Fiber depolymerization: fracture, fragments, vanishing times, and stochastics in sickle hemoglobin.
The well-characterized rates, mechanisms, and stochastics of nucleation-dependent polymerization of deoxyhemoglobin S (HbS) are important in governing whether or not vaso-occlusive sickle cell crises will occur. The less well studied kinetics of depolymerization may also be important, for example in achieving full dissolution of polymers in the lungs, in resolution of crises and/or in minimizing gelation-induced cellular damage. We examine depolymerization by microscopic observations on depolymerizing HbS fibers, by Monte Carlo simulations and by analytical characterization of the mechanisms. We show that fibers fracture. Experimental scatter of rates is consistent with stochastic features of the analytical model and Monte Carlo results. We derive a model for the distribution of vanishing times and also show the distribution of fracture-dependent fiber fragment lengths and its time dependence. We describe differences between depolymerization of single fibers and bundles and propose models for bundle dissolution. Our basic model can be extended to dissolution of gels containing many fibers and is also applicable to other reversible linear polymers that dissolve by random fracture and end-depolymerization. Under the model, conditions in which residual HbS polymers exist and facilitate repolymerization and thus pathology can be defined; whereas for normal polymers requiring cyclic polymerization and depolymerization for function, conditions for rapid cycling due to residual aggregates can be identified.
Wang
Jiang Cheng
JC
Department of Physiology and Biophysics, Albert Einstein College of Medicine, The Bronx, New York, USA.
Kwong
Suzanna
S
Ferrone
Frank A
FA
Turner
Matthew S
MS
Briehl
Robin W
RW
D001704:Biopolymers / Q000737:chemistry; D003198:Computer Simulation; D006451:Hemoglobin, Sickle / Q000737:chemistry*; D006801:Humans; D007700:Kinetics; D008857:Microscopy, Interference; D008956:Models, Chemical; D009010:Monte Carlo Method; D013269:Stochastic Processes; D013997:Time Factors
D001704:Biopolymers; D006451:Hemoglobin, Sickle