Genetic Networks of Alzheimer’s Disease, Aging and Longevity in Humans

Balmorez, Timothy; Sakazaki, Amy; Murakami, Shin

doi:10.1101/2023.01.06.23284252

Cited by 4 publications

(1 citation statement)

References 49 publications

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“…Interestingly, the study of two traits (IP6K3 and IPMK) suggests that gene–gene interactions may be more meaningful than single polymorphisms [ 50 ]. Thus, studies of interaction networks are needed [ 51 ].…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence-Assisted Meta-Analysis of the Frequency of ACE I/D Polymorphisms in Centenarians and Other Long-Lived Individuals

Murakami

2023

IJMS

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Current research on the angiotensin-converting-enzyme (ACE) gene has yielded controversial results on whether different ACE polymorphisms are linked with human longevity. ACE polymorphisms are a risk factor for Alzheimer’s disease and age-onset diseases that may contribute to the mortality of older people. Our goal is to consolidate existing studies, using artificial intelligence-assisted software to come to a more precise understanding of the role of the ACE gene in human longevity. The I (insertion) and D (deletion) polymorphisms in the intron are correlated with the levels of circulating ACE; homozygous D (DD) is high, and homozygous I (II) is low. Here, we performed a detailed meta-analysis of the I and D polymorphisms using centenarians (100+ years old), long-lived subjects (85+ years old), and control groups. ACE genotype distribution was analyzed across a total of 2054 centenarians and 12,074 controls, as well as 1367 long-lived subjects between the ages of 85–99, using the inverse variance and random effects methods. The ACE DD genotype was found to be favored in centenarians (OR: 1.41 (95% CI: 1.19–1.67), p < 0.0001) with a heterogeneity of 32%, and the II genotype slightly favored the control groups (OR: 0.81 (95% CI: 0.66–0.98), p = 0.03) with a heterogeneity of 28%, corroborating results from previous meta-analyses. Novel to our meta-analysis, the ID genotype was found to be favored in control groups (OR: 0.86 (95% CI: 0.76–0.97), p = 0.01) with a heterogeneity of 0%. The long-lived group showed a similar positive association between the DD genotype and longevity (OR: 1.34 (95% CI: 1.21–1.48), p < 0.0001) and a negative association between the II genotype and longevity (OR: 0.79 (95% CI: 0.70–0.88), p < 0.0001). The long-lived ID genotype did not show significant findings (OR: 0.93 (95% CI: 0.84–1.02), p = 0.79). In conclusion, the results suggest a significant positive association of the DD genotype with human longevity. However, despite the previous study, the results do not confirm a positive association of the ID genotype with human longevity. We suggest a few important paradoxical implications: (1) inhibition of ACE can increase longevity in model systems from nematodes to mammals, seemingly opposite to the finding in humans; (2) exceptional longevity associated with homozygous DD is also associated with age-related diseases with higher mortality risks in homozygous DD. We discuss ACE, longevity, and age-related diseases.

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Section: Discussionmentioning

confidence: 99%

Artificial Intelligence-Assisted Meta-Analysis of the Frequency of ACE I/D Polymorphisms in Centenarians and Other Long-Lived Individuals

Murakami

2023

IJMS

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Predicting Alzheimer’s Cognitive Resilience Score: A Comparative Study of Machine Learning Models Using RNA-seq Data

Kitani,

Matsui

2024

Preprint

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Alzheimer’s disease (AD) is an important research topic. While amyloid plaques and neurofibrillary tangles are hallmark pathological features of AD, cognitive resilience (CR) is a phenomenon where cognitive function remains preserved despite the presence of these pathological features. This study aimed to construct and compare predictive machine learning models for CR scores using RNA-seq data from the Religious Orders Study and Memory and Aging Project (ROSMAP) and Mount Sinai Brain Bank (MSBB) cohorts. We evaluated support vector regression (SVR), random forest, XGBoost, linear, and transformer-based models. The SVR model exhibited the best performance, with contributing genes identified using Shapley additive explanations (SHAP) scores, providing insights into biological pathways associated with CR. Finally, we developed a tool called the resilience gene analyzer (REGA), which visualizes SHAP scores to interpret the contributions of individual genes to CR. REGA is available athttps://igcore.cloud/GerOmics/REsilienceGeneAnalyzer/.

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Microglial Transforming Growth Factor-β Signaling in Alzheimer’s Disease

Vidovic,

Spittau

2024

IJMS

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Novel technologies such as single-cell RNA and single-nucleus RNA sequencing have shed new light on the complexity of different microglia populations in physiological and pathological states. The transcriptomic profiling of these populations has led to the subclassification of specific disease-associated microglia and microglia clusters in neurodegenerative diseases. A common profile includes the downregulation of homeostasis and the upregulation of inflammatory markers. Furthermore, there is concordance in few clusters between murine and human samples. Apolipoprotein E, which has long been considered a high-risk factor for late-onset Alzheimer’s disease, is strongly regulated in both these murine and human clusters. Transforming growth factor-β plays an essential role during the development and maturation of microglia. In a pathological state, it attenuates their activation and is involved in numerous cell regulatory processes. Transforming growth factor-β also has an influence on the deposition of amyloid-beta, as it is involved in the regulation of key proteins and molecules. Taken together, this review highlights the complex interaction of apolipoprotein E, the triggering receptor on myeloid cells 2, and transforming growth factor-β as part of a regulatory axis in microglia at the onset and over the course of Alzheimer’s disease.

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Genetic Networks of Alzheimer’s Disease, Aging and Longevity in Humans

Cited by 4 publications

References 49 publications

Artificial Intelligence-Assisted Meta-Analysis of the Frequency of ACE I/D Polymorphisms in Centenarians and Other Long-Lived Individuals

Artificial Intelligence-Assisted Meta-Analysis of the Frequency of ACE I/D Polymorphisms in Centenarians and Other Long-Lived Individuals

Predicting Alzheimer’s Cognitive Resilience Score: A Comparative Study of Machine Learning Models Using RNA-seq Data

Microglial Transforming Growth Factor-β Signaling in Alzheimer’s Disease

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