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

识别老年痴呆症中老年保护剂协同作用的系统方法

• 批准号: 10403520
• 负责人: DEREK Major HUFFMAN
• 金额: $ 50.81万
• 依托单位: ALBERT EINSTEIN COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10403520
• 关键词: Age; Aging; Alzheimer' s Disease; Alzheimer' s disease model; Alzheimer' s disease pathology; Amyloidosis; Animals; Appearance; Biological Aging; Biological Models; Brain region; Caloric Restriction; Chronic Disease; Cognition; Complex; Data; Data Set; Disease; Disease Progression; Etiology; Exercise; Female; Formulation; Geroscience; Hippocampus (Brain); Histone Deacetylase Inhibitor; Inflammation; Insulin-Like Growth Factor I; Intermittent fasting; Intervention; Late Onset Alzheimer Disease; Longevity; Metabolism; Modeling; Mus; Onset of illness; Pathology; Pathway Analysis; Pathway interactions; Peripheral; Phenotype; Plasma; Pre-Clinical Model; Prefrontal Cortex; Probability; Process; Regulator Genes; Risk Factors; Sex Differences; Signal Transduction; Sirolimus; Spermidine; Support System; Symptoms; System; Systems Analysis; Tauopathies; Testing; Tissues; Translating; Validation; base; burden of illness; combinatorial; comparative; comparative genomics; design; gene network; gene regulatory network; healthspan; human disease; improved; in silico; in vivo; in vivo evaluation; male; mouse model; multiscale data; novel; presenilin-1; preservation; prevent; proteostasis; response; synergism; transcriptome sequencing; transcriptomics; treatment strategy

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.

项目概要 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 的策略提供重要的路线图，正如 由老年科学假说提出。虽然单一干预措施和策略的例子导致 临床前模型中存在延长寿命、健康寿命和降低 AD 负担的效果，现在有证据表明 旨在同时针对衰老的多种途径和支柱的组合策略可能会导致 比单一药剂更有效。当尝试翻译这些内容时，这一点可能尤其重要 人类发现疾病病因是多因素且复杂的。鉴于潜力巨大 老年保护剂组合，凭经验确定治疗哺乳动物 LOAD 的最有效选择 系统根本不可行或高效。然而，集成基于多级数据的系统方法 根据 AD 模型中的比较和基因组效应，有可能做出强有力的、明智的预测 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进行经验测试和验证。因此，响应 RFA-AG- 20-013，我们假设最有效地针对 AD 将需要比较和系统的集成 老年科学方法来确定可协同作用的配方，以最佳地针对衰老支柱和途径 预防或延缓 AD 的效果超出了单一方法所能达到的效果。为此，目标 1 将确定 延迟衰老策略调节衰老、认知和基因网络重要支柱的相对能力 淀粉样变性小鼠模型（APP/PSEN1）。这一目标将使用多种方法来描述已建立的 以及新兴策略及其对衰老支柱的影响，包括蛋白质稳态、炎症、新陈代谢和 12月龄时相关脑区和周围组织发生大分子损伤。我们将进一步履行 对预防病理和保留认知的延缓衰老策略进行比较分析，并执行 RNAseq 支持系统分析。接下来我们将构建 AD 利用的系统老年科学视图 多尺度数据并对每种干预措施的相对功效、与其影响相关的支柱进行排名，以及 相关基因调控网络具有修改 AD 相关表型的能力，以构建系统视图 的互动。基于该模型，Aim 2 将使用系统方法来确定目标协同作用 候选策略中淀粉样变性小鼠模型的病理学和症状。这首先会发生在 构建系统模型来识别具有潜在协同作用的组合，以超越单一策略。 预计在计算机中产生最大协同效应的组合将在体内得到验证： 与对照和单独的每次干预进行比较，从而证明系统的潜在效用 老年科学确定治疗或预防 AD 的新型组合治疗策略。