Biological Pathways and Molecular Mechanisms of Dementia—Second Edition

A special issue of Cells (ISSN 2073-4409). This special issue belongs to the section "Cellular Aging".

Deadline for manuscript submissions: 30 April 2025 | Viewed by 645

Special Issue Editors


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Guest Editor
1. Department of Neurology and Neurological Science, Tokyo Medical and Dental University, Bunkyo-ku, Tokyo 113-8510, Japan
2. Memory Clinic Ochanomizu, Bunkyo-ku, Tokyo 113-8510, Japan
Interests: Alzheimer’s disease; neurodegenerative diseases; amyloid-β; neurotoxicity; mild cognitive impairment; dementia; secretases; proteinopathy
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Guest Editor
Department of Immunology and Nano-Medicine, Herbert Wertheim College of Medicine, Florida International University, Miami, FL, USA
Interests: molecular mechanisms of Aβ generation and tau hyperphosphorylation; senescence and Alzheimer’s disease; autophagy–lysosome pathway and longevity; sirtuins, mitophagy and healthspan; role of novel Alzheimer’s risk genes in senescence and neuroinflammation; development of novel cellular assays and discovery of multi-target drugs for Alzheimer’s disease
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Dementia is one of the most serious health problems among the elderly, for which only limited medical treatments are available. Molecular pathological studies of dementia disorders, such as Alzheimer’s disease (AD), frontotemporal dementia (FTD), and dementia with Lewy bodies (DLB), have greatly advanced along with the discoveries of the genes associated with their familial forms as well as their risk genes. Although genome-wide association studies have also identified genes which increase the risk, the etiologies of the sporadic forms of these disorders remain elusive. Notably, recent evidence suggests that pathological hallmark proteins, such as amyloid-β (Aβ), tau, TDP-43, FUS, and α-synuclein, are linked to various biological and molecular pathways: enzymes including proteases (e.g., α-, β-, γ-secretases), kinases, phosphatases, acetylcholine esterase, and glucocerebrosidase; neurotransmitters, neurotrophic factors, hormones, and other biological ligands and their receptors; lipids and apolipoproteins and their receptors; cell death pathways, including apoptosis and necroptosis; intracellular transport systems, including axonal transport; proteostasis, including ubiquitin proteasome systems (UPSs), autophagy, and mitophagy; molecules involved in stress responses, including sirtuins; inflammatory molecules, including cytokines and chemokines; transcription factors; microRNAs; and pathways of Aβ clearance from the brain. These biological pathways and factors are generally maintained in a healthy status, but their dysregulation and/or functional perturbation may be critically involved in the pathological mechanisms of dementia disorders. Elucidating the molecular mechanisms underlying the characteristic neuropathological conditions would help to develop new therapeutic approaches.

This Special Issue will present original research articles that investigate the molecular mechanisms of dementia disorders from the above-mentioned aspects, as well as comprehensive review articles that cover such specific topics.

Dr. Wataru Araki
Dr. Madepalli K. Lakshmana
Guest Editors

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Keywords

  • Alzheimer’s disease
  • frontotemporal dementia
  • dementia with Lewy bodies
  • amyloid-β
  • tau
  • TDP-43
  • FUS
  • α-synuclein
  • neurodegeneration
  • neuropathology
  • neuroinflammation

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Published Papers (1 paper)

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Research

25 pages, 4018 KiB  
Article
A Transcriptomics-Based Machine Learning Model Discriminating Mild Cognitive Impairment and the Prediction of Conversion to Alzheimer’s Disease
by Min-Koo Park, Jinhyun Ahn, Jin-Muk Lim, Minsoo Han, Ji-Won Lee, Jeong-Chan Lee, Sung-Joo Hwang and Keun-Cheol Kim
Cells 2024, 13(22), 1920; https://doi.org/10.3390/cells13221920 - 19 Nov 2024
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Abstract
The clinical spectrum of Alzheimer’s disease (AD) ranges dynamically from asymptomatic and mild cognitive impairment (MCI) to mild, moderate, or severe AD. Although a few disease-modifying treatments, such as lecanemab and donanemab, have been developed, current therapies can only delay disease progression rather [...] Read more.
The clinical spectrum of Alzheimer’s disease (AD) ranges dynamically from asymptomatic and mild cognitive impairment (MCI) to mild, moderate, or severe AD. Although a few disease-modifying treatments, such as lecanemab and donanemab, have been developed, current therapies can only delay disease progression rather than halt it entirely. Therefore, the early detection of MCI and the identification of MCI patients at high risk of progression to AD remain urgent unmet needs in the super-aged era. This study utilized transcriptomics data from cognitively unimpaired (CU) individuals, MCI, and AD patients in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort and leveraged machine learning models to identify biomarkers that differentiate MCI from CU and also distinguish AD from MCI individuals. Furthermore, Cox proportional hazards analysis was conducted to identify biomarkers predictive of the progression from MCI to AD. Our machine learning models identified a unique set of gene expression profiles capable of achieving an area under the curve (AUC) of 0.98 in distinguishing those with MCI from CU individuals. A subset of these biomarkers was also found to be significantly associated with the risk of progression from MCI to AD. A linear mixed model demonstrated that plasma tau phosphorylated at threonine 181 (pTau181) and neurofilament light chain (NFL) exhibit the prognostic value in predicting cognitive decline longitudinally. These findings underscore the potential of integrating machine learning (ML) with transcriptomic profiling in the early detection and prognostication of AD. This integrated approach could facilitate the development of novel diagnostic tools and therapeutic strategies aimed at delaying or preventing the onset of AD in at-risk individuals. Future studies should focus on validating these biomarkers in larger, independent cohorts and further investigating their roles in AD pathogenesis. Full article
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