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

在退伍军人事务部的医疗保健系统中，大约25%的退伍军人患有糖尿病 2010财年，截肢造成的经济负担超过2亿美元。 除了经济成本，这些退伍军人失去机动性和独立性还有很大的影响 对退伍军人及其照顾者的生活质量的影响。尽管在两个伤口护理方面都有创新 在糖尿病治疗方面，糖尿病溃疡仍然是退伍军人管理局患者截肢的主要原因。 健康人的正常伤口愈合启动很快，并通过良好的 具有特点的迭代步骤；然而，在糖尿病伤口中，愈合过程在过渡阶段停滞不前 在消解炎症和启动组织重组之间。在健康的个体中，这一点 过渡的特征是远离炎症和与之相关的人口转移 巨噬细胞(Mф)。已经很好地证实，炎症和 糖尿病；然而，皮肤慢性炎症在糖尿病患者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 MΦS(交替激活，抗炎) 炎症性)。M2 MΦS又进一步分为M2a、M2b、M2c和M2d亚型。我们的 初步数据显示，伤口内的新陈代谢状况是 愈合并支持我们的总体观点，即创伤相关MΦS的免疫调节是 对于伤口的解决来说是必要的。这项研究项目的主要目标是开发一种初步的 能够准确预测伤口是否有反应的生物标志物模型 目前的护理标准。 为了实现这一目标，我们将利用体外MΦ极化模型来量化 宿主代谢健康(基于供体血液A1c水平)对MΦ功能表型的影响。MΦ 可塑性将使用复杂系统生物学方法进行量化，其中包括多路 细胞因子/趋化因子/生长因子与髓系基因表达的关系，全球代谢组学，半 靶向脂质组学，以及实时、活的细胞代谢图谱。而我们的体外MΦ模型使用 从人类捐赠者收集的原代细胞，我们的候选生物标记物的确认将需要 使用我们的复杂系统生物学方法现场确认候选生物标记物可以 用临床样本进行检测。一次伤口清创样本将随着时间的推移而收集，对于 应用定量免疫组织化学和原位荧光技术寻找候选生物标志物 杂交。最后，初级伤口组织将随着时间的推移用目标代谢物进行分析。 确认作为临床靶点的生物标记物的有效性。 最后，利用基于接收曲线特征(ROC)曲线的生物标志物发现统计 分析，生物标志物将被选为包括在我们的预测模型中。预测建模将 利用随机森林机器学习和基于基准的预测模型的有效性测试 目前的临床护理，我们选择的生物标志物，或两者的组合。曾经的统计实力 预测模型确定最佳匹配，该模型将在临床上与 关心。最终，我们的希望是为更好地预测伤口治疗方案奠定基础， 推动创面护理新疗法设计，迈向精准医学的第一步 为我们的糖尿病退伍军人提供伤口护理。

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.