Glomerular Hyperfiltration, Charge Selectivity, and the Low-Dimensional Structure of Glomerular Transport

Background The relative contributions of molecular size, electrostatic charge, and filtration rate to glomerular transport remain controversial. We hypothesized that glomerular sieving data contain a limited number of underlying transport modes that can be identified directly from experimental measurements. Methods Glomerular sieving coefficients were measured in anesthetized rats using neutral and anionic polysucrose during baseline conditions and glucagon-induced hyperfiltration. Data were analyzed using aligned-rank two-factor ANOVA, nonlinear mixed-effects regression of an electrostatic distributed two-pore model, pairwise correlation analysis, and principal component analysis. Results Hyperfiltration reduced the sieving of small and intermediate polysucrose molecules, whereas anionic polysucrose exhibited lower sieving coefficients than neutral polysucrose over a broad range of molecular sizes. An electrostatic distributed two-pore model accurately reproduced the observed effects of filtration rate and molecular charge and yielded an effective pore-wall charge density of 5.4 mC/m2 (95% confidence interval, 4.5 to 6.6). Pairwise correlation analysis revealed strong coupling between neighboring molecular sizes throughout the entire measured size range. Principal component analysis of the 2.5-8.0 nm size-selective region showed that the first principal component explained 96.3% of the variance and the first two principal components explained 99.9% of the variance. Separate analyses of the 2.5-5.0 nm and 5.0-8.0 nm transport regions showed that the first principal component explained 99.4% and 89.5% of the variance, respectively. Conclusions Glomerular sieving curves exhibited a highly constrained low-dimensional structure despite differences in molecular charge, filtration rate, and individual animals. The observed transport structure was consistent with distinct small-pore and large-pore transport domains and enabled highly effective principal component-based denoising of experimental sieving data.

Öberg, C. M.

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