Identification of diagnostic and prognostic biomarkers to improve the management of diabetes-related ulcers
Published on Jun 1, 2014in Asian Pacific Journal of Tropical Disease
· DOI :10.1016/S2222-1808(14)60521-1
Abstract Introduction Diabetes-related ulcers are a common and severe complication of diabetes which is expected to increase in prevalence in line with projected global growth in rates of diabetes. Caring for these chronic wounds imposes a multi-billion dollar burden on the health care systems. These ulcers can prove lethal if untreated or not recognised and can lead to critical health complications. Methods To investigate underlying causes of wound chronicity, proteomic analyses of swab samples collected weekly from healing and non-healing diabetic foot ulcers was performed. Protein profiling was conducted based on Surface Enhanced Laser Desorption Ionisation Time of Flight (SELDI-TOF) mass spectrometry and statistical softwares were used to short list potential biomarkers. In addition, bottom-up proteomics was performed on healing and non-healing samples by SDS-PAGE and LC-MS/MS analysis using an AB SCIEX Triple TOF ® 5600 System. Trans-proteomic pipeline was used for data analyses and X! Tandem was used to search the database. Label-free quantitative proteomic analyses were performed using a computational tool called Abacus. Results (1) Statistical analyses of healing and non healing samples analysed on SELDI-TOF revealed 5 and 7 potential biomarkers ( m/z ) for samples with Texas score A1 and C1 respectively. (2) Bottom-up proteomics approach from both healing and non-healing samples identified 15 unique (healing) and 16 unique (non-healing) potential biomarkers. (3) Quantitative proteomic analyses resulted in 24 and 45 up-regulated healing candidates and 67 and 43 up-regulated nonhealing candidates for Texas score A1 and C1 wounds. (4) Relative quantification of 23 proteins related to oxidative stress has been identified through Abacus and 5/23 proteins have been validated using ELISA. Conclusions We are investigating these potential biomarkers using various biochemical, bioinformatics and statistical tools. A thorough investigation and study of the patterns may generate new protein candidates that can be used as potential prognostic and diagnostic biomarkers to improve the management of diabetic ulcers.