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

项目总结 老龄化是大多数慢性病的主要潜在风险因素，包括散发性或晚发性阿尔茨海默氏症 疾病(AD)。衰老的几个支柱，被认为是生物衰老及其疾病的基础， 可以为确定针对和预防AD的战略提供重要路线图，一如以往 由老年科学假说提出。虽然单一干预和战略的例子导致 在临床前模型中存在延长寿命、健康寿命和降低AD负担的情况，现在有证据表明 旨在同时针对多个衰老途径和支柱的组合策略可能会导致 比单一药物更有效。在尝试翻译这些文件时，这可能特别重要 在疾病病因是多因素和复杂的情况下，人类的发现。鉴于潜在的巨大数量 GeroProtector组合，经验性地确定治疗哺乳动物负荷的最有效选项 这一系统根本不可行，也不高效。然而，集成多层数据的系统方法基于 根据AD模型中的比较和基因组效应，可能会做出强有力的、知情的预测 关于看似不相关的化合物和干预措施之间产生协同效应的可能性。 此外，这些预测可以在体内得到经验检验和验证。因此，为了回应RFA-AG- 20-013，我们假设最有效地瞄准AD将需要比较和系统的集成 老年科学方法，以确定协同作用的配方，以最佳地针对衰老支柱和途径 预防或延迟AD的发生，超出了单一方法所能达到的效果。为此，目标1将决定 延缓衰老策略调节衰老、认知和基因网络的重要支柱的相对能力 淀粉样变性小鼠模型(APP/PSEN1)。这一目标将使用几种方法来描述已建立的 和新兴的策略及其对衰老支柱的影响，包括蛋白代谢、炎症、代谢和 12mo龄时相关脑区及周围组织大分子损伤。我们将进一步演出 延龄防病保智策略的比较分析 RNAseq支持支持系统分析。接下来，我们将构建AD利用的系统老年学视图 多尺度数据并对每种干预措施的相对有效性、影响其效果的支柱进行排名，以及 相关的基因调控网络为其修饰AD相关表型的能力，构建了系统的观点 互动的方式。基于这一模式，AIM 2将使用系统方法来确定目标的协同效应 候选策略中的淀粉样变性小鼠模型的病理和症状。这将首先发生在 构建一个系统模型，以确定组合(S)与潜在的协同效应，以超越单一战略。 预测将在硅胶中产生最大协同效应的组合将在体内得到验证 与单独的控制和每一次干预相比，从而展示了系统的潜在效用 老年学以确定治疗或预防阿尔茨海默病的新组合治疗策略。

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.