Exploring Biomarkers Related to Autophagy in Alzheimer's Disease Based on Pathway Crosstalk Analysis

Abstract The pathological mechanism of Alzheimer's disease (AD) involves multiple pathways, and the crosstalk between autophagy and other pathways plays an increasingly prominent role in AD. However, current methods are primarily based on single-gene analysis or a single signal pathway to find therapeutic targets for AD, which are somewhat limited. The aim of our study is to identify autophagy-related biomarkers in AD based on the crosstalk between autophagy and other pathways. The pathway analysis method (PAGI) was applied to find the feature mRNAs involved in the crosstalk between autophagy and many other AD-related pathways. Then, the weighted gene co-expression network analysis (WGCNA) was used to construct a co-expression module of feature mRNAs and differential lncRNAs. Finally, clinical information was used to screen the biomarkers related to the prognosis of AD in the co-expressed gene modules. The experiment finally identified 8 mRNAs and 2 lncRNAs (TLN1, ARRB1, FZD4, AKT1, JMJD7-PLA2G4B, STAT5A, SMAD7, ZNF274; AC113349.1, AC015878.2) as biomarkers of AD, and they all interact directly or indirectly with autophagy. In summary, we provide an effective method for extracting autophagy-related biomarkers based on pathway crosstalk in AD. This method enriches the therapeutic targets of AD and provides new insights into the molecular mechanism of autophagy in AD.

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Faculty Opinions recommendation of Investigation of the human brain metabolome to identify potential markers for early diagnosis and therapeutic targets of Alzheimer's disease.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.718010574.793476741 ◽

2013 ◽

Author(s):

Laetitia Davidovic

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Early Diagnosis ◽

Human Brain ◽

Therapeutic Targets

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

Biomedicines ◽

10.3390/biomedicines9010034 ◽

2021 ◽

Vol 9 (1) ◽

pp. 34

Author(s):

Taesic Lee ◽

Hyunju Lee

Keyword(s):

Diabetes Mellitus ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Protein Interactions ◽

Expression Patterns ◽

Protein Protein Interactions ◽

Hub Genes ◽

Gene Modules ◽

Gene Regulatory ◽

The Brain

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.

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Artificial intelligence-based computational framework for drug-target prioritization and inference of novel repositionable drugs for Alzheimer’s disease

Alzheimer s Research & Therapy ◽

10.1186/s13195-021-00826-3 ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Shingo Tsuji ◽

Takeshi Hase ◽

Ayako Yachie-Kinoshita ◽

Taiko Nishino ◽

Samik Ghosh ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Deep Learning ◽

Drug Target ◽

Target Genes ◽

Drug Repositioning ◽

Therapeutic Targets ◽

Machine Learning Techniques ◽

Computational Framework ◽

Novel Therapeutic

Abstract Background Identifying novel therapeutic targets is crucial for the successful development of drugs. However, the cost to experimentally identify therapeutic targets is huge and only approximately 400 genes are targets for FDA-approved drugs. As a result, it is inevitable to develop powerful computational tools that can identify potential novel therapeutic targets. Fortunately, the human protein-protein interaction network (PIN) could be a useful resource to achieve this objective. Methods In this study, we developed a deep learning-based computational framework that extracts low-dimensional representations of high-dimensional PIN data. Our computational framework uses latent features and state-of-the-art machine learning techniques to infer potential drug target genes. Results We applied our computational framework to prioritize novel putative target genes for Alzheimer’s disease and successfully identified key genes that may serve as novel therapeutic targets (e.g., DLG4, EGFR, RAC1, SYK, PTK2B, SOCS1). Furthermore, based on these putative targets, we could infer repositionable candidate-compounds for the disease (e.g., tamoxifen, bosutinib, and dasatinib). Conclusions Our deep learning-based computational framework could be a powerful tool to efficiently prioritize new therapeutic targets and enhance the drug repositioning strategy.

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Genetic evidence for microglia-associated pathways as therapeutic targets for Alzheimer's disease

Neurobiology of Aging ◽

10.1016/j.neurobiolaging.2016.01.036 ◽

2016 ◽

Vol 39 ◽

pp. S6

Author(s):

Claudio Villegas-Llerena ◽

Mar Matarin ◽

John Hardy ◽

Jennifer Pocock

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Therapeutic Targets ◽

Genetic Evidence

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Editorial: Signaling Molecules as Biomarkers and Therapeutic Targets for Alzheimer's Disease: A New Perspective

Current Alzheimer Research ◽

10.2174/156720501504180222162435 ◽

2018 ◽

Vol 15 (4) ◽

pp. 302-302

Author(s):

Sandra Moreno

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Therapeutic Targets ◽

Signaling Molecules ◽

New Perspective

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Single-channel Ca2+ imaging implicates Aβ1–42 amyloid pores in Alzheimer’s disease pathology

The Journal of Cell Biology ◽

10.1083/jcb.201104133 ◽

2011 ◽

Vol 195 (3) ◽

pp. 515-524 ◽

Cited By ~ 101

Author(s):

Angelo Demuro ◽

Martin Smith ◽

Ian Parker

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Ion Channels ◽

Single Channel ◽

Membrane Integrity ◽

Cytosolic Calcium ◽

Xenopus Laevis Oocytes ◽

Permeable Membrane ◽

Membrane Pores ◽

Pathological Mechanism

Oligomeric forms of Aβ peptides are implicated in Alzheimer’s disease (AD) and disrupt membrane integrity, leading to cytosolic calcium (Ca2+) elevation. Proposed mechanisms by which Aβ mediates its effects include lipid destabilization, activation of native membrane channels, and aggregation of Aβ into Ca2+-permeable pores. We distinguished between these using total internal reflection fluorescence (TIRF) microscopy to image Ca2+ influx in Xenopus laevis oocytes. Aβ1–42 oligomers evoked single-channel Ca2+ fluorescence transients (SCCaFTs), which resembled those from classical ion channels but which were not attributable to endogenous oocyte channels. SCCaFTs displayed widely variable open probabilities (Po) and stepwise transitions among multiple amplitude levels reminiscent of subconductance levels of ion channels. The proportion of high Po, large amplitude SCCaFTs grew with time, suggesting that continued oligomer aggregation results in the formation of highly toxic pores. We conclude that formation of intrinsic Ca2+-permeable membrane pores is a major pathological mechanism in AD and introduce TIRF imaging for massively parallel single-channel studies of the incorporation, assembly, and properties of amyloidogenic oligomers.

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