A spatially resolved joint cortical metabolome and proteome in aging and menopause for the rhesus macaque

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PROJECT SUMMARY / ABSTRACT The preclinical phase of late-onset Alzheimer’s disease (LoAD) is a crucial time period for diagnosis and intervention that is not well-modeled by the transgenic animal models commonly used in aging research. This lack of strong models exists despite the fact that LoAD accounts for well over 90% of Alzheimer’s disease (AD) cases and thus represents the bulk of the disease burden. At particularly high risk for LoAD are female carriers of the ε4 apolipoprotein allele, a risk that has been linked to the menopause transition (MT). Macaque monkeys well-model the preclinical phase of LoAD; this species recapitulates patterns of accumulation of amyloid and tau pathology seen with aging in humans, and shows accompanying memory impairment equivalent to mild cognitive impairment in humans. Furthermore, female macaques undergo the MT, and there is only one macaque apolipoprotein isoform, equivalent to the human ε4 allele; all female macaques are therefore at high risk for LoAD-like pathology. The macaque monkey thus offers a unique opportunity to study preclinical brain changes in aging and LoAD, in a species with a brain that is structurally and functionally very similar to that of humans. It has long been known that in humans during the preclinical phase of LoAD, amyloid and tau pathology show characteristic sites of origin and (particularly for tau) patterns of spread; the mechanisms behind these spatial features of the disease are not yet known. In order to understand the spatial neurochemistry of the aging primate brain, we will study peri- and post-MT female macaques, and age- matched male controls, delivering within-subject spatial atlases of metabolite levels (spatial metabolome), protein expression (spatial proteome), and extant pathology for the cortex and cerebellum. The metabolome and proteome will be obtained from the same samples, allowing trans-omic integration to understand spatial patterns of biochemical pathway activity across the brain. The histopathological data will come from the opposite hemisphere of the same individual. We will use spatial principal components analysis and a novel spatial adaptation of multiple correspondence analysis to identify individual biomarkers and combinations of biomarkers that account for spatial position in the cortex, and to identify biomarkers and biomarker combinations that are associated with observed (in an individual) and predicted (from the literature) spatial patterns of pathology. These crucial data will yield unprecedented insights into early aging and the critical prodromal phase of LoAD, offering possibility of identifying biochemical factors that confer risk or resilience upon local brain circuits. Factors promoting resilience are opportunities for intervention, while risk factors hold the possibility for early diagnosis and screening; both of these outcomes will be crucial if we are to understand and effectively address the most prevalent form of this devastating brain disease.