Aging is the major underlying risk factor for most chronic diseases, including sporadic or late onset Alzheimer's disease (AD). Several pillars of aging, which have been proposed to underlie biological aging and its diseases, could provide an important roadmap for identifying strategies to target and protect against AD, as has been proposed by the geroscience hypothesis. While examples of single interventions and strategies resulting in improved lifespan, healthspan and lower AD burden exist in preclinical models, there is now evidence that combinatorial strategies designed to simultaneously target multiple pathways and pillars of aging can result in greater efficacy than single agents. This may be particularly critical when attempting to translate these discoveries to humans where disease etiology is multifactorial and complex. Given the sheer number of potential geroprotector combinations, empirically identifying the most efficacious options to treat LOAD in a mammalian system is simply not feasible or efficient. However, a systems approach that integrates multi-level data based upon comparative and genomic effects in AD models, could potentially make powerful, informed predictions regarding probability of synergistic effects between seemingly unrelated compounds and interventions. Moreover, these predictions can be empirically tested and validated in vivo. Therefore, in response to RFA-AG- 20-013, we hypothesize that most effectively targeting AD will require the integration of comparative and systems geroscience approaches to identify formulations that synergize to optimally target aging pillars and pathways to prevent or delay AD beyond what can be achieved with single approaches. To this end, Aim 1 will determine the relative ability of age-delaying strategies to modulate important pillars of aging, cognition, and gene networks in a mouse model of amyloidosis (APP/PSEN1).
This aim will use several approaches to characterize established and emerging strategies and their effects on aging pillars, including proteostasis, inflammation, metabolism, and macromolecular damage in relevant brain regions and peripheral tissues at 12 mo of age. We will further perform a comparative analysis among age-delaying strategies to prevent pathology and preserve cognition, and perform RNAseq to support to support systems analysis. We will next build a systems geroscience view of AD leveraging multiscale data and rank the relative efficacy of each intervention, the pillars implicated in their effects, and associated gene regulatory networks for their ability to modify the AD-related phenotype, to build a systems view of interactions. Based upon this model, Aim 2 will use a systems approach to identify synergy for targeting pathology and symptoms in a mouse model of amyloidosis among candidate strategies. This will first occur by constructing a systems model to identify combination(s) with potential synergy to outperform single strategies. The combination predicted to produce the greatest synergistic effect in silico will then be validated in vivo as compared to controls and each intervention alone, thereby demonstrating the potential utility of systems geroscience to identify novel combinatorial treatment strategies to treat or prevent AD.
This proposal is aimed to determine if a systems geroscience approach can be leveraged to identify potential synergy among age-delaying strategies, which when given alone, can delay pathology and symptoms in mouse models of AD. As such, we will use several approaches to characterize established and emerging strategies on major aging pillars, including proteostasis, inflammation, metabolism, and macromolecular damage in relevant brain regions, along with omics datasets to build a systems view of aging pillars and AD. Based upon these data, we will use our systems approach to predict possible synergistic effects in silico among these strategies and this model will then be validated in vivo as compared to controls and each intervention alone, thereby demonstrating the potential utility of systems geroscience to identify novel treatment strategies for AD.
