BIOMED PhD Thesis Defense

Title:
Understanding Molecular Differences Between Healing Outcomes in Tissue Repair and Regeneration
 
Speaker:
Jessica M. Eager, PhD Candidate
School of Biomedical Engineering, Science and Health Systems
Drexel University
 
Advisor:
Kara Spiller, PhD
Professor
School of Biomedical Engineering, Science and Health Systems
Drexel University

Details:
The prevalence of poor wound healing continues to increase leading to significant morbidity, mortality, and economic consequences. Understanding the molecular mechanisms behind improper tissue repair and regeneration is important for developing new therapies and particularly personalized medicine approaches such as biomarkers. The reasons for impaired are complex and still being uncovered, but dysregulated inflammation is a known major contributor. Following injury, the cascade begins with an inflammatory state that is progressively dampened as the tissue returns to homeostasis. Macrophages, the primary cells of the immune response, are key regulators throughout all phases of healing due to their inherent plasticity in activation states. At early stages of healing, they exist primarily as the pro-inflammatory (M1) phenotype and transition to phenotypes associated with the resolution of healing (M2 subtypes). Therefore, the goal of this work was to identify molecular differences related to inflammation and macrophage phenotypes to lay groundwork for future biomarker studies in two injuries: torn rotator cuff tendons and chronic diabetic foot ulcers.

Impaired healing following rotator cuff repair is a major concern, with re-tear rates as high as 94%. A method to predict whether patients are likely to experience poor surgical outcomes would change clinical practice. While various patient factors such as age and tear size have been linked to poor functional outcomes, it is currently very challenging to predict outcome prior to surgery. The purpose of this study was to evaluate gene expression differences in tissue collected during surgery between patients who ultimately went on to have good outcomes or who experienced a re-tear in an effort to determine if surgical outcomes can be predicted. A case-control study was conducted to elucidate if differences in gene expression of tendon at the time of surgery was associated with surgical outcome. Rotator cuff tissue was collected at the time of surgery from 140 patients. Patients were tracked for a minimum of 6 months to identify those with good or poor outcomes using clinical functional scores and follow up magnetic resonance imaging to confirm failure to heal or re-tear. Gene expression differences between 8 patients with poor outcomes and 28 patients with good outcomes were assessed using a multiplex gene expression analysis via NanoString ™and a custom-curated panel of 145 genes related to various stages of rotator cuff healing. Although significant differences in the expression of individual genes were not observed, gene set enrichment analysis highlighted major differences in gene sets, with patients who had poor healing outcomes showing greater expression of gene sets related to extracellular matrix production which is critical for cell and tissue structure, cellular biosynthetic pathways, and the pro-inflammatory (M1) macrophage phenotype. Together, these results suggest that a more pro-inflammatory, fibrotic environment prior to repair may play a role in poor healing outcome.

Diabetic foot ulcers (DFUs) are a common complication and notoriously and difficult to treat. Here, we analyzed a focused panel of 227 inflammation- and macrophage-related genes in DFU tissue samples collected from 27 subjects. Paired samples were analyzed for changes over time and their relationships to the wound microbiome, determined by 16s ribosomal RNA sequencing. Although inflammation-related genes were generally expressed at higher levels in non-healing DFUs compared to healing DFUs, these effects resulted from only about half of the subjects with non-healing DFUs. The other half of the subjects expressed similar or lower levels of inflammation-related genes compared to healing DFUs, suggesting that heterogeneity in the wound microenvironment may contribute to variability in treatment response. Partial least squares discriminant analysis (PLS-DA) further confirmed that these two subtypes of non-healing DFUs were quite different from each other. Healing and non-healing DFUs also differed in how they changed over time; for example, expression of CCL1 increased in healing DFUs and decreased in non-healing DFUs over time, suggesting possible utility as a biomarker. When the data were normalized to account for differences in immune cell number, opposite trends in expression of TNAIP6 and RPL37A over time were observed. In non-healing DFUs, many genes were correlated with microbial diversity and with Staphylococcus species. These trends were not observed in healing DFUs, suggesting less communication with microbiota. Overall, the results suggest that differences in inflammation and crosstalk with the microbiome contributes to the substantial heterogeneity observed in the response of human chronic DFUs to various treatments.

Altogether, these findings indicate that it is possible to detect molecular differences related to inflammation and macrophage phenotype in local tissues between healing outcomes. With validation in larger cohorts, these results may ultimately lead to diagnostic methods to predict poor outcomes, guide treatment options, develop novel therapeutics, and future biomarker studies.