Comparative Analysis of Multiple Neurodegenerative Diseases Based on Advanced Epigenetic Aging Brain

Shi, Feitong; He, Yudan; Chen, Yao; Yin, Xinman; Sha, Xianyi; Wang, Yin

doi:10.3389/fgene.2021.657636

Cited by 11 publications

(8 citation statements)

References 89 publications

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“…have been linked to AD and other neurodegenerative diseases. 99,138,[140][141][142][143][144][145][146][147][148] Our transcriptome data strongly suggests that splicing and transcriptional abnormalities due to TDP-43 acetylation impacts synaptic plasticity, neurotransmission, and neuronal survival pathways in the TDP-43 KQ/KQ model, which overlaps with abnormalities found in human FTLD-TDP patients. We suspect that this prominent RNA signature may underlie the cognitive decline in TDP-43 KQ/KQ neocortex and hippocampus.…”

Section: Discussionsupporting

confidence: 52%

RNA-binding deficient TDP-43 drives cognitive decline in a mouse model of TDP-43 proteinopathy

Necarsulmer,

Simon,

Evangelista

et al. 2023

Preprint

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TDP-43 proteinopathies including frontotemporal lobar degeneration (FTLD) and amyotrophic lateral sclerosis (ALS) are neurodegenerative disorders characterized by aggregation and mislocalization of the nucleic-acid binding protein TDP-43 and subsequent neuronal dysfunction. Here, we developed endogenous models of sporadic TDP-43 proteinopathy based on the principle that disease-associated TDP-43 acetylation at lysine 145 (K145) alters TDP-43 conformation, impairs RNA-binding capacity, and induces downstream mis-regulation of target genes. Expression of acetylation-mimic TDP-43K145Q resulted in stress-induced nuclear TDP-43 foci and loss of TDP-43 function in primary mouse and human induced pluripotent stem cell (hiPSC)-derived cortical neurons. Mice harboring the TDP-43K145Q mutation recapitulated key hallmarks of FTLD, including progressive TDP-43 phosphorylation and insolubility, TDP-43 mis-localization, transcriptomic and splicing alterations, and cognitive dysfunction. Our study supports a model in which TDP-43 acetylation drives neuronal dysfunction and cognitive decline through aberrant splicing and transcription of critical genes that regulate synaptic plasticity and stress response signaling. The neurodegenerative cascade initiated by TDP-43 acetylation recapitulates many aspects of human FTLD and provides a new paradigm to further interrogate TDP-43 proteinopathies.

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Section: Discussionsupporting

confidence: 52%

RNA-binding deficient TDP-43 drives cognitive decline in a mouse model of TDP-43 proteinopathy

Necarsulmer,

Simon,

Evangelista

et al. 2023

Preprint

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“…There are many studies focused on neurodegenerative disorder, which includes Alzheimer’s disease, Parkinson’s disease, Dementia, and any type of cognitive disorders. Different types of deep neural networks were applied and compared with Cox proportional hazard models to predict the neurodegenerative disorders using population-based datasets [ 66 ], and another comparative analysis was done to understand the association between ageing process and these diseases [ 58 ]. Among all kinds of neurodegenerative diseases, Alzheimer’s disease was focused a lot.…”

Section: Resultsmentioning

confidence: 99%

Application of machine learning in measurement of ageing and geriatric diseases: a systematic review

Das,

Dhillon

2023

BMC Geriatr

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Background As the ageing population continues to grow in many countries, the prevalence of geriatric diseases is on the rise. In response, healthcare providers are exploring novel methods to enhance the quality of life for the elderly. Over the last decade, there has been a remarkable surge in the use of machine learning in geriatric diseases and care. Machine learning has emerged as a promising tool for the diagnosis, treatment, and management of these conditions. Hence, our study aims to find out the present state of research in geriatrics and the application of machine learning methods in this area. Methods This systematic review followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and focused on healthy ageing in individuals aged 45 and above, with a specific emphasis on the diseases that commonly occur during this process. The study mainly focused on three areas, that are machine learning, the geriatric population, and diseases. Peer-reviewed articles were searched in the PubMed and Scopus databases with inclusion criteria of population above 45 years, must have used machine learning methods, and availability of full text. To assess the quality of the studies, Joanna Briggs Institute's (JBI) critical appraisal tool was used. Results A total of 70 papers were selected from the 120 identified papers after going through title screening, abstract screening, and reference search. Limited research is available on predicting biological or brain age using deep learning and different supervised machine learning methods. Neurodegenerative disorders were found to be the most researched disease, in which Alzheimer’s disease was focused the most. Among non-communicable diseases, diabetes mellitus, hypertension, cancer, kidney diseases, and cardiovascular diseases were included, and other rare diseases like oral health-related diseases and bone diseases were also explored in some papers. In terms of the application of machine learning, risk prediction was the most common approach. Half of the studies have used supervised machine learning algorithms, among which logistic regression, random forest, XG Boost were frequently used methods. These machine learning methods were applied to a variety of datasets including population-based surveys, hospital records, and digitally traced data. Conclusion The review identified a wide range of studies that employed machine learning algorithms to analyse various diseases and datasets. While the application of machine learning in geriatrics and care has been well-explored, there is still room for future development, particularly in validating models across diverse populations and utilizing personalized digital datasets for customized patient-centric care in older populations. Further, we suggest a scope of Machine Learning in generating comparable ageing indices such as successful ageing index.

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“…Similarly, Nos1, encoding the neuronal nitric oxide synthase (nNOS) protein that regulates LTP and synaptic plasticity via the production of nitric oxide 142,143 , showed aberrant splicing patterns in TDP-43 KQ/KQ mice. Importantly, all of these synaptic regulators (Nos1, Lrp8, Argef2, Sema5b) and other signi cantly altered splice variants (e.g., Adipor2, Mapk14, Smarca4, Sort1, Mapt) have all been linked to AD and other neurodegenerative diseases 101,141,[143][144][145][146][147][148][149][150][151] . Altogether, our transcriptome data strongly suggests that splicing and transcriptional abnormalities due to TDP-43 acetylation impacts synaptic plasticity, neurotransmission, and neuronal survival pathways in the TDP-43 KQ/KQ model, which overlaps with abnormalities found in human FTLD-TDP patients.…”

Section: Discussionmentioning

confidence: 99%

RNA-binding deficient TDP-43 drives cognitive decline in a mouse model of TDP-43 proteinopathy

Necarsulmer

Simon

Evangelista

et al. 2023

Preprint

View full text Add to dashboard Cite

TDP-43 proteinopathies including frontotemporal lobar dementia (FTLD) and amyotrophic lateral sclerosis (ALS) are neurodegenerative disorders characterized by aggregation and mislocalization of the nucleic-acid binding protein TDP-43 and subsequent neuronal dysfunction. Here, we developed an endogenous model of sporadic TDP-43 proteinopathy based on the principle that disease-associated TDP-43 acetylation at lysine 145 (K145) alters TDP-43 conformation, impairs RNA-binding capacity, and induces downstream mis-regulation of target genes. Expression of acetylation-mimic TDP-43K145Q resulted in stress-induced nuclear TDP-43 foci and loss-of-TDP-43-function in primary mouse and human induced pluripotent stem cell (hiPSC)-derived cortical neurons. Mice harboring the TDP-43K145Q mutation recapitulated key hallmarks of FTLD, including progressive TDP-43 phosphorylation and insolubility, TDP-43 mis-localization, transcriptomic and splicing alterations, and cognitive dysfunction. Our study supports a model in which TDP-43 acetylation drives neuronal dysfunction and cognitive decline through aberrant splicing and transcription of critical genes that regulate synaptic plasticity and stress response signaling. The neurodegenerative cascade initiated by TDP-43 acetylation recapitulates many aspects of FTLD and provides a new paradigm to further interrogate TDP-43 proteinopathies.

show abstract

Comparative Analysis of Multiple Neurodegenerative Diseases Based on Advanced Epigenetic Aging Brain

Cited by 11 publications

References 89 publications

RNA-binding deficient TDP-43 drives cognitive decline in a mouse model of TDP-43 proteinopathy

RNA-binding deficient TDP-43 drives cognitive decline in a mouse model of TDP-43 proteinopathy

Application of machine learning in measurement of ageing and geriatric diseases: a systematic review

RNA-binding deficient TDP-43 drives cognitive decline in a mouse model of TDP-43 proteinopathy

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