(WHS-P20) OPTIMIZATION OF REAL-TIME QPCR FOR MEASURING INFLAMMATION INDEX IN DIABETIC FOOT ULCER WOUNDS IN MODEL TISSUE SAMPLES.
Friday, May 17, 2024
7:30 AM – 5:00 PM East Coast USA Time
Inflammation and wound healing are complex, linked processes that are alternated in nonhealing diabetic foot ulcers (DFU). Our research has shown that while initial pro-inflammatory activation of immune cells is critical for the initiation of wound healing processes, prolonged activation directly impairs it. After recognizing that transition from the early inflammatory to the late resolution phase is required for successful healing, we developed a composite biomarker using the ratio of 4 early-stage pro-inflammatory gene markers to 3 late-stage inflammation-resolution biomarkers, referred to as the Inflammation Index. The Inflammation Index is an indirect measurement of the wound’s healing stage. Our previous studies measured the Inflammation Index via RT-qPCR using RNA extracted from debrided wound tissue, suggesting that this score might have the potential to identify those wounds that are more likely to respond to conservative treatment versus those that may benefit from a more aggressive approach. To evaluate the expression of biomarkers that comprise the Inflammation Index, quality RNA is essential. The chronic wound environment is particularly damaging for RNA because of its high levels of enzymes and cellular debris containing RNases. Therefore, our goal in this project was to optimize biomarker detection and determine the minimum sample quality and quantity in which the Inflammation Index can be reliably detected using RT-qPCR. By using an experimental model of intact and partially degraded RNA from in vitro-cultivated macrophages derived from human primary monocytes, we proved how quality control (QC) metrics affect biomarker expression. We determined that degradation-influenced shifts of threshold values (Ct-values) can be compensated by calculating delta-Ct values between test genes and the mean values of several control genes. We demonstrated that the Inflammation Index can be measured on samples with even low-quality and quantity RNA. Additionally, we validated how sample storage/shipping conditions affect RNA QC metrics. Based on these results, we conclude that by using controllably degraded cell samples in vitro to model damaged tissue, the measurement of the Inflammation Index in DFU samples was appropriately optimized. From a translational perspective, these results validate the biomarker detection method (RT-qPCR) and determine the minimum QC metrics that must be satisfied for the biomarkers to be reliably measured, using real-world samples collected from the Diabetic Foot Consortium (DFC). By using samples from the DFC, we will measure the Inflammation Index’s ability to predict healing in response to the standard of care, in order to ultimately personalize treatment for patients with hard-to-heal ulcers and to refine entry criteria for clinical trials of new treatments.