Active mechanics of sea star oocytes
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Abstract
Actomyosin is a canonical example of an active material, driven out of equilibrium in part through the injection of energy by myosin motors. This influx of energy allows actomyosin networks to generate cellular-scale contractility, which underlies cellular processes ranging from division to migration. While the molecular players underlying actomyosin contractility have been well characterized, how cellular-scale deformation in disordered actomyosin networks emerges from filament-scale interactions is not well understood. Here, we address this question in vivo using the meiotic surface contraction wave of Patiria miniata oocytes. Using pharmacological treatments targeting actin polymerization, we find that the cellular deformation rate is a nonmonotonic function of cortical actin density peaked near the wild type density. To understand this, we develop an active fluid model coarse-grained from filament-scale interactions and find quantitative agreement with the measured data. This model further predicts the dependence of the deformation rate on the concentration of passive actin crosslinkers and motor proteins, including the surprising prediction that deformation rate decreases with increasing motor concentration. We test these predictions through protein overexpression and find quantitative agreement. Taken together, this work is an important step for bridging the molecular and cellular length scales for cytoskeletal networks in vivo .
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This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/11186843.
In the paper Active mechanics of sea star oocytes by Foster et al., the authors study the contractility of a disordered actomyosin network in vivo, which they then combined with a model to explain cell-scale observations on filament scale.
The authors first present the phenomenon of surface contraction waves in sea star oocytes. These waves travel through the unordered actomyosin network which forms the oocyte cortex from the vegetal to the animal pole. The observed wave can be quantified by the deformation rate at the cortex, which is significantly lowered by changes in the actin density.
Based on this data, an active fluid model is built to look into further factors affecting the …
This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/11186843.
In the paper Active mechanics of sea star oocytes by Foster et al., the authors study the contractility of a disordered actomyosin network in vivo, which they then combined with a model to explain cell-scale observations on filament scale.
The authors first present the phenomenon of surface contraction waves in sea star oocytes. These waves travel through the unordered actomyosin network which forms the oocyte cortex from the vegetal to the animal pole. The observed wave can be quantified by the deformation rate at the cortex, which is significantly lowered by changes in the actin density.
Based on this data, an active fluid model is built to look into further factors affecting the contractility of actomyosin networks. The model describes mechanical interactions between actin filaments, molecular motors and passive crosslinkers by taking their contributions to active stress and viscosity into account. Most importantly, crosslinkers and motors are modelled to be distributed unequally on the actin filaments, such that an asymmetry between contraction and extension appears.
Lastly, the model's predictions for the deformation rate as function of in actin, crosslinker and motor concentration are tested. The results indicate that all changes compared to the wild type concentrations lead to a decrease in deformation rate. This finding is quite unexpected as an increase in motor concentration was connected to saturating effects in previous in vitro studies. Together with already established models, this paper is an important step on the way to a full biophysical description of active mechanics of cytoskeletal networks.
The paper's structure helps to follow the thoughts step by step. Every section builds on the previous ones, peaking in a beautiful conclusion of all results and predictions to a comprehensive picture. For occasional explanations however, it feels like the authors jump to statements too soon or without providing enough context.
In conclusion, this paper gives very interesting results based on a solid experimental execution. But several problems regarding the understanding of their analysis should be addressed before publication.
Major comments:
• Referring to two plots (Fig. 2e and 5), the authors state that "the deformation rate is maximum near the wild-type density". I assumed that "wild-type" refers to the results in the first section regarding untreated oocytes and to the data cluster in Fig. 2e representing the control group. But comparing this group's mean value of the characteristic deformation rate (dc ≈ 0.033min−1) with the value of the first measurements in the section before (dc = 0.017±0.002min−1) shows a high discrepancy (which reappears in Fig. 5). Additional context would be helpful to understand where this arises from.
• The model's details are presented and discussed only superficially with reference to a previous paper, thus missing out in giving a better understanding at some points. While helpful in building intuition, some mentioned (mathematical) background lacks explanation. This applies especially to the motor and crosslinker concentration as introduced in Eq. 5. In my opinion, an example of a distribution and how it leads to values for in the supplementary material could help.
• Fig. 2d could profit from more context. Neither do the authors explain what the x-axis label "distance" refers to, nor do they give background information regarding the measurement, analysis or interpretation. As it - in its current state - does not lead to additional information, the plot could also be left out of the paper without limiting overall understanding.
Minor comments:
• In my opinion, a measure of quality is missing in Fig. 5 regarding the comparison of data and prediction. Stating that the data points "collapse to the curve" could be expanded by information about uncertainties. Also, the data corresponding to the increasing part of the curve seem to show much less variance than the rest. Maybe a different choice of the scale can highlight whether that indeed is the case.
• When choosing the configuration of the model, four different realisations are called plausible in the supplementary material, but only one is implemented. It would be interesting to see the biological meaning and a comparison regarding the predictions of the other three models. Additionally, the biological justification for the model of choice seems to be inconsistent or to lack a depth of explanation, as the crosslinker compatible with the modelling (Arp2/3) is different to the one tested for (Alpha-Actinin).
• Adding to the partial lack of mathematical background: the jump from proportionality to equality of deformation rate and the ratio of active stress to viscosity in Eq. 8 is unclear and not explained.
• In the labels of some plots (Fig. 2 a/b/e, 4 b/d, and 5), the written variable name "deformation rate" instead of "characteristic deformation rate" is confusing. I'd suggest using abbreviations, e.g. "char. deform. rate dc" to have a compromise between space usage, thus readability, and clarity. That the subscript "c" of dc is quite small might add to the confusion but seems to be inevitable.
Competing interests
The authors declare that they have no competing interests.
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