Dynamics of correlation between erythrocyte electrophoretic mobility and volume in stress

Abstract

At rest, rat erythrocyte electrophoretic mobility varied independently on cell volume. Emotional and physical stress gave rise to a short-lived moderate negative correlation between erythrocyte volume and the coefficient of asymmetry of electrophoretic mobility distribution, probably originating from accompanying metabolic and systemic influences.Key Words: erythrocyte electrophoretic mobility; erythrocyte volume; rats; stress; correlation Electrophoretic mobility (EPM) of erythrocytes as a function of their surface charge is a vitally important parameter determining cell aggregation resistance, blood fluidity, and microcirculation in capillaries. It is of interest, therefore, to identify the factors participating in EPM regulation. Cell volume may be one of such factors [7]. Surface charge is proportional to cell size [11]. Young erythrocytes have higher mobility compared to old cells [ 10]. Young mature erythrocytes are larger than aging cells [2], and therefore are characterized by higher EPM, To clarify this question we studied the correlation between EPM and erythrocyte volume using the model of emotional and physical stress in rats. MATERIALS AND METHODSExperiments were carried out on 30 outbred male albino rats weighing 160-180 g. The animals were forced to swim in ice-cold water until exhaustion and decapitated under ether narcosis 10 min (n=15) and 1 h (n=15) after swimming session. EPM parameters, erythrocyte and reticulocyte count, hematocrit, and mean erythrocyte volume were determined. The conInstitute of Physiology, St. Petersburg State University; Bashkirian State University, Ufa; Institute of Evolutional Physiology and Biochemistry, Russian Academy of Sciences, St, Petersburg trol group (n=10) comprised intact rats of the same weight.Erythrocyte EPM was measured automatically with a "Parmoquant-2" apparatus at 25~ in a 0.02-ml blood sample diluted (1:200) with 0.1 M phosphate buffer (pH=7.4). Electrophoretic scanograms were input to a computer. The mean EPM for each sample was calculated by averaging data of 250 individual measurements. Coefficients of asymmetry (As) and excess (Ex) for EPM distribution were determined for each sample and for the whole groups. The significance of differences was evaluated by Student's t test. Correlation analysis was performed using standard software.