Metabolic Immunomodulation of Wound-Associated Macrophage Functional Plasticity as a Novel Diagnostic Target in Diabetic Veterans

伤口相关巨噬细胞功能可塑性的代谢免疫调节作为糖尿病退伍军人的新诊断目标

• 批准号: 10370267
• 负责人: Mary Cloud Bosworth Ammons
• 金额: --
• 依托单位: BOISE VA MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10370267
• 关键词: Activities of Daily Living; Address; Algorithms; Amputation; Anti-Inflammatory Agents; Assessment tool; Benchmarking; Biological Assay; Biological Markers; Blinded; Caregivers; Caring; Cells; Cellular Metabolic Process; Characteristics; Chronic; Clinical; Complex; Computer Models; Data; Debridement; Development; Diabetes Mellitus; Disabled Persons; Economic Burden; Etiology; Fluorescent in Situ Hybridization; Foundations; Functional disorder; Future; Gene Expression; Goals; Growth Factor; Health; Healthcare Systems; Human; Immunity; Immunohistochemistry; In Situ; Individual; Inflammation; Inflammatory; Knowledge; Lower Extremity; Machine Learning; Medical; Metabolic; Metabolism; Modeling; Molecular; Myelogenous; Natural Immunity; Patients; Phenotype; Population; Process; Proteomics; Quality of life; Research; Research Project Grants; Resolution; Role; Sampling; Serum; Skin; Specimen; System; Systems Biology; Testing; Therapeutic; Therapeutic Intervention; Time; Tissues; Translating; Translations; Treatment Protocols; United States Department of Veterans Affairs; Veterans; Wound models; base; biomarker discovery; candidate marker; candidate selection; chemokine; clinical care; clinically actionable; clinically relevant; computerized tools; cost; cytokine; design; diabetes management; diabetic; diabetic patient; diabetic ulcer; diabetic wound healing; economic cost; efficacy testing; functional plasticity; healing; high dimensionality; immunoregulation; innovation; limb amputation; lipidomics; macrophage; metabolic phenotype; metabolic profile; metabolomics; microbiome; military veteran; neutrophil; new therapeutic target; non-healing wounds; novel; novel diagnostics; pre-clinical; precision medicine; predictive modeling; predictive tools; random forest; responders and non-responders; standard of care; statistics; targeted biomarker; temporal measurement; tool; wound; wound care; wound healing; wound treatment

Within the Veterans Affairs healthcare system, around 25% of military veterans have diabetes and the economic burden of lower limb amputations exceeded $200 million for fiscal year 2010. Beyond the economic costs, the loss of mobility and independence in these veterans has a significant impact on veteran quality of life and that of their caregivers. Despite innovations in both wound care and diabetes management, diabetic ulcers remain the leading cause of amputation for VA patients. Normal wound healing in healthy individuals initiates quickly and proceeds through well- characterized, iterative steps; however, in diabetic wounds, the healing process stalls at the transition between resolution of inflammation and initiation of tissue reorganization. In healthy individuals, this transition is characterized by a shift away from inflammation and an associated population shift in macrophages (Mф). It has been well established that there is a correlation between inflammation and diabetes; however, the role of chronic inflammation at the skin in diabetics has not been explored. MΦs display remarkable functional plasticity and are generally are divided into M1 MΦs (classically activated, pro-inflammatory) and a broad set of M2 MΦs (alternatively activated, anti- inflammatory). M2 MΦs have been further subdivided into M2a, M2b, M2c, and M2d subtypes. Our preliminary data demonstrate that metabolic landscape within the wound is an important variable in healing and supports our overarching idea that immunomodulation of wound-associated MΦs is necessary for wound resolution. The primary goal of this research project is to develop a preliminary model of biomarkers that can accurately predict whether a wound will either respond or not respond to current standards of care. To achieve this goal, we will utilize an ex vivo MΦ polarization model to quantify the impact of host metabolic health (based on donor HemA1c serum levels) on MΦ functional phenotype. MΦ plasticity will be quantified using a Complex Systems Biology approach, incorporating multiplexed cytokine/chemokine/growth factor profile with myeloid gene expression, global metabolomics, semi- targeted lipidomics, and real-time, live cell metabolism profiling. While our ex vivo MΦ model uses primary cells collected from human donors, confirmation of our candidate biomarkers will require using our Complex Systems Biology approach in situ to confirm that candidate biomarkers can be detected with clinical samples. Primary wound debridement samples will be collected over time and for probed for candidate biomarkers by quantitative immunohistochemistry and fluorescence in situ hybridization. Finally, primary wound tissue will be profiled over time with targeted metabolite biomarkers to confirm efficacy of biomarkers as clinical targets. Finally, utilizing biomarker discovery statistics based on receiver-curve-characteristic (ROC) curve analysis, biomarkers will be selected for inclusion in our predictive model. Predictive modeling will utilize Random Forest machine learning and test efficacy of predictive models based on benchmarks of current clinical care, our selected biomarkers, or a combination of both. Once statistical strength of predictive model determines best fit, the model will be assessed clinically in parallel with standard of care. Ultimately, our hope is to lay the foundation for better prediction of wound treatment protocols, promote design of novel wound-care therapeutics, and take the first step towards Precision Medicine wound care for our diabetic veterans.

在退伍军人事务医疗系统中，约有25％的退伍军人患有糖尿病 2010财政年度的下肢截肢的经济燃烧超过了2亿美元。 除了经济成本之外，这些退伍军人的流动性和独立性的丧失还有很大的 对退伍军人生活质量及其照料者的影响。尽管两种伤口护理都进行了创新 糖尿病性溃疡仍然是VA患者截肢的主要原因。 健康个体的正常伤口愈合会迅速发起，并通过 特征是迭代步骤；但是，在糖尿病伤口中，愈合过程停滞在过渡 在炎症的分辨率和组织重组的主动性之间。在健康的个体中，这个 过渡的特征是从炎症转移和相关的种群转移 巨噬细胞（MO）。已经很好地确定炎症与 糖尿病;但是，尚未探索慢性炎症在糖尿病患者中皮肤中的作用。 MφS显示出显着的功能可塑性，通常被分为M1Mφ （经典激活，促炎）和一组M2MφS（替代激活，抗 炎症）。 M2MφS已进一步细分为M2A，M2B，M2C和M2D亚型。我们的 初步数据表明，伤口内的代谢景观是一个重要的变量 治愈并支持我们的总体观念，即伤口相关Mφ的免疫调节是 解决伤口所必需的。该研究项目的主要目标是开发初步 可以准确预测伤口是否会反应的生物标志物模型 当前护理标准。 为了实现这一目标，我们将利用一个离体Mφ极化模型来量化 宿主代谢健康（基于供体HEMA1C血清水平）在Mφ功能表型上。 mφ 将使用复杂的系统生物学方法对可塑性进行量化，并结合多路复用 细胞因子/趋化因子/生长因子谱，髓样基因表达，全球代谢组学，半 有针对性的脂质组学和实时的活细胞代谢分析。而我们的离体Mφ模型使用 从人类捐助者中收集的原代细胞，我们的候选生物标志物的确认需要 原位使用我们的复杂系统生物学方法来确认候选生物标志物可以是 用临床样品检测到。随着时间和 通过定量免疫组织化学和原位探测候选生物标志物 杂交。最后，随着时间的推移，原发性伤口组织将与靶向代谢物进行分析 生物标志物确认生物标志物作为临床靶标的效率。 最后，使用基于接收器 - 曲线特征（ROC）曲线的生物标志物发现统计数据 分析，将选择生物标志物将其纳入我们的预测模型。预测建模将 利用基于基准的预测模型的随机森林机器学习和测试效率 当前的临床护理，我们选择的生物标志物或两者的组合。曾经的统计强度 预测模型确定了最佳拟合 关心。最终，我们的希望是为更好地预测伤口治疗方案的基础， 促进新颖的世界护理疗法的设计，并迈出迈向精密医学的第一步 对我们的糖尿病退伍军人的伤口护理。