A systems approach for identifying geroprotector synergy in Alzheimer’s disease

项目摘要 衰老是大多数慢性疾病的主要潜在危险因素，包括散发性或迟发性阿尔茨海默病 疾病（AD）。衰老的几个支柱，已经被提出来作为生物衰老及其疾病的基础， 可以提供一个重要的路线图，用于确定针对和防范AD的战略， 这是老年科学假说提出的。虽然单一的干预措施和战略的例子， 在临床前模型中，存在寿命延长、健康寿命延长和AD负担降低，现在有证据表明， 设计用于同时靶向多种途径和衰老支柱的组合策略可导致 比单一药物更有效。这一点在试图翻译这些 发现人类的疾病病因是多因素和复杂的。考虑到潜在的 老年保护剂组合，经验性地确定治疗哺乳动物中LOAD的最有效选择。 这个系统根本不可行，也没有效率。然而，一个系统的方法，集成多层次的数据为基础的 基于AD模型中的比较和基因组效应，可能会做出强有力的，明智的预测 关于看似无关的化合物和干预措施之间协同效应的可能性。 此外，这些预测可以在体内进行经验测试和验证。因此，为了响应RFA-AG- 20-013，我们假设，最有效地针对AD将需要整合比较和系统 老年科学的方法，以确定配方，协同作用，以最佳目标老化支柱和途径， 预防或延迟AD超出了单一方法所能达到的效果。为此，目标1将确定 延缓衰老策略调节衰老、认知和基因网络重要支柱的相对能力， 淀粉样变性（APP/PSEN 1）的小鼠模型。这一目标将使用几种方法来表征已建立的 和新兴的战略及其对衰老支柱的影响，包括蛋白质稳态，炎症，代谢， 12月龄时相关脑区和外周组织的大分子损伤。我们将进一步执行 对预防病理和保护认知的延缓衰老策略进行了比较分析， RNAseq支持系统分析。接下来，我们将建立一个系统老年科学的观点， 多尺度数据，并对每种干预措施的相对功效、影响其效果的支柱进行排名， 相关基因调控网络的能力，以修改AD相关的表型，建立一个系统的观点 的互动。根据这一模式，目标2将采用系统方法来确定目标的协同作用 病理学和症状在淀粉样变性的小鼠模型中的候选策略。这将首先发生在 构建系统模型，以识别具有潜在协同作用的组合，从而优于单一策略。 然后将在体内验证预测产生最大协同效应的组合， 与对照组和单独的每种干预措施相比， 老年科学，以确定新的组合治疗策略，以治疗或预防AD。

PROJECT SUMMARY 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.