Abstract
Clinical proteomics is an exciting new sub-discipline of proteomics that involves the application of proteomic technologies at the bedside to identify new biomarkers, associated with specific diseases. In this study to compare serum protein profiles between identical age-matched groups of fracture and non-fracture controls, we looked at the initial proteomic profile of 10 patients who had fractures and compared them to age-matched controls to see if there was any specific difference indicative of fracture.
Materials and Methods
10 patients with single fractures of the long bones, wrist or ankle gave a blood sample upon presentation at the fracture clinic. 10 healthy, age-matched, non-fracture volunteers also donated blood. Plasma was isolated and the albumin and IgG fractions removed before loading equal amounts of each sample onto 2 dimensional polyacrylamide gels for analysis by isoelectric point in the first dimension and molecular mass in the second dimension. Protein profiles between fracture patients and non-fracture controls were contrasted using Phoretix 2D analysis software.
Data analysis differentiated between the average gel of the patient group and the average gel of the control group. More than 300 protein spots were observed in both the control and patient group. Seven protein spots were identified which showed a statistically significant (p<0.05) difference between the control and patient samples. Of these, three spots (X, Y, Z) were clear, distinct and present in at least 80% of these gels. All the three spots were up regulated in the patient group as opposed to the control group. These proteins are currently being investigated further by MALDI-TOF TOF for specific protein identification.
Discussion
Proteomic analysis is already a powerful tool in the identification of disease markers. We aim to show here that there are differences seen in blood plasma profiles in fracture patients compared to non-fracture healthy controls. The differences seen may help us to understand the fracture repair process better.
