1. Cell Biology
  2. Developmental Biology

Combinatorial deployment of F-actin regulators to build complex 3D actin structures in vivo

  1. Yi Xie
  2. Rashmi Budhathoki
  3. J Todd Blankenship  Is a corresponding author
  1. Department of Biological Sciences, University of Denver, United States
https://doi.org/10.7554/eLife.63046
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  1. Yi Xie
  2. Rashmi Budhathoki
  3. J Todd Blankenship
(2021)
Combinatorial deployment of F-actin regulators to build complex 3D actin structures in vivo
eLife 10:e63046.
https://doi.org/10.7554/eLife.63046
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Abstract

Despite extensive studies on the actin regulators that direct microfilament dynamics, how these regulators are combinatorially utilized in organismal tissues to generate 3D structures is an unresolved question. Here, we present an in-depth characterization of cortical actin cap dynamics and their regulation in vivo. We identify rapid phases of initiation, expansion, duplication, and disassembly and examine the functions of seven different actin and/or nucleator regulators (ANRPs) in guiding these behaviors. We find ANRPs provide distinct activities in building actin cap morphologies – specifically, while DPod1 is a major regulator of actin intensities, Cortactin is required for continued cortical growth, while Coronin functions in both growth and intensity and is required for Cortactin localization to the cap periphery. Unexpectedly, cortical actin populations recover more rapidly after regulator disruption, suggestive of a deep competition for limited G-actin pools, and we measure in vivo Arp2/3 recruitment efficiencies through an ectopic relocalization strategy. Our results illustrate how the coordination of multiple actin regulators can orchestrate organized and dynamic actin structures in a developmental system.

Introduction

The mechanisms by which complex actin-based structures form are essential to shaping cell and tissue morphologies. This capacity to rapidly direct filamentous actin assembly is key to a cell’s ability to either maintain or abruptly distort its cell shape. During development, the rapid changes in tissue morphologies are often a result of the remodeling of cortical actin and myosin activities (reviewed in Munjal and Lecuit, 2014; Heer and Martin, 2017). In keeping with this importance of regulating actin structures to achieve discrete cell shapes, a multitude of actin regulators are present within the genomes of eukaryotic animals (Siripala and Welch, 2007; Swaney and Li, 2016; Pegoraro et al., 2017). Some of the foremost examples of actin regulators are the nucleation and assembly complexes of the Formin and Arp2/3 complex families. Additionally, there are a host of actin and nucleator regulatory proteins present in the genomes of most higher animals (Siripala and Welch, 2007). However, although the biochemical activities of a broad array of actin regulators have been examined in vitro, how these combined activities are utilized by development in vivo to generate three-dimensional structures is less clear. Furthermore, many actin regulators have been implicated in a variety of different processes ranging from the control of filament branching and turnover to the direct regulation and stabilization of nucleator complex function. Thus, the baseline effects of how these proteins contribute to building cortical structures is unclear. Here, we examine the in vivo function of seven major families of actin and/or nucleator regulatory proteins (ANRPs – DPod1, Coronin, Cortactin, Scar, Wasp, Wash, Carmil) in an intact organismal tissue context. We are using the dynamic furrowing processes in the early syncytial fly embryo to study the rapid formation and disassembly of apical actin networks. In the end, the combined activities of different actin regulatory pathways will drive the cell-shaping events necessary for the development and generation of a wide array of cell morphologies.

The Drosophila syncytium has a series of rapid transient cleavage cycles that are driven by actin polymerization and membrane trafficking pathways (Warn, 1986; Foe et al., 2000; Riggs et al., 2003; Pelissier et al., 2003; Grosshans et al., 2005; Yan et al., 2013; Holly et al., 2015; Mavor et al., 2016; Xie and Blankenship, 2018; Zhang et al., 2018). Following fertilization and the fusion of the male and female pronuclei, the zygotic nucleus undergoes 13 rounds of replication in the absence of cell division to generate a single-celled embryo with approximately 5000 nuclei (Hartenstein, 1993; Mazumdar and Mazumdar, 2002; Schmidt and Grosshans, 2018). The first nine rounds of replication occur deep in the yolk of the embryo; however, at cycle 10, nuclei migrate to the periphery and begin organizing the formation of cortical actin structures at the apical surface (apical F-actin caps) that will then seed the formation of cytokinetic-like furrows that serve to separate mitotic spindles in the syncytium.

Apical actin cap and cleavage furrow behaviors are highly transient, forming during each syncytial cell cycle where they compartmentalize and anchor mitotic spindles, before then regressing (Foe and Alberts, 1983; Sullivan et al., 1993; Cao et al., 2010; Holly et al., 2015). Embryos undergo four rapid rounds of actin cap and furrow formation (cycles 10–13), followed by a final fifth round of furrow ingression that results in the permanent packaging of nuclei into individual cells and the formation of an epithelium through a process known as cellularization. The ability to form these apical actin caps and ingressing furrows is essential to genomic stability – when these morphogenetic processes are disrupted, chromosomal segregation defects occur (Sullivan et al., 1993; Holly et al., 2015; Xie and Blankenship, 2018). Cap failure leads to aneuploid and polyploid nuclei, and defective nuclei are subsequently jettisoned into the deep yolk layer where they do not contribute further to development. This illustrates the importance of effective cortical actin cap function in the early embryo. These cleavage cycles of actin and furrow formation are rapid, with each full round of cap assembly and disassembly occurring within 7 (cycle 10) to 20 min (cycle 13). F-actin regulation is therefore highly dynamic, and these stages represent an intriguing system to analyze how three-dimensional forms can be swiftly generated.

Here, we use 4D live-imaging of filamentous actin to determine phasic behaviors during cortical cap formation. Formation of these actin structures progresses through rapid periods encompassing growth, stabilization, elongation, and remodeling activities. Formin and Arp2/3 networks are responsible for building these cortical structures, and individual ANRPs have distinct functions in guiding cap growth and cap-associated actin intensities. We also generate an ANRP-related toolkit of genomic transgenes for these actin regulators and employ a mito-tag strategy to assess the strength of Arp2/3 nucleator recruitment in vivo. Finally, we explore how the disruption of ANRPs leads to faster actin network recoveries, which may be suggestive of a competition for a limited G-actin pool that controls actin assembly dynamics in the embryo.

Results

Rapid formation and dissolution of apical actin cap structures

The apical actin caps in the early syncytial Drosophila embryo are highly active structures that undergo cyclic behaviors of formation and disassembly on relatively short time scales. As such, they represent a unique opportunity to unravel the mechanisms that guide formation of complex three-dimensional actin-based structures. As a starting point, we live-imaged wild-type actin caps by labeling filamentous actin with an actin-binding domain construct derived from moesin (mCh:MoeABD). This method of labeling has been used extensively in the Drosophila embryo and well-represents endogenous filamentous actin dynamics while avoiding problems that occur when fluorescent proteins are directly attached to actin or other labeling paradigms (such as Lifeact; Kiehart et al., 2000; Blankenship et al., 2006; Spracklen et al., 2014; Figure 1—figure supplement 1A–F). Actin cap formation was imaged through the four cycles that occur in the syncytial blastoderm (nuclear cycles 10–13), and actin growth behaviors were analyzed (Figure 1A, Figure 1—figure supplement 2A–G; Figure 1—source data 1).

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Rapid formation and dissolution of cortical actin cap structures.

(A) Still images from live-imaging of apical F-actin dynamics (UAS:moeABD:mCherry, cycle 11) at t = 0, 20, 90, 180, 270, 360, 540 s. Scale bar = 5 μm. (B) WT actin cap area dynamics from cycle 11 (measured cap n = 15, from embryo N = 4). Cap areas are normalized to the size at t = 0 s. Four different phases are labeled (Exp.: Expansion; Stab.: Stabilization; Elong.: Elongation; and Frag.: Fragmentation phases). (C) WT actin cap expansion rate from 30 s rolling window (cycle 11, n = 15, N = 4). Inset has y-axis re-scaled to visualize changes after expansion. (D) WT actin cap area (μm2) from cycle 11 at t = 0, 60, 120, 180, 240, 300, 360, 420, 480, 540 s (n = 15, N = 4). (E) WT actin cap area change in different phases (cycle 11, n = 15, N = 4). The values are calculated by the cap area at the end point divided by the area at the beginning of each phase. (F) Average WT actin cap area expansion rate (μm2/s) in different phases (cycle 11, n = 15, N = 4). (G) Average WT actin cap intensity (A.U.) from cycle 11 at t = 0, 60, 120, 180, 240, 300, 360, 420, 480, 540 s (n = 12, N = 3). (H) WT actin cap heterogeneity dynamics from cycle 11 (n = 15, N = 4). The heterogeneity is measured as the intensity standard deviation normalized to the value at t = 0 s. (I) WT actin cap heterogeneity from cycle 11 at t = 0, 60, 120, 180, 240, 300, 360, 420, 480, 540 s (n = 15, N = 4).

Figure 1—source data 1

Cap dynamics data.

https://cdn.elifesciences.org/articles/63046/elife-63046-fig1-data1-v2.xlsx

Our analysis shows that apical actin caps experience an initial period of rapid exponential growth (~6 fold growth, see Materials and methods). During this phase, the major increase in cap dimensions occurs in as little as 120 s (Figure 1A–C, Figure 1—figure supplement 2). This is followed by cap stabilization and cap elongation, which correlates with spindle duplication and separation (Figure 1A–C; Cao et al., 2010). Lastly, caps disassemble and then reform as small proto-caps around two central hubs to begin the next cycle of cap behaviors (Figure 1A,B, Figure 1—figure supplement 1A,B; Figure 1—figure supplement 2E–G). In each cycle, the cap dynamics share similar features, albeit with moderate differences (especially in the very short cycle 10) – as a result, we will focus on cycle 11 caps going forward (Figure 1—figure supplement 2A–G).

During the expansion phase, cycle 11 actin caps rapidly expand from small proto-caps to caps 15–18 µm in diameter and 206 µm2 in area on average within 2 min (Figure 1A–F). Apical cap size increases almost sixfold during this period and growth rates peak early during expansion (Figure 1E,F). Interestingly, despite the exponential growth in cap size, the actin intensity increases only mildly, with a ~39% increase by the end of stabilization (Figure 1G). This suggests that actin recruitment is carefully regulated spatially such that the cap grows rapidly in total area but regional intensities do not.

The following stabilization phase lasts for ~60 s with caps largely maintaining their size. As the cell cycle continues, caps elongate and slightly increase in size (Figure 1A–F). However, cap intensities begin to decrease and heterogeneity within the cap drops by approximately one quarter of the maximum at the end of stabilization phase (Figure 1G–I). Interestingly, the morphology of the cap during elongation switches from round to an elongated doughnut-like structure and caps begin to lose intensity in internal actin populations (Figure 1A). As the cell cycle reaches mitosis and chromosomal segregation, the caps fragment and disassemble. Overall cap area begins to decline as F-actin gradually disbands leaving low-intensity gaps in the middle of the elongating figure and along cap edges (Figure 1A, Figure 1—figure supplement 1A). During actin cap disassembly, the average intensity of caps drops, as well as the measured heterogeneity (Figure 1G–I). These results demonstrate that the apical cap is a dynamic and complex F-actin structure, providing an interesting contextual model for the investigation of filamentous behaviors in vivo.

Diaphanous and Arp2/3 networks direct actin cap dynamics

After describing the filamentous actin dynamics above, we wanted to examine the major actin networks that drive these behaviors. The Formin Diaphanous and the Arp2/3 complex have been previously implicated in regulating actin nucleation at these syncytial stages (Stevenson et al., 2002; Zallen et al., 2002; Grosshans et al., 2005; Cao et al., 2010; Zhang et al., 2018). According to published studies, Diaphanous appears to be the major regulator of furrow-associated F-actin, while Arp2/3 has been implicated in apical actin formation (Grosshans et al., 2005; Cao et al., 2010; Zhang et al., 2018), although comprehensive time-lapse-based quantitation has been lacking. We therefore performed our own quantitative analysis of these protein’s function in the early syncytial stages. Consistent with previous results, disruption of Diaphanous function deeply affects furrow-associated actin (Figure 2—figure supplement 1). However, Diaphanous also shows a significant contribution to the early expansion of actin caps. Dia disrupted embryos have a ~ 35% reduction in cap area expansion, but a relatively mild 18% reduction in actin intensities and cap expansion rate (Figure 2A–H, Figure 2—source data 1). We then examined the contribution of the Arp2/3 complex to actin behaviors. By contrast to Diaphanous, when Arp2/3 function is compromised there is an almost complete absence of cap expansion and cap actin intensities are reduced to 47% of wild-type levels (Figure 2A–H). Interestingly, the remaining actin structure appear to be hollowed out and missing internal actin populations (Figure 2A,B). As recent evidence has shown that Arp2/3 and Formin networks can demonstrate a degree of interdependence (Suarez et al., 2015; Chan et al., 2019), we examined Diaphanous and Arp2/3 intensities when each network was disrupted. Although defective cap structures characteristic of the above described results were observed, disruption of Diaphanous function did not significantly affect Arp2/3 intensities (as judged by ArpC1:GFP), nor did Arp2/3 disruption affect anti-Diaphanous intensities in the remaining cap structures (anti-Diaphanous immunostaining; Figure 2—figure supplement 1B–E). These results show that Diaphanous and Arp2/3 both contribute to structuring apical actin caps, but that Arp2/3 is the major regulator of actin intensities and cap growth. The hollowed out internal cap structures as well as the failure in cap expansion also suggests a possible model in which Arp2/3 polymerizes F-actin internally, and the cap possesses a dispersion mechanism that flows F-actin filaments towards the edges so as to maintain consistent internal F-actin intensities.

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Quantitative dynamics of Formin and Arp2/3-driven actin networks.

(A) Still images from live-imaging of apical F-actin dynamics (UAS:moeABD:mCherry, cycle 11) from control, Dia shRNA and ArpC4 shRNA lines at t = 20, 90, 180, 360, 540 s. Four different phases are labeled (Exp.: Expansion; Stab.: Stabilization; Elong.: Elongation; and Frag.: Fragmentation phases). Scale bar = 5 μm. (B) Still images showing F-actin cap intensities by live-imaging (UAS:moeABD:mCherry, cycle 11) from control, Dia shRNA and ArpC4 shRNA lines at t = 120 s. First three panels are leveled and imaged equivalently, with the last panel optimized for visualization. Scale bar = 5 μm. (C) Actin cap area dynamics of control (black, n = 15, N = 4), Dia shRNA (grey, n = 10, N = 3) and ArpC4 shRNA (red, n = 11, N = 3) from cycle 11. Cap areas are normalized to the size at t = 0 s. (D) Actin cap area (μm2) of control (n = 15, N = 4), Dia shRNA (n = 10, N = 3), and ArpC4 shRNA (n = 11, N = 3) at t = 120 s in cycle 11. *: p<0.05, ***: p<0.0005. (E) Actin cap area change of control (n = 15, N = 4), Dia shRNA (n = 10, N = 3), and ArpC4 shRNA (n = 11, N = 3) from t = 120 s to t = 0 s in cycle 11. ***: p<0.0005. (F) Average intensity of apical cap structures of control (n = 12, N = 3), Dia shRNA (n = 10, N = 3), and ArpC4 shRNA (n = 11, N = 3) at t = 120 s in cycle 11. *: p<0.05, ***: p<0.0005. (G) Actin cap area expansion rate of control (n = 15, N = 4), Dia shRNA (n = 10, N = 3), and ArpC4 shRNA (n = 11, N = 3) from 0 to 120 s in cycle 11. *: p<0.05, ***: p<0.0005. (H) Actin cap heterogeneity (intensity standard deviation) of control (n = 15, N = 4), Dia shRNA (n = 10, N = 3), and ArpC4 shRNA (n = 11, N = 3) at t = 120 s in cycle 11. ns: not significant, ***: p<0.0005.

Figure 2—source data 1

Arp and Dia disrupted data.

https://cdn.elifesciences.org/articles/63046/elife-63046-fig2-data1-v2.xlsx

Different ANRPs have distinct roles in building apical actin structures

Given the above rapid dynamics of cap formation and nucleator function, we wanted to identify how actin and/or nucleator regulatory proteins (ANRPs) are deployed to control actin activities spatiotemporally in the construction of cortical actin caps. Extensive work across a variety of systems has revealed a diverse array of actin regulatory proteins, many of which have also been implicated, to varying degrees, in controlling Arp2/3 activity or stability, although additional actin-related functions for these proteins exist (Siripala and Welch, 2007; Swaney and Li, 2016). Here we are focusing on seven of these families of proteins (DPod1, Coronin, Cortactin, Scar, Carmil, Wasp, and Wash) – each of these families is represented within the Drosophila genome by single orthologs (Figure 3A). Cortactin and Carmil have been shown to regulate Arp2/3 complex function as well as branch point stability (Cortactin) or filament capping (Carmil), and Scar/Wash/Wasp super-family proteins are known potent activators of Arp2/3 nucleation (Jung et al., 2001; Uruno et al., 2001; Weaver et al., 2001; Ammer and Weed, 2008; Pollitt and Insall, 2009). Scar has previously been suggested to be the most relevant member regulating Arp2/3 function in the early embryo, although this has not been tested systematically (Zallen et al., 2002; Levayer et al., 2011). Drosophila DPod1 contains WD40 domains and has similarity to Coronin-family proteins, which have been implicated in recruiting Arp2/3 complexes in the presence of preexisting actin filaments, as well as regulating cofilin function (Gandhi and Goode, 2008). Given the many associated functions of the ANRPs, we wanted to determine their baseline behaviors in regulating in vivo actin structures, and therefore analyzed actin cap dynamics in embryos compromised for each of these ANRPs (Figure 3B, Figure 3—source data 1).

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ANRPs have distinct roles in building cortical actin structures.

(A) Schematics of different ANRPs domain organization. Domains are collected from Flybase (Pfam/SMART) and UniProt. WD40: WD40 repeats; CC: coiled-coil domain; Hs1/Cortactin: Hs1/Cortactin repeats; SH3: SH3 domain; PRD: proline-rich domain; WCA: WH2/verprolin, cofilin, acidic domains; LRR: Leucine-rich repeats; PH: pleckstrin homology domain; LLR: Leucine-rich repeats; C-terminal: Carmil c-terminal domain; DUF: domains of unknown function. Domain size is not to scale. (B) Cycle 11 apical actin cap area dynamics in control, Arp2/3, and ANRP disrupted embryos: control (black, n = 15, N = 4), DPod1 shRNA (green, n = 11, N = 3), Coronin shRNA (blue, n = 9, N = 3), Cortactin shRNA (red, n = 12, N = 3), Scar shRNA (orange, n = 11, N = 3), Carmil shRNA (grey, n = 9, N = 3), and ArpC4 shRNA (magenta, n = 11, N = 3). Cap areas are normalized to the size at t = 0 s. (C, D, F, H, J, L) Still images from live-imaging of apical F-actin dynamics (UAS:moeABD:mCh, cycle 11) at t = 180, 270, 360 s, from control (C), DPod1 shRNA (D), Coronin shRNA (F), Cortactin shRNA (H), Scar shRNA (J), and Carmil shRNA (L) embryos. Images are identically leveled and imaged. Scale bar = 5 μm. (E, G, I, K, M) Apical actin cap area dynamics (cycle 11) in control (black, n = 15, N = 4), DPod1 shRNA (green, n = 11, N = 3), Coronin shRNA (blue, n = 9, N = 3), Cortactin shRNA (red, n = 12, N = 3), Scar shRNA (orange, n = 11, N = 3), Carmil shRNA (gray, n = 9, N = 3), and ArpC4 shRNA (magenta, n = 11, N = 3). Cap areas are normalized to the size at t = 0 s. (E’, G’, I’, K’, M’) Absolute actin cap areas (μm2) in control and ANRP-compromised embryos from cycle 11 at indicated time points. *: p<0.05. (E’’, G’’, I’’, K’’, M’’) Actin cap average intensity in control and ANRP-compromised embryos from cycle 11 at indicated time points. *: p<0.05. Bar graphs without * labeled in (E’–E’’, G’–G’’, I’–I’’, K’–K’’, M’–M’’) are not significant. (N) Still images of endogenous CRISPR Arp3:GFP behavior in control and actin regulator disrupted embryos at t = 120 s in cycle 11. Scale bar = 5 μm. (O) CRISPR Arp3:GFP intensity in control and different actin regulators functional disruption embryos at t = 120 s in cycle 11. control: n = 13, N = 3; ArpC4 shRNA: n = 18, N = 3; DPod1 shRNA: n = 34, N = 3; Coronin shRNA: n = 30, N = 3; Scar shRNA: n = 24, N = 3; Cortactin shRNA: n = 27, N = 3; Dia shRNA: n = 28, N = 3. ns: not significant, *: p<0.05, **: p<0.005; ***: p<0.0005.

Figure 3—source data 1

ANRP measurement data.

https://cdn.elifesciences.org/articles/63046/elife-63046-fig3-data1-v2.xlsx

Interestingly, these results identify distinct functions for Cortactin, DPod1, Coronin, and Scar in building F-actin caps (Figure 3D–K), while disrupting Carmil, Wasp, and Wash had little effect on caps (Figure 3L,M, Figure 3—figure supplement 1J–M). Compromising Coronin function causes an immediate defect in the expansion phase of cap formation (Figure 3G,G’), with actin intensities at ~70% of control levels (Figure 3C,F,G’’). By contrast, disrupting DPod1 function produces caps that expand at near wild-type levels and possess wild-type areas until they fail to maintain area size in the later stages of apical cap function (Figure 3E,E’). However, these embryos have dramatically reduced actin intensities throughout the cap area (Figure 3E’’). In embryos with compromised Cortactin function, there is a normal burst of actin expansion, but after ~120 s actin caps do not continue to grow and steadily diminish in size, suggesting a role for Cortactin in growth at the cap periphery (Figure 3H,I–I’’). Interestingly, F-actin intensities within the smaller cap are at higher levels than control embryos (Figure 3I’’). Disrupting Scar function produces actin caps that show an early depletion of F-actin intensities followed by a delayed expansion phase in which the caps cannot fully reach control cap areas (Figure 3J,K–K’’). The specificity and reproducibility of these phenotype was confirmed with second, independent shRNA lines (Figure 3—figure supplement 1).

Given these effects on the size, shape, and intensity of apical actin caps, and our results demonstrating that the Arp2/3 complex is the major regulator of actin behaviors in the apical cortex, we next determined the degree to which Arp2/3 complex recruitment (as proxied by an endogenous CRISPR Arp3:GFP) to the apical cortex was compromised in these various backgrounds. Disrupting the Formin Diaphanous had no effect on Arp3:GFP localization at apical structures (Figure 3N,O). By contrast, disruption of ArpC4, one of the Arp2/3 complex subunits, almost completely abolishes apical Arp3:GFP localization and intensity (Figure 3N,O). Interestingly, DPod1, which had the deepest impact on overall F-actin intensities, also had the largest effect on Arp3:GFP localization, while Coronin, Scar, and Cortactin showed intermediate Arp3:GFP recruitment defects. Together, these results reveal distinct functions for individual ANRPs, with DPod1 being required for overall actin intensities, while Cortactin and Scar are necessary for cap expansion and maintenance. These results are also consistent with Coronin having an early function, possibly in the cap center, and Cortactin/Scar possessing a later function in controlling cap growth and maintenance of cap areas at the periphery. Coronin and Scar have both intensity (at early phases) and cap size functions, and Carmil, Wasp, and Wash have either minor or no contributions to cap dynamics.

Coronin directs Cortactin localization to the cap periphery

To examine the expression and localization of the ANRPs during cap formation, we first used qPCR to examine the relative mRNA levels of each ANRP in the early syncytium. These results show that Coronin, Cortactin, and DPod1 are highly expressed and Carmil and Wash are present at low levels during syncytial stages (Figure 4—figure supplement 1G). Dia, Wasp, and Scar have intermediate expression. To further investigate the localization of each regulator, we generated an ANRP toolkit of expression constructs. We first generated either N- or C-terminal UAS GFP expression transgenic constructs (and, in many cases, both N- and C- terminal; Table 1), and then followed up with either CRISPR/Cas9-mediated homologous recombination to knock-in GFP at endogenous loci or generated antibodies to examine endogenous localization. A CRISPR-generated GFP knock-in at the endogenous Arp3 locus shows a strong localization to the apical cap with little localization to furrows, consistent with our functional data (Figure 3N, Figure 4—figure supplement 1A). Neither UAS-driven Carmil nor Wasp localized to apical actin caps (Figure 4E, Figure 4—figure supplement 1E), again consistent with the above functional analysis indicating their disruption had little impact on apical cap dynamics. UAS-driven Coronin, Cortactin, Scar, and DPod1 all displayed varying degrees of localization to actin caps (Figure 4A–D, Figure 4—source data 1), with endogenous CRISPR constructs or antibody stains showing similar patterns (Figure 4—figure supplement 1B–D). One intriguing facet of these regulators’ localization, however, is that Coronin and Cortactin possessed a complementary localization in mature caps, with Cortactin enrichment occurring at the cap periphery, while Coronin possesses an enrichment in the cap interior (temporal overlays in Figure 4F–I). This is also consistent with our functional analysis, in which Cortactin was required for cap growth late in the expansion phase and during later size maintenance, while Coronin was required for early cap growth. The complementary localization also suggests a possible antagonism between Coronin and Cortactin. To examine this, we imaged GFP:Cortactin embryos when Coronin was disrupted. Remarkably, this revealed that, in the absence of Coronin function, Cortactin fails to transition to the cap periphery, indicating that Coronin contributes to the ability of Cortactin to localize to the cell periphery to direct actin growth and the maintenance of cap edges (Figure 4J,K).

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ANRP localization at apical actin caps.

(A–E) Coronin (UAS:Coronin:GFP), Cortactin (UAS:GFP:Cortactin), DPod1 (UAS:DPod1:GFP), Scar (UAS:Scar:GFP), and Carmil (UAS:Carmil:GFP) localization on apical cap structures at t = 20, 180, 270, and 360 s. Additional Cortactin CRISPR and DPod1 CRISPR allele and anti-Coronin immunostaining data in Fig. Supplement 4-1. Scale bar = 5 μm. (F–H) Cortactin transitions to actin periphery through Coronin antagonism during cap growth. Overlapped images from t = 360 s (magenta) to t = 180 s (green) from live-imaging. Coronin (F) and Cortactin (H) images are derived from live-imaging of UAS:Coronin:GFP (F) and CRISPR GFP:Cortactin (Fig S6B), respectively. Scale bar = 2 μm. (J) CRISPR GFP:Cortactin t = 180 s (green) and 360 s (magenta) overlapped images in Coronin shRNA disrupted embryo. Scale bar = 2 μm. (G, I, K) Intensity profiles from (F), (H), and (J) yellow lines, respectively. Distance = 0 μm indicates the center of apical cap structures.

Figure 4—source data 1

RT PCR source data.

https://cdn.elifesciences.org/articles/63046/elife-63046-fig4-data1-v2.xlsx
Table 1
Arp2/3 and ANRPs toolkit.
ConstructVectorChromosome
CRISPR Arp3:GFPEndogeousIII
CRISPR GFP:CortactinEndogeousIII
CRISPR GFP:DPod1EndogeousX
UAS:GFP:CortactinpUASTX, II, III
UAS:Cortactin:GFPpUAST,pUASpX, II, III
UAS:mCherry:CortactinpUASpII, III
UAS:DPod1:GFPpUASTX, II, III
UAS:Coronin:GFPpUASTII, III
UAS:GFP:Carmil FLpUASTX, II, III
UAS:Carmil FL:GFPpUASTX, II, III
UAS:Scar:GFPpUASpII, III
UAS:mito:mCherry:CortactinpUASpII, III
UAS:mito:mCherry:CoroninpUASpX, II, III
UAS:mito:mCherry:DPod1pUASpII, III
UAS:mito:mCherry:ScarpUASpII, III
UAS:mito:mCherrypUASpII, III

Faster recovery of F-actin networks after ANRP disruption

We next examined the dynamics of how actin networks in the early embryo form. To do so, we analyzed recovery rates after photobleaching. As a starting point, we measured recovery in cortical cap populations and in furrow-associated actin populations at ~120 s (when caps are approaching their early maximum in size) into cycle 11. These results revealed that actin is highly dynamic, with a half-time of recovery (T50) of only 8.2 s in the cap and a low immobile fraction of 17% (Figure 5A–D, Figure 5—source data 1). Furrow-associated actin is more stable with a T50 of 15.1 s (Figure 5C). These recovery rates are nearly identical when actin is directly labeled with GFP, again demonstrating that tracking actin cap behaviors with the MoeABD:GFP accurately reflects actin dynamics and that any on–off actin-binding rates by MoeABD:GFP are significantly slower than actin turnover rates (Figure 1—figure supplement 1D–F).

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Arp2/3 and Formin network analysis suggests competition for free G-actin.

(A) Still images from FRAP recovery of F-actin live-imaging (UAS:moeABD:mCh) in cycle 11 embryos at t = −1s (1 s before photobleaching), 0 s (photobleaching), 4 s, 8 s, 12 s, 24 s, and 32 s. Scale bar = 5 μm. (B) FRAP recovery dynamics in control apical actin cap at cycle 11. Intensity is normalized to the value at t = −1s. (C, D) FRAP T50 and immobile fraction of F-actin at apical cap (n = 6) and furrow (n = 3) structures in cycle 11. (E, F) FRAP T50 and immobile fraction of F-actin at apical cap structures from control (n = 13), ArpC4 shRNA (n = 14), and Dia shRNA (n = 9) in cycle 11 embryos showing faster actin recovery rates in ArpC4 and Dia shRNA embryos. ns: not significant, ***: p<0.005. (G) Schematic of Arp2/3 and Dia competition for limited G-actin pool.

Figure 5—source data 1

FRAP actin network source data.

https://cdn.elifesciences.org/articles/63046/elife-63046-fig5-data1-v2.xlsx

We then measured recovery rates when the two major actin nucleating factors in the early embryo, Arp2/3 and Diaphanous, are disrupted. Our expectation was that as these networks are essential for cap growth and actin intensities, we would observe a longer recovery time after photobleaching. Surprisingly, we found that actin recovery is much faster when either Arp2/3 or Diaphanous function is disrupted. Indeed, although actin intensities are much reduced, the half-time to recovery of these intensities is nearly twice as fast as in wild-type embryo (4.2 s and 4.1 s in ArpC4 and Diaphanous shRNA embryos, respectively; Figure 5E). To examine this further, we analyzed what would happen to recovery rates when more actin is bound into stable filaments and less G-actin is available. We therefore injected embryos with low levels of jasplakinolide to stabilize F-actin – under these conditions, recovery times increased (Figure 5—figure supplement 1C–D). Similarly, reducing the G-actin availability by low-dose Latrunculin B injection also slowed recovery times (Figure 5—figure supplement 1C–D). These results are intriguing, and at least two potential models could explain these behaviors: (1) the enhanced FRAP recovery times are driven by increased rates of nucleation and polymerization due to higher free G-actin pools or (2) higher FRAP recovery rates could be caused by increased actin turnover. Given that both Diaphanous and Arp2/3 complex function are deeply implicated in directing actin growth and nucleation, and the fact that they are chronically depleted in these experiments, we would suggest that these results support the first model and argues that these complexes are in a strong competition for a limited pool of available G-actin monomers. Consistent with this, FRAP analysis after disrupting Cofilin function (known as twinstar in Drosophila) did not significantly slow down FRAP recovery times (Figure 5—figure supplement 1A,B).

Finally, we measured cap recovery rates when the individual ANRPs were disrupted. Similar to disrupting Arp2/3 or Dia function, compromising DPod1, Coronin, Cortactin, or Scar led to faster recovery times, although to varying degrees (Figure 5—figure supplement 1A–B,E). Interestingly, DPod1 disruption was almost comparable to disrupting Arp2/3 function in its effect on recovery times and immobile fractions. This is consistent with our data indicating that DPod1 has the strongest impact on F-actin intensities in the cap. By analogy to the above, this could also suggest that actin intensities in the various disrupted backgrounds appear to well-reflect the degree to which specific actin networks are the predominant G-actin utilizing networks in the embryo.

Nucleator recruitment strengths of ANRP regulators

As we have examined the localization and function of the different ANRPs in cortical cap formation, and as the Arp2/3 complex is the major regulator of new actin in the apical cortex, we wanted to test the strength of Arp2/3 recruitment by each ANRP in vivo. Although, as discussed above, the ANRPs have been implicated in several different mechanisms of actin-regulation, many of the ANRPs have been shown to either activate or stabilize Arp2/3 complex function (Uruno et al., 2001; Weaver et al., 2002; Uetrecht and Bear, 2006; Pollitt and Insall, 2009; Bhattacharya et al., 2016). We therefore adapted a mitochondrial-tagging assay (Shin et al., 2020) to recruit ANRPs to the mitochondria and then tested the degree to which Arp3 and F-actin become ectopically localized. Since DPod1, Cortactin, Coronin, and Scar had the strongest effects on actin cap formation, we fused each of these ANRPs to mCherry and an outer mitochondrial membrane mito-tag (Tom70-HA, 58 amino acids). Intriguingly, the mito-tagged ANRPs are each capable of recruiting Arp3:GFP, although an ANRP-less mito-tag control did not (Figure 6A–J, Figure 6—source data 1). Furthermore, they also appear able to activate Arp2/3 complex function, as filamentous actin is observed at the mito-tag puncta (Figure 6—figure supplement 1B–E). To measure the strength of recruitment, we quantified the colocalization percentage and relative Arp3:GFP intensity as normalized to mito-ANRP:mCherry intensity. Of the tested ANRPs, mito-DPod1 possessed the strongest colocalization and recruitment ability (Figure 6C–D). By contrast, Coronin had the lowest colocalization and recruitment ability, while Cortactin and Scar had intermediate Arp3-recruiting activities (Figure 6E–J). These results are consistent with our functional analysis, which indicated that DPod1 is most important for overall actin cap intensities and suggests that a high potency ANRP, DPod1, has been selected to drive overall actin levels, while Coronin, Cortactin, and Scar largely have a spatial function in driving cap expansion. These results also provide some insight on the ANRPs relative potencies, as Cortactin demonstrates a high percent of colocalization, but relatively low recruitment ability, while Scar has moderate Arp3 colocalization but high recruitment ability (Figure 6O, P). Although it was not possible to recover flies that had pair-wise mito-tag combinations of every mito-ANRP, we were able to analyze embryos that possessed both Coronin and Cortactin mito-tags, as well as embryos that had both DPod1 and Scar mito-tags. Intriguingly, the Arp3 localization and recruitment ability was very low in the double mito-Coro+mito-Cort, while Arp3 recruitment was very high in the double mito-DPod1+mito-Scar background (Figure 6O,P). This is again consistent with a potential inhibitory interaction between Coronin and Cortactin and with DPod1 and Scar being potent activators of Arp2/3 recruitment and activity.

Figure 6 with 1 supplement see all
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In vivo nucleator recruitment strengths of actin regulatory proteins.

(A, C, E, G, I, K, M) Images of CRISPR Arp3:GFP with mito-tagged mCherry control (A) or mCherry:ANRPs (C, E, G, I, K, M) in cells at stage 12. Scale bar = 3 μm. (B, D, F, H, J, L, N) Intensity line plots of CRISPR Arp3:GFP and different mito-ANRP measured from yellow dashed lines in (A, C, E, G, I, K, M). (O) Percent of Arp3:GFP-positive compartments that colocalize with mito-ANRP puncta. Mito-control tag (no ANRP): n = 150; N = 4; DPod1: n = 338, N = 6; Cortactin: n = 198, N = 3; Coronin: n = 80, N = 4; Scar: n = 74, N = 6; Coro + Cort: n = 28, N = 4; DPod1 + Scar: n = 282, N = 4. Similar data trends were observed after calculation of Pearson correlation coefficients with Mito-control tag (no ANRP): r = −0.036 ± 0.025; DPod1: r = 0.625 ± 0.033; Cortactin: r = 0.492 ± 0.039; Coronin: r = 0.234 ± 0.028; Scar: r = 0.313 ± 0.024; Coro + Cort: r = 0.0231 ± 0.012; DPod1 + Scar: r = 0.551 ± 0.020. Reported Pearson values are (mean) ± (standard error of mean). (P) Arp3 recruitment ability (normalized GFP:mCherry intensity ratio in Arp3:GFP-positive mito-ANRP compartments) by mito-tagged DPod1, Cortactin, Coronin, and Scar. DPod1: n = 26, N = 3; Cortactin: n = 29, N = 3; Coronin: n = 27, N = 4; Scar: n = 40, N = 3; Coro + Cort: n = 25, N = 4; DPod1 +Scar: n = 46, N = 5.

Figure 6—source data 1

Mito-localization source data.

https://cdn.elifesciences.org/articles/63046/elife-63046-fig6-data1-v2.xlsx

ANRPs function in building cap properties essential for apical nuclear anchoring

Finally, we wanted to determine what the physiological impact of having cortical actin caps with different expansion rates, sizes, and intensities would be on development. As F-actin caps have been implicated in the apical anchorage and positioning of nuclei (Foe and Alberts, 1983; Sullivan et al., 1993; Blankenship and Wieschaus, 2001), we examined what the critical actin properties are that mediate nuclear anchorage against the substantial mitotic flows during division cycles. Loss of apical anchorage is readily apparent in Arp2/3 compromised embryos, with multiple nuclear fallout events being observed in a single cycle (Figure 7A, Figure 7—source data 1). In previous work, we have shown that a failure to properly segregate chromosomes led to aneuploid or polyploid nuclei and subsequent loss of apical nuclear positioning (Xie and Blankenship, 2018). However, here a different mechanism is at work, as we observed that nuclei that underwent apparently normal cell divisions still lost apical positioning in the Arp2/3 compromised background (Figure 7A). We therefore correlated nuclear fallout with cortical cap properties such as intensity and cap expansion rates in the various ANRP backgrounds. This analysis revealed that the key property for nuclear anchorage was the growth in cap areas (Figure 7B), while overall cap intensities had little correlation to nuclear fallout rates (Figure 7C). These data suggest that cortical actin cap expansion and organization, as mediated by Cortactin, Coronin, and Scar, are essential for nuclear positioning and the maintenance of apical nuclear-cortex attachment sites.

Figure 7
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Requirement for filamentous actin cap ANRP function in anchoring embryonic nuclei.

(A) Nuclei (marked by Histone:RFP) lose apical anchorage and fall into the embryonic interior in ArpC4 shRNA embryos during cycle 12 at t = 0 s, 90 s, 180 s, 270 s, and 540 s. Medial z-layer (−5 μm from apical most portion of embryo) indicates plane of normal nuclear positioning, and basal layer (−10 μm) images are shown. Asterisk, arrowhead, and arrow indicate individual falling-out nuclei. Scale bar = 5 μm. (B) Correlation of nuclear fallout rates to cap area expansion rates (t = 0–120 s) in indicated backgrounds (cycle 13 embryos). (C) Correlation of nuclear fallout rates to average actin cap intensities (t = 120 s) in indicated backgrounds (cycle 13 embryos). (B, C) Control (n > 12, N > 3), Dia shRNA (n = 10, N = 3), ArpC4 shRNA (n = 11, N = 3), DPod1 shRNA (n = 11, N = 3), Coronin shRNA (n = 9, N = 3), Cortactin shRNA (n = 12, N = 3), Scar shRNA (n = 11, N = 3), and Carmil shRNA (n = 9, N = 3). Dashed lines indicate linear regression fitting. P: Pearson’s correlation coefficient, R2: coefficient of determination.

Figure 7—source data 1

Nuclear fallout source data.

https://cdn.elifesciences.org/articles/63046/elife-63046-fig7-data1-v2.xlsx

Discussion

Cells have a variety of actin regulatory proteins to select from in the construction of cortical structures that support cell shape and function. Here, we used the early Drosophila syncytium as a system to study rapidly developing actin structures and tested the function of seven different ANRP family members in directing specific properties of the apical cortex. We created an ANRP toolkit composed of 18 different transgenic constructs (Table 1) to analyze the interplay of actin regulators in an intact morphogenetic organism. This toolkit should form a useful reagent collection for the fly community and has revealed that unique ANRPs were used to drive specific aspects of the growing actin cap. We observed that DPod1 has an essential function in supporting the overall actin intensities in the cap, but does not appear to function in directing the expansion of the actin cap. In contrast, Cortactin does not contribute to actin intensities, but plays a key role driving the continued growth and expansion of the cap. Interestingly, Coronin, which shares similar WD40 and DUF domain architectures to DPod1, has a dual role in supporting both actin intensity and cap growth. Coronin also shows the earliest function in directing cap growth, while Cortactin and Scar have cap growth rates that become compromised during the late portions of the expansion phase. Interestingly, our results also showed a potential antagonism between Cortactin and Coronin that may underlie these early and late functions of the two regulatory proteins. Cortactin localizes to the cap periphery in later cortical caps, but fails to undergo this transition when Coronin function is disrupted. This suggests that centrally located Coronin may aid in directing Cortactin to a peripheral enrichment, and is consistent with a previous study showing a competition between Coronin and Cortactin in binding at actin branching points (Cai et al., 2008). These results are consistent with a combinatorial model for structuring the apical cortex in which DPod1 supports overall amounts of actin filaments, Coronin supports very early actin cap growth, and Cortactin and Scar promote mid-to-late cap growth and maintenance, although there are varying degrees of overlap in these functions. We also observed that several ANRPs (Carmil, Wasp, and Wash) appeared to have limited or no function at regulating actin cap formation at these stages, as judged both at the level of localization and function, although it is possible that residual gene function after shRNA disruption could obscure potential phenotypes.

We also comprehensively quantified apical actin dynamics when the major Formin and Arp2/3 actin networks are disrupted. Earlier works in the fly embryo suggested that the actin cap is largely dependent on Arp2/3 function, while the filamentous actin supporting ingressing furrows is largely Diaphanous/Formin driven (Grosshans et al., 2005; Cao et al., 2010; Zhang et al., 2018). Our results are broadly consistent with this viewpoint, although they also point to a lesser, but still substantial, Formin function in the cap. Recent work has suggested that Formin proteins and Arp2/3 complex function possess an intriguing interplay in apical caps, with Diaphanous-based actin bundles being displaced by Arp2/3 actin nucleation function (Jiang and Harris, 2019), and other data has shown a degree of interdependence between Arp2/3 and Formin network function (Suarez et al., 2015; Chan et al., 2019), although our measurements suggest that the levels of Diaphanous and Arp2/3 are not greatly changed after disruption of the counterpart network. Given the depth of the actin cap disruption in Arp2/3 compromised embryos, we cannot rule out that the developmental regulation of the phases of actin behaviors is altered in this background, though we would favor a more direct mechanical role of Arp2/3 in directing F-actin formation.

Our results also reveal a potential deep competition between the Diaphanous/Formin and Arp2/3 networks over the available G-actin pools. Somewhat surprisingly, actin fluorescent recovery times were approximately twice as fast when either network was compromised. Although either increases in filament turnover or filament assembly could explain these faster recovery times, the fact that chronic disruption of Diaphanous and Arp2/3 function (both of which are implicated in directing nucleation and filament growth) led to faster network recoveries is suggestive, to us, that this illustrates the degree to which G-actin availability limits filament assembly. These results would also be consistent with studies in S. pombe that have similarly suggested an upregulation of specific Formin or Apr2/3 networks when one network is disrupted (Burke et al., 2014). However, it should be noted that Coronin and DPod1 have each been implicated in regulating actin turnover, in addition to potential roles in regulating growth and nucleation (more on this below; Cai et al., 2008; Gandhi and Goode, 2008; Mikati et al., 2015). Contrary to this possibility, disruption of actin destabilizing Cofilin did not produce faster FRAP recovery. We find these results intriguing, as even given the volume of the Drosophila embryo (9.02 × 106 µm3; Markow et al., 2009) and the relatively few actin caps (~500–2000 caps during cycles 10–12) present in the early cortical cycles (i.e., per unit volume), these data suggest that local concentrations of G-actin can still become limiting at the cortex. Finally, we would note that the observed actin FRAP recovery times are faster than has been previously observed (Cao et al., 2010), although this may be due to differences in the measured syncytial cycle (cycle 11 versus cycle 12) or other methodological/instrumental differences.

To test the relative Arp2/3 recruiting potencies of the ANRPs, we chose to employ an ectopic relocalization strategy (Wong and Munro, 2014). This mito-tag technique has the advantage of testing factors in an intact tissue and cytoplasm, as opposed to artificially, buffered conditions in vitro. Although this approach does not differentiate between direct and indirect interactions, it interestingly revealed that DPod1 most potently recruited Arp2/3 to ectopic sites at the mitochondria, which correlates well with the importance of DPod1 for F-actin intensities at the cortical actin cap. Coronin had the weakest recruiting ability, possibly suggesting a primary role for Coronin in the regulation of Cortactin function and consistent with studies that suggest a complicated, and at times contradictory, function in Arp2/3 regulation (reviewed in Gandhi and Goode, 2008, and discussed below). Interestingly, when Scar was found to colocalize with Arp3:GFP, it was a very potent recruiter of Arp3/F-actin, but only a subset of mitochondrial Scar appeared active (~50% colocalization with Arp3:GFP). Embryos with disrupted DPod1, Cortactin, and Scar function also showed changes in actin stability and recovery rates (as indicated by immobile fractions and T50s) that mimicked the changes observed when Arp2/3 complex function was compromised. The partial colocalization of mito-tag Scar with Arp3:GFP additionally suggests a possible regulation and/or partial activation of Scar, which may be limiting in terms of Scar function and may explain why DPod1 is the most potent regulator of actin network function at these stages despite the similar strength with which Scar appears capable of recruiting Arp2/3 complex function. It may also be that this regulation is limiting in the relocalization assay and suggests one of the caveats to this approach (namely, that although this technique had the advantage of being in vivo, it still represents recruitment to an unnatural compartment that may have its own limitations imposed by the presence or absence of upstream signals and lipid bilayer composition). Nevertheless, this approach is a nicely complementary technique to in vitro biochemical measurements and may provide a useful alternative approach for assaying protein recruitment abilities in the early fly embryo in vivo.

Finally, although much of our focus has been on the ANRPs in terms of guiding F-actin nucleation through the Arp2/3 complex, it should be pointed out that these regulators have been implicated in other actin-related processes (such as the control of filament branching and turnover) that may be responsible for their relative effects on actin growth and intensities. Our functional results illustrate the final outcomes of disrupting ANRP function on actin morphologies, and, combined with the mito-tag assay, suggest the strength of Arp2/3-dependent regulation, but it is clear that several of these proteins have been implicated in additional biochemical processes other than Arp2/3 activation. For example, Coronin has been observed to both promote and inhibit Arp2/3 function, as well as directing F-actin turnover through cofilin/GMF function (Gandhi and Goode, 2008; Mikati et al., 2015). The Scar/Wasp/Wash family of proteins is typically viewed as directly activating Arp2/3 nucleating activities (reviewed in Molinie and Gautreau, 2018), and Cortactin can also activate nucleation at high concentrations, but additionally inhibits Arp2/3 debranching after nucleation has begun (Weaver et al., 2001; Uruno et al., 2001; Weaver et al., 2002; Cai et al., 2008). In other systems, Cortactin and Coronin have been found to compete in either stabilizing or destabilizing Arp2/3 branch points, and Cortactin is often preferentially found in newer filaments of migrating lamellipodia (Cai et al., 2008). It is tempting to speculate that this new-branch stabilizing function of Cortactin could be a reason why Cortactin has been selected to support the edge outgrowth of the caps. Vertebrate homologs of DPod1 (Coronin7) have also been shown to bind and regulate Cdc42 and/or Rac function, suggesting one potential mechanism of actin/Arp2/3 regulation (Swaminathan et al., 2015; Bhattacharya et al., 2016). However, regardless of the above various activities, our results show the final products of these factors on the apical, cortical actin networks that form and position nuclei in the early fly embryo. It will be interesting in future experiments to begin to further examine the biochemical partners that may help mediate the activities observed in this study.

Materials and methods

Fly stock and genetics

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All stocks were maintained at 25°C. Genotypes used in this study are listed in Tables 1 and 2. To generate endogenous GFP reporter constructs, the CRISPR/Cas9 system was used to knock-in an N-terminal (downstream of ATG site) or C-terminal (upstream of stop codon) GFP tag through the use of a donor construct with 1 kb or 1.5 kb homologous sequences flanking GFP. The homology donors were constructed in pBluescript SK(-). The upstream and downstream guide RNAs were designed in flyCRISPR (https://flycrispr.org/) and inserted into pU6-BbsI-chiRNA. Genomic PAM sites were pre-verified by DNA sequencing to avoid single-nucleotide polymorphisms present in different Drosophila lines. Donor constructs (500 ng/µL) and guide RNA constructs (100 ng/µL) were mixed and injected into nos-Cas9 expressing embryos (BestGene). Potential insertions were balanced, and flies were screened by genomic PCR (Platinum Taq DNA polymerase, Invitrogen) after genome extraction from larva or adults (E.Z.N.A insect DNA kit, Omega Biotek).

Table 2
Stocks and genetics.
StocksSourceIdentifier
General stocks
P[mat-tub-Gal4] mat67D. St Johnston
P[mat-tub-Gal4] mat15D. St Johnston
UAS:mCherry:MoesinABDT. Millard
Histone:RFPBDSCBDSC 23650 III; BDSC
23651 II
UAS:GFP:Act88FBDSCBDSC #9253
Wasp:sGFPVDRCVDRC #318474
Wash:GFPBDSCBDSC #81644
YFP:mitoBDSCBDSC #7194
UAS:mCh:mitoOMMBDSCBDSC #66532, 66533
nos-Cas9BestgeneNIG-FLY #CAS-0001, CAS-0003
w1118Bestgene
shRNA (Valium) lines
ArpC4 shRNADRSC/TRiPBDSC #41888
Dia shRNADRSC/TRiPBDSC #35479
DPod1 shRNADRSC/TRiPBDSC #41705
Coronin shRNADRSC/TRiPBDSC #40841
Cortactin shRNADRSC/TRiPBDSC #44425
Carmil shRNADRSC/TRiPBDSC #41686
Scar shRNADRSC/TRiPBDSC #51803
Wasp shRNADRSC/TRiPBDSC #51802
Wash shRNADRSC/TRiPBDSC #62866
Cofilin shRNADRSC/TRiPBDSC #33670
DPod1 shRNA 2VDRC/KKVDRC #108886
Coronin shRNA 2VDRC/KKVDRC #109644
Cortactin shRNA 2VDRC/KKVDRC #105289

To generate UAS GFP-tagged fly stocks, N-terminal or C-terminal eGFP was inserted into pUASp or pUASt along with the coding sequence for a given gene. Mito-tagged constructs were made by inserting Tom70-HA (generous gift of S. Munro lab) at the N-terminus of mCh:ANRPs in pUASp. The constructs were injected into embryos (BestGene) for transgene recovery and balanced. UAS constructs were crossed to P[mat-tub-Gal4] mat67; P[mat-tub-Gal4] mat15 (mat 67; 15) maternal drivers for Gal4-driven expression. To knockdown gene function, shRNA lines were also crossed to mat-tub-Gal4 lines, and females were recovered from either mat-67-Gal4; mat-15-Gal4 double Gal4 lines for high shRNA expression or to individual mat-67-Gal4 or mat-15-Gal4 for moderate shRNA expression.

Microscopy and time-lapse imaging

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Spinning-disk confocal microscopy was performed on a Zeiss/Solamere Technologies Group spinning-disk with a 63 × 1.4 NA objective lens (image stacks were acquired every 5 s and were composed of 15 z-layers with 0.3 μm z-steps), or Olympus Fluoview FV3000 confocal laser scanning microscope with 40× or 60× 1.35 NA objective lens (images acquired every 5 s at 12 ms/pixel exposure settings). Embryos were collected on yeasted apple juice agarose plates. After dechorionation in 50% bleach, embryos were transferred to an air-permeable membrane and mounted in Halocarbon 27 oil (Sigma). A coverslip was placed on embryos for live-imaging. For FRAP experiments, Olympus Fluoview FV3000 confocal laser scanning microscope with 40× or 60× 1.35 NA objective lens was used. Images were acquired every 1 s at 2 ms/pixel exposure settings. For drug injection, after dechorionation embryos were glued on a coverslip and dehydrated for 12–15 min, covered in Halocarbon oil 700 (Sigma). Jasplakinolide (20 µg/mL) and Latrunculin B (200 nM) were injected into embryos, followed by regular imaging protocol for FRAP and live-imaging. MicroManager 1.4, FIJI/ImageJ, and Olympus Fluoview software were used for image collection and analysis. All movies were acquired at 25°C.

Embryo fixation, antibodies, immunostaining, and imaging

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Dechorionated embryos were fixed at the interface of heptane and either 18.5% paraformaldehyde (Electron Microscopy Sciences) (Postner and Wieschaus, 1994) for 30 min for actin cap staining or 4% paraformaldehyde for 70 min for mito-tagged embryo staining, in 0.1 M sodium phosphate buffer (pH 7.4). The embryos were manually devitellinized and stained with rabbit anti-GFP (1:1000, Invitrogen) and/or anti-dsRed (1:500, Invitrogen). Alexa 546 or 647-phalloidin (1:200, Invitrogen), or secondary antibodies conjugated with Alexa 488 or 568 (Invitrogen), were used at 1:500. Coronin peptide antibody was used at 1:100 dilution. Coronin peptide antibody was generated by GenScript as peptide-KLH conjugation in New Zealand rabbits (sequence in Table 3). The affinity-purified antibody was used at 1:100 dilution (~10 μg/mL). Embryos were mounted in ProLong Gold (Life Technologies). Olympus Fluoview FV3000 confocal laser scanning microscopy was used for immunostained embryos imaging. Exposure settings of 8 or 12 ms/pixel were used for image acquisition.

Table 3
Reagents.
ReagentSourceIdentifier
Antibodies and dyes
Rabbit anti-GFPInvitrogenA11122
Mouse anti-dsRedClontech632393
Alexa Fluor Goat anti rabbit 488InvitrogenA11034
Alexa Fluor Goat anti mouse 568InvitrogenA11031
Alexa 568-PhalloidinInvitrogenCat# A12380
Alexa 647-PhalloidinInvitrogenCat# A22287
Rabbit anti-DiaWasserman lab
Chemicals and kits
Halocarbon oil 27Cat# H8773
Halocarbon oil 700Cat# H8898
ParaformaldehydeElectron Microscopy SciencesCat# 15714
ProLong GoldInvitrogenCat# P36931
JasplakinolideSanta Cruz BiotechCat# sc202191
Latrunculin BSigmaCat# L5288
QIAShredderQIAGENCat# 79654
Quick-RNA MicroPrepZymo ResearchCat# R1050
QuantiTech Reverse Transcription KitQIAGENCat# 205310
QuantiTech SYBR Green RT-PCRQIAGENCat# 204141
Q5 site-directed mutagenesis
EZNA insect DNA kit		NEB
Omega Biotek		Cat# E0554S
Cat# D0926-01
Software
iQ5Bio-Radbio-rad.com
FIJI/ImageJSchindelin et al., 2012Fiji.sc
Micromanager 1.4Edelstein et al., 2014micro-manager.org
OriginProOriginLaboriginlab.com
PhotoshopAdobeadobe.com
IllustratorAdobeadobe.com
Peptides and oligonucleotides
Coronin peptide for antibodyGenScriptCLPAKKAGNILNKPR
TOM70-HAS. Munro lab
qPCR primers
Sqh(MRLC)QuantiTectCat# QT00499065
Rh3QuantiTectCat# QT00978481
DPod1 set1QuantiTectCat# QT00499464
DPod1 set2Eurofins5′-TCCTCACCAAGAACCACTGC
Eurofins5′-GTGGGTGGGAACAGATCGTC
Coronin set1QuantiTectCat# QT00940737
Coronin set2Eurofins5′-ACAGGCTTCAACCGTAGCTC
Eurofins5′-GAACATTACGCCGTTGGACG
Cortactin set1QuantiTectCat# QT00979020
Cortactin set2Eurofins5′-TTCGGAGTGCAAGAGGATCG
Eurofins5′-GCACTCCAAATTTGCCTCCG
Arp14DQuantiTectCat# QT00923419
ArpC1(sop2)QuantiTectCat# QT00936222
Dia set1QuantiTectCat# QT00939477
Dia set2Eurofins5′-CAAATCGAAGGAGGAGCGACA
Eurofins5′-CCCATTCTGCAGGTATTCCAC
Wasp set1QuantiTectCat# QT00984641
Wasp set2Eurofins5′-ATGGCATGGAGGTGGTCAAG
Eurofins5′-TTACGCGTCTCTATGGTGGC
Scar set1QuantiTectCat# QT00934584
Scar set2Eurofins5′-ACGATCCATAGAACCCGTGC
Eurofins5′-GGCGAATGATGTTCGTCAGC
Carmil set1Eurofins5′-CCACTGGTGGGTCGTAAGTC
Eurofins5′-GGCATAGACGTCTCCTCAGC
Carmil set2Eurofins5′-GCTGAGGAGACGTCTATGCC
Eurofins5′-ATAACACTACCCTCGCCTGC
WashEurofins5′-GCGTAGGAAGAGTGTGGGAC
Eurofins5′-GTGATGGAATTGCGCTCGTC
Guide RNAs for CRISPR
Arp3:GFP
chiRNA1Eurofins5′-CTTCGCTATCAGGTGTGTCACACGA
Eurofins5′-AAACTCGTGTGACACACCTGATAGC
chiRNA2Eurofins5′-CTTCGCCAGTTCAACCCCCTATCTA
Eurofins5′-AAACTAGATAGGGGGTTGAACTGGC
GFP:Cortactin
chiRNA1Eurofins5′-CTTCGGGGCCGACAAAGCCGGATC
Eurofins5′-AAACGATCCGGCTTTGTCGGCCCC
chiRNA2Eurofins5′-CTTCGGTGGCCTGAATCTGGTGAC
Eurofins5′-AAACGTCACCAGATTCAGGCCACC
GFP:DPod1
chiRNA1Eurofins5′-CTTCGAGCGACTGAGAGGGAGCCAC
Eurofins5′-AAACGTGGCTCCCTCTCAGTCGCTC
chiRNA2Eurofins5′-CTTCGCGATGTTGTTACCGTACGTC
Eurofins5′-AAACGACGTACGGTAACAACATCGC
DPod1 mutated PAM sites in homologous constructsThis study5′-CCACCGGACTAGTGACACTCGAC
5′-GCAGCGCACAACTGACACTCGAC
This study5′-GTGGGCAGCTACCAGACGTACGG
5′-GTGGGCAGTTATCAAACCTATGG

Actin cap dynamics measurements

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Apical cap dynamics were measured by live-imaging embryos with the F-actin marker UAS:mCh:MoeABD. UAS:mCh:MoeABD displays very similar dynamics to directly labeled actin (Figure 1—figure supplement 1), suggesting that on/off rates of the moesin actin binding domain are much slower than actin assembly/disassembly rates, and mCh:MoeABD marks similar structures as phalloidin reveals (Figure 1—figure supplement 1; Blankenship et al., 2006). UAS:mCh:MoeABD has also been used extensively in Drosophila as an F-actin reporter (Kiehart et al., 2000; Blankenship et al., 2006; Spracklen et al., 2014). The measured apical cap region was determined by the region 0.9 μm (3 z-planes) below the apical most layer in which the embryo could be detected. The apical cap area was selected through manual segmentation based on the cap F-actin boundary after background subtraction. Area, average intensity (average intensity values of the measured pixels, and not summed intensities across the total cap), and standard deviation were quantified in FIJI/ImageJ. Normalization was used to reduce variability due to different initial absolute cap areas that occurs naturally in different embryos. To do so, cap measurement traces were aligned to the first time point of cap elongation phase when the centrosomes separate. Cap behaviors were then tracked backwards to the first moment that a new cap was clearly present (this is approximately t = −180 s from the alignment point at cycle 11), and cap area at this time was used as the initial t = 0 area. Cap areas were normalized to the cap size at cap initiation. This gave the most robust and reproducible measurements of changes in cap area.

Intensity measurements

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For Arp3:GFP intensity measurements, a circular region (7705 px2, ~207 μm2) was quantified in each cap with FIJI/ImageJ. For line intensity measurements, the intensity profiles were quantified in FIJI/ImageJ and smoothed by averaging three neighboring points.

FRAP intensity measurements

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Photobleaching was performed on an Olympus Flouview FV3000 confocal laser scanning microscope with 60 × 1.35 NA objective lens as described above. Five hundred and sixty-one nanometer laser photobleaching of ROIs were drawn to encompass single individual caps, or specific regions of caps (center versus periphery), and ROI data was used for analysis of actin recovery rates. Each single cap FRAP was collected from a single embryo (multiple cap FRAP data were not acquired from a given embryo). Images were acquired every 1 s at 2 ms/pixel exposure settings for a total of 60–120 s. ImageJ was used for measuring cap total intensity from FRAP data. To minimize potential effects of focal plane or biological drift, two non-FRAP caps were measured and used for normalization. Normalized intensity = ((FRAPed cap intensity − background) * FRAPed cap area) / ((non-FRAPed cap intensity − background) * non-FRAPed cap area). To calculate the t50 and immobile fraction, we fitted the FRAP data to two-phase association in Graphpad Prism eight with SpanFast = (Plateau-Y0) (%Fast*0.01); SpanSlow = (Plateau-Y0) (100-%Fast*0.01); Y=Y0 + (SpanFast) (1-exp(-KFast*X)) + (SpanSlow) (1-exp(-KSlow*X)). X is time in seconds; Y is total cap intensity over time. Y0 is the cap total intensity immediately after FRAP (t = 0 s). We assumed that the fast half-time measurement indicates the fluorescence recovery by the surrounding G-actin pool addition and turnover, while the slow half-time was produced through the minor cap expansion and intensity increases during this short time period. Fast half-time measurements and fractions are reported as the measured t50 and immobile fractions. Immobile fractions = (Y-1− PlateauFast) / (Y-1− Y0). PlateauFast = 2 * SpanFast * (1-exp(-KFast*t50)) + Y0. Y-1 is the cap intensity 1 s before FRAP.

Real-time PCR

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shRNA lines were crossed to P[mat-tub-Gal4] mat67; P[mat-tub-Gal4] mat15. The F1 embryos were collected by standard protocol, shredded (QIAShredder, QIAGEN), and RNA extracted (Quick-RNA MicroPrep, Zymo Research). The RNA extracts were reverse transcribed (QuantiTech Reverse Transcription Kit, Invitrogen) and used for real-time PCR (QuantiTech SYBR Green RT-PCR, Invitrogen; Bio-Rad iQ5). The primers used for RT-PCR are listed in Table 3.

Statistics and repeatability

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A Shapiro-Wilk test was performed in OriginPro to test for statistical normality of data. Cap area, area change, expansion rate, intensity, heterogeneity, and heterogeneity change data were tested for statistical significance using Student’s t-test for all normal data. ns: p>0.05; *: p<0.05; **: p<0.005; ***: p<0.0005. Each cycle 11 cap was measured for ~110 time points (every 5 s), with all measurements being quantified from at least nine individual caps from a minimum of three embryos. n represents the total number of individual structures measured, and N represents the total embryos tested. For mito-tag colocalization, mitochondrial puncta greater than 8 µm2 were measured and puncta that shared a minimum of at least 10 pixels in common were determined as possessing colocalization. Pearson coefficient calculations were performed with ImageJ Coloc two plug-in using Costes threshold regression with PSF = 3.0 and Costes randomization = 10. Data for the 0–60 s periods of rapid expansion fits the following exponential function, Y = 1.642 * exp*(0.09733*X), with an R value of 0.9139 (as calculated in Graphpad Prism 8).

Image editing and figure preparation

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Spinning-disk and laser scanning confocal microscopy images were edited by FIJI/ImageJ and Adobe Photoshop. Images were uniformly leveled for optimal channel appearance except where noted. Actin cap curves (average values and errors), bar graphs (average values and errors), box and whisker plots (boxes as 25–75% values, whiskers as minimal and maximal values, and lines in the boxes as median), and other graphs were made in OriginPro. Error bars are shown as S.E.M. Figures were prepared and labeled in Adobe Illustrator.

Data availability

All data generated or analyzed during this study are included in the manuscript and supporting files. Transgenic stocks have been made freely available.

References

    1. Blankenship JT
    2. Wieschaus E
    (2001)
    Two new roles for the Drosophila AP patterning system in early morphogenesis
    Development 128:5129–5138.
  12. Book
    1. Gandhi M
    2. Goode BL
    (2008) Coronin: the double-edged sword of actin dynamics
    In: Clemen C. S, Eichinger L, Rybakin V, editors. The Coronin Family of Proteins. Springer. pp. 72–87.
    https://doi.org/10.1007/978-0-387-09595-0_7
  14. Book
    1. Hartenstein V
    (1993)
    Atlas of Drosophila Development
    Cold Spring Harbor Laboratory Press.
    1. Warn RM
    (1986)
    The cytoskeleton of the early Drosophila embryo
    Journal of Cell Science 1986:311–328.

Decision letter

  1. Alphee Michelot
    Reviewing Editor; Institut de Biologie du Développement, France
  2. Anna Akhmanova
    Senior Editor; Utrecht University, Netherlands
  3. Alphee Michelot
    Reviewer; Institut de Biologie du Développement, France
  4. Anne Cecile Reymann
    Reviewer

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Acceptance summary:

This work shows the molecular complexity involved in the assembly of actin networks, and this in-depth characterization will be important for the field. The FRAP data showing faster recovery of actin networks after regulator disruption is interesting. It questions how this excessive monomeric actin reservoir is maintained and consumed under these conditions, and what it represents in terms of size control for these actin networks. Future modeling would be greatly beneficial to understand how actin fluxes are controlled in this system.

Decision letter after peer review:

Thank you for submitting your article "Combinatorial deployment of F-actin regulators to build complex 3D actin structures in vivo" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, including Alphee Michelot as the Reviewing Editor, and the evaluation has been overseen by Anna Akhmanova as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Anne Cecile Reymann (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.

As the editors have judged that your manuscript is of interest, but as described below that additional experiments are required before it is published, we would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). First, because many researchers have temporarily lost access to the labs, we will give authors as much time as they need to submit revised manuscripts. We are also offering, if you choose, to post the manuscript to bioRxiv (if it is not already there) along with this decision letter and a formal designation that the manuscript is "in revision at eLife". Please let us know if you would like to pursue this option. (If your work is more suitable for medRxiv, you will need to post the preprint yourself, as the mechanisms for us to do so are still in development.)

Summary:

Xie and Blankenship present here a characterization of the apical actin architectures during the syncytial cell cycles of the fly early embryo. These actin architectures undergo a series of rapid and transient reorganization correlated with cell cycle. The authors examine the functions of one formin (diaphanous), of the Arp2/3 complex, and of 7 other proteins (called here ANRPs) involved in the activation of the Arp2/3 complex and/or in various other actin regulation activities. Their results reveal a clear complexity, as these proteins display different effects on actin caps dynamics and in anchoring embryonic nuclei.

This work also includes information of ANRPs localization at actin caps, and their relative role in recruiting the Arp2/3 complex. Among those observations, the authors find a clear antagonism between cortactin and coronin, which was shown in previous studies but is nicely confirmed here.

In the last part of this work, which is more debatable, the authors performed FRAP experiments on actin caps, and find surprisingly that recovery of actin signals are faster in mutant conditions than in WT. These results, together with the fact that treatment with LatB or Jasp increases on the contrary characteristic times of recovery, suggest to the authors that the pool of monomeric actin might be increased in mutant conditions, potentially revealing a competition between actin networks for a limited pool of monomeric actin.

Overall, the Reviewers agree that tools and results presented here will be valuable for the community. The experimental procedures are adapted and well performed, and revisions should require limited experimental work. However, the Reviewers regret some lack of explanation on the quantification and statistical analysis of the experiments, notably regarding the lack of detailed explanation on the procedures (segmentation procedure, measure of intensity, calculation of errors, FRAP analysis, colocalization etc).

Essential revisions:

1. Quantification of the data:

Could the authors use a more automatized or robust method, such as via an autocorrelation function, to measure the size of the caps without the bias of using a threshold or a manual segmentation? Could they also provide videos for each conditions as Supplementary data?

How initial time points of cap formation are determined is not clear. Segmentation or thresholding methods to obtain area and intensities are not clearly described. Measured Area is often shown as normalized over the initial area, but this initial time point seems prone to fluctuations or errors of segmentation.

Regarding actin intensity: is it a total intensity or a mean density which is important as the area is indeed changing? Important to know if we are considering total actin filaments change or local density changes. Making this distinction would also be interesting while discussing shRNA results. There is also no mention on the impact of bleaching even though 5s interval of 4D acquisitions are performed over several minutes.

From this quantification and the definition of clear parameters, it should also appear clearly why the authors report that cap dynamics share similar features in cycle 10-13. From an observer's eye, it seems on the contrary that some of these phases are absent from certain cycles. Also, Figure 2C, 3B,E,G,I,K and M are not clear, because each phases are defined based on WT conditions only. It would be important to report how phase durations are modified in shRNAs lines.

Finally, some results from this quantification are curious. For example, between time 0 and 60s in WT, cap intensity only increases moderately (Figure 1G), while cap area increases a lot (Figure 1D). This suggests that actin density should decrease a lot, which seems opposite to what is observed in Figure 1A.

2. Statistical analysis:

In Figures 5 and S7 the number of caps analyzed seems fairly small (n=3 or 4 in some cases) for FRAP experiments. The authors should increase their sample size and show the degree of variability in the FRAP parameters that they measured using box plots and standard deviations (rather than standard error).

The authors should also clarify what the variation observed for cells within a single embryo is vs. the variation for all embryos considered in this study.

3. The authors should report the efficiency of silencing in the shRNA experiments. This would enable readers to evaluate if residual activity of these proteins is expected or not. When some shRNA as Carmil, Wasp or Wash have little impact on cap dynamics, could it be just a question of efficiency of silencing? In the absence of such measurements, we would be more careful with the conclusions of these experiments. In some other systems too, wasp or wave silencing has little effect on cortical dynamics, to compensate gex-3 can be depleted and is more efficient. Did the author consider this option?

4. Based on the hollowed appearance of actin caps in Arp2/3 shRNA embryos, the authors propose that Arp2/3 may promote central actin assembly that then flows outwards. This seems easy enough to test, for example by photobleaching a region in the center of the cap and examining whether that photobleached region propagates outwards. The authors should conduct the same experiment upon Cortactin shRNA or Scar shRNA treatment, which seem to impede actin flow outwards (Figure 3H-I).

5. Regarding FRAP, the authors decided to use MOE:GFP as a proxy for actin dynamics. This rational is not well explained (line 266) and should be more justified.

For example, line 114: "This method of labeling has been used extensively in the Drosophila embryo, and well-represents endogenous filamentous actin dynamics while avoiding problems that occur when fluorescent proteins are directly attached to actin or other labeling paradigms". One could always argue that any fluorescent peptide binding to actin potentially impacts actin dynamics in some ways. What would be important for this study is to justify why this specific marker does not interfere for the specific observations made here. Additionally, it would be important to note that FRAP recovery times are equivalent to GFP:Act88F, indicating that recovery times represent rates of actin turnover rather than dynamics of MoeABD binding/unbinding.

Note that in figure S2 one could show the recovery curves in addition to the extracted value to justify the similarity of the process. Comparison with other published data is not mentioned. For example, injected rhodamine actin followed during cycle 12 (2010 Cao et al. Current Biology) values are t1/2 = 18.9{plus minus}1.7 s and 87.3% of recovery (so 13% immobile). So more than twice the t1/2 value presented here (8.2s). The quantification is also not well documented. What intensity is used? Is it normalized to pre-bleach as well as to the value post bleach? Is there any curve fitting to exponential functions to extract the parameters? The authors could try to use some simple models of actin polymerization to estimate rates of assembly and disassembly from the FRAP data and try to get at which one is changing when Dia or Arp2/3 are lost (e.g. Kobb et al., MBoC, 2019). The authors should also discuss the choice of FRAP timing (performed at max Area 120s) so at a critical point in terms of assembly and reorganization of caps architectures.

Also, FRAP recovery images are provided only for WT condition, but not for shRNA embryos. The authors should provide these images in the Supplementary data.

6. There is an over simplification in this work, in considering that formin (diaphanous) and Arp2/3 networks assemble fully independently. There is clear evidence now, for example in lamellipodia, that formins and Arp2/3 can be synergistic, and it is not demonstrated that dia and Arp2/3 networks assemble independently here. The authors should take into account this possibility when discussing their results, and modify Figure 5G.

https://doi.org/10.7554/eLife.63046.sa1

Author response

Essential revisions:

1. Quantification of the data:

Could the authors use a more automatized or robust method, such as via an autocorrelation function, to measure the size of the caps without the bias of using a threshold or a manual segmentation? Could they also provide videos for each conditions as Supplementary data?

We have been very interested in an automated approach, and have tried a variety of segmentation algorithms/approaches. However, largely due to the very spiky periphery of caps and the low SNR (signal-to-noise) at the cap edge under the current imaging conditions, we have not found a segmentation approach that determined cap outlines as well as our manual measurements. For now, this appears to be the most accurate way to capture these area. We would note that this is part of the reason for the moderate n numbers – careful manual measurements of cap sizes means that each cycle 11 cap takes ~2 hours to determine (for example, Figure 1 represents ~35 cumulative hours of manual measurements).

How initial time points of cap formation are determined is not clear. Segmentation or thresholding methods to obtain area and intensities are not clearly described. Measured Area is often shown as normalized over the initial area, but this initial time point seems prone to fluctuations or errors of segmentation.

This is a good point, and we have clarified this in the Methods. The reviewer(s) are correct that, especially in the early time points, individual cap behaviors have some degree of variability. To address this, we temporally aligned caps to the first moment of cap duplication when the centrosomes separate (beginning of the elongation phase). We then went back to the first moment that a new cap is present that will clearly become the cycle 11 cap (this is approximately t = -180 sec from the alignment point). Data on cap area was then the normalized to the size at this starting point to account for variation that is induced from caps either having smaller or larger initial starting sizes. This produced the best comparable data as shown by the error envelopes. Both absolute and relative cap areas are measured and are shown in Figure 1. Both absolute and relative areas show similar dynamics and are not significantly different (0 minute in Figure 3 E’, G’, I’, K’, M’). For example, cap areas dynamics of Cortactin shRNA in Figure 3I (relative area) and I’ (absolute area) are similar, but normalized area dynamics better showed how cap areas evolved over time with less variation due to absolute changes in initial starting cap sizes. Please note that we did not observe that any of our shRNA lines affected the timing of centrosome separation.

Regarding actin intensity: is it a total intensity or a mean density which is important as the area is indeed changing? Important to know if we are considering total actin filaments change or local density changes. Making this distinction would also be interesting while discussing shRNA results. There is also no mention on the impact of bleaching even though 5s interval of 4D acquisitions are performed over several minutes.

Thank you for pointing this out. We are reporting average densities and not summed total actin intensities – we clarify this in the Methods (Lines 567-568). In terms of the potential photobleaching through live-imaging, this is one of the advantages of the fly embryo – we rarely observe much in the way of photobleaching. We attribute this in part to: 1) the use of moderate laser powers and exposure time (100ms) to reduce bleaching, and 2) the large volume of the embryo, most of which is not in the illuminated focal plane. We have analyzed long live-imaging videos that covered cycles 10 through 13 (~1 hour of acquisitions) and did not find significant reductions/trends of changes in total actin intensities with our imaging parameters.

From this quantification and the definition of clear parameters, it should also appear clearly why the authors report that cap dynamics share similar features in cycle 10-13. From an observer's eye, it seems on the contrary that some of these phases are absent from certain cycles. Also, Figure 2C, 3B,E,G,I,K and M are not clear, because each phases are defined based on WT conditions only. It would be important to report how phase durations are modified in shRNAs lines.

Yes, we believe that there is a similarity of phases in each of the cell cycles, but agree that there are moderate differences, especially in the short cycle 10. We have clarified the language in the manuscript to match this. Also, to make this similarity of phasing more apparent, we have added image panels to the Supplemental Figure S2 better illustrating the cap behaviors in cycles 10, 12, and 13. It appears that the Expansion and Fragmentation phases are clearly present in all cycles. In terms of the stabilization and elongation phases, cycle 10 is very short, so the stabilization phase is less clear, but the stabilization and elongation phases (as distinguished by the separation/elongation of the cap into an elongated, and eventually separated, cap) also appear to clearly occur in cycles 11-13. This is less obvious in the simple graph of cap area measurements, but changes in the cap dimensions marking elongation are apparent in the new panels in Figure Supplement1-2E-G. In terms of changes in phased behaviors in the shRNA backgrounds, it appears that each of the phases are generally present in each of the backgrounds (at least it does not appear that a phase has been deleted or that the timing of the cell cycle is deeply disrupted) except for in the background where Arp2/3 function is disrupted. In the Arp2/3 background, the cell cycle is ~60 seconds shorter and there is an absence of phased behaviors – we have added a short description of this to the Discussion section on Arp2/3 function.

Finally, some results from this quantification are curious. For example, between time 0 and 60s in WT, cap intensity only increases moderately (Figure 1G), while cap area increases a lot (Figure 1D). This suggests that actin density should decrease a lot, which seems opposite to what is observed in Figure 1A.

Yes, we believe this goes to what reviewers asked in 1.3, the cap intensity we showed here is average intensity, meaning that the actin density only moderately (~30%) increase from 0-120s (Figure 1G).

2. Statistical analysis:

In Figures 5 and S7 the number of caps analyzed seems fairly small (n=3 or 4 in some cases) for FRAP experiments. The authors should increase their sample size and show the degree of variability in the FRAP parameters that they measured using box plots and standard deviations (rather than standard error).

Thank you for pointing it out. We have repeated our FRAP and increased our sample size. The control, ArpC4, and Dia shRNA data have been replaced with box and distribution plots.

The authors should also clarify what the variation observed for cells within a single embryo is vs. the variation for all embryos considered in this study.

We only FRAP one cap in a given embryo. We have clarified this in the Methods section (“FRAP intensity measurements” section).

3. The authors should report the efficiency of silencing in the shRNA experiments. This would enable readers to evaluate if residual activity of these proteins is expected or not. When some shRNA as Carmil, Wasp or Wash have little impact on cap dynamics, could it be just a question of efficiency of silencing? In the absence of such measurements, we would be more careful with the conclusions of these experiments. In some other systems too, wasp or wave silencing has little effect on cortical dynamics, to compensate gex-3 can be depleted and is more efficient. Did the author consider this option?

Another good point. Single embryo Westerns are difficult to obtain consistent results, and antibodies do not exist for several of the silenced proteins, so we have used at least two independent shRNAs to show the specificity of the observed phenotypes – this has generally been the standard for work in the early fly embryo. Very similar effects are observed between independent shRNA lines (Figure Supplement 4-1), suggesting that knockdown efficacy in different lines with different shRNA target sequences produces representative and reproducible defects (Figure Supplement 4-1). There is also a good correlation between the localization and functional data – for example, little-to-no localization was observed for Carmil, Wasp, and Wash in the early embryo, which also fits with the lack of phenotypes observed after disruption of these genes. Valium20 and Valium22 shRNA constructs have worked efficiently in the embryo in our lab, but it remains a possibility that residual activity could remain that obscures a phenotype. We have added a short discussion of this possibility to the Discussion section (Lines 402-405).

4. Based on the hollowed appearance of actin caps in Arp2/3 shRNA embryos, the authors propose that Arp2/3 may promote central actin assembly that then flows outwards. This seems easy enough to test, for example by photobleaching a region in the center of the cap and examining whether that photobleached region propagates outwards. The authors should conduct the same experiment upon Cortactin shRNA or Scar shRNA treatment, which seem to impede actin flow outwards (Figure 3H-I).

Yes, we thought this was a good idea and tried similar experiments prior to our first submission. We also tried this again after receiving the reviewer comments – we were disappointed by our current confocal microscope systems ability to specifically FRAP small subregions of actin caps – this could be either because of system limitations or because of optical imaging limitations with the vitelline membrane and intervening perivitelline space causing too much light scattering. It may be the latter as this inability to FRAP small regions was consistent across two different photobleaching systems (a new Olympus Fluoview FV3000 laser scanning confocal system and a spinning disc-mounted Andor Micropoint system). We did get preliminary data consistent with the our and the reviewers’ expectations, but thought the diffuseness of the photobleached regions was not up to our publication standards.

Below is the data of region specific FRAP – as discussed above, we have not been satisfied with the quality of the FRAP, so prefer not to include it in the main manuscript. Peripheral regions show a slower recovery than central regions, and compromising Cortactin function disrupts the regional differences of the resulting smaller actin caps (n>10 for all).

Author response image 1
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5. Regarding FRAP, the authors decided to use MOE:GFP as a proxy for actin dynamics. This rational is not well explained (line 266) and should be more justified.

For example, line 114: "This method of labeling has been used extensively in the Drosophila embryo, and well-represents endogenous filamentous actin dynamics while avoiding problems that occur when fluorescent proteins are directly attached to actin or other labeling paradigms". One could always argue that any fluorescent peptide binding to actin potentially impacts actin dynamics in some ways. What would be important for this study is to justify why this specific marker does not interfere for the specific observations made here. Additionally, it would be important to note that FRAP recovery times are equivalent to GFP:Act88F, indicating that recovery times represent rates of actin turnover rather than dynamics of MoeABD binding/unbinding.

Yes, always a concern – on the one hand directly labeling actin subunits can lead to altered dynamics and disruption of filamentous networks, but indirectly labeling actin has its own concerns. We strengthened our justification in lines 283-287. MoeABD:GFP/MoeABD:mCherry or Utrophin:GFP have generally been the standard for visualizing actin dynamics in the early embryo. Both rely on the recognition of filamentous actin, but appear to have the most moderate effects on actin networks in terms of the available labeling methods. This is why we have compared our imaged dynamics with those of directly labeled actin as well as phalloidin stained actin caps (newly combined Figure Supplement 1-1).

Note that in figure S2 one could show the recovery curves in addition to the extracted value to justify the similarity of the process. Comparison with other published data is not mentioned. For example, injected rhodamine actin followed during cycle 12 (2010 Cao et al. Current Biology) values are t1/2 = 18.9{plus minus}1.7 s and 87.3% of recovery (so 13% immobile). So more than twice the t1/2 value presented here (8.2s). The quantification is also not well documented. What intensity is used? Is it normalized to pre-bleach as well as to the value post bleach? Is there any curve fitting to exponential functions to extract the parameters? The authors could try to use some simple models of actin polymerization to estimate rates of assembly and disassembly from the FRAP data and try to get at which one is changing when Dia or Arp2/3 are lost (e.g. Kobb et al., MBoC, 2019). The authors should also discuss the choice of FRAP timing (performed at max Area 120s) so at a critical point in terms of assembly and reorganization of caps architectures.

Also, FRAP recovery images are provided only for WT condition, but not for shRNA embryos. The authors should provide these images in the Supplementary data.

Thank you for pointing out this out – it’s true that our Methods section needed to be better detailed, which have done in the revised manuscript. We also adapted a method of FRAP measurements similar to that outlined by Kobb et al. (as suggested) with correction to adjacent, nonbleached caps. This should better adjust for changes in z-levels and/or timing. However, this did not significantly change our reported results. The faster recovery times we observe than has been seen in Cao et al. is interesting, and could be because of the different cycle times (cycle 11 vs cycle 12), methodology, or instrumentation – we comment briefly on this in the Discussion (Lines 438-441). We also added the requested additional FRAP images (Figure Supplement 5-1E).

6. There is an over simplification in this work, in considering that formin (diaphanous) and Arp2/3 networks assemble fully independently. There is clear evidence now, for example in lamellipodia, that formins and Arp2/3 can be synergistic, and it is not demonstrated that dia and Arp2/3 networks assemble independently here. The authors should take into account this possibility when discussing their results, and modify Figure 5G.

Yes, an interesting point – however, how to interpret this is an interesting, and complex, discussion. On the one hand, we actually see a faster recovery of the intact actin nucleating network when the other network is disrupted, which could argue against synergistic effects. Contrary to this, it is also true that decreases in overall actin levels could be due to possible indirect decreases in the surviving networks activity. This was interesting, so to look at it more closely, we examined Diaphanous cap intensities after Arp2/3 disruption, and Arp2/3 cap levels after Diaphanous disruption (new Figure Supplement 2-1B-E). Interestingly, neither Arp2/3 levels after Diaphanous disruption, nor Diaphanous levels after Arp2/3 disruption were significantly decreased. We have revised the Results and Discussion sections to mention these findings.

https://doi.org/10.7554/eLife.63046.sa2

Article and author information

Author details

  1. Yi Xie

    Department of Biological Sciences, University of Denver, Denver, United States
    Contribution
    Conceptualization, Formal analysis, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing
    Competing interests
    No competing interests declared

  2. Rashmi Budhathoki

    Department of Biological Sciences, University of Denver, Denver, United States
    Contribution
    Formal analysis, Investigation, Methodology
    Competing interests
    No competing interests declared

  3. J Todd Blankenship

    Department of Biological Sciences, University of Denver, Denver, United States
    Contribution
    Conceptualization, Formal analysis, Supervision, Funding acquisition, Investigation, Methodology, Writing - original draft, Writing - review and editing
    For correspondence
    jblanke4@du.edu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8687-9527

Funding

National Institute of General Medical Sciences (R01GM127447)

  • J Todd Blankenship

National Institute of General Medical Sciences (R01GM126422)

  • J Todd Blankenship

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

The authors declare no competing financial interests. We thank members of the Blankenship lab for critical reading and constructive comments on the manuscript. This work was supported by grants from the NIH NIGMS: R01GM127447 and R15 GM126422 to JTB.

Senior Editor

  1. Anna Akhmanova, Utrecht University, Netherlands

Reviewing Editor

  1. Alphee Michelot, Institut de Biologie du Développement, France

Reviewers

  1. Alphee Michelot, Institut de Biologie du Développement, France
  2. Anne Cecile Reymann

Version history

  1. Received: September 11, 2020
  2. Accepted: May 4, 2021
  3. Accepted Manuscript published: May 5, 2021 (version 1)
  4. Version of Record published: May 25, 2021 (version 2)

Copyright

© 2021, Xie 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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  1. Yi Xie
  2. Rashmi Budhathoki
  3. J Todd Blankenship
(2021)
Combinatorial deployment of F-actin regulators to build complex 3D actin structures in vivo
eLife 10:e63046.
https://doi.org/10.7554/eLife.63046

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    Thao DV Le, Dianxin Liu ... Julio E Ayala
    Research Article Updated

    The canonical target of the glucagon-like peptide-1 receptor (GLP-1R), Protein Kinase A (PKA), has been shown to stimulate mechanistic Target of Rapamycin Complex 1 (mTORC1) by phosphorylating the mTOR-regulating protein Raptor at Ser791 following β-adrenergic stimulation. The objective of these studies is to test whether GLP-1R agonists similarly stimulate mTORC1 via PKA phosphorylation of Raptor at Ser791 and whether this contributes to the weight loss effect of the therapeutic GLP-1R agonist liraglutide. We measured phosphorylation of the mTORC1 signaling target ribosomal protein S6 in Chinese Hamster Ovary cells expressing GLP-1R (CHO-Glp1r) treated with liraglutide in combination with PKA inhibitors. We also assessed liraglutide-mediated phosphorylation of the PKA substrate RRXS*/T* motif in CHO-Glp1r cells expressing Myc-tagged wild-type (WT) Raptor or a PKA-resistant (Ser791Ala) Raptor mutant. Finally, we measured the body weight response to liraglutide in WT mice and mice with a targeted knock-in of PKA-resistant Ser791Ala Raptor. Liraglutide increased phosphorylation of S6 and the PKA motif in WT Raptor in a PKA-dependent manner but failed to stimulate phosphorylation of the PKA motif in Ser791Ala Raptor in CHO-Glp1r cells. Lean Ser791Ala Raptor knock-in mice were resistant to liraglutide-induced weight loss but not setmelanotide-induced (melanocortin-4 receptor-dependent) weight loss. Diet-induced obese Ser791Ala Raptor knock-in mice were not resistant to liraglutide-induced weight loss; however, there was weight-dependent variation such that there was a tendency for obese Ser791Ala Raptor knock-in mice of lower relative body weight to be resistant to liraglutide-induced weight loss compared to weight-matched controls. Together, these findings suggest that PKA-mediated phosphorylation of Raptor at Ser791 contributes to liraglutide-induced weight loss.

    1. Cell Biology
    2. Developmental Biology

    Cylicins are a structural component of the sperm calyx being indispensable for male fertility in mice and human

    Simon Schneider, Andjela Kovacevic ... Hubert Schorle
    Research Article

    Cylicins are testis-specific proteins, which are exclusively expressed during spermiogenesis. In mice and humans, two Cylicins, the gonosomal X-linked Cylicin 1 (Cylc1/CYLC1) and the autosomal Cylicin 2 (Cylc2/CYLC2) genes, have been identified. Cylicins are cytoskeletal proteins with an overall positive charge due to lysine-rich repeats. While Cylicins have been localized in the acrosomal region of round spermatids, they resemble a major component of the calyx within the perinuclear theca at the posterior part of mature sperm nuclei. However, the role of Cylicins during spermiogenesis has not yet been investigated. Here, we applied CRISPR/Cas9-mediated gene editing in zygotes to establish Cylc1- and Cylc2-deficient mouse lines as a model to study the function of these proteins. Cylc1 deficiency resulted in male subfertility, whereas Cylc2-/-, Cylc1-/yCylc2+/-, and Cylc1-/yCylc2-/- males were infertile. Phenotypical characterization revealed that loss of Cylicins prevents proper calyx assembly during spermiogenesis. This results in decreased epididymal sperm counts, impaired shedding of excess cytoplasm, and severe structural malformations, ultimately resulting in impaired sperm motility. Furthermore, exome sequencing identified an infertile man with a hemizygous variant in CYLC1 and a heterozygous variant in CYLC2, displaying morphological abnormalities of the sperm including the absence of the acrosome. Thus, our study highlights the relevance and importance of Cylicins for spermiogenic remodeling and male fertility in human and mouse, and provides the basis for further studies on unraveling the complex molecular interactions between perinuclear theca proteins required during spermiogenesis.

    1. Cell Biology
    2. Structural Biology and Molecular Biophysics

    Baited reconstruction with 2D template matching for high-resolution structure determination in vitro and in vivo without template bias

    Bronwyn A Lucas, Benjamin A Himes, Nikolaus Grigorieff
    Research Advance

    Previously we showed that 2D template matching (2DTM) can be used to localize macromolecular complexes in images recorded by cryogenic electron microscopy (cryo-EM) with high precision, even in the presence of noise and cellular background (Lucas et al., 2021; Lucas et al., 2022). Here, we show that once localized, these particles may be averaged together to generate high-resolution 3D reconstructions. However, regions included in the template may suffer from template bias, leading to inflated resolution estimates and making the interpretation of high-resolution features unreliable. We evaluate conditions that minimize template bias while retaining the benefits of high-precision localization, and we show that molecular features not present in the template can be reconstructed at high resolution from targets found by 2DTM, extending prior work at low-resolution. Moreover, we present a quantitative metric for template bias to aid the interpretation of 3D reconstructions calculated with particles localized using high-resolution templates and fine angular sampling.