Integrative computational evaluation of genetic markers for Alzheimer's disease

Proteomics of brain, CSF, and plasma identifies molecular signatures for distinguishing sporadic and genetic Alzheimer's disease

Proteomic studies for Alzheimer's disease (AD) are instrumental in identifying AD pathways but often focus on single tissues and sporadic AD cases. Here, we present a proteomic study analyzing 1305 proteins in brain tissue, cerebrospinal fluid (CSF), and plasma from patients with sporadic AD, TREM2 risk variant carriers, patients with autosomal dominant AD (ADAD), and healthy individuals. We identified 8 brain, 40 CSF, and 9 plasma proteins that were altered in individuals with sporadic AD, and we replicated these findings in several external datasets. We identified a proteomic signature that differentiated TREM2 variant carriers from both individuals with sporadic AD and healthy individuals. The proteins associated with sporadic AD were also altered in patients with ADAD, but with a greater effect size. Brain-derived proteins associated with ADAD were also replicated in additional CSF samples. Enrichment analyses highlighted several pathways, including those implicated in AD (calcineurin and Apo E), Parkinson's disease (α-synuclein and LRRK2), and innate immune responses (SHC1, ERK-1, and SPP1). Our findings suggest that combined proteomics across brain tissue, CSF, and plasma can be used to identify markers for sporadic and genetically defined AD.

The common genes involved in the pathogenesis of Alzheimer's disease and type 2 diabetes and their implication for drug repositioning

BACKGROUND

The prevalences of Alzheimer's disease (AD) and type 2 diabetes (T2D) continuously increase with the aging of world population. Clinical and epidemiological studies indicate that T2D is an important risk factor for AD. However, the mechanisms underlying the linkage of the two disorders are still not fully elucidated. The aim of this study is to explore the molecular mechanisms of their comorbidity and potential drug targets for AD treatment.

METHODS

We first compiled comprehensive lists of genes associated with AD and T2D, respectively. Then, we investigated the signatures of the shared genes and screened for interactions between the hub genes. Subsequently, we used Autodock Vina to perform molecular docking to predict new drug candidates. Lastly, structure and dynamics of docking results were examined by molecular dynamics simulation to verify drug reliability.

RESULTS

We obtained 917 AD-associated genes, 631 T2D-associated genes and 175 shared genes between the two disorders for subsequent analyses. Functional analysis revealed that metabolic process, lipid and atherosclerosis, AMPK signaling pathway, insulin resistance, chemokines and cytokines were enriched in the shared genes. In addition, 50 central hub genes were identified, including IL6, TNF, INS, IL1B, AKT1, VEGFA, IL10, TP53, PTGS2, TLR4, and others. Finally, we predicted new drug candidates (verdoheme and stannsoporfin) that could be potentially used for AD treatment.

CONCLUSIONS

Our study confirmed that there are important shared genes and pathways between AD and T2D, which may provide clues to reveal the molecular mechanism underlying the pathophysiology of the two diseases and help us to discover novel drug candidates for the treatment of AD. The results may also provide clues into identification of new targets and strategies for prevention and therapy of T2D that predisposes to AD.

Exploration of novel biomarkers in Alzheimer's disease based on four diagnostic models

Background

Despite tremendous progress in diagnosis and prediction of Alzheimer's disease (AD), the absence of treatments implies the need for further research. In this study, we screened AD biomarkers by comparing expression profiles of AD and control tissue samples and used various models to identify potential biomarkers. We further explored immune cells associated with these biomarkers that are involved in the brain microenvironment.

Methods

By differential expression analysis, we identified differentially expressed genes (DEGs) of four datasets (GSE125583, GSE118553, GSE5281, GSE122063), and common expression direction of genes of four datasets were considered as intersecting DEGs, which were used to perform enrichment analysis. We then screened the intersecting pathways between the pathways identified by enrichment analysis. DEGs in intersecting pathways that had an area under the curve (AUC) > 0.7 constructed random forest, least absolute shrinkage and selection operator (LASSO), logistic regression, and gradient boosting machine models. Subsequently, using receiver operating characteristic curve (ROC) and decision curve analysis (DCA) to select an optimal diagnostic model, we obtained the feature genes. Feature genes that were regulated by differentially expressed miRNAs (AUC > 0.85) were explored further. Furthermore, using single-sample GSEA to calculate infiltration of immune cells in AD patients.

Results

Screened 1855 intersecting DEGs that were involved in RAS and AMPK signaling. The LASSO model performed best among the four models. Thus, it was used as the optimal diagnostic model for ROC and DCA analyses. This obtained eight feature genes, including ATP2B3, BDNF, DVL2, ITGA10, SLC6A12, SMAD4, SST, and TPI1. SLC6A12 is regulated by miR-3176. Finally, the results of ssGSEA indicated dendritic cells and plasmacytoid dendritic cells were highly infiltrated in AD patients.

Conclusion

The LASSO model is the optimal diagnostic model for identifying feature genes as potential AD biomarkers, which can supply new strategies for the treatment of patients with AD.

Proteomic Analysis of Adult Human Hippocampal Subfields Demonstrates Regional Heterogeneity in the Protein Expression

Background: Distinct hippocampal subfields are known to get affected during aging, psychiatric disorders, and various neurological and neurodegenerative conditions. To understand the biological processes associated with each subfield, it is important to understand its heterogeneity at the molecular level. To address this lacuna, we investigated the proteomic analysis of hippocampal subfields─the cornu ammonis sectors (CA1, CA2, CA3, CA4) and dentate gyrus (DG) from healthy adult human cohorts. Findings: Microdissection of hippocampal subfields from archived formalin-fixed paraffin-embedded tissue sections followed by TMT-based multiplexed proteomic analysis resulted in the identification of 5,593 proteins. Out of these, 890 proteins were found to be differentially abundant among the subfields. Further bioinformatics analysis suggested proteins related to gene splicing, transportation, myelination, structural activity, and learning processes to be differentially abundant in DG, CA4, CA3, CA2, and CA1, respectively. A subset of proteins was selected for immunohistochemistry-based validation in an independent set of hippocampal samples. Conclusions: We believe that our findings will effectively pave the way for further analysis of the hippocampal subdivisions and provide awareness of its subfield-specific association to various neurofunctional anomalies in the future. The current mass spectrometry data is deposited and publicly made available through ProteomeXchange Consortium via the PRIDE partner repository with the data set identifier PXD029697.

Identification of Hub Genes Related to Alzheimer's Disease and Major Depressive Disorder

BackgroundAlthough many studies reported a close relationship between depression and Alzheimer's disease (AD), the underlying pathophysiological mechanism remains unclear. The present study aimed to investigate the mechanism of AD and major depressive disorder (MDD). Method: The datasets were downloaded from the Gene Expression Omnibus. After screening differentially expressed genes (DEGs), gene ontology and pathway analysis were performed and protein-protein interaction, TF-target gene, and miRNA-target gene networks were established. Results: 171 DEGs of AD-related datasets and 79 DEGs shared by AD and MDD were detected. Functional analysis revealed that AD and MDD common genes were significantly enriched in circadian entrainment and long-term depression signaling pathways. Five hub genes were identified after construction of networks and validation of hub gene signatures. In conclusion, DYNC1H1, MAPRE3, TTBK2, ITGB1, and WASL may be potential targets for the diagnosis and treatment of AD and MDD.

Case report of first-episode psychotic symptoms in a patient with early-onset Alzheimer's disease

BACKGROUND

Alzheimer's disease (AD) is a neurodegenerative disorder featuring the behavioral and psychological symptoms of dementia. Patients with early-onset AD that exhibits first as psychotic symptoms usually lack obvious cognitive impairment, so they may be misdiagnosed with late-onset schizophrenia.

CASE PRESENTATION

We report a patient who had prominent psychotic symptoms at the age of 60 and was initially diagnosed with very-late-onset-schizophrenia-like psychosis. Psychotic symptoms disappeared rapidly after treatment with olanzapine, and the patient later showed extrapyramidal symptoms and decline in cognitive function. Brain magnetic resonance imaging (MRI) showed frontotemporal atrophy, and positron emission tomography (PET) showed extensive areas of hypometabolism in the frontal cortex and head of the caudate nucleus. The patient's SORL1 gene was found to carry a heterozygrous mutation (c.296A > G). The patient was eventually diagnosed with early-onset AD.

CONCLUSIONS

Our case suggests that clinicians should consider the possibility of early-onset AD in middle-aged or elderly patients whose first symptoms are the behavioral and psychological symptoms of dementia. To distinguish early-onset AD from late-onset schizophrenia, clinicians should evaluate cognitive function, perform MRI and PET, and search for SORL1 mutations.

Differential Expression of mRNAs in the Brain Tissues of Patients with Alzheimer's Disease Based on GEO Expression Profile and Its Clinical Significance

Background

Early diagnosis of Alzheimer's disease (AD) is an urgent point for AD prevention and treatment. The biomarkers of AD still remain indefinite. Based on the bioinformatics analysis of mRNA differential expressions in the brain tissues and the peripheral blood samples of Alzheimer's disease (AD) patients, we investigated the target mRNAs that could be used as an AD biomarker and developed a new effective, practical clinical examination program.

Methods

We compared the AD peripheral blood mononuclear cells (PBMCs) expression dataset (GEO accession GSE4226 and GSE18309) with AD brain tissue expression datasets (GEO accessions GSE1297 and GSE5281) from GEO in the present study. The GEO gene database was used to download the appropriate gene expression profiles to analyze the differential mRNA expressions between brain tissue and blood of AD patients and normal elderly. The Venn diagram was used to screen out the differential expression of mRNAs between the brain tissue and blood. The protein-protein interaction network map (PPI) was used to view the correlation between the possible genes. GO (gene ontology) and KEGG (Kyoto Gene and Genomic Encyclopedia) were used for gene enrichment analysis to determine the major affected genes and the function or pathway.

Results

Bioinformatics analysis revealed that there were differentially expressed genes in peripheral blood and hippocampus of AD patients. There were 4958 differential mRNAs in GSE18309, 577 differential mRNAs in GSE4226 in AD PBMCs sample, 7464 differential mRNAs in GSE5281, and 317 differential mRNAs in GSE129 in AD brain tissues, when comparing between AD patients and healthy elderly. Two mRNAs of RAB7A and ITGB1 coexpressed in hippocampus and peripheral blood were screened. Furthermore, functions of differential genes were enriched by the PPI network map, GO, and KEGG analysis, and finally the chemotaxis, adhesion, and inflammatory reactions were found out, respectively.

Conclusions

ITGB1 and RAB7A mRNA expressions were both changed in hippocampus and PBMCs, highly suggested being used as an AD biomarker with AD. Also, according to the results of this analysis, it is indicated that we can test the blood routine of the elderly for 2-3 years at a frequency of 6 months or one year. When a patient continuously detects the inflammatory manifestations, it is indicated as a potentially high-risk AD patient for AD prevention.