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Alzheimer disease involves structural changes in the brain that begin long before symptoms appear. The most distinctive features are extracellular neuritic plaques and intracellular neurofibrillary tangles.Neuritic plaques form in the cerebral cortex and around blood vessels. These plaques contain a dense core of beta-amyloid (Aβ)—a toxic protein fragment that clumps outside neurons. The core is surrounded by damaged neuronal extensions, as well as reactive astrocytes and microglia. Abnormal...
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Alzheimer disease is a chronic, progressive, and irreversible neurodegenerative disorder and the most common cause of dementia in older adults. It leads to gradual neuronal loss, causing cognitive decline, behavioral changes, and loss of functional independence.Risk Factors and EtiologyThe disease is multifactorial. Age is the strongest risk factor, with prevalence doubling every 5 years after age 65. Genetic factors include mutations in genes such as APP, PSEN1, and PSEN2, which are associated...
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Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration
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Decoding Pathogenic Mutational Landscapes in Alzheimer's Disease Through Integrated Transcriptomics.

Wan Ma1, Fenfang Zhou1, Huaying Cai2

  • 1Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China, zju.edu.cn.

Human Mutation
|May 14, 2026
PubMed
Summary
This summary is machine-generated.

Genetic mutations, especially in TUBB2A, are key drivers of Alzheimer's disease (AD). Identifying these mutations offers new avenues for early diagnosis and personalized treatments for AD.

Keywords:
Alzheimer′s disease (AD)TUBB2Ainflammatory responsemutationstranscriptomic alterations

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Area of Science:

  • Genetics
  • Neuroscience
  • Computational Biology

Background:

  • Alzheimer's disease (AD) is increasingly recognized as a complex disorder influenced by genetic mutations beyond amyloid and tau pathology.
  • Understanding the role of genetic variations is crucial for deciphering AD pathogenesis.

Purpose of the Study:

  • To identify key genes associated with Alzheimer's disease mutations.
  • To explore the potential of these genes as diagnostic biomarkers.
  • To investigate distinct Alzheimer's disease subtypes based on genetic and expression profiles.

Main Methods:

  • Integration of multiple Alzheimer's disease gene expression datasets.
  • Application of machine learning algorithms (random forest, XGBoost, LASSO, SVM).
  • Identification of differentially expressed genes and hub genes, including TUBB2A.
  • Analysis of mutation data, unsupervised clustering, and pseudotemporal trajectory analysis.

Main Results:

  • 172 differentially expressed genes were identified, with TUBB2A, RTN4, and YWHAZ as top mutation-associated hub genes.
  • TUBB2A demonstrated significant diagnostic potential (AUC = 0.822) and harbored somatic mutations.
  • Two distinct AD subtypes were revealed, one with widespread gene overexpression and another (Cluster 2) with endoplasmic reticulum stress.
  • Trajectory analysis indicated a progression towards Cluster 2, potentially initiated by a pivotal mutational event.

Conclusions:

  • Somatic and germline mutations, particularly in TUBB2A, play a central role in Alzheimer's disease pathogenesis.
  • A mutation-centric approach is recommended for biomarker development.
  • SNP-based strategies could enable early diagnosis and personalized therapies for Alzheimer's disease.