scholarly journals Co-Expression Analysis of microRNAs and Proteins in Brain of Alzheimer’s Disease Patients

Cells ◽  
2022 ◽  
Vol 11 (1) ◽  
pp. 163
Author(s):  
Callum N. Watson ◽  
Ghazala Begum ◽  
Emma Ashman ◽  
Daniella Thorn ◽  
Kamal M. Yakoub ◽  
...  
Keyword(s):  
Gene Expression ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Early Intervention ◽  
Early Treatment ◽  
Late Stage ◽  
Early Phase ◽  
Therapeutic Targets ◽  
Protein Profiles ◽  
Molecular Fingerprint

Alzheimer’s disease (AD) is the most common form of dementia globally; however, the aetiology of AD remains elusive hindering the development of effective therapeutics. MicroRNAs (miRNAs) are regulators of gene expression and have been of growing interest in recent studies in many pathologies including AD not only for their use as biomarkers but also for their implications in the therapeutic field. In this study, miRNA and protein profiles were obtained from brain tissues of different stage (Braak III-IV and Braak V-VI) of AD patients and compared to matched controls. The aim of the study was to identify in the late stage of AD, the key dysregulated pathways that may contribute to pathogenesis and then to evaluate whether any of these pathways could be detected in the early phase of AD, opening new opportunity for early treatment that could stop or delay the pathology. Six common pathways were found regulated by miRNAs and proteins in the late stage of AD, with one of them (Rap1 signalling) activated since the early phase. MiRNAs and proteins were also compared to explore an inverse trend of expression which could lead to the identification of new therapeutic targets. These results suggest that specific miRNA changes could represent molecular fingerprint of neurodegenerative processes and potential therapeutic targets for early intervention.

[P3-091]: EFFECTIVE ANALYSIS OF GENE EXPRESSION FOR THE DISCOVERY OF BIOMARKERS AND THERAPEUTIC TARGETS FOR ALZHEIMER'S DISEASE

2017 ◽  
Vol 13 (7S_Part_20) ◽  
pp. P968-P969
Author(s):  
Juan Luis Fernández-Martínez ◽  
Enrique deAndrés-Galiana ◽  
Stephen Sonis ◽  
Manuel Menendez-Gonzalez
Keyword(s):  
Gene Expression ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Therapeutic Targets ◽  
Effective Analysis

Covert versus overt self-recognition in late stage Alzheimer's disease

1997 ◽  
Vol 3 (2) ◽  
pp. 195-198 ◽  
Author(s):  
SANDRA M. BOLOGNA ◽  
CAMERON J. CAMP
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Late Stage ◽  
Memory Systems ◽  
Self Recognition ◽  
Nondeclarative Memory

Some persons with Alzheimer's disease (AD) lose the ability to recognize themselves, as when they cannot overtly recognize their reflection in a mirror. There is evidence, however, that covert or unconscious self-recognition might be displayed in such individuals. In this study, 3 persons with AD lacking the ability to overtly self-recognize demonstrated multiple instances of unconscious or covert self-recognition. A variety of interventions, inspired by research with prosopagnosics, was implemented to remediate this loss. Interventions enabled all participants to exhibit overt self-recognition, though each did so with the aid of a different intervention. In addition, successful overt self-recognition required a verbal probe and was entirely intervention-dependent: When the intervention was removed, overt self-recognition was lost. Results support a dissociation between explicit–declarative versus implicit–nondeclarative memory systems, and extends this dissociation into the realm of self-recognition in AD. (JINS, 1997, 3, 195–198.)

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

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.

scholarly journals Association of neurogranin gene expression with Alzheimer's disease pathology in the perirhinal cortex

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Xiaoyan Sun ◽  
Qian Wang ◽  
Kaj Blennow ◽  
Henrik Zetterberg ◽  
Micheline McCarthy ◽  
...  
Keyword(s):  
Gene Expression ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Perirhinal Cortex

Lentivirus-Mediated Expression of Human Secreted Amyloid Precursor Protein-Alpha Promotes Long-Term Induction of Neuroprotective Genes and Pathways in a Mouse Model of Alzheimer’s Disease

2020 ◽  
pp. 1-16
Author(s):  
Margaret Ryan ◽  
Valerie T.Y. Tan ◽  
Nasya Thompson ◽  
Diane Guévremont ◽  
Bruce G. Mockett ◽  
...  
Keyword(s):  
Gene Expression ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Mouse Model ◽  
Amyloid Precursor Protein ◽  
Precursor Protein ◽  
Integrated Analysis ◽  
Empty Vector ◽  
Mouse Transcriptome

Background: Secreted amyloid precursor protein-alpha (sAPPα) can enhance memory and is neurotrophic and neuroprotective across a range of disease-associated insults, including amyloid-β toxicity. In a significant step toward validating sAPPα as a therapeutic for Alzheimer’s disease (AD), we demonstrated that long-term overexpression of human sAPPα (for 8 months) in a mouse model of amyloidosis (APP/PS1) could prevent the behavioral and electrophysiological deficits that develop in these mice. Objective: To explore the underlying molecular mechanisms responsible for the significant physiological and behavioral improvements observed in sAPPα-treated APP/PS1 mice. Methods: We assessed the long-term effects on the hippocampal transcriptome following continuous lentiviral delivery of sAPPα or empty-vector to male APP/PS1 mice and wild-type controls using Affymetrix Mouse Transcriptome Assays. Data analysis was carried out within the Affymetrix Transcriptome Analysis Console and an integrated analysis of the resulting transcriptomic data was performed with Ingenuity Pathway analysis (IPA). Results: Mouse transcriptome assays revealed expected AD-associated gene expression changes in empty-vector APP/PS1 mice, providing validation of the assays used for the analysis. By contrast, there were specific sAPPα-associated gene expression profiles which included increases in key neuroprotective genes such as Decorin, betaine-GABA transporter, and protocadherin beta-5, subsequently validated by qRT-PCR. An integrated biological pathways analysis highlighted regulation of GABA receptor signaling, cell survival, and inflammatory responses. Furthermore, upstream gene regulatory analysis implicated sAPPα activation of Interleukin-4, which can counteract inflammatory changes in AD. Conclusion: This study identified key molecular processes that likely underpin the long-term neuroprotective and therapeutic effects of increasing sAPPα levels in vivo

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