advance, followed by timely early intervention for patients.

Methods

Transcriptomic data on peripheral blood mononuclear cells (PBMCs) from patients with AD and the control group were collected, and preliminary data processing was completed using standardized analytical methods. PBMCs were initially segmented into distinct subpopulations, and the divisions were progressively refined until the most significantly altered cell populations were identified. A combination of high-dimensional weighted gene co-expression analysis (hdWGCNA), cellular communication, pseudotime analysis, and single-cell regulatory network inference and clustering (SCENIC) analysis was used to conduct single-cell transcriptomics analysis and identify key gene modules from them. Genes were screened using machine learning (ML) in the key gene modules, and internal and external dataset validations were performed using multiple ML methods to test predictive performance. Finally, bidirectional Mendelian randomization (MR) analysis, regional linkage analysis, and the Steiger test were employed to analyze the key gene.

Result

A significant decrease in non-classical monocytes was detected in PMBC of AD patients. Subsequent analyses revealed the inherent connection of non-classical monocytes to AD, and the NAP1L1 gene identified within its gene module appeared to exhibit some association with AD as well.

Conclusion

The NAP1L1 gene is a potential predictive biomarker for AD.

Exploring the Potential Role of Oligodendrocyte-Associated PIP4K2A in Alzheimer's Disease Complicated with Type 2 Diabetes Mellitus via Multi-Omic Analysis

Alzheimer's disease (AD) and type 2 diabetes mellitus (T2DM) are two common diseases that affect the elderly population worldwide. The identification of common genes associated with AD and T2DM holds promise for potential biomarkers and intriguing pathogenesis of these two complicated diseases. This study utilized a comprehensive approach by integrating transcriptome data from multiple cohorts, encompassing both AD and T2DM. The analysis incorporated various data types, including blood and tissue samples as well as single-cell datasets, allowing for a detailed assessment of gene expression patterns. From the brain region-specific single-cell analysis, PIP4K2A, which encodes phosphatidylinositol-5-phosphate 4-kinase type 2 alpha, was found to be expressed mainly in oligodendrocytes compared to other cell types. Elevated levels of PIP4K2A in AD and T2DM patients' blood were found to be associated with key cellular processes such as vesicle-mediated transport, negative regulation of autophagosome assembly, and cytosolic transport. The identification of PIP4K2A's potential roles in the cellular processes of AD and T2DM offers valuable insights into the development of biomarkers for diagnosis and therapy, especially in the complication of these two diseases.

Blood-Based Transcriptomic Biomarkers Are Predictive of Neurodegeneration Rather Than Alzheimer's Disease

Alzheimer's disease (AD) is a growing global health crisis affecting millions and incurring substantial economic costs. However, clinical diagnosis remains challenging, with misdiagnoses and underdiagnoses being prevalent. There is an increased focus on putative, blood-based biomarkers that may be useful for the diagnosis as well as early detection of AD. In the present study, we used an unbiased combination of machine learning and functional network analyses to identify blood gene biomarker candidates in AD. Using supervised machine learning, we also determined whether these candidates were indeed unique to AD or whether they were indicative of other neurodegenerative diseases, such as Parkinson's disease (PD) and amyotrophic lateral sclerosis (ALS). Our analyses showed that genes involved in spliceosome assembly, RNA binding, transcription, protein synthesis, mitoribosomes, and NADH dehydrogenase were the best-performing genes for identifying AD patients relative to cognitively healthy controls. This transcriptomic signature, however, was not unique to AD, and subsequent machine learning showed that this signature could also predict PD and ALS relative to controls without neurodegenerative disease. Combined, our results suggest that mRNA from whole blood can indeed be used to screen for patients with neurodegeneration but may be less effective in diagnosing the specific neurodegenerative disease.

Identification of peripheral blood immune infiltration signatures and construction of monocyte-associated signatures in ovarian cancer and Alzheimer's disease using single-cell sequencing

Background

Ovarian cancer (OC) is a common tumor of the female reproductive system, while Alzheimer's disease (AD) is a prevalent neurodegenerative disease that primarily affects cognitive function in the elderly. Monocytes are immune cells in the blood that can enter tissues and transform into macrophages, thus participating in immune and inflammatory responses. Overall, monocytes may play an important role in Alzheimer's disease and ovarian cancer.

Methods

The CIBERSORT algorithm results indicate a potential crucial role of monocytes/macrophages in OC and AD. To identify monocyte marker genes, single-cell RNA-seq data of peripheral blood mononuclear cells (PBMCs) from OC and AD patients were analyzed. Enrichment analysis of various cell subpopulations was performed using the "irGSEA" R package. The estimation of cell cycle was conducted with the "tricycle" R package, and intercellular communication networks were analyzed using "CellChat". For 134 monocyte-associated genes (MRGs), bulk RNA-seq data from two diseased tissues were obtained. Cox regression analysis was employed to develop risk models, categorizing patients into high-risk (HR) and low-risk (LR) groups. The model's accuracy was validated using an external GEO cohort. The different risk groups were evaluated in terms of immune cell infiltration, mutational status, signaling pathways, immune checkpoint expression, and immunotherapy. To identify characteristic MRGs in AD, two machine learning algorithms, namely random forest and support vector machine (SVM), were utilized.

Results

Based on Cox regression analysis, a risk model consisting of seven genes was developed in OC, indicating a better prognosis for patients in the LR group. The LR group had a higher tumor mutation burden, immune cell infiltration abundance, and immune checkpoint expression. The results of the TIDE algorithm and the IMvigor210 cohort showed that the LR group was more likely to benefit from immunotherapy. Finally, ZFP36L1 and AP1S2 were identified as characteristic MRGs affecting OC and AD progression.

Conclusion

The risk profile containing seven genes identified in this study may help further guide clinical management and targeted therapy for OC. ZFP36L1 and AP1S2 may serve as biomarkers and new therapeutic targets for patients with OC and AD.

Transcriptomic analysis of glutamate-induced HT22 neurotoxicity as a model for screening anti-Alzheimer's drugs

Glutamate-induced neurotoxicity in the HT22 mouse hippocampal neuronal cell line has been recognized as a valuable cell model for the study of neurotoxicity associated with neurodegenerative diseases including Alzheimer's disease (AD). However, the relevance of this cell model for AD pathogenesis and preclinical drug screening remains to be more elucidated. While there is increasing use of this cell model in a number of studies, relatively little is known about its underlying molecular signatures in relation to AD. Here, our RNA sequencing study provides the first transcriptomic and network analyses of HT22 cells following glutamate exposure. Several differentially expressed genes (DEGs) and their relationships specific to AD were identified. Additionally, the usefulness of this cell model as a drug screening system was assessed by determining the expression of those AD-associated DEGs in response to two medicinal plant extracts, Acanthus ebracteatus and Streblus asper, that have been previously shown to be protective in this cell model. In summary, the present study reports newly identified AD-specific molecular signatures in glutamate-injured HT22 cells, suggesting that this cell can be a valuable model system for the screening and evaluation of new anti-AD agents, particularly from natural products.

Identification of Chromatin Regulatory Factors Related to Immunity and Treatment of Alzheimer's Disease

Alzheimer's disease is one of the common neurodegenerative diseases in the elderly, which mainly manifests as progressively severe cognitive impairment, which seriously affects the quality of life of patients. Chromatin regulators have been shown to be associated with a variety of biological processes, and we mainly explore the relationship between chromatin regulators and Alzheimer's disease. Eight hundred seventy chromatin regulators were collected from previous studies, and data related to Alzheimer's disease patients were downloaded from the GEO database. Finally, we screened chromatin regulators related to Alzheimer's disease immunity, established prediction models, and screened related drugs and miRNAs. We screened 160 differentially expressed CRs, constructed an interaction network, obtained 10 hub genes, successfully constructed a prediction model based on immune-related 5 CRs, and obtained 520 related drugs and 3 related miRNA, which provided an idea for the treatment of Alzheimer's disease. Our study identified 5 chromatin regulators related to Alzheimer's disease, which are expected to be new targets for Alzheimer's disease immunotherapy.

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.

Exploring the common pathogenesis of Alzheimer's disease and type 2 diabetes mellitus via microarray data analysis

Background

Alzheimer's Disease (AD) and Type 2 Diabetes Mellitus (DM) have an increased incidence in modern society. Although more and more evidence has supported that DM is prone to AD, the interrelational mechanisms remain fully elucidated.

Purpose

The primary purpose of this study is to explore the shared pathophysiological mechanisms of AD and DM.

Methods

Download the expression matrix of AD and DM from the Gene Expression Omnibus (GEO) database with sequence numbers GSE97760 and GSE95849, respectively. The common differentially expressed genes (DEGs) were identified by limma package analysis. Then we analyzed the six kinds of module analysis: gene functional annotation, protein-protein interaction (PPI) network, potential drug screening, immune cell infiltration, hub genes identification and validation, and prediction of transcription factors (TFs).

Results

The subsequent analyses included 339 common DEGs, and the importance of immunity, hormone, cytokines, neurotransmitters, and insulin in these diseases was underscored by functional analysis. In addition, serotonergic synapse, ovarian steroidogenesis, estrogen signaling pathway, and regulation of lipolysis are closely related to both. DEGs were input into the CMap database to screen small molecule compounds with the potential to reverse AD and DM pathological functions. L-690488, exemestane, and BMS-345541 ranked top three among the screened small molecule compounds. Finally, 10 essential hub genes were identified using cytoHubba, including PTGS2, RAB10, LRRK2, SOS1, EEA1, NF1, RAB14, ADCY5, RAPGEF3, and PRKACG. For the characteristic Aβ and Tau pathology of AD, RAPGEF3 was associated significantly positively with AD and NF1 significantly negatively with AD. In addition, we also found ADCY5 and NF1 significant correlations with DM phenotypes. Other datasets verified that NF1, RAB14, ADCY5, and RAPGEF3 could be used as key markers of DM complicated with AD. Meanwhile, the immune cell infiltration score reflects the different cellular immune microenvironments of the two diseases.

Conclusion

The common pathogenesis of AD and DM was revealed in our research. These common pathways and hub genes directions for further exploration of the pathogenesis or treatment of these two diseases.

Literature review on the interdisciplinary biomarkers of multi-target and multi-time herbal medicine therapy to modulate peripheral systems in cognitive impairment

Alzheimer's disease (AD) is a chronic progressive neurodegenerative disease characterized by the deposition of amyloid-beta (Aβ) peptide and neurofibrillary tangles in the brain. The approved drug for AD has certain limitations such as a short period of cognitive improvement effect; moreover, the development of drug for AD therapeutic single target for Aβ clearance in brain ended in failure. Therefore, diagnosis and treatment of AD using a multi-target strategy according to the modulation of the peripheral system, which is not only limited to the brain, is needed. Traditional herbal medicines can be beneficial for AD based on a holistic theory and personalized treatment according to the time-order progression of AD. This literature review aimed to investigate the effectiveness of herbal medicine therapy based on syndrome differentiation, a unique theory of traditional diagnosis based on the holistic system, for multi-target and multi-time treatment of mild cognitive impairment or AD stage. Possible interdisciplinary biomarkers including transcriptomic and neuroimaging studies by herbal medicine therapy for AD were investigated. In addition, the mechanism by which herbal medicines affect the central nervous system in connection with the peripheral system in an animal model of cognitive impairment was reviewed. Herbal medicine may be a promising therapy for the prevention and treatment of AD through a multi-target and multi-time strategy. This review would contribute to the development of interdisciplinary biomarkers and understanding of the mechanisms of action of herbal medicine in AD.

Bioinformatics-based study reveals that AP2M1 is regulated by the circRNA-miRNA-mRNA interaction network and affects Alzheimer's disease

Alzheimer's disease (AD) is a progressive neurological disease that worsens with time. The hallmark illnesses include extracellular senile plaques caused by β-amyloid protein deposition, neurofibrillary tangles caused by tau protein hyperphosphorylation, and neuronal loss accompanying glial cell hyperplasia. Noncoding RNAs are substantially implicated in related pathophysiology, according to mounting data. However, the function of these ncRNAs is mainly unclear. Circular RNAs (circRNAs) include many miRNA-binding sites (miRNA response elements, MREs), which operate as miRNA sponges or competing endogenous RNAs (ceRNAs). The purpose of this study was to look at the role of circular RNAs (circRNAs) and microRNAs (miRNAs) in Alzheimer's disease (AD) as possible biomarkers. The Gene Expression Omnibus (GEO) database was used to obtain an expression profile of Alzheimer's disease patients (GSE5281, GSE122603, GSE97760, GSE150693, GSE1297, and GSE161435). Through preliminary data deletion, 163 genes with significant differences, 156 miRNAs with significant differences, and 153 circRNAs with significant differences were identified. Then, 10 key genes, led by MAPT and AP2M1, were identified by the mediation center algorithm, 34 miRNAs with obvious prognosis were identified by the cox regression model, and 16 key circRNAs were selected by the database. To develop competitive endogenous RNA (ceRNA) networks, hub circRNAs and mRNAs were used. Finally, GO analysis and clinical data verification of key genes were carried out. We discovered that a down-regulated circRNA (has_circ_002048) caused the increased expression of numerous miRNAs, which further inhibited the expression of a critical mRNA (AP2M1), leading to Alzheimer's disease pathology. The findings of this work contribute to a better understanding of the circRNA-miRNA-mRNA regulating processes in Alzheimer's disease. Furthermore, the ncRNAs found here might become novel biomarkers and potential targets for the development of Alzheimer's drugs.

Exploring the key ferroptosis-related gene in the peripheral blood of patients with Alzheimer’s disease and its clinical significance

Introduction

Alzheimer’s disease (AD) is the most common type of dementia, and there is growing evidence suggesting that ferroptosis is involved in its pathogenesis. In this study, we aimed to investigate the key ferroptosis-related genes in AD and identify a novel ferroptosis-related gene diagnosis model for patients with AD.

Materials and methods

We extracted the human blood and hippocampus gene expression data of five datasets (GSE63060, GSE63061, GSE97760, GSE48350, and GSE5281) in the Gene Expression Omnibus database as well as the ferroptosis-related genes from FerrDb. Differentially expressed ferroptosis-related genes were screened by random forest classifier, and were further used to construct a diagnostic model of AD using an artificial neural network. The patterns of immune infiltration in the peripheral immune system of AD were also investigated using the CIBERSORT algorithm.

Results

We first screened and identified 12 ferroptosis-related genes (ATG3, BNIP3, DDIT3, FH, GABARAPL1, MAPK14, SOCS1, SP1, STAT3, TNFAIP3, UBC, and ULK) via a random forest classifier, which was differentially expressed between the AD and normal control groups. Based on the 12 hub genes, we successfully constructed a satisfactory diagnostic model for differentiating AD patients from normal controls using an artificial neural network and validated its diagnostic efficacy in several external datasets. Further, the key ferroptosis-related genes were found to be strongly correlated to immune cells infiltration in AD.

Conclusion

We successfully identified 12 ferroptosis-related genes and established a novel diagnostic model of significant predictive value for AD. These results may help understand the role of ferroptosis in AD pathogenesis and provide promising therapeutic strategies for patients with AD.

Exploring Early Physical Examination Diagnostic Biomarkers for Alzheimer’s Disease Based on Least Absolute Shrinkage and Selection Operator

Neurodegenerative diseases such as Alzheimer’s disease (AD) are an increasing public health challenge. There is an urgent need to shift the focus to accurate detection of clinical AD at the physical examination stage. The purpose of this study was to identify biomarkers for AD diagnosis. Differential expression analysis was performed on a dataset including prefrontal cortical samples and peripheral blood samples of AD to identify shared differentially expressed genes (DEGs) shared between the two datasets. In addition, a minimum absolute contraction and selection operator (LASSO) model based on shared-DEGs identified nine signature genes (MT1X, IGF1, DLEU7, TRIM36, PTPRC, WNK2, SPG20, C8orf59, and BRWD1) that accurately predict AD occurrence. Enrichment analysis showed that the signature gene was significantly associated with the AD-related p53 signaling pathway, T-cell receptor signaling pathway, HIF-1 signaling pathway, AMPK signaling pathway, and FoxO signaling pathway. Thus, our results identify not only biomarkers for diagnosing AD but also potentially specific pathways. The AD biomarkers proposed in this study could serve as indicators for prevention and diagnosis during physical examination.

Alzheimer's Disease Blood Biomarkers Associated With Neuroinflammation as Therapeutic Targets for Early Personalized Intervention

The definitive diagnosis of Alzheimer's Disease (AD) without the need for neuropathological confirmation remains a challenge in AD research today, despite efforts to uncover the molecular and biological underpinnings of the disease process. Furthermore, the potential for therapeutic intervention is limited upon the onset of symptoms, providing motivation for studying and treating the AD precursor mild cognitive impairment (MCI), the prodromal stage of AD instead. Applying machine learning classification to transcriptomic data of MCI, AD, and cognitively normal (CN) control patients, we identified differentially expressed genes that serve as biomarkers for the characterization and classification of subjects into MCI or AD groups. Predictive models employing these biomarker genes exhibited good classification performances for CN, MCI, and AD, significantly above random chance. The PI3K-Akt, IL-17, JAK-STAT, TNF, and Ras signaling pathways were also enriched in these biomarker genes, indicating their diagnostic potential and pathophysiological roles in MCI and AD. These findings could aid in the recognition of MCI and AD risk in clinical settings, allow for the tracking of disease progression over time in individuals as part of a therapeutic approach, and provide possible personalized drug targets for early intervention of MCI and AD.

Identification of potential blood biomarkers for early diagnosis of schizophrenia through RNA sequencing analysis

Schizophrenia (SCZ) is a highly heritable, polygenic complex mental disorder with imprecise diagnostic boundaries. Finding sensitive and specific novel biomarkers to improve the biological homogeneity of SCZ diagnosis is still one of the research hotspots. To identify the blood specific diagnostic biomarkers of SCZ, we performed RNA sequencing (RNA-seq) on 30 peripheral blood samples from 15 first-episode drug-naïve SCZ patients and 15 healthy controls (CTL). By performing multiple bioinformatics analysis algorithms based on RNA-seq data and microarray datasets, including differential expression genes (DEGs) analysis, WGCNA and CIBERSORT, we first identified 6 specific key genes (TOMM7, SNRPG, KRT1, AQP10, TMEM14B and CLEC12A) in SCZ. Moreover, we found that the proportions of lymphocyte, monocyte and neutrophils were significantly distinct in SCZ patients with CTL samples. Therefore, combining various features including age, sex and the novel blood biomarkers, we constructed the risk prediction model with three classifiers (RF: Random Forest; SVM: support vector machine; DT: decision tree) through repeated k-fold cross validation ensuring better generalizability. Finest result of Area under Receiver Operating Characteristic (AUROC) score of 0.91 was achieved by RF classifier and with a comparable good performance of AUROC 0.77 in external validation dataset. A lower AUROC of 0.63 was demonstrated when it was further applied to a Bipolar disorder (BPD) cohort. In conclusion, the study identified three peripheral core immunocytes and six key genes associated with the occurrence of SCZ, and further studies are required to test and validate these novel biomarkers for early diagnosis and treatment of SCZ.

Screening of Crucial Differentially-Methylated/Expressed Genes for Alzheimer's Disease

Background: We aimed to make an integrated analysis of published transcriptome and DNA methylation dataset to ascertain the key differentially methylated and differentially expressed genes for Alzherimer's disease (AD). Methods: Two gene expression microarrays and 1 gene methylation microarray were downloaded for identification of differentially expressed genes and differentially methylated genes. Then, we used various biological information databases to annotate the functions of the differentially-methylated/expressed genes, and screen out key genes and important signaling pathways. Finally, we validate the differentially-methylated/expressed genes in the additional online datasets and in blood from AD patients.Results: A total of 8 hub hypomethylated-high expression genes were obtained, including Rac family small GTPase 2, FGR proto-oncogene, Src family tyrosine kinase, LYN proto-oncogene, Src family tyrosine kinase, protein kinase C delta, myosin IF, integrin subunit alpha 5, semaphorin 4D, and growth arrest specific protein 7. Some enriched signaling pathways of hypomethylated-high expression genes were identified, including regulation of actin cytoskeleton, chemokine signaling pathway, Fc gamma R-mediated phagocytosis, and axon guidance. Conclusion: Differentially-methylated/expressed genes are likely to be associated with AD.

Large-scale plasma proteomic profiling identifies a high-performance biomarker panel for Alzheimer's disease screening and staging

INTRODUCTION

Blood proteins are emerging as candidate biomarkers for Alzheimer's disease (AD). We systematically profiled the plasma proteome to identify novel AD blood biomarkers and develop a high-performance, blood-based test for AD.

METHODS

We quantified 1160 plasma proteins in a Hong Kong Chinese cohort by high-throughput proximity extension assay and validated the results in an independent cohort. In subgroup analyses, plasma biomarkers for amyloid, tau, phosphorylated tau, and neurodegeneration were used as endophenotypes of AD.

RESULTS

We identified 429 proteins that were dysregulated in AD plasma. We selected 19 "hub proteins" representative of the AD plasma protein profile, which formed the basis of a scoring system that accurately classified clinical AD (area under the curve  = 0.9690-0.9816) and associated endophenotypes. Moreover, specific hub proteins exhibit disease stage-dependent dysregulation, which can delineate AD stages.

DISCUSSION

This study comprehensively profiled the AD plasma proteome and serves as a foundation for a high-performance, blood-based test for clinical AD screening and staging.

A transcriptome-wide association study identifies novel blood-based gene biomarker candidates for Alzheimer's disease risk

Alzheimer's disease (ad) adversely affects the health, quality of life and independence of patients. There is a critical need to identify novel blood gene biomarkers for ad risk assessment. We performed a transcriptome-wide association study to identify biomarker candidates for ad risk. We leveraged two sets of gene expression prediction models of blood developed using different reference panels and modeling strategies. By applying the prediction models to a meta-GWAS including 71 880 (proxy) cases and 383 378 (proxy) controls, we identified significant associations of genetically determined expression of 108 genes in blood with ad risk. Of these, 15 genes were differentially expressed between ad patients and controls with concordant directions in measured expression data. With evidence from the analyses based on both genetic instruments and directly measured expression levels, this study identifies 15 genes with strong support as biomarkers in blood for ad risk, which may enhance ad risk assessment and mechanism-focused studies.

Whole Blood Expression Pattern of Inflammation and Redox Genes in Mild Alzheimer's Disease

Background

Although Alzheimer’s disease (AD) is associated with alterations of the central nervous system, this disease has an echo in blood that might represent a valuable source of biomarkers for improved diagnosis, prognosis and for monitoring drug response.

Methods

We performed a targeted transcriptomics study on 38 mild Alzheimer’s disease (AD) patients and 38 matched controls for evaluating the expression levels of 136 inflammation and 84 redox genes in whole blood. Patients were diagnosed as mild AD based on altered levels of total TAU, phospho-TAU and Abeta_(1–42) in cerebrospinal fluid, and Abeta_(1–40), Abeta_(1–42) and total TAU levels in plasma. Whenever possible, blood and brain comparisons were made using public datasets.

Results

We found 48 inflammation and 34 redox genes differentially expressed in the blood of AD patients vs controls (FC >1.5, p < 0.01), out of which 22 pro-inflammatory and 12 redox genes exhibited FC >2 and p < 0.001. Receiver operating characteristic (ROC) analysis identified nine inflammation and seven redox genes that discriminated between AD patients and controls (area under the curve >0.9). Correlations of the dysregulated inflammation and redox transcripts indicated that RELA may regulate several redox genes including DUOX1 and GSR. Based on the gene expression profile, we have found that the master regulators of inflammation and redox homeostasis, NFκB and NRF2, were significantly disturbed in the blood of AD patients, as well as several zinc finger and helix-loop-helix transcription factors.

Conclusion

The selected inflammation and redox genes might be useful biomarkers for monitoring anti-inflammatory therapy in mild AD.

Alterations in T-Cell Transcription Factors and Cytokine Gene Expression in Late-Onset Alzheimer's Disease

BACKGROUND

Late-onset Alzheimer's disease (LOAD) is associated with many environmental and genetic factors. The effect of systemic inflammation on the pathogenesis of neurodegenerative diseases such as AD has been strongly suggested. T helper cells (Th) are one of the important components of the immune system and can easily infiltrate the brain in pathological conditions. The development of each Th-subset depends on the production of unique cytokines and their main regulator.

OBJECTIVE

This study aimed to compare the mRNA levels of Th-related genes derived from peripheral blood mononuclear cells of LOAD patients with control. Also, the identification of the most important Th1/Th2 genes and downstream pathways that may be involved in the pathogenesis of AD was followed by computational approaches.

METHODS

This study invloved 30 patients with LOAD and 30 non-demented controls. The relative expression of T-cell cytokines (IFN-γ, TNF-α, IL-4, and IL-5) and transcription factors (T-bet and GATA-3) were assessed using real-time PCR. Additionally, protein-protein interaction (PPI) was investigated by gene network construction.

RESULTS

A significant decrease at T-bet, IFN-γ, TNF-α, and GATA-3 mRNA levels was detected in the LOAD group, compared to the controls. However, there was no significant difference in IL-4 or IL-5 mRNA levels. Network analysis revealed a list of the highly connected protein (hubs) related to mitogen-activated protein kinase (MAPK) signaling and Th17 cell differentiation pathways.

CONCLUSION

The findings point to a molecular dysregulation in Th-related genes, which can promising in the early diagnosis or targeted interventions of AD. Furthermore, the PPI analysis showed that upstream off-target stimulation may involve MAPK cascade activation and Th17 axis induction.

Revelation of Pivotal Genes Pertinent to Alzheimer's Pathogenesis: A Methodical Evaluation of 32 GEO Datasets

Alzheimer’s disease (AD), a dreadful neurodegenerative disorder that affects cognitive and behavioral function in geriatric populations, is characterized by the presence of amyloid deposits and neurofibrillary tangles in brain regions. The International D World Alzheimer Report2018 noted a global prevalence of 50 million AD cases and forecasted a threefold rise to 139 million by 2050. Although there exist numerous genetic association studies pertinent to AD in different ethnicities, critical genetic factors and signaling pathways underlying its pathogenesis remain ambiguous. This study was aimed to analyze the genetic data retrieved from 32 Gene Expression Omnibus datasets belonging to diverse ethnic cohorts in order to identify overlapping differentially expressed genes (DEGs). Stringent selection criteria were framed to shortlist appropriate datasets based on false discovery rate (FDR) p-value and log FC, and relevant details of upregulated and downregulated DEGs were retrieved. Among the 32 datasets, only six satisfied the selection criteria. The GEO2R tool was employed to retrieve significant DEGs. Nine common DEGs, i.e., SLC5A3, BDNF, SST, SERPINA3, RTN3, RGS4, NPTX, ENC1 and CRYM were found in more than 60% of the selected datasets. These DEGs were later subjected to protein–protein interaction analysis with 18 AD-specific literature-derived genes. Among the nine common DEGs, BDNF, SST, SERPINA3, RTN3 and RGS4 exhibited significant interactions with crucial proteins including BACE1, GRIN2B, APP, APOE, COMT, PSEN1, INS, NEP and MAPT. Functional enrichment analysis revealed involvement of these genes in trans-synaptic signaling, chemical transmission, PI3K pathway signaling, receptor–ligand activity and G protein signaling. These processes are interlinked with AD pathways.

PICALM mRNA Expression in the Blood of Patients with Neurodegenerative Diseases and Geriatric Depression

BACKGROUND

Phosphatidylinositol-binding clathrin assembly protein (PICALM) is a validated genetic risk factor for late-onset Alzheimer's disease (AD) and is associated with other neurodegenerative diseases. However, PICALM expression in the blood of neurodegenerative diseases remains elusive.

OBJECTIVE

This study aimed to assess the usefulness of PICALM expression levels in the blood of patients with AD, Parkinson's disease (PD), dementia with Lewy bodies (DLB), and geriatric major depressive disorder (MDD) as a diagnostic biomarker.

METHODS

In total, 45, 20, 21, and 19 patients with AD, PD, DLB, and geriatric MDD, respectively, and 54 healthy controls (HCs) were enrolled in the study. Expression data from Gene Expression Omnibus database (GSE97760), (GSE133347) and (GSE98793), (GSE48350), and (GSE144459) were used to validate the ability of biomarkers in the blood of patients with AD, PD, geriatric MDD, and a postmortem human AD brain and animal model of AD (3xTg-AD mouse), respectively.

RESULTS

PICALM mRNA expression in human blood was significantly increased in patients with AD compared with that in HCs. PICALM mRNA expression and age were negatively correlated only in patients with AD. PICALM mRNA expression in human blood was significantly lower in patients with PD than in HCs. No changes in PICALM mRNA expression were found in patients with DLB and geriatric MDD.

CONCLUSION

PICALM mRNA expression in blood was higher in patients with AD, but lower in patients with PD, which suggests that PICALM mRNA expression in human blood may be a useful biomarker for differentiating neurodegenerative diseases and geriatric MDD.

Differentially Expressed Circular RNAs in Peripheral Blood Mononuclear Cells of Patients with Parkinson's Disease

Background

New noninvasive and affordable molecular approaches that will complement current practices and increase the accuracy of Parkinson's disease (PD) diagnosis are urgently needed. Circular RNAs (circRNAs) are stable noncoding RNAs that accumulate with aging in neurons and are increasingly shown to regulate all aspects of neuronal development and function.

Objectives

Τhe aims of this study were to identify differentially expressed circRNAs in blood mononuclear cells of patients with idiopathic PD and explore the competing endogenous RNA networks affected.

Methods

Eighty‐seven circRNAs were initially selected based on relatively high gene expression in the human brain. More than half of these were readily detectable in blood mononuclear cells using real‐time reverse transcription‐polymerase chain reaction. Comparative expression analysis was then performed in blood mononuclear cells from 60 control subjects and 60 idiopathic subjects with PD.

Results

Six circRNAs were significantly down‐regulated in patients with PD. The classifier that best distinguished PD consisted of four circRNAs with an area under the curve of 0.84. Cross‐linking immunoprecipitation‐sequencing data revealed that the RNA‐binding proteins bound by most of the deregulated circRNAs include the neurodegeneration‐associated FUS, TDP43, FMR1, and ATXN2. MicroRNAs predicted to be sequestered by most deregulated circRNAs have the Gene Ontology categories “protein modification” and “transcription factor activity” mostly enriched.

Conclusions

This is the first study that identifies specific circRNAs that may serve as diagnostic biomarkers for PD. Because they are highly expressed in the brain and are derived from genes with essential brain functions, they may also hint on the PD pathways affected. © 2021 Biomedical Research Foundation, Academy of Athens. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Shared Blood Transcriptomic Signatures between Alzheimer's Disease and Diabetes Mellitus

Alzheimer's disease (AD) and diabetes mellitus (DM) are known to have a shared molecular mechanism. We aimed to identify shared blood transcriptomic signatures between AD and DM. Blood expression datasets for each disease were combined and a co-expression network was used to construct modules consisting of genes with similar expression patterns. For each module, a gene regulatory network based on gene expression and protein-protein interactions was established to identify hub genes. We selected one module, where COPS4, PSMA6, GTF2B, GTF2F2, and SSB were identified as dysregulated transcription factors that were common between AD and DM. These five genes were also differentially co-expressed in disease-related tissues, such as the brain in AD and the pancreas in DM. Our study identified gene modules that were dysregulated in both AD and DM blood samples, which may contribute to reveal common pathophysiology between two diseases.

Differential Expression of mRNAs in Peripheral Blood Related to Prodrome and Progression of Alzheimer's Disease

Alzheimer's disease (AD) is a chronic progressive neurodegenerative disease that affects the quality of life of elderly individuals, while the pathogenesis of AD is still unclear. Based on the bioinformatics analysis of differentially expressed genes (DEGs) in peripheral blood samples, we investigated genes related to mild cognitive impairment (MCI), AD, and late-stage AD that might be used for predicting the conversions. Methods. We obtained the DEGs in MCI, AD, and advanced AD patients from the Gene Expression Omnibus (GEO) database. A Venn diagram was used to identify the intersecting genes. Gene Ontology (GO) and Kyoto Gene and Genomic Encyclopedia (KEGG) were used to analyze the functions and pathways of the intersecting genes. Protein-protein interaction (PPI) networks were constructed to visualize the network of the proteins coded by the related genes. Hub genes were selected based on the PPI network. Results. Bioinformatics analysis indicated that there were 61 DEGs in both the MCI and AD groups and 27 the same DEGs among the three groups. Using GO and KEGG analyses, we found that these genes were related to the function of mitochondria and ribosome. Hub genes were determined by bioinformatics software based on the PPI network. Conclusions. Mitochondrial and ribosomal dysfunction in peripheral blood may be early signs in AD patients and related to the disease progression. The identified hub genes may provide the possibility for predicting AD progression or be the possible targets for treatments.

Relationship between Wine Consumption, Diet and Microbiome Modulation in Alzheimer's Disease

Alzheimer's disease (AD) is a progressive neurodegenerative disorder leading to the most common form of dementia in elderly people. Modifiable dietary and lifestyle factors could either accelerate or ameliorate the aging process and the risk of developing AD and other age-related morbidities. Emerging evidence also reports a potential link between oral and gut microbiota alterations and AD. Dietary polyphenols, in particular wine polyphenols, are a major diver of oral and gut microbiota composition and function. Consequently, wine polyphenols health effects, mediated as a function of the individual's oral and gut microbiome are considered one of the recent greatest challenges in the field of neurodegenerative diseases as a promising strategy to prevent or slow down AD progression. This review highlights current knowledge on the link of oral and intestinal microbiome and the interaction between wine polyphenols and microbiota in the context of AD. Furthermore, the extent to which mechanisms bacteria and polyphenols and its microbial metabolites exert their action on communication pathways between the brain and the microbiota, as well as the impact of the molecular mediators to these interactions on AD patients, are described.

Identifying Blood Transcriptome Biomarkers of Alzheimer's Disease Using Transgenic Mice

The testing of pathological biomarkers of Alzheimer's disease (AD), such as amyloid beta and tau, is time-consuming, expensive, and invasive. Here, we used 3xTg-AD mice to identify and validate putative novel blood transcriptome biomarkers of AD that can potentially be identified in the blood of patients. mRNA was extracted from the blood and hippocampus of 3xTg-AD and control mice at different ages and used for microarray analysis. Network and functional analyses revealed that the differentially expressed genes between AD and control mice modulated the immune and neuroinflammation systems. Five novel gene transcripts (Cdkn2a, Apobec3, Magi2, Parp3, and Cass4) showed significant increases with age, and their expression in the blood was collated with that in the hippocampus only in AD mice. We further assessed previously identified candidate biomarker genes. The expression of Trem1 and Trem2 in both the blood and brain was significantly increased with age. Decreased Tomm40 and increased Pink1 mRNA levels were observed in the mouse blood. The changes in the expression of Snca and Apoe mRNA in the mouse blood and brain were similar to those found in human AD blood. Our results demonstrated that the immune and neuroinflammatory system is involved in the pathophysiologies of aging and AD and that the blood transcriptome might be useful as a biomarker of AD.

Screening and Identification of Potential Peripheral Blood Biomarkers for Alzheimer's Disease Based on Bioinformatics Analysis

BACKGROUND Alzheimer's disease (AD) is the leading cause of dementia worldwide; however, the molecular mechanisms underlying its pathogenesis remain unclear. The present study aimed to discover some potential peripheral blood biomarkers for early detection of patients with AD. MATERIAL AND METHODS Publicly available AD datasets - GSE18309 and GSE97760 - were obtained from the Gene Expression Omnibus database, and limma package from Bioconductor was employed to search for differently expressed genes (DEGs). Weighted correlation network analysis was performed to identify DEGs with highly synergistic changes, and functional annotation of DEGs was performed using gene set enrichment analysis and Metascape. STRING and Cytoscape were used to construct protein-protein interaction networks and analyze the most significant hub genes. Thereafter, the Comparative Toxicogenomics Database (CTD) was used to identify hub genes associated with AD pathology, and Connectivity Map was used to screen small molecule drugs for AD. Finally, hub genes coupled with corresponding predicted miRNAs involved in AD were assessed via TargetScan, and functional annotation of predicted miRNAs was performed using DIANA database. RESULTS Our analyses revealed 5042 DEGs; based on functional analyses, these DEGs were mainly associated with oligosaccharide lipid intermediate biosynthetic process, cyclin binding, signaling pathways regulating pluripotency of ubiquitin mediated proteolysis, and extracellular matrix-receptor interaction. UBB, UBA52, SRC, MMP9, VWF, GP6, and PF4 were identified as the hub genes. The CTD showed that these hub genes are closely related with AD or cognition impairment. CONCLUSIONS The identified hub genes and corresponding miRNAs might be useful as potential peripheral blood biomarkers of AD.

Molecular docking analysis of amyloid precursor protein with compounds from the Australian cowplant

Amyloid precursor protein is linked with Alzheimer's disease (AD). The Australian cowplant Gymnema sylvestre is known in Indian and Chinese medicine. Therefore, it is of interest to screen the Amyloid precursor protein with compounds from the Australian cowplant. We report five compounds (Gymnemasaponin 5, Gymnemasin D, Gymnemoside A, Gymnemoside E, Gymnemoside F) derived from the Australian cowplant as the poteinal inhibitors of Amyloid precursor protein with optimal binding features for further consideration.

Identification of dysregulated genes and pathways of different brain regions in Alzheimer's disease

Background: Alzheimer's disease (AD) is a degenerative neurologic disease. The study aimed to identify the key differentially expressed genes (DEGs) and pathways in AD pathogenesis and obtain potential biomarkers in AD diagnosis.Methods: An integrated analysis of publicly available Gene Expression Omnibus datasets of AD was performed. DEGs in hippocampus tissue (HIP), temporal gyrus tissue (TG), frontal gyrus tissue (FG) and whole blood (WB) were identified. Bioinformatics analyses were used to insight into the functions of DEGs. The expression levels of candidate DEGs were preliminarily validated in GSE1297. The discriminatory ability of candidate DEGs in WB samples of AD patients and healthy individuals was evaluated in GSE63060 and GSE63061 datasets through receiver operating characteristic (ROC) analysis.Results: The DEGs in HIP, TG and FG tissues of AD were identified. Functions involved in regulation of apoptotic process, apoptotic process and cell death were significantly enriched from DEGs in AD. MAPK signaling pathway and Wnt signaling pathway were significantly enriched. YAP1, MAPK9 and GJA1 were the hub proteins in protein-protein interaction network in HIP, TG and FG. The expression levels of 14 DEGs in GSE1297 dataset were consistent with our integrated analysis. Moreover, 7 out of 14 DEGs had the diagnostic value in distinguishing AD patients from healthy controls in both GSE630060 and GSE630061 datasets.Conclusion: The DEGs including YAP1, MAPK1, GJA1 and pathways including MAPK signaling pathway and Wnt signaling pathway may be related to AD progression. RAD51C, SAFB2, SSH3 and TXNDC9 might be potential biomarkers in AD diagnosis.

Dysregulated Fc gamma receptor-mediated phagocytosis pathway in Alzheimer's disease: network-based gene expression analysis

Transcriptomics has become an important tool for identification of biological pathways dysregulated in Alzheimer's disease (AD). We performed a network-based gene expression analysis of blood-based microarray gene expression profiles using 2 independent cohorts, Alzheimer's Disease Neuroimaging Initiative (ADNI; N = 661) and AddNeuroMed (N = 674). Weighted gene coexpression network analysis identified 17 modules from ADNI and 13 from AddNeuroMed. Four of the modules derived in ADNI were significantly related to AD; 5 modules in AddNeuroMed were significant. Gene-set enrichment analysis of the AD-related modules identified and replicated 3 biological pathways including the Fc gamma receptor-mediated phagocytosis pathway. Module-based association analysis showed the AD-related module, which has the 3 pathways, to be associated with cognitive function and neuroimaging biomarkers. Gene-based association analysis identified PRKCD in the Fc gamma receptor-mediated phagocytosis pathway as being significantly associated with cognitive function and cerebrospinal fluid biomarkers. The identification of the Fc gamma receptor-mediated phagocytosis pathway implicates the peripheral innate immune system in the pathophysiology of AD. PRKCD is known to be related to neurodegeneration induced by amyloid-β.

Transcriptomic and Network Analysis Highlight the Association of Diabetes at Different Stages of Alzheimer's Disease

Alzheimer's disease (AD) and type 2 diabetes (T2D) are among the most prevalent chronic diseases affecting the aging population. Extensive research evidence indicates that T2D is a well-established risk factor for AD; however, the molecular mechanisms underlying this association have not been fully elucidated. Furthermore, how T2D may contribute to the progression of AD is a subject of extensive investigation. In this study, we compared the blood transcriptome of patients with mild cognitive impairment (MCI), AD, and advanced AD to those afflicted with T2D to unveil shared and unique pathways and potential therapeutic targets. Blood transcriptomic analyses revealed a positive correlation between gene expression profiles of MCI, AD, and T2D in seven independent microarrays. Interestingly, gene expression profiles from women with advanced AD correlated negatively with T2D, suggesting sex-specific differences in T2D as a risk factor for AD. Network and pathway analysis revealed that shared molecular networks between MCI and T2D were predominantly enriched in inflammation and infectious diseases whereas those networks shared between overt AD and T2D were involved in the phosphatidylinositol 3-kinase and protein kinase B/Akt (PI3K-AKT) signaling pathway, a major mediator of insulin signaling in the body. The PI3K-AKT signaling pathway became more significantly dysregulated in the advanced AD and T2D shared network. Furthermore, endocrine resistance and atherosclerosis pathways emerged as dysregulated pathways in the advanced AD and T2D shared network. Interestingly, network analysis of shared differentially expressed genes between children with T2D and MCI subjects identified forkhead box O3 (FOXO3) as a central transcriptional regulator, suggesting that it may be a potential therapeutic target for early intervention in AD. Collectively, these results suggest that T2D may be implicated at different stages of AD through different molecular pathways disrupted during the preclinical phase of AD and more advanced stages of the disease.

Meta-Analysis of Gene Expression Changes in the Blood of Patients with Mild Cognitive Impairment and Alzheimer's Disease Dementia

Background: Dementia is a major public health concern affecting approximately 47 million people worldwide. Mild cognitive impairment (MCI) is one form of dementia that affects an individual’s memory with or without affecting their daily life. Alzheimer’s disease dementia (ADD) is a more severe form of dementia that usually affects elderly individuals. It remains unclear whether MCI is a distinct disorder from or an early stage of ADD. Methods: Gene expression data from blood were analyzed to identify potential biomarkers that may be useful for distinguishing between these two forms of dementia. Results: A meta-analysis revealed 91 genes dysregulated in individuals with MCI and 387 genes dysregulated in ADD. Pathway analysis identified seven pathways shared between MCI and ADD and nine ADD-specific pathways. Fifteen transcription factors were associated with MCI and ADD, whereas seven transcription factors were specific for ADD. Mir-335-5p was specific for ADD, suggesting that it may be useful as a biomarker. Diseases that are associated with MCI and ADD included developmental delays, cognition impairment, and movement disorders. Conclusion: These results provide a better molecular understanding of peripheral changes that occur in MCI and ADD patients and may be useful in the identification of diagnostic and prognostic biomarkers.

Multi-omics signature of brain amyloid deposition in asymptomatic individuals at-risk for Alzheimer's disease: The INSIGHT-preAD study

BACKGROUND

One of the biggest challenge in Alzheimer's disease (AD) is to identify pathways and markers of disease prediction easily accessible, for prevention and treatment. Here we analysed blood samples from the INveStIGation of AlzHeimer's predicTors (INSIGHT-preAD) cohort of elderly asymptomatic individuals with and without brain amyloid load.

METHODS

We performed blood RNAseq, and plasma metabolomics and lipidomics using liquid chromatography-mass spectrometry on 48 individuals amyloid positive and 48 amyloid negative (SUVr cut-off of 0·7918). The three data sets were analysed separately using differential gene expression based on negative binomial distribution, non-parametric (Wilcoxon) and parametric (correlation-adjusted Student't) tests. Data integration was conducted using sparse partial least squares-discriminant and principal component analyses. Bootstrap-selected top-ten features from the three data sets were tested for their discriminant power using Receiver Operating Characteristic curve. Longitudinal metabolomic analysis was carried out on a subset of 22 subjects.

FINDINGS

Univariate analyses identified three medium chain fatty acids, 4-nitrophenol and a set of 64 transcripts enriched for inflammation and fatty acid metabolism differentially quantified in amyloid positive and negative subjects. Importantly, the amounts of the three medium chain fatty acids were correlated over time in a subset of 22 subjects (p < 0·05). Multi-omics integrative analyses showed that metabolites efficiently discriminated between subjects according to their amyloid status while lipids did not and transcripts showed trends. Finally, the ten top metabolites and transcripts represented the most discriminant omics features with 99·4% chance prediction for amyloid positivity.

INTERPRETATION

This study suggests a potential blood omics signature for prediction of amyloid positivity in asymptomatic at-risk subjects, allowing for a less invasive, more accessible, and less expensive risk assessment of AD as compared to PET studies or lumbar puncture. FUND: Institut Hospitalo-Universitaire and Institut du Cerveau et de la Moelle Epiniere (IHU-A-ICM), French Ministry of Research, Fondation Alzheimer, Pfizer, and Avid.

Global expression profiling of cognitive level and decline in middle-aged monozygotic twins

Only few studies have investigated the genomewide transcriptome of normative cognitive aging. We therefore aimed at investigating blood gene expression patterns associated with cognitive aging using a population-based sample of 235 middle-aged monozygotic twin pairs with longitudinal data on cognitive function. This unique setup enabled examination of gene expression differences associated with individual and intrapair differences in cognitive level and change while controlling for underlying genetic variation and shared early environment. Overall, increased expression of several gene sets was found to strongly correlate with a lower cognitive level and cognitive decline. The most significantly correlated gene sets were related to protein metabolism, translation, RNA metabolism, infectious disease, and the immune system, which are all processes previously linked to transcription signatures of pathological and normal brain aging, and aging in blood. The results of our study thus suggest that gene expression patterns of cognitive level and decline in our sample mirror those seen in cognitively impaired individuals, which could point toward a more generic response to cognitive aging and aging in general.

Integrated identification of key genes and pathways in Alzheimer's disease via comprehensive bioinformatical analyses

Background

Alzheimer's disease (AD) is known to be caused by multiple factors, meanwhile the pathogenic mechanism and development of AD associate closely with genetic factors. Existing understanding of the molecular mechanisms underlying AD remains incomplete.

Methods

Gene expression data (GSE48350) derived from post-modern brain was extracted from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were derived from hippocampus and entorhinal cortex regions between AD patients and healthy controls and detected via Morpheus. Functional enrichment analyses, including Gene Ontology (GO) and pathway analyses of DEGs, were performed via Cytoscape and followed by the construction of protein-protein interaction (PPI) network. Hub proteins were screened using the criteria: nodes degree≥10 (for hippocampus tissues) and ≥ 8 (for entorhinal cortex tissues). Molecular Complex Detection (MCODE) was used to filtrate the important clusters. University of California Santa Cruz (UCSC) and the database of RNA-binding protein specificities (RBPDB) were employed to identify the RNA-binding proteins of the long non-coding RNA (lncRNA).

Results

251 & 74 genes were identified as DEGs, which consisted of 56 & 16 up-regulated genes and 195 & 58 down-regulated genes in hippocampus and entorhinal cortex, respectively. Biological analyses demonstrated that the biological processes and pathways related to memory, transmembrane transport, synaptic transmission, neuron survival, drug metabolism, ion homeostasis and signal transduction were enriched in these genes. 11 genes were identified as hub genes in hippocampus and entorhinal cortex, and 3 hub genes were identified as the novel candidates involved in the pathology of AD. Furthermore, 3 lncRNAs were filtrated, whose binding proteins were closely associated with AD.

Conclusions

Through GO enrichment analyses, pathway analyses and PPI analyses, we showed a comprehensive interpretation of the pathogenesis of AD at a systematic biology level, and 3 novel candidate genes and 3 lncRNAs were identified as novel and potential candidates participating in the pathology of AD. The results of this study could supply integrated insights for understanding the pathogenic mechanism underlying AD and potential novel therapeutic targets.

Network meta-analysis correlates with analysis of merged independent transcriptome expression data

Background

Using meta-analysis, high-dimensional transcriptome expression data from public repositories can be merged to make group comparisons that have not been considered in the original studies. Merging of high-dimensional expression data can, however, implicate batch effects that are sometimes difficult to be removed. Removing batch effects becomes even more difficult when expression data was taken using different technologies in the individual studies (e.g. merging of microarray and RNA-seq data). Network meta-analysis has so far not been considered to make indirect comparisons in transcriptome expression data, when data merging appears to yield biased results.

Results

We demonstrate in a simulation study that the results from analyzing merged data sets and the results from network meta-analysis are highly correlated in simple study networks. In the case that an edge in the network is supported by multiple independent studies, network meta-analysis produces fold changes that are closer to the simulated ones than those obtained from analyzing merged data sets. Finally, we also demonstrate the practicability of network meta-analysis on a real-world data example from neuroinfection research.

Conclusions

Network meta-analysis is a useful means to make new inferences when combining multiple independent studies of molecular, high-throughput expression data. This method is especially advantageous when batch effects between studies are hard to get removed.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2705-9) contains supplementary material, which is available to authorized users.

Point-of-Care Devices Using Disease Biomarkers To Diagnose Neurodegenerative Disorders

Neurodegenerative disorders such as Alzheimer's, Parkinson's, and Huntington's diseases are highly prevalent and immensely destructive to the health and well-being of individuals and their families across the globe. Neurodegenerative diseases are characterized by the gradual loss of neural tissue in the central nervous system. Clearly, early diagnosis of the onset of neurodegeneration is vital and beneficial. Current diagnostic methods rely heavily on symptoms or autopsy results, thus overlooking early diagnosis, the only opportunity for amelioration. However, appropriately selected and used biomarker diagnostics provide a solution. This article reviews the development and application of biomarker-related diagnostics for neurodegenerative disease with specific recommendations for point-of-care (POC) methodology. These advantageous approaches may offer a solution to existing obstacles and limitations to neurodegenerative disease treatment.

Gene expression parallels synaptic excitability and plasticity changes in Alzheimer's disease

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by abnormal accumulation of β-amyloid and tau and synapse dysfunction in memory-related neural circuits. Pathological and functional changes in the medial temporal lobe, a region essential for explicit memory encoding, contribute to cognitive decline in AD. Surprisingly, functional imaging studies show increased activity of the hippocampus and associated cortical regions during memory tasks in presymptomatic and early AD stages, whereas brain activity declines as the disease progresses. These findings suggest an emerging scenario where early pathogenic events might increase neuronal excitability leading to enhanced brain activity before clinical manifestations of the disease, a stage that is followed by decreased brain activity as neurodegeneration progresses. The mechanisms linking pathology with synaptic excitability and plasticity changes leading to memory loss in AD remain largely unclear. Recent studies suggest that increased brain activity parallels enhanced expression of genes involved in synaptic transmission and plasticity in preclinical stages, whereas expression of synaptic and activity-dependent genes are reduced by the onset of pathological and cognitive symptoms. Here, we review recent evidences indicating a relationship between transcriptional deregulation of synaptic genes and neuronal activity and memory loss in AD and mouse models. These findings provide the basis for potential clinical applications of memory-related transcriptional programs and their regulatory mechanisms as novel biomarkers and therapeutic targets to restore brain function in AD and other cognitive disorders.