Fragility of foot process morphology in kidney podocytes arises from chaotic spatial propagation of cytoskeletal instability

Kidney podocytes’ function depends on fingerlike projections (foot processes) that interdigitate with those from neighboring cells to form the glomerular filtration barrier. The integrity of the barrier depends on spatial control of dynamics of actin cytoskeleton in the foot processes. We determined how imbalances in regulation of actin cytoskeletal dynamics could result in pathological morphology. We obtained 3-D electron microscopy images of podocytes and used quantitative features to build dynamical models to investigate how regulation of actin dynamics within foot processes controls local morphology. We find that imbalances in regulation of actin bundling lead to chaotic spatial patterns that could impair the foot process morphology. Simulation results are consistent with experimental observations for cytoskeletal reconfiguration through dysregulated RhoA or Rac1, and they predict compensatory mechanisms for biochemical stability. We conclude that podocyte morphology, optimized for filtration, is intrinsically fragile, whereby local transient biochemical imbalances may lead to permanent morphological changes associated with pathophysiology

Fragility of foot process morphology in kidney podocytes arises from chaotic spatial propagation of cytoskeletal instability.

Kidney podocytes' function depends on fingerlike projections (foot processes) that interdigitate with those from neighboring cells to form the glomerular filtration barrier. The integrity of the barrier depends on spatial control of dynamics of actin cytoskeleton in the foot processes. We determined how imbalances in regulation of actin cytoskeletal dynamics could result in pathological morphology. We obtained 3-D electron microscopy images of podocytes and used quantitative features to build dynamical models to investigate how regulation of actin dynamics within foot processes controls local morphology. We find that imbalances in regulation of actin bundling lead to chaotic spatial patterns that could impair the foot process morphology. Simulation results are consistent with experimental observations for cytoskeletal reconfiguration through dysregulated RhoA or Rac1, and they predict compensatory mechanisms for biochemical stability. We conclude that podocyte morphology, optimized for filtration, is intrinsically fragile, whereby local transient biochemical imbalances may lead to permanent morphological changes associated with pathophysiology

Fragility of foot process morphology in kidney podocytes arises from chaotic spatial propagation of cytoskeletal instability.

Kidney podocytes' function depends on fingerlike projections (foot processes) that interdigitate with those from neighboring cells to form the glomerular filtration barrier. The integrity of the barrier depends on spatial control of dynamics of actin cytoskeleton in the foot processes. We determined how imbalances in regulation of actin cytoskeletal dynamics could result in pathological morphology. We obtained 3-D electron microscopy images of podocytes and used quantitative features to build dynamical models to investigate how regulation of actin dynamics within foot processes controls local morphology. We find that imbalances in regulation of actin bundling lead to chaotic spatial patterns that could impair the foot process morphology. Simulation results are consistent with experimental observations for cytoskeletal reconfiguration through dysregulated RhoA or Rac1, and they predict compensatory mechanisms for biochemical stability. We conclude that podocyte morphology, optimized for filtration, is intrinsically fragile, whereby local transient biochemical imbalances may lead to permanent morphological changes associated with pathophysiology

Fragility of foot process morphology in kidney podocytes arises from chaotic spatial propagation of cytoskeletal instability.

Kidney podocytes' function depends on fingerlike projections (foot processes) that interdigitate with those from neighboring cells to form the glomerular filtration barrier. The integrity of the barrier depends on spatial control of dynamics of actin cytoskeleton in the foot processes. We determined how imbalances in regulation of actin cytoskeletal dynamics could result in pathological morphology. We obtained 3-D electron microscopy images of podocytes and used quantitative features to build dynamical models to investigate how regulation of actin dynamics within foot processes controls local morphology. We find that imbalances in regulation of actin bundling lead to chaotic spatial patterns that could impair the foot process morphology. Simulation results are consistent with experimental observations for cytoskeletal reconfiguration through dysregulated RhoA or Rac1, and they predict compensatory mechanisms for biochemical stability. We conclude that podocyte morphology, optimized for filtration, is intrinsically fragile, whereby local transient biochemical imbalances may lead to permanent morphological changes associated with pathophysiology

Processed and Reconstructed SBEM Imaging Data for Podocytes

This data package contains the segmented binary images (Level 1) and the Gaussian-smoothened reconstructed volumes (Level 2) of individual rat kidney podocytes that were used to create our dynamical models. Images have 11 nm/pixel in-plane (XY)-resolution and 210 nm out-of-plane (Z)-resolution. Reconstructed volumes all use a single coordinate system. Technical details of segmentation and reconstruction are provided in the Supplementary Information file associated with the manuscript

Spatial simulations of the response to perturbed bundling activity, β_b, highlighting potential compensatory mechanisms for actin instability.

<p><b>A-C</b>. Progressive loss of FPs due to a continued decrease of β_b imposed at time t = 0; snapshots at time (<b>A</b>) t = 0, <b>(B)</b> t = 500, and <b>(C)</b> t = 1500. <b>D-J.</b> Tests of combinations of transient perturbation in β_b and α_f to explore compensatory mechanisms. <b>(D)</b> Return to baseline β_b at time t₁ = 500 results in <b>(E)</b> recovery of the majority of the remaining foot process bundle concentrations at t₂ = 1500. <b>(F)</b> Decrease of α_b at time t₁ while holding β_b constant results in <b>(G)</b> similar stabilization. Finally, <b>(I)</b> increase of α_f while holding β_b constant <b>(J)</b> produces similar spatial results. All three interventions prevent progressive effacement (compare C with E, G and J). <b>(H)</b> Timecourses for spatial average of bundle concentration in the FPs identified by arrows in snapshots E, G and J (at time 1500, gray arrowhead). Linestyle follows the same pattern as arrows. The same color scale is used for all the 3-D snapshots of bundle concentrations. Parametric perturbations are listed in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005433#pcbi.1005433.s002" target="_blank">S1 Table</a>.</p

Analysis of bundle stability in the face of spatial differences in positive feedback (α_f), using an ODE model composed of 2 FP compartments.

<p>One FP fractional compartment (FP₂) is subject to a stimulus that increases actin polymerization by Δα_f, whereas the remaining fraction (FP₁) is subject to nominal polymerization conditions, α_f. This stimulus may either be sustained (shown in A) or transient (shown in B-G). <b>(A)</b> A 3-D plot showing steady state bundles on the vertical axis as a function of Δα_f/α_f and FP₂ (log scale). For this sustained enhancement, the bundle intensity in FP₁ (blue mesh) and FP₂ (red mesh) depend on the intensity Δα_f and the relative proportions of FP₂ to FP₁ (FP₁ + FP₂ = 1). As Δα_f increases, FPs with stronger feedback form stronger bundles. If the fraction of FPs with enhanced feedback (FP₂, red mesh) is small, the FPs with normal α_f (FP₁, blue mesh) are unperturbed, while the bundles in FP₂ are strengthened. Because there is a fixed total amount of actin, stronger bundling in FP₂ drains actin available for bundles in FP₁ until a threshold is reached at which collapse of actin bundles in FP₁ is observed. (<b>B)</b> Time course of a transient stimulus applied at t = 40. In C-E, the value of Δα_f follows this time course, with varying intensities; equal volume fractions for FP₁ and FP₂ were used, with red curves corresponding to FP₂ and blue to FP₁. (<b>C</b>) A small perturbation allows the system to return to the pre-stimulus steady state. (<b>D</b>) When the maximum Δα_f/α_f = 1 (i.e. FP₂ transiently reaches twice that of FP₁), a new stable steady state is generated where bundles have collapsed in FP₁ and increased in FP₂. (<b>E</b>) When the maximum Δα_f/α_f = 2 (i.e. FP₂ transiently reaches 3 times that of FP₁) there is a transient increase in FP₂ bundles followed by collapse and enhanced bundles in FP₁. The behavior displayed in C, D and E is the hallmark of a tristable system. See the text for an explanation. (<b>F</b>). The steady state values for concentration of bundles in fractions FP₁ (blue) and FP₂ (red) are shown as a function of stimulus intensity, consistent with C-E. (<b>G</b>) Different fractions of FP₁ and FP₂ will impact the steady state values (see also <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005433#pcbi.1005433.s008" target="_blank">S6 Fig</a>).</p