(2023) 24:32
Zhao et al. BMC Molecular and Cell Biology
https://doi.org/10.1186/s12860-023-00492-3

BMC Molecular andCell Biology

Open Access

RESEARCH

From network analysis to experimental
validation: identification of regulatorsof non‑muscle myosin II contractility using
the folded‑gastrulation signaling pathway
Andy Zhao1, Sophia Varady1, Madelyn O’Kelley‑Bangsberg1, Vicki Deng1, Amy Platenkamp1, Petra Wijngaard1,
Miriam Bern1, Wyatt Gormley1, Elaine Kushkowski1, Kat Thompson1, Logan Tibbetts1, A. Tamar Conner1,
David Noeckel1, Aidan Teran1, Anna Ritz1*† and Derek A. Applewhite1*†

Abstract
The morphogenetic process of apical constriction, which relies on non-muscle myosin II (NMII) generated constrictionof apical domains of epithelial cells, is key to the development of complex cellular patterns. Apical constriction occurs
in almost all multicellular organisms, but one of the most well-characterized systems is the Folded-gastrulation (Fog)induced apical constriction that occurs in Drosophila. The binding of Fog to its cognizant receptors Mist/Smog results
in a signaling cascade that leads to the activation of NMII-generated contractility. Despite our knowledge of key
molecular players involved in Fog signaling, we sought to explore whether other proteins have an undiscovered rolein its regulation. We developed a computational method to predict unidentified candidate NMII regulators using
a network of pairwise protein–protein interactions called an interactome. We first constructed a Drosophila interac‑
tome of over 500,000 protein–protein interactions from several databases that curate high-throughput experiments.
Next, we implemented several graph-based algorithms that predicted 14 proteins potentially involved in Fog signal‑
ing. To test these candidates, we used RNAi depletion in combination with a cellular contractility assay in Drosophila
S2R + cells, which respond to Fog by contracting in a stereotypical manner. Of the candidates we screened using thisassay, two proteins, the serine/threonine phosphatase Flapwing and the putative guanylate kinase CG11811 were
demonstrated to inhibit cellular contractility when depleted, suggestive of their roles as novel regulators of the Fog
pathway.
Keywords Non-muscle myosin II, Contractility, Folded gastrulation, Apical constriction, Drosophila melanogaster†Anna Ritz and Derek A. Applewhite contributed equally to this project.

*Correspondence:
Anna Ritz
aritz@reed.edu
Derek A. Applewhite
applewhd@reed.edu
1
Reed College Department of Biology, 3203 SE Woodstock Blvd, Portland,
OR 97202, USA

Background
Apical constriction, the asymmetric constriction of theapical domain of polarized epithelial cells, is criticalto a number of physiological processes. During vertebrate development, the cells at the hinge of the neuralgroove apically constrict to form the neural tube [1]while individual neural crest cells delaminate from theneural plate via apical constriction driven ingression,
allowing them to migrate and differentiate into distinct
structures [2]. Tissue folding and invagination, also

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Zhao et al. BMC Molecular and Cell Biology(2023) 24:32

driven by apical constriction, facilitate the internalization of mesoderm and endoderm precursor cells during
gastrulation for both vertebrates and invertebrates [3,4]. Finally, the apical constriction of cells below the top
sheet of the epidermis and around the periphery of thewound allows for wound contraction and healing [5].
Apical constriction is largely driven by non-muscle
myosin II (NM II) which, when coupled with the actin
cytoskeleton, forms an actomyosin network which candrive contractility. The NMII holoenzyme is a hexamer
consisting of two heavy chains, each of which is madeup of a globular head domain where ATP hydrolysis
and actin binding occurs as well as a central rod domain
which facilitates oligomerization, two essential lightchains (ELCs), which are thought to play structural roles,
and two regulatory light chains (RLCs), which play arole in regulating the activation-inactivation cycle of the
motor protein. NMII is maintained in an autoinhibited,
inactive conformation due to interactions between thehead tail of the molecule [6], preventing oligomerization
and actin binding. Upon phosphorylation of the RLC,
the autoinhibition is relieved and NMII heavy chain
opens into an assembly-competent form, oligomerizing
and associating with actin in an antiparallel orientation.
Hydrolysis of ATP leads to the sliding of actin filaments
and the subsequent generation of force [7].
Studies in Drosophila have contributed immensely
to our understanding of apical constriction. In particular, decades of research on ventral furrow formation
during gastrulation has pieced together the pathway
from cell fate specification via transcription factors andG-protein coupled receptor (GPCR) signaling cascades,
to force generation and cell shape change. Ventral furrow formation is regulated by the Folded-gastrulation(Fog) pathway, whereby binding of the secreted ligand
Fog by the heterotrimeric GPCRs Mist and Smog, triggers the exchange of GDP for GTP in the ­G⍺13 subunit,
Concertina (Cta), leading to its activation and subsequent disassociation from the complex [8]. Simultaneously, T48, a transmembrane protein which localizes tothe apical domain, recruits a Rho Guanine Exchange Factor, RhoGEF2. Cta binds to RhoGEF2, which promotes
the recruitment and activation of Rho1 [9]. Rho1 activates Rho kinase (Rok), which phosphorylates the RLC,
encoded by spaghetti squash (sqh), in flies [6], activating
the NMII complex. Additionally, Rok phosphorylates amyosin phosphatase complex, creating a conformationalchange which inhibits phosphatase activity [10]. Withan actomyosin meshwork situated on the apical surface,ratchet-like pulses of apical constriction drive a reduction in apical surface area [11]. This contractile network
pulls on apical adherens junctions, which transmit the

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force across sheets of cells, coordinating morphologicalchanges across tissues.
While the Fog pathway has been elegantly pieced
together through many decades of research, we wondered
if there were other molecular players in the pathway thatstill remained unidentified. While much of our understanding of ventral furrow formation was gleaned fromexperiments using Drosophila embryos, cultured Drosophila melanogaster cells also serve as a useful model.
Robust whole genome-wide screens, capitalizing on thesensitivity of S2 and S2R + cells to RNAi, have furthered
our understanding of many developmental processes
including the Fog pathway [12–17]. In fact, Mist, the long
predicted GPCR that binds Fog, was uncovered in a cellbased contractility assay in S2R + cells [18]. Despite theease and efficacy of whole genome-wide screens using
Drosophila tissue culture cells, they remain time consuming and expensive. Furthermore, genome-wide screensproduce many false positives, the validation of whichwastes valuable resources and time [19, 20]. A computerbased protein–protein interactome network analysis is an
attractive alternative to genome-wide screening and has
been used as a discovery tool for the identification of newmolecular partners [21–23]. When coupled with a robust
bench assay, network analysis has proven to be a cheaper,
faster replacement to whole genome-wide screening.
Drosophila S2R + cells recapitulate the Fog signaling
pathway despite lacking the apical-basal polarity of epithelial cells. The addition of Fog-conditioned media leads to anincrease in phosphorylated Sqh and the contraction of theperinuclear actomyosin network, leading to a constriction
characterized by phase-dark and phase-light ruffles when
imaged through phase-contrast microscopy and linear
actin structures radiating from the nucleus, or “star-bursts,”
when stained with fluorescently labeled phalloidin [16, 18].
We developed a computational pipeline to identify
potential new regulators of NMII within the context of
Fog signaling. Using a custom-built Drosophila interactome, which represents 233,054 experimentally-observed
protein–protein interactions among 11,473 proteins, wedeveloped three network-based methods to identify potential candidate regulators of Fog signaling. After combining
the outputs of the methods and curating this ranked list ofpredictions to ensure that the candidates are expressed in
S2R + cells, we ended with a list of 14 predicted regulators
not previously associated with the Fog pathway (our protein candidates). We depleted these 14 candidates using
RNAi in S2R + cells and performed a cellular contractility
assay to elucidate the validity of their putative regulatory
function. A secondary screen helped to eliminate a false
positive from the initial results, leaving us with two potential novel regulators of the Fog pathway- Flapwing (Flw),a catalytic subunit (PP1c) of the Protein Phosphatase typeZhao et al. BMC Molecular and Cell Biology(2023) 24:32

1 (PP1), as well as CG11811, a putative guanylate kinase.
Depletion of these targets led to a decrease in the fractionof the cells that underwent contractility in response to theapplication of Fog-conditioned media, suggesting theirrole as regulators of NMII contractility.

Results
A computational framework for predicting potential
regulators of Fog signaling

Before we describe the computational methods we used
to predict candidate Fog signaling regulators, we first built
a comprehensive resource of Drosophila protein–protein
interactions from the literature. This information canbe represented as a protein–protein interactome, which
describes a set of proteins and connections between those
proteins. An interactome is mathematically formulated
as a graph, where the nodes (proteins) are connected byedges (protein–protein interactions). We combined six
existing Drosophila resources of protein–protein interaction data to build an interactome with 11,473 nodes andPage 3 of 21

233,054 undirected edges, ignoring self-loops and mapping all proteins to FlyBase identifiers (Table 1). Eachedge in the interactome may be supported by multiple
databases; while DroID contributes the largest amount of
data, 10,654 edges (5%) are not supported by DroID andwould be missed by a single-database analysis. Each edgein the interactome includes literature citations as well assources of experimental or other evidence. In total, theinteractions are supported by 21,065 pubmed-indexed
literature citations and 58 PSI-MI evidence sources [24].
Our computational goal was to identify candidates of
NMII regulation by Fog signaling using this fly-specificinteractome. We began by pooling three sets of proteins thatare known to be relevant to Fog signaling [18], apical constriction (Gene Ontology 0003383), and gastrulation (Gene
Ontology 0007369) to produce a list of 104 known protein
regulators (called positives; Supplementary Table 2). Using
the fly interactome, we applied three graph algorithms toidentify protein candidates that are “near” the known protein regulators (Fig. 1). The Steiner Tree Approximation

Table 1 Data sources used to build the fly interactome
Data Source

Data Source Type# ­Interactionsa

FlyMine [25]Data warehouse for functional genomic & proteomic datasets

278,370

DroID [26]Experimentally detected interactions curated from literature and external databases

262,179

Mentha [27]Experimentally detected physical interactions

45,669MyProteinNet [28]Protein interaction data compiled from 11 PPI databases

41,530SignaLink 2 [29]Protein interactions curated from literature, integration of interactions from PPI databases,
and integration of transcription factors and their regulatory interactions

5,236

FlyReactome [30]Expert-authored knowledgebase of reactions and pathways612

aAccessed fall of 2017

Fig. 1 Graph algorithms used to identify protein candidates. A Algorithms developed to identify candidate proteins from the interactome
and the positive proteins. Brighter/lighter green indicates a higher Ranked Paths score. B Venn diagram of protein candidates from each method;
see Supplementary Table 1 for candidate list

Zhao et al. BMC Molecular and Cell Biology(2023) 24:32

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method aims to connect the positives with as few edges as
possible; candidates are nodes that are used to connect thepositives. The Paths to NMII method calculates the shortest path from each positive to Sqh; candidates are nodes
that are on these paths. Finally, the Ranked Paths method
calculates, for each unlabeled node, the shortest path fromeach positive to that node and assigns a score that is proportional to the average length of all shortest paths. All nodes in
the Ranked Paths method are ranked; the nodes with a normalized score greater than 0.7 were selected as candidates(Fig. 1A). See the Methods for more details about the graphalgorithms employed.
There is surprisingly little overlap among the candidates from the three methods (Fig. 1B). This is due tothe differences in each method’s goal. For example, a
Steiner tree will connect positives using the fewest possible non-positive nodes, so these predictions are often
connected to many known positives (Supplementary
Fig. 1). Calculating the paths from positives to NMII, on
the other hand, may have multiple predictions on eachpath (though many of these paths overlap in practice, see
Supplementary Fig. 2). Finally, the node score from theranked paths approach indicates how close the node is to
all positives, so the top predictions tend to be clustered
together (Supplementary Fig. 3). A combined network of
all predicted nodes, along with the adjacent positives, isshown in Supplementary Fig. 4.
Only four candidates, Cell division cycle 5 (Cdc5),
Netrin-B (NetB), Spinophilin (Spn), and Ubiquitin-63E(Ubi-p63E), were identified by all three methods. NetB
is not expressed in S2R + cells and thus was not pursued
further [31]. Depletion of Spn is known to inhibit cellular contractility as assessed by a cellular contractility
assay ([32], for details see below and Methods section)
while depletion of Ubi-p63E and Cdc5 led to apoptosis preventing further assessment (unpublished data).
Seventeen candidates were identified by two or moremethods, and a total of 99 protein candidates were identified by any of the methods (Supplementary Table 2 andSupplementary Fig. 4). We excluded candidates that had
already been shown to be a part of the Fog signaling pathway or were known Sqh interactors. As our contractility
assay was performed in S2R + cells we further excluded
candidates that were not expressed in these cells according to Harvard Fly RNAi database [17]. From this list of
candidates 13 proteins were selected as promising forfollow-up study (Table 2).
Preliminary screen of computational putative targets

We initially screened all 13 candidates (Table 2) using acellular contractility assay [16, 34], which capitalizes on the
Fog-signaling pathway [35]. Briefly, this cell-based contractility assay uses exogenously expressed Fog-myc harvested
from a stable S2:Fog-myc cell line, that, when applied to
S2R + cells, leads to the phosphorylation of Sqh via Rho1
signaling, leading to the contraction of cells [18, 34]. Indicative of this contractility is the observation of phase-dark
and phase-light ruffling or “bonneting” when imaged byphase-contrast microscopy (Fig. 2A, B). S2R + cells weretreated with RNAi for seven days and then challenged tocontract. Following this initial screen we identified two
candidates that failed to contract following perfusion
with Fog: the PP1 catalytic subunit Flapwing (Flw) andCG10347 which, according bioinformatic queries, is putatively a member of the heat shock protein (HSP) 20 family. Both Flw and CG10347 were predicted by the Steinerand Paths to NMII methods. A second round of computational predictions that used hits from Table 2 as positive proteins revealed CG11811 (hereafter we refer to as
Oya [oya]) which, according to bioinformatic queries andprevious studies, is a guanylate kinase [36, 37]. We havesubsequently named CG11811 the putative Drosophila
guanylate kinase, Oya after the Yoruba goddess of water
who is associated with fertility and acts of creation. While
Oya wasn’t predicted in the first round of candidates, it has
been shown to directly interact with Sqh via coimmunoprecipitation [38] (Supplementary Fig. 5).

Table 2 Preliminary screen of computational identified candidates
Drosophila gene name

SymbolaCG ­Numberb

Drosophila gene name

SymbolaCG ­Numberb

Ras opposite

Rop

CG15811

Meltrin

Meltrin

CG764914–3-3zeta

14–3-3zeta

CG17870

microtubule star

mts

CG7109CG10347

CG10347

CG10347

Kinesin heavy chain

Khc

CG7765Tenascin major

Ten-mCG5723corkscrew

csw

CG3954

flapwing

flw

CG2096seven in absentia

SinA

CG9949downstream of receptor kinase

drk

CG6033

Calcineurin B2

CanB2

CG11217

grouchogro

CG8384aGene symbol from Flybase [33]B

Annotation symbol from Flybase [33]Zhao et al. BMC Molecular and Cell Biology(2023) 24:32

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Fig. 2 Computationally identified candidates lead to the inhibition of NMII contractility. A-H Phase-contrast images of S2R + cells treatedwith control (A and B), CG10374 (C and D), Flapwing (E and F), or Oya (G and H) RNAi. Cells were either perfused with control cell media (A, C, E, G)or treated with Fog-conditioned media (B, D, F, H); yellow arrows indicate cells that have contracted following Fog perfusion. The white box in (A)denotes uncontracted cells shown at higher magnification while the yellow box in (B) denotes the contracted cells shown at higher magnification(right). Scale bars 10 µm. I-K Scatter plots quantifying the fraction of contracted cells with and without Fog perfusion following RNAi treatmentswith CG10274 RNAi (I), Flapwing RNAi (J), and Oya RNAi (K). I There was no statistically significant difference between control and CG10274 RNAicells with and without Fog treatment, however in there was an inhibition of cellular contractility following RNAi treatment with Flapwing and Oya
as compared to control RNAi treated cells (****p-values < 0.0001, Student’s T-test, N = 3). (L & M) Results from RT-qPCR indicating the efficacy of RNAitreatments for cells treated with Flapwing RNAi (L) and Oya (M). There was a statistically significant reduction in Flw mRNA as compared to control
treatments (****p-value > 0.0001, Student’s t-test, N = 3), similarly, we observed a statistically significant reduction in Oya mRNA following RNAitreatment as compared to controls (** p-value = 0.0024, ****p-value > 0.0001, one-way ANOVA, N = 5)

Using an identical protocol we performed a secondary screen focused on these three candidates (Flw,
CG10347, and Oya). This screen revealed that cells
depleted of CG10347 still contracted, with the fractionof contracted cells following the addition of Fog not statistically different from that of control RNAi treated cells(Fig. 2A-D & I), while cells depleted of Flw and Oya failed
to substantially contract following perfusion with Fog (pvalue < 0.0001, Student’s t-test, N = 3, N = 54–129 image
fields per condition) (Fig. 2E-H, J & K). Given that theRNAi depletion CG10347 failed to inhibit contractility inthis assay we decided to no longer pursue this candidate.
To determine the efficacy of our RNAi depletion, we
performed three independent rounds of reverse transcription quantitative PCR (RT-qPCR) to determine thetranscriptional abundance of either flw or oya in ourRNAi treated cells. Cells treated with either flw, oya, oroya 5’-untranslated region (UTR) RNAi were comparedagainst control treated cells using elongation factor-1(ef1) as an internal reference (Fig. 2L, M). The RT-qPCR
data showed a statistically significant decrease in flw

mRNA as compared to control RNAi treated samples(p-value < 0.001, Student’s t-test, N = 3) (Fig. 1L). Similarly, in cells treated with dsRNA directed against either
coding region or 5’ UTR of oya we observed a statisticallysignificant decrease in oya mRNA as compared to control RNAi treated samples albeit, the dsRNA targetingthe coding region showed a far more substantial decrease(p-value = 0.0024, and p-value > 0.0001 for 5’UTR andthe coding region dsRNA respectively, ANOVA, N = 5).
Given the results of initial contractility assay and efficacy of our RNAi we decided to explore further how thedepletion of Flw and Oya can lead to a decrease in NMII
contractility.
Oya depletion affects the abundance and spatial distributionof phosphomyosin

In order to further probe the hypocontractility observed
upon depletion of CG1181, we investigated both theabundance and spatial distribution of phosphomyosin in CG1181 depleted cells. To this end, cells weretreated with either control, Oya, or Sqh RNAi for sevenZhao et al. BMC Molecular and Cell Biology(2023) 24:32

days and then challenged to contract by perfusion of
Fog-enriched media. We then immunostained the cellswith an antibody raised against a synthetic phosphopeptide mimicking phosphorylated NMII [39] as well as
fluorescently-labeled phalloidin to visualize actin filaments (Fig. 3A-C). Imaging the cells by epifluorescencemicroscopy, we quantified the phosphomyosin staining following the Fog and RNAi treatments (Fig. 3D, E).
This analysis revealed that Oya depleted cells have levels of phosphomyosin that are significantly lower thanthose of control treated cells but greater than that of Sqh
RNAi treated cells (p-value < 0.00062, ordinary one-way
ANOVA, N = 3, N = 289–510 cells) (Fig. 3D). Taking thePage 6 of 21

fluorescence as an assay of phosphomyosin abundance,such a result suggests that, upon Fog induction, theamount of active, phosphorylated myosin is reduced in
Oya depleted cells, likely contributing to cellular hypocontractility. However, Oya depletion does not completely
inhibit the phosphorylation of the regulatory light chain,
as evidenced by the significantly higher fluorescence values compared to Sqh depleted cells.
We then turned to probing the effects of Oya knockdown on the spatial distribution of phosphomyosin.
Following Fog treatment in S2R + cells the NMII network reorganizes assembling into ordered peri-nuclear
structures such as rings which are highly reminiscentFig. 3 RNAi depletion of Oya disrupts the localization of phosphorylated non-muscle myosin II. A-C Drosophila S2R + cells stained for actin(right panel, cyan in merged image) and phosphomyosin (middle panel, red in merged image) treated with (A) control, (B) Oya, or (C) Sqh
RNAi. Yellow arrowheads denote coalesced phosphomyosin contractile network while cyan arrowheads denote a dispersed network. Scalebar is 10 µm. D The quantification of the ratio of the fluorescent intensities of phoshomyosin to actin following RNAi treatments. The mean (± SEM)
phosphomyosin:actin ratio in Oya depleted cells (magenta circles) was statistically significantly lower than that of control treated cells (yellow
circles) but greater than that of Sqh depleted cells (blue circles) (**** p < 0.0001, one-way ANOVA with Tukey’s post-hoc analysis). (E) Quantificationof the coalescence index measuring the degree of phosphomyosin coalescence in cells treated with control (yellow circles), Oya (magenta circles),and Sqh RNAi (blue circles). The mean (± SEM) coalescence in Oya depleted cells is less than control treated cells yet higher than Sqh depleted cells(** p-value = 0.00295, **** p-value < 0.0001, one-way ANOVA with Tukey’s post-hoc analysis N = 3)

Zhao et al. BMC Molecular and Cell Biology(2023) 24:32

of medioapical polarization observed in epithelial cells
in vivo [40]. In order to quantify NMII distribution weused a previously established coalescence index [39, 41].
The higher coalescence index value the more organized (or less diffuse) the actomyosin network. This
analysis reveals that Oya depletion results in a phosphomyosin distribution pattern significantly more diffuse
than control RNAi treated cells yet more ordered thanSqh RNAi treated cells, mirroring the same pattern weobserved in quantifying the amount of phosphomyosin(p-value = 0.002947, Ordinary one-way ANOVA, N = 3,
N = 135–161 cells) (Fig. 3E). Thus, phosphomyosin distribution of Oya treated cells presented an “intermediate”
phenotype where certain cells (Fig. 3B, yellow arrows)
managed to recruit the ring of phosphomyosin seen incontrol treated cells while others displayed diffuse patterns of phosphomyosin (Fig. 3B, red arrows) similar to
Sqh dsRNA treated cells. It also should be noted that
depletion of Sqh leads to failed cytokinesis and subsequently larger cells. In addition we also imaged the distribution of EGFP-tagged Sqh in live cells following control,Oya, or Sqh dsRNA treatment (Supplemental Fig. 6A-D).
Similar to our phosphmyosin staining, we observed anintermediate, diffuse pattern of Sqh-EGFP following Oyadepletion whereas a clear perinuclear organization of Sqh
can be observed in control depleted cells (Supplementary
Fig. 6A, C and D). Depletion of Sqh led to a highly diffuse NMII pattern as expected (Supplementary Fig. 6B).
Together, Oya knockdown appears to have a moderate
effect on the recruitment and organization of NMII filaments, reducing the abundance of phosphomyosin while
inhibiting the assembly of higher-order myosin structures which is likely contributing to the hypocontractility
phenotype we observed in contractility assay.
Depletion of Oya may alter cytoplasmic concentrations GTP

Previous studies suggested Oya is a putative guanylatekinase due to its sequence similarity with mammalianguanylate kinases as well as its ability to phosphorylate GMP and dGMP using ATP as a phosphate donor[42]. Considering that depletion of guanylate kinases has
been demonstrated to decrease intracellular concentrations of both GDP and GTP [43] as well as the centrality of GTP-mediated signaling in the Fog pathway via
the Rho family of GTPases, we hypothesized that Oya’s
regulation of the pathway could be due its putativerole in regulating intracellular GTP levels. To test thishypothesis, we treated cells with control or Oya RNAi,or treated them with Mizoribine selective inhibitor of
inosine-5’-monophosphate dehydrogenase (IMPDH)
and guanosine monophosphate synthetase as a control
and then fixed and stained the cells with an anti-tubulin
antibody (Supplemental Fig. 7). Given the importance ofPage 7 of 21

GTP-tubulin to the formation of microtubules, changes
to microtubule fluorescence intensity would be an indicator of cytoplasmic GTP levels. We quantified anti-tubulin fluorescence intensity and found that both treatment
with Oya RNAi and Mizoribine led to a statisticallysignificant decrease in tubulin staining as compared
to control treated samples (p-value = 0.0001 One-way
ANOVA, N = 38–84 cells) (Supplemental Fig. 7). Further,
Oya RNAi and Mizoribine treated cells were statisticallyindistinguishable suggesting that RNAi Oya affects theincorporation of tubulin subunits into microtubules possibly through lowering cytoplasmic GTP levels. In termsof the NMII contractility and the Fog pathway, decreased
cytoplasmic levels could affect the function of Rho family GTPases. This possibility warrants further exploration
but is beyond the scope of this study. For the remainderof this study we shifted our focus to the PP1 complex
component Flw.
Depletion of other PP1 components does not phenocopy
depletion of Flapwing

Nurse cells mutant for flw exhibited hyper-contracted
ring canals [44], and thus our results from the cellular
contractility assay (Fig. 1E, F & J) are seemingly contradictory given that PP1 complex is commonly understoodto function as phosphatase for targets such as the regulatory light chain of NMII. In order to further interrogate role of the PP1 complex in cellular contractility, we
turned our attention to myosin binding subunit (MBS)and myosin phosphatase targeting subunit 75D (MYPT75D) which are both involved in the targeting of the PP1complex [43–45]. MYPT-75D contains a prenylation
motif suggesting that it may be involved in membranetargeting while MBS lacks this prenylation [44–46]. We
depleted MBS, MYPT-75D, and Flw, as well as Flw andMBS, and Flw and MYPT-75D in combination, and following our cellular contractility assay, challenged thecells to contract upon the addition of Fog (Fig. 4A-G).
We again observed a statistically significant inhibition ofcellular contractility following the addition of Fog in cells
depleted of Flw as compared to control RNAi treatedcells (p-value < 0.0001, ANOVA, N = 3–4, N = 50–75 cells)
however, depletion of MBS failed to inhibit contractility
and was no different from control RNAi treated samples(Fig. 4A-C & H). Depletion of MYPT-75D yielded mixed
phenotype, with the fraction of cells undergoing contractility following the addition of Fog being statistically different from both control RNAi treated cells and that of
Flw RNAi treated cells (p-values = 0.0389 and < 0.0001
respectively, ANOVA N = 3, N = 25–40 image fields)(Fig. 4A-E & H). Double depletion of Flw and MBS and
Flw and MYPT-75D did lead to a slight rescue, withthe fraction of contracted cells following Fog perfusion

Zhao et al. BMC Molecular and Cell Biology(2023) 24:32

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Fig. 4 Depletion of PP1 complex has distinct effects S2R + cellular contractility. A-G Phase-contrast imaging of S2R + cells from the cellular
contractility assay (A) in the absence of Fog or (B-G) following the perfusion of Fog. Cells were treated with (A and B) control, (C) MBS, (D) Flw, (E)
MYPT-75D RNAi or double-depleted with (F) Flw and MBS, or (G) Flw and MYPT-75D RNAi. The white box in (A) denotes an uncontracted cell shownat higher magnification while the yellow box in (B) denotes a contracted cell shown at higher magnification.Yellow arrowheads indicate contracted
cells, cyan arrowheads indicate rounded cells. Scale bars 10 µm. H and I Quantification of the mean (± SEM) fraction of contracted cells and fractionof rounded cells following treatment with control (yellow circles), Flw (blue circles), MBS (green circles), MYPT-75D (peach circles), Flw and MBS(magenta circles), and Flw and MYPT-75D RNAi (purple circles). The fraction of Flw depleted cells was statistically significantly lower than all otherconditions, while depletion of MBS was no different than control RNAi treated samples. Depletion of MYPT-75D was also statistically significant
from both control RNAi and Flw RNAi treated cells showing an intermediate phenotype. Double depletion of Flw and MBS and Flw and MYPT-75D
also showed an intermediate phenotype, being statistically significantly different from control RNAi as well as single RNAi treatments of Flw, MBS,and MYPT-75D (**p-value = 0.0090,***p-value = 0.0004, ****p-value < 0.0001, one-way ANOVA with Kruskal–Wallis test, N = 3). (I) We observed
a statistically significant increase in the fraction of rounded cells following Flw RNAi as compared to all other conditions. The fraction of rounded
cells following MBS or MYPT-75D depletion was no different than control RNAi treated cells, while double depletion led to an intermediatecell rounding phenotype statistically significantly different from that of Flw, MBS, or MYPT-75D RNAi treated cell, as well as control cells(*p-value = 0.0417, ***p-value = 0.002, ****p-value < 0.0001, one-way ANOVA with Kruskal–Wallis test, N = 3)

being statistically significant from both Flw RNAi(p-value = 0.004 and 0.0017, respectively ANOVA, N = 3,
N = 25–40 image fields), but this rescue was incomplete
as these RNAi conditions were as also statistically different from control RNAi treated cells (p-value < 0.0001,

ANOVA, N = 3, N = 25–40 image fields) (Fig. 4A-H).
These results indicate that despite potentially being a partof the same complex, the targeting subunits, MBS and
MYPT-75D, and the catalytic subunit, Flw play differential roles in regulating the contractility of cells.

Zhao et al. BMC Molecular and Cell Biology(2023) 24:32

and I). Double depletion of Flw and MBS and Flw
and MYPT-75D increased the number of rounded cells,
indicative of Flw’s penetrance (Fig. 4F, G and I). These
results closely mirror that of our contractility assay andagain point to differences in the regulation of cellular
contractility between PP1 components.
Flapwing, MYPT‑75D, and MBS have distinct localization
patterns and differentially regulate phosphomyosin
distribution

NMII generated contractility relies on the concerted
phosphorylation of the regulatory light chain, which
results in a conformational change to the overall NMII
holoenzyme allowing for oligomerization and the subsequent binding and contraction of actin filaments. Traditionally, the activity of phosphatases such as the PP1complex, opposes this phosphorylation and maintains
the NMII holoenzyme in the “closed” conformation.
Previously it has been reported that MYPT-75D localizes to the cell membrane [47] while MBS can be found

Page 9 of 21

throughout the cytoplasm [46]. As it is likely that these
spatial differences add yet another level of regulation,
we sought to determine if these localization patterns
are consistent in S2R + cells (Fig. 5). Consistently, weobserved Flw forming a ring inside the peri-nuclear network of NMII in the center of these cells (Fig. 5A). This
is in contrast to MBS, which rather than forming a distinct ring, could be found in the center of the this perinuclear NMII ring as an amorphous cloud (Fig. 5B), while
MYPT-75D had a more global localization pattern forming a haze throughout the cytoplasm (Fig. 5C). Thus, itappears that there are differences in localization between
these components of the PP1 complex which may translate to differences in the regulation of NMII contractility.
The distinct localization of PP1 complex proteins
likely influences the spatiotemporal distribution ofphosphorylated NMII regulatory light chain, andthus cellular contractility. To interrogate the distribution of phosphorylated NMII regulatory light chain
we depleted S2R + cells of Flw, MBS, MYPT-75D or

Fig. 5 PP1 complex members have distinct localization patterns. A-C S2R + cells co-expressing TagRFP-tagged (A) Flapwing (left panel, cyan
in merge), (B) MBS (left panel, cyan in merge), or (C) MYPT-75D (left panel, cyan in merge) with SQH-EGFP (middle panel, red in merge) imaged
by TIRF microscopy. Yellow lines indicate a representative region of interest where line scans were taken and graphically represented in D-F. Scalebar 10 µm. D-F Line scans from cells co-expressing TagRFP-tagged (D) Flw, (E) MBS, and (F) MYPT-75D (all in cyan), with EGFP-Sqh (red). Graphs
represent the Normalized Fluorescent Intensity of 7–10 cells per condition

Zhao et al. BMC Molecular and Cell Biology(2023) 24:32

treated them with control RNAi, and then immunostained them using an anti-phosphomyosin antibody we previously used (Fig. 6A-D). Following
fixation and immunostaining we imaged and quantified the mean fluorescence pixel intensity of cells
depleted of Flw, MBS, MYPT-75D or treated control RNAi in the absence of Fog in order to obtain abasal phosphorylation rate (Fig. 6A-E). We found that
depletion of both Flw and MYPT-75D lead to a statistically significant increase in phosphomyosin staining as compared to control or MBS RNAi conditions(p-value < 0.0001, ANOVA, N = 30–57 cells) (Fig. 6E).
The observed hypocontractility, when viewed in conjunction with the increased abundance of phosphorylated myosin, suggests that Flw depleted cells might
display defects in spatial or temporal regulation NMII.

Page 10 of 21

In order to explore this latter possibility we decided
to quantify the spatial distribution of the phosphorylated regulatory light chain using our coalescence index[39, 41]. Again, the greater the coalescence index themore punctate the distribution which in turn indicates a more organized phosphomyosin network.
This analysis revealed that while being statisticallyindistinguishable between one another, the phosphomyosin distribution of Flw or MYPT-75D was significantly more diffuse than MBS or control RNAi treatedcells (p-value < 0.0001, ANOVA, N = 25–108 cells)
both with and without the perfusion of Fog (Fig. 6G
and H). The Fog pathway has been previously investigated in S2R + cells, where it was observed that theaddition of Fog and the subsequent contractility coincided with a "purse string" structure of NMII circling

Fig. 6 Depletion of Flw increases the amount of phosphomyosin in S2R + cells, but leads to a less organized contractile network. A-D) S2R + cells
fixed and stained for phosphomyosin in the absence of Fog (left panels), or after the perfusion of Fog (right panels) following treatment with (A)control, (B) Flw, (C) MBS, or MYPT-75D RNAi. Yellow arrowheads denote cells with a coalesced phosphomyosin contractile network in the formof peri-nuclear rings, while cyan arrowheads indicate a more diffuse phosphomyosin network. Yellow boxes denote cells with peri-nuclear rings
shown at higher magnification, while cyan boxes denote cells with a diffuse phosphomyosin network shown at higher magnification. Scale bars
10 µm. E–H Quantification of the mean (± SEM) (E) Normalized Fluorescence Intensity, (F & G) Coalesce Index, (H) Number of cells with definedrings for cells following treatment with control (yellow circles), Flw (blue circles), MBS (green circles), and MYPT-75D (peach circles). E Depletionof Flw and MYPT-75D led to a statistically significant increase in normalized mean phosphomyosin fluorescence intensity while the depletionof MBS was no different than that of control RNAi treated samples. F The coalescence index indicated that depletion of Flw and MYPT-75D were
statistically indistinguishable from one another but were statistically less organized as compared to MBS and control RNAi treated samples. G
In comparing the number of defined rings, Flw depletion led to a lower number of cells with rings as compared to all other RNAi conditions.
(**p-value = ,***p-value = , ****p-value < 0.0001, one-way ANOVA with Tukey’s post-hoc analysis, N = 3, N = 29–89 image fields)

Zhao et al. BMC Molecular and Cell Biology(2023) 24:32

the organelle-rich center domain of the cell. The contraction of this circular structure resulted in the "bonneted" morphology of S2R + cells plated on con A(Fig. 2B) [12]. Another proxy for the coalescence index
is the prevalence of this phosphomyosin ring. In control treated cells, upon addition of Fog, we observed
defined rings of phosphorylated myosin surrounding
the central region of the cell (Fig. 6A), consistent withthe literature. However, in Flw depleted cells, we saw
a significant decrease in the proportion of cells displaying defined rings of phosphomyosin following Fog
addition as compared to control, MBS, and MYPT-75D
RNAi treated cells (p-value < 0.0001, one-way ANOVA,N = 29–89 image fields) (Fig. 6B and F). Thus, thedepletion of either Flw or MYPT-75D results in an
increase in the phosphorylation state of the regulatory
light chain network but a decrease in its organization.
Further, given some of the overlap in phenotypes, it
may suggest a larger role for MYPT-75D in targeting
Flw over MBS in these cells. These results also indicatethat increased phosphorylation of the regulatory lightchain is not enough to induce cellular contractility; itmust also be spatially organized as well.

Page 11 of 21

Loss of contractility following flapwing depletion is partiallymediated through moesin activity

Flw is known to be able to dephosphorylate and thus
inactivate the Ezrin-Radixin-Moesin (ERM) protein
Moesin [48]. Considering that Moesin has been implicated in driving cellular rounding [49], a phenotype
observed in Flw depleted cells, we hypothesized that thephenotypes observed upon Flw knockdown could be due
to an increased abundance of phosphorylated, active,
Moesin. In order to test this hypothesis, S2R + cells weredepleted of Moesin, Flw, and Flw and Moesin in combination or were treated with control RNAi and thenwere challenged to contract following perfusion of Fog(Fig. 7). Depletion of Moesin led to a hypercontractile
phenotype, with a larger fraction of cells undergoing
Fog-induced contractility than control RNAi treated cells(p-value = 0.00230, One-way ANOVA, N = 3). This finding may be indicative of less resistance to NMII contractility as depletion of Moesin is likely leading to inability of
the cortical actin network to maintain tension. Additionally, the hypocontractile phenotype in Flw depleted cells
was partially rescued by the double depletion of Flw andMoesin (p-value = 0.001634, One-way ANOVA N = 3).

Fig. 7 Flw regulates cellular contractility through its interaction with Moesin. A-D Phase-contrast images of S2R + cells following perfusion of Fog
and treatment with (A) control, (B) Flw, (C) Moesin, and (D) Flw and Moesin RNAi. Yellow arrowheads denote contracted cells and cyan arrowheads
denote rounded cells. Scale bar 10 µm. E and F Quantification of the fraction (± SEM) of (E) Contracted cells and (F) Rounded cells, treatedwith control (yellow circles), Moesin (mint green circles), Flw (blue circles), and Flw and Moesin (magenta circles) RNAi. E Depletion of Moesin
led to a statistically significant increase in the fraction of contracted cells, while double depletion of Moesin and Flw only led to a partial rescue(**p-value = 0.002304,***p-value = 0.000258, ****p-value < 0.0001, one-way ANOVA with Tukey’s post-hoc analysis, N = 3). F Conversely, depletionof Moesin did not affect the fraction rounded cells we observed, while depletion of Flw led to a statistically significant increase. The double
depletion of Moesin and Flw failed to rescue this cell-rounding phenotype ( ****p-value < 0.0001, one-way ANOVA with Tukey’s post-hoc analysis,
N = 3)

Zhao et al. BMC Molecular and Cell Biology(2023) 24:32

We also quantified the fraction of rounded cells following treatment with control, Moesin, Flw, and Moesin
and Flw RNAi and observed a trend similar to our contractility assay. Moesin depleted cells were no different than control RNA depleted cells, and Flw RNAi led
to stark increase in the fraction of rounded cells which
was as compared to Moesin and control RNAi treatedcells (p-value < 0.0001, One-way ANOVA, N = 3). However, the fraction of rounded cells failed to be rescued
by depletion of Moesin and Flw in tandem. Collectively, these results indicate an antagonistic relationshipbetween Moesin and Flw and may suggest that the hypocontractility we observed following Flw may be the resultof increased cortical tension, a consequence of hyperphosphorylated Moesin. This increased tension and theloss of spatial resolution of the phosphorylated regulatory light chain of NMII is enough to inhibit contractility
in our assay following Flw RNAi.
To further interrogate how the phosphorylation stateof Moesin affects contractility, likely through regulating cortical tension, we generated both phosphomimetic (T559E) and non-phosphorylatable Moesin point
mutants (T559A) and then tested them in our contractility assay. We depleted the endogenous pool of Moesin
by using dsRNA generated against the 3’-untranslated
region (UTR) of Moesin and then transiently expressed
EGFP-tagged Moesin-T559A and -T559E which are
refractory to this dsRNA (Fig. 8). The cells were thensubjected to the aforementioned cellular constriction
assay. Cells expressing phosphomimetic Moesin failed
to constrict in response to Fog induction and displayed
increased proportions of rounded cells, recapitulating the phenotype observed upon treatment with Flw
dsRNA (Fig. 8A, C and D), while cells expressing thenon-phosphorylatable form of Moesin behaved in a similar manner to Moesin depleted cells in both the fractionof contracted cells and fraction of rounded cells (Fig. 8BD). Together, these results suggest that Flw’s regulationof cellular contraction is mediated, in part, through theincreased abundance of active Moesin following Flw
knockdown.
Phosphomyosin abundance and distribution is perturbed
by phospho‑mimetic moesin mutants

Having demonstrated that the phosphorylation state ofMoesin impacts cellular contractility, we then aimed toinvestigate if this phenotype was the result of aberrations in phosphomyosin abundance and distribution. To
this end, we treated cells with either control or Moesin
3’UTR dsRNA and then transiently expressed fluorescently tagged phosphomimetic and non-phosphorylatable Moesin mutants (Fig. 9). Cells were then stimulatedwith Fog-conditioned before immunostaining with thePage 12 of 21

aforementioned phosphomyosin antibody. This immunostaining revealed that cells depleted of Moesin displayed the characteristic pattern phosphomyosin seen incontrol cells and referenced previously. The expression of
non-phosphorylatable Moesin-T559A did not impair theformation of these myosin rings while the expression of
the phosphomimetic T559E Moesin mutant resulted in amore diffuse distribution of phosphomyosin, with atypical concentrations around the cellular cortex (Fig. 9A-D).
Statistical analysis revealed a significant decrease in boththe abundance and coalescence index of phosphorylated NMII in cells expressing the T559E Moesin mutant,suggesting that phosphorylated Moesin can inhibit therecruitment and higher-order assembly of phosphomyosin (Fig. 9E and F). This phenotype could be the result ofMoesin’s known function as an antagonist to Rho pathway [50] as the phosphorylation of NMII by Rok is triggered by signaling from Rho1.
Considering that Flw knockdown results in increased
amounts of phosphorylated, active Moesin, we would
expect to see a decrease in phosphomyosin abundance in
Flw depleted cells, similar to the phosphomimetic mutant
expressing cells, rather than the observed increase. Thisapparent contradiction suggests that Flw directly regulates the phosphorylation state of NMII, as the increasein phosphomyosin abundance cannot be accounted forby regulation of Moesin alone. Together, these observations suggest that Flw regulates cellular contractility
through both Moesin-mediated and Moesin-independent mechanisms.

Discussion
The Fog signaling pathway converges on NMII contractility leading to apical constriction, a morphogenetic eventthat is critical to the tissue folding needed for processes
such as gastrulation and neural tube formation in vertebrates, to wound healing responses and the epithelialto-mesenchymal transition (EMT) of cells delaminatingfrom surrounding tissues [5]. Despite being an insect specific pathway many of the molecular players involved in
Fog signaling have mammalian counterparts with highly
conserved functions [35, 51, 52]. Thus, the implicationsof this research extend beyond the Fog signaling pathway
and into mammalian homeostasis and development.
Despite the intense scrutiny of the Fog pathway
through the years, we wondered whether any previously unidentified proteins may also be contributing
to this signaling. To address this, we built a Drosophila
specific protein–protein interactome and developed aframework that predicts Fog signaling regulators fromnetwork-based algorithms. We have coupled this computational approach to identify potential candidates with acell-based contractility assay which provided a means forZhao et al. BMC Molecular and Cell Biology(2023) 24:32

Page 13 of 21

Fig. 8 Moesin’s phosphorylation state affects cellular contractility. A and B Phase-contrast (left) and fluorescent imaging (right, green in merge)of S2R + cells following Fog perfusion treated with Moesin 3’UTR RNAi and expressing EGFP tagged (A) Moesin T559E or (B) Moesin T559A. Scalebar 10 µm. C and D Quantification of the fraction (± SEM) of (C) Contracted and (D) Rounded cells following RNAi treatment with control (yellow
circles) and Moesin 3’UTR (mint green circles) RNAi. We also expressed EGFP-tagged Moesin T559A (plum circles) and Moesin T559E (brown
circles) following Moesin 3’UTR RNAi. C The hypercontractile phenotype following Moesin 3’UTR RNAi could not be rescued by the expressionof EGFP-Moesin T559A, while expression of EGFP-Moesin T559E led to a statistically significant decrease in the number of contracted cells(***p-value = 0.000174, ****p-value < 0.0001, one-way ANOVA with Tukey’s post-hoc analysis, N = 3). D Conversely, the number of rounded cells
following rescue with EGFP-Moesin T559A was no different than Moesin 3’UTR RNAi alone, but expression EGFP-Moesin T559E led to a significant
increase in the number of rounded cells (*p-value = 0.0254,****p-value < 0.0001, one-way ANOVA with Tukey’s post-hoc analysis, N = 3)experimental validation [16]. Using this framework, wehave uncovered two new proteins involved in the regulation of Fog signaling pathway and actomyosin contractility, the phosphatase Flw, and the guanylate kinase Oya.
Neither Flw nor Oya, have been previously associatedwith Fog signaling and our results indicate that they both
may play roles in its regulation, albeit indirectly, throughthe regulation of the phosphorylation state of Sqh andthe levels of cytoplasmic GTP levels, respectively (Fig. 2).
Oya is a putative guanylate kinase [42, 53], which isa class of enzymes that phosphorylate the nucleotide

GMP into GDP using ATP as a phosphate donor. Thus,
Oya could play a role in maintaining cellular concentrations of GDP. As GTP synthesis is contingent on thephosphorylation of GDP by a variety of enzymes suchas nucleoside-diphosphate kinases (NDPKs), reducing levels of cellular GDP through Oya depletion could
also potentially decrease the amount of cellular GTP by
reducing de novo GTP biosynthesis. In support of this, aprevious study demonstrated that knockdown of a guanylate kinase led to an increase of intracellular GMP anda corresponding decrease of GDP and GTP [43, 54]. InZhao et al. BMC Molecular and Cell Biology(2023) 24:32

Page 14 of 21

Fig. 9 Moesin’s phosphorylation state feeds back to the phosphorylation state of NMII’s regulatory light chain. A-D TIRF images S2R + treatedwith (A) control RNAi or (B-D) Moesin 3’UTR RNAi and cells fixed and stained for phosphomyosin (left panel, red in merge). C and D Cells
were also expressing EGFP-tagged (C) Moesin T559A (middle panel, cyan in merge) or (D) Moesin T559E (middle panel cyan in merge). Scalebar 10 µm. E and F Quantification of the mean (± SEM) (C) Fluorescence and (D) Coalescence Index of cells following RNAi treatment with control(yellow circles) and Moesin 3’UTR (mint green circles) RNAi. We also expressed EGFP-tagged Moesin T559A (plum circles) and Moesin T559E(brown circles) following Moesin 3’UTR RNAi. E Expression of Moesin T559E following Moesin 3’UTR RNAi led to a statistically significant
decrease in the amount of phosphorylated NMII regulatory light chain (**p-value = 0.004123,***p-value = 0.00057, one-way ANOVA with Tukey’s
post-hoc analysis, N = 3). Similarly, expression of Moesin T559E following Moesin 3’UTR RNAi led to a decrease in Coalescence (*p-value = 0.0149,**p-value = 0.00495,***p-value = 0.00566, one-way ANOVA with Tukey’s post-hoc analysis, N = 3)

support of this, we observed a decrease in tubulin fluorescence upon Oya depletion, which was on par with
Mizoribine treatment, a selective inhibitor of inosine5’-monophosphate dehydrogenase (IMPDH) and guanosine monophosphate synthetase (Supplemental Fig. 2)
suggesting that Oya depletion may reduce the amount ofcytoplasmic GDP and GTP in S2R + cells. This alteringof the ratio of cellular GDP/GTP could disrupt several
GTP-binding signaling components of the Fog pathway,
such as the G-protein coupled receptor Mist/Smog and
its associated ­G⍺12/13 Concertina, as well as small GTPase

Rho. Indeed, while depletion of Oya inhibited NMII contractility, led to a decrease in Sqh phosphorylation, anddisrupted NMII localization (Figs. 2 and 3), in manycases these phenotypes were intermediate (Supplemental
Fig. 1). These more subtle phenotypes lend further support to a global decrease in cytoplasmic concentrations
rather than outright inhibition of NMII contractility. Itshould also be noted that the cellular contractility assay
captures whether a cell contracts or not while some ofthe other measurements we explore in this manuscript(total phosphomyosin staining and coalescence index)

Zhao et al. BMC Molecular and Cell Biology(2023) 24:32

capture more nuanced phenotypes. It is both an increasein phosphomyosin and the reorganization of the NMII/
actin filament network that is needed for cellular contractility. We previously observed similar differences inphosphomyosin staining and reorganization upon depletion of the Rab GAP, RN-tre [39]. Further experiments,
outside of the scope of this study, will need to be conducted in order to more directly test this hypothesis, but
this unexpected result expands the possible mechanismsof NMII regulation.
The Drosophila protein phosphatase-1 (PP1) complex
consists of a catalytic subunit Flw, one of two myosin
phosphatase targeting subunits, MYPT-75D or MBS,
which are homologous to mammalian MYPT3 andMBS85 respectively, and an M20 unit of unknown function. These proteins are highly conserved across eukaryotes [46, 47, 55]. The PP1 complex catalyzes the removalof phosphate groups from a variety of substrates. Flw(PP1b9C) encodes for the PP1b subtype, which is homologous to mammalian PP1b, and is sometimes referred
to as PP1d. Although there is nearly 90% shared aminoacid identity between PP1 complex isoforms, structuraldifferences are conserved across homologues, suggesting
evolutionarily derived differences in function [56]. Flw
precipitates with Sqh and MYPT-75D in vitro [47], while
mammalian MYPT3 has been shown to target PP1cδ tothe regulatory light chain of myosin [45]. Flw also binds
to MBS, Drosophila mbs mutants have been shown tohave higher phosphorylated Sqh relative to controls,suggesting its role in targeting the PP1 complex to myosin [57]; and complete colocalization between MBS and
Flw was observed in germ cells undergoing incomplete
cytokinesis [44]. However, while MYPT-75D shows binding specificity for Flw, in vitro studies have shown that
MBS lacks binding specificity for Flw as it also binds to
PP1-87B [47], a PP1α subtype that was previously demonstrated to account for ∼80% of myosin phosphataseactivity in third instar larvae [58]. Notably, MBS contains
an inhibitory Rok phosphorylation site, while MYPT75D does not (reviewed in [46]). Therefore, Rok directly
antagonizes MBS function, but could work synergisticallywith the MYPT-75D/Flw complex. Evidence suggeststhat the MYPT regulatory subunits confer many of thedifferences in myosin phosphatase substrate specificity;
however, the exact relationship between regulatory subunits and Flw is not well understood.
We observed distinct localization patterns of Flw, MBS,and MYPT-75D in S2R + cells respectively which adds a
spacial aspect to their roles in regulating NMII contractility (Fig. 5). Previously, MBS has been described as localizing apically alongside DE-cadherin in imaginal wingdiscs [59]. It was also found to be apically during ventral
furrow formation and during germ band extension where

Page 15 of 21

MBS was found to be enriched in apical junctions and in
the medial pulsatile contractions along with NMII colocalizing with NMII foci [60, 61]. Flapwing also colocalizes
with NMII at the ventral furrow in developing Drosophila embryos [62], while in the follicular epitheliumof Drosophila egg chambers, Flw was found at the tipsof basal actomyosin bundles in early stages (S6) whereas
in later stages (S9) it was found dotted along the lengthof these actomyosin bundles [63]. MYPT-75D localized
to the cell periphery in imaginal wing discs, likely due toits prenylation [47]. While three PP1 complex members
coincided with the peri-nuclear organization of NMII
bundles, Flw appeared more ring-like in nature, whileboth MBS and MYPT-75D took up more diffuse patterns
in the cell center. Our cell culture system, while not epithelia in nature, has allowed us to observe these proteinsat higher resolutions and these distinct localization differences may ultimately dictate phosphatase function andbehavior.
Drosophila germ cells mutant for flw exhibited hypercontracted ring canals and Flw was required for therachet-like cycle of NMII in the basal actomyosin network of the follicular epithelial again where it is thought
to counter the activity of Rok [44]. Moreover, lethal,
hypomorphic alleles of flw can be rescued by reducing
the expression of the myosin heavy chain zipper, or byhypomorphic alleles of rok, rhogef2, or expression of nonphosphorylatable Sqh [47], suggesting that a reduction incellular contractility can ameliorate the reduction in Flw.
Despite its connection to Sqh phosphorylation, a role Flw
has yet to be explored in the context of apical constriction during Fog signaling.
Flw depleted cells exhibited hypocontractility in ourcellular constriction assay which is puzzling given its
role as part of PP1 complex (Figs. 2 and 4). Yang and colleagues previously demonstrated a crucial role of Flw
in the regulation of Moesin family proteins, Merlin andMoesin. It was reported that Flw forms a complex withboth Merlin and Moesin and activity of both proteins
was altered by dephosphorylation [48]. Further, reduced
Flw expression caused changes in the levels of Merlinand Moesin and was associated with defective epithelial
integrity. More specifically, the reduction of Flw corresponded with an increase in Armadillo, Coracle, andF-actin expression, putatively indicating a loss of apicalbasal polarity [48]. We also observed a cell rounding phenotype following Flw depletion. Again, this phenotype
may be the result of Flw’s association with Moesin whichplays a role in regulating cell cortex rigidity. A decrease in
Flw expression leads to an increase in levels of activatedMoesin, increased cell cortex rigidity and a rounded cellmorphology [48, 49]. Thus, the hypocontractile phenotype we observed upon Flw depletion may be the result ofZhao et al. BMC Molecular and Cell Biology(2023) 24:32

Page 16 of 21

increased cell cortex rigidity, countering the Fog stimulated NMII activation. In further support of this hypothesis, we observed a hypercontractile phenotype upon
Moesin depletion. This may suggest that a weakened cellcortex has less resistance to NMII generated contractility
and thus more easily constricts in our assay.
Our results also suggest a larger role for Moesin in
the regulation of NMII constractility, with this function
being somewhat independent of Moesin’s own regulation by Flw. Expression of a phosphomimetic version
Moesin (T559E) inhibited Fog-stimulated cellular contractility, an overall decrease in phosphorylated Sqh, anda decrease in NMII coalescence. Moesin has been documented to antagonize Rho signaling in both Drosophila
wing discs epithelium and LLC-PK1 epithelial cells [50],and given the centrality of Rho signaling to the Fog pathway Moesin should also be added to the growing list of
Fog signaling associated proteins.

subgraph that is no more than twice the cost of the optimal Steiner tree [64]. The candidate regulators are theunlabeled nodes that are used to connect the positives(Fig. 1A and Supplementary Fig. 1).

Conclusion
We have used computational approaches combined withexperimental validation and have identified two proteinspreviously not associated with Fog signaling. We showthat depletion of either Oya or Flw results in a decrease inphosphorylated Sqh, a disruption of NMII localization andhypocontractility following Fog perfusion. Despite disrupting NMII contractility, their mechanism of action is very
different, with depletion of Oya likely leading to a global
decrease in GTP levels, while depletion of Flw likely affects
Moesin phosphorylation and the rigidity of the cell cortex.

Finally, there may be regulators that are not near NMII
but interact with many positive nodes in the graph. We
calculated the shortest path from each unlabeled node
to all positives, which generates a list of 104 shortestpath lengths for each unlabeled node (call these distancesd1 , d2 , . . . , d104 ). The score for a node is the sum
1of the multiplicative inverses of the distances: 104
i=1 di .
These scores are then normalized to sum to one. The candidate regulators are the nodes with a normalized score
greater than 0.7 (Fig. 1A and Supplementary Fig. 3).

Methods
Computational methods

We applied three different network-based algorithms topredict candidate NMII regulators from the Drosophilainteractome. Mathematically, the interactome is represented as an undirected, unweighted graph G = (V,E) witha set V of nodes (proteins) and a set E of edges (physical interactions). We also have 104 proteins known to be
relevant to Fog signaling, apical constriction, or gastrulation. These relevant proteins are called positive nodes
in the graph G; the rest of the nodes in G are unlabeled.
Computationally, the algorithms aim to identify unlabeled nodes in G that could be predicted by regulators
based on their proximity to the positive nodes in G.
Steiner tree approximation

Given a graph G and a set of positive nodes, a Steiner
tree is a minimum weight tree that is a subgraph of G
and includes all of the positive nodes (and possibly
other unlabeled nodes). Finding a minimum Steiner
tree in a graph is NP-Hard, so we implemented an
approximation algorithm that is guaranteed to return a

Paths to NMIIThe Steiner tree approximation does not explicitly focus
on NMII, so we developed a shortest-paths algorithm
that accounts for the proximity of NMII in the network. Dijkstra’s algorithm efficiently finds the shortest
path between any two nodes in a graph. Given a graph
G and a set of positive nodes, we calculated the shortest path from each positive node to Sqh using Dijkstra’salgorithm, totalling 104 distinct paths. The candidate
regulators are the unlabeled nodes that lie on these paths(Fig. 1A and Supplementary Fig. 2).
Ranked paths

Cell culture

For detailed instructions on Drosophila tissue culture
please see [14, 65]. S2R + cells were cultured at 25 °C
in Shields and Sang media (S&S; Sigma S8398, 0.5 g/l
NaHCO3, 1 g/l yeast extract, 2.5 g/l bactopeptone) with
10% fetal bovine serum (FBS) (Thermo Fisher Scientific,
Waltham, MA) and 1 × Antibiotic–Antimycotic (LifeTechnologies; 15,240–062. Cells were grown in cell-culture flasks and passed every 4–5 days (Rogers & Rogers,
2008). Transfections were carried out using the FuGENE
HD Transfection Reagent (Promega, Madison, WI) following the manufacturer’s instructions. Briefly, FuGENE
Reagent was brought to room temperature. Plasmid DNA(2 g) was added to up to 100 µl of water. FuGENE Reagent (6 µl) was added to DNA and mixed by pipetting
immediately. The mixture was incubated at room temperature for 10 min. The DNA–water mixture was thenadded dropwise to cells that were ∼80–100% confluent
in 1 ml media six-well plate. CuSO4 (1 mM) was added
to cells if necessary for induction of pMT constructs. For
Mizoribine treatment, cells were incubated with 290 µM
Mizoribine (Sigma-Aldrich, St. Luis, MO) for 15 minprior to imaging.

Zhao et al. BMC Molecular and Cell Biology(2023) 24:32

RNAi

For instructions on how to obtain dsRNA suitable for
insect culture, see [14]. Cells were incubated with 1 µl of
dsRNA in 1 ml of S&S media in six-well plates at 25 °C for
24 h. Media and dsRNA were aspirated off and replenished each day of treatment, typically 5–7 d. See Table 3
for primers used to generate dsRNA.
qPCR validation

Flapwing, oya, and control (gfp) RNAi-treatments were
performed as outlined above Following 6 days of treatment, cells were pelleted by centrifugation at 300 × g
for 3 min, the media was decanted, and the cells wereimmediately placed on ice for RNA extraction. RNA wasextracted using a Promega Maxwell® 16 Instrument andPromega Maxwell® 16 Low Elution Volume simplyRNA
Cells Kit, according to the protocol (Promega, Madison, WI), and eluted in 30 ml nuclease-free water. After
extraction, the RNA samples were snap frozen on a mixture of dry ice and 70% ethanol, and stored at -80 °C for
up to 24 h. The RNA concentration was determined witha NanoDrop ND-1000. RNA quality was visualized on
a 1% w/v agarose gel with 1% bleach in TAE at 60 V for
one hour. RNA quality was assessed by the presence anddefinition of a 28S/18S band which should occur prominently at 2,000 bp, as the 28S is processed in two fragments which run similarly to the 18S band Thermo Fisher
Scientific). Following RNA extraction, reverse transcription was carried out by Promega GoScript Reverse
Transcription System (#A5000; Promega, Madison,
WI). 3.25 μg of RNA was added to 0.5 μg of Oglio(dT)15
primer in nuclease water for a total volume of 5 ml. The
RNA primer was incubated at 70ºC for 5 min, chilled on
ice for 5 min, then spun down for 5 s. Reverse transcription reactions included 7.2 μl nuclease-free water, 4 μl
GoScript 5X Reaction Buffer, 1.2 μl ­MgCl2 (1.5 mM final),
1 μl PCR nucleotide mix (0.5 mM final), 20U Recombinant RNAsin Ribonuclease Inhibitor, and 1 μl GoScript

Page 17 of 21

Reverse transcriptase. Thermocycling parameters were
programmed accordingly: 25 °C for 5 min, 42 °C for
1 h, then 70 °C for 15 min. cDNA products were aliquoted into 4 ml portions, snap frozen on a mixture of
dry ice and 70% ethanol, and stored at -80 °C for up to
24 h. Samples were subject to only one freeze–thaw
before qPCR. Quantitative PCR (qPCR) primers wereordered from QIAGEN; the exact sequences are proprietary. Elongation factor 1 (EF1), was used as a reference
gene for qPCR analysis. An 8-point cDNA serial dilution
curve was run in triplicate to validate the efficacy of eachof the three targets (Flapwing, Oya, and EF1). Promega
GoTaq qPCR Master Mix (#A6001) was used for qPCR
reactions, which employs a SYBR Green dye for quantification (Promega, Madison, WI). cDNA was diluted
ten-fold and added separately from a master mix into
plates (#ML9651, BioRad, Hercules, CA), and sealed with
Microseal ‘B’ film (#MSB1001, BioRad, Hercules, CA),in a BioExpress AirClean 600 PCR Workstation hood.
Reactions were plated with three replicates per dilution
and a no template and no reverse transcriptase control.
Reactions were incubated at 95.0 °C for 2 min and then
40 rounds of 95.0 °C for 15 s, 55.0 °C for 40 s, and 72 °C
for 30 s before a fluorescence measurement. To determine melting temperatures (Tm), primers were incubated for 5 s at 0.5 °C increasing increments from 65.0 °C
to 97.0 °C. Melting temperature, gene quantification andprimer efficiency were determined with BioRad CFXManager 3.1 software (BioRad, Hercules, CA). Primers
were considered to be efficient within 90–110%.
Plasmid construction

The Gateway entry clone of Drosophila Moesin cDNA
encoding the D isoform [66] was purchased from
Addgene (Watertown, MA) and using standard Gateway cloning procedures (Thermo Fisher Scientific,
Waltham, MA) was inserted into pMT-His A vector with
an N-terminal EGFP tag (DGRC, Bloomington, IN). The

Table 3 T7 Primers for dsRNACG#

Forward Primer (5’ to 3’)a

Flw

CG2096

Oya

CG11811

CCT​TGT​CCT​ATG​CGG​TCC​

dsRNA
Control (gfp)

Reverse Primer 5’ to 3’)a

Reference

TGG​CCC​ACC​C TC​GTGA​

GCG​GAT​C TT​GAA​GTT​CAC​C TTG​

This study

CTG​GCC​AAA​GTC​AAC​GAT​

AAG​TCC​GGA​TGT​GAG​GAC​AC[31]

ATT​GAT​GAT​C TT​GTG​GAA​GTTG​[31]
This study

5’UTR Oya

CG11811

TTT​GTG​AAC​TGA​TCG​ACG​AC

TCT​GTA​TCT​TCG​GAA​AGT​T T

MBS

CG32156

CTG​GCC​AAA​GTC​AAA​CGA​T

AAG​TCC​GGA​TGT​GAG​GAC​AC[12]

MYPT-75D

CG6896

CTG​TGA​AGA​TCC​CGA​AAT​CACGT​AGC​AGT​TCA​TCG​T TT​TC

[31]

Sqh

CG3595GCC​CGG​GAT​CAA​C TT​CAT​GTT​CCT​C

GTC​C TT​GGC​ACC​GTG​C TT​AAGG​[12]

MoesinCG10701

AGA​CGC​T TT​GTC​TCC​TCA​TCC​

GAA​AAG​AAG​CAG​CAG​GAG​TACG​[31]

3’UTR Moesin

CG10701

GTC​GTT​T TG​TCT​CGC​AGC​T T

AGA​GTC​GGT​CCT​TCC​TAA​CC

This studyaPrimers have the T7 promoter sequence (taatacgactcactatagg) at their 5′ endZhao et al. BMC Molecular and Cell Biology(2023) 24:32

Moesin point mutations (T559A and T559E) were generated using standard PCR based site-directed mutagenesis using the following primers: 5’-CTC​GCG​GAG​CGC​
CTT​GTA​CTT-3’ and 5’-AAG​TAC​AAG​GCG​CTC​CGC​
GAG-3’ for T559A and 5’-CTC​GCG​GAG​CTC​CTT​GTA​
CTT-3’ and 5’-AAG​TAC​AAG​GAG​CTC​CGC​GAG-3’ for
T559E.
Immunofluorescence and imaging

Concanavalin A (con A) was applied to glass bottom
dishes, removed, and the dishes were left to air dry. Once
dry, cells in Shang’s and Shields media were allowed toattach for 45 min. Cell culture media was removed fromthe attached cells, and to the Fog-treated cells, 150 µlof Shang’s and Shields media and 50 µl concentrated
Fog media was added. Control cells were supplied with
200 µl of control-conditioned Shang’s and Shields media
as described in [16]. After 8 min, cells were fixed witha 10% paraformaldehyde solution (Electron Microscopy Sciences, Hatfield, PA) in PEM buffer, 100 mM
PIPES, 1 M ­MgCl2, 1 mM EGTA, pH to 6.8) for 15 minat room temperature. The cells were rinsed 3 × with Trisbuffered Solution (TBS; 20 mM Tris, 150 mM NaCl, pH7.4), and cells were blocked with 5% goat serum in TBS
for 10 min. The block was removed and a 1:200 dilution
rabbit anti-phosphomyosin (Ser21 light chain) primary
antibody (Cell Signaling Technology, Danvers, MA) in
5% goat serum in TBS was added and left to incubate atroom temperature for one hour, or at 4 °C overnight. The
cells were rinsed 3 × with TBS and then incubated with asolution of 1:100 anti-Rabbit Alexa Fluor 598 secondary
antibody (Jackson ImmunoResearch Laboratories, Inc.,
West Grove, PA), with 1:100 Alexa Fluor 488 Phalloidin
and 1:10,000 Hoesct (Thermo Fisher Scientific, Waltham,MA), in TBS plus 0.1 Triton-X (Sigma-Aldrich, St. Louis,MO) and incubated in the dark at room temperaturefor one hour. The cells were rinsed 3 × with TBS, andmounted using Dako Fluorescence Mounting Medium(Agilent, Santa Clara, California). Cells were imaged
using a Nikon Eclipse Ti-E inverted microscope (Nikon,
Tokyo, Japan) using a 100x/1.49NA oil immersion TIRF
objective or a 40x/0.75NA objective to visualize constriction. For quantification of phosphomyosin to actin ratios(see Fig. 3D), the integrated fluorescence intensity of theanti-phosphomyosin (Ser21 light chain) antibody was
divided by the corresponding integrated fluorescenceintensity of the phalloidin staining. The Normalized
Mean Fluorescent Intensity (see Fig. 6E) was obtained
from fluorescence intensity of the anti-phosphomyosin
and was calculated by taking the fluorescent intensitiesof each cell as defined by its phalloidin staining and normalizing the intensity by dividing by the highest intensity

Page 18 of 21

value. The Fluorescence Intensity (as reported in Fig. 9E)
is the integrated fluorescence intensity of each cell.
Cell contractility assay

The cell contractility assay was performed as describedin (Peters et al., 2018; and Platenkamp et al., 2020).
Briefly, After cells were allowed to attach con A covered
glass-bottom dishes. Cell culture was removed and thenreplaced with either Fog-conditioned media or control
media. The cells were then fixed and immunostained(see above). The number of constricted and relaxed cells
were counted per image frame, excluding ambiguous cellstates. For control and Oya RNAi conditions the rate of
ambiguous cells ranged between 2–8%, while depletionof Flw led to an increase in the number of ambiguous cellstates (10–20%). These results were presented as a ratioof constricted cells over total cells by treatment per frame
and analyzed for statistical significance using Prism 7 by
ANOVA (Graphpad, LaJolla, CA). The percentage of contracted cells in control RNAi conditions treated with Fog
is approximately 30%, however we do observe some variances across experiments. To mitigate these variances we
prepare large batches of Fog (which has an approximate
shelf life of three months at 4C and cannot be frozen forlong term storage) and use low passaged cells. Despite
these efforts some variance remains.
Coalescence index

The coalescence index was used to measure the distribution of phosphorylated NMII Sqh at Ser21 in eachtreatment [39]. The coalescence index was adapted froma punctate/diffuse index [41] used to compare the progression of cytochrome c dispersal across a cell during
apoptosis. To perform the measurement images of cells
were thresholded in ImageJ and the cell was outlined.
All values for the XY coordinates surrounded by theoutline were obtained and normalized to a scale of 0–1.
The mean over the SD of the normalized values was designated the “coalescence index” and compared acrossconditions. High values indicate more fluorescent pixel
coalescence, while low values indicate dispersed fluorescent pixels. The relative coalescence index results wereanalyzed for statistical significance using Prism 7 by
ANOVA (Graphpad, LaJolla, CA).
Data availability

All data, data sets, and analyses are available upon
reasonable request from the corresponding authors
D.A.A. and A.R. The computational data (including
the code for predicting candidates and generating networks) and interactive networks are available in the
public GitHub repo https://​g ithub.​com/​Reed-​CompB​
io/​nmii-​contr​actil​ity-​regul​ators.

Zhao et al. BMC Molecular and Cell Biology(2023) 24:32

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s12860-​023-​00492-3.
Additional file 1: Supplementary Table 1. Positive proteins used for thecomputational method. Below is a list of the three sets of proteins usedas positives for the computational method. The three protein sets werecombined into one set of positives for analysis. Supplemental Table 2.
Predicted proteins from each computational method. Below is a list of thepredicted protein candidates by computational method. This list does notinclude positive proteins that were captured by the methods.
Additional file 2: Supplementary Figure 1. Network visualization ofproteins predicted by the Steiner tree algorithm. The Steiner tree method
aims to connect all the positive proteins by using as few interactions (andintermediate nodes) as possible. Gray nodes are positive proteins andred nodes are predicted proteins. Supplementary Figure 2. Network
visualization of proteins predicted by calculating paths to NMII. Thismethod calculates the shortest path from each positive protein to NMII.
Gray nodes are positive proteins and green nodes are predicted proteins.
Supplementary Figure 3. Network visualization of proteins predicted by
the ranked paths method. The ranked paths method aims to find proteinsthat are close to many positive proteins. The predicted proteins are shownin blue; for context, the positive Sqh is shown in gray. Supplementary
Figure 4. Combined network of predictions from all three methods. Nodesize indicates the number of methods that found the predicted protein(large white nodes indicate proteins found by all three methods). Gray:
positive (known) proteins; red: proteins predicted by only the Steiner
method; green: proteins predicted by only paths to NMII; blue: proteinspredicted by only the ranked paths approach; yellow: proteins predicted
by Steiner & paths to NMII; cyan: proteins predicted by paths to NMII and
ranked paths; magenta: proteins predicted by Steiner and ranked paths
methods. The number of each color corresponds to the Venn diagramin Figure 2 of the main text. Supplementary Figure 5. Oya’s immediateneighbors in the network. Oya’s direct interacting partners are shown
along with any interactions among those neighbors. The network includes
the positive Sqh (gray) and two Ubiquitinases (green) that are found by atleast one of the three algorithms. Supplemental Figure 6. RNAi Deple‑
tion of Oya leads to a disruption of NMII organization. (A-D) Live-cell TIRF
imaging of Drosophila S2R+ cells expressing EGFP-tagged Sqh following
treatment with (A) control, (B) Sqh, and (C & D) Oya RNAi. Scale bar 10µm. Supplemental Figure 7. RNAi depletion of Oya leads to a decreasein tubulin fluorescence. (A-C) S2R+ cells fixed and stained with anti-alphatubulin antibody following treatment with (A) control RNAi, (B) Oya RNAi,or (C) 290µM Mizoribine. Images are presented using the same grayscale for direct comparison. Scale bar is 10 µm. (D) Quantification of themean (± SEM) of the integrated tubulin fluorescence intensity for control
RNAi (dark blue circles), Oya RNAi (medium blue circles), and 290 µM
Mizoribine (cyan blue circles). There was a statistically significant decrease
in integrated tubulin fluorescence following Oya RNAi treatment or treat‑ment with Mizoribine as compared to control RNAi treated cells (*p-value= 0.0143,****p-value <0.0001, one-way ANOVA with Tukey’s post-hoc
analysis, N = 43-84 cells).
Acknowledgements
We acknowledge the Drosophila Genomics Resources Center (DGRC)(National Institutes of Health [NIH] grant 2P40OD010949 to DGRC). Membersof Reed College’s Computational Systems Biology and Cell Biology courses,
Greta Glover and Kristine Hayes for their support with equipment andreagents, and members of the Ritz and Applewhite Lab for their thoughtful
discussions.
Sources of cell culture
All cell lines were initially obtained from the Drosophila Genomics Resource
Centers (DGRC, Bloomington, IN).
Authors’ contributions
A.R. and D.A.A. acquired funding and oversaw all experiments, analyses, andinterpretation of the data. A.Z., S.V., M.O.B., V.D., D.A.A, and A.R. wrote thePage 19 of 21

manuscript. A.Z., D.A.A, and A.R. prepared the figures. A.Z., M.O.B., V.D., A.P., A.C.,
D.N., and A.T., conducted all the cell-based experiments and performed thecorresponding data analysis. P.W., M.B., W.G., E.K., K.T., and L.T. performed andanalyzed computational experiments. All authors reviewed the manuscript in
preparation for publication.
Funding
This work was supported by the National Science Foundation (NSF 716964 to
D.A.A. and A.R., and NSF DBI 1750981 to A.R.) and the Reed College Depart‑
ment of Biology.
Availability of data and materials
All data and materials pertinent to this study will be made available upon
reasonable request.

Declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Received: 4 July 2023 Accepted: 29 September 2023

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