PRKAG2 Gene Expression Is Elevated and its Protein Levels Are Associated with Increased Amyloid-β Accumulation in the Alzheimer’s Disease Brain

Bharadwaj, Prashant; Martins, Ralph N.

doi:10.3233/jad-190948

Cited by 12 publications

(8 citation statements)

References 27 publications

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“…For example, a PPARD null mouse model shows cognitive impairment 42 , and PPARD is highly expressed in the brain 43 and implicated in type 2 diabetes and obesity 44 , which are risk factors for AD. In addition, there is experimental evidence to suggest that JUN and PRKAG2 regulate or interact with APP 45 , 46 , which is of interest in AD given APP is cleaved to beta-amyloid. Thus, these findings from the literature provide complementary support that EWASplus identifies disease-relevant findings and is likely to provide fresh insight into AD.…”

Section: Discussionmentioning

confidence: 99%

A machine learning approach to brain epigenetic analysis reveals kinases associated with Alzheimer’s disease

et al. 2021

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Alzheimer’s disease (AD) is influenced by both genetic and environmental factors; thus, brain epigenomic alterations may provide insights into AD pathogenesis. Multiple array-based Epigenome-Wide Association Studies (EWASs) have identified robust brain methylation changes in AD; however, array-based assays only test about 2% of all CpG sites in the genome. Here, we develop EWASplus, a computational method that uses a supervised machine learning strategy to extend EWAS coverage to the entire genome. Application to six AD-related traits predicts hundreds of new significant brain CpGs associated with AD, some of which are further validated experimentally. EWASplus also performs well on data collected from independent cohorts and different brain regions. Genes found near top EWASplus loci are enriched for kinases and for genes with evidence for physical interactions with known AD genes. In this work, we show that EWASplus implicates additional epigenetic loci for AD that are not found using array-based AD EWASs.

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Section: Discussionmentioning

confidence: 99%

A machine learning approach to brain epigenetic analysis reveals kinases associated with Alzheimer’s disease

et al. 2021

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“…Recent findings from our group show that expression of the PRKAG2 gene was increased three-fold in AD-hippocampus and the AD-frontal cortex, and its protein levels positively correlated with Aβ accumulation in the brain [108]. In yeast, we showed that Aβ42 expression activates autophagy and co-expression of SNF4 (PRKAG2 homolog) markedly reduced the levels of Aβ42 aggregates and autophagic activity [109]. Overall, these findings show that increased autophagy activation and expression of PRKAG2 could be a response to increased Aβ accumulation in the AD brain.…”

Section: Role Of Autophagy In Ad Pathogenesismentioning

confidence: 65%

Activate or Inhibit? Implications of Autophagy Modulation as a Therapeutic Strategy for Alzheimer’s Disease

Krishnan

Shrestha

Jayatunga

et al. 2020

IJMS

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Neurodegenerative diseases result in a range of conditions depending on the type of proteinopathy, genes affected or the location of the degeneration in the brain. Proteinopathies such as senile plaques and neurofibrillary tangles in the brain are prominent features of Alzheimer’s disease (AD). Autophagy is a highly regulated mechanism of eliminating dysfunctional organelles and proteins, and plays an important role in removing these pathogenic intracellular protein aggregates, not only in AD, but also in other neurodegenerative diseases. Activating autophagy is gaining interest as a potential therapeutic strategy for chronic diseases featuring protein aggregation and misfolding, including AD. Although autophagy activation is a promising intervention, over-activation of autophagy in neurodegenerative diseases that display impaired lysosomal clearance may accelerate pathology, suggesting that the success of any autophagy-based intervention is dependent on lysosomal clearance being functional. Additionally, the effects of autophagy activation may vary significantly depending on the physiological state of the cell, especially during proteotoxic stress and ageing. Growing evidence seems to favour a strategy of enhancing the efficacy of autophagy by preventing or reversing the impairments of the specific processes that are disrupted. Therefore, it is essential to understand the underlying causes of the autophagy defect in different neurodegenerative diseases to explore possible therapeutic approaches. This review will focus on the role of autophagy during stress and ageing, consequences that are linked to its activation and caveats in modulating this pathway as a treatment.

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“…As an energy source, MCFAs could be rapidly transported and absorbed, and metabolized into ketones which had been demonstrated to provide energy for the brain in place of glucose metabolism to overcome the brain energy gap [42]. The accumulation of amyloid β‐protein (Aβ) was considered one of the major pathological hallmarks of AD [43], and MCFAs were demonstrated to increase total Aβ degradation [44]. Mitochondrial dysfunction was one of the AD pathogenesis [45], which would impair the β‐oxidation of LCFAs and cause the accumulation of LCFAs [46].…”

Section: Resultsmentioning

confidence: 99%

Simultaneous quantification of fatty acids in serum with dual derivatization by liquid chromatography‐tandem mass spectrometry

Lin

et al. 2023

J of Separation Science

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Fatty acids have multitudinous biological functions and play a crucial role in many biological processes, but due to poor ionization efficiency and lack of appropriate internal standards, the comprehensive quantification of fatty acids by liquid chromatography‐tandem mass spectrometry is still challenging. In this study, a new, accurate, and reliable method for quantifying 30 fatty acids in serum using dual derivatization was proposed. Indole‐3‐acetic acid hydrazide derivants of fatty acids were used as the internal standard and indole‐3‐carboxylic acid hydrazide derivants of them were used to quantify. The derivatization conditions were systematically optimized and the method validation results showed good linearity with R2 > 0.9942, low detection limit (0.03–0.6 nM), precision (1.6%–9.8% for intra‐day and 4.6%–14.1% for inter‐day), recovery (88.2%–107.2% with relative standard deviation < 10.5%), matrix effect (88.3%–105.2% with the relative standard deviation < 9.9%) and stability (3.4%–13.8% for fatty acids derivants in 24 h at 4°C and 4.2%–13.8% for three freeze‐thaw cycles). Finally, this method was successfully applied to quantify fatty acids in serum samples of Alzheimer's patients. In contrast to the healthy control group, nine fatty acids showed a significant increase in the Alzheimer's disease group.

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PRKAG2 Gene Expression Is Elevated and its Protein Levels Are Associated with Increased Amyloid-β Accumulation in the Alzheimer’s Disease Brain

Cited by 12 publications

References 27 publications

A machine learning approach to brain epigenetic analysis reveals kinases associated with Alzheimer’s disease

A machine learning approach to brain epigenetic analysis reveals kinases associated with Alzheimer’s disease

Activate or Inhibit? Implications of Autophagy Modulation as a Therapeutic Strategy for Alzheimer’s Disease

Simultaneous quantification of fatty acids in serum with dual derivatization by liquid chromatography‐tandem mass spectrometry

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