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: 10533319
• 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/10533319
• 关键词: Activities of Daily Living; Address; Algorithms; Amputation; Anti-Inflammatory Agents; Assessment tool; Benchmarking; Biological Assay; Biological Markers; Blinded; Caregivers; Caring; Cellular Metabolic Process; Characteristics; Chronic; Clinical; Collecting Cell; 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; Macrophage; 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; Veterans; Veterans Health Administration; Wound models; biomarker discovery; biomarker identification; biomarker selection; candidate marker; candidate selection; chemokine; clinical care; clinically actionable; clinically relevant; computerized tools; cost; cytokine; design; diabetes management; diabetic; diabetic patient; diabetic ulcer; economic cost; efficacy testing; functional plasticity; healing; high dimensionality; immunoregulation; innovation; limb amputation; lipidomics; 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.

在退伍军人事务部医疗保健系统中，约 25% 的退伍军人患有糖尿病 2010财年，下肢截肢造成的经济负担超过2亿美元。 除了经济成本之外，这些退伍军人失去流动性和独立性也造成了重大影响。 尽管伤口护理方面有所创新，但仍对退伍军人及其护理人员的生活质量产生影响。 和糖尿病管理一样，糖尿病溃疡仍然是 VA 患者截肢的主要原因。 健康个体的正常伤口愈合迅速开始并通过良好的过程进行。 然而，在糖尿病伤口中，愈合过程在过渡过程中停滞不前。 在健康个体中，炎症消退和组织重组开始之间。 转变的特点是远离炎症和相关的人口转变 巨噬细胞（Mф）已被证实炎症与巨噬细胞之间存在相关性。 糖尿病；然而，皮肤慢性炎症在糖尿病中的作用尚未被探索。 MΦs表现出显着的功能可塑性，一般分为M1 MΦs （经典激活，促炎）和广泛的 M2 MΦ（选择性激活，抗炎） M2 MΦ 进一步细分为 M2a、M2b、M2c 和 M2d 亚型。 初步数据表明，伤口内的代谢状况是一个重要的变量 愈合并支持我们的总体观点，即伤口相关 MΦ 的免疫调节是 该研究项目的主要目标是开发一个初步的伤口解决方案。 生物标志物模型，可以准确预测伤口是否有反应 当前的护理标准。 为了实现这一目标，我们将利用离体 MΦ 极化模型来量化 宿主代谢健康（基于供体 HemA1c 血清水平）对 MΦ 功能表型的影响。 可塑性将使用复杂系统生物学方法进行量化，结合多重 细胞因子/趋化因子/生长因子谱与骨髓基因表达、整体代谢组学、半代谢组学 靶向组脂质和实时活细胞代谢分析，而我们的离体 MΦ 模型使用。 从人类捐赠者收集的原代细胞，确认我们的候选生物标志物将需要 使用我们的复杂系统生物学方法原位确认候选生物标志物可以 随着时间的推移，将收集主要伤口清创样本来检测。 通过定量免疫组织化学和原位荧光探测候选生物标志物 最后，随着时间的推移，将用目标代谢物对原发性伤口组织进行分析。 生物标志物，以确认生物标志物作为临床目标的功效。 最后，利用基于接受者曲线特征（ROC）曲线的生物标志物发现统计数据 分析后，生物标志物将被选择包含在我们的预测模型中。 随机森林机器学习和基于基准测试预测模型的有效性 当前的临床护理、我们选择的生物标志物或两者的组合一旦统计强度。 预测模型确定最佳拟合，该模型将与标准同时进行临床评估 最终，我们的希望是为更好地预测伤口治疗方案奠定基础， 推动新型伤口护理疗法的设计，迈出精准医疗的第一步 为我们的糖尿病退伍军人提供伤口护理。