Effect of clycation on surface charge and indentation stiffness of individual collagen fibrils

Abstract

Collagen is the most abundant and arguably most important structural protein in mammals. In soft tissues the protein molecules predominantly arrange into brils by forming dierent types of intermolecular bonds, one of which being Advanced Glycation Endproducts (AGEs). They are the result of a complicated cascade of non-enzymatic chemical reactions, commonly referred to as glycation, of the collagen proteins with sugar compounds and have been shown to contribute to the tissue deterioration observed in old age or common degenerative diseases such as diabetes mellitus. The exact mechanisms of tissue change and deterioration,however, are not fully understood and are therefore worthy of examination.In this study we model the physiological eects of AGEs by in-situ measurements of individual collagen brils from mouse tail tendon glycated with Methylglyoxal(MGO), a highly reactive compound that is also found as intermediate product in the physiological AGE reaction cascade.Nanoindentation measurements in phosphate buered saline (PBS) through AtomicForce Microscopy (AFM) were conducted on the same brils (N=10) before and after glycation to assess paired changes in bril stiness. In a similar fashion Kelvin Probe Force Microscopy (KPFM) in air was conducted on the very same brils before and after glycation to test for changes in electrical surface potential,which can be an important property for cell interaction and adhesion. Results show statistically signicant dierent changes in both bril stiness as well as surface potential compared to a similarly treated but unglycated control group(N=10). While means of elastic modulus Emod went up for both control DEmod=+15%(32%SD) and MGO DEmod=+42%(33%SD) groups, surface potential Phi means shifted slightly up for the control DPhi =+20%(21%SD) and significantly down for MGO DPhi=173%(86%SD) (SD...Standard Deviation). This was the first time that the stiffening of collagen fibrils caused by glycation could be directly linked to the change in the electrical surface potential.In the course of data analysis, for the determination of the contact point in AFM force curve analysis a novel approach through convolutional neural networks(CNNs) has been developed, trained and deployed. A comparison to other state of the art methods shows it to be signicantly more precise and robust.