Multiscale Growth Kinetics of Model Biomolecular Condensates Under Passive and Active Conditions

TL;DR

This work addresses how enzymatic activity regulates the growth kinetics and local structure of biomolecular condensates formed from a cationic phosphorylatable peptide and polyU RNA. Using a multiscale approach that combines time-resolved USAXS, confocal microscopy, and CALVADOS-based coarse-grained MD, the authors contrast passive, charge-driven phase separation with active, phosphatase-driven regulation. In the passive regime, growth follows nucleation and Brownian coalescence with $\ angle R\rangle=(Kt)^{1/3}$ and $N\sim t^{-1}$, with peptide-decorated RNA subunits driving coalescence and eventual self-limitation by Coulomb repulsion. Under active conditions, condensates form a network-like structure with mass fractal dimension $D<3$ that matures before coarsening, and FRAP reveals faster nanoscale diffusion ($D_c\approx0.024~\mu\mathrm{m}^2\mathrm{s}^{-1}$) than in the passive case. Overall, enzymatic activity alters local structure and kinetic pathways, linking formation and dissolution of condensates to functional nanoscale dynamics across four decades in time and multiple length scales.

Abstract

Living cells exhibit a complex organization comprising numerous compartments, among which are RNA- and protein-rich membraneless, liquid-like organelles known as biomolecular condensates. Energy-consuming processes regulate their formation and dissolution, with (de-)phosphorylation by specific enzymes being among the most commonly involved reactions. By employing a model system consisting of a phosphorylatable peptide and homopolymeric RNA, we elucidate how enzymatic activity modulates the growth kinetics and alters the local structure of biomolecular condensates. Under passive condition, time-resolved ultra-small-angle X-ray scattering with synchrotron source reveals a nucleation-driven coalescence mechanism maintained over four decades in time, similar to the coarsening of simple binary fluid mixtures. Coarse-grained molecular dynamics simulations show that peptide-decorated RNA chains assembled shortly after mixing constitute the relevant subunits. In contrast, actively-formed condensates initially display a local mass fractal structure, which gradually matures upon enzymatic activity before condensates undergo coalescence. Both types of condensate eventually reach a steady state but fluorescence recovery after photobleaching indicates a peptide diffusivity twice higher in actively-formed condensates consistent with their loosely-packed local structure. We expect multiscale, integrative approaches implemented with model systems to link effectively the functional properties of membraneless organelles to their formation and dissolution kinetics as regulated by cellular active processes.

Figures (9)

• Figure 1: (a) Schematic representation of RRASLRRASL peptide (red) and polyU RNA (blue) forming condensates upon Coulomb interaction. (b) A solution of condensates in a quartz cuvette exhibits a high level of turbidity. (c) Confocal microscopy image of condensates obtained with 1,000 µM of peptide including 0.5% of TAMRA-peptide, and 0.5 g.L-1 of RNA. (d) Contour map of turbidity measured at 500 nm as a function of peptide and RNA concentrations. The red dashed line indicates electroneutrality.
• Figure 2: TR-USAXS measurements under passive conditions. (a) Scattering curves at different times of a solution containing 1,000 µM of peptide and 0.5 g.L-1 of RNA. (b) Mean radius $\langle R\rangle$ of condensates as a function of time for various peptide concentrations. The numbers in brackets are the peptide-to-RNA charge ratios. $\langle R\rangle$ is estimated by fitting the scattering curves with a model of lognormally-distributed spheres. The dashed line represents a scaling law in $t^{1/3}$. (c) Arbitrarily scaled number density of condensates versus time at various peptide concentrations. The dashed line is a scaling law in $t^{-1}$. The RNA concentration in (b) and (c) is 0.5 g.L-1 except for the highest peptide concentration of 2,000 µM where the RNA concentration is 1 g.L-1.
• Figure 3: Coarse-grained MD simulation of passively-formed condensates. The graph plots the number of peptides in the largest cluster as a function of the simulation time steps in logarithmic scale. The snapshots illustrate the coalescence events as labeled on the graph. Peptides (800 µM) are in green and RNA (0.5 g.L-1) in purple.
• Figure 4: (a) Confocal microscopy image of condensates obtained with a peptide concentration of 1,000 µM including 0.5% of TAMRA-peptide and 0.5 g.L-1 of RNA. The image is acquired 25 min after mixing components at 37 °C. (b) Condensate radius distribution ($N=707$) inferred from a larger view of (a). The red line is a fit with a lognormal distribution.
• Figure 5: TR-USAXS measurements under active conditions. (a) Scattering curves at various times (black solid lines) fitted with a polydisperse mass fractal model (blue dashed lines). The solution contains 1,000 µM of initially phosphorylated peptide, 0.5 g.L-1 of RNA and 800 U.mL-1 of LPP. The inset compares two scattering curves collected with peptide and RNA concentrations of 1,000 µM and 0.5 g.L-1, respectively, under passive (gray dashed line) and active (black solid line; 800 U.mL-1 of LPP), at times where the $I_0$ values are similar. (b) Mean condensate size $\langle\xi\rangle$ as a function of time for various peptide concentrations. LPP concentration is 800 U.mL-1 except in one experiment (gray discs; 1,200 U.mL-1). The dashed line indicates a scaling law in $t^{1/3}$. (c) Mass fractal dimension $D$ versus time for the same experimental conditions as in (b).