Divergence in the metabolome between natural aging and Alzheimer’s disease

Abstract

Alzheimer's disease (AD) is a progressive and debilitating neurodegenerative disorder and one of the leading causes of death in the United States. Although amyloid plaques and fibrillary tangles are hallmarks of AD, research suggests that pathology associated with AD often begins 20 or more years before symptoms appear. Therefore, it is essential to identify early-stage biomarkers in those at risk for AD and age-related cognitive decline (ARCD) in order to develop preventative treatments. Here, we used an untargeted metabolomics analysis to define system-level alterations following cognitive decline in aged and APP/PS1 (AD) mice. At 6, 12, and 24 months of age, both control (Ctrl) and AD mice were tested in a 3-shock contextual fear conditioning (CFC) paradigm to assess memory decline. AD mice exhibited memory deficits across age and these memory deficits were also seen in naturally aged mice. Prefrontal cortex (PFC), hippocampus (HPC), and spleen were then collected and analyzed for metabolomic alterations. A number of significant pathways were altered between Ctrl and AD mice and naturally aged mice. By identifying systems-level alterations following ARCD and AD, these data could provide insights into disease mechanisms and advance the development of biomarker panels. Alzheimer's disease (AD), a progressive and debilitating neurodegenerative disorder, characterized by senile amyloid plaques and tau fibrillary tangles, is the leading cause of dementia 1. Amyloid plaques are aggregates of various amyloid peptides derived from the amyloid precursor protein (APP). APP accumulates when presenilin 1 and 2 mutant genes (PS1 and PS2) enhance the γ-secretase-mediated processing of APP 2. Therefore, we have chosen the APP/PS1 AD mouse model to best replicate the human amyloid cascade. This amyloid accumulation then promotes the spread of tau tangles, consisting of hyperphosphorylated tau protein, inevitably leading to cognitive decline 3. However, research suggests that the pathology associated with AD often begins 20 or more years before symptoms appear 4 , which makes this a critical time window for possible preventative treatments. Because aging is the greatest risk factor for developing AD 5 , it is also important to examine age-related brain changes and age-related cognitive decline (ARCD). ARCD is a normal process of aging where conceptual reasoning, memory, and processing speed gradually decline over time (fluid intelligence) 6-8. However, ARCD differs from AD in that certain abilities, such as vocabulary or other skills that have accumulated throughout life, are resilient to brain aging (crystallized intelligence) 9. It is important to understand how metabolites influence ARCD and AD, as our current aging population is expected to double in the next 40 years 10. One of the biggest problems facing AD research is the lack of biomarkers for diagnosis and treatment. Currently, there are only 3 FDA-approved diagnostic tests for AD, all of which are positron emission tomography (PET) neuroimaging scans for amyloid 11-1...

Tissue-Specific Landscape of Metabolic Dysregulation during Ageing

“…A decreased concentration of taurine, a sulfur-containing amino acid that augments the proliferative responses of T-cells, indicates a defect in this process in old mice [63] and points to a reduced potential for detoxification of reactive oxygen species [64]. In line with previous results [65], we observed decreased uridine levels in the ageing spleen, suggesting uridine and taurine as robust spleen-specific biomarker candidates for the ageing process. Accordingly, reduced levels of uridine might be associated with increased cellular senescence in all tissues, as uridine has been shown to affect senescence in human mammary epithelial cells [66].…”

Role of adipocyte Na,K-ATPase oxidant amplification loop in cognitive decline and neurodegeneration

“…Loss of neuronal function and synapses in the hippocampus, a characteristic of neurodegenerative disorders ( Canchi et al., 2019 ), strongly correlates with cognitive impairment and high levels of Aβ production ( Sheng et al., 2012 ). Other investigators have found evidence of the involvement of many of these pathways, including pathways for axon guidance ( Lee et al., 2019 ) and histidine metabolism ( Hunsberger et al., 2020 ; Smith et al., 2010 ). Neuroinflammation contributes to increased oxidative stress, phosphorylation of tau, reduced Aβ clearance, synaptic degeneration, and neuronal death ( Chen et al., 2016 ; Kempuraj et al., 2017 ).…”

Integrating Environment and Aging Research: Opportunities for Synergy and Acceleration

“…Combined with additional epigenetic and microbiome and phenotypic data, the contribution of environmental mixtures on biological systems and aging biology can begin to be explored. For example, in animal models metabolomics has been used to differentiate normative aging from aging related to Alzheimer’s disease ( Hunsberger et al, 2020 ). This same experimental design could be used to better understand the contribution of toxic chemicals in altering the normative aging metabolic profiles and impacts on the Alzheimer’s disease profiles.…”