Spatial control of irreversible protein aggregation
Abstract
Liquid cellular compartments form in the cyto or nucleoplasm and can regulate aberrant protein aggregation. Yet, the mechanisms by which these compartments affect protein aggregation remain unknown. Here, we combine kinetic theory of protein aggregation and liquidliquid phase separation to study the spatial control of irreversible protein aggregation in the presence of liquid compartments. We find that even for weak interactions aggregates strongly partition into the liquid compartment. Aggregate partitioning is caused by a positive feedback mechanism of aggregate nucleation and growth driven by a flux maintaining the phase equilibrium between the compartment and its surrounding. Our model establishes a link between specific aggregating systems and the physical conditions maximizing aggregate partitioning into the compartment. The underlying mechanism of aggregate partitioning could be used to confine cytotoxic protein aggregates inside dropletlike compartments but may also represent a common mechanism to spatially control irreversible chemical reactions in general.
https://doi.org/10.7554/eLife.42315.001Introduction
Spatial control within living cells is essential to many cellular activities, ranging from the local control of protein activity to the uptake of pathogens or the management of wastes (Alberts, 2017). Understanding the mechanisms underlying regulation of cell activities in space and time is key not only for biological function, but also in view of understanding and eventually controlling cellular dysfunction (Knowles et al., 2011; Knowles et al., 2014; Chiti and Dobson, 2006; Gitler et al., 2017; Michaels et al., 2018). The spatial organization of cellular activities is often associated with membranebound organelles that ensure permeation only for certain molecules of specific molecular structure (Neupert and Herrmann, 2007; Wiedemann and Pfanner, 2017; Dukanovic and Rapaport, 2011). Recently, new types of organelles have been discovered that do not possess a membrane. They are referred to as nonmembranebound compartments and they share most hallmark properties with actual liquidlike droplets (Brangwynne et al., 2009; Brangwynne, 2013; ElbaumGarfinkle et al., 2015; Zhu and Brangwynne, 2015; Banani et al., 2017). Unlike organelles surrounded by membranes, these nonmembranebound compartments are formed by liquidliquid phase separation. In many cases, this phase separation is driven by disfavoring interactions between the constituent molecules of the compartment and the surrounding cyto or nucleoplasm (Hyman et al., 2014; Brangwynne et al., 2015). The partitioning of other intracellular molecules into such dropletlike compartments is then controlled by their relative interactions with the constituent molecules of the compartment.
These dropletlike compartments are ubiquitous inside living cells (Banani et al., 2017). For instance, they emerge prior to cell division (Brangwynne et al., 2009; Parker and Sheth, 2007), and form as a response to cellular stress (Patel et al., 2015; Malinovska et al., 2013; Molliex et al., 2015). They have been shown to enrich proteins (HernándezVega et al., 2017; Woodruff et al., 2017; Mateju et al., 2017) and genetic material (Parker and Sheth, 2007; Saha et al., 2016; Zhang et al., 2015) providing distinct environments for chemical reactions and biological function. The molecules hosted inside these compartments may even be protected against other agents from the cytoplasm (Franzmann et al., 2018) or face conditions facilitating their molecular repair (Mateju et al., 2017; Ganassi et al., 2016; Alberti et al., 2017; Alberti and Carra, 2018; Jain et al., 2016; Specht et al., 2011). In addition to these roles, recent evidence suggests that liquid cellular compartments could play an important role in regulating pathological protein aggregation (Alberti and Hyman, 2016; Shin and Brangwynne, 2017). An example is the irreversible assembly of amyloids into fibrillar aggregates, a process that is linked to a large variety of currently incurable diseases (Dobson, 2003; Knowles et al., 2014; Lashuel et al., 2002; Catalano et al., 2006; Benilova et al., 2012; Campioni et al., 2010), such as Alzheimer’s and Parkinson’s diseases, amyloidosis or typeII diabetes. As another example, a chaperone in yeast uses a prionlike, intrinsically disordered domain to bind and sequester misfolded proteins in protein deposition sites (Grousl et al., 2018; Boczek and Alberti, 2018). Moreover, misfolded and pathological proteins can accumulate inside liquidlike stress granules triggering the aggregation kinetics inside these compartments. The presence of this phase separated compartment can promote the formation of fibrillar aggregates, and prevent aggregation outside the stress granules (Molliex et al., 2015; Mateju et al., 2017). Thus, the corresponding cytotoxic effects of protein aggregates are expected to be strongly localized in space as well. However, whether weak protein interactions are sufficient to significantly change the aggregate concentration in the compartment relative to homogeneous aggregation and how the physical parameters of aggregation and phase separation determine the partitioning of aggregates remains an open question.
Here, we combine the kinetics of irreversible protein aggregation with the theory of liquidliquid phase separation to develop a model of irreversible assembly of protein fibrils in the presence of dropletlike compartments. We use this model to predict the partitioning of aggregates into the liquid compartment as a function of the fundamental physical parameters underlying aggregation kinetics and phase separation. We find that relatively weak interactions between the protein monomers and the liquid compartment molecules are sufficient to enrich the concentration of aggregates within the liquid compartment by several orders of magnitudes relative to homogeneous aggregation (Figure 1). This strong enrichment of aggregates emerges because the liquid compartment acts as continuous sink of monomers during the aggregation dynamics, thus promoting intracompartment aggregation but suppressing aggregation outside of the compartment. Moreover, we find that aggregate partitioning is more pronounced for larger (smaller) compartments depending on the relative values of the reaction orders for primary and secondary nucleation. Our results suggest that cellular liquid compartments are ideal to control irreversible protein aggregation in space. In particular, the compartment volume, which is determined by the mean concentration of phase separated protein, represents a relevant control parameter for intracompartment positioning of aggregate amount and size. The underlying physical mechanism might also be relevant in the context of spatial regulation of other irreversible chemical reactions where liquid compartments act as biomolecular microreactors.
Model for liquid compartments controlling protein aggregation
To capture the interplay between liquid phase separation and protein aggregation kinetics we start with a model of two coexisting phases. One phase could be rich in proteins for example and coexist with a phase rich in another protein component, lipid, or water. Monomers that are prone to aggregate can partition differently into these phases. This partitioning is determined by the relative interactions between the majority components of each phase with the monomers. We consider the case where the partitioning of monomers is close to equilibrium during the kinetics of aggregation. This assumption is well justified since small, weakly interacting molecules such as the aggregating monomers diffuse between seconds and minutes through a cell of size in the order of tens of $\mu m$ (Brangwynne et al., 2009; Griffin et al., 2011), while typical time scales of aggregation in vitro are in the order of hours (see, for example, Cohen et al., 2013). Furthermore, the diffusion of aggregates is highly hindered as long fibrillar aggregates experience a much larger hydrodynamic drag force and can get entangled with cytoskeletal filaments and other assembled fibrils (de Gennes, 1971; Rubinstein, 1987). Finally, at large enough density and size, fibrils may even form solidlike gels (Mateju et al., 2017) further slowing down their mobility. All these effects imply that we may safely neglect diffusion of large aggregates and consider the typical case that monomers diffuse quickly relative to their aggregation kinetics.
We also consider the case where monomers and aggregates are dilute enough to neglect their influence on the composition of the two coexisting protein phases. Typical values of volume fractions for monomers of Amyloidβ, ${c}_{\mathrm{m}}{\nu}_{\mathrm{m}}$ (radius of gyration in the range 1–2 nm [Sajfutdinow et al., 2018]), at physiological concentrations between 100 pM to 1 nM are in the range of 10^{9} to 10^{8}. Time scale separation and dilute monomers together ensure that the compartment can coexist at thermodynamic equilibrium while the partitioning kinetics of monomers may weakly deviate from the partitioning equilibrium. Thus, we first discuss the partitioning of monomers into phase separated compartments at equilibrium and then consider small deviations from this equilibrium to understand its consequences for protein aggregation.
Phase separation and partitioning of monomers at equilibrium
We consider a system of total volume $V$ hosting a single liquid compartment (a droplet for example) of a condensed phase I of volume $V}^{\mathrm{I}$. The compartment itself forms by liquidliquid phase separation between the two components $A$ and $B$. Compartment I is composed of the component $A$ and a small fraction of component $B$, while compartment II has a small amount of $A$ and a large amount of $B$, as depicted in Figure 1. Each compartment creates a distinct environment for the aggregating monomers.
For simplicity, we discuss the case of an incompressible system where the aggregating monomers ‘m’ and aggregates ‘a’ are dilute, that is ${c}_{\mathrm{m}}{\nu}_{\text{m}}\ll 1$ and ${c}_{\mathrm{a}}{\nu}_{\text{a}}\ll 1$, with ${c}_{\text{m}}$ and ${c}_{\text{a}}$ denoting the concentrations of monomers and aggregates and ${\nu}_{\text{m}}$ and ${\nu}_{\text{a}}$ are the respective molecular volumes. The assumption of dilute monomers and aggregates imply that for an incompressible system, the volume fractions ${\varphi}_{A}$ and ${\varphi}_{B}$ of the protein components $A$ and $B$ obey, ${\varphi}_{A}+{\varphi}_{B}=1{c}_{\mathrm{m}}{\nu}_{\mathrm{m}}{c}_{\text{a}}{\nu}_{\text{a}}\simeq 1$, where we abbreviate ${\varphi}_{A}=\varphi $ in the following. As a result, the monomers may partition differently into the respective minority and majority phases, but, due to their dilute concentrations, they do not affect the degree of phase separation. Under these circumstances and in the absence of binding processes, the partitioning of monomers in the two phases is governed by the relative interaction strength $\mathrm{\Delta}\chi $ between the monomers with the $A$ and the $B$ components, respectively. If $\mathrm{\Delta}\chi $ is large and positive, monomers favor the presence of the majority component $A$ in compartment I. In this case, we expect a more pronounced partitioning of monomers into compartment I. Contrariwise, when $\mathrm{\Delta}\chi $ is large and negative, monomers favorably partition into compartment II. The degree of monomer partitioning at equilibrium can be calculated using the condition that the chemical potentials of monomers associated with compartment I and II are balanced (see Figure 2(a), and Appendix 1 for the derivation), and allows us to define the monomer partitioning
where $\mathrm{c}}_{\mathrm{m}}^{\mathrm{I}$, $\mathrm{c}}_{\mathrm{m}}^{\mathrm{I}$ are the monomer concentrations in phases I and II, respectively, $\nu $ denotes the molecular volume of $A$ and $B$ molecules, and ${\varphi}^{\mathrm{I}}{\varphi}^{\mathrm{I}\mathrm{I}}\in [0,1]$ is the degree of phase separation of the $A$component. Then the relative partitioning of the total monomer concentration, ${c}_{\mathrm{m}}^{\mathrm{t}\mathrm{o}\mathrm{t}}=\left({c}_{\mathrm{m}}^{\mathrm{I}}{V}^{\mathrm{I}}+{c}_{\mathrm{m}}^{\mathrm{I}\mathrm{I}}{V}^{\mathrm{I}\mathrm{I}}\right)/V$, is given by the expressions $c}_{\mathrm{m}}^{\mathrm{I}}=\xi (\overline{\varphi})\mathrm{\Gamma}{c}_{\mathrm{m}}^{\mathrm{t}\mathrm{o}\mathrm{t}$ and $c}_{\mathrm{m}}^{\mathrm{I}\mathrm{I}}=\xi (\overline{\varphi}){c}_{\mathrm{m}}^{\mathrm{t}\mathrm{o}\mathrm{t}$, where the partition degree
captures the impact of the relative size of the compartment volume ${V}^{\mathrm{I}}(\overline{\varphi})/V$. The volume of the compartment I is in turn controlled by the mean volume fraction $\overline{\varphi}$ of $A$ molecules in the system in terms of the relationship ${V}^{\mathrm{I}}(\overline{\varphi})=V(\overline{\varphi}{\varphi}^{\mathrm{I}\mathrm{I}})/({\varphi}^{\mathrm{I}}{\varphi}^{\mathrm{I}\mathrm{I}})$, where we neglected the volume contribution of monomers and aggregates due to the considered dilute conditions. For finite sized compartments, the equilibrium volume fractions, $\varphi}^{\mathrm{I}$ and $\varphi}^{\mathrm{I}\mathrm{I}$, are slightly increased due to the Laplace pressure. However, for compartments significantly exceeding the size of the molecules the relative increase is weak and is thus neglected in the following (see Appendix 1).
Model for protein aggregation coupled to nonequilibrium monomer partitioning
Due to the separation of time scales of monomer diffusion and monomer aggregation, the partitioning of monomers into the compartment is close to equilibrium at all times of the aggregation kinetics and thus the relative fraction of monomers is approximately governed by the monomer partitioning $\mathrm{\Gamma}$, Equation 1. However, as the aggregation kinetics decreases the amount of monomers inside each phase, aggregation couples to the partitioning. This coupling is represented by a diffusive flux of monomer with a rate ${J}^{\alpha}$ in each phase, that attempts to maintain the monomer partitioning close to equilibrium. In the limit of a sharp interface separating the liquid compartment from the bulk, there is no aggregation at the interface, ${J}^{\mathrm{I}}={J}^{\mathrm{I}\mathrm{I}}\equiv J$. Furthermore, to linear order, the flux $J$ between the phases is proportional to the difference of monomer partitioning with respect the equilibrium value $\mathrm{\Gamma}$ (see Appendix 2 for the derivation) and is of the form:
where $M}_{\mathrm{m}}^{\alpha}={c}_{\mathrm{m}}^{\alpha}{m}_{\mathrm{m}$ (with $m}_{\mathrm{m}$ as monomer mass) is the monomer mass concentration in compartment $\alpha =\mathrm{I},\mathrm{I}\mathrm{I}$, and $k$ denotes the rate at which monomer partitioning relaxes back to the equilibrium given by Equation 1. For simplicity, we consider the case where diffusion of monomers is constant and equal in each phase, and not affected by the aggregates.
Very generally, in a homogeneous solution, irreversible protein aggregation results from the combined action of several microscopic events, including (i) primary nucleation, whereby monomers spontaneously interact to form the smallest stable aggregate structures, (ii) fibril elongation, and (iii) secondary (i.e. aggregatedependent) nucleation processes (Michaels and Knowles, 2014; Michaels et al., 2016; Arosio et al., 2016; Michaels et al., 2018; Törnquist et al., 2018). Secondary nucleation mechanisms (Törnquist et al., 2018) have been found to be active in many aggregating protein systems, ranging from prions to amyloidogenic proteins (Zhu et al., 2003; Kundel et al., 2018; Ruschak and Miranker, 2007; Meisl et al., 2014; Cohen et al., 2013); key examples of such secondary nucleation processes include fibril fragmentation, lateral branching and surfacecatalyzed secondary nucleation.
In the presence of a liquid compartment, irreversible protein aggregation of fibrillar structures occurs within each phase as a consequence of both primary and secondary nucleation, and growth of aggregates via their ends, each event occurring with rate constants ${k}_{1}$, ${k}_{2}$, and ${k}_{+}$(Michaels and Knowles, 2014; Michaels et al., 2016; Arosio et al., 2016; Michaels et al., 2018). We have seen that the key term in our model is the difference between the monomer concentration inside and outside of the compartment which leads to the diffusive flux of monomers ${J}^{\alpha}$ between the phases (Equation 3a), which connects the effects of phase separation and protein aggregation. The coupled equations describing protein aggregation kinetics in both phases can be written as
Here, Equation (3b) describes the rate of formation of new fibrils in each compartment ($\alpha =\mathrm{I},\mathrm{I}\mathrm{I}$) through primary nucleation, fragmentation or surface catalyzed secondary nucleation. In the case of primary nucleation, the rate of formation of new aggregates depends solely on the concentration of monomers, where the reaction order ${n}_{1}$ describes the concentration dependence of nucleation. For secondary processes, including fragmentation and surfacecatalyzed secondary nucleation, the rate of formation of new aggregates is proportional to the aggregate mass concentration; the dependence of the rate on the monomer concentration is described by the reaction order ${n}_{2}$ (the case ${n}_{2}=0$ corresponding to fragmentation). Note that both primary and secondary nucleation of aggregates are nonclassical, multistep nucleation processes; hence, the reaction orders ${n}_{1}$ and ${n}_{2}$ do not necessarily correlate to the physical size of nuclei (Šarić et al., 2016). Equation (3c) captures the buildup of aggregate mass within each compartment due to elongation of existing aggregates, which occurs by monomer addition at their ends. Finally, Equation (3d) models the population balance of monomers in each compartment as a result of two effects: (i) monomer depletion due to aggregate growth (see Equation (3c)) and (ii) the monomer flux between compartments I and II; this flux is given by Equation (3a) and ensures that partitioning is maintained close to the monomer partitioning factor $\mathrm{\Gamma}$.
While the monomer partitioning factor $\mathrm{\Gamma}$ (Equation 1) governs the constant ratio of the time dependent concentrations in compartment I and II, the partitioning degree $\xi $ (Equation 2) determines how the total monomer concentration, which decays over time as a result of aggregation, is split between the two compartments at any time point during the kinetics of aggregation. As we will see, both parameters will be crucial in controlling the degree of aggregate partitioning into the compartments.
Irreversible aggregation in the presence of phase separated compartments
To understand how protein aggregation kinetics couples to the two phase separated compartments in terms of the physical parameters $\mathrm{\Gamma}$ and $\xi $, we constructed explicit analytical solutions to the set of nonlinear kinetic Equation (3) by exploiting an analogy to classical mechanics (Michaels et al., 2016 and Appendix B for details of the calculations), and compared these with numerical solutions of (Equation 3).
Monomer partitioning affects nucleation and growth of aggregates between the compartments
In the limit of fast monomer diffusion, the aggregation kinetics in each compartment is controlled by a set of effective rate parameters. The relative magnitude of these effective rates between compartment I and II at early times scales with the monomer partitioning as ${\mathrm{\Gamma}}^{{n}_{1}}$, while at late times, the corresponding ratio of these rates scales with ${\mathrm{\Gamma}}^{({n}_{2}+1)/2}$ (see Appendix 3, Equation (39) and Equation (40)). Thus, the aggregate growth inside compartment I is faster than in compartment II if there is enrichment of monomers in the condensed phase ($\mathrm{\Gamma}\phantom{\rule{thinmathspace}{0ex}}>\phantom{\rule{thinmathspace}{0ex}}1$). Moreover, the relative magnitudes of growth rate at early times solely depends on the reaction order of primary nucleation, ${n}_{1}$, while at late times, relative growth is determined by the reaction order of secondary nucleation, ${n}_{2}$.
Phase separated compartments mediate a positive feedback for aggregate growth
This difference in growth rates between the phases can be qualitatively explained by the rapid preference of monomers to recover phase equilibrium (Figure 3(a)). The enhanced monomer concentration in compartment I causes aggregates to nucleate first inside compartment I. As a consequence, elongation of aggregates is more pronounced inside compartment I leading to a stronger consumption of monomers. This difference in monomer consumption between the compartments couples to the flux (Equation 3), which forces more monomers to diffuse into compartment I to maintain partitioning equilibrium, even as aggregates grow. This positive feedback mechanism in compartment I is accompanied by negative feedback for compartment II, which continuously loses monomers leading to a slowing down of the aggregation kinetics outside. Thus, the coupling between the aggregation kinetics and phase separation, mediated by diffusion of monomers (Equation 3), is key to determine aggregate enrichment/depletion in each phase.
Positive feedback for aggregate growth causes strong aggregate partitioning
To understand this feedback mechanism, we study the time evolution of the aggregate concentration inside each phase, ${c}_{\mathrm{a}}^{\mathrm{I}}(t)$ and ${c}_{\mathrm{a}}^{\mathrm{I}\mathrm{I}}(t)$ (Figure 3(b)). The first aggregates are initiated by primary nucleation and solely determined by the monomer concentration. Because monomer concentrations in the compartments are slaved due to the rapid flux that maintains partitioning equilibrium, the time evolution of the aggregate concentrations in the early regime of the aggregation kinetics are slaved as well, following $c}_{\mathrm{a}}^{\mathrm{I}}(t)/{c}_{\mathrm{a}}^{\mathrm{I}\mathrm{I}}(t)\propto {\mathrm{\Gamma}}^{{n}_{1}$. When aggregates start consuming monomers via elongation, the flux of monomers from compartment II to I causes a saturation of the aggregate concentration outside the compartment II, while the concentration of aggregates in compartment I increases significantly. This rapid increase of growth is facilitated by the continuous influx of monomers (positive feedback). As monomers get depleted in the entire system the growth of aggregates also saturates in compartment I. Most importantly, the resulting asymptotic concentrations at large time scales, ${c}_{\mathrm{a}}^{\mathrm{I}}(\mathrm{\infty})$ and ${c}_{\mathrm{a}}^{\mathrm{I}\mathrm{I}}(\mathrm{\infty})$, can differ by several orders of magnitude, even for modest values of $\mathrm{\Gamma}$ corresponding to weak relative interactions.
Enrichment and depletion relative to homogeneous aggregation is determined by the reaction orders
To elucidate the impact of the reaction orders on the aggregation kinetics, we first consider the enrichment of aggregates relative to the case of homogeneous aggregation, that is for $\mathrm{\Gamma}=1$. For large values of monomer partitioning, the asymptotic concentrations in compartments I and II at large times relative to the homogeneous aggregate concentration $c}_{\mathrm{a}}^{\mathrm{t}\mathrm{o}\mathrm{t}}{}_{{\varphi}^{\mathrm{I}}={\varphi}^{\mathrm{I}\mathrm{I}}$ at large times read
where $w$ is a dimensionless numerical prefactor (Appendix 3, Equation 52). We see that for a large monomer partitioning factor $\mathrm{\Gamma}$, the partitioning of aggregates inside compartment I gets more pronounced, while aggregates in compartment II are more depleted relative to the homogeneous case (Figure 3(c)). Most importantly, the value of the terminal values of aggregate concentrations for given monomer partitioning factor are controlled by the reaction orders for primary and secondary nucleation, ${n}_{1}$ and ${n}_{2}$. The role of ${n}_{1}$ and ${n}_{2}$ results directly from the interplay between aggregate growth and nucleation and their dependence on the monomer concentration.
Aggregate concentration in the compartments is controlled by compartment volume
Having understood the role of the monomer partitioning factor $\mathrm{\Gamma}$ in aggregation kinetics, we now turn to how the asymptotic concentrations of aggregates in each compartment depend on the volume of the compartments. The dependence on compartment volume is given the partition degree $\xi (\overline{\varphi})$. From Equation 2, we see that the partition degree $\xi (\overline{\varphi})\in [1,{\mathrm{\Gamma}}^{1}]$, where the value of one is relevant for small compartments (Figure 2(b)). Following Equations (4) and (5), we see that for a small volume of compartment I, enrichment and depletion exhibit an inverse scaling, i.e. $c}_{\mathrm{a}}^{\mathrm{I}}(\mathrm{\infty})\propto {\left({c}_{\mathrm{a}}^{\mathrm{I}\mathrm{I}}(\mathrm{\infty})\right)}^{1}\propto {\mathrm{\Gamma}}^{\frac{{n}_{2}+1}{2}$, which is solely dependent on the reaction order for secondary nucleation. Contrariwise, when the volume of compartment I is large, enrichment of aggregates inside I vanishes, while depletion inside compartment II then solely depends on the reaction order for primary nucleation, $c}_{\mathrm{a}}^{\mathrm{I}\mathrm{I}}(\mathrm{\infty})\propto {\mathrm{\Gamma}}^{{n}_{1}$.
This switch between aggregate partitioning governed by secondary nucleation, to a partitioning solely determined by primary nucleation, arises from primary nucleation events occurring first inside compartment I due to a higher monomer concentration ($\mathrm{\Gamma}\phantom{\rule{thinmathspace}{0ex}}>\phantom{\rule{thinmathspace}{0ex}}1$). Once the first aggregates have formed via primary nucleation inside compartment I, small and large compartments behave fundamentally differently. If compartment I is small, only a few aggregates can form via primary nucleation due to the small compartment size. As aggregates begin to grow earlier in compartment I, the unbalance of monomers causes a flux from II to I. As a consequence of this continuous flux, the secondary nucleation events quickly overwhelm primary nucleation events inside compartment I, while secondary nucleation is suppressed in compartment II. However, if compartment I is large, the aggregation kinetics is similar to that for a homogeneous system because the monomer mass concentration is very close to the total monomer mass in the system and there is only a negligible amount of monomers entering from compartment II. Additionally, in the smaller compartment II where aggregates grow via primary nucleation, the coupling flux continuously removes monomers suppressing primary nucleation. Since compartment I is large, it shows little or no enrichment of aggregates relative to the homogeneous case while inside the small compartment II, aggregates are depleted determined by the lack of primary nucleation events relative to the homogeneous case.
Changes in compartment volume switch the driving mechanism for aggregate partitioning
To quantify the switch in aggregate partitioning as a function of compartment volume, we define the asymptotic aggregate partitioning ratio
As the compartment volume enters the partitioning factor $\epsilon (\overline{\varphi})$ solely via the partition degree $\xi (\overline{\varphi})$, the sign of ${n}_{2}{n}_{1}+1$ determines whether larger or smaller compartments lead to a larger partitioning (Figure 3(e)). Indeed, we find that the slope of the partitioning factor scales as $\epsilon {(\overline{\varphi})}^{\prime}\propto ({n}_{1}{n}_{2}1)$. Thus, for ${n}_{1}\phantom{\rule{thinmathspace}{0ex}}>\phantom{\rule{thinmathspace}{0ex}}{n}_{2}+1$, increasing the compartment volume by increasing the amount of $A$material $\overline{\varphi}$ causes a larger relative partitioning. Conversely, for ${n}_{1}\phantom{\rule{thinmathspace}{0ex}}<\phantom{\rule{thinmathspace}{0ex}}{n}_{2}+1$, larger partitioning can be found for smaller compartment sizes. Consistently, if the nucleation coefficients obey ${n}_{2}={n}_{1}1$, compartment volume has no impact on the partitioning factor $\epsilon $.
This qualitative switch in the mechanism for aggregate partitioning raises the question which systems favor large or small compartment volumes in order to maximize aggregate partitioning $\epsilon (\overline{\varphi})$. Figure 4 depicts the regimes in terms of the reaction orders characterizing primary and secondary nucleation, ${n}_{1}$ and ${n}_{2}$, for which the maximal aggregate partitioning corresponds to smaller and larger compartment volumes. This prediction can be related to specific aggregating systems for which the values of the reaction orders ${n}_{1}$ and ${n}_{2}$ have been experimentally determined (References see caption of Figure 4). Using these values for the reaction orders, our model predicts that largest partitioning is obtained for large compartments in systems of aggregating tau and yeast prion Ure2p. These two examples belong primarily to the class of systems where the mechanism responsible for the formation of new aggregates in the late stage is fragmentation which has a zero secondary reaction order, ${n}_{2}=0$ (i.e. nucleation is monomer independent). For nonfragmenting systems with ${n}_{2}\phantom{\rule{thinmathspace}{0ex}}>\phantom{\rule{thinmathspace}{0ex}}0$, our model predicts different scenarios for aggregating systems: largest aggregate partitioning for large compartment volumes occurs in the case branching systems, such as actin in the presence of the complex Arp2/3, as well as systems proliferating through monomer dependent secondary nucleation with ${n}_{2}\phantom{\rule{thinmathspace}{0ex}}<\phantom{\rule{thinmathspace}{0ex}}{n}_{1}1$, such as the Islet Amyloid Polypeptide (IAPP). In contrast, largest aggregate partitioning is reached for small compartments in the case of the 40 and 42residue forms of Amyloidβ peptide (Aβ40 and Aβ42).
Compartment volume and monomer partitioning control the total amount of aggregates
Our results have demonstrated that aggregates can be effectively partitioned inside liquidlike compartments, raising the question: can compartments also control the total amount of aggregates or their average size? To test this possibility, we compute the difference between the total amount of aggregates formed in the presence of liquid compartments, ${c}_{\mathrm{a}}^{\mathrm{t}\mathrm{o}\mathrm{t}}=\left({c}_{\mathrm{a}}^{\mathrm{I}}(\mathrm{\infty}){V}^{\mathrm{I}}+{c}_{\mathrm{a}}^{\mathrm{I}\mathrm{I}}(\mathrm{\infty}){V}^{\mathrm{I}\mathrm{I}}\right)/V$, compared to the number of aggregates formed in the homogeneous system without compartments, $c}_{\mathrm{a}}^{\mathrm{t}\mathrm{o}\mathrm{t}}{}_{{\varphi}^{\mathrm{I}}={\varphi}^{\mathrm{I}\mathrm{I}}$. The homogeneous case can be studied by considering equal compositions of both compartments, that is $\varphi}^{\mathrm{I}}={\varphi}^{\mathrm{I}\mathrm{I}$. This difference between the homogeneous case and the case with compartments be quantified by introducing the relative asymptotic aggregate concentration, $\mathcal{\mathcal{C}}(\overline{\varphi},\mathrm{\Gamma})=({c}_{\mathrm{a}}^{\mathrm{t}\mathrm{o}\mathrm{t}}{c}_{\mathrm{a}}^{\mathrm{t}\mathrm{o}\mathrm{t}}{}_{{\varphi}^{\mathrm{I}}={\varphi}^{\mathrm{I}\mathrm{I}}})/{c}_{\mathrm{a}}^{\mathrm{t}\mathrm{o}\mathrm{t}}{}_{{\varphi}^{\mathrm{I}}={\varphi}^{\mathrm{I}\mathrm{I}}}$, which is positive for an increased pool of aggregates, and negative for a lowered pool of aggregates relative to the homogeneous state. We find that compartments can affect the total number of aggregates relative to the homogeneous system depending on the relative values of the reaction orders for secondary nucleation and aggregate growth, the value of monomer partitioning $\mathrm{\Gamma}$ and the amount of compartment material $\overline{\varphi}$ that in turn regulates compartment size ${V}^{\text{I}}$. In particular, for reaction orders ${n}_{2}\phantom{\rule{thinmathspace}{0ex}}<\phantom{\rule{thinmathspace}{0ex}}1$, the presence of the liquid droplet always reduces the total amount of aggregates formed relative to the homogeneous system for all values of $\overline{\varphi}$ and $\mathrm{\Gamma}$ and thereby always leading to larger aggregates (Figure 5(a)). However, for ${n}_{2}\phantom{\rule{thinmathspace}{0ex}}>\phantom{\rule{thinmathspace}{0ex}}1$, we find a different behaviour. For low partitioning factors $\mathrm{\Gamma}$, the presence of liquid compartments decreases the total number of aggregates, corresponding to a larger average aggregate size, while for larger values of $\mathrm{\Gamma}$, more and thereby shorter aggregates form compared to the homogeneous system (Figure 5(b)). This behavior is also affected by compartment volume; the corresponding boundary in the $\overline{\varphi}$$\mathrm{\Gamma}$ diagram separates these two regimes corresponding to more but smaller or less but larger aggregates (Figure 5(c)). The role of the reaction order for secondary nucleation ${n}_{2}$ on total aggregate concentration and average size can be explained as follows. In a homogeneous system proliferating through secondary nucleation pathways, the average aggregate size in the saturating regime of the aggregation kinetics at long times scales as $\sqrt{{k}_{+}/{k}_{2}}{[{M}_{\mathrm{m}}^{\mathrm{tot}}]}^{(1{n}_{2})/2}$ (Michaels et al., 2015). Larger values of $\mathrm{\Gamma}$ lead to an increase of monomers in the compartment, favouring both secondary nucleation and aggregate growth by elongation inside the compartment. If ${n}_{2}\phantom{\rule{thinmathspace}{0ex}}>\phantom{\rule{thinmathspace}{0ex}}1$, the rate of secondary nucleation is increased by $\mathrm{\Gamma}$ more than elongation, which results in more numerous aggregates and hence shorter aggregates due to the limiting and fixed amount of total monomer mass in the system. The opposite trend is observed when ${n}_{2}\le 1$. In summary, a strong partitioning of aggregates inside compartments caused by a strong monomer partitioning (large $\mathrm{\Gamma}$) is accompanied by an increase of the total number of aggregates in the system in the presence of secondary nucleation, while in the absence of secondary nucleation, the total amount of aggregates decreases.
Discussion
By combining the theories of irreversible protein aggregation kinetics and phase separation, we have shown how liquid compartments can control the position and the total amount of aggregates. The coupling of slow aggregation and rapid phase separation leads to a mechanism whereby even a weak partitioning of monomers is amplified into a relatively large accumulation of aggregates in the compartment. Such partitioning of aggregates is a nonequilibrium effect and thereby not only determined by the phase separation parameters relevant at equilibrium (monomer partitioning $\mathrm{\Gamma}$ and partitioning degree $\xi $) but in addition, it depends on kinetic parameters characterizing the aggregation kinetics (e.g. reaction orders ${n}_{1}$ and ${n}_{2}$ for primary and secondary nucleation). However, several other effects may influence or limit the resulting degree of aggregate partitioning.
Model validity
In our model, we have considered the case that monomers and aggregates do not affect phase separation and phase separation is driven by the competition between the entropic tendency to mix and interactions favoring demixing. Future work could be devoted to extending our model by a coupling between aggregates and the liquid compartment or by entropically driven phase separation, relevant for the assembly of coacervates (Overbeek and Voorn, 1957) or mixtures with depletion interactions. Moreover, our model is restricted to time scales when aggregates hardly diffuse and monomer diffusion is not affected by rheological properties of the aggregates. The observed strong aggregate partitioning may diminish if aggregates significantly diffuse, or if they slow down diffusion of monomers. Furthermore, we have focussed on the case where both phases have the same reaction rates of aggregation. This assumption may be inaccurate for proteinrich phases (Wei et al., 2017) but can be scrutinized using our model (see Appendix 3). We find that due to the power law dependence of the monomer partitioning $\mathrm{\Gamma}$ (Equation 6) differences in aggregation rates must be very large to significantly affect the partitioning of aggregates. A lowered partitioning of aggregates could be caused by the coarsening dynamics of many droplets (Ostwald, 1897; Lifshitz and Slyozov, 1961; Bray, 1994). While coarsening via coalescence would not affect our results at all because aggregates remain confined inside the droplets, dissolving droplets undergoing Ostwald ripening would diminish the degree of aggregate partitioning. However, because the aggregation kinetics varies with compartment size, aggregates in droplets of different size may compete about monomers. This nonequilibrium competition could cause accumulation of more aggregates either in smaller or larger compartments. Overall, for systems where partitioning of monomers is fast relative to the aggregation kinetics, the mechanism underlying the strong partitioning of aggregates proposed in this study could be relevant for several phenomena in living cells. It could have impact on strategies of drug design or serve as a principle to speed up irreversible chemical reactions and can be tested experimentally.
Invitro realization
Our quantitative predictions of strong aggregate enrichment inside a liquidlike compartment (Figure 3 (b–e) and Figure 4) are experimentally testable using recently developed bulk and microfluidic assays. For example, synthetic liquid biocompartments of tuneable size and composition can be used to locally affect reaction rates and partition proteins (Faltova et al., 2018) and thereby represent attractive platforms to investigate the partitioning and aggregation of different amyloidogenic peptides and proteins, including Amyloidβ. These synthetic compartments are highly flexible and allow to validate the effect of several parameters predicted in this work. For instance, the monomer partitioning factor $\mathrm{\Gamma}$ could be varied in vitro by changing the degree of phase separation (Equation (1) and Figure 2) or by conjugating the proteins with specific sequences capable of tuning recruitment into the liquid compartments (Faltova et al., 2018). Moreover, the compartment volumes can be adjusted by the initial supersaturation via changes in temperatures, which affect the kinetic rate constants only weakly (Cohen et al., 2018). Measuring the concentration or size of aggregates inside and outside of the compartment by epifluorescence spectroscopy as a function of time and parameters such as the partitioning factor and compartment volume will allow for tracking aggregate enrichment as a function of compartment volume and test both the scaling predictions and the crossover of the scaling exponent from ${n}_{2}+1$ at small volume to ${n}_{1}$ at large volumes. It would be particularly interesting to test this prediction for different amyloidforming protein systems or for varying the reaction orders $n}_{1$ and $n}_{2$ by adjusting the total amount of monomers (Meisl et al., 2014); see Figure 4.
In vivo relevance and implications for drug design
Our model may already provide a framework to explain the phenomena of aggregate partitioning inside living cells. An example of such phenomena could be the partitioning of pericentriolar material into centrosomes (Zwicker et al., 2014) and the spatial organization of aggregates inside stress granules (Molliex et al., 2015; Mateju et al., 2017). The propensity of aggregates to solidify the compartment as reported in Mateju et al. (2017) could be accounted for in our model through a gelsol transition (Stockmayer, 1943; Harmon et al., 2017). Including the solidification induced by aggregates could lead to additional volume changes of the compartment which in turn may affect the aggregation kinetics. Furthermore, the enrichment of toxic aggregates inside liquid compartments may trigger new directions for drug design against aberrant protein aggregation. Our results suggest to design drugs not only with respect to their ability to interfere with the aggregation kinetics (Arosio et al., 2014) but also with respect to their partitioning properties into the liquid compartments. This strategy is reminiscent of quantifying the potency of lowmolecular weighted anesthetics via the MeyerOverton correlation based on solubility of the anaesthetics in oil (Meyer, 1937; Franks and Lieb, 1978).
General speedup mechanism for chemical reactions
The reported feedback mechanism of aggregate growth mediated by liquid compartments may represent a general principle to spatially confine and speed up other irreversible chemical processes or to control aggregate amount and average size. Examples may include precipitation of proteins or polymerization kinetics of actin and microtubules (see also Figure 4). Indeed, a speed up of the chemical reactions could be expected due to the increased concentration of educts inside the liquid compartments. Thus, liquid compartments are ideal biomolecular microreactors that enrich the amount of products by dynamically exchanging reactants with their surroundings.
Appendix 1
Partition coefficient for dilute monomers at equilibrium
We consider phase separation of an incompressible, ternary mixture composed of monomers, component $A$ and $B$ (we neglect the interactions between the aggregates and phase separation for simplicity) described by the following FloryHuggins free energy density (Flory, 1942; Huggins, 1942)
where the logarithmic terms correspond to the mixing entropy. The interactions between $A$ and $B$ are described by the parameter $\mathrm{\Lambda}$ while the interactions with the monomers are characterized by ${\mathrm{\Lambda}}_{mi}$, $i=A,B$. These interaction parameters have the unit $[1/\text{volume}]$. Here we define a dimensionless interaction parameters by writing $\mathrm{\Lambda}=\chi /\nu $ and ${\mathrm{\Lambda}}_{mi}={\chi}_{mi}/\nu $ with $\nu ={\nu}_{A}$. Moreover, for simplicity, we consider equal molecular volumes of $A$ and $B$, $\nu ={\nu}_{B}$. Thus we arrive at
With ${\varphi}_{B}=1{\varphi}_{A}{\nu}_{\mathrm{m}}{c}_{\mathrm{m}}$ and monomers being dilute, ${\nu}_{\mathrm{m}}{c}_{\mathrm{m}}\ll 1$, we can expand $f$ in ${\nu}_{\mathrm{m}}{c}_{\mathrm{m}}$ up to the first order:
where we defined ${\varphi}_{A}\equiv \varphi $ and neglected all terms of order $\mathcal{O}\left[{\left({\nu}_{\mathrm{m}}{c}_{\mathrm{m}}\right)}^{2}\right]$. The corresponding chemical potentials read
At equilibrium the chemical potentials of each component inside (I) and outside (II) the compartment are balanced leading to relationships between the concentration values inside and outside. Specifically, if phase separation equilibrium is reached the following conditions are fulfilled
The relations above allow to calculate the equilibrium concentration in each phase, for component $A$, $\varphi}^{\mathrm{I}$ and $\varphi}^{\mathrm{I}\mathrm{I}$, and the monomers, $c}_{\mathrm{m}}^{\mathrm{I}$ and $c}_{\mathrm{m}}^{\mathrm{I}\mathrm{I}$. An analytic result of the equilibrium concentrations is very difficult to obtain. However, we can focus on the leading contributions for the balance of the chemical potentials inside and outside taking advantage that monomers are dilute and thereby obtain an approximation for the equilibrium values inside and outside.
Considering that the dimensionless interaction parameters $\chi $ are all of $\mathcal{O}(1)$, the impact of the dilute monomers on the phase equilibrium between $A$ and $B$ is negligible and we can approximate
The resulting chemical potential Equation (12) simply corresponds to the chemical potential of a binary, incompressible FloryHuggins mixture. From the equilibrium condition Equation (11a), we can calculate the binodal line described by the condition
which solely depends on the interaction parameters between $A$ and $B$, $\chi $. For fixed interaction parameters $\chi $, the binodal gives the equilibrium volume fractions inside and outside the compartment, $\varphi}^{\mathrm{I}$ and $\varphi}^{\mathrm{I}\mathrm{I}$. For finite sized compartments, these equilibrium volume fractions are slightly increased due to the Laplace pressure. This increase is described by the GibbsThomson relationships, ${\varphi}_{\text{eq}}^{\alpha}={\varphi}^{\alpha}(1+{\mathrm{\ell}}_{\gamma}^{\alpha}/R)$, where ${\mathrm{\ell}}_{\gamma}^{\alpha}$ denotes the capillary length for each phase. For strong phase separation of compartment I, $\varphi}^{\mathrm{I}}\gg {\varphi}^{\mathrm{I}\mathrm{I}$, the capillary lengths obey $\ell}_{\gamma}^{\mathrm{I}}\ll {\ell}_{\gamma}^{\mathrm{I}\mathrm{I}$ (Weber et al., 2018). Moreover, the capillary length in the minority phase II can be estimated by ${\ell}_{\gamma}^{\mathrm{I}\mathrm{I}}\simeq 2\gamma \nu /({\varphi}^{\mathrm{I}}{k}_{\text{B}}T)$, which is typically closer to the molecular size than the size of micronsized compartments of radius $R$. Thus, for compartments significantly exceeding the size of the molecules we can neglect Laplace and surface tension effects on the equilibrium volume fraction and approximate ${\varphi}_{\text{eq}}^{\alpha}\simeq {\varphi}^{\alpha}$ in the following.
By means of equilibrium condition Equation (11b), we can calculate the impact of the phase separated compartment on the monomer distribution leading to the monomer partitioning factor
where the relative interaction strength reads $\mathrm{\Delta}\chi =\left({\chi}_{B,\text{m}}{\chi}_{A,\text{m}}\right)$. Thus, there is partitioning of monomers into the condensed phase ($\mathrm{\Gamma}\phantom{\rule{thinmathspace}{0ex}}>\phantom{\rule{thinmathspace}{0ex}}1$) if monomers favor the presence of $A$ relative to $B$, that is $\chi}_{A,\mathrm{m}}\phantom{\rule{thinmathspace}{0ex}}<\phantom{\rule{thinmathspace}{0ex}}{\chi}_{B,\mathrm{m}$.
Appendix 2
Intercompartment flux of monomers close to equilibrium
This flux can be calculated for a maintained concentration difference $\varphi}^{\mathrm{I}}{\varphi}^{\mathrm{I}\mathrm{I}$ using the chemical potential for the monomers ${\mu}_{\mathrm{m}}$ (Equation 10b). To this end, let us consider that the compartment is spherical of radius $R$. Perturbing the concentrations in both phases may lead to an unbalance of the chemical potential and thus a flux between the phases. The net radial flux of monomers $J={J}_{\text{r}}^{\mathrm{I}}{J}_{\text{r}}^{\mathrm{I}\mathrm{I}}$ [unit: mass per time] arises from a flux contribution of each phase through the interface, where the radial flux in each phase reads
and ${\mathit{\bm{e}}}_{r}$ is the radial unit vector pointing normal to the spherical interface. The local fluxes at the interface, ${{\mathit{\bm{j}}}^{\alpha}}_{R}$, can be calculated in the limit where the aggregation kinetics is slow compared to diffusion in each phase. In this limit we can use a quasi static approximation, $\nabla \cdot \left({\xi}^{\alpha}\nabla {\mu}_{\text{m}}^{\alpha}(r)\right)\simeq 0$. For a constant mobility coefficient in each phase we can solve a Laplace equation for the chemical potential, ${\nabla}^{2}{\mu}_{\text{m}}^{\alpha}(r)\simeq 0$, with the following boundary conditions for the chemical potentials: ${\mu}_{\text{m}}^{\alpha}$ deeply inside each phase and ${{\mu}_{\text{m}}^{\alpha}}_{R}$ at the interface $r=R$. For simplicity, we discuss the case of a small compartment I with a homogeneous chemical potential inside, thus $\mu}_{\mathrm{m}}^{\mathrm{I}}(r)={\mu}_{\mathrm{m}}^{\mathrm{I}$ is constant and equal to the chemical potential right inside the interface $\mu}_{\mathrm{m}}^{\mathrm{I}}{}_{R$. For compartment II, we then find by solving the Laplace equation ${\mu}_{\mathrm{m}}^{\mathrm{I}\mathrm{I}}(r)={\mu}_{\mathrm{m}}^{\mathrm{I}\mathrm{I}}+\left({\mu}_{\mathrm{m}}^{\mathrm{I}\mathrm{I}}{}_{R}{\mu}_{\mathrm{m}}^{\mathrm{I}\mathrm{I}}\right)R/r$ where $\mu}_{\text{m}}^{\text{II}$ is the chemical potential far from the interface. The flux at the interface can be calculated by ${\mathit{j}}^{\mathrm{I}\mathrm{I}}{}_{R}={\mathit{e}}_{r}{m}_{\mathrm{m}}{\xi}^{\mathrm{I}\mathrm{I}}{\mathrm{\partial}}_{r}{\mu}_{\mathrm{m}}^{\mathrm{I}\mathrm{I}}(r){}_{r=R}={\mathit{e}}_{r}{m}_{\mathrm{m}}{\xi}^{\mathrm{I}\mathrm{I}}\left({\mu}_{\mathrm{m}}^{\mathrm{I}\mathrm{I}}{}_{R}{\mu}_{\mathrm{m}}^{\mathrm{I}\mathrm{I}}\right)/R$. The absence of aggregation at the interface allows that diffusion can equilibrate the chemical potentials, that is $\mu}_{\mathrm{m}}^{\mathrm{I}\mathrm{I}}{}_{R}={\mu}_{\mathrm{m}}^{\mathrm{I}}{}_{R$ which is also equal to the chemical potential deeply inside the phase $\mu}_{\mathrm{m}}^{\mathrm{I}$ because phase $\text{I}$ is considered to be homogeneous. The net flux of monomers, $J={J}_{\text{r}}^{\mathrm{I}}{J}_{\text{r}}^{\mathrm{I}\mathrm{I}}$ with ${J}_{\text{r}}^{\text{I}}=0$, is
Using the chemical potential of the monomers, Equation (10b), we can approximate the chemical potential difference as
where we expanded $M}_{\mathrm{m}}^{\mathrm{I}}={M}_{\mathrm{m},\mathrm{e}\mathrm{q}}^{\mathrm{I}}+\delta {M}_{\mathrm{m}}^{\mathrm{I}$ and $M}_{\mathrm{m}}^{\mathrm{I}\mathrm{I}}={M}_{\mathrm{m},\mathrm{e}\mathrm{q}}^{\mathrm{I}\mathrm{I}}+\delta {M}_{\mathrm{m}}^{\mathrm{I}\mathrm{I}$ up to linear order around the equilibrium concentrations $M}_{\mathrm{m},\mathrm{e}\mathrm{q}}^{\mathrm{I}$ and $M}_{\mathrm{m},\mathrm{e}\mathrm{q}}^{\mathrm{I}\mathrm{I}}={M}_{\mathrm{m},\mathrm{e}\mathrm{q}}^{\mathrm{I}}/\mathrm{\Gamma$, respectively. Thus net change of monomers due to the exchange of material through the interface reads
where the diffusion constant in phase II is $D}_{\mathrm{m}}={k}_{\mathrm{B}}T{m}_{\mathrm{m}}{\xi}^{\mathrm{I}\mathrm{I}}/{M}_{\mathrm{m},\mathrm{e}\mathrm{q}}^{\mathrm{I}$ and the rate to relax back to monomer partitioning at equilibrium is $k(R)=4\pi R{D}_{\mathrm{m}}$. To ease notation we omitted the “$\delta $” to indicate linear deviations from equilibrium.
Appendix 3
Analytical solution to aggregation kinetics with liquid compartments
In this appendix, we discuss the detail associated with the derivation of analytical solutions to the aggregation kinetics in the presence of liquid compartments, Equation(3), main text.
Initial layer dynamics
Due to the separation of timescales between monomer equilibration between the two compartments and protein aggregation, the system described by Equation (3) (main text) will develop initially through a rapid phase of equilibration, before any aggregation occurs in either compartment. During this initial layer phase, which is the temporal equivalent of a boundary layer (Astarita and Marucci, 1974; Danckwerts, 1951), the initial values of the monomer concentration in each compartment, ${M}_{\mathrm{m}}^{\mathrm{I}}(0)$ and ${M}_{\mathrm{m}}^{\mathrm{I}\mathrm{I}}(0)$, relax quickly to equilibrium such that the condition ${M}_{\mathrm{m}}^{\mathrm{I}}(t)=\mathrm{\Gamma}{M}_{\mathrm{m}}^{\mathrm{I}\mathrm{I}}(t)$ is satisfied before aggregation is initiated. This early equilibration kinetics is described by setting the aggregation terms in Equation (3) (main text) to zero, yielding the following equations:
The solution to Equation (21) is:
where $A={k}^{\mathrm{I}}+\mathrm{\Gamma}{k}^{\mathrm{I}\mathrm{I}}$, and $B=[{k}^{\mathrm{I}\mathrm{I}}{M}_{\mathrm{m}}^{\mathrm{I}}(0)+{k}^{\mathrm{I}}{M}_{\mathrm{m}}^{\mathrm{I}\mathrm{I}}(0)]/A$. Note that the kinetics described by Equation (22) ‘pushes’ the system towards the slow manifold, which is described by ${M}_{\mathrm{m}}^{\mathrm{I}}(t)=\mathrm{\Gamma}{M}_{\mathrm{m}}^{\mathrm{I}\mathrm{I}}(t)$. Hence, when ${M}_{\mathrm{m}}^{\mathrm{I}}(0)=\mathrm{\Gamma}{M}_{\mathrm{m}}^{\mathrm{I}\mathrm{I}}(0)$, there is no initial phase of ‘correction’ of the initial conditions. At the end of this initial boundary layer phase, the monomer concentrations in the two compartments are given by:
Since we are not very much interested in this initial phase of redistribution of the initial conditions, in the following we shall assume for simplicity that the initial monomer concentrations in compartments I and II satisfy the relationship ${M}_{\mathrm{m}}^{\mathrm{I}}(0)=\mathrm{\Gamma}{M}_{\mathrm{m}}^{\mathrm{I}\mathrm{I}}(0)$. This assumption does not affect the generality of our results. In fact, if this condition was not satisfied initially, then, according to Equation (22), rapid equilibration between the two compartments would correct these initial conditions, eventually leading to a ’corrected’ set of initial conditions that lie in the slow manifold.
Solving aggregation kinetics in the slow manifold
After an initial, rapid phase of monomer redistribution through the two compartments, the system enters a slower phase of dynamics, where, at leading order, the system stays on the slow manifold $M}_{\mathrm{m}}^{\mathrm{I}}=\mathrm{\Gamma}{M}_{\mathrm{m}}^{\mathrm{I}\mathrm{I}$ at all times. For simplicity, let us assume that the initial concentrations of monomers in the two compartments obey the relationship ${M}_{\mathrm{m}}^{\mathrm{I}}(0)=\mathrm{\Gamma}{M}_{\mathrm{m}}^{\mathrm{I}\mathrm{I}}(0)$ (otherwise there will be a fast equilibration of the initial conditions such that this relationship is satisfied). To describe the aggregation process in the slow manifold, we write ${M}_{\mathrm{m}}^{\mathrm{I}}\mathrm{\Gamma}{M}_{\mathrm{m}}^{\mathrm{I}\mathrm{I}}\simeq 0$ for all times in Equation (3) (main text) and find:
It is useful to introduce the total monomer concentration in the system as:
Note that this concentration may vary in time as aggregates are nucleated and grow, however, the total mass concentration
is conserved at all times. Using the condition $M}_{\mathrm{m}}^{\mathrm{I}}=\mathrm{\Gamma}{M}_{\mathrm{m}}^{\mathrm{I}\mathrm{I}$, we can write the following relationships linking the concentrations of monomers in compartments I and II to the total concentration of monomers in the system:
where we abbreviated the partitioning degree
Thus, using Equation (27), we can rewrite Equation (24) as:
Earlytime dynamics for aggregate number and mass concentrations in compartments I and II
Before discussing the full time course of aggregation, it is useful to consider the earlytime kinetics of the system, which emerges when the monomers in the system have not been depleted significantly (Michaels et al., 2018). This limit is obtained by assuming in Equation (29) that the total monomer concentration is constant in time, that is ${M}_{\mathrm{m}}(t)\approx {M}_{\mathrm{m}}^{\mathrm{tot}}$ (Michaels et al., 2018). This assumption transforms the kinetic Equation (29) into the following simpler set of linear differential equations:
where we have introduced the parameters:
and
with
The solution to Equation (30) subject to the condition that no aggregates are present initially reads
for the aggregate mass concentrations in compartments I and II, and
for the aggregate number concentrations in compartments I and II. Here, we have introduced the kinetic coefficients
and
with
being the effective rates characterizing the proliferation of aggregates due to primary and secondary nucleation, respectively (Michaels et al., 2018). According to Equations (34) and (35), the aggregate number and mass concentrations in both compartments grow exponentially with time. The effective growth rates ${\kappa}_{\mathrm{I}}$ and ${\kappa}_{\mathrm{II}}$ are different for each compartment and depend on $\mathrm{\Gamma}$ and $\xi $. Since $\mathrm{\Gamma}\gg 1$, aggregate growth in the early times is much faster in compartment I compared to compartment II. In particular, the ratio of the growth rates in the two compartments is independent of $\xi $ and is given by:
Also primary nucleation is enhanced inside compartment I relative to compartment II:
Analytical solution for full time course of monomer concentrations in compartments I and II
We now construct analytical solutions for the monomer and aggregate mass concentrations that are valid for the entire duration of the aggregation reaction. In the previous section, we have seen that for $\mathrm{\Gamma}\gg 1$ two timescales, $1/{\kappa}_{\mathrm{I}}$ and $1/{\kappa}_{\text{II}}$, characterize the earlytime aggregation in the two compartments. Since the growth rate in compartment I, ${\kappa}_{\mathrm{I}}$, is much larger than that in compartment II, ${\kappa}_{\mathrm{II}}$, monomers in compartment I will be consumed by aggregation much faster than those in compartment II. However, the relationship ${M}_{\mathrm{m}}^{\mathrm{I}}(t)=\mathrm{\Gamma}{M}_{\mathrm{m}}^{\mathrm{I}\mathrm{I}}(t)$ must hold at all times. Thus, to compensate the fast aggregation in compartment I, there will be a flux of monomers from compartment II to compartment I. Eventually, the vast majority of monomers will end up as part of aggregates in compartment I and the parameter ${\kappa}_{\mathrm{I}}$ will naturally control the depletion of monomers in both compartments. We can make this argument more quantitative by using Equation (34) as follows. Monomers in compartment I are consumed over a timescale of the order $1/{\kappa}_{\mathrm{I}}$. The amount of aggregate mass that will be formed in compartment II during this time period will be of the order
Since ${\kappa}_{\mathrm{II}}/{\kappa}_{\mathrm{I}}\ll 1$, we can expand the cosh function as a Taylor series, $\mathrm{cosh}x=1+{x}^{2}/2+\mathcal{O}({x}^{5})$. At leading order, we find:
Thus, the ratio between the mass of aggregates formed in compartments I and II over a timescale $1/{\kappa}_{\mathrm{I}}$ is
Since $\mathrm{\Gamma}\gg 1$, the aggregate mass in compartment I will be much larger than that in compartment II. We can thus neglect at leading order the contribution from ${M}_{\mathrm{a}}^{\mathrm{I}\mathrm{I}}(t)$ to the conservation of total mass relationship. Doing so, and using Equation (27), we can write the conservation of mass relationship as follows
Using Equation (44), we can reduce the kinetic Equations (29) to a system of two coupled different equations:
where ${\stackrel{~}{k}}_{+}={k}_{+}\mathrm{\Gamma}/(\mathrm{\Gamma}+1)$ and $\stackrel{~}{k}}_{2}={k}_{2}(\mathrm{\Gamma}+1)/\mathrm{\Gamma$. Conveniently, Equation (45) are exactly the fundamental kinetic equations describing the dynamics of protein aggregation in a pure system, that is without compartment, but with effective rate parameters that depend on the degree of phase separation (Michaels et al., 2018; Michaels et al., 2016). Thus, we can adapting the results in Michaels et al. (2016) to Equation (45), we find the following solution for the time varying monomer concentration in compartment I:
where $\theta =\sqrt{2/[{n}_{2}({n}_{2}+1)]}$. Using Equation (44), we then obtain an expression for the aggregate mass concentration:
Finally, the time course of the monomer concentration in compartment II is obtained using the relationship ${M}_{\mathrm{m}}^{\mathrm{I}}(t)=\mathrm{\Gamma}{M}_{\mathrm{m}}^{\mathrm{I}\mathrm{I}}(t)$. This yields:
The accuracy of our analytical solutions Equations (46), (47) and (48) against numerical integration of Equation (3) (main text) is shown in Figure 1.
Scaling relationships for the aggregate number concentrations in compartments I and II
From the knowledge of the time varying monomer concentration, Equation (46), we can obtain an expression for the aggregate number concentration in compartment I using Equation (45a) by simple differentiation of Equation (46), $c}_{\mathrm{a}}^{\mathrm{I}}(t)=1/[2{\stackrel{~}{k}}_{+}\phantom{\rule{thinmathspace}{0ex}}{M}_{\mathrm{m}}^{\mathrm{I}}(t)]\mathrm{d}{\mathrm{M}}_{\mathrm{m}}^{\mathrm{I}}(\mathrm{t})/\mathrm{d}\mathrm{t$. This yields the following expression:
where
is the number concentration of aggregates at steady state. It is interesting to extract from Equation (50) the key dependence of ${c}_{\mathrm{a}}^{\mathrm{I}}(\mathrm{\infty})$ on the parameters $\xi $ and $\mathrm{\Gamma}$:
Note that the prefactor defines the homogeneous concentration in the absence of compartments,
Thus, using $\mathrm{\Gamma}\gg 1$ we find the following scaling relationship for the steadystate number concentration of aggregates in compartment I:
The scaling relationship Equation (53) can be rationalized as follows. The terminal concentration of aggregates in compartment I is given as ${c}_{\mathrm{a}}^{\mathrm{I}}(\mathrm{\infty})\simeq {M}_{\mathrm{a}}^{\mathrm{I}}(\mathrm{\infty})/{L}^{\mathrm{I}}(\mathrm{\infty})$, where ${L}^{\mathrm{I}}(\mathrm{\infty})$ is the average size of aggregates in compartment I. ${L}^{\mathrm{I}}(\mathrm{\infty})$ is determined by the ratio between aggregate growth and nucleation as $\simeq \sqrt{{\mu}^{\mathrm{I}}/{\beta}^{\mathrm{I}}}$ (Michaels and Knowles, 2014; Michaels et al., 2015); indeed, increasing the rate of growth over the rate of nucleation leads to more monomers being consumed in aggregate elongation, hence to longer aggregates. Since $M}_{\mathrm{a}}^{\mathrm{I}}(\mathrm{\infty})/{M}_{\mathrm{m}}^{\mathrm{t}\mathrm{o}\mathrm{t}}\simeq \xi \mathrm{\Gamma$ and using Equation (31), we find $L}^{\mathrm{I}}(\mathrm{\infty})\simeq (\xi \mathrm{\Gamma}{)}^{\frac{1{n}_{2}}{2}$ and, hence, $c}_{\mathrm{a}}^{\mathrm{I}}(\mathrm{\infty})\simeq (\xi \mathrm{\Gamma})/(\xi \mathrm{\Gamma}{)}^{\frac{1{n}_{2}}{2}}\simeq (\xi \mathrm{\Gamma}{)}^{\frac{{n}_{2}+1}{2}$.
A similar scaling relationship to Equation (53) can be derived also for the steadystate number concentration of aggregates in compartment II as follows. We recall that the earlytime dynamics of aggregation in compartment II is characterized by a timescale $1/{\kappa}_{\mathrm{I}\mathrm{I}}$, which is much slower than the timescale of aggregation in compartment I, $1/{\kappa}_{\mathrm{I}}$. Thus, ${c}_{\mathrm{a}}^{\text{II}}$ can be considered to be still in the exponential growing phase even when the aggregate concentration in compartment I is equilibrating. Eventually, the assembly in compartment II is arrested abruptly as soon as aggregation in compartment I is fully saturated, since no monomer is left in either compartment. Since the timescale for saturation of aggregation in compartment I is proportional to $1/{\kappa}_{\mathrm{I}}$ (see Equation 46), we can estimate the concentration of aggregates in compartment II at the end of the reaction as:
where in the last step we used Equation (39). Since we are interested in the limit of relatively large monomer partitioning $\mathrm{\Gamma}\gg 1$, the argument of the $\mathrm{sinh}$ function is much smaller than unity. Hence, we can simplify Equation (54) by using a Taylor expansion of the $\mathrm{sinh}$ function to first order, $\mathrm{sinh}x=x+\mathcal{O}({x}^{3})$, yielding the following scaling relationship after extracting the $\xi $ dependence of ${\nu}_{\mathrm{II}}$ and ${\kappa}_{\mathrm{II}}$:
where $w={k}_{1}/({k}_{2}\theta ){\left({M}_{\mathrm{m}}^{\mathrm{tot}}\right)}^{{n}_{1}{n}_{2}1}$. Combining Equation (53) with Equation (55), we obtain one of the key results of our paper, namely the scaling behavior of aggregate partitioning between compartments I and II with $\mathrm{\Gamma}$:
where the impact of compartment volume on the relative degree of monomer characterized by $\xi \left(\overline{\varphi}\right)$ is given in Equation (28).
Effect of different rates inside and outside the compartment
So far, we have assumed constant growth rates inside and outside of the compartment; in general, this assumption will not hold for all phase separating systems combined with aggregation. We can extend our framework to take into account the effect of different rates for the condensed ($\alpha =\mathrm{I}$) and dilute ($\alpha =\mathrm{I}\mathrm{I}$) phases:
and can then follow the same approach as for the case of homogeneous rates to determine how the presence of different rates affects the various scaling relationships discussed above. For instance, Equations (39) and (40) become:
Moreover, Equations (53) and (55) become:
such that
Note that there is an asymmetry in the dependence of ${c}_{\mathrm{a}}^{\mathrm{I}}(\mathrm{\infty})$ and ${c}_{\mathrm{a}}^{\mathrm{I}\mathrm{I}}(\mathrm{\infty})$ on the rate constants of primary and secondary nucleation; it originates from the fact that, due to separation of timescales and due to $\mathrm{\Gamma}\ll 1$, aggregation in compartment II is arrested abruptly as soon as aggregation in I reaches saturation (see Equation 54). This effect makes ${c}_{\mathrm{a}}^{\mathrm{I}\mathrm{I}}(\mathrm{\infty})$ depend on the rate of primary nucleation. Importantly, from the expressions Equation (59), we see that different growth rates of the compartments enter as multiplicative prefactors. Hence, even though having different rates for the condensed and dilute phases can certainly affect aggregate partitioning (enhanced partitioning occurs for larger growth rates in I, and vice versa), the effect on the rate constants must be sufficiently large (at least one oder of magnitude) to dominate the effect of monomer partitioning $\mathrm{\Gamma}$. This suggest that the mechanism of aggregate partitioning proposed in this study is likely to be robust if aggregation rates differ among the compartments.
Data availability
All data generated or analysed during this study are included in the manuscript and supporting files.
References

Granulostasis: protein quality control of RNP granulesFrontiers in Molecular Neuroscience 10:84.https://doi.org/10.3389/fnmol.2017.00084

Quality control of membraneless organellesJournal of Molecular Biology 430:4711–4729.https://doi.org/10.1016/j.jmb.2018.05.013

Chemical kinetics for drug discovery to combat protein aggregation diseasesTrends in Pharmacological Sciences 35:127–135.https://doi.org/10.1016/j.tips.2013.12.005

Biomolecular condensates: organizers of cellular biochemistryNature Reviews Molecular Cell Biology 18:285–298.https://doi.org/10.1038/nrm.2017.7

The toxic aβ oligomer and Alzheimer's disease: an emperor in need of clothesNature Neuroscience 15:349–357.https://doi.org/10.1038/nn.3028

One domain fits all: using disordered regions to sequester misfolded proteinsThe Journal of Cell Biology 217:1173–1175.https://doi.org/10.1083/jcb.201803015

Phase transitions and size scaling of membraneless organellesThe Journal of Cell Biology 203:875–881.https://doi.org/10.1083/jcb.201308087

Polymer physics of intracellular phase transitionsNature Physics 11:899–904.https://doi.org/10.1038/nphys3532

Theory of phaseordering kineticsAdvances in Physics 43:357–459.https://doi.org/10.1080/00018739400101505

A causative link between the structure of aberrant protein oligomers and their toxicityNature Chemical Biology 6:140–147.https://doi.org/10.1038/nchembio.283

The role of amyloidbeta derived diffusible ligands (ADDLs) in Alzheimer's diseaseCurrent Topics in Medicinal Chemistry 6:597–608.https://doi.org/10.2174/156802606776743066

Protein misfolding, functional amyloid, and human diseaseAnnual Review of Biochemistry 75:333–366.https://doi.org/10.1146/annurev.biochem.75.101304.123901

Significance of LiquidFilm coefficients in gas absorptionIndustrial & Engineering Chemistry 43:1460–1467.https://doi.org/10.1021/ie50498a055

Reptation of a polymer chain in the presence of fixed obstaclesThe Journal of Chemical Physics 55:572–579.https://doi.org/10.1063/1.1675789

Multiple pathways in the integration of proteins into the mitochondrial outer membraneBiochimica Et Biophysica Acta (BBA)  Biomembranes 1808:971–980.https://doi.org/10.1016/j.bbamem.2010.06.021

Thermodynamics of high polymer solutionsThe Journal of Chemical Physics 10:51–61.https://doi.org/10.1063/1.1723621

Neurodegenerative disease: models, mechanisms, and a new hopeDisease Models & Mechanisms 10:499–502.https://doi.org/10.1242/dmm.030205

A prionlike domain in Hsp42 drives chaperonefacilitated aggregation of misfolded proteinsThe Journal of Cell Biology 217:1269–1285.https://doi.org/10.1083/jcb.201708116

Some properties of solutions of Longchain compoundsThe Journal of Physical Chemistry 46:151–158.https://doi.org/10.1021/j150415a018

Liquidliquid phase separation in biologyAnnual Review of Cell and Developmental Biology 30:39–58.https://doi.org/10.1146/annurevcellbio100913013325

The amyloid state and its association with protein misfolding diseasesNature Reviews Molecular Cell Biology 15:384–396.https://doi.org/10.1038/nrm3810

Measurement of tau filament fragmentation provides insights into Prionlike spreadingACS Chemical Neuroscience 9:1276–1282.https://doi.org/10.1021/acschemneuro.8b00094

The kinetics of precipitation from supersaturated solid solutionsJournal of Physics and Chemistry of Solids 19:35–50.https://doi.org/10.1016/00223697(61)900543

Protein disorder, prion propensities, and selforganizing macromolecular collectivesBiochimica Et Biophysica Acta (BBA)  Proteins and Proteomics 1834:918–931.https://doi.org/10.1016/j.bbapap.2013.01.003

Contributions to the theory of narcosisTransactions of the Faraday Society 33:1062–1064.https://doi.org/10.1039/tf9373301062

Dynamics of protein aggregation and oligomer formation governed by secondary nucleationThe Journal of Chemical Physics 143:054901.https://doi.org/10.1063/1.4927655

Hamiltonian dynamics of protein filament formationPhysical Review Letters 116:038101.https://doi.org/10.1103/PhysRevLett.116.038101

Chemical kinetics for bridging molecular mechanisms and macroscopic measurements of amyloid fibril formationAnnual Review of Physical Chemistry 69:273–298.https://doi.org/10.1146/annurevphyschem050317021322

Meanfield master equation formalism for biofilament growthAmerican Journal of Physics 82:476–483.https://doi.org/10.1119/1.4870004

Translocation of proteins into mitochondriaAnnual Review of Biochemistry 76:723–749.https://doi.org/10.1146/annurev.biochem.76.052705.163409

Studien über die bildung und umwandlung fester körperZeitschrift Für Physikalische Chemie 22:289–330.https://doi.org/10.1515/zpch18972233

Phase separation in polyelectrolyte solutions. Theory of complex coacervationJournal of Cellular and Comparative Physiology 49:7–26.https://doi.org/10.1002/jcp.1030490404

Discretized model of entangledpolymer dynamicsPhysical Review Letters 59:1946–1949.https://doi.org/10.1103/PhysRevLett.59.1946

Kinetics of spontaneous filament nucleation via oligomers: insights from theory and simulationThe Journal of Chemical Physics 145:211926.https://doi.org/10.1063/1.4965040

Hsp42 is required for sequestration of protein aggregates into deposition sites in Saccharomyces cerevisiaeThe Journal of Cell Biology 195:617–629.https://doi.org/10.1083/jcb.201106037

Theory of molecular size distribution and gel formation in branched‐chain polymersThe Journal of Chemical Physics 11:45–55.https://doi.org/10.1063/1.1723803

Secondary nucleation in amyloid formationChemical Communications 54:8667–8684.https://doi.org/10.1039/C8CC02204F

Mitochondrial machineries for protein import and assemblyAnnual Review of Biochemistry 86:685–714.https://doi.org/10.1146/annurevbiochem060815014352

RNA controls PolyQ protein phase transitionsMolecular Cell 60:220–230.https://doi.org/10.1016/j.molcel.2015.09.017

Nuclear bodies: the emerging biophysics of nucleoplasmic phasesCurrent Opinion in Cell Biology 34:23–30.https://doi.org/10.1016/j.ceb.2015.04.003
Decision letter

Frank JülicherReviewing Editor; Max Planck Institute for the Physics of Complex Systems, Germany

Arup K ChakrabortySenior Editor; Massachusetts Institute of Technology, United States

Alexander GrosbergReviewer; NYU, United States
In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.
Thank you for submitting your article "Spatial control of irreversible protein aggregation" for consideration by eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by Frank Jülicher as Reviewing Editor and Arup Chakraborty as the Senior Editor. The following individual involved in the review of your submission has agreed to reveal his identity: Alexander Grosberg (Reviewer #3).
The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.
Summary:
The work on "Spatial control of irreversible protein aggregation" presents novel ideas and a theoretical approach to show that protein aggregates could predominantly form in liquid cellular compartments and strongly enrich there. Such compartments could therefore play a role to reduce protein aggregation outside such compartments and to control where irreversible aggregation is permitted to occur. The approach used is simple and elegant. The paper has a number of insights gleaned from both FloryHuggins and kinetic theories of phase separation and aggregation kinetics, respectively. Presented equations are formulated to provide insight into the rich interplay between monomer partitioning, condensate volume fraction, and aggregation mechanism on the resulting aggregation location and rates under a quantitative framework. The main results are: 1) When monomers that tend to aggregate partition preferentially in a liquid droplet this results in a positive feedback driving aggregation inside droplets, as it causes a flux of monomers from the dilute phase to the droplet. 2) A phase diagram reveals a switch between regions where large or small compartments show maximal aggregate enrichment, respectively. This switch depends on the orders of the primary and secondary aggregate nucleation reactions. This is a strong and interesting paper which presents important theoretical ideas that are relevant for cell biology. The work presented is in principle suitable for eLife. However the authors should carefully consider several specific points raised by the reviewers and in a revised manuscript they should formulate testable predictions more explicitly and improve the links to potential experimental tests.
Essential revisions:
1) The main entrance point to the model is Equation 1 (same as 14). It operates with phase segregation between Arich and Apoor liquid phases in which monomers and aggregates are also dissolved. This idea is not explained well, because terms (monomer, aggregate, A, B, etc.) are used without definitions. More importantly, what are A's and B's? Presumably A's and B's are also proteins of some sort. It is unclear why it is fair to talk about equilibrium and kinetics of proteins called monomers while assuming other proteins (in fact, perhaps many protein species!) to passively maintain equilibrium volume fractions ϕ^{I} and ϕ^{II}. One would actually expect that even a small amount of a particular protein called "monomer" could have a significant effect on the balancing of coexisting densities given the multitude of components hidden inside notations A and B.
2) In a regular theory of liquid nuclei (like, e.g., Lifshitz and Slyozov, 1961) an important part of the setup is dependence of equilibrium volume fractions on the nucleus size (the Laplace pressure effect); it is unclear why present theory neglects this altogether. To some extent authors may make an argument that concentration of "monomers" is small and does not affect the droplet size; but as irreversible aggregation continues, aggregates accumulate, and they seem to be bound to start affecting the droplet size eventually.
3) It is also not clear why in the kinetics part it is possible to neglect the fact that one of the phases might be dramatically more viscous than the other. The authors themselves cite works on polymer reputation and even possible solidification, but do not seem to consider the viscosity increase in the subsequent considerations, such as Equation 3. Again, this is possibly justifiable at the initial stage, but perhaps not so easily on a later time. I think that the authors have in mind some selfconsistent system of conditions where all these questions may be answered, but they did not present these conditions in a clear way. These conditions and the terminology should be clarified.
4) In the subsection “Mathematical model for liquid compartments controlling protein aggregation”, the authors present an interesting approach to understanding the partition coefficient that is quite useful. However, the authors comment that the partitioning is solely governed by the relative interaction strength Δχ between the monomers with the A and the B components. This statement seems overstated, since other issues such as the relative density of the components and the potential of nonχ type interactions (e. g. direct binding between A and B) may not captured.
5) The text around Equations 3B3D needs to be elaborated. n_{1} and n_{2} need to be defined and the description of the processes being considered requires more detail.
6) It is worth noting that the type of analyses carried out in this paper, including the analogy with Boundary Layer theory, have been considered extensively in the past in the context of gasliquid reactions that are used extensively in the chemical industry for separation processes. The work of G. Astarita and P.V. Danckwerts are just two examples. Some reference to these studies may be appropriate.
7) It is important to note whether the biological systems placed on the "phase diagram" in Figure 4 are known to correspond to the big or small compartment limits. If not, how might the prediction be tested.
8) More generally the authors should formulate testable predictions more explicitly and improve the links to potential experimental tests.
9) The model assumes that the condensed phase and dilute phase have the same kinetics of growth for aggregation. This is likely inaccurate and thus should be addressed. For example, others have noticed (e.g. Wei et al., 2017), a significant size originated nonideality in partitioning occurs in many proteinrich phases in vivo and in vitro. This nonideality would weaken the kinetics of growth for aggregates and potentially limit their size. It is unclear how much such nonideal contributions would perturb the conclusions thus requiring some discussion.
10) The authors focus on cytoplasm vs. specific biomoleculedense liquid phases. The authors should not limit themselves to the cytoplasm, since these principles would apply to aggregation in the nucleoplasm as well, and are thus worth at least commenting on.
11) The authors discuss the concept of primary vs. secondary nucleation, and indeed their analysis focuses on key differences in these two modes. But I think they should be introduced, at least in a few sentences, in a qualitative way, prior to diving into the mathematical details.
12) The authors focus on droplet size as a key factor, but the analysis seems to be exclusive with the simplest mapping from volume fraction to droplet size, by taking the condensed phase as all having coalesced into a single droplet. The authors should at least comment on the expected effects of how a distribution of droplet sizes, such as those commonly reported in the intracellular phase separation literature, would manifest in this context.
[Editors' note: further revisions were requested prior to acceptance, as described below.]
Thank you for resubmitting your work entitled "Spatial control of irreversible protein aggregation" for further consideration at eLife. Your revised article has been favorably evaluated by Arup Chakraborty (Senior Editor) and a Reviewing Editor.
The manuscript has been improved but there is one remaining issue that needs to be addressed before acceptance, as outlined below:
You have addressed most points raised and have improved the manuscript. However, an important criticism of the reviewers was formulated:
"8) More generally the authors should formulate testable predictions more explicitly and improve the links to potential experimental tests."
The reviewers felt that it was rather unclear how the ideas of the paper could be tested in experiments and whether there are clear predictions from the work for future experiments. In the revision of the manuscript this point was addressed somewhat superficially. This is not easy to do, but we would be grateful if you might try again to address this point more carefully. We anticipate that the paper will be accepted after that.
https://doi.org/10.7554/eLife.42315.013Author response
Essential revisions:
1) The main entrance point to the model is Equation 1 (same as 14). It operates with phase segregation between Arich and Apoor liquid phases in which monomers and aggregates are also dissolved. This idea is not explained well, because terms (monomer, aggregate, A, B, etc.) are used without definitions. More importantly, what are A's and B's? Presumably A's and B's are also proteins of some sort. It is unclear why it is fair to talk about equilibrium and kinetics of proteins called monomers while assuming other proteins (in fact, perhaps many protein species!) to passively maintain equilibrium volume fractions ϕ^{I} and ϕ^{II}. One would actually expect that even a small amount of a particular protein called "monomer" could have a significant effect on the balancing of coexisting densities given the multitude of components hidden inside notations A and B.
The reviewers suggest improving the discussion and presentation of the paragraph about the three component phase separation model and why a consideration at phase separation equilibrium of the AB mixture is valid. We have now revised the corresponding paragraphs and explicitly mention what species A and B could represent (note, in any case, that an exact definition is unnecessary because only physical parameter enter the model). Moreover, we now briefly explain why the binary phase separation between A and B is well equilibrated while the partitioning of monomer deviates weakly from the partitioning equilibrium. Such assumptions are well satisfied because of two reasons: 1) Monomer species are very dilute and thus cannot affect AB phase equilibrium. 2) Moreover, aggregation kinetic is slow relative to the partitioning of monomers via diffusion. Thus, at each time point during the aggregation kinetics, monomer partitioning is very close to the partitioning equilibrium. For better overview we have now combined all essential assumptions of our model in the first section of the model description.
2) In a regular theory of liquid nuclei (like, e.g., Lifshitz and Slyozov, 1961) an important part of the setup is dependence of equilibrium volume fractions on the nucleus size (the Laplace pressure effect); it is unclear why present theory neglects this altogether. To some extent authors may make an argument that concentration of "monomers" is small and does not affect the droplet size; but as irreversible aggregation continues, aggregates accumulate, and they seem to be bound to start affecting the droplet size eventually.
We agree with the reviewer that we have neglected the effect of the compartment radius R on the equilibrium concentrations. For a binary AB mixture, this effect can be captured by the GibbsThomson relationship stating that the relative increase on both equilibrium concentrations inside and outside increases, cαlγα/R, where lγα denotes the capillary length inside and outside. However, the capillary length is typically in the order of a few molecular sizes. Thus, as long as the compartment size significantly exceeds the molecular size (which is the typical case inside cells for example), the actual equilibrium concentration values would differ only very weakly relative to the equilibrium concentrations for large R. Of course, this little concentration difference is key for the coarsening kinetics (Ostwald ripening) where the small fraction, lγα/R, competes with another very small value, namely the supersaturation. However, in our work we only consider one compartment and not the coarsening of many compartments. In the presence of monomers the arguments above apply as well because the considered monomer concentrations are so dilute that we can neglect their impact on the equilibrium concentrations and also on the surface tension (in the case they would act as surfactants). We have added a comment to the main text and a paragraph to the Appendix discussing when Laplace pressure effects can be neglected.
Please note that we have estimated typical values for homogeneous volume fraction of aggregating monomers to be 10^{9} – 10^{8}. Hence, even for large partitioning factors, such as e.g. Γ=100, the local concentrations of monomers remain dilute; this allows us to safely neglect the contributions of monomers to droplet volume. Based on the reviewer’s comments, we have now commented on the role of Laplace pressure effects in the appendix (see text after Equation 13) and the impact of monomers on droplet in the main text (see end of paragraph “Model for liquid compartments controlling protein aggregation”).
3) It is also not clear why in the kinetics part it is possible to neglect the fact that one of the phases might be dramatically more viscous than the other. The authors themselves cite works on polymer reputation and even possible solidification, but do not seem to consider the viscosity increase in the subsequent considerations, such as Equation 3. Again, this is possibly justifiable at the initial stage, but perhaps not so easily on a later time. I think that the authors have in mind some selfconsistent system of conditions where all these questions may be answered, but they did not present these conditions in a clear way. These conditions and the terminology should be clarified.
We agree that very complex rheological properties could emerge in the later stage of the aggregation kinetics. It is correct that our assumption of a constant monomer diffusion coefficient, while aggregates hardly diffuse, may no more be quantitively correct. One may expect that the monomers could pick up a thickening behaviour due to the interactions with the aggregates on long time scales that would lower their diffusivity as a function of time. We have not included these effects in our model. However, we think that they are worth studying for specific cellular and aggregating systems because rheological properties, in particular, time dependent ones, vary from system to system. Please note that in our manuscript, we present a study of a mechanism which is expected to be qualitatively robust against different rheological properties – of course, quantitative changes of the degree of aggregate partitioning may occur. Based on the reviewer’s comment above we have decided to add a comment to the main text and the outlook.
4) In the subsection “Mathematical model for liquid compartments controlling protein aggregation”, the authors present an interesting approach to understanding the partition coefficient that is quite useful. However, the authors comment that the partitioning is solely governed by the relative interaction strength Δχ between the monomers with the A and the B components. This statement seems overstated, since other issues such as the relative density of the components and the potential of nonχ type interactions (e. g. direct binding between A and B) may not captured.
We thank the reviewers for this comment. Our statement only holds for dilute monomer volume fraction (typical case for the considered aggregating systems discussed in our manuscript) and in the absence of binding processes. We have corrected this statement in our manuscript.
5) The text around Equations 3B3D needs to be elaborated. n_{1} and n_{2} need to be defined and the description of the processes being considered requires more detail.
The reviewers suggest elaborating on the presentation about the primary and secondary reaction orders. Accordingly, we have revised the text around Equations (3B3D). In particular, we now give a physical interpretation of the reaction orders n_{1} and n_{2}.
6) It is worth noting that the type of analyses carried out in this paper, including the analogy with Boundary Layer theory, have been considered extensively in the past in the context of gasliquid reactions that are used extensively in the chemical industry for separation processes. The work of G. Astarita and P.V. Danckwerts are just two examples. Some reference to these studies may be appropriate.
We thank the reviewers for suggestions of references on the boundary layer theory. We have included the proposed references to the discussion of boundarylayer dynamics (see text before Equation 21 in the Appendix).
7) It is important to note whether the biological systems placed on the "phase diagram" in Figure 4 are known to correspond to the big or small compartment limits. If not, how might the prediction be tested.
To the best of our knowledge, no experiments are currently available that would allow us to judge which system corresponds to the small or large compartment limit. However, we appreciate the suggestion to think more of how to test the “phase diagram”. We thus revised the paragraph in the conclusion section, where we have already suggested how our prediction could be tested. Now we also include a short discussion related to Figure 4.
8) More generally the authors should formulate testable predictions more explicitly and improve the links to potential experimental tests.
We have revised the paragraphs in the conclusion section, which now present explicit experimental tests. Moreover, we tried to stress the link to experimental systems by structuring the conclusion section more clearly using subheadings.
9) The model assumes that the condensed phase and dilute phase have the same kinetics of growth for aggregation. This is likely inaccurate and thus should be addressed. For example, others have noticed (e.g. Wei et al., 2017), a significant size originated nonideality in partitioning occurs in many proteinrich phases in vivo and in vitro. This nonideality would weaken the kinetics of growth for aggregates and potentially limit their size. It is unclear how much such nonideal contributions would perturb the conclusions thus requiring some discussion.
It is correct that the assumption of equal kinetic parameters inside and outside will not hold for all phase separating systems combined with aggregation. In our manuscript we wanted to make the point that a strong partitioning of aggregates already occurs for equal kinetic parameters inside and outside. To test how deviations from our ideal assumption affect our conclusions we have derived an equation for the partitioning of aggregates in the case of different growth rates inside and outside. We have now added a corresponding discussion to the main text and show the extended equations in the appendix. We also cite the reference mentioned by the reviewers and discuss Brangwynne et al.’s finding of a semidilute mesh of proteins forming droplets that can affect physical properties inside the compartment.
10) The authors focus on cytoplasm vs. specific biomoleculedense liquid phases. The authors should not limit themselves to the cytoplasm, since these principles would apply to aggregation in the nucleoplasm as well, and are thus worth at least commenting on.
Great point. We now also mention the nucleoplasm (see e.g. Abstract).
11) The authors discuss the concept of primary vs. secondary nucleation, and indeed their analysis focuses on key differences in these two modes. But I think they should be introduced, at least in a few sentences, in a qualitative way, prior to diving into the mathematical details.
Following the reviewers’ suggestion, we have included (before Equation 3) a detailed discussion of primary and secondary nucleation in protein aggregation (see also reply to point 5).
12) The authors focus on droplet size as a key factor, but the analysis seems to be exclusive with the simplest mapping from volume fraction to droplet size, by taking the condensed phase as all having coalesced into a single droplet. The authors should at least comment on the expected effects of how a distribution of droplet sizes, such as those commonly reported in the intracellular phase separation literature, would manifest in this context.
This is a fascinating comment. We would expect that either the smallest or the largest droplets in the system would initially enrich more of the aggregates (depending on the reaction orders of primary vs. secondary nucleation). During the aggregation kinetics there would be a competition about the monomers between the droplets of different size. Droplets of different size thus end up with different aggregate fraction. We now mention these thoughts in the conclusion of our manuscript.
[Editors' note: further revisions were requested prior to acceptance, as described below.]
You have addressed most points raised and have improved the manuscript. However, an important criticism of the reviewers was formulated:
"8) More generally the authors should formulate testable predictions more explicitly and improve the links to potential experimental tests."
The reviewers felt that it was rather unclear how the ideas of the paper could be tested in experiments and whether there are clear predictions from the work for future experiments. In the revision of the manuscript this point was addressed somewhat superficially. This is not easy to do, but we would be grateful if you might try again to address this point more carefully. We anticipate that the paper will be accepted after that.
We were delighted by the positive response of the reviewers to our work. We have included suggestions into our manuscript in the context of experimentally testable predictions. We have also added a new short section on a calculation of the amount of aggregates in the compartments – summarized in a new Figure 5, which we think is a complementary perspective on the rest of the paper.
https://doi.org/10.7554/eLife.42315.014Article and author information
Author details
Funding
Deutsche Forschungsgemeinschaft
 Christoph Weber
Swiss National Science Foundation
 Thomas Michaels
MacArthur Foundation
 L Mahadevan
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Acknowledgements
CAW thanks the German Research Foundation (DFG) for financial support and TCTM acknowledges the support from the Swiss National Science foundation.
Senior Editor
 Arup K Chakraborty, Massachusetts Institute of Technology, United States
Reviewing Editor
 Frank Jülicher, Max Planck Institute for the Physics of Complex Systems, Germany
Reviewer
 Alexander Grosberg, NYU, United States
Publication history
 Received: September 26, 2018
 Accepted: April 1, 2019
 Version of Record published: May 14, 2019 (version 1)
Copyright
© 2019, Weber et al.
This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.
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Funding:
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