Addendum: REST and stress resistance in ageing and Alzheimer’s disease

Lu, Tao; Aron, Liviu; Zullo, Joseph M.; Pan, Y. Albert; Kim, Haeyoung; Chen, Yiwen; Yang, Tun-Hsiang; Kim, Hyun‐Min; Drake, Derek; Liu, X Shirley; Bennett, David A.; Colaiácovo, Mónica P.; Yankner, Bruce A.

doi:10.1038/nature20579

Cited by 13 publications

(1 citation statement)

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“…Twenty-one candidate genes that were previously identified as risk genes for AD (Lambert et al, 2013; Lu et al, 2014) were used in this study. These sets of genetic variants were also previously used to examine the G×E influence on the aging brain (Ge et al, 2015).…”

Section: Methodsmentioning

confidence: 99%

A Set-Based Mixed Effect Model for Gene-Environment Interaction and Its Application to Neuroimaging Phenotypes

Sun

Guillaume

et al. 2017

Front. Neurosci.

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Imaging genetics is an emerging field for the investigation of neuro-mechanisms linked to genetic variation. Although imaging genetics has recently shown great promise in understanding biological mechanisms for brain development and psychiatric disorders, studying the link between genetic variants and neuroimaging phenotypes remains statistically challenging due to the high-dimensionality of both genetic and neuroimaging data. This becomes even more challenging when studying gene-environment interaction (G×E) on neuroimaging phenotypes. In this study, we proposed a set-based mixed effect model for gene-environment interaction (MixGE) on neuroimaging phenotypes, such as structural volumes and tensor-based morphometry (TBM). MixGE incorporates both fixed and random effects of G×E to investigate homogeneous and heterogeneous contributions of multiple genetic variants and their interaction with environmental risks to phenotypes. We discuss the construction of score statistics for the terms associated with fixed and random effects of G×E to avoid direct parameter estimation in the MixGE model, which would greatly increase computational cost. We also describe how the score statistics can be combined into a single significance value to increase statistical power. We evaluated MixGE using simulated and real Alzheimer's Disease Neuroimaging Initiative (ADNI) data, and showed statistical power superior to other burden and variance component methods. We then demonstrated the use of MixGE for exploring the voxelwise effect of G×E on TBM, made feasible by the computational efficiency of MixGE. Through this, we discovered a potential interaction effect of gene ABCA7 and cardiovascular risk on local volume change of the right superior parietal cortex, which warrants further investigation.

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Section: Methodsmentioning

confidence: 99%

A Set-Based Mixed Effect Model for Gene-Environment Interaction and Its Application to Neuroimaging Phenotypes

Sun

Guillaume

et al. 2017

Front. Neurosci.

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LSD1 is an environmental stress-sensitive negative modulator of the glutamatergic synapse

Longaretti

Forastieri

Toffolo

et al. 2020

Neurobiology of Stress

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Immunosenescence: molecular mechanisms and diseases

Qiang

Ren

Guo

et al. 2023

Sig Transduct Target Ther

154

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Infection susceptibility, poor vaccination efficacy, age-related disease onset, and neoplasms are linked to innate and adaptive immune dysfunction that accompanies aging (known as immunosenescence). During aging, organisms tend to develop a characteristic inflammatory state that expresses high levels of pro-inflammatory markers, termed inflammaging. This chronic inflammation is a typical phenomenon linked to immunosenescence and it is considered the major risk factor for age-related diseases. Thymic involution, naïve/memory cell ratio imbalance, dysregulated metabolism, and epigenetic alterations are striking features of immunosenescence. Disturbed T-cell pools and chronic antigen stimulation mediate premature senescence of immune cells, and senescent immune cells develop a proinflammatory senescence-associated secretory phenotype that exacerbates inflammaging. Although the underlying molecular mechanisms remain to be addressed, it is well documented that senescent T cells and inflammaging might be major driving forces in immunosenescence. Potential counteractive measures will be discussed, including intervention of cellular senescence and metabolic-epigenetic axes to mitigate immunosenescence. In recent years, immunosenescence has attracted increasing attention for its role in tumor development. As a result of the limited participation of elderly patients, the impact of immunosenescence on cancer immunotherapy is unclear. Despite some surprising results from clinical trials and drugs, it is necessary to investigate the role of immunosenescence in cancer and other age-related diseases.

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Addendum: REST and stress resistance in ageing and Alzheimer’s disease

Cited by 13 publications

References 1 publication

A Set-Based Mixed Effect Model for Gene-Environment Interaction and Its Application to Neuroimaging Phenotypes

A Set-Based Mixed Effect Model for Gene-Environment Interaction and Its Application to Neuroimaging Phenotypes

LSD1 is an environmental stress-sensitive negative modulator of the glutamatergic synapse

Immunosenescence: molecular mechanisms and diseases

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