Quantitative analysis of mechanisms that govern red blood cell age structure and dynamics during anaemia.
Mathematical modelling has proven an important tool in elucidating and quantifying mechanisms that govern the age structure and population dynamics of red blood cells (RBCs). Here we synthesise ideas from previous experimental data and the mathematical modelling literature with new data in order to test hypotheses and generate new predictions about these mechanisms. The result is a set of competing hypotheses about three intrinsic mechanisms: the feedback from circulating RBC concentration to production rate of immature RBCs (reticulocytes) in bone marrow, the release of reticulocytes from bone marrow into the circulation, and their subsequent ageing and clearance. In addition we examine two mechanisms specific to our experimental system: the effect of phenylhydrazine (PHZ) and blood sampling on RBC dynamics. We performed a set of experiments to quantify the dynamics of reticulocyte proportion, RBC concentration, and erythropoietin concentration in PHZ-induced anaemic mice. By quantifying experimental error we are able to fit and assess each hypothesis against our data and recover parameter estimates using Markov chain Monte Carlo based Bayesian inference. We find that, under normal conditions, about 3% of reticulocytes are released early from bone marrow and upon maturation all cells are released immediately. In the circulation, RBCs undergo random clearance but have a maximum lifespan of about 50 days. Under anaemic conditions reticulocyte production rate is linearly correlated with the difference between normal and anaemic RBC concentrations, and their release rate is exponentially correlated with the same. PHZ appears to age rather than kill RBCs, and younger RBCs are affected more than older RBCs. Blood sampling caused short aperiodic spikes in the proportion of reticulocytes which appear to have a different developmental pathway than normal reticulocytes. We also provide evidence of large diurnal oscillations in serum erythropoietin levels during anaemia.
Savill
Nicholas J
NJ
Centre for Infectious Diseases, Institute of Immunology and Infection Research, University of Edinburgh, Edinburgh, United Kingdom. nick.savill@ed.ac.uk
Chadwick
William
W
Reece
Sarah E
SE
D000740:Anemia / Q000097:blood* / Q000139:chemically induced; D000818:Animals; D001499:Bayes Theorem; D001854:Bone Marrow Cells / Q000378:metabolism / Q000473:pathology; D004195:Disease Models, Animal; D004905:Erythrocyte Aging / Q000502:physiology*; D004906:Erythrocyte Count; D004912:Erythrocytes / Q000378:metabolism / Q000473:pathology*; D004921:Erythropoietin / Q000378:metabolism; D025461:Feedback, Physiological / Q000502:physiology; D008390:Markov Chains; D051379:Mice; D009010:Monte Carlo Method; D010659:Phenylhydrazines; D017701:Reticulocyte Count; D012156:Reticulocytes / Q000378:metabolism / Q000473:pathology*
D010659:Phenylhydrazines; C030299:phenylhydrazine; D004921:Erythropoietin