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.

Grant Number: 5I01BX005344-04
NIH Institute/Center: VA
Principal Investigator: Mary Cloud Ammons