Spatial patterning of contractility by a self-organized mechanogen activity gradient underlies Drosophila gastrulation

Domain: neuroscience
Published: 2026-01-23
Editorial outcome: minor_revision

Abstract
During development cell deformations are spatially organized, however, how cellular mechanics is spatially controlled is unclear. Spatial control of cell identity often determines local cellular mechanics in a two-tiered mechanism. Theoretical studies also proposed that molecular gradients, so called “mechanogens”, spatially control mechanics. We report evidence of a similar mechanism required for Drosophila gastrulation. We show that the GPCR ligand Fog, expressed in the posterior endoderm, diffuses and acts in a concentration-dependent manner to activate actomyosin contractility at a distance during a wave of tissue invagination. While Fog is uniformly distributed in the extracellular space, it forms a surface-bound gradient that recruits Myosin-II via receptor oligomerization. This activity gradient self-renews as the wave propagates and is shaped by both receptor endocytosis and modulation of GPCR signalling by integrins upon adhesion to the vitelline membrane. This exemplifies how chemical, mechanical and geometrical cues underly the emergence of a self-organized mechanogen activity gradient.

Introduction
Embryonic development entails the spatial and temporal control of both cell fate determination to generate different cell types and of cell mechanics to generate tissue shapes during morphogenesis. Classically, the latter is considered to be a consequence of the former. For instance, the vertebrate nervous system is first specified in the dorsal region of the ectoderm and subsequently shaped into the neural tube. Similarly, the Drosophila mesoderm is first specified in the ventral part of the embryos, and then invaginates.

Typically, spatial patterning in a field of cells involves morphogens, i.e., diffusing molecules that form concentration gradients and impart positional information within tissues. Morphogens are produced from a source, from where they diffuse, and are degraded within the target tissue, thereby producing exponential gradients. Receiving cells interpret the positional information imparted by morphogen gradients by activating the expression of different genes at different morphogen concentration thresholds. This mechanism partitions the field of receiving cells into discrete domains with different cell identities. For example, the morphogens Shh, BMP and Wnt specify the identity of different neuron progenitors in the vertebrate neural tube through the expression of different combinations of transcription factors. In Drosophila, the morphogen Dorsal specifies mesoderm identity on the ventral side of embryos via the expression of the transcription factors Twist and Snail. Within this framework, the specification of cell identities directs downstream morphogenetic processes through the expression of molecular effectors that regulate cell mechanics, such as cell contractility, cell motility and cell adhesion. For example, the differential expression of adhesion molecules downstream of the Shh morphogen controls sorting of neural progenitors in the zebrafish neural tube. In the Drosophila ventral mesoderm, the transcription factors Twist and Snail, in response to the morphogen Dorsal, induce the expression of Fog, a GPCR ligand that leads to the apical activation of the small GTPase Rho1, the kinase Rok and non-muscle Myosin-II (MyoII). This induces cell-autonomous apical constriction and culminates with the invagination of the mesoderm in the ventral part of the embryos. Thus, morphogens direct morphogenesis via a two-tiered mechanism whereby the spatial control of cell identities determines downstream local cellular mechanics.

By analogy to the concept of morphogens, theoretical studies proposed that so-called “mechanogens”, i.e., “external diffusible bio-molecules”, may form spatial gradients that “influence mechanical properties, such as cell-cell adhesion and cellular contractility (and therefore, cell shape), and create spatial gradients in cell structure in a tissue”. Recent studies reported that classical morphogens can also function as mechanogens by regulating collective tissue mechanical properties without specifying cell fates. For instance, a gradient of Nodal in Zebrafish specifies mesendoderm invagination by two mechanisms: (1) triggering a motility-driven unjamming transition in protrusive leader cells able to autonomously internalize, and (2) promoting adhesion to their immediate followers, which results in a collective and ordered mode of internalization. Similarly, in the developing feather buds, the morphogens BMP and FGF define two adjacent domains with different supracellular mechanical properties, a rigid elastic core and an active fluid-like margin, that drive mesenchymal budding. In both cases, the morphogens Nodal and BMP can be considered to act as mechanogens as they tune collective, supracellular mechanical properties; however, they most likely do it indirectly via transcriptional regulation of target genes. Instead, mechanogens have been proposed to form chemical gradients that directly regulate cell mechanics (e.g., cell contractility) without the intermediary of gene transcription.

Here, we demonstrate that the GPCR ligand Fog, a signaling molecule directly controlling cellular contractility, in addition to its autocrine function in the Drosophila posterior midgut anlage, it diffuses away from its zone of production and acts in a concentration-dependent manner to control a gradient of MyoII activation during the propagation of a wave of tissue invagination. We show that, although Fog is homogenously distributed in the extracellular perivitelline fluid, it forms a multicellular scale activity gradient on the cell surface that constantly renews and propagates towards the anterior as the wave propagates. We propose that the Fog activity gradient patterns contractility in a manner similar to a mechanogen.

Results
GPCR signaling is required in the propagation zone for MyoII activation
The morphogenesis of the Drosophila posterior endoderm begins with MyoII-dependent apical constriction in the posterior-most region of the embryo, the endoderm primordium. Here, the terminal transcription factors Hückebein and Tailless control the localized expression and secretion of the ligand Fog, which activates tissue autonomous recruitment of MyoII apically and tissue bending. This induces a polarized flow towards the embryo dorsal-anterior due to the high curvature of the tissue in this region. This initial flow and tissue bending triggers a wave of MyoII activation and tissue invagination that propagates anteriorly within the dorsal epithelium across a region denominated “wave propagation zone (PZ)”. Wave propagation does not depend on sustained gene transcription and is driven by a self-replicating cycle of 3D cell deformation where cells anterior to the furrow undergo a sequence: (1) contact with the vitelline membrane (VM), (2) integrin-mediated adhesion, (3) MyoII activation, and (4) detachment from the VM due to contractility in the furrow, as they get closer to the invaginating front. During the wave, MyoII is initially recruited at low levels in cells distant from the furrow upon their contact with the vitelline membrane. Then, when cells get closer to the furrow, MyoII recruitment rapidly increases via positive feedback, where integrins promote MyoII recruitment, which in turn stabilizes focal complexes in contact with the VM. Previous studies ruled out that the propagation of MyoII activation solely reflects the movement of the diffusion front of an expanding gradient of the ligand Fog away from the endoderm primordium. However, the requirement for Fog-GPCR signaling in the activation of MyoII in cells of the PZ was not directly tested. Furthermore, since in the absence of integrins (KO of the integrin α-PS3, also known as Scab), MyoII activation, which remains pulsatile at low levels, is not completely abolished during wave propagation, other factors, such as Fog-GPCR signaling, could be involved.

The small GTPase Gα Concertina (hereafter Gα) is known to transduce Fog-GPCR signaling that activates medio-apical MyoII contractility and tissue invagination in the endoderm and mesoderm primordia. In the absence of Gα (null mutants Gα−/−), invagination of the endoderm primordium, which triggers the subsequent wave propagation, is blocked. Thus, to test the role of Fog-GPCR signaling in cells of the PZ, we rescued Gα activity in Gα−/− embryos only in the primordium to be able to induce its invagination. To this end, we expressed a WT form of Gα using a hückebein (hkb) promoter, which drives expression specifically in the posterior endoderm primordium and not in the neighboring PZ, in Gα−/− embryos (Gα−/− hkb-Gα hereafter). We visualized cell contours with E-cadherin::GFP and MyoII recruitment with MyoII Regulatory Light Chain MRLC::mCherry. As expected, in Gα−/− hkb-Gα embryos, cells in the primordium recruited apical MyoII and constricted apically. This led to the formation of an initial invagination with kinetics similar to those in WT embryos, indicating a functional rescue of Gα activity in the primordium. However, despite a normal invagination of the endoderm primordium, in Gα−/− hkb-Gα embryos, the subsequent wave propagation and the dorsal-anterior movement of the posterior endoderm were severely affected. Indeed, MyoII propagation occurred on average only in 2.5 ± 0.06 (mean ± s.e.m.) rows of cells compared to an average of 7.1 ± 0.3 (mean ± s.e.m.) cells in WT embryos. Beyond the first two cells at the border with the endoderm primordium, in the PZ of Gα−/− hkb-Gα, MyoII was not recruited at all. As a result, non-contractile cells joined the furrow, which blocked detachment from the VM of more anterior cells once they reached the edge of the invaginating furrow.
Together, we conclude that Gα signaling in cells of the PZ is required to activate MyoII and sustain wave propagation.

Fog diffusion from the primordium is necessary for MyoII activation in the propagation zone
In Drosophila, Gα signaling inducing MyoII-dependent tissue invagination is activated by the secreted GPCR ligand Fog. We thus considered that Fog might be the ligand activating Gα signaling in the PZ. Consistent with this possibility, Fog protein was detected by immunohistochemistry in the PZ. In the posterior endoderm, fog is expressed zygotically at high levels only in the primordium region, but maternally deposited fog transcripts have been detected at low levels everywhere in the embryo and could give rise to Fog detected in the PZ. To test the roles of these two possible sources of Fog, we generated embryos where fog is expressed only zygotically in the endoderm primordium. To this end, we blocked both maternal and zygotic endogenous expression by injection of dsRNAs against fog (fog RNAi) and restored fog expression only in the endoderm primordium using an RNAi-resistant fog transgene expressed under the hkb promoter (hereafter hkb-fog res) (Fig. 2a and Supplementary Fig. 2a). Contrary to the expression of an RNAi-sensitive hkb-fog transgene (hkb-fog sen hereafter), expression of hkb-fog res in fog RNAi embryos rescued the invagination and the dorsal-anterior movement of the posterior endoderm, indicating that fog is indeed resistant to RNAi. Notably, hkb-fog res rescued apical MyoII recruitment, cell apical constriction and tissue invagination in the endoderm primordium, which were defective in hkb-fog sen embryos. Furthermore, hkb-fog res also rescued the propagation of MyoII in fog RNAi embryos. Propagation of MyoII in these embryos spanned on average 5.3 ± 0.2 (mean ± s.e.m.) cells while it was completely blocked in hkb-fog sen embryos injected with fog dsRNAs. Moreover, in hkb-fog res embryos injected with fog dsRNAs the levels of MyoII were almost as high as in hkb-fog res embryos injected with water, showing that fog expression in the endoderm primordium is sufficient to rescue endoderm invagination and wave propagation in fog RNAi embryos. We conclude that Fog produced in the endoderm primordium acts non-autonomously in the PZ to activate GPCR-dependent MyoII activation there.
Next, we investigated the mechanism by which Fog acts at a distance in the PZ. Fog is a large (100 kDa), secreted molecule and previous reports did not convincingly determine whether Fog acts in a paracrine manner. In light of the non-autonomous rescue of MyoII recruitment in the PZ reported above, we hypothesized that Fog produced and secreted in the primordium might diffuse into the PZ to activate MyoII. To test this, we sought to inhibit Fog dispersal from its source of production. We constructed a membrane-tethered version of Fog by fusing the entire fog coding sequence with a truncated form (Δ1–421, see methods for details) of the vesicular stomatitis virus (VSV) G protein, a type-II transmembrane protein, and tested its ability to disperse from a localized production source. We expressed Fog-VSV-GΔ1–421 (Fog VSV-G hereafter) in stripes in the embryo using the wingless-Gal4 driver and visualized it with an anti-Fog antibody. Contrary to a WT version of Fog that was detected also into the neighboring non-expressing tissue, Fog VSV-G was only observed within the producing cells. To test the ability of Fog VSV-G to activate MyoII, we overexpressed it homogeneously in the embryo during gastrulation. The resulting MyoII pattern with low-level recruitment in the entire dorsal epithelium and strong MyoII apical recruitment in the endoderm primordium and the PZ, was similar to that of an overexpressed WT Fog. This confirmed that Fog VSV-G, although unable to diffuse, is able to activate GPCR signaling and MyoII contractility cell-autonomously in the embryo. We next used this tool to test whether Fog produced in the primordium diffuses to the PZ to activate MyoII there. We expressed an RNAi-resistant version of Fog VSV-G, in the primordium (using the hkb promoter) in embryos injected with fog dsRNA to remove endogenous maternal and zygotic Fog (hereafter referred to as hkb-fog VSV-G, Fig. 2b). In hkb-fog VSV-G embryos, cell apical constriction and endoderm primordium invagination were similar to hkb-fog res. However, the subsequent anterior movement of the endoderm was blocked as was wave propagation (on average only 1.7 ± 0.1 (mean ± s.e.m.) cells). Indeed, in hkb-fog VSV-G embryos, MyoII was not recruited apically in cells of the PZ beyond the first 1–2 cell rows, and cells did not detach from the VM.
Altogether, we conclude that Fog produced in the primordium diffuses into the zone of wave propagation, where it is required to activate apical MyoII contractility and cell detachment. Our findings demonstrate that Fog acts in a tissue non-autonomous manner during wave propagation.

Fog production and uptake tune an exponential gradient of MyoII
Since Fog diffuses from the primordium to activate MyoII at a distance, we next explored the spatial dynamics of Fog activity. During wave propagation, apical MyoII is graded in a restricted domain of 2–3 cell rows over ~30–40 μm from the advancing furrow, with high MyoII levels in cells just anterior to the furrow and lower levels more anteriorly. We considered the possibility that Fog diffusion from the primordium and degradation or removal in the receptive field may set the amplitude and range of this graded pattern of MyoII in a manner similar to morphogen activity gradients. We found that at each moment in time, as the invaginating furrow moves toward the anterior, the spatial distribution of apical MyoII mean intensity in front of the furrow fits a one-phase exponential decay curve with an average length-scale (λ) of 3.18 ± 0.29 (mean ± s.e.m.) μm. The shape of the activity gradient of morphogens typically depends on (1) the rate of morphogen production at the source, (2) the rate of its removal in the target tissue, and (3) its diffusion coefficient. We tested whether similar mechanisms also control the gradient of MyoII in the PZ. To this end, we first increased Fog production in the primordium (the source) by expressing two additional copies of a WT form of Fog using the hkb promoter. This increased both the amplitude in front of the invaginating furrow (at x=0 μm, WT: 11.55 ± 0.10 A.U and hkb-fog: 32.59 ± 0.32 A.U (mean ± s.e.m.)) and the range of the gradient (WT: 27.08 ± 5.72 μm, hkb-fog: greater than 38.01 ± 1.92 μm (mean ± s.e.m.), see methods) as expected of a concentration gradient that defines the activation profile of MyoII. Interestingly, the length scale of the exponential MyoII gradient also increased (WT: 3.18 ± 0.29 μm and hkb-fog: 5.26 ± 0.58 μm, (mean ± s.e.m.)), suggesting that diffusion or degradation/removal of Fog may depend on Fog concentration. Next, we tested whether the spatial gradient of MyoII also depends on Fog removal within the PZ. Since Fog binding to its GPCR induces endocytosis of the receptor-ligand complex, we sought to interfere with Fog removal by blocking GPCR endocytosis. Gprk2, a kinase that promotes GPCR endocytosis upon ligand binding, is the only GRK known to interfere with Fog signaling and endocytosis in the embryo. Knock-down of Gprk2 by RNAi (gprk2 RNAi) resulted in an increase in the amplitude (Control: 9.72 ± 0.08 A.U and gprk2 RNAi: 14.25 ± 0.14 A.U (mean ± s.e.m.)), the range (Control: 20.86 ± 5.72 μm and gprk2 RNAi: greater than 33.59 ± 2.5 μm (mean ± s.e.m.)) and length-scale (Control: 2.96 ± 0.45 μm and gprk2 RNAi: 5.48 ± 0.58 μm (mean ± s.e.m.)) of the exponential MyoII profile similar to hkb-fog, indicating that Fog removal/endocytosis also tunes the MyoII gradient.
Thus, Fog produced in the endoderm primordium sets an exponential gradient of MyoII in the PZ by diffusion/degradation. Furthermore, since we find that the shape of this gradient is not perturbed when gene transcription was blocked with α-amanitin, Fog acts non transcriptionally in the PZ. These findings suggest that Fog might act as a mechanogen directly controlling cellular contractility. It is worth noting that this exponential gradient, whose maximum is located at the edge of the tissue invagination, is not static but translocates anteriorly as the wave of invagination propagates to the anterior.

Fog is uniformly distributed in the extracellular space
Since Fog regulates the gradient of MyoII in the PZ, we next tested whether Fog also forms a concentration gradient there. Immunohistochemistry (IHC) against Fog in fixed embryos revealed a concentration gradient of cellular Fog protein in the PZ, although shallower than that of MyoII, consistent with the integrin-dependent amplification of MyoII activation closer to the invaginating furrow. This concentration gradient may reflect intracellular or surface-bound Fog since the soluble extracellular fraction is washed away during fixation and permeabilization. To visualize the entire pool of Fog protein in living embryos (extracellular and intracellular), we engineered an endogenous Fog::SYFP2 protein fusion at the fog locus using CRISPR/Cas9 (referred to as Fog::YFP). Fog::YFP homozygote flies were viable and their embryos showed no defects in gastrulation. Furthermore, IHC to detect Fog::YFP with antibodies anti-GFP (to visualize the YFP tag) and anti-Fog (to visualize the Fog portion of the fusion protein) showed (1) a localization consistent with that of the known endogenous protein in both the endoderm primordium (subapical vesicular accumulation) and the PZ (shallow apical gradient) and (2) a high degree of co-localization between the two detection methods. Altogether these results indicate that Fog::YFP is functional and correctly localized. In particular, they also suggest that Fog::YFP is not cleaved in the embryo despite the presence of three putative protein cleavage sites in the Fog protein sequence. To further test this latter possibility, we measured the diffusion coefficient (D) of Fog::YFP in the extracellular space using fluorescence correlation spectroscopy (FCS). Within the extracellular space between the VM and the invaginated posterior endoderm, Fog::YFP exhibited significantly slower diffusion dynamics than secreted GFP at comparable excitation power. To circumvent artifacts induced by photophysical effects, we interpolated the values of D obtained from FCS measurements at different excitation powers. We thereby obtained for Fog::YFP a D of 55 μm2 s−1, as predicted for a globular 127 kDa protein diffusing in an aqueous fluid and for a secreted version of GFP a significantly higher D = 87 μm2 s−1 (sec::GFP, 27 kDa). Thus, Fog::YFP is a good Fog reporter in vivo and we used it to image Fog distribution during wave propagation.
In contrast to our expectations and the fact that intracellular Fog forms a gradient, we observed that Fog::YFP was present in the extracellular perivitelline space at uniform concentration. The distribution of Fog::YFP across the region of wave propagation was similar to that of sec::GFP, which reflects the available space between the apical surface of cells and the VM. Thus, endogenous Fog::YFP is uniformly distributed in the extracellular perivitelline space during wave propagation. This does not depend on the presence of a maternal protein pool since a similar distribution was observed also when Fog::YFP was expressed only zygotically with a hkb promoter.
We conclude that extracellular Fog does not exhibit a concentration gradient in the PZ. Yet, intracellular Fog and its signaling (MyoII activation) are restricted to a dynamic domain of few cells anterior to the advancing furrow. This argues that Fog dependent signaling is organized at the cell surface in a dynamic spatial gradient, as the tissue invagination wave propagates anteriorly.

GPCR endocytosis tunes a gradient of cell-surface bound Fog and receptor oligomerization
This led us to investigate how a gradient of MyoII activation emerges in the PZ in spite of uniform extracellular Fog in the vitelline fluid. To test this, we analyzed the diffusion dynamics of Fog at the apical surface of cells in the PZ using FCS. In contrast to our measurements in the extracellular space within the posterior invagination, in the PZ we observed two populations of Fog, characterized by two distinct diffusion coefficients. A large fraction of Fog (84% of the total pool) displayed an average diffusion coefficient D of 51 ± 2 (mean ± s.e.m.) μm2 s−1 (D fast), similar to that measured in the posterior invagination and indicative of free diffusion. A minor fraction of Fog (16% of the total pool) showed much slower diffusion dynamics, with an average D of 1.6 ± 0.2 (mean ± s.e.m.) μm2 s−1 (D slow). This presumably corresponds to ligands bound to or interacting with the apical plasma membrane of cells, since it was undetectable in measurements within the extracellular space performed inside the invagination. Interestingly, the average diffusion coefficient of the Fog receptor Smog::GFP (0.54 ± 0.04 (mean ± s.e.m.) μm2 s−1) at the apical cell surface in the PZ measured by FCS was in the same range, but nonetheless significantly lower than D slow of Fog. This suggests that the slow-diffusing fraction of Fog (Fog slow) could correspond to ligands transiently bound to their receptor in the PZ. To further test this, we sought to increase the half-life of the receptor at the surface by inhibiting GPCR endocytosis (gprk2 RNAi) and measured Fog slow in the PZ. We found that in gprk2 RNAi embryos the Fog slow fraction increased to 30 ± 15% (mean ± s.e.m.) of the total pool from 16 ± 1% (mean ± s.e.m.) in WT, consistent with the idea that Fog slow is receptor-bound on the surface of cells in the PZ. We next examined whether Fog slow forms a spatial gradient in the PZ. We found that in WT embryos Fog slow was higher closer to the invagination (0–20 μm) than further away (20–70 μm) and that a spatial gradient with a negative slope could be detected within each embryo. Furthermore, blocking GPCR endocytosis not only globally increased Fog slow but also significantly reduced the average slope of its gradient, which was now not different from 0. These results are consistent with the observed higher levels of MyoII and its expanded gradient in gprk2 RNAi embryos and suggest that the Fog activity gradient might emerge from a gradient of ligand-receptor binding within the PZ.
Next, we sought to measure Fog receptor activation within the PZ to see if that also formed a gradient as predicted from the distribution of Fog slow. GPCR ligand binding and activation is known to induce receptor clustering (oligomerization), which can be measured by FCS. Using FCS, we measured the per-particle brightness, which reflects the degree of molecular oligomerization, of Smog::GFP on the cell apical surface in the PZ. Consistent with previous studies, we found that the molecular brightness of Smog::GFP decreased upon depletion of the ligand Fog and increased upon depletion of Gprk2 without changing its diffusion coefficient and only slightly affecting its total concentration. We next compared molecular brightness of Smog::GFP with that of the known trimeric protein VSV-G::GFP and found that the brightness of VSV-G::GFP was significantly higher than that of Smog::GFP in both the PZ and the lateral ectoderm. Furthermore, since VSV-G::GFP brightness was not affected by neither Fog nor Gprk2 depletion, these results confirmed that the observed effects on Smog::GFP are receptor specific and that its oligomerization is a proxy for GPCR activation. The comparison of molecular brightness with VSV-G::GFP also suggests that, in the simplest scenario, Smog::GFP is present as a mixture of dimers and monomers in WT embryos, while oligomerization increases upon blocking receptor endocytosis and decreases in the absence of the ligand Fog during wave propagation. Since our data indicate that Smog::GFP exists as monomers and higher-order oligomers (dimers, trimers, etc.), we next estimated the fraction of oligomers from the measured brightness, assuming a linear dependence of the brightness on the number of protomers. Since only the average brightness can be directly extracted from FCS measurements, the exact type of oligomeric species cannot be determined. Nevertheless, for a particular model, i.e., a two-species mixture of monomers and oligomers, the fraction of monomers and oligomers can be estimated. We calculated these numbers for several possible scenarios (monomers with either dimers, trimers or tetramers) in WT, fog and gprk2 RNAi embryos taking previously reported photophysical properties of GFP into account. Depleting the ligand Fog by RNAi reduced the probability of higher-order oligomer formation for Smog::GFP by ~8 folds, i.e., from ~68% dimers in WT embryos to less than 8% in fog RNAi embryos. Conversely, blocking receptor endocytosis significantly increased oligomer formation by ~21% when considering the coupled dimers/monomers (from 79 to 100%) or by ~55% when considering the coupled trimers/monomers (from 21 to 76%). To test whether Smog::GFP oligomers are distributed in a gradient in the PZ, we measured Smog::GFP brightness on the apical cell surface and at lateral junctions as a function of the distance from the invaginating furrow in the PZ. In WT embryos we found higher Smog::GFP brightness closer to the invagination (0–20 μm) than at larger distances (20–70 μm), and spatial gradients with negative slopes within each embryo on the apical surface and similar, although less significant, trends at junctions. gprk2 RNAi reduced this spatial difference by increasing Smog::GFP brightness both close and at a distance from the invagination and significantly changed the average slope of the gradients observed within each embryo, which were now not different from 0 or even slightly positive, indicating that the gradient of Smog::GFP oligomerization depends on GPCR endocytosis. Importantly, this spatial dependence of oligomerization was specific to Smog::GFP, as no gradient was observed for VSV-G::GFP. Altogether, our data indicate that Fog binding to the cell apical surface induces a gradient of receptor activation and oligomerization, which is tuned by receptor endocytosis.

Integrin activation by contact with the vitelline membrane tunes the Fog activity gradient
The gradient of GPCR activation and oligomerization tunes the multicellular spatial pattern of MyoII activation during wave propagation. Integrins, which are activated when cells of the PZ contact the VM, are also involved in MyoII activation. We showed earlier that the αPS3 integrin Scab amplifies MyoII levels in a positive feedback mechanism. Consistent with this, in αPS3 null mutants (αPS3−/−), MyoII was recruited at much lower levels than in WT embryos, although higher than those observed in the absence of Gα signaling. Moreover, in these mutants, MyoII recruitment in the PZ still followed an exponential gradient with a range similar to WT, albeit with lower amplitude, which led to an increased length scale compared to WT embryos. This suggests that amplification of MyoII activation by integrins is not uniform in the PZ, as the gradient length scale should not change with a uniform amplification by a constant factor. This led us to address whether integrins only amplify MyoII activation in the cell near the edge of invagination front, as previously suggested, or whether they also have a role in regulating the spatial range of the multicellular Fog/GPCR activity gradient. Since amplitude, range and length scale of the gradient are increased significantly when GPCR endocytosis is inhibited in gprk2 RNAi embryos, we tested whether integrins are required for this effect and thus for the process of gradient formation by acting everywhere in the field where Fog and GPCR signaling can be active. Strikingly, removing integrins in embryos depleted for Gprk2 (gprk2 RNAi + αPS3−/−) dramatically reduced both the levels and the range of MyoII to levels similar to those of the knock-out of integrins (αPS3−/−). These results show that integrins are required for both the expanded gradient and the higher levels of MyoII observed following the hyperactivation of Fog-GPCR signaling. Additionally, it shows that blocking endocytosis has no impact on the shape of the gradient if integrins are absent. We conclude that integrins are part of the multicellular gradient formation mechanism and do not simply amplify MyoII activation in the cell near the invagination front. Next, we tested whether integrins directly promote oligomerization of GPCR and thus MyoII recruitment as part of the gradient formation mechanism. We measured the molecular brightness of Smog::GFP by FCS in the PZ of embryos depleted of the αPS3 integrin Scab by RNAi (αPS3 RNAi), and we found that this was not decreased compared to WT embryos, contrary to what would be expected if integrins promoted Smog oligomerization. Smog::GFP molecular brightness was instead increased in the PZ compared to WT, suggesting that in the absence of integrins, Smog::GFP oligomers are no longer active (decreased MyoII levels, despite higher molecular clustering). We conclude that integrins do not directly promote GPCR clustering as part of the gradient formation mechanism, but rather they act as co-factors for GPCR oligomer signaling.
Integrins accumulate and form bright puncta in the PZ upon apical cell contact with the overlying vitelline membrane. The range of this contact might thus contribute to determining the range of the gradient of MyoII and GPCR signaling. Consistent with this idea, integrin puncta, visualized with the endogenously GFP-tagged βPS integrin Myospheroid (βPS::GFP hereafter), accumulated at highest levels closer to the invaginating furrow and decreased more anteriorly forming a gradient with a range identical to that of MyoII activation in the PZ. This integrin gradient formed within the region of contact of the tissue with the VM, as revealed by the exclusion of dextran injected in the perivitelline space from the region where βPS::GFP forms bright puncta. Furthermore, the depletion of Gprk2 increased the amplitude and the range of the gradient of βPS::GFP as well as the range of tissue contact with the VM (monitored by the exclusion of dextran and by the distance of E-cad junctions from the VM), suggesting that the increased range of the MyoII gradient observed in these mutants stems from a larger zone of contact with the VM in front of the invaginating furrow.
Together, we conclude that during wave propagation, MyoII is activated where integrins are in contact with the VM and that the range of the exponential gradient of MyoII during wave propagation depends on the contact of the tissue with the VM in a manner that involves integrin activation and their cross-talk with Fog-GPCR signaling.

Methods
Fly strains and genetics
The following mutant alleles and insertions were used ctaRC10 (gift from Leptin lab), pUASt-fog12, pUASp-sec::eGFP, hkb-fog, pUAS-gprk2shRNA (Bloomington stock #35326), scbKO, ubi-VSV-G::GFP, sqh-smogC::GFP (long ORF of the RC isoform of Smog, chromosome 3) were described earlier. hkb-Gα, hkb-fog res, pUASt-fog VSV-G, hkb-fog VSV-G, and fog::SYFP2KIN were generated in this study (see below). Live-cell imaging of MRLC, spaghetti squash (sqh, Genebank ID: AY122159) in Drosophila was carried out using a sqh-Sqh::mCherry transgene inserted either on chromosome 2 (at the VK18 site, located at 53B2 or a previously generated insertion) or on chromosome 3 (VK27 site located at 89E11). Live imaging of E-cadherin (shg in Drosophila, FlyBase ID: FBgn0003391) and of the main beta integrin βPS (myospheroid (mys), in Drosophila FlyBase ID: FBgn0004657) was carried out with EGFP knock-in alleles at the locus generated by homologous recombination, respectively E-cad::EGFPKIN and mys::EGFPKIN. For the expression of Fog in stripes (Supplementary Fig. 3b, c), virgin females ; ; wg-Gal4 were crossed to males ; ; pUASt-fog12 or ; ; UAS-fog VSV-G and the F1 progeny (embryos) was analyzed. For the homogeneous overexpression of Fog (Supplementary Fig. 3e), virgin females ; 67-Gal4, E-cad::EGFPKin, sqh-Sqh::mCherry were crossed with males ; pUASt-fog12; or ; ; pUASt-fog VSV-G and the F1 progeny was analyzed. The presence of the UAS transgene was assessed based on the phenotype. 67-Gal4 (matα4-GAL-VP16) is ubiquitous and maternally supplied.
For the expression of shRNA against G protein-coupled receptor kinase 2 (gprk2, FlyBase ID: FBgn0261988), F1 progeny was analzyed from females ; 67-Gal4, E-cad::EGFPKIN, sqh-Sqh::Cherry/+; pUAS-gprk2shRNA/+ crossed to males ; ; pUAS-Gprk2shRNA.
For homogenous expression of secreted GFP (sec::GFP), females ; 67-Gal4/+; UAS-sec::GFP/+ were crossed to males ; ; UAS-sec::GFP and F1 progeny was analzyed. All fly constructs and genetics are listed below (Table 1).

Constructs and transgenesis
hkb-Gα. We re-built the plasmid containing hkb-fog from ref. 33. This plasmid contain the sequence from nucleotide −2498 bp upstream of the ATG to the ATG itself of the hkb gene upstream of an hsp70 basal promoter and the cDNA of the complete fog-RB mRNA (NM_001038771.3). hkb-Gα was cloned into a pCasper5 transformation vector containing an attB site for PhiC31-mediated transgenesis. The pCasper5-attB backbone was purified after EcoRI digest of pRB14 (Addgene 52522, a pCasper5-attB_Cas9 plasmid from Klaus Foerstemann). The final plasmid was called the C5-hkb-Fog plasmid. To build hkb-Gα, the fog ORF in the C5-hkb-Fog plasmid was replaced with the Gα ORF (cta, NM_001273767.1).
hkb-fog res. To build a fog mRNA resistant to the dsRNAs (fog res) under the control of the hkb promoter, the sequence of fog-RB mRNA targeted by the injected dsRNAs (861 bp: from 24 bp before the ATG to 834 bp after the ATG) in the C5-hkb-Fog plasmid was modified every 4 to 8 bp using codon redundancy to generate the same amino acidic sequence (see Supplementary Fig. 2a, detailed sequence available in Supplementary Table 1).
hkb-fog VSV-G. Membrane-tethered Fog was built by cloning a truncated version of type-I transmembrane protein VSV-G (vesicular stomatitis virus-G protein, Genebank NP041715, amino acids E422-R508, ACK77584) at the C-terminus of the fog ORF (U03717.1). The C5-hkb-Fog plasmid was modified by inserting a DNA fragment containing 2xHA tags surrounded by two SGGGGS flexible linkers plus the truncated VSV-G before the stop codon of fog ORF. For transgenesis, hkb-fog res was inserted into the attP2 landing site (Chr3, 68A4) and hkb-fog VSV-G into the attP40 landing site (Chr2, 25C6).
scab ko allele. A KO-attP founder line was generated by CRISPR/Cas9 gene editing as previously described in ref. 30. Briefly, the entire ORF and a part of the UTRs were deleted (from −281 nucleotide to +8670 nucleotide from ATG of scab-RB, FlyBase ID: FBtr0087369) and replaced by an attP-3xP3-RFP selection marker cassette containing: forward attP (49 bp) and a floxed 3xP3-RFP eye selection marker which was flipped out by Cre recombinase in a second step. The deletion was verified by genomic PCR and Sanger sequencing. This “KO-attP founder” allele is a null allele of scab. Flies are not homozygous viable and are maintained over CyO dYFP balancer.
fog::SYFP2KIN. We used SYFP2 to generate endogenously tagged Fog. SYFP2 was used for tagging because it is reported to be a very rapidly maturing monomer. Endogenous fog tagged to SYFP2 (fog::SYFP2KIN) was generated by CRISPR/Cas9 gene editing (Wellgenetics Fly Genome Editing Service, Taipei, Taiwan), using a donor vector containing in sequence (Supplementary Fig. 5a): (1) the 5’-fog homology arm, (2) a 2xHA tags flanked by two SGGGGS flexible linkers, (3) the SYFP2 inserted before the TAA stop codon of the fog gene, (4) a PiggyBac DsRed eye marker screening cassette, composed of 3xP3- selection cassette flanked by the 5’ and 3’ PiggyBac terminal repeats, and cloned just after the TAA stop codon, and (5) the 3’-fog homology arm. Following eye color selection to identify successful gene editing, the 3xP3-DsRed cassette was flipped out by PiggyBac Transposase, generating a fog allele (fog::SYFP2KIN) encoding a Fog::YFP fusion protein at the endogenous locus. The fog::SYFP2KIN allele is homozygously viable and shows no defects in germ band extension (Supplementary Fig. 5b, c). Correct gene editing was verified by genomic PCR and Sanger sequencing.
FASTA sequences of all plasmids are available upon request.

Sample preparation and embryo microinjections
Flies were kept in cages at 25°C except for experiments using the UAS-GAL4 system (wg-Gal4 and 67-Gal4) in which they were kept at 18°C. Embryos were prepared as previously described. Briefly, they were collected using apple cider plates smeared with yeast paste and transferred to a mesh basket, rinsed with water, dechorionated with 2.6% bleach for 90s, and then rinsed copiously with water before transferring them back to clean agar. For live imaging, embryos were staged and selected (early stages of cellularization) under a dissection microscope and were aligned with the dorsal side facing up. They were then transferred to a glass coverslip coated with homemade glue. A drop of Halocarbon 200 Oil (Polysciences) was placed on the embryos to avoid drying during imaging. For microinjections, embryos were similarly harvested from agar plates, where flies were allowed to lay for 0.5–1 h. Embryos were then rinsed, dechorionated and aligned on coverslip coated with homemade glue. Following a brief desiccation period, embryos were covered with halocarbon oil and were injected with dsRNAs, RNase-free water or chemical inhibitors. The embryos were kept at 22°C until live imaging. All injections in this study were performed on the lateral side at 50–80% egg length from the posterior.

dsRNAs preparation and drug injections
dsRNA probes directed against fog, gprk2, and scab were generated from PCR products containing the T7 promoter sequence (TAATACGACTCACTATAGGG) fused to 18–21 nucleotides specific to each target gene.
The fog dsRNA probe is 861 bp in length and corresponds to nucleotides 1546–2406 of the fog transcript. The gprk2 dsRNA probe is 529 bp long and targets nucleotides 659–1187 within the 5′ UTR of the gprk2-RA transcript. The scab dsRNA probe targets nucleotides 2652–3101 (450 bp) of the scab-RB coding region. Gel-purified PCR products served as templates for in vitro transcription using T7 polymerase (Ribomax, Promega, P1300). Resulting dsRNAs were purified with Sure-Clean Plus (Bioline, BIO-37047) and diluted in RNase-free water to final concentrations of 5–30 μM for fog, 5 μM for gprk2 and 10 μM for scab. Microinjections with dsRNAs were carried out as described in the sample preparation section above. The sequence of the primers used to generate the dsRNA probes are listed in Table 2.
Dextran injections (Fig. 7e and Supplementary Fig. 8g) were performed using dextran 647, 10,000 MW, anionic, fixable from Thermo Fischer Scientific (D22914) at a concentration of 1 mg/mL. Embryos were injected in the perivitelline space at stage 6–7 directly on the microscope and imaged a few minutes later to ensure dextran equilibration in the perivitelline fluid.
α-Amanitin was injected at a concertation of 500 mg/ml at stage 7 directly on the microscope about 2–3 min before imaging.

Live imaging
Live imaging of embryos was performed at stage 7 in the dorsal-posterior region for 12–50 min at room temperature (21–22°C), depending on the experiment. Dual color time-lapse imaging for EGFP and mCherry was performed using simultaneous acquisition on a spinning disc confocal (CSU-X1, Yokogawa) Nikon Eclipse Ti inverted microscope equipped with two cameras (Rolera EM-C2, Q-Imaging or Kinetix22, Photometrics) distributed by Roper and using a 40X/1.25 NA Apo water-immersion or a 100X/1.45 NA Plan Apo oil-immersion objectives from Nikon, depending on the experiment. Live imaging was performed by simultaneously exciting with 491-nm (EGFP) and 561-nm (mCherry) lasers, using a dichroic mirror to collect emission signals on two cameras. For live imaging with 40X magnification, a z-series of 33 planes (spacing 0.8 μm) spanning about 26.4 μm from the apex of cells in the PZ was acquired with a frame rate of 1 stack every 20s. For live imaging of E-cad and MyoII with 100X magnification, a z-series of ten planes spanning 5 μm (spacing 0.5 μm) was acquired with a frame rate of 1 stack every 5s. Live imaging of sec::GFP in Fig. 4h was performed with 100X magnification, a z-series of ten planes spanning 5 μm (spacing 0.5 μm) below the vitelline membrane and with a frame rate of 1 stack every 5s. Live imaging of βPS and MyoII was performed with 100X magnification. Z-series of two planes spanning 1 μm (spacing 0.5 μm) just below the vitelline membrane were acquired with a frame rate of 1 stack every 4s. Similar settings were used to image sec::GFP in Supplementary Fig. 8h.
Live imaging of endogenous Fog::YFP and hkb-Fog::YFP was performed with a Zeiss 880 confocal microscope using a 40X/1.2 NA water-immersion or 63X/1.4 NA oil objective. GaAsP hybrid detectors were utilized with a 514-nm excitation laser. Z-stacks of 4–5 planes (spacing 0.5 μm) spanning about 2–2.5 μm just below the vitelline membrane were acquired every 15–90s for 15–45 min.
Imaging conditions (line averaging, camera exposure time, laser power, etc.) were optimized and kept constant during the experiment performed at room temperature (22°C). Laser powers were measured with a power meter at the back aperture of the objectives for every imaging session.
For brightfield imaging in Supplementary Figs. 1b, 2b, d, f, 5b, embryos were prepared and immobilized for imaging as described above. Brightfield time-lapse images were collected on a Zeiss Axiovert 200M inverted microscope using a 20X/0.75 NA objective and a programmable motorized stage to record different positions over time (Mark & Find module from Zeiss). The system was controlled by AxioVision software (Zeiss). Time-lapse data in WT or injected embryos were performed over 300 min with a frame rate of one image every 1 min.

Antibody staining
Embryos were fixed and permeabilized with 3.7% formaldehyde for 20 min, the vitelline membrane was removed by shaking in heptane/methanol, and then embryos were stained according to standard procedures. The Fog protein was detected with a rabbit antibody (1:1000, a gift from ref. 36), sqh-MRLC::mCherry was detected with a rat antibody (1:1000, anti-RFP, Chromotek, Catalog no. 5f8), and Fog::YFP was detected with a chicken antibody (1:1000, anti-GFP, Aveslabs, Catalog no. GFP-1020). The Fog antibody was pre-adsorbed by incubating overnight at 4°C with fixed y-w- embryos less than 1 h old. Patched was detected with a mouse antibody (1:100, Drosophila Ptc (apa1) from DSHB, Catalog no. AB_528441). Secondary antibodies used were donkey anti-rabbit Alexa Fluor 568 (1:500, Thermo Fischer Scientific, Catalog no. A10042), donkey anti-rat Alexa Fluor 568 (1:500, Thermo Fischer Scientific, Catalog no. A78946), donkey anti-chicken Alexa Fluor 488 IgY (1:500, Thermo Fischer Scientific, Catalog no. 703 545 155), donkey anti-mouse Alexa Fluor 647 (1:500, Thermo Fischer Scientific, Catalog no. A32787). Stained embryos were mounted in Aqua-Polymount (Polysciences) and imaged with a Zeiss 880 confocal microscope using a C-Apochromat 63X/1.4 NA oil immersion objective. Image stacks with a spacing of 0.5 μm were collected, and maximum projections of three to seven planes were analysed.

Image processing, segmentation, and cell tracking
All image manipulation and processing were performed using ImageJ (version 2.140/1.54f).
To generate the top views of 40X image stacks, image projections of selected z-planes around the cell apex were performed as previously described using a custom procedure exploiting the Stack Focuser plugin (M. Umorin). The procedure restricts the projection to a narrow set of z-planes surrounding the apical surface, improving both outline definition and signal-to-noise compared with standard maximum-intensity projections. Briefly, the macro applies the stack focuser plugin with a 30-pixel (∼6 μm) kernel to locally identify the apical plane based on the E-cadherin::GFP signal. Subsequently, four z-planes for E-cadherin and seven for MyoII centered on this plane are projected as maximum-intensity projections. The resulting 2D-projected cells were then tracked either manually (by manually drawing ROIs) or automatically (Tissue Analyzer) to measure mean intensity and cell apical area.
Side views from 40X image stacks along the posterior-anterior axis (Figs. 1d, 2d) were generated by a single line re-slice of the hyper-stacks across the dorsal midline. The side views were then smoothened with a Gaussian blur of two pixels (0.15 μm) for representation.
For 100X image stacks, maximum-intensity projections were used for measurements of fluorescence intensity and cell segmentation. Before projection, z-stacks were rotated, cropped and smoothened with a mean filter of kernel 0.5 pixels (0.04 μm) to increase the signal-to-noise (SNR). The 2D-projected stacks were then segmented, and cells were tracked as previously described using Ilastik (v.1.4) and Tissue Analyzer. In brief, cell outlines were visualized using E-cadherin::GFP. The 2D projections were band-pass filtered using cutoff frequencies of 1 μm (low-pass) and 8 μm (high-pass), after which they were processed with Ilastik to produce outline probability maps. Initial segmentation was carried out on these Ilastik predictions, followed by manual corrections on the original images. Final segmentation and cell tracking were then completed using Tissue Analyzer.
Side views from 100X (Supplementary Fig. 8k) image stacks were generated by re-slicing a rectangle ROI (6-μm wide) across the dorsal midline on images smoothened with a mean blur of one pixel (0.08 μm).
The distance of E-cad from the vitelline membrane (Figs. 1l, 2k and Supplementary Fig. 8l) was measured on height map images that carried the information of the plane where E-cad junctions were most in focus in the stack. These height map images were generated from E-cad hyper-stacks using the Stack Focuser plugin with a kernel of 11 pixels (0.88 μm). Height map images of E-cad were generated for all timepoints, and a mask based on E-cad intensity in 2D max projections was applied to mask out regions where E-cad junctions were not present.
Kymographs for E-cad::GFP (Fig. 1m) along the posterior-anterior axis were obtained with a method similar to the generation of side views but using maximum-intensity projection time series as an input for re-slicing. Kymographs are obtained using a single line of line width of 20 pixels (1.6 μm) across the medio-apical region of the cell.

Data analysis
Measurement of posterior endoderm displacement from brightfield imaging (Supplementary Figs. 1c, 2c, e, g, 5c). The extent of posterior endoderm displacement during gastrulation was measured from brightfield movies for 60 min after the time when the cellularization front reaches the basal side of the nuclei on the dorsal side of the embryo (time 0). The cumulated displacement was measured by manually tracking the posterior edge of the invagination and by calculating the cumulative traveled distance of this point, I = sqrt((x-x0)^2 + (y-y0)^2), where (x0, y0) is its position at time 0. This was normalized to the maximum embryo length (L) measured as the distance between the anterior-most and posterior-most points of the embryo. Normalized posterior endoderm (PE) displacement = l/L.
Measurements of fluorescence intensity and cell shape. Measurements of fluorescence intensity and cell shape parameters were extracted using ImageJ and further analyzed with Matlab (R2022b, including curve fitting toolbox, image processing toolbox, statistics and machine learning toolbox). For all intensity measurements, local background intensities were subtracted as previously described. Briefly, background intensities were removed by first masking out the structures of interest, like high and low intensity MyoII clusters or integrin puncta and then subtracting the remaining signal. Images were preprocessed with the “background subtraction” tool from ImageJ using a radius of ~4 μm (50 pixels) for MyoII and of ~0.4 μm (five pixels) for βPS to minimize uneven illumination across the field of view. Masks were generated using manually defined intensity thresholds applied to these preprocessed images. The residual background images were then smoothed with a Gaussian blur with a radius ~0.3–0.6 μm (4–8 pixels) prior to subtraction.
For measurements of the mean intensity of MyoII and apical area in 40X images (Figs. 1f, 2g), fluorescence intensity and apical surface area were measured on stack-focused projections of 40X image stacks. Individual cells were manually tracked by drawing ROIs (about 6 cells per embryo). The mean gray value and area were measured using ImageJ.
For measurements of the mean/integrated intensity of MyoII in 100X images (Figs. 1j, k, 2j and Supplementary Fig. 8b), fluorescence intensities were measured on standard maximum-intensity projections for 100X image stacks. For individual cell measurements, mean and integrated intensities were measured within a region of interest (ROI) obtained by automated segmentation and tracking, and then shrunken by 10 pixels to remove junctional signals. Cell tracking was performed based on the E-cad signal in maximum-intensity projections of E-cad. Time traces of MyoII and cell apical area in individual cells were registered based on the time when the apical area reached a maximum. This time is defined as time 0 in Figs. 1j, l, 2k and Supplementary Fig. 8b.
Measurements of the invagination depth (Figs. 1g, 2f). To measure the invagination depth, a straight line was manually drawn in side views from the deepest point of the apical surface of the invagination to the overlying vitelline membrane. The distance measured at 8 min (for Fig. 1g) or at 5 min (for Fig. 2f) was plotted for the indicated conditions. Time 0 is defined by synchronizing embryos relatively to the time of onset of cell divisions in the dorsal posterior (see embryo synchronization below).
Manual measurement of the number of cell rows recruiting MyoII during wave propagation (Figs. 1h, 2h). To measure the propagation of the MyoII wave, we manually recorded the time of MyoII recruitment in cells of the PZ along 3–4 rows of cells parallel to the anterior-posterior axis in each embryo. For each embryo, a mean value was obtained by averaging the 3–4 rows and different embryos were averaged to obtain plots in Figs. 1h, 2h. Time 0 is defined as the time of MyoII recruitment in the first cell in the PZ for each embryo.
Space-intensity plot (Figs. 3b, c, e, f, h, i, 4c, e, j, k, 7b, f, i, j and Supplementary Figs. 3c, 4a, c, d, 5f, g, 8d, e, g, i). In Supplementary Fig. 3c, space-intensity plots were measured from still images. First, a maximum-intensity projection of four planes (2 μm) was obtained. The Ptc signal was used as a proxy to define the posterior boundary of the wgGal4 driven-Fog expression domain. The mean intensity of Fog along three lines with a thickness of 100 pixels (9 μm each) was measured from this posterior boundary (0 μm) and plotted as a function of the distance along the AP-axis. The plots obtained were normalized (using min-max normalization) for representation.
In Fig. 4c, space-intensity plots were measured from still images. The mean intensity of Fog and MyoII was measured on maximum intensity projection of four planes (2 μm), and an average background (across three embryos) was subtracted. The resultant mean intensity was plotted across the distance from the invagination along the AP-axis. To control that the observed profiles do not depend on a confocal sectioning effect, image stacks with the same z-settings were acquired using the 405 nm laser line to capture embryo autofluorescence (gray background profile). Intensity profiles of the autofluorescence were measured to ensure that this value was constant. For Fig. 4e, the mean intensity of GFP and Fog was plotted similarly to Fig. 4c, without background subtraction.
In Fig. 3b, c, e, h, and Supplementary Figs. 4a, c, 7b, f, i, j, 8d, g space-intensity plots were measured from registered time series where the position of the invagination front was kept constant. For this, the position of the front of the invagination was tracked manually over time using the ImageJ plugin (Manual tracking, https://imagej.nih.gov/ij/plugins/track/track.html). Each individual timepoint was then translated to keep the invagination front always in the same position. Then, an average intensity projection over time of the registered time series of MyoII was performed after background intensity subtraction (as shown in Figs. 3d (right), 3g (right), 7e (right), 7h (middle and right) and Supplementary Fig. 8). Additionally, the junctional MyoII signal was removed before the time average projection using a mask based on E-cad intensity above a threshold. The mean intensity of MyoII was then measured in each embryo using three-line profiles (200-pixel, 16 μm wide each) along the AP-axis of the embryo on the dorsal side. For each embryo, data were averaged across the three-line profiles and plotted against the distance from the front of the invagination along the AP-axis (shown as embryo profiles in Fig. 3b and Supplementary Fig. 4a).
The value R2 in Fig. 3b, c and Supplementary Fig. 8d quantifies the goodness of fit in GraphPad Prism. R2 (a fraction between 0.0 and 1.0) compares the fit of the one-phase exponential decay model to the fit of a horizontal line through the mean of all values on the Y-axis. R2 is computed as a ratio of the sum of the squares of distances of the points from the best-fit curve determined by the fitting of the one-phase exponential decay model (SSresiduals) normalized to the sum of squares of the distances of the points from a horizontal line through the mean of all Y values (SStotal). R2 is calculated using the equation: R2 = 1.0 – (SSresiduals/SStotal). An R2 value close to 1.0 indicates a good fit of the experimental data. In Fig. 3c and Supplementary Fig. 4a we fitted an exponential decay model to the intensity profiles of individual embryos (Supplementary Fig. 4a) or individual timepoints within a single embryo (Fig. 3c). In Supplementary Fig. 8d, the exponential decay model was fitted to the intensity profiles from all the measured embryos (fit to the embryo profiles derived by averaging the three-line profiles measured in each embryo).
For quantification of the length-scale (Fig. 3f, i and Supplementary Figs. 4d, 8e), the mean intensity profiles from individual embryos were fitted with a one-phase decay exponential Y = (Y0 - a) * e(-x/λ) + a, where Y0 is the measured intensity value at X=0, a is the plateau intensity value at X→∞ and λ is the decay length. Decay length across all measured embryos is represented as mean ± s.d.
In Fig. 4k and Supplementary Figs. 5g, 7f (dextran), 8g, space-intensity profiles were also measured from registered time series relative to the position of the invaginating front. Here, average projections over time of maximum intensity projections were obtained without local background subtraction. Average time projections were then used to measure the intensity using three-line profiles of 100-pixel (9 μm) line width along the AP-axis of the embryo at the dorsal side.
In Fig. 4j and Supplementary Fig. 5f, ratios of each line profile from one condition to all line profiles of the other condition were calculated. The average of all measured ratios was plotted against the distance from the front of the invagination.
In Fig. 7f and Supplementary Fig. 8g, i, the normalization is a max-min normalization.
Amplitude and range of MyoII and βPS (Figs. 3e, h, 7c, d, g, k–n and Supplementary Fig. 8j). For the amplitude, descriptive statistics of the space-intensity profiles of each embryo were obtained in GraphPad Prism, and the maximum values for each embryo were plotted in Fig. 7b, i, j.
For the data in Fig. 7 and Supplementary Fig. 8, the range was estimated as follows. The mean intensity and standard deviation (s.d.) of MyoII and βPS were measured in the region of very low/no recruitment at the embryo anterior of control embryos (the region from 50 to 60 μm anterior to the invagination front). An average of the values corresponding to 3 s.d. above the mean for each control embryo was used as a global threshold of intensity to define recruitment for all conditions. The x-axis values of the point where the intensity profiles overcame this threshold were plotted as the range of activation for each embryo. For Fig. 7c, g, each embryo intensity profile was smoothened (80–90 neighbors on each side to average and 0th order of the smoothing polynomial) in GraphPad Prism before thresholding to achieve a precise value of the range. For Supplementary Fig. 8j, a threshold of 10% of the normalized intensity was used to define a region of contact with the vitelline membrane.
For the data in Fig. 3 (Fig. 3e, h), the activation threshold was defined as the mean + 3 s.d. estimated from the average curves of the controls in the region from 35 to 40 μm anterior to the invagination front, since the measurements did not cover a larger region. This global threshold (indicated by the dotted lines in Fig. 3e, h) was used to estimate the range in individual embryos for all conditions as above. Since the measurements did not cover regions beyond 40 μm anterior to the invagination front, in the text, we indicated the lower limit of the possible range of MyoII activation for the mutant conditions.
Embryo synchronization. Synchronization was performed to compare mutant to wild-type embryos and to register them in developmental time before data pooling and averaging.
For brightfield movies (Supplementary Figs. 1, 2, 5b, c), time 0 is defined as the time when the cellularization front reaches the basal side of the nuclei on the dorsal side of the embryos.
For 40X time lapses, synchronization is based on the time of onset of cell divisions in the dorsal-posterior ectoderm. This phenomenon is independent of the morphogenesis of the posterior endoderm. Time 0 is defined as 22 min (for Fig. 1d, f) and 19 min (for Fig. 2g, h) prior to the first cell division in an embryo to coincide with the onset of MyoII recruitment in the primordium for control embryos.
For experiments in fixed samples, embryos were staged manually based on similarity with events observed in time-lapse experiments.

Fluorescence correlation spectroscopy (FCS)
Data acquisition. FCS measurements were performed at room temperature (22°C) on an inverted Zeiss LSM880 system (Carl Zeiss, Oberkochen, Germany) using a Plan-Apochromat 100x, 1.4 NA DIC M27 oil-immersion objective. Fluorescence was excited with the 488 nm (Smog::GFP, VSV-G::GFP) or 514 nm (Fog::YFP, sec::GFP) line of an Argon laser in spot mode. Fluorescence was detected in the range of 491–700 and 516–700 nm (for measurements in Figs. 4g, h, 5b–h, and Supplementary Fig. 6a–e) or 491–558 nm (for measurements performed in Fig. 6a, b, d–g and Supplementary Figs. 7a–d, f–n, 8f) on a 32 channel GaAsP array detector operating in photon counting mode, with a pinhole set to one airy unit. Prior to FCS measurements and at embryonic stages preceding wave propagation, the vitelline membrane was pre-bleached to reduce autofluorescence background in the measurements. For this, the apical-most plane of the embryo (containing the vitelline membrane) was scanned in full field-of-view and continuous scan mode (scan speed 6) for 4 min with the 488 nm line at a power of ca. 15 μW or for 1.5 min with the 514 nm line at ca. 5 μW. Laser powers were measured at the exit of the objective. Four to five FCS measurements per embryo were then acquired within the extracellular space of the invaginating furrow at an approximated depth of 5–10 μm below the vitelline membrane (spot I) during wave propagation. In addition, up to ten measurements per embryo were performed at the apical surface of cells in the propagation zone (spot PZ), in a focal plane ~0.1 μm below the vitelline membrane. Each FCS trace was acquired for 30–60s with a time resolution of 1.23 μs, at 0.2–2 μW excitation laser power for Fog::YFP (0.25 μW in Fig. 4g for Fog::YFP) and 0.2–6 μW for sec::GFP (2 μW in Fig. 4g for sec::GFP) and Smog::GFP/VSV-G::GFP. This power range was selected to evaluate the potential bias in the estimated diffusion coefficients by light-induced photophysical transitions of the fluorophores (see below). For oligomerization measurements of Smog::GFP and VSV-G::GFP, additional measurements of the residual autofluorescence background were performed in yw embryos, i.e., embryos not expressing any fluorescent construct, on the same day.
Data analysis. FCS measurements were manually exported as TIFF files using the ZEN software (Zen Black 2.3 SP1 FP3), imported and analyzed using custom-written MATLAB (The MathWorks, Natick, MA, USA; version R2020a) code. To correct for signal decrease due to photobleaching, a two-component exponential function, f(t), was fitted to the raw fluorescence time series Fraw(t) and the following correction formula was applied, resulting in the corrected time series Fcorr(t): Fcorr(t) = Fraw(t) / (f(t)/f(0)) + f(0) * (1 - f(t)/f(0)).
For the analysis of Smog and VSV-G in cells of the PZ, an additional correction was applied to remove residual background from the vitelline membrane. To this end, background intensity time traces acquired in multiple pre-bleached yw embryos were averaged, and a two-component exponential fit was fitted to the average intensity time trace. The obtained fit function was then subtracted from the intensity time trace of each individual measurement acquired in the actual sample, i.e., embryos expressing Smog::GFP or VSV-G::GFP. Due to local variations in the background intensity, this correction procedure occasionally led to very low or even slightly negative intensity values, inducing high noise, particularly in the brightness readout. We therefore removed all measurements with average intensities below 10 kHz after background subtraction (typically 10–20% of measurements at maximum). Generally, background correction by subtraction is valid in the case of a non-correlated background, which we confirmed by FCS analysis of the background measurements (as also shown in ref. 38). Note that the applied correction scheme accounts, on average, for temporal decay of the background intensity due to photobleaching. In fact, intensity time traces of Smog::GFP or VSV-G::GFP did not show substantial photobleaching after background subtraction.
From the resulting intensity time trace F(t), the autocorrelation function (ACF) was calculated as follows, using a multiple tau algorithm: G(τ) = <δF(t)δF(t+τ)> / <F(t)>^2, where δF(t) = F(t) - <F(t)>.
To avoid artefacts caused by rarely occurring long-term instabilities, ACFs were calculated segment-wise (5–10 segments) and then averaged. Segments showing clear distortions were manually removed from the analysis. In addition, the first segment was always removed since it was occasionally corrupted by residual intensity changes due to temporal variations of background photobleaching that were not properly captured by the average background correction.
For measurements inside the invagination (spot I), a model for three-dimensional diffusion and Gaussian confocal volume geometry was fitted to the average ACFs: G(τ) = 1/N * (1 + T/(1-T) * e^(-τ/τb)) * (1 + τ/τd)^-1 * (1 + τ/(τd * S^2))^-0.5. Here, N denotes the particle number, τd the diffusion time, T is the fraction of fluorophores undergoing photophysical transitions with an average time constant τb, and S is the structure parameter. From the diffusion time, the diffusion coefficient D was determined by D = ω0^2 / 4τd. The confocal volume was calibrated from measurements of Alexa Fluor 488 (Thermo Fisher Scientific, Waltham, MA, USA) dissolved in water at 20 nM, using the previously determined diffusion coefficient of D = 435 μm2. Calibration measurements were performed at ca. 1 μW (at 488 nm) or 10 μW laser power (to compensate for suboptimal excitation of AF488 at 514 nm) and 2 μm depth to minimize aberrations. The structure parameter was fixed to the average value determined in calibration measurements, typically around 6.
Underthe assumption of an aqueous environment and globular protein conformation, the relative diffusion dynamics of two proteins (i.e., diffusion coefficients D1 and D2) of different size (i.e. of molecular weights MW1 and MW2) diffusing freely in 3D can be estimated using the Stokes-Einstein relation, D = kB T / 6πηR (with kB denoting the Boltzmann’s constant, T the temperature and η the viscosity of the solvent, and R the protein’s hydrodynamic radius) and the fact that the hydrodynamic radius approximately scales with the cubic root of the molecular weight (for proteins of similar mass density). Thus, D1/D2 = (MW2/MW1)^(1/3). Using secreted GFP (molecular weight of ca. 27 kDa) as a ruler, it is thus expected that Fog::YFP (molecular weight of ca. 127 kDa) diffuses about 1.7 times slower than secreted GFP. With careful analysis of photophysical effects (see details below), a relative ratio of 1.6 was obtained with the determined diffusion coefficients of 55 μm2/s for Fog::YFP and 87 μm2/s for sec::GFP in the invagination.
For measurements in the propagation zone (spot PZ), a two-component diffusion fit model was applied: G(τ) = 1/N * (1 + T/(1-T) * e^(-τ/τb)) * (F * (1 + τ/τd1)^-1 * (1 + τ/(τd1 * S^2))^-0.5 + (1-F) * (1 + τ/τd2)^-1).
Here, F denotes the fraction of the first component, and τd1 and τd2 the diffusion times. The photophysics term G(τ) = 1 + T/(1-T) * e^(-τ/τb) does not contain the photophysics term. The first component is 3D diffusion and the second component is 2D diffusion. The photophysics term converged to time constants of ca. 5–50 μs.
Molecular brightness measurements using FCS. The molecular brightness was quantified from the average fluorescence intensity and the particle number determined from the FCS analysis, B = I/N. For a mixture of a monomeric species (brightness B1) and an oligomeric species (i.e., n-mers, brightness Bn = nB1), the average brightness is calculated as follows, B = fn * Bn + (1 - fn) * B1, where fn is the relative fraction of the oligomeric species and f1 = 1 - fn the monomeric fraction. Inverting this equation allows computing the oligomeric fraction, fn = (B - B1) / (Bn - B1). Further details on the calculation of the oligomeric fraction are provided in the main text. In particular, dark fluorophore states have to be taken into account, which can effectively be modeled with a single parameter, the fluorescence probability pf. For an n-mer, the brightness Bn(pf) is then calculated as follows, Bn(pf) = (Σ i=1 to n (n choose i) * pi * (1-pf)^(n-i) * (i * B1)^2) / (Σ i=1 to n (n choose i) * pi * (1-pf)^(n-i) * (i * B1)), assuming that all fluorescent protein subunits are independent and in a fluorescent state with a probability p. Further details on the calculation of the oligomeric fraction are provided in the main text.
In our analysis, we first calculated the brightness of monomeric GFP assuming that VSV-G::GFP is exclusively present as trimers and then used this value to calculate the oligomer fraction for a putative monomer-oligomer mixture of Smog::GFP, given the experimentally determined average brightness B. We calculated oligomer fractions for pf = 1 (i.e., absence of dark GFP states, since they have not been characterized in Drosophila embryos yet) and pf = 0.7 (i.e., the value that has been previously measured for GFP in mammalian cells).
Gradient analysis using FCS. FCS measurements were performed at the apical cell surface at increasing distances from the moving invagination. Before each measurement, a confocal image capturing the position of the invagination front and the anterior part of the embryo was acquired where the position of the FCS spot was marked. In the analysis, the position of the invagination front was manually determined in the image. Then, FCS and image metadata were extracted (using custom-written MATLAB code) to determine the distance of the FCS spot from the invagination for each FCS measurement. Estimates for the relative fractions (Ffast and Fslow) of fast and slow-diffusing components and molecular brightness values resulting from FCS analysis were pooled for several measurements in each embryo and across multiple embryos. These values were then plotted as a function of distance from the invagination.
Calculation of gradient slopes (Fig. 5g, n and Supplementary Fig. 7h, l, m). Measurements at different distances from the invagination within individual embryos were fitted with a simple linear regression from 0 to 70 μm from the invagination using the linear regression model in GraphPad Prism. The best-fit values of the slope of each individual embryo were then plotted in the dot plots in Fig. 5g, n and Supplementary Fig. 7h, l, m. Their mean ± s.d is reported in the main text.

Statistics
For all experiments, data points from different cells, measurements or embryos from at least two independent experiments were pooled to estimate the plotted mean, s.d., and s.e.m.
In order to determine whether the two datasets were significantly different, a two-sided unpaired t-test was performed using GraphPad Prism. It was assumed that the datasets are sampled from Gaussian populations but have unequal variances (Welch’s correction). In Fig. 5h, n and Supplementary Fig. 7h, l, m, a two-sided one-sample t-test was carried out to compare the sample mean to a hypothetical mean of zero. For the control sample (water) in 5h, n, and Supplementary Fig. 7h the test was one sided to the hypothesis that mean of the distribution is below 0. The test assumes sampling from a gaussian distribution. No statistical method was used to predetermine the sample size. The experiments were not randomized, and the investigators were not blinded to allocation during experiments and outcome assessment. The experimental procedure did not allow identifying the sex of the embryos at the time of the experiment.

Repeatability. All measurements were performed in 3–9 embryos. In experiments involving living embryos, we considered each embryo as an independent experiment. In immunostainings, independent experiments correspond to distinct staining procedures. Representative images, which are shown in Figs. 1–7 and Supplementary Figs. 1–8 were repeated at least twice and up to more than ten times. The experiments in Figs. 1e, m, 2 were repeated independently for four embryos in both conditions with similar results. The experiment in Supplementary Fig. 5d was repeated independently for 5 embryos in both conditions with similar results.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "A previous study from the same group has shown that the GPCR ligand Fog regulates tissue invagination during Drosophila\ngastrulation via a GPCR signaling and controls Myosin II and cellular contractility. The current article provides evidence that\nthe requirement of a diffusible fraction of Fog by comparing the action of “membrane-tethered” Fog and freely diffusible Fog.\nThe authors also demonstrate that although the extracellular Fog is uniform, the Fog::YFP and Smog::GFP homoclusters\nform a gradient shaped by a receptor endocytosis and an integrin-mediated positive feedback mechanism, which in turn\ncontributes to the MyoII activity gradient.\nThese results led authors to propose that the ligand Fog, act as a “mechanogen”, because it induces a concentration-\ndependent activation of Myosin II in a propagating wave of tissue invagination directly through signaling pathway but not\nthrough new gene transcription.\nFrom the perspective of this reviewer, there are no significant issues in the current version of the manuscript. However, the\nmechanism determining Fog activity gradient despite its uniform distribution in the extracellular space is still unclear.\nMajor points:\n1. It is unclear what the top hierarchy of this gradient formation mechanism is. Since production rate and the removal of Fog\nseem to be essential, is there a possibility that Fog protein may be modified differently within the cell during the production\nprocess (before the secretion)? Without this mechanism, I feel the impact of this manuscript may be too weak to suggest that\nFog works as a concentration-dependent mechanogen.\n2. The authors mention the presence of two sources of Fog (maternal and zygotic). Does maternally provided protein not\nhave a function at all? If so, what is the potential reason behind this?\n3. In their previous work (Bailles et al., 2019 Nature, ref#6), it was stated that MyoII wave dynamics is independent of Fog\ndelivery based on the result that a higher Fog copy number did not increase the speed of wave propagation. This conclusion\nappears to contradict the current work. I understand that these conclusions are based on different experiments, and no data\nis contradictory. However, including the results section below requires a more thorough explanation of why the authors\npropose opposite interpretations.\n“Although previous studies (reference cited for #6) ruled out that the propagation of MyoII activation reflects the propagation\nof a wavefront of diffusion/transport of the ligand Fog, the role of Fog-\nGPCR signalling in activating MyoII in cells of the PZ was not directly tested. Furthermore, the absence of integrins (KO of\nthe (cid:1)-PS3 integrin, Scab) does not completely abolish MyoII activation, which remains pulsatile at low levels, during wave\npropagation, indicating that other factors, such as Fog-GPCR signalling, could be involved.”", "reviewer_2": "The manuscript “Spatial patterning of contractility by a mechanogen gradient underlies Drosophila gastrulation” by Mundhe\net al., presents evidence of the existence of mechanogens, molecular gradients that spatially control cell mechanics in a\nconcentration dependent manner. Using the Drosophila gastrulating embryo, the authors show that the GPCR ligand Fog is\nexpressed in the posterior endoderm and diffuses towards the propagation zone (PZ) of an actomyosin contractility\nwave.\nUsing live imaging, FCS and genetic perturbations they show that although Fog is distributed uniformly in the extracellular\nspace, it forms a surface-bound gradient. The authors propose that this gradient generates patterns of receptor\noligomerization and endocytosis resulting in Myosin-II activity gradient. Last, the interplay of Fog/actomyosin and vitelline\nmembrane attachment via integrins is further investigated leading to the conclusion that integrins self-renew this gradient.\nOverall, the experiments are rigorous and the quantitative analysis is accurate. The existence of a\nmechanogen, which is the main goal of this paper, is very convincing, changing our view of how\ndiffusive molecules can drive patterning, beyond eliciting transcription-dependent responses. Below some comments that (in my opinion) can prove even further the\nexistence of a pure “mechanogen” and clarify the proposed mechanistic interplay between Fog/Smog/Myosin and integrins.\nMajor comments:\n• Transcription-dependency. According to the definition of a “mechanogen” given by the authors, such a diffusive molecule\nshould trigger changes in cell mechanics directly, meaning that an intermediate step involving transcription is not required.\nCan the authors support somehow the claim that indeed transcription is dispensable for Fog-dependent Myosin-II gradient?\n• Length-scales of Myosin vs Smog oligomerization vs Fog ligand. The steepness of the Myosin-II gradient does not match\nthe steepness neither of Fog (as the authors mention) nor of the oligomerized receptor. The authors showed compelling\nevidence for Smog receptor oligomerization patterns being responsible for the Fog-dependent GPCR activation, but still is\nthe Smog oligomerization length-scale as steep as the myosin length-scale? Another possibility is that the expression of\nSmog is pre-patterned as a steep gradient, where cells closer to the myosin front express much more Smog than cells further\naway. This would also fit with the observation that despite the high extracellular abundance of Fog (84%), it is not\ncontributing to longer-range signalling in the PZ. Could this be this be due to the absence of Smog from cells further away\nand thus they cannot receive excess Fog?\n• Interplay with integrins. Although integrins are involved in the Myosin-II pattern it is unclear if this is an independent parallel\nregulation or it is involved in a functional interplay as illustrated in the scheme in Fig. 6O. For instance, is integrin attachment\nalso affecting the Smog/Fog gradient? Or is it possible that Fog manipulations could affect the integrin complexes that\nimpact the myosin gradient? I suggest that the authors perform live imaging or immunostainings of i) integrin localization in\nthe diffusible vs tethered Fog or gprk2 embryos, and ii) Fog/smog gradient in the integrin manipulations. This\ncharacterization would support the self-renew hypothesis.\nMinor comments:\n• Although the authors checked that the tethering manipulation is successful when performed in the wingless stripe, it is\nadvisable to perform evidence of the tethering effect of FogVSV-G in the primordium. Moreover, antibody staining or\nFog::YFP live imaging in the primordium and propagation zone would confirm the conditions in Fig. 2A, Fig. 2B.", "reviewer_3": "In this study, Mundhe and colleagues identify an extracellular gradient of a GPCR ligand, Fog, and explore how it can\ndirectly contribute to the formation of intracellular gradients of Myo2, a contractile protein essential for successful Drosophila\ngastrulation. Their findings are interesting and novel, and the authors go at length to characterize and quantify the dynamics\nof this system, specially how the observed gradients can rapidly shape and influence the endoderm invagination\nmorphogenesis.\nBoth conceptually and methodologically, the work is relevant to the field. While the idea of ‘mechanogen’ is not new, the\nidentification of mechanochemical coupling via the establishment of chemical gradients that lead to direct activation of\nmechanically active downstream targets (independently of transcription) is an important finding.\nAs is, the current study and respective analyses support part of the authors' claims. Further crucial evidence and\nclarifications are required as detailed below to strengthen and validate the proposed conclusions.\nMajor points:\n1) Validation of the Fog::YFP line.\nWhile the overlap in anti-Fog signals generated from the KI line and wt conditions is rather nice (Fig. 4E), as it would\nindicate that Fog regardless of being tagged is being produced and secreted to similar amounts to wildtype, it is somewhat\nunexpected to have a lot more GFP signal than anti-Fog in the KI line. Is this just a consequence of the methodology (i.e.\nsecondary antibody used against anti-GFP is brighter? Can Fog::YFP rescue a fog-/- mutant?\n2) Diffusion coefficients’ comparison.\nThe finding and characterization of the 2 fractions of Fog diffusing in the extracellular space makes sense with the\nphenomenon being described. By perturbing endocytosis of the GPCR receptor (gprk2 RNAi) the authors show that the\nfraction of Fog that is slow diffusing (Fog slow) does not change. In this case, shouldn’t the diffusion of Smog::GFP and Fog\nSlow decrease, as more Fog molecules are bound to the receptor, which cannot be internalized? This in turn leads to more\nMyo2 recruitment (i.e. ‘activation’), which is nicely shown in gprk2 RNAi.\n3) Fog slow spatial gradient.\nCould the authors show images of the Fog slow spatial gradient (Fig. 5E) in wt and gprk2 RNAi conditions? Please include\nR2 to the linear fits shown in Figs 5E and 5G. Also, it would be great to see the comparison of the slopes obtained between\nthese conditions, since 5G appears flat – this is a better comparison between gradient shapes than the uneven bin\ncomparison in Figs 5F and 5H. In alternative to that comparison, an average of signal by bins of similar magnitudes is more\nprecise (e.g. 20 um across the distance from invagination) and can perhaps illustrate the gradient more easily, rather than 2\nbins of uneven sizes.\n4) Smog::GFP expression.\nWhere is Smog::GFP expressed? Is anti-Smog expression uniform across the PZ? The analysis between Smog monomers,\ndimers and oligomers points towards a graded expression of the oligomerized (activated) form of Smog (Fig. 5L), could\nimages be shown? If indeed this is true, then overexpressing Smog in a way to make it uniform across the PZ, would lead in\nprinciple to uniform Myo2 recruitment, flattening this gradient. Does smog-/- lead to no Myo2 gradients?\n5) Integrins, Fog and Smog relationships\nLooks like removing Integrins abolishes Myo2 gradient - are these still exponentials?\nStatistically comparing slopes between conditions would be important to the authors claim, in addition to the\nshown Myo2 recruitment (amplitude) or range.\nFurthermore, what's the relationship between Smog and the Integrins? Finally, could the needed role of integrins for Fog\ngradient formation be purely mechanical, to ensure Fog confinement into a small extracellular region and generate higher\nprobability of interactions between Fog and its receptor Smog?\nMinor points:\nMyo2 ‘activation’ - shouldn’t this in fact be called recruitment? As Myo2 is being recruited to the cortex. Activation assumes\nphosphorylation and contractility, which of course are related, but are not shown by the transgenic line.\nFig.3B - Please do fits to the individual datapoints of the cumulative 4 measured embryos, not to their mean intensity, as this\nis misleading to the R2 value obtained."}, "author_response": "We thank the reviewer for their useful and constructive comments on our manuscript. Below we\nprovide a point-by-point response illustrating all changes that we made to our manuscript. We think\nthis has significantly improved the manuscript.\nOur responses are highlighted in the blue text while the comments from the reviewers are reported in\nthe italic black text. In the manuscript major changes to the text are highlighted in yellow.\nReviewer #1 (Remarks to the Author):\nA previous study from the same group has shown that the GPCR ligand Fog regulates tissue\ninvagination during Drosophila gastrulation via a GPCR signaling and controls Myosin II and\ncellular contractility. The current article provides evidence that the requirement of a diffusible\nfraction of Fog by comparing the action of “membrane-tethered” Fog and freely diffusible Fog. The\nauthors also demonstrate that although the extracellular Fog is uniform, the Fog::YFP and\nSmog::GFP homoclusters form a gradient shaped by a receptor endocytosis and an integrin-mediated\npositive feedback mechanism, which in turn contributes to the MyoII activity gradient. These results\nled authors to propose that the ligand Fog, act as a “mechanogen”, because it induces a\nconcentration-dependent activation of Myosin II in a propagating wave of tissue invagination directly\nthrough signaling pathway but not through new gene transcription.\nFrom the perspective of this reviewer, there are no significant issues in the current version of the\nmanuscript. However, the mechanism determining Fog activity gradient despite its uniform\ndistribution in the extracellular space is still unclear.\nResponse: We agree with the reviewer that the mechanism that determines the Fog activity gradient\nfrom its uniform distribution in the extracellular space is not fully understood. Our paper reports\nevidence that this activity gradient is self-organized and involves contact of the cells with the vitelline\nmembrane, integrin activation, and a functional interaction between GPCR and integrins to promote\nGPCR signalling. For this revision we performed additional experiments which exclude a direct role\nof integrins in GPCR oligomerization and suggest that they rather act as co-factors for signalling (see\nbelow responses to point 3 from Reviewer 2 and point 5 from Reviewer 3 and new Fig. S8F in the\nmanuscript). Other factors (e.g. post-translation modifications of Fog, as the reviewer suggests, and\nits subsequent processing in the extracellular space, see below) may also be involved in the\nmechanism of gradient formation but their investigation, which would require significant work for\nthe set up and validation of the experimental methods, is well beyond the scope of this paper.\nFurthermore, the finding that Fog may be post-translationally modified would not change main\nfindings and conclusions of the paper, namely 1) the uncovering of a gradient of Fog activity directly\npatterning contractility during wave propagation and 2) the finding that this gradient self organizes\nand is constantly renewed with the movement of the invaginating furrow through the mechanism\nmentioned above. We expanded the discussion to acknowledge the limitations of our study and to\ndiscuss other factors that may have a role in the formation of the Fog activity gradient (pp. 12, lines\n3-15).\n“Our study presents some limitations. The exact mechanism of the Fog activity gradient formation is\nnot fully elucidated. Our data indicate that in contact with the vitelline membrane a functional\ninteraction between integrins and ligand-bound GPCR might shape gradient with integrins acting as\nco-factors for GPCR signalling. However, the precise molecular basis of this functional interaction\nremains to be investigated. Furthermore, it is also possible that the Fog activity gradient might\nrequire post-translational modification (PTM) and/or an extracellular regulation of Fog protein\nactivity by proteins capable of binding and/or activating/inactivating Fog or its receptor at the cell\n1\nsurface. The formation of a Dpp morphogen gradient in the vitelline space requires binding to its\ninhibitor Sog and a protease Tld that enables Dpp activity in a narrower dorsal domain than where\nDpp is expressed and diffuses43, 44. Similarly, PTM of the zygotic fraction of Fog protein may explain\nits higher activity compared to the maternal pool. The maternal pool is more abundant (Fig. S5G),\nyet it does not rescue a zygotic fog null mutant4 and an extracellular inhibitor or an enzyme modifying\nFog PTMs may spatially restrict Fog activity and thereby be part of the gradient formation\nmechanism.”\nMajor points:\n1. It is unclear what the top hierarchy of this gradient formation mechanism is..\nResponse: Our data indicate that the activity gradient is self-organized from the unrolling of the 3D\ncycle of cell deformation accompanying the wave, rather than being hierarchically controlled. As\nsuch, the formation and the shape of the gradient emerge from the interactions of all the events\ninvolved (Fog diffusion, tissue contact with the vitelline membrane, integrin activation, receptor\nclustering, MyoII activation, tissue deformation), rather than being instructed by something “on top\nof the hierarchy”. Consistent with this idea, a key feature of the activity gradient we describe is that\nit self-renews maintaining constant range and amplitude as the wave sweeps/travels through the PZ\nof the embryo. Note that if the gradient was deterministically defined by something “on top of the\nhierarchy” as a prepattern, then its amplitude and range would progressively decrease as the furrow\nmoves towards the embryo anterior, unless the element “on top of the hierarchy” (e.g. a prepattern)\ntranslocates together with the moving furrow with similar kinetics. The fact that blocking\ntranscription (with α-amanitin) does not block propagation of the gradient, further argues against the\nidea of a deterministic/hierarchical control due to a prepattern. In this revision we modified the title\nto “Spatial patterning of contractility by a self-organized mechanogen activity gradient underlies\nDrosophila gastrulation” to better reflect the self-organized nature of the gradient we describe and\nwe also discuss this more clearly in the discussion (pp. 11, lines 18-35).\n“Thus, the Fog activity gradient is not governed by a concentration gradient of the ligand Fog in the\nextracellular space. Rather, the gradient emerges from contacts with the vitelline membrane through\nthe mutual interactions between Fog-GPCR signalling, tissue deformations, integrins, and MyoII\nrecruitment, which in a repeated cycle self-renew the activity gradient as the wave sweeps through\nthe tissue.\nThe self-organized Fog activity gradient is constantly renewed over time. This feature allows the\ngradient to maintain a constant amplitude and range over time as it translocates with the moving\nfront of tissue invagination towards the embryo anterior. In contrast, a gradient deterministically\ncontrolled by a hierarchical mechanism (e.g. by the concentration gradient of a factor, e.g. a receptor\nor a co-factor/co-receptor, at the beginning of the invagination process) would not display such a\nfeature and its amplitude and range would progressively decrease over time as the furrow moves\nanteriorly within the embryo, away from the source and thus down the initial concentration gradient.\nNotably, this is also true when de novo transcription is blocked, which rules out that expression of\nputative factors propagates as a wave to shape the gradient and rather suggests that its activation\nwould have to be part of the mechanism of gradient formation. The factors (e.g. a receptor or a co-\nfactor/co-receptor) could be expressed in a fixed but broad enough domain to encompass the whole\nPZ, and some specific event would potentiate its activity, for instance the contact with the VM.\nInterestingly, this is what happens for integrins. Once expressed in the PZ before induction of wave\npropagation, integrin transcription is no longer required and integrin activity is potentiated/activated\nby the contact to the VM which itself propagates as a wave.”\nprotein may be modified differently within the cell during the production process (before the\nsecretion)? Without this mechanism, I feel the impact of this manuscript may be too weak to suggest\nthat Fog works as a concentration-dependent mechanogen\nResponse: In the paper we show that Fog, produced from a restricted source (the endoderm\nprimordium), acts at a distance in the target tissue (the PZ) via protein diffusion (full phenotypic\nrescue of MyoII activation and tissue invagination in the PZ with a diffusible but not with a membrane\nbound Fog produced solely in the endoderm primordium, Fig. 2). We further show (Fig. 3) that Fog\nactivity (MyoII activation) forms an exponential gradient modulated by its rate of production at the\nsource and removal in the target tissue, similar to morphogen activity gradients. Based on this we\npropose that Fog shows key features of mechanogens, that is: 1) a concentration-dependent non-\nautonomous activity on cells of the receptive field, and 2) a direct activation, i.e. independent of gene\ntranscription (see new Fig. S4B-D), of cellular mechanics (contractility). Furthermore, in this revised\nversion we toned down a few statements in the abstract, introduction and discussion to propose that\nFog “acts with a mechanism similar to a mechanogen” rather than stating that “Fog is a mechanogen”.\nWe agree with the reviewer that a different post-translational modification, PTM, (e.g. glycosylation)\nof the zygotically produced Fog within the producing cells is possible and would explain the\nobservation that Fog maternal protein is less potent than the zygotically produced protein despite\nbeing present at higher levels (see Fig. S4E-G, now Fig. S5E-G and response to point 2 below).\nHowever, this alone would still not explain the formation of the activity gradient because, as shown\nin Fig. S4E-F (now Fig. S5E-F), zygotically produced Fog also does not form a concentration gradient\nin the extracellular space, consistent with its fast diffusion dynamics (estimated D=55 µm2s-1) which\nallow the secreted protein to cover very large distances in just tens of seconds (e.g. ca. 50 µm in 10\ns). Therefore, this mechanism would still need to be coupled to some extracellular regulation of Fog\nprotein (e.g. modification of PTM residues, binding and/or inactivation of Fog) able to locally restrict\nFog activity only very close to the moving invaginating furrow. A rigorous test of this hypothesis\nwould imply performing glyco/phosphoproteomics, IP of Fog, identification and mutagenesis of\nputative PTM sites as well as the identification of the enzymes responsible of modifying the protein\nboth in producing cells and in the extracellular space. This work is a full 3-4 years project on its own\n(which we actually proposed in a new grant and plan to start soon) and we feel it is beyond the scope\nof this manuscript. The possibility of an extracellular regulation of Fog activity in the extracellular\nspace was briefly discussed in the original manuscript (pp.10 lines 20-22) and we now extended the\ndiscussion of this point in the revised version to include the possibility of a different PTM of zygotic\nFog in the producing cells (pp. 12 lines 7-9 and 11-15).\n“Furthermore, it is also possible that the Fog activity gradient might require post-translational\nmodification (PTM) and/or an extracellular regulation of Fog protein activity by proteins capable of\nbinding and/or activating/inactivating Fog or its receptor at the cell surface.”\nand\n“Similarly, PTM of the zygotic fraction of Fog protein may explain its higher activity compared to the\nmaternal pool. The maternal pool is more abundant (Fig. S5G), yet it does not rescue a zygotic fog\nnull mutant4 and an extracellular inhibitor or an enzyme modifying Fog PTMs may spatially restrict\nFog activity and thereby be part of the gradient formation mechanism.”\nmechanogen activity gradient which patterns contractility) and it does not explain per se how the\ngradient self-renews as the wave propagates across the tissue.\n2. The authors mention the presence of two sources of Fog (maternal and zygotic). Does maternally\nprovided protein not have a function at all? If so, what is the potential reason behind this?\nOur data and data from the Wieschaus lab indicate that the maternal pool of Fog is less potent, than\nzygotic fog, at activating MyoII contractility during gastrulation. This is based on the following:\n1. Zygotic Fog has strong activity although being less abundant than maternal Fog:\nEvidence 1) fog zygotic null mutants have a strong invagination phenotype (mesoderm +\nendoderm) with invagination completely blocked in the endoderm (Sweeton et al. 1991,\nPMID: 1935689, Costa et al. 1994, PMID: 8137424) and with medio-apical MyoII\nrecruitment dramatically reduced and delayed (see Annexed Fig.1 below). Evidence 2) our\ndata in Figs. 2 and S2 show that Fog expressed zygotically in the endoderm primordium (with\nthe hkb promoter) is sufficient to fully rescue endoderm invagination and wave propagation\nin embryos lacking both maternal and zygotic endogenous pool of Fog. Evidence 3) This\ncomplement of Fog protein although sufficient at rescuing endoderm invagination is present\n(uniform in the extracellular space) at lower levels compared to Fog maternal protein (Fig.\nS4E-G, now S5E-G).\n2. Maternal Fog has little activity: In fog zygotic null mutants, where the maternal Fog pool is\nstill present, apical MyoII recruitment is reduced and delayed in the endoderm primordium\nbut not abolished. This phenotype is less strong than the phenotype of fog RNAi, where both\nmaternal and zygotic Fog are downregulated and where medio-apical MyoII recruitment is\ncompletely abolished (see Annexed Fig.1 below). This shows that some activity of the\nmaternal Fog protein is detectable, but it is not sufficient to drive tissue invagination. This is\nconsistent with data from the Wieschaus lab (Costa et al. 1994 PMID: 8137424, Zusman and\nWieschaus 1995, PMID: 3930314) where, based on germline clones and fog gene dosage\nincrease in mothers, the authors conclude that “the maternal contribution of fog supplements\nthe zygotic activity but cannot substitute for it”.\nAltogether, considering that maternal Fog cannot drive tissue invagination alone (while zygotic Fog\ncan) though it is more abundant, one has to conclude that it is less potent than zygotic Fog at activating\nMyoII apical contractility and cell apical constriction in the posterior endoderm. This difference in\nactivity could stem from different PTM of the zygotic protein and we now discuss this more clearly\nin the discussion of the paper (pp. 12, lines 12-15).\n“The maternal pool is more abundant (Fig. S5G), yet it does not rescue a zygotic fog null mutant4\nand an extracellular inhibitor or an enzyme modifying Fog PTMs may spatially restrict Fog activity\nand thereby be part of the gradient formation mechanism. ”\n10 min 10 min\n20μm 20μm\nfog4a fog RNAi\n25 min 25 min\n20μm 20μm\nfog4a fog RNAi\nAnnexed Fig. 1: Stills from time lapse of representative embryos of the indicated conditions at approximately 10 and 25 minutes from the\nonset of gastrulation. On the left a fog zytotic null mutant (fog4a) and on the right an embryo where the maternal and zytgotic\ncopmplements of fog have been depleted by RNAi.\n3. In their previous work (Bailles et al., 2019 Nature, ref#6), it was stated that MyoII wave dynamics\nis independent of Fog delivery based on the result that a higher Fog copy number did not increase\nthe speed of wave propagation. This conclusion appears to contradict the current work. I understand\nthat these conclusions are based on different experiments, and no data is contradictory. However,\nincluding the results section below requires a more thorough explanation of why the authors propose\nopposite interpretations.\n“Although previous studies (reference cited for #6) ruled out that the propagation of MyoII activation\nreflects the propagation of a wavefront of diffusion/transport of the ligand Fog, the role of Fog-\nGPCR signalling in activating MyoII in cells of the PZ was not directly tested. Furthermore, the\nabsence of integrins (KO of the 𝛼-PS3 integrin, Scab) does not completely abolish MyoII activation,\nwhich remains pulsatile at low levels, during wave propagation, indicating that other factors, such\nas Fog-GPCR signalling, could be involved.”\nResponse: The reviewer raises a valid point and we agree that we need to better clarify the apparent\ncontradiction between the conclusions in our previous study and those in this study.\nIn a nutshell in Bailles and Collinet et al., 2019 we showed that Fog diffusion/delivery does not\ndetermine the dynamics of the MyoII wave, such as if the wave reflected the non-steady state\ndynamics of a diffusion front. We add now that Fog delivery is nonetheless required to make a\ngradient to begin with. The wave reflects the propagation of a steady state gradient pattern.\nIn our previous study (Bailles and Collinet et al., 2019 Nature, PMID: 31413363) we falsified the\nhypothesis that the propagation of MyoII activation reflected the dynamic expansion of a Fog gradient\nbefore reaching a steady state, due to diffusion and degradation. In this case the expansion speed of\nthe gradient would decay over time as would the amplitude at the front. To account for the observed\nwave kinematics (constant speed of propagation and constant amplitude) we considered a model that\ncouples Fog diffusion and saturable receptor binding and compared its predictions with experiments.\nWhile the model predicts that increasing Fog production at the source should increase the speed of\nthe MyoII wave propagation, the experiments showed that this remained constant despite increased\nMyoII levels in cells of the PZ. We also showed that the homogenous overexpression of fog in the\n5\nentire embryo did not abolish the wave propagation of MyoII in the PZ and we concluded that the\nwave dynamics of MyoII activation, i.e. its propagation in the PZ, does not solely depend on a\nwavefront of Fog protein delivery (we wrote “patterned Fog signalling does not determine the\ndynamics of the MyoII wave. Fog may be required, but MyoII activation is not governed solely by\nFog-dependent Rho1 activation in the propagation region.”). Notably, this conclusion leaves the door\nopen and even suggests that Fog signalling may be required, but together with other factors (e.g.\ncontact with the vitelline membrane, activation of integrins and their modulation of signalling from\nGPCR).\nHere we propose that Fog needs to diffuse to form an activity gradient that is at a steady state at any\nmoment in front of the invaginating furrow. The wave reflects and is coupled to the movement of the\nself-organized reconstitution of the gradient (which involves tissue contact with the VM, integrin\nactivation, receptor clustering, etc.). If tissue movements were blocked, the gradient would maintain\nits shape and position.\nWe now better explain this at the beginning of the result section (pp.3, lines 23-25) and we inserted a\nparagraph in the discussion (pp. 10, lines 35-41).\nWe write at the beginning of the result section: “Previous studies6 ruled out that the propagation wave\nof MyoII activation solely reflects the anterior movement of the diffusion front of an expanding\ngradient of the ligand Fog away from the endoderm primordium.”\nand in the discussion: “Notably, this is not inconsistent with previous reports excluding the possibility\nthat the wave propagation of MyoII recruitment towards the anterior solely reflects the expansion of\na non-steady state gradient of the ligand Fog diffusing away from the endoderm primordium6. If this\nwere the case the expansion rate of the gradient would decay over time. Instead, the speed of\npropagation and the amplitude of the MyoII gradient in front of the invaginating furrow are constant.\nTherefore, the wave of MyoII recruitment reflects a steady-state activity gradient of the ligand Fog\nthat is constantly renewed as it travels anteriorly across the tissue.”.\nReviewer #2 (Remarks to the Author):\nThe manuscript “Spatial patterning of contractility by a mechanogen gradient underlies Drosophila\ngastrulation” by Mundhe et al., presents evidence of the existence of mechanogens, molecular\ngradients that spatially control cell mechanics in a concentration dependent manner. Using the\nDrosophila gastrulating embryo, the authors show that the GPCR ligand Fog is expressed in the\nposterior endoderm and diffuses towards the propagation zone (PZ) of an actomyosin contractility\nwave. Using live imaging, FCS and genetic perturbations they show that although Fog is distributed\nuniformly in the extracellular space, it forms a surface-bound gradient. The authors propose that this\ngradient generates patterns of receptor oligomerization and endocytosis resulting in Myosin-II\nactivity gradient. Last, the interplay of Fog/actomyosin and vitelline membrane attachment via\nintegrins is further investigated leading to the conclusion that integrins self-renew this gradient.\nOverall, the experiments are rigorous and the quantitative analysis is accurate. The existence of a\nmechanogen, which is the main goal of this paper, is very convincing, changing our view of how\ndiffusive molecules can drive patterning, beyond eliciting transcription-dependent responses. Below\nsome comments that (in my opinion) can prove even further the existence of a pure “mechanogen”\nand clarify the proposed mechanistic interplay between Fog/Smog/Myosin and integrins.\nMajor comments:\n6\n• Transcription-dependency. According to the definition of a “mechanogen” given by the authors,\nsuch a diffusive molecule should trigger changes in cell mechanics directly, meaning that an\nintermediate step involving transcription is not required. Can the authors support somehow the claim\nthat indeed transcription is dispensable for Fog-dependent Myosin-II gradient?\nResponse: We thank the reviewer for raising this point. In a previous study (Bailles and Collinet et\nal., 2019 Nature, PMID: 31413363) we provided evidence that the propagation of MyoII activation\nis independent of gene transcription. We reported that the injection of α-amanitin, a potent inhibitor\nof the RNA polymerase, did not block the wave propagation of MyoII activation, which occurred\nwith kinetics similar to WT embryos (Fig. 2 in that paper). We now provide new data where we\ndirectly analyzed the effects of blocking gene transcription on the formation and maintenance of the\ngradient of MyoII that we describe here. Our results show that in embryos injected with α-amanitin\nMyoII forms a gradient similar to WT embryos and that the gradient remains unchanged over time.\nWe conclude that the establishment of the MyoII gradient does not require gene transcription. These\ndata are shown in a new Fig. S4B-D.\n• Length-scales of Myosin vs Smog oligomerization vs Fog ligand. The steepness of the MyoII\ngradient does not match the steepness neither of Fog (as the authors mention) nor of the oligomerized\nreceptor. The authors showed compelling evidence for Smog receptor oligomerization patterns being\nresponsible for the Fog-dependent GPCR activation, but still is the Smog oligomerization length-\nscale as steep as the myosin length-scale? Another possibility is that the expression of Smog is pre-\npatterned as a steep gradient, where cells closer to the myosin front express much more Smog than\ncells further away. This would also fit with the observation that despite the high extracellular\nabundance of Fog (84%), it is not contributing to longer-range signalling in the PZ. Could this be\nthis be due to the absence of Smog from cells further away and thus they cannot receive excess Fog?\nResponse: We answer these two great questions below:\nSteepness of the MyoII and Smog oligomerization gradient: In front of the invaginating furrow\nSmog brightness decreases by about half of its dynamic range, i.e. the max brightness (corresponding\napproximately to that of trimers) minus the min brightness (corresponding approximately to that of\nmonomers), within a range of about 30 µm, similar to the Fog fraction. More precisely, the\nslow\noligomer fraction decreases to half of its initial value (measured at distance 0) at a distance of 30-35\nµm (e.g. from 26% to 13% trimers or 100% to 50% dimers). As noted by the reviewer, this decrease\nis less steep compared to the decrease of MyoII intensity, which in the same region decreases more\nthan 10 folds and follows an exponential decay. This is not unexpected since the signalling cascade\nthat leads to MyoII activation downstream of GPCRs includes many steps (interactions with integrins,\nactivation of heterotrimeric G proteins, Rho1, Rock, etc.) which may lead to non-linear signal\namplification and thus increase the steepness of the MyoII gradient. In fact, we know that integrins\nare part of this amplification (based on the shape of the gradient in the αPS3 mutant, Fig. 6B and\nS7D, now S8D).\nIs the MyoII gradient due to a gradient of Smog receptor?: The Smog receptor is expressed\nhomogenously in the epithelial blastoderm (Kerridge et al 2016, PMID: 26780298). Therefore, the\nMyoII gradient does not depend on a gradient of total Smog receptor. Furthermore, a pre-patterned\ngradient of expression of the receptor would provide a static protein gradient in the referential of the\ntissue with high concentration close to the primordium and lower concentrations at a distance. If the\ngradient of MyoII was due this static gradient of receptor, then its amplitude and the range would\ndecrease over time as the invagination front (and thus the MyoII gradient) moves across the tissue\nand, therefore across gradient of receptor. This is not what we observe. We observe that the MyoII\n7\ngradient maintains a constant amplitude and range over time despite the movement of the invaginating\nfurrow across the tissue (Fig. 3C). Note that this is also true when de novo transcription is blocked,\nwhich rules out that expression of a putative co-receptor propagates as a wave to shape the gradient.\nThe constant amplitude and range of the gradient is naturally emerging from the self-organized\nmechanism that we propose here.\nFor a putative co-factor/co-receptor to be involved, its activation would have to be part of the\nmechanism of gradient formation. The co-factor could be expressed in a fixed but broad enough\ndomain to encompass the whole PZ, and some specific event would potentiate its activity, for instance\nthe contact with the VM. This is precisely what we document for integrins. Once expressed in the PZ\nbefore induction of wave propagation, integrin transcription is no longer required and integrin activity\nis potentiated/activated by the contact to the VM which itself propagates as a wave.\nAltogether, these data and these considerations support the fact that the MyoII gradient does not\nsimply emerge from a gradient of the Smog receptor expression/protein concentration and this is not\nwhat limits longer-range signalling in the PZ.\nWe now discuss this more explicitly in the discussion (pp. 11, lines 22-35).\n“The self-organized Fog activity gradient is constantly renewed over time. This feature allows the\ngradient to maintain a constant amplitude and range over time as it translocates with the moving\nfront of tissue invagination towards the embryo anterior. In contrast, a gradient deterministically\ncontrolled by a hierarchical mechanism (e.g. by the concentration gradient of a factor, e.g. a receptor\nor a co-factor/co-receptor, at the beginning of the invagination process) would not display such a\nfeature and its amplitude and range would progressively decrease over time as the furrow moves\nanteriorly within the embryo, away from the source and thus down the initial concentration gradient.\nNotably, this is also true when de novo transcription is blocked, which rules out that expression of\nputative factors propagates as a wave to shape the gradient and rather suggests that its activation\nwould have to be part of the mechanism of gradient formation. The factors (e.g. a receptor or a co-\nfactor/co-receptor) could be expressed in a fixed but broad enough domain to encompass the whole\nPZ, and some specific event would potentiate its activity, for instance the contact with the VM.\nInterestingly, this is what happens for integrins. Once expressed in the PZ before induction of wave\npropagation, integrin transcription is no longer required and integrin activity is potentiated/activated\nby the contact to the VM which itself propagates as a wave.”\n• Interplay with integrins. Although integrins are involved in the Myosin-II pattern it is unclear if this\nis an independent parallel regulation or it is involved in a functional interplay as illustrated in the\nscheme in Fig. 6O. For instance, is integrin attachment also affecting the Smog/Fog gradient? Or is\nit possible that Fog manipulations could affect the integrin complexes that impact the myosin\ngradient? I suggest that the authors perform live imaging or immunostainings of i) integrin localization in\nthe diffusible vs tethered Fog or gprk2 embryos, and ii) Fog/smog gradient in the integrin manipulations. This\ncharacterization would support the self-renew hypothesis.\nResponse: Our data in Fig. 6A-D show that integrins are required for the enhanced Fog-GPCR\nsignalling in gprk2 RNAi and suggest that integrins may regulate directly receptor activation and/or\nits downstream signalling to MyoII. Notably, if integrins and GPCR signalling were parallel pathways\nto regulate MyoII, we would observe that the effects of loss of integrins (i.e. decreased amplitude\nwith similar range of the gradient) and loss of Gprk2 (i.e. increased amplitude and range of the\ngradient) would add to one another in the double LOF condition (gprk2 RNAi + αPS3-/-) and produce\nan intermediate phenotype. The expected phenotype would then be an increased range of the MyoII\ngradient compared to WT with an amplitude in between that of αPS3-/- and gprk2 RNAi embryos.\nInstead, we observe that the phenotype of gprk2 RNAi + αPS3-/- is like that of αPS3-/- embryos,\n8\nindicating a functional interaction between GPCR signalling and integrins. We agree that this\nfunctional interaction is an important point that deserves further investigation.\nImpact of Fog on Integrin:\nConcerning the modulation of integrin complexes by Fog-GPCR signalling, we show in Fig. 6H-N\nthat in gprk2 RNAi embryos both the amplitude and the range of gradient of integrin focal complexes\nare increased in the PZ. These effects depend both on an increased activity of the positive feedback\nof MyoII on integrins (Collinet et al., 2024, PMID: 38103554), which sustains the formation of focal\ncomplexes, and on a larger zone of contact of the tissue with the vitelline membrane in gprk2 RNAi\nembryos (Fig. S7G-I and Fig. S7J-K, now S8G-I and S8J-K). The latter may result from an increased\nstiffness of the tissue due to increased MyoII levels; this would increase the range of tissue outward\nbending next to regions of invagination and thus the range of contact with vitelline membrane.\nImpact of Integrin on Fog signalling: To explain the functional interaction reported in Fig. 6A-D\nwe hypothesized that integrins might either directly promote receptor clustering or act as a co-factor\nnecessary for GPCR signalling amplification in the PZ. We thus measured the oligomerization of the\nFog receptor Smog in the PZ in αPS3 depleted (RNAi) embryos by FCS. We found that αPS3 RNAi\ndid not decrease Smog oligomerization in the PZ compared to WT embryos, as we expected if\nintegrins had a direct role of in receptor clustering. On the contrary, we observed an increase of Smog\noligomerization in αPS3 RNAi despite reduced signalling, as shown by the decreased MyoII levels\nin both αPS3-/- and αPS3-/- + gprk2 RNAi embryos, Fig. 6A-D. This may reflect either a different\ntopology of the apical membrane (e.g. more apical membrane grooves in the WT which may\nartificially decrease Smog molecular brightness) or a longer residence of inactive Smog oligomers at\nthe plasma membrane (the lack of receptor activation would also lead to its reduced endocytosis).\nTogether, based on these data we conclude that integrins do not regulate the Fog activity gradient by\npromoting receptor clustering, but may do so by acting as a co-factor for GPCR signalling\namplification. We now present these data in a new panel in Fig. S8F.\nThese data support the view that the Fog-GPCR activity gradient emerges from tissue bending,\ncontact with the vitelline membrane, integrin activation, MyoII activation and tissue detachment in a\nself-organised cycle.\nMinor comments:\n• Although the authors checked that the tethering manipulation is successful when performed in the\nwingless stripe, it is advisable to perform evidence of the tethering effect of FogVSV-G in the\nprimordium. Moreover, antibody staining or Fog::YFP live imaging in the primordium and\npropagation zone would confirm the conditions in Fig. 2A, Fig. 2B.\nResponse: The reviewer asks to verify the tethering effects of the Fog-VSV-G fusion directly in the\nprimordium and not just in the wingless stripe, as shown in the manuscript (Fig. S3A-C).\nDue to limitations of the experimental system, it is unfortunately not possible to perform antibody\nstaining on injected embryos (RNAi to remove the endogenous diffusible pool of Fog). Therefore, in\nembryos at gastrulation stage it is not possible to distinguish the exogenous pool of engineered Fog\n(RNAi-resistant diffusible or membrane tethered) from the endogenous pool of Fog in the cells next\nto the producing source (the primordium). For that reason, we performed the experiment at a later\nstage by expressing the diffusible and the membrane tethered forms of Fog ectopically (wingless\nstripe) in regions where Fog is not normally expressed (the lateral ectoderm of the embryo). These\nexperiments showed convincingly that the Fog-VSV-G fusion does not diffuse away from the\nexpressing cells contrary to a WT version of Fog.\nFog when expressed in the primordium (hkb promoter) does not act at a distance, contrary to a\nVSV-G\nWT diffusible version of Fog expressed in the same conditions. Indeed, while the diffusible version\nfully rescues the phenotype of the lack of protein (compare hkb-Fog + RNAi with hkb-Fog +\nsen res\nRNAi) the membrane tethered version expressed by the same promoter (hkb-Fog ) rescues MyoII\nVSV-G\nrecruitment and cell invagination only in the primordium but not in the PZ. Also, notably MyoII\nrecruitment was observed in the PZ when the Fog was expressed homogenously (Fig. S3D-E),\nVSV-G\nindicating that when present in this region Fog is capable of activating MyoII.\nVSV-G\nTaken together, we believe these data provide strong evidence to support the tethering effect of the\nFog also in the primordium at the stage of gastrulation.\nVSV-G\nReviewer #3 (Remarks to the Author):\nIn this study, Mundhe and colleagues identify an extracellular gradient of a GPCR ligand, Fog, and\nexplore how it can directly contribute to the formation of intracellular gradients of Myo2, a\ncontractile protein essential for successful Drosophila gastrulation. Their findings are interesting and\nnovel, and the authors go at length to characterize and quantify the dynamics of this system, specially\nhow the observed gradients can rapidly shape and influence the endoderm invagination\nmorphogenesis.\nBoth conceptually and methodologically, the work is relevant to the field. While the idea of\n‘mechanogen’ is not new, the identification of mechanochemical coupling via the establishment of\nchemical gradients that lead to direct activation of mechanically active downstream targets\n(independently of transcription) is an important finding.\nAs is, the current study and respective analyses support part of the authors' claims. Further crucial\nevidence and clarifications are required as detailed below to strengthen and validate the proposed\nconclusions.\nMajor points:\n1) Validation of the Fog::YFP line.\nWhile the overlap in anti-Fog signals generated from the KI line and wt conditions is rather nice\n(Fig. 4E), as it would indicate that Fog regardless of being tagged is being produced and secreted to\nsimilar amounts to wildtype, it is somewhat unexpected to have a lot more GFP signal than anti-Fog\nin the KI line. Is this just a consequence of the methodology (i.e. secondary antibody used against\nanti-GFP is brighter? Can Fog::YFP rescue a fog-/- mutant?\nResponse: In Fig. 4D-E detection of the Fog protein is done with two different antibodies, a\npolyclonal anti-GFP antibody raised in chickens and a polyclonal anti-Fog antibody raised in rabbits,\nwhich likely have very different affinities for their respective epitopes. Furthermore, the anti-Fog\nantibody was pre-adsorbed on embryos that do not express Fog to reduce non-specific binding and\nlabelling. This likely also reduces total signal. Finally, the two primary antibodies are revealed with\ndifferent secondary antibodies, labelled with different fluorophores (a donkey anti-chicken Alexa\nFluor 488 and a donkey anti-rabbit Alexa Fluor 568 to detect anti-GFP and anti-Fog respectively) and\nexcited at different laser powers. For all these reasons, in Fig. 4E the signals from the Fog and the\nGFP antibodies cannot be directly compared within the same sample (e.g. within the Fog::YFP\nsamples) but should only be compared across samples (i.e. WT vs Fog::YFP). This comparison shows\n10\nthat Fog::YFP in knock-in embryos is present at very similar levels and with a similar pattern than\nendogenous Fog in WT embryos (compare solid black and solid red curves in Fig. 4E).\nConcerning the rescue of the fog -/- mutant by Fog::YFP, we wish to note that the line used is a knock-\nin line where the SYFP2 ORF was added just before the stop codon of the endogenous fog gene.\nfog::SYFP2KIN homozygote flies are viable and embryos show no defects in gastrulation (Fig. S4B,\nnow Fig. S5B). Therefore, Fog::YFP is perfectly functional and rescues fog -/- mutants.\n2) Diffusion coefficients’ comparison.\nThe finding and characterization of the 2 fractions of Fog diffusing in the extracellular space makes\nsense with the phenomenon being described. By perturbing endocytosis of the GPCR receptor (gprk2\nRNAi) the authors show that the fraction of Fog that is slow diffusing (Fog slow) does not change. In\nthis case, shouldn’t the diffusion of Smog::GFP and Fog Slow decrease, as more Fog molecules are\nbound to the receptor, which cannot be internalized? This in turn leads to more Myo2 recruitment\n(i.e. ‘activation’), which is nicely shown in gprk2 RNAi.\nResponse: We thank the reviewer for this comment which we think requires some clarifications. The\nfraction of Fog in gprk2 RNAi embryos is actually increased (and not unchanged as stated by the\nslow\nreviewer) from 16% in WT to 30% in gprk2 RNAi (Fig. 5B). Thus, the effective average diffusion\ncoefficient (i.e. D = f *D + f *D ) does decrease, as the reviewer indicates should be the\neff fast fast slow slow\ncase. However, in Fig. 5D, we only show the diffusion coefficient of the slow component (i.e. D )\nslow\nand compare it with the diffusivity of the Fog receptor Smog::GFP. The reason for this comparison is\nthat, if Fog indeed corresponds to receptor-bound Fog::YFP, similar diffusion dynamics for D\nslow slow\nof Fog::YFP and D of Smog::GFP are expected, as we find (Fig. 5D). When more Fog molecules are\nbound to the receptor, i.e. when there is a higher slow fraction, D per se is not expected to change,\nslow\nas more receptor-ligand complexes are present but the diffusivity of each individual complex remains\nunchanged. In the FCS measurements of Fog::YFP, which locally detect the diffusive transits of free\nand receptor-bound ligands, statistically averaged over many single molecule events, the increased\nbinding of Fog to the receptor leads to an increased amplitude of the slow component of the FCS\nautocorrelation function (i.e. increased slow fraction since more of the detected ligands are bound to\nSmog receptors), but does not change the temporal decay dynamics of this slow component (i.e.\ncorresponding to the average diffusion coefficient of receptor-bound ligands). This holds true also\nwhen Smog oligomerization increases with more Fog binding or when more Fog molecules are bound\nto each individual receptor-ligand complex because diffusion of transmembrane inclusions in the\nplasma membrane is mostly dominated by the viscous membrane environment and only weakly\naffected by oligomerization or association of extracellular molecules (e.g. logarithmically reduced\nwith the radius of the membrane inclusion according to the Saffman-Delbrück relation, see Guigas\nand Weiss 2006, PMID: 16829562 and Petrov and Schwille 2008, PMID: 18192354 and only weakly\nmodulated by the mass of the extracellular domain, see Gurdap et al. 2022, PMID: 36110044). Thus,\nthe diffusivity of Smog::GFP is not expected to change significantly when more Fog molecules are\nbound to it or its oligomeric state increases. This explains why the detected diffusion coefficient for\nSmog::GFP is not significantly changed in gprk2 RNAi embryos as our data show in Fig S6A, now\nS7A.\n3) Fog slow spatial gradient.\nCould the authors show images of the Fog slow spatial gradient (Fig. 5E) in wt and gprk2 RNAi\nconditions? Please include R2 to the linear fits shown in Figs 5E and 5G. Also, it would be great to\nsee the comparison of the slopes obtained between these conditions, since 5G appears flat – this is a\n11\nbetter comparison between gradient shapes than the uneven bin comparison in Figs 5F and 5H. In\nalternative to that comparison, an average of signal by bins of similar magnitudes is more precise\n(e.g. 20 um across the distance from invagination) and can perhaps illustrate the gradient more easily,\nrather than 2 bins of uneven sizes.\nResponse: We thank the reviewer for this comment which allows us to clarify why images of the\nFog gradient were not shown and suggests a better display and statistical comparison of the data\nslow\nin Fig. 5E, 5G, 5L and 5N. Below we detail our response to the different questions/suggestions of the\nreviewer.\nImages of Fog\nslow\nWhile we can image the total signal of Fog::YFP in the tissue, the slow fraction of Fog is not obtained\nfrom images, but from spectroscopic measurements (FCS) performed at apical membranes in\ndifferent locations across the tissue. Statistical analysis of the temporal fluctuations recorded in these\nmeasurements provides a direct quantification of the diffusion dynamics of Fog::YFP and the relative\nfraction of the fast and the slow diffusing molecular pools of Fog (given by parameters D , D\nfast slow\nand the relative fractions of these species, in an approximation of two diffusing “states”, see also\nFig.S5E, now S6E showing the presence of two components corresponding to fast and slow diffusion\ndynamics).\nTherefore, it is not possible to show images of Fog spatial gradient.\nslow\nR2 of the linear fits for the scatter plots in Fig. 5E, 5G, Fig. 5L, 5N, S6F (now S7F), S6H (now\nS7H), S6J (now S7J) and S6L (now S7L)\nThe data in the scatter plots Fig. 5E, 5G, Fig. 5L, 5N, S6F (now S7F), S6H (now S7H), S6J (now\nS6J) and S6L (now S7L), where we added trendlines to illustrate the presence/absence of a spatial\ngradient in Fog fraction, oligomerized Smog::GFP and VSV-G::GFP, are pooled from\nslow\nmeasurements in multiple embryos and thereby influenced by a large embryo-to-embryo variability.\nTherefore, the R2 values (see Table 1 here below) extracted using linear fitting of the pooled data\nfrom several embryos are low and reflect this variability rather than a lack of a trend in the data.\nTable 1: R2 and slopes from fitting cumulated data points from all measured embryos\nFigure R2 Slope (best-fit value ± standard error)\n5E 0.09086 -0.001353± 0.0004876\n5G 0.0008572 -0.0002557± 0.0009883\n5L 0.06097 -0.1087± 0.04266\n5N 0.0009227 -0.02165± 0.08065\nS6F (new S7F) 0.06892 -0.04584± 0.01946\nS6H (new S7H) 3.237E-07 0.000287± 0.08407\nS6J (new S7J) 0.006787 -0.04864± 0.05884\nS6L (new S7L) 0.06943 0.06895±0.02915\nThis is confirmed by the fact that the average of R2 from linear fits of the data within each of the\nembryo (see table 2 here below) are significantly greater than that extracted from the from fits of the\npooled data.\nTable 2: R2 and slopes from fitting data points within each individual embryo:\nFigure mean R2 R2 SD mean slope slope SD\n12\n5E 0.2657 0.24 -0.002 0.01\n5G 0.1732 0.32 0.0007 0.01\n5L 0.2925 0.35 -0.0776 0.16\n5N 0.2201 0.35 0.0996 0.24\nS6F (new S7F) 0.1818 0.2 -0.0501 0.06\nS6H (new S7H) 0.3098 0.36 0.2434 0.29\nS6J (new S7J) 0.1441 0.13 0.0101 0.36\nS6L (new S7L) 0.1856 0.22 0.0528 0.1\nFor this reason, we decided to remove the trendlines in the scatter plots Fig. 5E, 5G, Fig. 5L, 5N, S6F,\nS6H, S6J and S6L and rather compare the slopes of the linear fits of each individual embryo as\ndetailed below.\nComparison of the slopes of the linear fits in WT and gprk2 RNAi embryos in Figs. 5 and S6\n(now S7)\nWe performed linear regression of the datapoints within each individual embryo, extracted the slope\nfor each and compared these slopes across conditions. We performed these comparisons for the\ndatasets in 5E vs 5G, 5L vs 5N, S6F (now S7F) vs S6H (now S7H) and S6J (now S7J) vs S6L (now\nS7L). The results are shown below and were added as new figure panels to G and N to Figs. 5 and\nnew panels H, L, M to Fig.S7.\n1.0\n0.5\n0.0\n-0.5\nUse of bins of similar bin size to represent the gradients Fog and Smog oligomerization in\nslow\nWT and gprk2 RNAi\nTo compare the gradients of Fog and Smog oligomerization in WT and gprk2 RNA we have chosen\nslow\nto use two bins of unequal size (in terms of area covered in the embryo) for biological rather than\nstatistical reasons. Indeed, the first bin (0–20 µm) corresponds to the region in the PZ where apical\nMyoII is recruited at the highest levels, while the second bin (20–70 µm) corresponds to the region\nwhere MyoII recruitment is low (see Fig. 3A-B in the manuscript as a reference). Equal size binning\nwith two bins of 35 µm (partitioning the PZ in more bins of smaller size would provide too few\ndatapoints/bin) would less well reflect this biologically meaningful boundary, but it would still yield\nsignificant differences (see Annexed Fig. 2 here below). The differences between the two regions in\nwater injected embryos are generally less significant (* is p<0.05 with bins of equal size instead of\n** corresponding to p<0.009 for Figs 5H and 5O in the manuscript for the same comparisons), as\n13\n)1-mμ\n* zHk(\nepolS\n1.0\n0.5\n0.0\n-0.5\nApical Junctional\n)1-mμ\n* zHk(\nepolS\nVSV-G::GFP + water\n0.006\nNS\n0.004\n0.002\nNS\n0.000\n-0.002\n-0.004\n-0.006\n)1-mμ(\nepolS\nFog::YFP\n1.0\n0.5\n0.0\n-0.5\nWater gprk2\nRNAi\n)1-mμ*\nzHk(\nepolS\nSmog::GFP\n1.0\n0.5\n0.0\nWater gprk2 -0.5\nRNAi\n)1-mμ\n* zHk(\nepolS\nFig.5G Fig.5N Fig.S7H Fig.S7L,M\nSmog::GFP: Junctional\nWater gprk2\nRNAi\nFig 5E vs 5G, * is p=0.015 Fig 5L vs 5N, * is p=0.035 Fig S6F (newS7F) vs S6H (new S7H), Fig S6J (newS7J), Fig S6L (newS7L),\n* is p=0.035 n.s. is p=0.72 n.s. is p=0.064\nexpected, because larger bins encompass both region where MyoII is activated at high levels and\nregions where MyoII is activated at low levels or not activated at all. In gprk2 RNAi embryos in both\ncases the differences between the two regions are not significant. Since the choice of binning does\nnot significantly change the results of the statistical comparisons, we chose to maintain our original\nselection of bins for Figs. 5H, 5O, S7I and S7N used for biological reasons.\nFig.5H\n1.0\n0.8\n0.6\n0.4\n0.2\n0.0\n4) Smog::GFP expression.\nWhere is Smog::GFP expressed? Is anti-Smog expression uniform across the PZ? The analysis\nbetween Smog monomers, dimers and oligomers points towards a graded expression of the\noligomerized (activated) form of Smog (Fig. 5L), could images be shown? If indeed this is true, then\noverexpressing Smog in a way to make it uniform across the PZ, would lead in principle to uniform\nMyo2 recruitment, flattening this gradient. Does smog-/- lead to no Myo2 gradients?\nResponse: The GPCR Smog is expressed homogenously in the epithelial blastoderm (Kerridge et al\n2016, PMID: 26780298) and therefore its expression is homogenous also in the PZ where MyoII\nforms a gradient. Thus, the MyoII gradient does not depend on the simple expression/presence of the\nSmog receptor on the cell surface but rather on its activation by ligand binding and oliogomerization.\nThis is well shown in gprk2 RNAi where the gradient of activated receptors is expanded and shallower\nand so does the gradient of MyoII. Consistent with this, homogenous overexpression of the Smog\nreceptor is not expceted to flatten the MyoII gradient since endogenous Smog is already\nhomogenously expressed but forms a gradient of activated receptors during wave propagation. This\nis also confirmed by the fact that the Smog::GFP construct used in this study (driven by the uniform\nsqh promoter and effectively generating an homogenous overexpression) rescues the smog -/- and\ndoes not produce defects in tissue/germband extension (Kerridge et al 2016, PMID: 26780298),\nwhich instead are produced when MyoII is activated everywhere in the embryo (Dawes-Hoang et al.\n2005, PMID: 16123312, Bailles and Collinet et al., 2019 Nature, PMID: 31413363).\nAltogeter, this confirms that the MyoII gradient does not simply depend on a gradient of expression\nof Smog in the PZ but rather on gradient of activated receptors that self organizes in contact with the\nvitelline membrane through interaction with integrins.\nAs for the request of showing images of the gradient of Smog oligomers, similar to the previous\nrequest (“…images of Fog slow…”), images of the oligomerization of the Smog::GFP receptor across\n14\nnoitcarf\nwolS\nFog::YFP\nNS 60\n40\n20\n0-35μm 35-70μm 0-35μm 35-70μm 0\nWater gprk2 RNAi\n)zHk(\nssenthgirB\nFig.5O\nSmog::GFP\nNS\n30\n20\n10\n0 0-35μm 35-70μm 0-35μm 35-70μm\nWater gprk2 RNAi\n)zHk(\nssenthgirB\nFig.S7I\nSmog::GFP: Junctional\nNS 60\n40\n20\n0-35μm 35-70μm 0-35μm 35-70μm 0\nWater gprk2 RNAi\n)zHk(\nssenthgirB\nEqual size binning, same data as:\nFig.S7N\nVSV-G::GFP + water\nNS\nNS\n0-35μm 35-70μm 0-35μm 35-70μm\nApical Junctional\n* is p=0.026, NS is p=0.47 * is p=0.032, NS is p=0.6 ** is p=0.0063, NS is p=0.84 NS is p≥0.64\nAnnexed Figure 2: Fog (left), Smog brighness in the apical (middle left) and junctional (middle right) regions and VSV-G brighness\nslow\nin the indicated conditions and within bins of the indicated distance from the invaginating furrow. p-valies are indicated at the bottom.\nComparisons to the data shown in the manuscript are indicated on the top\nthe PZ cannot be shown because this is obtained by locally measuring Smog::GFP molecular\nbrightness with FCS. FCS is performed by measuring intensity fluctuations over time within a\nconfocal volume placed at fixed positions at different distance from the invaginating furrow. As such,\nthe technique provides local measurements of receptor oligomerization (molecular brightness) but\nnot images. More in details, the diffusion time and number of diffusing molecules are obtained from\nthe statistical analysis of the recorded fluctuations via an autocorrelation function. From the particle\nnumber and the average intensity, the brightness (i.e. photon counts per diffusing entity) is\ndetermined, which provides a direct measure of receptor oligomerization (i.e. oligomers correspond\nto several fluorophores per diffusing complex and thus a higher brightness than monomers, which\nonly carry one GFP per molecule). This is explained in the methods in the section “Fluorescence\nCorrelation Spectroscopy (FCS)”, pp. 46-50.\nFinally, we previously reported that in smog-/- embryos invagination of the posterior endoderm is\naffected to a variable degree and that embryos show gastrulation defects (Kerridge et al 2016, PMID:\n26780298). In particular, the fast phase of germband extension, which is known to depend on the\ninvagination of the posterior endoderm (initial invagination of the primordium + subsequent wave\npropagation, Irvine & Wieshaus 1994, PMID:7600960 and Collinet at al. 2015, PMID: 26389664),\nis delayed in smog-/- embryos. Thus, the Fog receptor Smog is involved in the invagination of the\nposterior endoderm, however, at least another Fog receptor (Mist) is known to be expressed in the\nposterior endoderm (Manning et al. 2013, PMID: 24222713) and could participate to the process of\nMyoII activation in both the primordium and the PZ. This is consistent with the finding that, contrary\nto fog-/- embryos, in smog-/- invagination of the posterior endoderm is not completely blocked. Thus,\nsmog-/- is not expectd to completely abolish MyoII activation and its gradient during wave\npropagation. We wish to note that in this study we do not claim that the Smog receptor is the only\nreceptor involved in the formation of the MyoII gradient, rather we use Smog as a prototype of Fog\nreceptor to show that a gradient of Fog activity arises in the PZ despite a uniform distribution of the\nligand Fog in the extracellular space.\n5) Integrins, Fog and Smog relationships\nLooks like removing Integrins abolishes Myo2 gradient - are these still exponentials?\nStatistically comparing slopes between conditions would be important to the authors claim, in addition to the\nshown Myo2 recruitment (amplitude) or range.\nResponse: In 𝛼PS3-/- (as in 𝛼PS3-/- + gprk2 RNAi) embryos there is still a MyoII gradient and the\ngradients are still exponential. This was shown in Fig. 6B and S7D (now S8D). We now provide the\nR2 values of the exponential fits that are: 0.79 for WT, 0.93 for gprk2 RNAi, 0.59 for 𝛼PS3-/- and 0.81\nfor 𝛼PS3-/- + gprk2 RNAi in Fig. S8D. Since in all these conditions the gradients are exponential,\nwe argue that a comparison of the decay length scale in addition to the amplitude and range is more\nappropriate than comparing the slopes. A statistical comparison of the lenght scales of the exponential\ngradients of MyoII in the different conditions (WT, gprk2 RNAi, 𝛼PS3-/- and 𝛼PS3-/- + gprk2 RNAi)\nwas shown in Fig. S7E (now S8E). However, the length scale of the gradient also depends on signal\namplification in a complex manner (for instance in 𝛼PS3-/- embryos the gradient length scale increases\nbecause the max amplitude is lower, consistent with the reported MyoII amplification role of\nintegrins, but the range of the gradient is simiar to WT). Therefore, we found more useful to\nstatistically compare amplitude and range of the MyoII gradient to describe the effects of integrins\non the expanded gradient in gprk2 RNAi embryos. Our data show that the higher amplitude and range\nof the gradient observed upon hyperactivation of Fog-GPCR signalling (gprk2 RNAi) requires\nintegrins, which supports the conclusion that integrins do not simply amplify MyoII activation in the\ncell near the invagination front (as previously reported in Collinet et al. 2024, PMID: 38103554) but\n15\nalso further away from the invagination in the field of cells where the MyoII gradient is observed. As\nsuch, integrins are part of the multicellular gradient formation mechanism described in this study.\nFurthermore, what's the relationship between Smog and the Integrins? Finally, could the needed role\nof integrins for Fog gradient formation be purely mechanical, to ensure Fog confinement into a small\nextracellular region and generate higher probability of interactions between Fog and its receptor\nSmog?\nResponse: We agree with the reviewer that the relationship between Smog activation and integrins\nrequired further investigation. In particular, the reviewer wonders whether the binding of integrins to\nthe VM could generate small micro-environments that confine Fog diffusion in the extracellular space\nand increase the probability of binding to the receptor. Trapping of Fog in such micro-environments\nwould reduce the diffusivity D of the unbound fraction of Fog (Fog ), if they were in the range\nfast fast\nof few hundreds of nm (i.e. the size of the focal volume or smaller). However, in the PZ the measured\nD is not different from that measured in the extracellular space in the invagination where the ligand\nfast\nFog can freely diffuse, indicating that, if present, such regions of confinement cannot be that small.\nModelling and molecular simulations would be required to determine the size at which such micro-\nenvironments would increase the probability of binding between Fog and its receptor. Furthermore,\nsince Fog , the fraction that forms a gradient,shows similar diffusivity as its receptor Smog and\nslow\nsince this fraction responds to modification of the surface abundance of the receptor (gprk2 RNAi),\nwe believe that Fog represents a membrane-bound fraction rather than a fraction trapped in the\nslow\nextracellular space by integrins. Together, although we cannot completely exclude it, we believe it is\nunlikely that integrins contribute to the mechanism of gradient formation by confining Fog into small\nmicro-environments.\nInstead, as also suggested by reviewer 2, we tested whether integrins directly promote Smog receptor\noligomerization or act as co-factors for downstream signalling to MyoII as part of the mechanism of\ngradient formation (see also response to point 3 from reviewer 2 above). Our results (included in a\nnew Fig.S8F and also described above) indicate that integrins do not directly promote Smog\nclustering but rather promote GPCR signalling by acting as co-factor for receptor activation. We\nincluded these new results in a new Fig. S8F and in the main text pp.9, lines 23-31.\n“Next, we tested whether integrins directly promote oligomerization of GPCR and thus MyoII\nrecruitment as part of the gradient formation mechanism. We measured the molecular brightness of\nSmog::GFP by FCS in the PZ of embryos depleted of the 𝛼PS3 integrin Scab by RNAi (𝛼PS3 RNAi)\nand we found that this was not decreased compared to WT embryos (Fig. S8F), contrary to what was\nexpected if integrins promoted Smog oligomerization. Smog::GFP molecular brightness was instead\nincreased in the PZ compared to WT, suggesting that in the absence of integrins Smog::GFP\noligomers are no longer active (decreased MyoII levels, despite higher molecular clustering). We\nconclude that integrins do not directly promote GPCR clustering as part of the gradient formation\nmechanism but rather they act as co-factors for GPCR oligomer signalling.”\nMinor points:\nMyo2 ‘activation’ - shouldn’t this in fact be called recruitment? As Myo2 is being recruited to the\ncortex. Activation assumes phosphorylation and contractility, which of course are related, but are not\nshown by the transgenic line.\nResponse: The reviewer is right in noting that our measurements of MyoII intensity specifically\nmeasure the cortical recruitment of MyoII minifilaments and not its activation by phosphorylation.\ndephosphorylated MyoII is almost immediately removed from the cortex. This is based on our previus\nexperiments showing that blocking the Rho kinase ROCK using chemical inhibitors leads to an\nalmost complete disappearance of MRLC::mCherry signal from the cortex within seconds (see for\ninstance Fig. 3 in Bailles and Collinet et al., 2019 Nature, PMID: 31413363 or Fig. 4 in Collinet et\nal., 2024 PMID: 38103554). Here, with the term “activation” we refer to the process of MyoII\nactivation by signalling which ultimately leads to recruitment of MyoII to the apical cell cortex. We\nagree that the term “cortical recruitment” is more accurate when we describe our data and we changed\n“activation” to “cortical recruitment” in the manuscript wherever this was relevant.\nFig.3B - Please do fits to the individual datapoints of the cumulative 4 measured embryos, not to their\nmean intensity, as this is misleading to the R2 value obtained.\nResponse: We thank the reviewer for this comment which arises from the legend of Fig. 3B which\nwas not sufficiently clear. The R2 shown in Fig. 3B is not the R2 of the fit of the mean of the 4 embryos,\nbut rather the R2 of the exponential fit from all the data points of the 4 embryos. We have now\ncorrected the legend (pp.21, lines 4-7) to specify this more clearly.\n“(B): Spatial profile of MyoII mean intensity in the PZ in 4 WT embryos along with their fit with a\nsingle exponential decay function (red dashed line). n=4 embryos. The fit was performed on the\nindividual datapoints from all the 4 embryos.”\nIn addition, since the R2 value can be reduced due to embryo-to-embryo variations (e.g. due to the\nexpression of the transgene (MRLC::mCherry) and/or fluctuations in the laser intensity across\ndifferent imaged embryos) while each individual embryo could still very well fit an exponential\ndecay, we decided to also show in a new Fig. S4A the MyoII intensity profiles and their fits in the 4\nindividual embryos. Notably, the R² values obtained from the individual fits are 0.9944,\n0.993,\n0.9923, and 0.9786, yielding a mean R2 ± SD of 0.9892 ± 0.0080, which is greater the R2 obtained\nby fitting the pooled data of the 4 embryos. This indicates that the MyoII intensity profile in each\nembryo very well fits an exponential decay\nFig. S4A\n15\n10\n5\n0\n0 10 20 30 40\nDistance from invagination (μm)\n17\n)U.A(\nytisnetni\nnaem\nIIoyM\n15\nembryo #1 embryo #2 embryo #3 embryo #4\n10\n5\n0\n0 10 20 30 40\nDistance from invagination (μm)\n)U.A(\nytisnetni\nnaem\nIIoyM\n15\n10\n5\n0\n0 10 20 30 40\nDistance from invagination (μm)\n)U.A(\nytisnetni\nnaem\nIIoyM\n15\n10\n5\n0\n0 10 20 30 40\nDistance from invagination (μm)\n)U.A(\nytisnetni\nnaem\nIIoyM\nExponential decay fit, Exponential decay fit, Exponential decay fit, Exponential decay fit,\nR2 =0.9944 R2 =0.9930 R2 =09923 R2 =0.9786\n18"}, {"round": 2, "reviewer_comments": {"reviewer_1": "Upon revision, the authors have remarkably addressed all issues I pointed, and I find it is acceptable for publication.", "reviewer_2": "All concerns have been addressed. This paper is very thorough and I recommend it for publication.", "reviewer_3": "I appreciate the authors comments and efforts to improve the paper. They authors have constructively responded to\ncomments with new data and changes to the manuscript. The manuscript is much improved and I have no further questions."}, "author_response": ""}]

Decision letter