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

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.