CELLULAR MECHANISMS THAT PROMOTE THE COLLECTIVE
MIGRATORY BEHAVIOR OF DROSOPHILA BORDER CELLS
by
GEORGE GIL F. ARANJUEZ
Submitted in partial fulfillment of the requirements for the degree of
Doctor of Philosophy
Department of Genetics and Genome Sciences
CASE WESTERN RESERVE UNIVERSITY
August, 2015 CASE WESTERN RESERVE UNIVERSITY
SCHOOL OF GRADUATE STUDIES
We hereby approve the dissertation of
George Gil F. Aranjuez
candidate for the degree of Doctor of Philosophy.
Committee Chair
Dr. Helen Salz
Committee Member / Mentor
Dr. Jocelyn McDonald
Committee Member
Dr. Heather Broihier
Committee Member
Dr. Hua Lou
Date of Defense
July 7, 2015
*We also certify that written approval has been obtained
for any proprietary material contained therein.
2 TABLE OF CONTENTS
LIST OF TABLES ...... 7
LIST OF FIGURES ...... 8
ACKNOWLEDGEMENTS ...... 11
ABSTRACT ...... 13
CHAPTER 1. INTRODUCTION ...... 15
COLLECTIVE MIGRATION DURING NORMAL DEVELOPMENT ...... 16
Collective movement of two-dimensional cell sheets ...... 16
Sprouting and branching involves leading tip cells that pull the cells
behind to form new structures ...... 18
Freely-migrating collectives ...... 19
COLLECTIVE CELL MIGRATION IN CANCER ...... 21
Maintenance of cell-cell adhesions ...... 22
Response to growth factors ...... 22
Remodeling and responding to the environment ...... 23
HALLMARKS OF COLLECTIVE MIGRATION ...... 24
Control of protrusion formation and directionality ...... 24
Constant cell-cell interaction ...... 25
Migration of border cells occur in a ligand-guided fashion ...... 29
Precise control of border cell protrusions is essential for efficient cluster
migration ...... 30
3 OUTSTANDING QUESTIONS PERTAINING TO COLLECTIVE BORDER
CELL MIGRATION ...... 32
CHAPTER 2. ON THE ROLE OF PDZ DOMAIN-ENCODING GENES IN
DROSOPHILA BORDER CELL MIGRATION ...... 42
INTRODUCTION ...... 43
RESULTS ...... 46
RNAi knockdown of PDZ domain-encoding genes in border cells ...... 46
Validation of candidates ...... 50
Investigation of two genes, bbg and CG6509, reveals distinct functions in
border cells ...... 53
DISCUSSION ...... 57
RNAi knockdown of specific classes of genes to identify regulators of
border cell migration ...... 57
Epithelial polarity and cytoskeletal-associated genes are highly
represented hits ...... 60
Roles of Bbg and CG6509 in cell migration ...... 63
MATERIALS AND METHODS ...... 64
Drosophila Genetics ...... 64
In vivo RNAi Knockdown ...... 65
Quantitative RT-PCR Analysis of Gene Expression ...... 66
Immunostaining and Microscopy ...... 67
Calculation of Stat92E/DAPI Intensity Ratio ...... 68
Graphs, Statistics, and Figures ...... 69
4 CHAPTER 3. DOP KINASE IS REQUIRED FOR STEREOTYPIC PROTRUSION
FORMATION IN COLLECTIVELY MIGRATING BORDER CELL
CLUSTERS IN DROSOPHILA ...... 100
INTRODUCTION ...... 101
RESULTS AND DISCUSSION ...... 104
Loss of dop in border cells results in failure to complete migration ...... 104
Loss of dop does not affect border cell position in the cluster ...... 107
Loss of dop results in multiple protrusions ...... 108
Dop mutant clusters extend misshapen protrusions ...... 110
Loss of dop does not alter the levels of F-actin or microtubules in border
cells ...... 110
Altering microtubule dynamics does not rescue the migration defects due
to dop RNAi ...... 111
CONCLUSION AND FUTURE DIRECTIONS ...... 112
MATERIALS AND METHODS ...... 114
Drosophila genetics ...... 114
Immunostaining and imaging ...... 115
Figures, Graphs, and Statistics ...... 115
CHAPTER 4. COLLECTIVE CELL SHAPE REQUIRES MYOSIN ACTIVITY
AT THE GROUP PERIPHERY DURING IN VIVO MIGRATION ...... 130
INTRODUCTION ...... 131
RESULTS AND DISCUSSION ...... 133
5 Myo-II is required for border cells to maintain cluster shape during
migration ...... 133
Activated Myo-II is localized to the cluster periphery and promotes cell
and cluster shape ...... 135
Maintenance of cluster shape requires dynamic Myo-II ...... 138
Nurse cell confinement influences border cell cluster shape and migration
...... 141
MATERIALS AND METHODS ...... 143
Drosophila strains and genetics ...... 143
Immunostaining and imaging ...... 145
RT-PCR ...... 146
Image Analyses, Figures, Graphs and Statistics ...... 146
CHAPTER 5. FINAL DISCUSSION ...... 165
Continue to develop experimental models of collective migration ...... 165
Strategies for identifying new components of collective migration
mechanisms ...... 168
Studying the interplay between collective migration and the environment
...... 170
Cortical tension at the collective level ...... 171
BIBLIOGRAPHY ...... 176
6 LIST OF TABLES
TABLE 2-1. High confidence PDZ domain-encoding genes in border cell migration
identified by RNAi knockdown ...... 82
TABLE 2-2. Expression Levels and RNAi Knockdown Efficiency as Measured by
Quantitative RT-PCR ...... 84
Table 2-S1. Complete results of the PDZ RNAi survey of border cell migration ...... 86
7 LIST OF FIGURES
Figure 1-1. Examples of collective migration in normal development ...... 34
Figure 1-2. Border cell migration occurs during mid-oogenesis ...... 36
Figure 1-3. Strict regulation of protrusion formation is the key to efficient migration of
the border cell cluster ...... 38
Figure 1-4. Components of the cluster-level cellular program that controls protrusion
formation in border cell clusters ...... 40
Figure 2-1. In vivo RNAi knockdown to identify PDZ domain-encoding genes required
for border cell migration ...... 70
Figure 2-2. Confirmation of positive and negative hit genes ...... 73
Figure 2-3. The multi-PDZ domain protein Big Bang regulates nuclear STAT levels in
border cells ...... 75
Figure 2-4. Markers of cell fate, cell adhesion, polarity and cytoskeleton in bbg RNAi
and CG6509 RNAi border cells ...... 78
Figure 2-5. The MAGUK famiy member CG6509 regulates border cell cluster
morphology ...... 80
Figure 2-S1. Drosophila PDZ-domain-containing proteins are functionally diverse and
involved in numerous processes ...... 92
Figure 2-S2. slbo-GAL4-driven RNAi expression targeting three positive candidates ... 94
Figure 2-S3. Gene expression levels in control and RNAi knockdown measured by
quantitative RT-PCR ...... 96
8 Figure 2-S4. Bbg function in early development of border cells ...... 98
Figure 3-1. Collectively migrating border cells in the Drosophila ovary and the
Drosophila Dropout protein ...... 116
Figure 3-2. RNAi knockdown of dop in border cells results in migration defects ...... 118
Figure 3-3. Mechanism for generating and identifying mosaic clones ...... 120
Figure 3-4. Dop mutant border cell clusters fail to complete migration ...... 122
Figure 3-5. Dop mutants do not affect single cell motility of border cells within the
cluster ...... 124
Figure 3-6. Loss of dop results in ectopic, misshapen protrusions ...... 126
Figure 3-7. Dop likely functions to control border cell microtubule dynamics ...... 128
Figure 4-1. Myo-II maintains the shape of the border cell cluster ...... 149
Figure 4-2. Activated Myo-II localizes to the cluster periphery and promotes cell and
cluster shape ...... 151
Figure 4-3. Cluster shape correlates with dynamic Myo-II activity at the cluster periphery
...... 153
Figure 4-4. Border cells have elevated Myo-II activity upon nurse cell compression ... 155
Figure 4-S1. Specificity and efficiency of RNAi knockdown ...... 157
Figure 4-S2. Expression patterns of c306-GAL4 and c306-GAL4, ts-GAL80 ...... 159
Figure 4-S3. Border cell migration and protrusion defects when Myo-II activity is altered
...... 161
9 Figure 4-S4. Localization of Rok, Rho and Sqh-EE in migrating border cells ...... 163
10 Acknowledgements
I am honored and blessed to cross paths with intelligent and supportive people during the 5 years of my graduate work. My growth as a scientist would not have been possible without their influence.
First, I would like to thank my mentor, Dr. Jocelyn A. McDonald. I will remember her as someone who strives to maintain a positive environment in the laboratory, which allows me and the other lab members to remain motivated and excited to work. Jocelyn believes firmly that a scientist should be able to communicate his/her work effectively to anyone. In this, she has put in a lot of her time and effort to help me become a better advocate of my work. Jocelyn also showed me that one of most exciting things in science is stumbling upon the unexpected. Jocelyn is a true mentor, in the purest sense of the word, and is very committed to the growth and success of the people in her lab, both in science and in life.
I also thank the members of my thesis committee, Drs. Heather Broihier, Hua
Lou, and Helen Salz. Together with my mentor, they were instrumental in guiding my progress and eventual success in earning my doctorate degree. They are quick to offer honest and constructive critiques on my work and are quicker to celebrate the breakthroughs and successes.
I would also like to acknowledge the Cleveland Drosophila community, a group of fun and quirky professors, postdocs, and students obsessed about fruit flies. They are an endless fountain of new ideas and points of view that keep our research fresh and exciting. In the same vein, I would like to acknowledge the larger, international
Drosophila community for exemplifying openness and collaboration.
11 I thank the past and present members of the McDonald lab for their help and support during the day-to-day business of doing research. I would also like to acknowledge the people and my peers from the CWRU Genetics and CCF Molecular
Genetics departments who shared this journey with me and have inspired me to work harder and do better.
I thank my family for their quiet sacrifice and unfailing support. A career in science demands a lot of time and energy, often at their expense. The weekends are often scheduled around visits to the lab. Dinner conversations turn into impromptu lab meetings. Through it all, my family has been my anchor.
Truly, I could not have accomplished this without each and everyone one of you.
12 Cellular Mechanisms that Promote the Collective
Migratory Behavior of Drosophila Border Cells
Abstract
by
GEORGE GIL F. ARANJUEZ
Collective migration is a type of cell movement wherein groups of cells move together as a coherent unit. Collective migration is involved in multiple aspects of development across many organisms. Different forms of collective migration are observed in many biological processes such as gastrulation, tubulogenesis, neural crest migration and wound healing. Cancer cells can also spread via collective migration in the form of cellular streams. The hallmark of collective migration is the action of cellular mechanisms that control and shape the motility of the individual cells in the group.
Despite the broad impact on normal development and disease, the molecular mechanisms that facilitate collective migration are still not well understood. I used border cell migration in the Drosophila ovary to study the mechanisms that promote collective movement. Border cell migration occurs in the egg chamber, which is composed of an oocyte and supportive nurse cells enveloped by a monolayer of follicle cell epithelium.
During oogenesis, border cells originate from the follicle epithelium, assemble into a cluster of 4-6 cells and migrate in a ligand-guided fashion in between the nurse cells to reach the oocyte. Through a genetic screen, I identified new genes that are important for
13 border cell migration. One of these genes, the serine/threonine kinase drop out (dop), promotes the stereotypic migratory behavior of the border cell cluster. Dop mutant border cell clusters extend ectopic protrusions and fail to complete migration. In vivo, collectively migrating cells maintain group cohesion while negotiating various physical hindrances such as the extracellular matrix or other cells. Border cell clusters squeeze in between larger cell types while maintaining a compact shape. I discovered waves of actomyosin contractility at the periphery of the cluster. Loss of peripheral actomyosin contractility leads to the deformation of the cluster and failure to complete migration. It is important to identify these supracellular mechanisms to understand collective migration.
14 Introduction
Collective cell migration is a type of cell movement wherein a population of cells moves together as a coordinated group. Different modes of collective migration feature prominently in different aspects of normal development, wound healing, as well as metastasis (Friedl and Gilmour, 2009). The key to efficient collective migration is the ability to orchestrate the migratory behavior of the individual cells to achieve coordinated movement. The mechanisms that facilitate this coordination are still poorly understood.
In particular, there is a need to improve our understanding of the specific cellular mechanisms that collectives use to orchestrate coordinated movement of individual cells.
Also, migrating collectives are subject to the physical constraints of their microenvironment (Friedl and Wolf, 2010). Cellular strategies used by collectives in response to outside forces that restrict migration are not well characterized.
Understanding how cells move collectively can help in the creation of interventions and therapies against developmental disorders and cancer metastasis.
We can study collective cell migration effectively using the unique model of border cell migration in the Drosophila ovary. Using this approach, we were able to identify new genes that facilitate collective migration and to study the interaction between migrating collectives and their microenvironment. Working with Drosophila allows for a high degree of genetic manipulation that facilitated the identification of novel regulators of border cell migration and the interrogation of their cellular function.
Drosophila border cell migration is also accessible to high-resolution microscopy of both fixed, immunostained tissue as well as live imaging. Microscopy is a powerful and necessary tool for studying collective migration due to its ability to visualize cell
15 morphology and dynamics. Established genetic tools allow for independent manipulation of the migrating border cells and its cellular environment. Therefore, border cell migration can be used to investigate the effect of the environment on migrating collectives.
Here, I describe my approach to identifying novel genes that regulate collective border cell migration. I discovered and characterized the role of a gene that is required for the coordination of individual border cell motility. I also uncovered a mechanism that actively maintains the compact border cell cluster organization as it squeezes in between other cells during migration. These discoveries contribute to our understanding of the mechanisms that collectively migrating cells use to coordinate their movement.
Collective migration during normal development
Collectively migrating cells can organize themselves in different configurations, each one utilizing a different strategy to accomplish cohesive cell movement. Here, I describe a few examples of collective migration during normal development and the specific mechanisms that are important for collective movement.
Collective movement of two-dimensional cell sheets
Epithelial cells form a contiguous monolayer or sheet of cells, held tightly together by cell-cell adhesions. Collective movement of cell sheets is characterized by the unified motion of a large number of cells with little to no rearrangement of cells
(Figure 1-1A).
Gastrulation involves complex movements of sheets of cells. Collective migration is observed as early as gastrulation--the earliest morphogenetic step in embryogenesis
16 that establish the primordial germ layers from which all adult structures stem from.
Individual cell tracking of Drosophila and zebrafish embryos undergoing gastrulation reveals a high degree of coordinated movement of individual cells (Arboleda-Estudillo et al., 2010; McMahon et al., 2008). The initiation, duration, and directionality of movement across a large number of cells is identical and synchronous (Arboleda-
Estudillo et al., 2010; McMahon et al., 2008). Interestingly, disruption of E-cadherin- based adhesions between mesendodermal cells of the Zebrafish embryo did not affect motility of individual cells but disrupted their coordinated movement (Arboleda-Estudillo et al., 2010).
Wound healing is accomplished through directed migration of epithelial sheets towards the cell-free space. Epithelial cells line the inner and outer surfaces of various organs in the body in a contiguous monolayer. Upon injury, the epithelial cells move into the wounded cell-free space and restore a contiguous epithelium through a process called sheet migration (Figure 1-1A). With the loss of its neighbors, the cells at the wound edge extend protrusions and migrate towards the cell-free space (Fenteany et al., 2000).
Interestingly, directed migration is observed in cells away from the leading edge. Careful imaging of Madine-Darby canine kidney (MDCK) cells away from the wound edge reveals cryptic protrusions that originate from the basolateral domain and extend under the adjacent cell and oriented towards the wound (Farooqui and Fenteany, 2005). Cell- cell adhesions between the epithelial cells not only maintain a contiguous epithelium but also transmit the pull of the leading edge pioneer cells to follower cells many rows away from the leading edge to orient the cells for coordinated migration (Vitorino and Meyer,
2008; Vitorino et al., 2011). The concerted migration of epithelial cells eventually fills in
17 the wound and restores epithelial integrity. Interestingly, loss of cell-cell adhesions does not inhibit single cell motility, as epithelial cells continue to migrate into the wound, albeit at a much slower rate (Vitorino et al., 2011). Furthermore, individual cells break off the leading edge and undergo single cell migration, which results in inefficient wound closure. Epithelial cells undergoing collective sheet migration utilize cell-cell adhesion to accomplish their biological role—to maintain an intact epithelium.
Sprouting and branching involves leading tip cells that pull the cells behind to form new structures
The growth of new blood vessels is an example of the sprouting type of collective migration. Blood vessel sprouting is led by a migratory endothelial tip cell that spearheads the growth of the nascent blood vessel towards gradients of Vascular
Endothelial Growth Factor (VEGF)-A (Figure 1-1B) (Gerhardt et al., 2003; Ruhrberg et al., 2002). Behind the tip cell are non-migratory endothelial stalk cells that maintain VE- cadherin-based adhesions with the tip cell and with each other to keep the blood vessel lumen open as the nascent blood vessel lengthens (Figure 1-1B) (Gerhardt et al., 2003).
Stalk cells are pulled behind the advancing tip cell but also undergo proliferation to provide a sufficient population of endothelial cells to support the growing blood vessel
(Gerhardt et al., 2003). The sprouting of blood vessels is necessary to maintain efficient gas exchange, nutrient delivery, and waste transport in the growing embryo as well during wound repair and regeneration. A similar progression of events occurs during the growth and branching of respiratory tracheal tubes in Drosophila (Affolter and
Caussinus, 2008).
18 Freely-migrating collectives
Neural crests efficiently migrate together in loose aggregates. Neural crest cells are a population of migratory cells originating from the interface of the neuroepithelium and the presumptive epidermis on the dorsal side of the developing vertebrate embryo.
Neural crests migrate as a stream of cells along distinct tracts, the sorting of which is mediated by ephrin/Eph receptor pairings and class3-semaphorins and neuropilin/plexin receptors (Figure 1-1C) (Theveneau and Mayor, 2012). Neural crest cells reach various targets such as the head, the heart, and the gut where they differentiate into multiple cell types that are required for the formation of various craniofacial structures such as the inner ear, normal heart development, adrenal gland development, and innervation of the gut (Mayor and Theveneau, 2013). There are multiple factors that positively regulate and guide neural crest cells, depending on the target tissue, such as the growth factors VEGF,
PDGF, FGF, and Stromal cell-derived factor 1 (Sdf1) (Theveneau and Mayor, 2012).
Neural crest cells are mesenchymal and do not form stable cell-cell adhesions. Instead, the counterbalanced effects of contact inhibition of locomotion (CIL) and short-range co- attractant signaling keeps the neural crest cells within close proximity of one another as they migrate as a group (Figure 1-1C) (Carmona-Fontaine et al., 2011). CIL is a phenomenon wherein a migrating cell, upon contact with another cell, collapses its protrusions and migrates in a different direction (Abercrombie and Heaysman, 1953).
Surprisingly, neural crest cells form transient cell adhesions upon contact, resulting in protrusion collapse via the inhibition of the small GTPase Rac1 and a change in migration direction by promoting microtubule collapse at the contact site (Moore et al.,
2013). Blocking CIL results in the failure of neural crest migration to progress (Carmona-
19 Fontaine et al., 2011; Moore et al., 2013). If CIL alone were active in neural crest cells, the result would be the eventual dispersal of neural crest cells (Carmona-Fontaine et al.,
2011). Neural crest cells migrate as an aggregate without the use of stable cell-cell adhesions, suggesting that another mechanism is counteracting the dispersing tendency of
CIL. Indeed, neural crest cells exhibit co-attraction mediated by paracrine chemokine signaling via the complement c3a and its cognate receptor, c3aR (Carmona-Fontaine et al., 2011) to counterbalance CIL to maintain the appropriate cell density for efficient collective migration.
Zebrafish lateral line. During zebrafish embryonic development, a population of cells called the lateral line primordium originates in the head region and migrates caudally along the midline on each side of the embryo. The primordium is made up of
>100 cells that migrate as group, seeding the midline with the precursors of the rosette- like mechanosensory structures, called neuromasts, of the adult lateral line sensory organ
(Ghysen and Dambly-Chaudière, 2007). Lateral line primordium cells express CXCR4 receptors that detect the SDF1 chemokine ligand along the midline axis on which it migrates. Loss of the SDF1 ligand or the CXCR4 receptor is detrimental to the lateral line primordium migration (David et al., 2002; Haas and Gilmour, 2006; Li et al., 2004).
Interestingly, the directionality of the migration is not via a gradient of SDF1, as no such gradient can be detected at the level of protein expression (Haas and Gilmour, 2006). In fact, upon encountering a gap in the SDF1 path, the primordium can occasionally reverse direction (Haas and Gilmour, 2006). If the environment does not set directionality, then it must be intrinsic to the collective itself. A key discovery is the identification of another receptor, CXCR7, that also binds SDF1 but is only expressed in the trailing cells of the
20 primordium (Burns et al., 2006). This break in symmetry is set up prior to the start of migration. Activation of CXCR4 in the leading cells inhibits expression of CXCR7
(Dambly-Chaudière et al., 2007). On the other hand, CXCR7 acts as an SDF1 sink in the trailing cells, sequestering the SDF1 ligand away and preventing activation of the
CXCR4 receptors (Dambly-Chaudière et al., 2007). The result is activation of the
CXCR4 receptors only in the leading cells and the establishment and maintenance of front-back polarity and directionality to the cluster. This mechanism for directionality is so robust that even a few CXCR4+ cells in a CXCR4- primordium is sufficient to restore directionality (Haas and Gilmour, 2006). Motility of the lateral line primordium cells does not depend on signaling from CXCR4 (Haas and Gilmour, 2006).
Collective cell migration in cancer
There are striking similarities between the proteins and molecules that promote collective migration in cancer spread and in normal development. The spread of individual epithelium-derived cancer cells (carcinomas) has been typically characterized by the downregulation of E-cadherin and loss of cell-cell adhesions in a process called epithelial-mesenchymal transition (EMT) (Thiery, 2002). However, cancer cells can also spread as collectives, both in the actual disease state, as seen via histological sections, or in in vitro culture models (Friedl et al., 2012). Collective cancer cell spread has been observed within the normal tissue stroma or within the bloodstream or lymphatic vessels
(Friedl et al., 2012). Due to inaccessibility for direct live imaging, our understanding of how cancer cells spread collectively is limited. Therefore, continued efforts to understand collective migration in normal development, coupled with improvements in in vivo live
21 imaging of cancer spread and in vitro 3D culture, will improve our understanding of collective cancer spread.
Maintenance of cell-cell adhesions
Collective cancer spread is characterized by the maintenance of cadherin expression and consequent cell-cell junctions during invasion (Ilina and Friedl, 2009).
Collective migration is favored by incompletely de-differentiated carcinoma cells that have not lost all epithelial characteristics (Christiansen and Rajasekaran, 2006). Aside from cadherin-based adhesions, cancer cells exhibit multicellular organization through the presence of desmosomes and tight junctions, as well as gap junctions where intercellular communication can occur (Friedl and Gilmour, 2009). Desmosomes are strong intercellular junctions made up proteins such as plakoglobin, desmosomal cadherins, desmocollin, and desmoglein. Of these, plakoglobin is of particular importance as it can act as signaling molecule to activate the Wnt/beta-catenin pathway and promote tumourigenesis and/or motility by inducing epithelial-mesenchymal transition (EMT)
(Chidgey and Dawson, 2007).
Response to growth factors
Collective cancer cell spread responds positively to the same permissive factors that act on normal collectively migrating cells such as Sdf1 (neural crests and Zebrafish lateral line primordium), hepatocyte growth factor (wound healing in human umbilical vein endothelial cells [HUVECs] in culture), fibroblast growth factor (FGF), and transforming growth factor beta (TGFbeta) and other morphogenetic factors (Grünert et al., 2003). These factors can promote motility by inducing EMT or act as chemoattractants that provide directional cues to spreading cancer cells.
22 Remodeling and responding to the environment
Spreading cancer cells are known to actively modify the extracellular matrix
(ECM), which eases the passage of cells and facilitates the collective cell movement away from the main tumor body. The ECM is made up of secreted molecules that provide structural support as well as a platform for signaling to the surrounding cells. Collagen is the most abundant protein in the ECM. Fibrillar collagen is secreted to the ECM, and along with other ECM components such as fibronectin, forms a meshwork of fibers.
Secretion of proteases by pioneer cancer cells, in particular, matrixmetalloproteinases
(MMPs), facilitate cell migration through the ECM (Friedl and Wolf, 2003). Proteolysis of ECM creates microtracks of cleaved, parallel fibers (Friedl and Wolf, 2009). As the pioneer cancer cells pave the way for follower cells in a contact-dependent fashion, more
ECM is modified which results in the widening of tracks, to the point that strand-like collective migration is sustained (Friedl and Wolf, 2003; 2008).
Conversely, the microenvironment strongly influences whether cancer cells spread singly or as a collective. Cancer cells of mesenchymal origin have no stable cell- cell adhesions and are able to migrate as single cells (Theveneau and Mayor, 2013).
However, when cultured in high-density 3D collagen, cancer cells from spheroid cultures stop migrating as single cells and acquire the ability to migrate collectively (Haeger et al.,
2014). Furthermore, these cells acquire cell-cell junctions and leader-follower group organization (Haeger et al., 2014). Environment-induced collective migration of mesenchymal cancer cells is dependent on ECM remodeling via MMPs (Haeger et al.,
2014). This emergent mode of collective migration of mesenchymal tumors due to a space-constraining microenvironment is termed ‘jamming’ (Sadati et al., 2013).
23 Hallmarks of collective migration
Despite the variety, from epithelial sheet migration to self-contained groups freely migrating within other tissues, all of collective migration relies on cellular mechanisms that specifically promote coordinated movement by orienting and controlling the migratory behavior of the individual cells. These mechanisms operate, not at the single- cell level, but at the level of the group or collective. Disruption of these cellular mechanisms does not impact single cell motility but is detrimental to collective migration.
Control of protrusion formation and directionality
Efficient group movement of a population of motile cells involves the precise coordination of individual cell motility to add up towards unified group movement. This relies on cellular mechanisms that can orchestrate the motile behavior of individual cells in the group.
Protrusions are membrane deformations that are absolutely required for any form of cell motility (Pollard and Borisy, 2003). The role of protrusions is to establish new anchor sites to the substrate, either the ECM or other cells, which determine the direction of migration (Ridley, 2011). Most migratory cells utilize F-actin polymerization to drive membrane extensions. A type of protrusion called blebs do not have an underlying F- actin structure but are instead generated by a focal decrease in cortical tension, allowing the intracellular hydrostatic pressure to create small, globular membrane extrusions
(Charras and Paluch, 2008; Fackler and Grosse, 2008; Paluch and Raz, 2013). In both cases, the cell is able to expand its contact with the substrate and establish new anchor sites, which help in the moving the cell forward.
24 In collective migration, mechanisms are in place to ensure that the combined protrusive activities of all cells result in efficient forward movement. For example, epithelial cells undergoing sheet migration during gastrulation or wound repair all orient their protrusions towards the direction of migration (McMahon et al., 2008; Vitorino et al., 2011). This behavior relies on stable adhesions between cells. Loss of cell-cell adhesion does not impact individual cell motility, as close observation shows that cells are still able to extend protrusions (Arboleda-Estudillo et al., 2010; Vitorino et al., 2011).
The protrusions, however, are oriented in random directions, which is highly detrimental to collective migration (Arboleda-Estudillo et al., 2010; Vitorino et al., 2011). Collective migration of Drosophila border cells, on the other hand, achieve efficient group movement by restricting protrusion formation to just the leading cell at any given moment (Prasad and Montell, 2007). This model of collective migration will be discussed in detail further into this chapter.
Constant cell-cell interaction
The most common hallmark of collective migration is the maintenance of stable contacts with the members of the collective. Cell-cell interactions can fulfill two functions in collective migration: 1) physically coupling cells together to maintain structural integrity during migration; and 2) mechanotransduction of cues to orient cells to the direction of migration.
Stable cell-cell adhesions maintain structural integrity of the collective. During blood vessel sprouting, only the leading tip cell is motile while the trailing follower cells are non-motile (Eilken and Adams, 2010; Gerhardt et al., 2003). Stable cell-cell adhesions allow the migratory tip cell to pull the non-motile follower cell as the nascent
25 blood vessel lengthens (Eilken and Adams, 2010; Gerhardt et al., 2003). A combination of proliferation and movement of follower cells into the growing blood vessel sustains the growth of the blood vessel(Eilken and Adams, 2010; Gerhardt et al., 2003). Also, stable cell-cell adhesions ensure that epithelial integrity is maintained as the epithelial sheet advances during wound closure (Li et al., 2012; Vitorino et al., 2011). In both situations, loss of cell-cell adhesion does not impact motility of the leading tip cell during sprouting or individual epithelial cells during wound healing (Gerhardt et al., 2003;
Vitorino et al., 2011)(ref). However, it is detrimental to the biological process that these cells participate in.
Cell-cell interactions influence individual migratory behavior. E-cadherin-based adhesions between epithelial cells ensure robust and uniform directionality of migration of cells far from the leading edge. In wound healing, the cells immediately adjacent to the wound find themselves at the leading edge of the migrating epithelial sheet, moving to fill in the cell-free space (Farooqui and Fenteany, 2005; Vitorino et al., 2011). There is an overt directional cue for leading edge cells provided by the loss of neighboring cells after wounding. However, the cells away from the leading edge do not experience this cue.
Instead, these cells rely on information relayed via E-cadherin-based adhesions to orient and sustain their migration (Farooqui and Fenteany, 2005). As the leading edge cells move towards the wounds, it exerts a pulling force on the cells immediately behind via E- cadherin-based adhesions. A pulling force from the front biases the follower cell's movement towards the forward direction (Vitorino et al., 2011). As the follower cell moves forward, it experiences resistance or drag from the cell immediately behind it, again via E-cadherin-based adhesion. Surprisingly, this drag from the rear enhances the
26 forward direction of the follower cell through a series of signaling pathways that originate from the rear E-cadherin-based adhesions to the protrusion machinery at the front (Weber et al., 2012). These two E-cadherin-based mechanisms ensure the directional and sustained migration of the epithelial sheet towards the cell free space during wound healing.
Drosophila border cells are an excellent model for studying freely-migrating collectives
Border cells migration occurs in the egg chamber, the basic unit of oogenesis
A pair of ovaries in the adult female abdomen can produce 50-70 mature eggs per day. Each ovary is made up of tube-like ovarioles, which appears and functions like an egg production line (Bastock and St Johnston, 2008). At the distal tip of the ovariole is the germarium, which houses both germline and somatic stem cells. Meiosis with incomplete cell division of the germline results in a 16-cell syncytium, which is then packaged by a single later of somatic follicle epithelium (Bastock and St Johnston, 2008).
This newly formed egg chamber buds off the germarium and continue its development as it travels down the length of the ovariole as new egg chambers are assembled behind it
(Figure 1-2A). One of germline cells becomes the oocyte and occupies the posterior pole of the egg chamber (Grieder et al., 2000). The 15 other germline cells become supportive nurse cells, manufacturing protein and RNA that is deposited into the growing oocyte via the network of interconnected ring canals (Bastock and St Johnston, 2008).
Border cell migration occurs during mid-oogenesis wherein 4-6 cells from the anterior follicle epithelium turn into migratory border cells (Montell et al., 2012). The border cells form a cluster and migrate in between the intervening nurse cells to reach the
27 anterior border of the oocyte (Figure 1-2B). In late oogenesis, the somatic follicle epithelium envelops the mature oocyte and forms the egg shell and external appendages
(Bastock and St Johnston, 2008). The border cell cluster reincorporates into the follicle epithelium and forms the micropyle, a pore on the eggshell and the site of sperm entry
(Montell et al., 1992). Thus, border cell migration is critical for fertility.
Border cells arise from non-motile follicle cells via local activation of JAK/STAT signaling
Paracrine signaling at the anterior pole of the egg chamber follicle epithelium transforms non-motile follicle cells into migratory border cells. A pair of specialized cells called polar cells sits at both poles of egg chamber follicle cell epithelium (Figure 1-2B).
The polar cells secrete the chemokine Unpaired to the surrounding follicle cell epithelium, activating JAK/STAT signaling (Beccari et al., 2002; Starz-Gaiano et al.,
2009; Van de Bor et al., 2011). The follicle cells with the strongest JAK/STAT signaling activation undergo a cell fate switch from non-migratory follicle cells to motile border cells.
This cell fate switch is marked by the expression of the C/EBP transcription factor, Slbo, which turns on a collection of genes important for border cell fate and motility (Montell et al., 1992; Wang et al., 2006). Genes upregulated by the Slbo transcription factor are involved in or predicted to play a role in actin dynamics (Wang et al., 2006). This is not surprising since cell motility is dependent on actin-based protrusions (Wang et al., 2006). Genes involved in the secretory and endocytic pathways are also upregulated in border cells, suggesting that trafficking to and from the membrane is important for normal border cell function (Wang et al., 2006). We now know that
28 endocytosis of active guidance receptors is important for signal transduction and subsequent normal migration via the action of Rab5- and Rab11-dependent endosomes
(Assaker et al., 2010; Ramel et al., 2013). Furthermore, maintenance of cell adhesions requires trafficking and turnover of E-cadherin(Baum and Georgiou, 2011; Langevin et al., 2005). Border cell clusters have a stereotypic pattern of E-cadherin localization that is maintained throughout migration, likely by E-cadherin trafficking to and from the membrane (Cai et al., 2014; Niewiadomska et al., 1999).
Around 4-6 border cells assemble into a cluster with the pair of non-motile polar cells at the middle (Figure 1-2C). This configuration is maintained by having stronger cell adhesion between the two polar cells, at polar cell-border cell junctions, and at border cell-border cell junctions and weaker adhesion between border cells and the surrounding nurse cells (Cai et al., 2014; Niewiadomska et al., 1999).
Migration of border cells occur in a ligand-guided fashion
Timing of border cell migration initiation is controlled via insect steroid hormone, ecdysone (Bai et al., 2000). Peak ecdysone levels coincide with the initiation of border cell migration (Figure 1-2A) (Montell et al., 2012). Border cells that do not express the ecdysone receptor complex or its coactivator, Taiman (Tai), results in failure or delay of border cell migration independent of Slbo-driven cell fate specification (Bai et al., 2000).
The efficient and directed migration of the border cell cluster depends on ligand guidance signaling from the oocyte (Figure 1-2B). Border cells express two receptor tyrosine kinases with confirmed ligands from the oocyte. One of the receptors is PVR
(platelet-derived growth factor [PDGF] / vascular endothelial growth factor [VEGF] receptor) and its ligand PVF1, expressed in the oocyte (Duchek et al., 2001). The other
29 receptor is EGFR (epidermal growth factor receptor) and two ligands, Keren and Spitz
(Duchek and Rørth, 2001; McDonald et al., 2006). The two receptors, PVR and EGFR, act in a redundant function and strong migration defects are only observed when both receptors are simultaneously disrupted (Duchek and Rørth, 2001; Duchek et al., 2001;
McDonald et al., 2006; 2003). A third potential receptor tyrosine kinase, Tie, was discovered through gene expression profiling of border cells (Wang et al., 2006). Mutants of Tie interact genetically with the other receptor tyrosine kinases in a manner consistent with a predicted guidance receptor function (Wang et al., 2006). However, no ligand has been identified. Active guidance receptors are enriched at the front of the cluster early in border cell migration, a phenomenon dependent on endocytosis (Assaker et al., 2010;
Jékely et al., 2005). Disrupting endocytosis results in the abolishment of active RTK at the front of the cluster and is associated with migration defects (Assaker et al., 2010;
Jékely et al., 2005).
Precise control of border cell protrusions is essential for efficient cluster migration
The border cell cluster only allows the leading border cell to extend a protrusion and inhibits protrusion formation from all other border cells (Figure 1-3A). This observation was made possible via the development of proper culture conditions of egg chambers, allowing for live imaging of border cell migration within an intact egg chamber ex vivo (Prasad and Montell, 2007). Remarkably, this precise protrusion control is maintained despite constant rearrangement of border cells within the cluster
(Figure 1-3A) (Prasad and Montell, 2007), which suggests that the incompletely understood cellular mechanism behind this protrusion behavior constantly verifies the direction of migration.
30 In conjunction with guiding migration, ligand guidance signaling is central to the characteristic protrusion behavior of border cell clusters. Loss of guidance receptor activity using dominant-negative mutants of PVR and DER results in ectopic protrusions from border cells away from the leading edge (Figure 1-3B) accompanied by strong migration defects (Poukkula et al., 2011; Prasad and Montell, 2007). This is contrary to the model of growth factor-mediated chemotaxis, wherein activation of guidance receptors results in protrusion formation and subsequent directed migration (Burridge and
Wennerberg, 2004). Therefore, the default behavior of border cells is to extend protrusions. Furthermore, ligand guidance signaling is a component of a cluster-level mechanism that facilitates collective migration of border cells by only allowing for protrusion formation from the leading border cell cluster and restricts protrusions everywhere else (Figure 1-4A).
Rac1 activation is localized to the front of the migrating border cell cluster and is dependent on ligand guidance signaling (Figure 1-4A, B). Rac1 is a small GTPase belonging to the Rho superfamily and is critical for cell motility, particularly in regulating actin dynamics at the membrane (Ridley, 2006; Wittmann et al., 2003). A transgenic biosensor expressed in border cells that reports Rac1 activation under live imaging reveals that Rac1 is active at the front of the cluster during migration (Wang et al., 2010b). This asymmetric activation is dependent on ligand guidance signaling (Wang et al., 2010b). Furthermore, direct activation of Rac1 using a photoactivatable construct is sufficient to cause a border cell protrusion to form at the site of activation (Wang et al.,
2010b). This is accompanied by retraction of protrusions away from the activation site
(Wang et al., 2010b). This is compelling evidence that the stereotypic protrusion behavior
31 of the border cell cluster is determined by Rac1 activation at the front of the cluster downstream of ligand guidance signaling.
Border cells communicate with each other using mechanotransduction via E- cadherin for normal protrusion control and efficient cluster migration (Figure 1-4B). High levels of E-cadherin are found at border cell-border cell boundaries (Niewiadomska et al.,
1999). Aside from its cell adhesion function, E-cadherin also plays a role in signaling via mechanotransduction between border cells in the cluster (Cai et al., 2014). Reduction of
E-cadherin in border cells results in multiple protrusions emanating from the cluster, mimicking the loss of guidance receptors (Cai et al., 2014). Furthermore, localized Rac1 activation at the front of the cluster disappeared upon E-cadherin reduction (Cai et al.,
2014). Last, targeted knockdown of E-cadherin in border cells with active Rac1 does not inhibit protrusion formation for the other border cells (Cai et al., 2014). In all, this shows that inter-border cell communication is mediated through E-cadherin.
Outstanding questions pertaining to collective border cell migration
The components of the mechanism that control border cell protrusions are not completely known. Recent studies have identified three important components of border cell protrusion control: 1) ligand guidance signaling; 2) asymmetric Rac1 activation at the front of the cluster; and 3) mechanotransduction via E-cadherin (Figure 1-4A). It is still unclear how Rac1 activation at the front of the cluster is maintained. There is no detectable difference in guidance receptor activation at the front and the rear of the cluster during migration (Cai et al., 2014). To fully understand collective border cell migration, it is important to identify more genes that play a role in maintaining the stereotypic protrusive behavior of border cell clusters.
32 How does the border cell cluster maintain its organization while squeezing in between the nurse cells? An underappreciated aspect of border cell migration is that the cluster has to migrate in between tightly packed nurse cells to reach the oocyte. Despite the mechanical constraints imposed by the nurse cells, the border cell cluster still migrates efficiently. It has not been shown whether the border cell cluster actively resists the compression from the nurse cells to maintain the collective.
Here I performed an RNAi-based screen to knockdown a curated list of genes in border cells, which led to the identification of new genes required for border cell migration. Detailed characterization of one of the candidates from the screen, Drosophila drop out (dop) / mammalian microtubule-associated serine/threonine kinase 2 (MAST), provides evidence that Dop is required for proper control of border cell protrusions necessary for efficient migration. Last, I uncovered a new role for the actomyosin contractility in maintaining the normal organization of the border cell cluster.
Actomyosin contractility is required for maintaining cortical tension at the level of the collective, which enables the cluster to resist the mechanical compression, imposed by the surrounding cellular environment. These discoveries contribute to our understanding of the mechanisms that collectively migrating cells use to coordinate their movement.
33
Figure 1-1. Examples of collective migration in normal development
34 Figure 1-1. Examples of collective migration in normal development. (A) Sheet migration during epithelial wound healing is important for restoring and maintaining an intact epithelium. Cells immediately adjacent to the wound extend protrusions and migrate towards the cell-free space. Stable E-cadherin-based adhesions transmit the pull of the leading edge cells to the cells behind it, which induces these cells to extend cryptic protrusions, all oriented towards the cell-free space. This unified motion enables the epithelium to migrate as an intact sheet to repair the wound. (B) Sprouting of nascent blood vessels is led by a motile tip cell. The tip cell extends protrusions and migrates towards the direction of growth. Stable cell-cell adhesions allow the tip cell to pull along the non-migratory stalk cells that make up the walls of the nascent blood vessel.
Furthermore, these cell-cell adhesions ensure that the lumen is preserved. Stalk cells undergo proliferation as the nascent blood vessel lengthens. (C) Neural crest cells migrate as a loose aggregate that is actively maintained through a balance of attractive and repulsive mechanisms. Neural crest cells migrate along defined tracts during embryogenesis. Lacking stable cell-cell adhesions, neural crests nonetheless stay together via co-attraction, an attractive paracrine signaling by mediated by the secreted cytokine, complement c3a. Co-attraction is counterbalanced by a repelling mechanism called contact inhibition of locomotion. When two neural crest cells come in contact, transient adhesions involving polarity proteins result in the destabilization of the microtubule array behind the leading edge. This causes collapse of the protrusion and formation of a new protrusion away from the site of contact.
35
Figure 1-2. Border cell migration occurs during mid-oogenesis
36 Figure 1-2. Border cell migration occurs during mid-oogenesis. (A) An ovariole is made up of the germarium, which is where egg chambers are assembled, and progressively more developed egg chambers, culminating in a mature egg. The oocyte
(pink) continuously grows throughout oogenesis. During mid-oogenesis, the border cell cluster (blue, arrowheads) forms at the anterior end of the egg chamber and migrates towards the oocyte. Initiation of migration coincides with a pulse of the steroid hormone, ecdysone. (B) A detailed illustration of mid-oogenesis shows the major stages of border cell migration. The follicle cell epithelium envelops the oocyte and supportive nurse cells. A pair of polar cells (red) at the anterior pole signal to the neighboring follicle cells, causing a cell fate switch to become migratory border cells (blue). The border cell cluster, composed of 4-6 border cells and a central pair of polar cells (C), detaches from the anterior follicle epithelium. The cluster migrates in between the nurse cells towards the oocyte, directed by a gradient of guidance ligands secreted by the oocyte. The border cells complete migration once they reach the oocyte.
37
Figure 1-3. Strict regulation of protrusion formation is the key to efficient migration of the border cell cluster
38 Figure 1-3. Strict regulation of protrusion formation is the key to efficient migration of the border cell cluster. (A) Live imaging studies revealed that the border cell cluster predominantly extends a single protrusion from the leading border cell towards the direction of migration. This phenomenon occurs despite the constant shuffling and rearrangement of border cell positions within the cluster. (B) The stereotypic wildtype protrusive behavior is lost when the border cell clusters cannot detect the gradient of guidance ligands coming from the direction of migration. Border cells away from the leading edge extend ectopic protrusions that are oriented away from the direction of migration. Loss of border cell protrusion control results in migration defects.
39
Figure 1-4. Components of the cluster-level cellular program that controls protrusion formation in border cell clusters
40 Figure 1-4. Components of the cluster-level cellular program that controls protrusion formation in border cell clusters. (A) Flowchart depicting the known and unknown components that promote the stereotypic protrusive behavior of border cell clusters. (B) The guidance ligands are detected by the receptors found on the surface of the border cells. This signaling information is integrated within the cluster and results in an asymmetric Rac1 activation in the leading edge of the cluster. Elevated Rac1 activity results in the formation of protrusion at the leading edge and suppression of protrusions elsewhere. Mechanotransduction via E-cadherin across border cell-border cell boundaries is required in this process, as well.
41 On the Role of PDZ Domain-encoding Genes in Drosophila Border Cell
Migration
George Aranjuez[1,2], Elizabeth Kudlaty[3], Michelle S. Longworth[2], and Jocelyn A.
McDonald[1,2]
[1] Genetics and Genome Sciences, Case Western Reserve University, Cleveland, OH,
USA. [2] Molecular Genetics, Lerner Research Institute, Cleveland Clinic Foundation,
Cleveland, OH, USA. [3] Program in Biological Sciences, Northwestern University,
Evanston, IL, USA.
Published in the journal Genes, Genome, Genetics on November 2012. doi: 10.1534/g3.112.004093
G3 November 1, 2012 vol. 2 no. 11 1379-139
G.A. and J.A.M. conceived and designed the experiments. G.A. and E.K. performed the experiments and analyzed the data. M.S.L. performed the qPCR analysis. G.A. and J.A.M prepared the manuscript.
This is an open-access article distributed under the terms of the Creative Commons
Attribution Unported License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
42 Introduction
Regulated cell movement is critical for embryonic development, adult wound healing, and normal immune system function. Determining how cells migrate during normal processes can help us better understand how misregulated cell migration contributes to pathologies such as tumor metastasis and inflammation. While some cells migrate singly, others move as small or large groups in a type of migration called collective migration (Friedl and Gilmour, 2009). Cells migrate collectively during gastrulation in the embryo and in epithelial sheet migration during wound closure.
Notably, this type of group migration has also been observed during tumor invasion and metastasis (Friedl and Gilmour, 2009; Friedl et al., 2012; Yilmaz and Christofori, 2010).
Migrating cells display striking morphological changes induced by dynamic rearrangement of actin filaments and cell-substrate adhesions, which together provide the necessary force for movement (Ridley, 2011). Cells migrating collectively further need to coordinate such individual cell motility to precisely modulate cell-cell adhesions and the cytoskeleton among cells in the group (Friedl and Gilmour, 2009). Our current understanding of the mechanisms that regulate these and other aspects of collective cell migration in tissues is fairly limited. Therefore, we have turned to a genetically amenable model, the Drosophila border cells, to identify new genes and pathways that control collective cell migration.
Border cells migrate as a cohesive cluster of six to ten cells during late oogenesis in a highly regulated process (Montell, 2003). Border cells are first specified in the anterior follicle cell epithelium at early stage 9. The follicular epithelium is a monolayer of ~600 cells that surrounds the germline-derived cells of the egg chamber, the basic
43 subunit of the Drosophila ovary. The cytokine-like protein Unpaired (Upd) is secreted from a pair of non-migratory cells, the polar cells, to activate Janus Kinase (JAK)/Signal
Transducer Activator of Transcription (STAT) signaling in the surrounding follicle cells
(Beccari et al., 2002; Ghiglione et al., 2002; Silver and Montell, 2001; Silver et al., 2005;
Xi et al., 2003). Cells expressing the highest levels of active JAK/STAT at the anterior end of the egg chamber become border cells. The border cells form a cluster around the polar cells and subsequently detach from the epithelium. Border cells then migrate over
~150 µm distance through the germline-derived nurse cell layer to reach the oocyte
(Figure 2-1A). Previous genetic screens identified multiple essential regulators of border cell migration, including the highly conserved steroid hormone receptor and receptor tyrosine kinase (RTK) signaling pathways (Bai et al., 2000; Duchek and Rørth, 2001;
Duchek et al., 2001; Liu and Montell, 1999; Mathieu et al., 2007; McDonald et al., 2003;
Silver and Montell, 2001). However, none of the screens to date were performed to saturation and, therefore, may have missed critical genes. Moreover, despite the discovery of these and other signaling pathways, in many cases the specific downstream effectors that interpret these signals to produce specific cellular responses in border cells remain unknown.
Correct establishment of cell polarity is critical for the motility of many types of cells including border cells (Etienne-Manneville, 2008; McDonald et al., 2008;
Niewiadomska et al., 1999; Pinheiro and Montell, 2004). Proteins that regulate epithelial polarity help orient migrating cells and promote motility of both single and collectively migrating cells by organizing the cellular membrane and cytoskeleton (Etienne-
Manneville, 2008; Hidalgo-Carcedo et al., 2011; Humbert et al., 2006). Moreover, many
44 of these proteins themselves localize in a polarized manner within cells, typically at the cell cortex. Epithelial polarity proteins have also emerged as potential tumor suppressors
(Etienne-Manneville, 2008; Humbert et al., 2008; Royer and Lu, 2011). A large number of polarity proteins implicated in cell migration, such as Par-3, Patj and Dlg1, contain
PDZ (PSD95/Dlg/ZO-1) domains. The PDZ domain is a small globular module that functions as a protein-protein interaction domain (Harris and Lim, 2001; Subbaiah et al.,
2011). Specifically, PDZ domains bind to short PDZ-binding motifs (PBMs) on target proteins that are mainly, although not exclusively, found at C-termini (Harris and Lim,
2001; Subbaiah et al., 2011). PDZ domains can occur alone or as multiple copies and are often found in combination with other protein-protein interaction domains and/or catalytic domains. Proteins with PDZ domains typically mediate the formation of large multi-protein scaffolding complexes that bring molecular components into proximity with each other within the cell (Ranganathan and Ross, 1997). PDZ domain-containing proteins regulate signaling, cytoskeletal dynamics and cell adhesion in addition to polarity, all of which are important for cell motility. Moreover, the polarity PDZ proteins
Bazooka (Baz; Par-3 homolog) and Par-6 organize the localization of membrane- associated proteins within the border cell cluster to promote migration (Pinheiro and
Montell, 2004). This raises the distinct possibility that other, unidentified PDZ domain complexes regulate the polarity and collective migration of border cells.
We sought to identify additional genes that participate in border cell migration using an RNAi knockdown approach. The recent availability of large collections of UAS-
RNAi transgenic lines have made it possible to systematically analyze the roles of the majority of genes in the Drosophila genome for specific phenotypes (Dietzl et al., 2007;
45 Ni et al., 2009). These lines are used to knock down gene function in a tissue- and temporal-specific manner using the GAL4/UAS system (Brand and Perrimon, 1993;
Perrimon et al., 2010). While multiple genome-wide in vivo RNAi screens have been performed (Cronin et al., 2009; Mummery-Widmer et al., 2009; Schnorrer et al., 2010), a substantial number of lines (>12,000) is needed to screen most of the genes in the genome at least once. Moreover, large-scale screens could miss more subtle phenotypes.
Several recent studies targeted specific classes of proteins, for example Rab GTPase- activating proteins and kinases, to identify new regulators of cell migration (Laflamme et al., 2012; Simpson et al., 2008). In the present study, we specifically targeted by RNAi knockdown 64 out of 66 genes predicted to encode PDZ domain-containing proteins. We identified 14 high confidence and 17 additional PDZ domain-containing proteins whose knockdown inhibited border cell migration. We provide additional evidence that two genes, big bang and CG6509, regulate specific features of border cells. The genes identified here thus represent a group of conserved signaling pathways and/or intracellular protein complexes that may regulate other types of collectively migrating cells.
Results
RNAi knockdown of PDZ domain-encoding genes in border cells
We first sought to identify all of the Drosophila genes that encode PDZ domain- containing proteins. We used a combination of the InterPro protein signatures
(http://www.ebi.ac.uk/interpro/) and FlyBase (http://flybase.org/) databases to identify genes that have at least one PDZ domain. While the human genome encodes more than
250 PDZ domain proteins (Tonikian et al., 2008), Drosophila has 66 PDZ genes (see
46 Table 2-S1) (Bilder, 2001). Many of these genes have alternatively-spliced isoforms, making the total number of PDZ domain proteins slightly higher (Sierralta and Mendoza,
2004). We performed a gene ontology analysis to determine the types of proteins that these genes encode along with predicted functions (see Materials and Methods).
Drosophila PDZ proteins are annotated predominantly to have protein binding, structural roles and regulation of enzyme activities (see Figure 2-S1). The relatively small number of PDZ domain genes in the Drosophila genome makes it a reasonable pool of candidates to test comprehensively for their role in border cell migration.
Knockdown of the multi-PDZ domain protein Baz, which regulates border cell migration, was used as a positive control (Figure 2-1; see Table 2-S1) (Pinheiro and
Montell, 2004). We tested different GAL4 drivers to determine the best one for UAS-
RNAi knockdown. A ubiquitous GAL4 driver, tubulin-GAL4, was lethal with baz RNAi
(line v2914) and therefore was not used. We next tested a follicle cell driver, T155-
GAL4, which is expressed early in the germarium in follicle cell precursors followed by expression in all follicle cells starting at stage 9 (Liu and Montell, 1999; Queenan et al.,
1997). We observed a high proportion of follicle cell defects when baz RNAi was driven by T155-GAL4 (Figures 2-1B and 2-1C; 33%, n = 136 egg chambers). Regions of the follicle cell epithelium were thin (Figure 2-1B) and multilayered (Figure 2-1C). In addition, some follicle cells did not complete their posterior-directed retraction to cover the oocyte at stage 10B (Figure 2-1C). These results are consistent with the known role for Baz in follicle cell polarity (Abdelilah-Seyfried et al., 2003; Cox et al., 2001; Huynh et al., 2001) and indicates that the RNAi line efficiently knocked down baz function.
47 However, we also observed a large proportion of degenerating egg chambers (33%, n =
136), which precluded us from scoring border cell migration.
We next tested two GAL4 drivers, slbo-GAL4 and c306-GAL4, which are expressed during border cell migration (Figures 2-1D and 2-1E) (Murphy and Montell,
1996; Rørth et al., 1998). We compared the expression patterns by crossing the GAL4 lines to UAS-mCD8:GFP. slbo-GAL4 begins to drive expression at high levels in the newly formed border cells at early stage 9 (Figure 2-1D). In contrast, c306-GAL4 turns on at earlier stages, beginning around stages 4/5 (Figure 2-1E). c306-GAL4 is expressed in a larger subset of follicle cells at the anterior end of the egg chamber that includes the presumptive border cells (Figures 2-1A and 2-1E). Both GAL4 lines are also expressed in a subset of posterior follicle cells (Figures 2-1D and 2-1E). We compared the border cell migration defects caused by knockdown of baz RNAi (line v2914) using slbo-GAL4 and c306-GAL4. This was scored as the percentage of border cells that migrated (complete) or did not migrate (incomplete) to the oocyte by stage 10 of oogenesis (Figures 2-1A and
2-1F). We consistently observed stronger inhibition of migration with baz RNAi driven by c306-GAL4 (Figure 2-1F), possibly because the earlier follicle cell expression allowed time for efficient knockdown of gene function in the presumptive border cells. Therefore, we selected c306-GAL4 for this study (Figure 2-1G).
We obtained available transgenic UAS-RNAi lines that target each of the PDZ domain-encoding genes from the Vienna Drosophila RNAi Center (VDRC)
(Figure 2-1G; see Table 2-S1). A few lines were acquired from two other collections
(NIG-Fly and TRiP). Whenever possible, multiple transgenic RNAi lines that target each gene were obtained. RNAi lines can potentially produce off-target effects by non-specific
48 knockdown of other genes (Perrimon et al., 2010). We excluded a few lines that were annotated to have a high number of potential off-target genes (more than 100), but in most cases were able to test alternative lines. For example, CG43955 had one line
(v31686) with 196 predicted off-target genes including taiman, which is required for border cell migration (Bai et al., 2000); we tested the alternative line v103267, which does not have any predicted off-target genes. The only available arc RNAi line (v16826) has 408 predicted off-targets that include taiman and another border cell migration gene, slow border cells (slbo) (Montell et al., 1992), so was not included in the PDZ gene survey. No lines were available for Mhcl. For the remaining 64 genes, 145 lines were tested for incomplete versus complete border cell migration (Figures 2-1A and 2-1G; see
Table 2-S1). More than 75% of the genes had multiple RNAi lines, either independent insertions of the same construct (e.g. baz) or independent constructs (e.g. bbg)
(see Table 2-S1). We knocked down the genes using UAS-RNAi lines driven by c306-
GAL4 (see Materials and Methods). baz RNAi was used as a positive control
(Figures 2-1A and 2-1F). RNAi against GFP was used as the negative control and did not significantly disrupt border cell migration (Figure 2-1F).
The percent incomplete migration for all of the tested lines are reported in Table
2-S1. The results were classified into two main groups, positive or negative hits. We calculated the minimum threshold for migration defects using our negative control data
(see Materials and Methods); migration defects of 9% or fewer of the analyzed egg chambers were considered negative hits. RNAi lines that resulted in more than 9% of egg chambers with border cell migration defects were classified as positive hits. To confirm the first-round positive hits, we retested the strongest RNAi lines for most genes (see
49 Materials and Methods; Figure 2-1G). Four genes that initially were categorized as positive hits in the first round did not repeat (see Table 2-S1). Moreover, upon retesting, some positive lines had stronger migration defects whereas others were milder; this suggests slight inherent variability of knockdown efficiency. Nonetheless, most RNAi lines when retested exhibited similar strength of migration defects; the variation between trials was generally ≤ 10% (see Table 2-S1). 33 genes fell into the negative hit category with the rest being positive hits (see Table 2-S1). For the 31 positive gene hits, RNAi knockdown caused migration defects ranging from 10 to 50% of the analyzed egg chambers; none of the lines completely blocked migration. Positive hits were further sorted based on the number of tested lines that had border cell migration defects (see
Table 2-S1). Genes with all or multiple RNAi lines producing migration defects were designated “high confidence” multiple hits (Table 2-1) (Booker et al., 2011). A total of
14 positive genes comprise the multiple hit category (Table 2-1) and 17 genes are single hits (see Table 2-S1). The positive hit genes have a range of predicted functions, although genes with known or predicted roles in epithelial polarity or cytoskeletal regulation together account for more than half of the hits (Table 2-1; see Figure 2-S1C).
Validation of candidates
To verify results from the systematic RNAi knockdown of PDZ genes, we performed additional tests for a subset of both positive and negative genes. We first performed a detailed analysis of the migration defects for selected positive first-round genes (see Materials and Methods). CG6498 has high homology to human microtubule- associated serine-threonine kinase 2 (MAST2) (NCBI Homologene; http://www.ncbi.nlm.nih.gov/homologene/); both genes encode proteins with a central
50 serine-threonine kinase domain and a single PDZ domain. Three CG6498 RNAi lines
(two different constructs) significantly disrupted migration (Figure 2-2A; http://flybase.org/reports/FBgn0036511.html). One independent construct, line v109282, did not cause migration defects, possibly due to inefficient knockdown (Figure 2-2A). In the strongest line, v35100, 21% of the egg chambers had incomplete migration
(Figure 2-2A). Most CG6498 RNAi border cells with a migration defect stopped midway to the oocyte (Figure 2-2B). Moreover, knockdown of CG6498 driven by the other border cell GAL4, slbo-GAL4, also disrupted border cell migration (see Figure 2-S2). We analyzed in more detail the migration defects caused by RNAi lines for two additional multi-hit positive genes, veli and CASK (Figure 2-2C). Closer examination of RNAi knockdown for both genes revealed migration defects similar to those observed in the first-round analysis (Figure 2-2C; see Table 2-S1). We confirmed that Veli was expressed in border cells using an antibody against Veli protein (Figure 2-2D). The strongest veli
RNAi line (v43094) has a predicted off-target match to estrogen receptor related (ERR).
However, RNAi for ERR did not disrupt border cell migration (Figure 2-2C). Moreover, veli RNAi (v43094) efficiently downregulated Veli levels in border cells (Figure 2-2D).
We next confirmed that two genes in the negative hit category did not disrupt border cell migration. X11L RNAi-induced phenotypes were close to the background cutoff migration defects observed in the first-round tests (see Table 2-S1). Upon detailed retesting, we verified that knockdown of X11L did not affect border cell migration
(Figure 2-2C). We also obtained two small deletion mutant alleles of the putative Rac guanine-nucleotide exchange factor (GEF) Protostome-specific GEF (PsGEF); these alleles are transcript null, viable and fertile (Higuchi et al., 2009). In agreement with the
51 RNAi results, egg chambers trans-heterozygous for PsGEF∆55/PsGEF∆21 were morphologically normal and did not disrupt border cell migration (Figure 2-2C).
Although most RNAi lines are expected to produce knockdown of the targeted genes, this has not been tested formally for most individual lines. Therefore, we performed quantitative real-time PCR (qRT-PCR) to ascertain the in vivo knockdown efficiency for selected RNAi lines. We analyzed 18 lines, which target four positive genes and 14 negative genes; this encompasses ~25% of the PDZ genes (Table 2-2). The tested lines were from multiple collections: the first-generation “GD” and second- generation “KK” long double-stranded hairpin RNA (dsRNA) libraries from VDRC; the long dsRNA (Valium1 and 10) and shRNA (Valium 20) libraries from the TRiP collection (http://flybase.org/reports/FBrf0208510.html) (Dietzl et al., 2007; Ni et al.,
2009; 2011). To determine whether these genes were expressed during oogenesis, qRT-
PCR was used to measure the relative expression levels in wild-type ovarian extracts
(Table 2-2). Most genes were expressed at low or low-to-moderate levels, with the exception of CG43955, which was not expressed. Next, we crossed the 18 RNAi lines to heat shock- (hs-) GAL4 and subjected adult flies to heat shock to induce RNAi transgene expression (see Materials and Methods). RNA isolated from whole female flies (ovaries removed) was used to analyze relative levels of transcript in RNAi knockdown flies versus a non-targeting control (RNAi to GFP). qRT-PCR performed on the resulting cDNA showed that 14 out of 18 RNAi lines achieved statistically significant knockdown of transcripts (Table 2-2; see Figure 2-S3). Knockdown ranged from mild (21% knockdown by PICK1 RNAi) to strong (90% knockdown by Grip RNAi), with most lines producing more than 40% knockdown. Finally, we compared lines we tested to those
52 identified in two genome-wide in vivo RNAi screens performed to identify genes that regulate Notch signaling or muscle morphogenesis (Mummery-Widmer et al., 2009;
Schnorrer et al., 2010). These genome-wide screens found specific phenotypes or lethality with 12 positive hit lines and 9 negative hit lines from our analysis
(see Table 2-S1). Together, these data confirm specificity for a number of RNAi lines.
Moreover, this suggests that the majority of lines tested in this study are expected to reduce relevant transcript levels.
Investigation of two genes, bbg and CG6509, reveals distinct functions in border cells
While the initial analysis of PDZ domain-encoding genes by RNAi knockdown focused on whether border cells completed their migration by the appropriate stage, we wanted to further determine specific function(s) of identified genes in border cells.
Earlier studies established that Baz and its partner Par-6 regulate polarity of border cells during detachment from the follicle cell epithelium and subsequent migration (McDonald et al., 2008; Pinheiro and Montell, 2004). None of the other high-confidence positive hits from this study have been analyzed previously in border cells, although a few have been found to regulate the migration of other cell types in Drosophila and/or in mammals
(Table 2-1). Two genes, big bang (bbg) and CG6509, were chosen for further tests because they encode different classes of PDZ domain-containing proteins.
bbg encodes a large multi-PDZ domain protein expressed at various stages of development (Kim et al., 2006). Little is known about the function of bbg in development except that mutants are mildly bang sensitive (Kim et al., 2006). The bbg gene locus spans over 120 kb and encodes multiple transcripts (5 of the 8 are shown; Figure 2-3A)
(Kim et al., 2006). Bbg protein isoforms are differentiated by the total number of PDZ
53 domains present (three in Bbg-PC/-PK, two in the other isoforms), by variations in the length of the N-terminal region, and the presence of two predicted coiled-coil domains
(Figure 2-3B) (Kim et al., 2006).
bbg is expressed in early oogenesis as well as in discrete patterns in the embryo and larval discs (Gustafson and Boulianne, 1996; Kim et al., 2006). We used qRT-PCR to verify that bbg was expressed in ovaries (Table 2-2). However, its expression during later stages of oogenesis has not been described. bbg was previously identified as the insertion site for the c96-GAL4 enhancer trap line, which has been shown to reliably report the bbg expression pattern (Kim et al., 2006). We analyzed the expression of bbg by crossing c96-GAL4 to UAS-mCD8:GFP (Figure 2-3C; see Figure 2-S4A). c96-GAL4 was restricted to a few follicle cells at the very anterior and posterior ends of the egg chamber at early stages (Figure 2-3C), in agreement with bbg transcript and protein (Kim et al.,
2006). Staining with Fasciclin III (FasIII), which marks the membrane between the pair of polar cells, confirmed that these cells are the anterior and posterior polar cells
(Figure 2-3C). The polar cells later recruit surrounding follicle cells to become border cells at late stage 8 (Silver and Montell, 2001; Xi et al., 2003). Starting at stage 8, c96-
GAL4-driven GFP expanded to the majority of follicle cells including border cells
(Figure 2-3C; see Figure 2-S4A).
We next confirmed that bbg RNAi-mediated knockdown disrupted border cell migration (Figure 2-3D). We retested three transgenic RNAi lines that target non- overlapping regions of bbg (Figure 2-3A). Line v15975 resulted in the strongest migration defects (14% of stage 10 egg chambers), whereas line v36111 had milder and more variable migration defects (Figure 2-3D). Upon retesting, the third RNAi line
54 against bbg (v101691) did not reliably disrupt migration. Knockdown of bbg using slbo-
GAL4 mildly disrupted border cell migration (see Figure 2-S2). The first two RNAi lines against bbg each have a predicted off-target gene. However, Irbp RNAi (off-target for v15975) and CG42724 RNAi (off-target for v36111) did not induce migration defects
(Figure 2-3D). Moreover, bbg RNAi line v15975 significantly knocked down bbg levels in vivo (Table 2-2; see Figure 2-S3).
To address whether Bbg regulates a specific aspect of border cell migration, we analyzed the levels and localization of several border cell-enriched proteins (Figure 2-4).
We first analyzed a marker of cell identity, the fascin homolog Singed (Sn). Border cells in which bbg was knocked down by the strongest RNAi line (v15975) had normal levels and localization of Sn compared to control border cells (Figure 2-4A). The cell adhesion protein E-cadherin and the membrane-associated polarity proteins atypical Protein Kinase
C (aPKC) and Discs Large 1 (Dlg1) were all localized correctly in bbg RNAi border cells
(Figures 2-4C-E). Moreover, bbg RNAi did not disrupt F-actin or α-tubulin, indicating no obvious cytoskeletal defects (Figures 2-4F and 2-4G). Thus, most aspects of border cell differentiation and membrane localization were unchanged when bbg levels were reduced. In contrast, we observed altered Stat92E subcellular localization when bbg was knocked down (Figures 2-3E and 2-4B).
JAK/STAT signaling specifies border cell fate, recruits border cells to form a cluster and promotes their motility (Beccari et al., 2002; Silver and Montell, 2001; Silver et al., 2005). Nuclear STAT localization reflects high levels of JAK/STAT signal activation (Vinkemeier, 2004). Stat92E (the Drosophila STAT homolog) becomes enriched in border cell nuclei as they are specified in the epithelium and is maintained
55 throughout their migration (Silver et al., 2005). Control migrating border cells have visibly higher nuclear Stat92E compared to the cytoplasm (Figure 2-3E). However, bbg
RNAi reduced the levels of nuclear Stat92E in most border cells (Figure 2-3E). To quantitate this effect, we measured the ratio of nuclear Stat92E to DAPI staining in stage
9 migrating border cells (see Materials and Methods). The ratio of nuclear STAT to
DAPI signal was reduced from ~2.0 in control border cells to ~1.5 in bbg RNAi border cells (Figure 2-3F). Nonetheless, high nuclear Stat92E was observed in premigratory bbg
RNAi border cells (see Figure 2-S4B). This result suggests that nuclear Stat92E was initially normal in bbg RNAi border cells, but was not maintained adequately after border cells began to migrate. bbg RNAi border cell clusters contain a similar number of cells compared to control clusters (see Figure 2-S4C). Thus, Bbg functions after border cells are specified and recruited into the cluster to maintain optimal STAT levels during border cell migration.
The second gene we analyzed in more detail, CG6509, encodes a member of the membrane-associated guanylate kinase (MAGUK) proteins (Figure 2-5A and 2-5B). The domain architecture of CG6509, a combination of PDZ, SH3 and guanylate kinase
(GUK) homology domains, is characteristic of members of the MAGUK family of scaffolding proteins (Oliva et al., 2012). Four other MAGUK-encoding genes were identified as positive hits: the multi-hit genes CASK ortholog (CASK) and stardust (sdt) and the single hit genes menage a trois (metro) and polychaetoid (pyd). We verified that knockdown of CG6509 with multiple RNAi lines disrupted border cell migration
(Figure 2-5C). Although the RNAi line v22496 initially fell below the migration defect cutoff, upon retesting it inhibited border cell migration in 26% of stage 10 egg chambers
56 (Figure 2-5C; see Table 2-S1). Moreover, line v22496 produced significant knockdown of CG6509 transcript levels (Table 2-2; see Figure 2-S3). Knockdown of CG6509 using the border cell-specific slbo-GAL4 mildly delayed migration, confirming a requirement in border cells (see Figure 2-S2). Three CG6509 RNAi lines (v22496, v46234 and v101596) do not have predicted off-target genes, further indicating that the phenotypes are specific to CG6509 knockdown.
Analysis of border cell markers in CG6509 RNAi border cells, similar to that performed for bbg (see above), did not reveal obvious changes compared to control
(Figure 2-4). Nonetheless, RNAi knockdown of CG6509 markedly affected the morphology of border cell clusters (Figures 2-4 and 2-5D). Control clusters are generally round and fairly compact (96%; n = 21). In contrast, 44% of CG6509 RNAi border cell clusters (n = 36) no longer had a compact shape and instead were dissociated or elongated (Figure 2-5D). Finally, overexpression of CG6509 in border cells also disrupted their migration; the strongest UAS-CG6509 line disrupted migration in 35% of stage 10 egg chambers (Figure 2-5C). These data together indicate that having proper levels of CG6509 is important for normal border cell migration and cohesion of the cluster.
Discussion
RNAi knockdown of specific classes of genes to identify regulators of border cell migration
The advantage of using RNAi to test a selected class of genes, like the one performed here, is the rapid identification of genes involved in a particular process such
57 as border cell migration. The less labor-intensive nature of this approach ensures that even those genes whose knockdown results in incompletely penetrant phenotypes are detected. Recently, this method was used to identify Evi5 as a new GTPase-activating protein for Rab11 in border cell migration (Laflamme et al., 2012). Moreover, targeted
RNAi knockdown of microtubule-associated proteins demonstrated a requirement for the
Lis-1 complex in border cells (Yang et al., 2012). In this study, we chose to systematically target 64 genes that encode PDZ domain-containing proteins because of their known functions in processes critical for cell migration, such as cell polarity, adhesion and signaling. The majority of these PDZ genes have not been examined for functions in cell migration in any organism. The 14 genes designated as high confidence hits likely represent new members of protein complexes required for border cell migration. Importantly, several positive genes, the multi-hit genes baz and par-6 and the single hit gene dishevelled (dsh), were previously identified as regulators of border cell migration (Bastock and Strutt, 2007; Pinheiro and Montell, 2004). Most genes in the positive class, which includes 17 additional genes with one phenotypic RNAi line, have direct or putative mammalian homologs. The results of this study thus provide a list of
PDZ genes whose roles in cell migration and motility are predicted to be conserved.
Knockdown of positive hit PDZ genes resulted in mild to moderate migration defects, with most border cells able to detach from the epithelium and migrate partway to the oocyte. These observations suggest that RNAi for these genes resulted in partially penetrant phenotypes, either due to partial knockdown of gene function or because the gene is not completely essential for full border cell motility. Incomplete knockdown could occur if the RNAi transgene is not expressed at the right time or at strong enough
58 levels (Booker et al., 2011; Perrimon et al., 2010). To overcome this potential problem, the c306-GAL4 driver was used because it is expressed early in follicle cells and maintained in the migrating border cells. Whenever possible, multiple independent insertion lines and/or constructs were tested to minimize the potential issue of inefficient
RNAi constructs. Multiple independent hits increase the likelihood that the migration defects caused by RNAi are specific. Our assessment of RNAi efficiency by qRT-PCR confirms that 75% of the tested RNAi lines effectively knocked down the relevant targeted transcript. While knockdown efficiency ranged from 20 to 90%, most RNAi lines decreased transcript levels by 40% or more. Furthermore, our results indicate that, at least for baz, partial knockdown (~40% reduction of transcripts) significantly disrupted border cell migration.
The relatively mild nature of the phenotypes alternatively suggests that these genes have support or partially redundant roles in border cell migration. A striking example of this is the two receptor tyrosine kinases, the epidermal growth factor receptor
(EGFR) and the PDGF/VEGF receptor related (PVR), which guide border cells to the oocyte in response to secreted growth factors (Duchek and Rørth, 2001; McDonald et al.,
2006). Loss of either receptor alone has modest effects, but simultaneous loss of both receptors severely inhibits posterior-directed migration (Duchek and Rørth, 2001;
Duchek et al., 2001; McDonald et al., 2003). This contrasts with other genes, such as slbo, that are required for early border cell fate and whose loss completely inhibits migration (Montell et al., 1992). It remains to be seen whether the genes identified in this study play partially redundant roles. Many of the genes identified lack classical mutant alleles and therefore RNAi is the most direct method to assess their functions at present.
59 Once loss-of-function alleles are tested and/or created, the mutant results can be compared to the RNAi knockdown results. In the future it will also be important to identify the cellular and membrane-associated proteins to which these PDZ domain proteins bind. The results from this study accordingly present a collection of candidates to search for PDZ-interacting substrates in border cells and other migratory cells.
Epithelial polarity and cytoskeletal-associated genes are highly represented hits
A key group of genes identified here are those involved in epithelial cell polarity.
Significantly, these and other epithelial polarity proteins are required for mammalian cell motility and have been implicated in tumor invasion and metastasis (Etienne-Manneville,
2008; Hidalgo-Carcedo et al., 2011; Martin-Belmonte and Perez-Moreno, 2012; Subbaiah et al., 2011). Border cells retain many epithelial characteristics during migration, including polarized localization of Par-6 and Baz and upregulation of E-cadherin
(Niewiadomska et al., 1999; Pinheiro and Montell, 2004). Nine of the positive hit genes
(baz, CASK, dsh, Lap1, par-6, Patj, pyd, sdt and veli) regulate apical-basal polarity to establish distinct membrane domains (Ashburner et al., 2000; Guillemot et al., 2008;
Martin-Belmonte and Perez-Moreno, 2012). This raises the possibility that these polarity proteins regulate the localization of junctional proteins in border cells to organize and promote migration, similar to the known functions of baz and par-6 (Llense and Martín-
Blanco, 2008; Pinheiro and Montell, 2004). Many of the proteins in the polarity group form known multi-protein complexes. Par-6 and Baz form a complex with the serine- threonine kinase aPKC in some contexts and Sdt, Patj and Veli form another complex with the transmembrane protein Crumbs (Martin-Belmonte and Perez-Moreno, 2012).
The identification of multiple members of these complexes in this study indicates that
60 specific complexes function in border cell migration and confirms the sensitivity of this approach.
Notably, most of the polarity proteins with phenotypes encode proteins that are associated with apical junctions of epithelial cells, for example the Baz and Crumbs complexes, rather than basolateral junctions (Laprise and Tepass, 2011; Martin-Belmonte and Perez-Moreno, 2012). Two basolateral polarity complex proteins that contain PDZ domains, Dlg1 and Scribbled (Scrib), regulate mammalian epithelial cell migration
(Chatterjee and Bohmann, 2012; Dow and Humbert, 2007). Moreover, Dlg1 is highly expressed in follicle cells and border cells (Szafranski and Goode, 2007). Surprisingly, dlg1 or scrib RNAi did not disrupt border cell migration even though their transcript levels were significantly knocked down. These proteins suppress cell invasion in ovarian follicle cells (Goode and Perrimon, 1997; Szafranski and Goode, 2007) and in a model of tumor invasion (Pagliarini and Xu, 2003), thus may have a different role in border cells.
Indeed, loss of dlg1 depolarizes the follicle cell epithelia, induces uncontrolled invasion, and may even stimulate border cell motility (Goode and Perrimon, 1997; Szafranski and
Goode, 2004). Therefore, the activity of Dlg1, and possibly Scrib, likely needs to be downregulated in border cells to allow their detachment and migration. We previously found that the basolateral protein Par-1 is required for detachment of border cells from the follicle cell epithelium and their subsequent motility (McDonald et al., 2008).
Therefore, border cells may use a different set of basolateral polarity proteins for migration compared to other types of epithelial cells.
The other major group of genes identified in this study encodes proteins with known or predicted roles in cytoskeletal regulation. This is consistent with established
61 roles for the actin cytoskeleton and microtubules in migrating cells (Kaverina and
Straube, 2011; Ridley, 2011). Like most migrating cells, border cells normally extend and retract actin-rich cellular protrusions that provide traction for migration and help them sense directional guidance cues (Fulga and Rørth, 2002; Murphy and Montell, 1996;
Prasad and Montell, 2007). Several microarray screens identified an enrichment of cytoskeletal-associated proteins in border cells compared to non-migratory cells
(Borghese et al., 2006; Wang et al., 2006). Moreover, regulators of actin and microtubules promote the formation of dynamic border cell protrusions (Kim et al., 2011;
Wang et al., 2010a; Yang et al., 2012). The cytoskeletal regulator Lim Kinase 1 (LIMK1), which encodes a serine-threonine kinase with two LIM domains in addition to a single
PDZ domain, was a multi-hit gene identified by our study. LIMK1 functions downstream of the Rac GTPase to regulate actin dynamics through the actin-regulatory protein cofilin
(Bernard, 2007). Moreover, LIMK1 mildly rescues the border cell migration defects caused by inactivation of Rac (Zhang et al., 2011). Our results demonstrate that LIMK1 itself is required for border cell migration. However, more work is needed to determine the extent to which LIMK1 functions primarily through Rac in border cells, as has been proposed (Zhang et al., 2011), or has any additional functions. Two Rac-GEFs, myoblast city and elmo, are required for border cell migration (Bianco et al., 2007; Geisbrecht et al., 2008). In contrast, we found that another putative Drosophila Rac-GEF (Higuchi et al., 2009), PsGEF, is not required for border cell migration. These results highlight the complex roles of Rac-effector proteins in specific cell and tissue contexts. Further investigation of the cytoskeletal-associated genes identified in this study is anticipated to provide new insights into the regulation of border cell motility.
62 Roles of Bbg and CG6509 in cell migration
While the targeted RNAi survey of PDZ gene function was designed to focus only on the extent of border cell migration, studies with bbg and CG6509 revealed genes that regulate distinct features of border cells. Our results indicate that Bbg regulates levels of active Stat92E within migrating border cells. Activation of the JAK/STAT pathway in the follicle cells surrounding the polar cells is the first step in the specification of border cell fate and recruitment of cells to form the border cell cluster (Silver and Montell, 2001).
Subsequently, JAK/STAT signaling is actively maintained during migration (Silver et al.,
2005). It was unclear from previous studies what mechanisms control nuclear Stat92E levels in border cells, although both active transport of Upd ligand mRNA and endocytosis appear to be important (Silver et al., 2005; Van de Bor et al., 2011). Despite the relatively mild migration defect caused by bbg knockdown, partially migrated bbg
RNAi border cells exhibited reduced nuclear Stat92E and presumably reduced
JAK/STAT activation. Moreover, STAT levels were unaffected in border cells prior to migration. Thus, Bbg is a new regulator of JAK/STAT signaling that upregulates and/or maintains nuclear Stat92E levels in migrating border cells. As the protein interaction partners of Bbg have yet to be identified, the mechanism for Bbg-mediated regulation of
STAT activity remains to be elucidated.
Border cells migrate as a morphologically distinct and interconnected group.
Knockdown of CG6509 in border cells disrupted this cluster organization in addition to delaying their migration. This suggests that CG6509 helps keep border cells together in a collective cluster. The predicted mammalian homolog of CG6509, Dlg5, has been implicated in regulating cell migration (Smolen et al., 2010) and epithelial polarity
63 (Nechiporuk et al., 2007). From mouse knockout studies, Dlg5 was proposed to maintain cell polarity through trafficking of cadherin-catenin complexes and stabilization of adherens junctions (Nechiporuk et al., 2007). The cluster morphology defects we observed with CG6509 knockdown are consistent with defects in cell polarity and/or cell- cell adhesion. Nonetheless, we did not observe gross alterations in the levels or localization of E-cadherin and polarity proteins in CG6509 RNAi border cells; however, we cannot rule out the possibility of subtle changes in these proteins and/or residual
CG6509 gene function. The disorganized cluster phenotypes produced by knockdown of
CG6509 resemble those caused by loss of JNK activity (Llense and Martín-Blanco, 2008;
Melani et al., 2008). JNK signaling promotes border cell cluster cohesion through regulation of cell polarity proteins such as Baz and cell-cell adhesion via Integrins and E- cadherin (Llense and Martín-Blanco, 2008). Further investigation will be needed to determine whether CG6509 regulates cell-cell contacts within the border cell cluster and if it functions downstream of or in parallel to JNK signaling.
Materials and Methods
Drosophila Genetics
All crosses were kept at 25° using standard protocols. c306-GAL4, slbo-GAL4, hsp70-GAL4 (hs-GAL4), T155-GAL4 (Bloomington Stock Center) and tubulin-GAL4
(from A. Page-McCaw) were used to drive UAS-RNAi expression. GAL4 lines were outcrossed to w1118 and used as controls. c96-GAL4, UAS-mCD8:GFP (from A. Zhu) was used to study the bbg expression pattern (Gustafson and Boulianne, 1996). The following lines for off-target genes were obtained from the Vienna Drosophila RNAi Center
(VDRC) or Harvard Transgenic RNAi Project (TRiP) from the Bloomington Stock
64 Center: UAS-ERR RNAi KK108422 (line v108349, VDRC); UAS-Irbp RNAi (line
JF03273, TRiP); UAS-CG42724 GD4280 RNAi (line v30629, VDRC). Additional fly stocks from the Bloomington Stock Center were: two insertions of UAS-CG6509.GFP
(http://flybase.org/reports/FBrf0211100.html); PsGef∆21, FRT19A; PsGef∆55, FRT19A; UAS- mCD8::GFP; and UAS-GFP dsRNA.
In vivo RNAi Knockdown
RNAi lines were obtained from the Vienna Drosophila RNAi Center (VDRC),
Harvard Transgenic RNAi Project (TRiP), NIG-Fly and the Bloomington Stock Center.
The complete list of RNAi lines is in Table 2-S1. Virgin c306-GAL4; UAS- mCD8:GFP/CyO flies were crossed with males from each UAS-RNAi line. Eight female progeny flies per cross were fattened by feeding with yeast paste for 20 hours at 29° prior to dissection to achieve maximal GAL4/UAS expression. The UAS-baz RNAi v2914 line
(VDRC) and the UAS-GFP dsRNA (line 143)
(http://flybase.org/reports/FBrf0191479.html) were used as positive and negative controls, respectively. The RNAi lines were tested in batches of 22 lines together with the controls in 24-well plates. Whole ovaries were dissected as described (McDonald and
Montell, 2005; Prasad et al., 2007). Ovaries were fixed with 4% formaldehyde in potassium phosphate buffer pH 7.2 for 10 min and washed with potassium phosphate buffer. Fixed ovaries were manually dissociated in 80% glycerol. UAS-mCD8:GFP fluorescence was used to visualize border cells in dissociated ovaries. Analysis of border cell migration was performed with a Zeiss Stereo Discovery V8 epi-fluorescent stereomicroscope. Crosses were set up independently and retested as above to confirm first-round candidates.
65 Quantitative RT-PCR Analysis of Gene Expression
Virgin hsp70-GAL4 (hs-GAL4) flies were crossed to male UAS-RNAi flies. To express RNAi ubiquitously, adult female progeny were heat shocked for 1 h, three times a day, at 37° for two days. Ovaries were dissected the following day. RNA was extracted from ovaries or adult female fly carcasses (ovaries removed) using Trizol (Invitrogen).
To determine endogenous expression levels, RNA was extracted from 15 to 20 ovary pairs dissected from hs-GAL4/UAS-GFP dsRNA females. The endogenous expression levels of rp49 and tub84B were measured as reference controls (see Table 2-2 for details). To determine RNAi knockdown, RNA was extracted from 10-15 hs-GAL4 >
UAS-RNAi female fly carcasses. Note that ovaries were removed because the germline is potentially refractory to long double-stranded hairpin RNA knockdown (Ni et al., 2011).
RNA was purified using the Qiagen RNAeasy Kit followed by cDNA synthesis using the
Taqman Reverse Transcription Kit (Applied Biosystems) and 1.5 µg of purified RNA. qRT-PCR was performed using the Roche Lightcycler 480 to run 15 µL reactions containing 0.5 µL cDNA, 0.5 µL 10 µM primer mix, and 7.5 µL of SYBR Green Master
Mix (Roche). All qRT-PCR experiments were performed in triplicate on three separate biological samples. In each experiment, UAS-baz RNAi was used as the positive control and UAS-dsRNA GFP (GFP RNAi) was used as the negative control. RNAi knockdown was calculated using the ∆∆CT method using rp49 gene expression for normalization.
The following primers were used: baz fwd, CAGGAGCTGCAGATGTCGGATG; baz rev, ctcgtgatcgccatcctccaaaag; bbg fwd, CAATCTCCACACAACGAGCTCCAC; bbg rev, ggagatgccgccaagcttagc; CG42788 fwd, GGAACGACCCTTGGAGTCTATGG;
CG42788 rev, gttcatgtgggcaaggagggg; CG43375 fwd,
66 GGCTTTGATAGCTGGGCGAGC; CG43375 rev, gggggccctgaacaagatgaag; CG43707 fwd, GCGGATGGTCGAAACGATATTGCG; CG43707 rev, cttcttgccggatgcattggcg;
CG6498 fwd, CCTGCTCCGGAAGATCTCCTATC; CG6498 rev, ctggtaacggagcggtcagttc; CG6509 fwd, CAGCATGATCAgaaggcgatccc; CG6509 rev, cacctgcatccgttccagcag; CG9588 fwd, GATGATCGTCTGTCGCGCCAG; CG9588 rev, cgtggaggcgcagatcaacag; cnk fwd, CTCCAGCTCGTATGGCCGTATG; cnk rev, ggcctacatcaacatcgccgag; dlg1 fwd, cccggcgacaatggcatctatg; dlg1 rev, ccagttcgtgcgttacgttctcc; dysc fwd, CTAGGATTGTATCACCGGGTCGC; dysc rev, gcgcgaccagcaaatcgatcatg; Grip fwd, CAGTCCCGACGAGGTGATGAC, Grip rev, cgggactccagtgtgctaaagc; PICK1 fwd, gattggcatcagcattgggggtg; PICK1 rev, cacgctcaccgaattcacagcc; RhoGAP100F fwd, CACGGGCTCAGCGATTTTCGTG;
RhoGAP100F rev, cgcacgggtagtgctgaaattgg; rp49 fwd,
TACAGGCCCAAGATCGTGAAG; rp49 rev, GACGCACTCTGTTGTCGATACC; scrib fwd, CAATGAAATTGGCCGCCTGCCG; scrib rev, cgaacttgggtatcgggttcgaac; Sif fwd, caaagtggcgagctgcccaatc; Sif rev, caggttgttgagcagcgaggg; Syn1 fwd, gaattgggcagggtgccgttc; Syn1 rev, ctggaaacggacttcctggcc; tub84B fwd,
GGCAAGGAGATCGTCGATCTGG; tub84B rev, GACGCTCCATCAGCAGCGAG; vari fwd, CTCGTTCACGATGACCATGTCGAAG; vari rev, cataagattcagctccagacgcgc;
X11L fwd, GCGTGTTGTTTCGGGCCAGATAC; X11L rev, cagtgctcggctgactttcgc.
Immunostaining and Microscopy
Ovarioles were dissected and fixed in 4% formaldehyde in 1× phosphate buffered saline (PBS) with 0.2% v/v Triton X-100 (PBT). Blocking, antibody incubations, and washes were done in PBT with 5 mg/mL BSA (PBT-BSA). The primary antibodies used
67 were: 1:400 mouse anti-alpha-tubulin (DM1A, Sigma); 1:200 rabbit anti-aPKC-zeta (sc-
216, Santa Cruz); 1:50 concentrated mouse anti-Dlg1 (4F3, Developmental Studies
Hybridoma Bank; DSHB); 1:150 mouse anti-Singed (sn 7c, DSHB); 1:150 rat anti-E- cadherin (DCAD-2, DSHB); 1:10 mouse anti-Fasciclin III (FasIII; 7G10, DSHB); 1:500 rabbit anti-GFP (Life Technologies); 1:1000 rabbit anti-Stat92E (a gift from S. Hou);
1:500 rabbit anti-Veli (a gift from E. Knust). Secondary antibodies conjugated to Alexa
Fluor 488, Alexa Fluor 568, or Alexa Fluor 647 (Life Technologies) were used at 1:400 dilution. Actin was visualized with phalloidin conjugated to Alexa Fluor 568 or Alexa
Fluor 647 (Life Technologies) used at 1:400 dilution. DAPI (0.05µg/mL, Sigma) was used to visualize nuclei. Stained egg chambers were mounted on slides in Aqua-
Poly/Mount (Polysciences, Inc.) and imaged with a Zeiss AxioImager Z1 epi-fluorescent compound microscope equipped with the ApoTome system and MRm CCD camera.
Either a 20× Plan-Apochromat 0.75 numerical aperture (NA) or a 40× Plan-Neofluar 1.3
NA objective were used. The microscope was controlled by Axiovision 4.8.1 software.
For detailed analyses of border cell migration and to verify first-round hits, GAL4/UAS-
RNAi crosses were independently set up and whole ovaries from the adult progeny were fixed and stained for Singed, phalloidin and DAPI as above. Manually dissociated ovaries were mounted on slides and analyzed as above using the same microscope.
Calculation of Stat92E/DAPI Intensity Ratio
Border cell clusters stained with anti-Stat92E and DAPI were imaged with multiple optical z-sections. A maximum intensity projection image was generated using the Axiovision Extended Focus module. For overlapping nuclei, separate projection images were generated to visually isolate the nuclei. Individual border cell nuclei were
68 first outlined in NIH ImageJ software. The mean DAPI and Stat92E intensities were measured using the “Measure” command in ImageJ. The ratio of Stat92E/DAPI for each nuclei was calculated by dividing the mean Stat92E intensity by the mean DAPI intensity.
Graphs, Statistics, and Figures
Graphs and statistical analysis were performed in GraphPad Prism 4. The threshold for determining RNAi-induced migration phenotypes was calculated by applying the three-sigma rule on the negative control data (c306-GAL4/+; UAS- mCD8:GFP/UAS-GFP dsRNA). Briefly, the background migration defect was 2.63 ±
2.32% (mean ± SD; data from 7 trials, n ≥ 50 egg chambers per trial). The threshold was calculated to be 9.59%, three standard deviation (SD) intervals from the mean. Statistical significance of RNAi knockdown by qRT-PCR was determined using the one-tailed unpaired t-test. In all other cases, the two-tailed unpaired t-test was used. Figures were assembled in Adobe Illustrator CS5. Minor image adjustments (brightness and/or contrast) were done in Axiovision 4.8.1 or Adobe Photoshop CS5. Gene ontology analyses were performed using PANTHER (http://www.pantherdb.org) (Thomas et al.,
2003) or AmiGO (http://amigo.geneontology.org) (Ashburner et al., 2000).
69 Figure 2-1. In vivo RNAi knockdown to identify PDZ domain-encoding genes required for border cell migration
70 Figure 2-1. In vivo RNAi knockdown to identify PDZ domain-encoding genes required for border cell migration. (A) Control border cells (arrowheads) migrate between the nurse cells (nc) from stages 9 to 10 of oogenesis to reach the oocyte (o).
Border cell clusters that have migrated past the dashed line (stage 10 control) are considered to have completed their migration. Border cells and follicle cells (brackets) express UAS-mCD8:GFP (green) driven by c306-GAL4 in egg chambers at the indicated stages; genotype is c306-GAL4/+; UAS-mCD8:GFP/+. Egg chambers were co-stained for actin (red) and DAPI (blue) to label cell membranes and nuclei, respectively. (Lower right panel) A stage 10 c306-GAL4/+; UAS-mCD8:GFP/UAS-baz RNAi v2914 (baz
RNAi) egg chamber in which border cells did not migrate. Scale bar is 20 µm. (B and C)
Knockdown of baz in follicle cells (bottom panels) using the follicle cell driver T155-
GAL4 disrupts the epithelium compared to control (top panels) at stage 9 (B) and stage
10 (C). Genotypes are T155-GAL4/+ (control) and UAS-baz RNAi/+; +/T155-GAL4.
Scale bar is 20 µm. (B) baz RNAi follicle cell layer is thin (dashed line) and some nuclei are misaligned (bracket) compared to control. (C) baz RNAi follicle cells are multilayered (arrow) and fail to retract over the oocyte (square bracket) like in control. (D and E) Ovarioles showing GAL4 expression patterns in border cells (arrowheads) and follicle cells (brackets) as visualized by UAS-mCD8:GFP (green); stages are indicated.
Egg chambers were co-stained for actin (red) and DAPI (blue). Scale bar is 50 µm. (D) slbo-GAL4 expression pattern (slbo-GAL4, UAS-mCD8:GFP/+). (E) c306-GAL4 expression pattern (c306-GAL4/+; UAS-mCD8:GFP/+). c306-GAL4 is also expressed in stalk cells, which connect egg chambers within the ovariole. (F) Quantification of migration in stage 10 egg chambers of the indicated genotypes, shown as the percentage
71 with complete (green) or incomplete (red) border cell migration. Error bars represent
SEM; n ≥ 50 egg chambers in each of 3 trials (**, p < 0.01; ***, p < 0.001; two-tailed unpaired t-test). (G) Outline of the scheme used to survey the role of PDZ genes in border cell migration. Anterior is to the left in this and all subsequent figures. Taken from
Aranjuez et al., 2012.
72 Figure 2-2. Confirmation of positive and negative hit genes
73 Figure 2-2. Confirmation of positive and negative hit genes. (A and C) Quantification of border cell migration at stage 10, shown as the percentage of border cells with complete (green) or incomplete (red) migration in egg chambers expressing RNAi to GFP
(control) or the indicated RNAi transgenes driven by c306-GAL4. Error bars represent
SEM; n ≥ 50 egg chambers in each of at least three trials (*, p < 0.05; **, p < 0.01; two- tailed unpaired t-test). (A) Knockdown of CG6498 using multiple transgenes disrupts border cell migration. (B) Representative example of an egg chamber with a border cell migration defect caused by CG6498 RNAi. Genotype is c306-GAL4/+; UAS- mCD8:GFP/UAS-CG6498 RNAi v35100. Border cells (green; arrowhead) stopped a little more than halfway to the oocyte (outlined). DAPI marks nuclei. Scale bar is 20 µm. (C)
Border cell migration defects by RNAi knockdown of veli (v43094) and CASK (v34185).
Normal border cell migration with RNAi knockdown of X11L (v28652) and in a PsGEF mutant (PsGEF∆55/PsGEF∆21). RNAi for ERR (line v108349), the predicted off-target gene for veli RNAi line v43094, did not disrupt border cell migration. (D) Border cells stained with an antibody to Veli. Control border cells (c306-GAL4/+; UAS- mCD8:GFP/+) had detectable Veli (red), which was strongly reduced in veli RNAi border cells (c306-GAL4/+; UAS-mCD8:GFP/UAS-veli RNAi v43094). GFP (green) shows GAL4 expression and DAPI (blue) labels nuclei. Scale bar is 10 µm. Taken from
Aranjuez et al., 2012.
74 Figure 2-3. The multi-PDZ domain protein Big Bang regulates nuclear STAT levels in border cells
75 Figure 2-3. The multi-PDZ domain protein Big Bang regulates nuclear STAT levels in border cells. (A) Schematic diagram of five bbg predicted transcripts (adapted from
FlyBase); coding exons are in orange. RNAi target sequences and c96-GAL4 insertion site are indicated. RNAi lines v15975 and v101691 target sequences common to all isoforms. RNAi line v36111 is specific to RC and RK (not shown; differs from RC only in a non-coding exon; see FlyBase) transcripts. (B) Schematic diagram of the 8 Bbg protein isoforms, which have either two or three PDZ domains. (C) Egg chambers showing c96-GAL4 expression pattern visualized by UAS-mCD8:GFP (green) at the indicated stages. Egg chambers were co-stained for E-cadherin (red) to mark cell membranes and DAPI (blue) to mark nuclei. Scale bar is 50 µm. (Top panel) c96-GAL4 expression in anterior and posterior polar cells (arrows) at early stages. Inset shows c96-
GAL4-positive polar cells (green) co-stained for FasIII (red; scale bar, 5 µm). (Bottom panels) c96-GAL4-driven GFP in border cells (arrowheads) and surrounding follicle cell epithelium during stages 9 to 10. (D) Quantification of border cell migration at stage 10, shown as the percentage of border cells with complete (green) or incomplete (red) migration in egg chambers expressing RNAi to GFP (control) or the indicated RNAi transgenes driven by c306-GAL4. Knockdown of bbg with RNAi line v15975 disrupted border cell migration. bbg RNAi line v36111 had variable effects and line v101691 did not disrupt migration. RNAi to the predicted off-target genes, CG42724 (line v30629) and Irbp (line JF03273), did not disrupt migration. Error bars represent SEM; n ≥ 50 egg chambers in each of at least three trials (**, p = 0.0071; two-tailed unpaired t-test). (E)
Reduction of Stat92E levels in border cell nuclei when bbg is knocked down. Stage 9 border cells stained for Stat92E (magenta) and DAPI (green). Stat92E is expressed at
76 higher levels in control border cell nuclei (yellow outline) compared to cytoplasm (c306-
GAL4/+; UAS-mCD8:GFP/+). Stat92E is expressed at low levels in bbg RNAi (c306-
GAL4/+; UAS-mCD8:GFP/UAS-bbg RNAi v15975) border cell nuclei (outlined). Polar cells (asterisks) were excluded from analyses. Scale bar is 5 µm. (F) Quantification of the fluorescence intensity ratio of STAT nuclear staining to DAPI staining for control (n =
59) or bbg RNAi (n = 88) border cells; genotypes as in (E). At least 16 individual clusters were analyzed. Error bars represent SEM (***, p < 0.001; two-tailed unpaired t-test).
Taken from Aranjuez et al., 2012.
77 Figure 2-4. Markers of cell fate, cell adhesion, polarity and cytoskeleton in bbg RNAi and CG6509 RNAi border cells
78 Figure 2-4. Markers of cell fate, cell adhesion, polarity and cytoskeleton in bbg
RNAi and CG6509 RNAi border cells. Representative immunofluorescent images of stage 9 control (c306-GAL4/+; UAS-mCD8:GFP/+), bbg RNAi (c306-GAL4/+; UAS- mCD8:GFP/UAS-bbg RNAi v15975), and CG6509 RNAi (c306-GAL4/+; UAS- mCD8:GFP/UAS-CG6509 RNAi v22496) border cells. (A and B) Border cells stained for antibodies to the cell fate markers Singed (A) and Stat92E (B). (A) Singed is enriched in the cytoplasm. (B) Stat92E is enriched in border cell nuclei compared to the cytoplasm.
The same cluster is presented with and without border cell nuclei outlined with a dotted line (taken from DAPI staining of nuclei, not shown). Polar cells are marked with an asterisk (*). (C) Border cells stained for the cell adhesion protein E-cadherin, which is high in central polar cells and at the membrane interface between border cells. (D and E)
Border cells stained for the cell polarity proteins aPKC (D) and Dlg1 (E). (D) aPKC is an apical cell marker and localizes between border cells; an apical view is shown. (E) Dlg1 is a basolateral cell marker that is enriched in the central polar cells and at lower levels at border cell membranes. (F and G) Border cells stained for the cytoskeletal markers α- tubulin to mark microtubules (F) and phalloidin to label F-actin (G). N ≥ 10 border cell clusters assayed for each genotype. Scale bar is 10 µm. Taken from Aranjuez et al., 2012.
79 Figure 2-5. The MAGUK famiy member CG6509 regulates border cell cluster morphology
80 Figure 2-5. The MAGUK family member CG6509 regulates border cell cluster morphology. (A) Schematic of the CG6509 transcripts, which differ only in the 5’ non- coding exons (adapted from FlyBase); coding exons in orange. RNAi target sequences are indicated. (B) Schematic of CG6509 protein showing the conserved domains. (C)
Quantification of border cell migration at stage 10, shown as the percentage of border cells with complete (green) or incomplete (red) migration in egg chambers expressing multiple RNAi lines or overexpression of full-length UAS-CG6509 (different insertions of same transgene) driven by c306-GAL4. Error bars represent SEM; n ≥ 50 egg chambers in each of at least three trials (*, p < 0.05; **, p < 0.01; two-tailed unpaired t- test). (D) Stage 9 border cells stained for GFP (green) and Singed (red) to reveal border cell cluster morphology. Representative example of a control (c306-GAL4/+; UAS- mCD8:GFP/+) border cell cluster. Two examples of CG6509 RNAi (c306-GAL4/+;
UAS-mCD8:GFP/UAS-CG6509 RNAi v22496) border cells in which the cluster is partially dissociated (middle panel) or elongated (right panel). Scale bar is 20 µm. Taken from Aranjuez et al., 2012.
81 TABLE 2-1. High confidence PDZ domain-encoding genes in border cell migration identified by RNAi knockdown GENE PUTATIVE OTHER DOMAINS # HITS / KNOWN ROLE IN CELL b d VERTEBRATE PRESENT TOTAL MIGRATION a c HOMOLOG LINES bazooka PARD3 (PAR3) Oligomerization domain 2 / 2 Pinheiro and Montell, Development 2004‡ Nakayama et al., Dev Cell 2008§ big PDZD2 none 2 / 3 — bang
CASK CASK Guanylate kinase domain 2 / 3 — ortholog L27 domain
Protein kinase, catalytic domain, inactive
SH3 domain
CG5921 Harmonin / none 2 / 2 — USH1C
CG6498 MAST2 Domain of unknown 3 / 3 — function
Protein kinase, catalytic domain
CG6509 DLG5 Guanylate kinase domain 2 / 3 Smolen et al., Genes Dev 2010§ Src homology 3 domain
Gef26 RAPGEF2 / PDZ- Cyclic nucleotide-binding 3 / 3 Huelsmann et al., GEF1 domain Development 2006‡
Guanine-nucleotide dissociation stimulator (RasGEF) Ras association domain
Ras-like guanine nucleotide exchange factor, N-terminal
Lap1 LRRC7 / ERBB2IP Leucine-rich repeats 2 / 2 —
82 LIMK1 LIMK1 LIM zinc-binding domain 2 / 3 Zhang et al., Development 2011‡ Protein kinase, catalytic domain Nishita et al., J Cell Biol 2005§ par-6 PARD6 (PAR6) PB1 domain 3 / 3 Pinheiro and Montell, Development 2004§
PatJ INADL / MPDZ L27 domain 2 / 3 Shin et al., EMBO Rep 2007§
Rim RIMS2 C2 domain 2 / 3 —
stardust MPP5 (PALS1) Guanylate kinase domain 2 / 3 —
L27 domain
Src homology 3 domain veli LIN7A / LIN7B / L27 domain 2 / 2 — LIN7C a Putative homologs were found using NCBI Homologene and UniProt. b Protein domains were identified using NCBI Conserved Domains Database and Interpro. c Number of RNAi lines that resulted in a migration defect out of all the lines tested. d Cited references describe the Drosophila gene or its homologs. ‡References pertaining to studies in Drosophila. §References pertaining to the mammalian homolog. Taken from Aranjuez et al., 2012.
83 TABLE 2-2. Expression Levels and RNAi Knockdown Efficiency as Measured by Quantitative RT- PCR
c GENE EXPRESSION RNAi LINE % KNOCKDOWN IN LEVEL IN THE WHOLE FLIESd
OVARY, CT a,b VALUE±S.D.
Positive Candidates
baz 25.7±0.350 (L-M) v2914 41% *** bbg 27.4±0.435 (L-M) v15975 89% **
CG6498 26.0±0.640 (L-M) v35100 53% * CG6509 26.3±0.868 (L-M) v22496 77% **
Negative Candidates
CG43707 34.5±0.885 (L) v25846 40% ns
CG43955 40.1±1.51 (N.E.) v103267 78% **
CG9588 24.3±0.318 (M-H) HM05013 82% ***
cnk 25.5±0.451 (L-M) HMS00238 67% ***
dlg1 25.4±0.575 (L-M) HMS01521 52% **
dysc 28.6±0.237 (L-M) v23278 32% *
Grip 28.4±0.671 (L-M) v103551 90% ***
PICK1 30.4±0.252 (L) JF01199 21% **
RHOGAP100F 31.9±0.874 (L) HMS00740 14% ns
scrib 25.8±0.512 (L-M) v105412 50% **
sif 31.2±0.0700 (L) v106832 16% ns
Syn1 31.2±0.500 (L) JF02654 32% ns
vari 25.7±0.0985 (L-M) HM05087 72% *** X11L 26.0±0.463 (L-M) v28652 55% ** a The mean CT value and standard deviation (S.D.) is calculated from three independent qPCR experiments. Expression summary: CT<20, very high expression (V.H.); CT=20-25, moderate to high (M-H); CT=25-30, low to moderate (L-M); CT=30-35, low (L); CT=35-40, none to low (N-L); CT>40, not expressed (N.E.). b For reference, the endogenous expression levels (mean CT value±SD) of rp49 and tub84b were 17.7±0.112 and 19.1±0.16, respectively. c RNAi lines used are from the Vienna Drosophila RNAi Center (prefixed with v-) and from the Harvard Transgenic RNAi Project (prefixed with HM-, HMS-, or JF-). d The extent of reduction of target gene expression compared to gfp dsRNA control, calculated using the ∆∆CT method using rp49 expression as reference. The one-tailed unpaired t-test was
84 used to test for significance (ns, p>0.05; *, p=0.01-0.05; **, p=0.001-0.01; ***, p<0.001). Taken from Aranjuez et al., 2012.
85 Table 2-S1. Complete results of the PDZ RNAi survey of border cell migration trial 1 trial 2 trial 3 Gene Annotation a Group Gene Name RNAi line % Symbol Symbol % defectb nc % defect n n defect
Pos. Mult. bazooka baz CG5055 2914 (M)d 43%e 116
2915 (M) 23% 137
Pos. Mult. big bang bbg CG42230 15975 19% 58 15% 112
101691 10% 132
36111 6% 78
Pos. Mult. CASK ortholog CASK CG6703 34185 (S†,M)f 23% 79 15% 80
34184 22% 122 10% 251
104793 2% 139
Pos. Mult. CG5921 CG5921 CG5921 37875 33% 94 23% 106
37874 18% 91
Pos. Mult. CG6498 CG6498 CG6498 35101 20% 97 11% 125
35100 17% 116 12% 164
109282 13% 176
Pos. Mult. CG6509 CG6509 CG6509 46234 (S†,M) 11% 54 12% 139
101596 11% 74
22496 (S†,M) 9% 87
Pos. Mult. Gef26 Gef26 CG9491 27017 (M) 19% 86 28% 197
27015 (M) 10% 98 20% 98
105159 15% 59
Pos. Mult. Lap1 Lap1 CG10255 18600 (M) 15% 112 13% 119
JF02362g 10% 134
1 2 Pos. Mult. LIM-kinase1 LIMK1 CG1848 25344 10% 125 8% 194 2% 20
25343 6% 128
2x RNAih 18% 50
Pos. Mult. par-6 par-6 CG5884 19732 (M†) 38% 58 26% 114
108560 44% 59
2x RNAii 14% 132
Pos. Mult. PatJ Patj CG12021 31620 13% 168 20% 128
86 12021R-3j 26% 184
101877 3% 63
12021R-4j lethal lethal
Pos. Mult. Rim Rim CG33547 39384 26% 92 12% 50
1 1 39385 24% 94 1% 78 2% 13
JF02610g 7% 254
Pos. Mult. stardust sdt CG32717 100685 39% 62 11% 57
29844 13% 182
29843 6% 197
Pos. Mult. veli veli CG7662 43094 (M†) 50% 88 27% 101
46963 (M) 31% 61
Pos. Single CG14168 CG14168 CG14168 17414 13% 55 21% 226
103225 2% 56
1 1 Pos. Single CG34375 CG34375 CG34375 23904 29% 63 5% 166 3% 43
102586 4% 56
Pos. Single CG42319 CG42319 CG42319 23928 41% 152
32835 (M) 8% 197
1 1 Pos. Single CG42788 CG42788 CG42788 22311 40% 72 5% 134 1% 96
108180 1% 123
45034 4% 72
Pos. Single CG6688 CG6688 CG6688 48260 15% 86 19% 214
106732 1% 139
Pos. Single Dishevelled dsh CG18361 101525 12% 93
JF01254g 6% 188
18361R-2j lethal
18361R-3j lethal
JF01253g 8% 89
Efa6 pleckstrin and Pos. Single Sec7 domain Efa6 CG31158 42321 14% 128 12% 109 containing
Pos. Single HtrA2 HtrA2 CG8464 24104 16% 75 9% 153 1 2
87 9% 60
24106 9% 132
Magi Magi CG30388 41735 13% 128 4% 167
41736 10% 62
Pos. Single menage a trois metro CG30021 29967 10% 73
29965 1% 128
110814 1% 129
Pos. Single polychaetoid pyd CG43140 38863 15% 111 23% 188
104159 6% 142
Pos. Single Ptpmeg Ptpmeg CG1228 38651 12% 165 22% 78
103740 3% 130
Pos. Single RhoGAP19D RhoGAP19D CG1412 43955 (M†) 42% 57 59% 165
HMS00352g 9% 161
dsRNA 2.1k 8% 217
dsRNA 3.1k 3% 62
Pos. Single Rhophilin Rhp CG8497 24111 13% 102 11% 204
110377 3% 152
Pos. Single Slip1 Slip1 CG1783 33006 (S) 22% 124 10% 168
101106 6% 71
Pos. Single Syntrophin-like 2 Syn2 CG4905 JF02999g 11% 157 6% 84
110602 10% 67
Z band alternatively Pos. Single Zasp52 CG30084 36563 (S†,M†) 37% 97 19% 124 spliced PDZ-motif protein 52
106177 3% 65
JF01133g 4% 227
Negative canoe cno CG42312 102686 3% 92
Negative CG10362 CG10362 CG10362 8317 4% 52
Negative CG15617 CG15617 CG15617 19148 37% 65 7% 162
19149 7% 94 6% 167
103267 2% 50
Negative CG15803 CG15803 CG15803 43635 3% 59
88 43636 1% 135
Negative CG32758 CG32758 CG32758 28457 4% 99
108542 4% 130
Negative CG3402 CG3402 CG3402 21485 (M†) 7% 58
110191 6% 125
Negative CG43707 CG43707 CG43707 25846S 8% 130
25847 (S†) 6% 89
Negative CG43955 CG43955 CG43955 103267 2% 50
Negative CG6619 CG6619 CG6619 15622 4% 67
Negative CG9588 CG9588 CG9588 47763 2% 95
100126 4% 167
HM05013g 0% 93
connector enhancer Negative cnk CG6556 107746 3% 135 of kar
HMS00238g 3% 152
Negative discs large 1 dlg1 CG1725 41134 (S†,M†) 4% 85
109274 2% 132
41136 lethal
HMS01521g lethal
Negative dyschronic dysc CG43749 23278 9% 92
14082 5% 59
110019 3% 135
Glutamate receptor Negative Grip CG14447 21003 (S†) 5% 57 binding protein
103551 6% 71
Negative Grasp65 Grasp65 CG7809 22564 1% 125
inactivation no Negative inaD CG3504 26211 6% 64 afterpotential D
Negative kermit kermit CG11546 109297 2% 131
Negative locomotion defects loco CG5248 9248 (M) 10% 67 6% 237
110275 7% 149
HMS00455g 3% 189
Negative PICK1 PICK1 CG6167 22268 4% 74
89 104486 1% 163
JF01199g 3% 222
Negative Prosap Prosap CG30483 21216 (M†) 5% 59
103592 4% 98
Protostome-specific Negative PsGEF CG43947 32088 8% 122 GEF
109769 1% 154
RhoGAP100 Negative RhoGAP100F CG1976 106241 0% 62 F
HMS00740g 1% 161
Negative RhoGEF2 RhoGEF2 CG9635 JF01747g 13% 145 6% 135
HMS01118g 7% 180
110577 3% 90
Negative scribbled scrib CG43398 105412 5% 172
HMS01490g lethal
Negative Spinophilin Spn CG16757 19658 (S) 4% 56
105888 1% 106
Negative sprite sprt CG30023 107873 2% 122
SRY interacting Negative Sip1 CG10939 16958 15% 65 9% 170 protein 1
Negative still life sif CG34418 26132 (M) 7% 89
106832 1% 106
Negative Syntrophin-like 1 Syn1 CG7152 27893 4% 73
104992 3% 111
JF02654g 3% 138
Negative varicose vari CG9326 24156 (M†) 4% 70
104548 6% 87
HM05087g 2% 124
Negative X11L X11L CG5675 28652 9% 110
27479 9% 114
Negative X11Lbeta X11Lbeta CG32677 8309 (S,M†) 2% 51
Z band Negative alternatively Zasp66 CG6416 102980 5% 131 spliced PDZ-motif
90 protein 66
Not tested arc a CG6741
Myosin heavy Not tested Mhcl CG31045 chain-like a Group designation are as follows: Pos. Mult. = Positive, Multiple Hits; Pos. Single = Positive, Single Hit; Negative = No Hit. b Percentage of stage 10 egg chambers in which border cell migration defects were observed upon RNAi expression. c Number of stage 10 egg chambers scored per trial. N ≥ 50 in every trial. d Lines that produced a phenotype (M) or were lethal (M†) in a genome-wide RNAi screen to study Notch signaling in Drosophila (Mummery-Widmer et al., 2009). e Values highlighted in orange represent a migration defect (≥10%). f Lines that produced a phenotype (S) or were lethal (S†) in a genome-wide RNAi screen to study Drosophila muscle morphogenesis and function (Schnorrer et al., 2010). g RNAi lines from the Transgenic RNAi Project (TRiP), Harvard Medical School. h LIMK1 double RNAi transgenic line (Ng insertions. Personal communication to FlyBase [FBrf0188570]). i par-6 double RNAi transgenic line from Pinheiro and Montell, 2004. j RNAi lines from NIG-Fly, Japan. k RhoGAP19D RNAi transgenic lines (Luo insertions. Personal communication to FlyBase [FBrf0141849]). Taken from Aranjuez et al., 2012.
91
Figure 2-S1. Drosophila PDZ-domain-containing proteins are functionally diverse and involved in numerous processes
92 Figure 2-S1. Drosophila PDZ-domain-containing proteins are functionally diverse and involved in numerous processes. (A-B) Of the 66 genes annotated to contain a PDZ domain, 51 were analyzed by PANTHER (http://www.pantherdb.org). (A) PDZ proteins are found in almost half (6/14) of the classifications for molecular function and predominantly annotated as having binding, enzyme regulator, structural, or catalytic activity. Molecular function is defined as the action that a protein performs on its direct target. A protein is assigned only one molecular function. (B) PDZ proteins contribute to
14 out of 19 annotated biological processes. A protein can contribute to multiple processes. (C) Positive hit genes were surveyed for roles in polarity or association with the cytoskeleton using AmiGO (http://amigo.geneontology.org). Genes can fall into both categories. The polarity genes include: baz, CASK, dsh, Lap1, par-6, Patj, pyd, sdt and veli. The following genes are associated with the cytoskeleton: CG14168, CG42319,
CG42788, CG5921, CG6498, dsh, Lap1, LIMK1, Patj, Ptpmeg, RhoGAP19D, Syn2, veli, and Zasp52. Taken from Aranjuez et al., 2012.
93
Figure 2-S2. slbo-GAL4-driven RNAi expression targeting three positive candidates
94 Figure 2-S2. slbo-GAL4-driven RNAi expression targeting three positive candidates.
Quantification of border cell migration at stage 10, shown as the percentage of border cells with complete (green) or incomplete (red) migration in egg chambers expressing
CG6498 RNAi v35100, bbg RNAi v15975, and CG6509 RNAi v22496 in border cells using slbo-GAL4. Two-tailed unpaired t-test was used to determine statistical significance (*, p < 0.05). At least 50 egg chambers were scored in at least three trials.
Taken from Aranjuez et al., 2012.
95
Figure 2-S3. Gene expression levels in control and RNAi knockdown measured by quantitative RT-PCR
96 Figure 2-S3. Gene expression levels in control and RNAi knockdown measured by quantitative RT-PCR. Ubiquitous expression of RNAi against a subset of (A) positive candidates and (B) negative candidates result in varying degrees of knockdown. The mean transcript levels were calculated from three biological replicates using ΔΔCT method and normalized to rp49 mRNA levels. Controls are RNAi to GFP. Error bars represent standard deviation. Taken from Aranjuez et al., 2012.
97
Figure 2-S4. Bbg function in early development of border cells
98 Figure 2-S4. Bbg function in early development of border cells. (A) c96-GAL4 expression (green) in egg chambers is limited to the polar cells (arrowhead) in the early stages but expands to most of the follicle cell epithelium starting at stage 8 of Drosophila oogenesis. The image was acquired to show the follicle cell epithelium. The nuclei are visualized with DAPI (blue). Scale bar is 50 µm. (B) STAT nuclear enrichment (yellow dashed circles) is observed in border cells expressing bbg RNAi line v15975 driven by c306-GAL4 before detachment (stage 9). Upon bbg knockdown, 72% (N=21) of detaching clusters still retain nuclear STAT enrichment in all or some border cells in the cluster. The images are of the same border cell cluster taken at different focal planes.
Scale bar is 10 µm. (C) Border cell recruitment is unaffected by bbg RNAi. On average, both control (N=35) and bbg RNAi (N=31) clusters have ~5 border cells. Error bars represent SEM. N.S., not statistically significant (two-tailed unpaired t-test). Taken from
Aranjuez et al., 2012.
99
Dop kinase is required for stereotypic protrusion formation in
collectively migrating border cell clusters in Drosophila
George Aranjuez[1,2], Alistair Langlands[3], H.-Arno Müller[3], and
Jocelyn A. McDonald[1,2]
[1] Genetics and Genome Sciences, Case Western Reserve University, Cleveland, OH,
USA. [2] Molecular Genetics, Lerner Research Institute, Cleveland Clinic Foundation,
Cleveland, OH, USA. [3] Cell and Developmental Biology, University of Dundee, UK.
Unpublished work
G.A. and J.A.M. conceived and designed the epxeriments. G.A. performed the experiments and analyzed the data. A.L. and H.-A.M. provided the dop mutant alleles.
100 Introduction
Border cell migration in the developing Drosophila ovary has been used as an excellent model for understanding the mechanisms that facilitate collective migration
(Montell et al., 2012; Rørth, 2012). The Drosophila ovary is composed of strings of ovarioles which are themselves made up of progressively more developed egg chambers.
Each egg chamber is made up of an oocyte and germline-derived nurse cells surrounded by a single layer of somatic follicle cell epithelium (Figure 3-1A). Border cells arise from the anterior follicle cell epithelium, delaminate as a group of 6-10 cells, and migrate in between the nurse cells to reach the anterior border of the oocyte (Figure 3-1A). This migration is very stereotypic and occurs as part of normal oogenesis.
The key to the collective migration of the border cell cluster is the ability to restrict protrusion formation to front-most border cell at any given time (Prasad and
Montell, 2007). The development of the live imaging technique revealed that normal border cell migration involves the extension of a single protrusion from the front of the cluster, which extends towards the direction of migration (Prasad and Montell, 2007;
Prasad et al., 2007). This phenomenon occurs despite the obvious rotation of the cluster and the intra-cluster rearrangement of border cell positions during migration (Prasad and
Montell, 2007). Thus, one of the hallmarks of collective border cell migration is the ability to orchestrate the protrusive behavior of the component border cells.
Ligand guidance signaling and asymmetric Rac1 activation are part of the mechanism that promotes the correct border cell protrusion behavior (Montell et al.,
2012). The cluster uses the ligand gradient to shape the cluster's protrusive behavior
(Montell et al., 2012). The inability to receive guidance cues secreted by the oocyte
101 causes border cells to simultaneously extend protrusions, regardless of where the cells are located in the cluster, resulting in migration defects (Poukkula et al., 2011; Prasad and
Montell, 2007). Ligand-receptor signaling leads to activation of Rac1 GTPase localized to the leading edge of the cluster (Ramel et al., 2013; Wang et al., 2010b). This promotes protrusion formation only at the front of the cluster. One approach to understand how the border cell cluster determines directionality is to identify additional genes that are required for maintaining a single, forward protrusion during border cell migration.
The RNAi screen described in the previous chapter was designed to identify new genes that regulate border cell migration as well as other collective behaviors (Aranjuez et al., 2012). In this chapter, I further characterize one of the identified genes from this
RNAi screen (Aranjuez et al., 2012), called drop out (dop), which is required for normal border cell migration. Data presented in this chapter shows that dop plays a role in the maintenance of the correct number of protrusions during border cell migration.
Dop encodes a serine/threonine kinase that also contains a PDZ domain and conserved domain of unknown function (DUF1908), which is only found in Dop and its mammalian homologs (Figure 3-1B). Dop was originally characterized in Drosophila for its role in cellularization during early embryogenesis (Galewsky and Schulz, 1992). The earliest step in embryogenesis is characterized by multiple rounds of nuclear division without cytokinesis, resulting in a syncytial blastoderm (Mazumdar and Mazumdar,
2002). Cellularization entails the de novo synthesis of cell membranes that encapsulate each nuclei, transforming the embryo into a cellularized blastoderm (Mazumdar and
Mazumdar, 2002). The name ‘drop out’ was coined from the observation that, in the dop mutant embryos, nuclei, are normally neatly arranged along the periphery of the embryo,
102 begin to drop out of formation (Galewsky and Schulz, 1992). Subsequent studies have shown that Dop is critical in the microtubule-based transport of during this process (Hain et al., 2014). Specifically, Dop phosphorylates a component of the microtubule motor protein complex dynein to promote minus-end directed transport (Hain et al., 2014).
Members of the Microtubule-Associated Serine/Threonine (MAST) family of kinases are the homologs of Drosophila Dop. MAST kinases were first identified in a screen for microtubule-associated proteins from a spermatid cDNA expression library
(Walden and Cowan, 1993). Originally called MAST205, this 205 kD protein was named based on biochemical studies that showed it immunoprecipitated with microtubule components from lysates and can phosphorylate a 75 kD unnamed substrate thought to be part of a microtubule complex (Walden and Cowan, 1993). However, MAST205 failed to bind to microtubules (MT) directly after performing in vitro binding studies, suggesting that MAST kinases interact with MTs via other MT-binding proteins (Walden and
Cowan, 1993). The MAST kinase family (composed of MAST1-4 and MAST-like) is expressed in multiple tissues based on RT-PCR and whole-mount immunostaining in rats
(Garland et al., 2008). Further studies on MAST kinases show relevance to diseases like muscular dystrophy, where it is thought to stabilize neuromuscular junctions by linking microtubules to the dystrophin complex (Lumeng et al., 1999), and cancer, where multiple gene rearrangements between MAST coding regions and various gene promoters have been found in a subset of breast carcinoma cell lines (Robinson et al., 2011). The ubiquitous expression of Dop/MAST kinases and the diversity of its described functions indicate that this family of kinases can function in multiple contexts.
103 Here, we present the initial characterization of the role of dop in border cell migration. Loss of dop in border cells results in failure to complete migration to the oocyte. Further characterization shows loss of dop results in ectopic border cell protrusions. This phenotype suggests that dop participates in the mechanism that controls proper protrusive behavior of the border cell cluster. This is the first described role of dop in the context of cell migration.
Results and Discussion
Loss of dop in border cells results in failure to complete migration
Wild type border cell clusters reach the oocyte and complete their migration by stage 10 of oogenesis (Figure 3-1A). Migration defects were quantified by dividing the migration path in stage 10 egg chambers into quartiles and noting the location of the border cell cluster (Figure 3-1A). RNAi knockdown using different lines targeted against different regions of the dop gene (Figure 3-2A) resulted in mild (~20% of dop RNAi clusters fail to reach the oocyte) migration defects. Use of multiple RNAi lines reduces the likelihood that the observed phenotype is a result of off-target effects (Aranjuez et al.,
2012). The mild migration defects could represent a hypomorphic condition due to inefficient RNAi knockdown. Hypomorphic mutants may fail to display phenotypes that can provide clues about the function of dop in border cell migration.
To address this, we used the strongest dop RNAi line (v35100), based on the border cell migration defects, and knocked down dop expression in a dop heterozygous mutant background. The dop[6] and dop[10] alleles were generated using chemical mutagenesis by the laboratory of Dr. H.-Arno Müller (University of Dundee, unpublished). The
104 dop[10] mutation is nucleotide substitution that results in an early truncation of the protein upstream of any of the conserved domains (Figure 3-1B, C). The dop[6] mutation is a nucleotide substitution that disrupts a conserved amino acid residue within the kinase domain (Figure 3-1B, C). Both alleles are early embryonic lethal as homozygotes, similar to other loss-of-function alleles of dop (Dr. Müller, personal communication).
Interestingly, knockdown of dop in any of the dop heterozygous mutant backgrounds does not enhance the migration defects caused by RNAi alone (Figure 3-2B, C).
Next, we examined clusters containing homozygous dop mutant border cells for border cell migration defects. Due to early embryonic lethality, mosaic analysis was used to generate a random clonal population of homozygous dop mutant cells within a heterozygous, viable fruit fly (Figure 3-3) (Xu and Rubin, 1993). These mutant cells are identified visually via the loss of a fluorescent marker (cytoplasmic GFP or nuclear RFP)
(Figure 3-4A). Clusters containing dop mutant border cells fail to complete migration at a higher rate compared to RNAi knockdown (Figure 3-4B, C). Interestingly, border cell clusters that contain dop[10] mutant clones have more frequent migration defects (43%) than clusters containing dop[6] mutant clones (14%), though the weaker phenotype is closer to what is observed with RNAi (Figure 3-4B, C). Consistent findings from both
RNAi knockdown and mosaic analysis experiments confirm a new role for dop in promoting border cell migration.
The disparity in migration defects between dop[6] and dop[10] clonal clusters may indicate that the two alleles affect dop function differently after embryonic lethality is bypassed. The dop[6] mutation may alter the kinase function of Dop while the other domains of Dop remain intact and are likely functional. It is possible that the process of
105 embryonic cellularization requires a fully functional Dop protein and that any mutation is detrimental. But once embryonic lethality is bypassed, the dop[6] allele may, in fact, be a hypomorphic allele in the context of border cell migration. On the other hand, the dop[10] mutation results in an early truncation upstream of the kinase and PDZ domains and also disrupts the DUF1908 domain (Figure 3-1B). The short N-terminal fragment, if not subject to degradation, is likely non-functional. This may explain the stronger migration defect observed in dop[10] clonal clusters compared to either the dop[6] clonal cluster or dop RNAi knockdown.
Due to the random nature of mosaic clonal analysis, the frequency of mutant border cells varies from just one mutant cell per cluster to all cells being mutant. Mutations that impact individual cell motility or the ecdysone hormone response each display a strong correlation between the severity of the migration defects and the number of mutant border cells in the cluster (Bai et al., 2000; Geisbrecht and Montell, 2004; McDonald et al., 2003). Therefore, it is informative to test whether the same phenomenon applies to border cells that lose dop function as a means to identify potential pathways that dop plays a role in. The number of border cell mosaic clones within a cluster was plotted against the extent of migration (quartiles) of control and dop mutant clonal clusters
(Figure 3-4D). All control clonal clusters completed their migration regardless of the number of clonal border cells present, as expected (Figure 3-4D). Interestingly, there is no correlation between the number of dop[6] or dop[10] mutant border cells and the severity of the migration defect (Figure 3-4D). The lack of correlation observed in dop mutant border cell clusters suggests that loss dop does not greatly hamper single cell motility or play a role in ecdysone hormone response. The mild migration defects
106 observed in dop mutant clonal clusters are consistent with this idea. Alternatively, dop may promote coordination at the collective level. To investigate this further, more detailed characterization of dop mutant border cell cluster morphology was performed.
Loss of dop does not affect border cell position in the cluster
Border cells within the cluster undergo position rearrangements throughout migration. There is no defined leader cell that is permanently at the front of the cluster.
Instead, border cells shuffle between the front and rear of the group throughout their migration (Prasad and Montell, 2007). Because mosaic border cell clones are fluorescently labeled, the location of control and dop mutant border cells within the cluster in immunostained egg chambers can be determined. Examining clusters in mid- migration, the frequency of finding a border cell clone in the front or rear half of the cluster was scored for both control and dop mutant mosaic clones. Control clones are found at the front and the rear of the cluster at roughly equal frequencies
(Figure 3-5A-C). When clusters with only single clones are analyzed separately, there is a slight tendency for clones to be found at the front (Figure 3-5C). Using the range of values observed in controls, dop mutant border cells sort no differently within the cluster, compared to control border cells.
Our finding that control clones are found equally throughout the cluster is consistent with the observed rearrangements of border cells during live migration (Prasad and Montell, 2007). Furthermore, the equal frequency indicates that border cells are a homogeneous population in terms of migratory behavior. Heterogeneity in migratory behavior manifests itself in the sorting or arrangement of cells in a population, with less motile cells occupying the rear. Clones of border cells mutant for ELMO/Ced-12, a GEF
107 (guanine nucleotide exchange factor) that promotes activation of Rac1 GTPase are always found in the rear of the cluster, as Rac is required for cell motility (Bianco et al.,
2007). Our findings indicate that dop mutant border cells are likely as motile as control border cells within the cluster. On the other hand, the presence of dop mutant border cells in a cluster affects movement of the group. This supports the hypothesis that the role for
Dop in border cell migration is specific to promoting collective migration of border cells and likely has little effect on individual border cell motility.
Loss of dop results in multiple protrusions
We discovered a dramatic deviation from the normal protrusive behavior in dop mutant border cell clusters. Closer examination of dop mutant clusters in mid-migration reveals the presence of multiple protrusions (Figure 3-6A). The majority of control clonal clusters (76%) do not have visible protrusions when examining fixed tissue, with 6% of clusters having >1 protrusions (Figure 3-6B). In contrast, clusters that contain dop[6] mutant border cells are much more protrusive, where a majority of the clusters (76%) exhibit one or more protrusions (Figure 3-6B). Furthermore, the frequency of multiple protrusions has increased six-fold to 38% of dop[6] clonal clusters observed versus control (Figure 3-6B). In contrast, dop[10] clonal clusters do not noticeably increase protrusion frequency. The majority of dop [10] clonal clusters (61%) do not exhibit protrusions, which is not that different from controls (Figure 3-6B). However, there is a three-fold increase in the frequency of multiple protrusions observed from dop[10] clonal clusters compared to controls (Figure 3-6B). The presence of multiple protrusions from control clonal clusters is unexpected. The genetic background required to generate mosaic clones is complex, involving multiple transgenes brought together into a single
108 fly line. It is possible that this complex genetic background causes the occasional appearance of multiple protrusions. Nonetheless, this does not appear to impact the ability of control clonal clusters to complete migration (Figure 3-4B, C).
Once again, the two dop mutant alleles exhibit dissimilar effects on protrusive behavior of border cell clusters (Figure 3-6B). The dop[6] allele increases the general likelihood of protrusions in clonal clusters and, strikingly, half of these protrusive clusters exhibit >1 protrusion. In contrast, the dop[10] allele does not drastically increase the baseline protrusive behavior of clusters. However, about half of the clusters that do exhibit protrusions contain more than one protrusion. In comparison, only a quarter of control clonal clusters that exhibit protrusion have more than one protrusion.
These data suggest that the dop[6] allele functions as a dominant negative to increase protrusive behavior with concomitant increase in multiple protrusions. Both alleles are predicted to affect the kinase activity of Dop. However, the dop[6] allele does not affect the function of the PDZ domain or the DUF1908 domain. The disparity of protrusion behavior in the two different mutant backgrounds might be due to the selective disruption of the kinase domain alone in the dop[6] background. Consistent with this, losing all conserved domains in the dop[10] background does not change the general protrusive behavior of the mutant clusters and only slightly increases the frequency of multiple protrusions.
Taken together, these findings indicate that dop promotes collective-level control of protrusion formation. Wild type clusters extend a single protrusion towards the direction of migration, which is key to efficient collective movement of border cell clusters.
Deviation from this stereotypical behavior represents a disruption of the cluster’s cellular
109 program for collective movement, as seen when the function of guidance receptors is blocked in border cells, resulting in multiple ectopic protrusions emanating from the cluster (Prasad and Montell, 2007). Dop may be part of the pathway involved in the mechanism of protrusion control in border cells.
Dop mutant clusters extend misshapen protrusions
Protrusions from dop mutant clusters also have aberrant morphology compared to controls. Normal protrusions have a tapered outline and a smooth margin (Figure 3-6C).
Conversely, protrusions from dop mutant clusters do not taper but are instead frayed or forked at the tip (Figure 3-6C). Protrusions are deformations in the membrane caused by an outward mechanical force generated by the underlying cytoskeleton. Polymerization of cortical F-actin is absolutely required for membrane protrusions (Pollard and Borisy,
2003). Microtubules also contribute to protrusion formation in glia as well as in long neuronal processes, a specialized form of membrane outgrowth (Conde and Cáceres,
2009). On the other hand, protrusions from migrating fish keratocytes do not require microtubules at all (Euteneuer and Schliwa, 1984). Therefore, the contribution of microtubules in protrusion formation is cell-dependent. Nonetheless, abnormalities in either F-actin or microtubules can result in abnormal protrusion morphologies (Etienne-
Manneville, 2013; Le Clainche and Carlier, 2008; Ridley, 2006; Waterman-Storer et al.,
1999).
Loss of dop does not alter the levels of F-actin or microtubules in border cells
We tested the possibility that Dop regulates the overall levels of actin and/or microtubules. F-actin and microtubule filaments were visualized in mosaic dop mutant border cell clusters while the rest of the border cells in the cluster acted as internal
110 controls. No observable change was detected in either F-actin (not shown) or microtubule levels in the dop mutant background (Figure 3-6D). These results suggest that dop is not required for the synthesis of cytoskeletal subunits or its assembly into filaments.
However, both F-actin and microtubules are highly dynamic structures that undergo further extension or collapse, dictated by numerous protein regulators (Desai and
Mitchison, 1997; Insall and Machesky, 2009). Disrupting the function of actin- or microtubule-binding proteins is known to impact cytoskeletal dynamics, with effects on cell shape and protrusions (Le Clainche and Carlier, 2008; Ridley, 2006; Ridley et al.,
2003). Visualizing the cytoskeleton in fixed, immunostained samples cannot reveal changes in cytoskeletal dynamics. Thus, further work is needed to analyze live cytoskeletal markers in dop mutant border cells.
Altering microtubule dynamics does not rescue the migration defects due to dop RNAi
The previous experiment revealed no gross change in cytoskeletal levels or arrangement in dop mutant border cells. However, this does not address a role for dop in regulating cytoskeletal dynamics in border cells. To explore this further, we tested whether dop genetically interacts with Eb1, a gene that encodes a microtubule-binding protein that promotes microtubule growth and stability. We assayed for rescue or enhancement of the dop RNAi migration defects after promoting either growth or collapse of microtubules through alteration of EB1 levels.
EB1 is a microtubule-binding protein that bind to the growing tip of microtubules and promotes stability and growth of microtubule filaments (Komarova et al., 2009;
Rogers et al., 2002; Vitre et al., 2008). Elevated amounts of EB1 promote growth and stability of microtubules in yeast, Drosophila, and mammalian cells (Tirnauer et al., J
111 Cell biol 2009, Rogers et alk., J Cell Biol 2002). Conversely, reduced amounts of EB1 results in the absence of growth (paused microtubule state) (Rogers et al., 2002; Tirnauer et al., 1999).
We compared the frequency of dop RNAi migration defects alone or in a background with either elevated or reduced EB1 levels. Overexpression of EB1 in a dop
RNAi background results in increased migration defects compared to dop RNAi alone
(Figure 3-7A). However, EB1 overexpression alone results in migration defects, as well
(Figure 3-7B). A more careful analysis will need to be performed to determine the presence of an additive effect in the EB1 overexpression + dop RNAi background.
Simultaneous RNAi knockdown of Eb1 and dop results in the same frequency of migration defects as dop RNAi alone (Figure 3-6A). RNAi knockdown of Eb1 does not increase the background rate of migration defects (Figure 3-7B) (Aranjuez et al., 2012).
The mild baseline phenotype caused by dop RNAi knockdown is a limitation of these experiments, as it is less sensitive in reporting suppression of the phenotype. This experiment needs to be repeated in a dop mutant background with a stronger, but not saturating, migration defect.
Conclusion and Future Directions
Efficient collective migration of border cells depends on a poorly understood cellular program that restricts protrusion formation to a single border cell at the front of the cluster. Part of this program involves active restriction of protrusions from every other border cell aside from the leading one. Dop likely contributes to this restriction, as loss of Dop function in border cells results in the presence of multiple, ectopic protrusions.
112 Ectopic protrusions could arise via two scenarios: 1) Protrusions from dop mutant border cells form and fail to retract; or 2) Protrusions form and retract independent of the location of a given border cell within the cluster. The first scenario is unlikely since dop mutant border cells sort within the cluster similar to control border cells. Failure to retract protrusions would increase the viscoelastic drag experienced by the mutant border cell, resulting in its sorting to the rear of the cluster. These scenarios can only be tested by observing dop mutant clusters using live time-lapse imaging.
Live imaging of dop mutant clusters will be invaluable in elucidating the role of dop in protrusion formation and border cell migration (Prasad et al., 2011). It is possible to create mosaic mutant border cell clusters where the mutant border cell membranes are differentially labeled from the rest of the cluster. This allows one to observe both mutant protrusions and protrusions from control border cells in the same cluster during the course of migration. Using this approach, we can determine how multiple protrusions arise in dop mutant clonal clusters and whether there are differences in the dynamics of protrusion formation and retraction between dop mutant and control border cells.
There is sufficient preliminary evidence to suggest that dop function in border cells is part of the cluster-level cellular mechanism that controls protrusion formation.
Loss of guidance receptor function results in ectopic protrusions (Poukkula et al., 2011;
Prasad and Montell, 2007), similar to what is observed in dop mutant border cell clusters.
Loss of guidance receptor function also abolishes increased Rac1 activity at the front of the cluster (Wang et al., 2010b). A key experiment to test this hypothesis is to determine whether high Rac1 activity is maintained at the leading edge of the dop mutant cluster.
113 Identifying the Dop kinase target in border cells is critical to arriving at a molecular mechanism of action. A component of the dynein microtubule motor complex is a confirmed substrate during cellularization of the Drosophila embryo (Hain et al.,
2014). However, it has been shown that dynein has no apparent role in border cells, but is instead required in the polar cells for secretion of the Jak ligand Upd (Van de Bor et al.,
2011). In support of this, knockdown of dop in polar cells alone result in border cell migration defects (Figure 3-7D). Knockdown of dop in both polar cells and border cells results in a higher frequency of migration defects compared to knockdown in either cell type alone (Figure 3-7D). This further confirms a role for dop in border cells and that
Dop likely has a different substrate in border cells that is important for protrusion formation. One approach is to identify Dop-interacting proteins via its PDZ domain.
Potential substrates can be tested genetically by characterizing the mutant phenotype, mutating the phosphorylated residues, and performing genetic interaction experiments with dop and the other components of the mechanism that control border cell protrusions, together with biochemical confirmation of substrate phosphorylation.
Materials and Methods
Drosophila genetics
Flies and crosses were kept at 25˚C unless otherwise stated. The following fly stocks were used: c306-GAL4, tsGAL80; upd-GAL4; slbo-GAL4; UAS-dop RNAi v35100 (Vienna Drosophila Resource Center [VDRC]); UAS-Eb1 RNAi v106233
(VDRC); UAS-EB1:GFP (gift from H. Broihier); hs-flp ; FRT2A, ubi>GFP (gift from A.
Zhu); FRT2A, dop[6] ; FRT2A, dop[10]-16 ; FRT2A, dop[10]-27. Dop alleles were a gift from H.-A. Muller.
114 Immunostaining and imaging
Egg chambers were dissected and immunostained as described previously
(Aranjuez et al., 2012). Briefly, 3-5 day old female flies were fattened overnight at 30C for optimum GAL4/UAS expression. Whole ovaries or ovarioles were dissected and fixed with 4% formaldehyde, methanol-free (Polysciences, Inc.) in phosphate-buffered saline + 0.1%v/v Triton-X-100 (PBT) for 10 mins with rocking and blocked in PBT +
0.5%w/v bovine serum albumin (PBT-BSA). Primary antibodies used and their concentrations are as follows: Singed, 1:60 (sn 7c, DSHB); GFP, 1:100 (Life
Technologies); alpha-tubulin, 1:1000 (Sigma). Alexa Fluor-conjugated secondaries (Life
Technologies) were used at 1:400 dilution. Egg chambers were mounted on slides using
AquaPolyMount (Polysciences, Inc.).
Images were acquired with a Zeiss AxioImager Z1 microscope equipped with the
Apotome system and MRm CCD camera and using either a 20x Plan-Apochromat 0.75
NA or a 40x Plan-Neofluar 1.3 NA oil-immersion objective.
Figures, Graphs, and Statistics
Graphs and statistical tests were performed on Graphpad Prism. Brightness and contrast adjustments were performed using Fiji, an ImageJ distribution. Figures and illustration were assembled in Adobe Illustrator.
115 Figure 3-1. Collectively migrating border cells in the Drosophila ovary and the Drosophila Dropout protein
116 Figure 3-1. Collectively migrating border cells in the Drosophila ovary and the
Drosophila Dropout protein. (A) Schematic of stage 10 egg chambers showing the border cell cluster (blue) finishing migration by reaching the oocyte. Border cell clusters arise from the anterior follicle cell epithelium (red) and migrate in between the intervening nurse cells (nc) to reach the oocyte. Migration defects can be quantified by scoring how far the cluster has migrated along the migration path, which is divided into quartiles, by stage 10 of oogenesis, when wildtype border cell cluster have completed migration. (B) The Dropout (Dop) protein is composed of a conserved kinase domain, a
PDZ domain, and a domain of unknown function DUF1908. Dop shares a very high homology across the conserved domains with its mammalian homolog microtubule- associated serine/threonine kinase MAST2. The resulting amino acid base change of the molecularly characterized alleles dop[6] and dop[10] are indicated. (C) Sanger DNA sequencing confirms the DNA base change in the dop[6] and dop[10] alleles (asterisks).
Overlapping chromatogram peaks represent the presence of wildtype and mutant dop sequence expected from heterozygotes.
117 Figure 3-2. RNAi knockdown of dop in border cells results in migration defects
118 Figure 3-2. RNAi knockdown of dop in border cells results in migration defects. (A)
Gene structure of dop with coding (orange) and non-coding exons (gray). Target sequences of various RNAi lines against dop are indicated by black bars. (B) Border cell migration score after RNAi knockdown of dop alone or in various dop mutant heterozygous backgrounds. UAS-dop RNAi v35100 was expressed in border cells using c306-GAL4, tsGAL80 by itself or in the indicated dop mutant heterozygous background.
The two dop[10] alleles were independently isolated lines but are identical upon Sanger sequencing. c306-GAL4, tsGAL80 / + used as control. One-way ANOVA was used to test for statistical significance; ****, p<0.0001; n.s., p>0.05. (C) Examples of migration defects after knockdown of dop by itself or in a dop[6] heterozygous background. Border cell clusters (arrowheads) fail to reach the oocyte (dashed lines) by stage 10 of oogenesis.
119 Figure 3-3. Mechanism for generating and identifying mosaic clones
120 Figure 3-3. Mechanism for generating and identifying mosaic clones. A heterozygous cell carries a dop mutation on one chromosome and a wild type homologous chromosome with a selectable marker such as GFP. Both chromosomes carry compatible FLP recombination target sequences (FRT). The heterozygous cell is expressing GFP.
Transient expression of the FLP enzyme during mitosis induces random recombination between FRT sequences. A subset of these recombination events will result in recombination of one chromosome arm to the homologous chromosome. Segregation of a chromosome and subsequent cell division results in two daughter cells: one containing both wild type chromosomes and the accompanying GFP marker; and another that is homozygous mutant for dop and absent any GFP expression. Proliferation of these daughter cells will result in adjacent populations of homozygous mutant cells and wild type cells, which can be distinguished based on the presence or absence of GFP expression.
121 Figure 3-4. Dop mutant border cell clusters fail to complete migration
122 Figure 3-4. Dop mutant border cell clusters fail to complete migration. (A) Border cell cluster containing control mosaic clones (GFP-negative, dashed outline). The whole cluster is visualized using Singed staining. (B) Frequency of migration defects (gray) observed in clusters containing control, dop[6], and dop[10] border cell clones.
Genotype same as above. (C) Egg chambers showing extent of migration of border cell clusters (arrowheads) containing mosaic clones (dashed outline) of indicated genotype.
Control clonal clusters reach the oocyte by stage 10 of oogenesis. Both dop[6] and dop[10] clonal clusters exhibit migration defects. Loss of GFP identifies mosaic clones;
Singed staining labels border cells; DAPI labels nuclei. Scale bar is 40µm. (D) Scatter plot showing the number of clonal cells within a border cluster versus the extent of migration in each of the indicated genotypes. Each dot represents a single cluster
(control, n=32 [no migration defect]; dop[6], n=37 [14% migration defect]; dop[10], n=24 [30% migration defect]). Mosaic clones were positively marked using nuclear RFP.
123 Figure 3-5. Dop mutants do not affect single cell motility of border cells within the cluster
124 Figure 3-5. Dop mutants do not affect single cell motility of border cells within the cluster. Graphs showing the frequency of clonal border cells of the indicated genotype being in the front or rear of the cluster in fixed samples when looking at: (A) all clusters that contain at least one clonal border cell; (B) clusters that contain multiple clonal border cells; and (C) clusters that contain exactly one clonal border cell.
125 Figure 3-6. Loss of dop results in ectopic, misshapen protrusions
126 Figure 3-6. Loss of dop results in ectopic, misshapen protrusions. (A) Border cell clusters with dop mutant border cells (dashed outlines) exhibit multiple, ectopic protrusions (yellow arrowheads). Absence of GFP marks mosaic border cell clones.
Singed labels the cluster and protrusions. (B) Frequency of observing clusters of the indicated genotype with no protrusions (gray), one protrusion (white), or multiple protrusions (red). Dop mutant cluster protrusions appear frayed or forked. A border cell cluster with control clones (dashed outline) exhibit a single, forward protrusion (yellow arrowhead) that has a smooth margin and tapers towards the tip. Dop[10] clonal clusters exhibit protrusions with abnormal morphology (arrowheads), either forked or frayed at the tip. A similar phenomenon occurs in dop[6] clones (not shown). Absence of nuclear
RFP marks mosaic border cell clones. Singed staining labels the cluster and protrusions.
DAPI labels the nuclei. (D) Loss of dop in border cells does not alter microtubule levels.
Similar levels of alpha-tubulin, a component of microtubule filaments, are observed between homozygous dop[10] border cell clones (asterisks) and neighboring control border cells within the same cluster. A similar finding was observed for dop[6] clonal border cell clusters (not shown).
127 Figure 3-7. Dop likely functions to control border cell microtubule dynamics
128 Figure 3-7. Dop likely functions to control border cell microtubule dynamics. (A)
Altering microtubule dynamics influences the migration defects caused by RNAi knockdown of dop in border cells. Border cell migration score in c306-GAL4, tsGAL80 /
+ ; UAS-dop RNAi v35100 / + alone or co-expressing either UAS-EB1:GFP or UAS-Eb1
RNAi v106233. Dashed lines represent the baseline migration defect found in c306-
GAL4, tsGAL80 / + controls. Error bars represent s.e.m. One-way ANOVA was performed to test for statistical significance: **, p<0.01; n.s., p>0.05. (B) Border cell migration score upon border cell expression of UAS-EB1:GFP alone or UAS-Eb1 RNAi v106233 alone. Overexpression of EB1 results in strong migration defects while knockdown of Eb1 expression in border cells had no effect on border cell migration. (C)
Overexpression of microtubule +-tip binding proteins EB1 or CLIP170 in border cells using c306-GAL4 (driving UAS-EB1:GFP) or slbo-GAL4 (driving UAS-CLIP170:GFP) result in ectopic, misshapen protrusions (yellow arrowheads). (D) Preliminary border cell migration score upon RNAi knockdown of dop in border cells (using c306-GAL4, tsGAL80), polar cells (using upd-GAL4), or in both. Knockdown in both polar cells and border cells results in a substantial increase in migration defects compared to knockdown in just one cell type.
129
Collective cell shape requires myosin activity at the group periphery during in vivo migration
George Aranjuez[1,2], Ashley Burtscher[1], Pralay Majumder[1,*] and Jocelyn A.
McDonald[1,2]
[1]Department of Genetics and Genome Sciences, School of Medicine, Case Western
Reserve University, Cleveland, OH. [2]Department of Cellular and Molecular Medicine,
Lerner Research Institute, Cleveland Clinic, Cleveland, OH.
*Present address: Department of Biological Sciences, Presidency University, Kolkata,
West Bengal, India
Unpublished work.
G.A., P.M. and J.A.M. conceived and designed the experiments. G.A., A.B., P.M. and
J.A.M. performed the experiments and analyzed the data. G.A. and J.A.M. wrote the manuscript with input from the other authors.
130 Introduction
Throughout development, cells frequently move in small to large groups, termed
“collectives”, to form, shape and remodel tissues and organs (Friedl and Gilmour, 2009).
Collective cell migration is vital for many processes that occur during embryonic development and accumulating evidence indicates that cancer cells can also invade as collectives (Friedl et al., 1995; 2012). Within the organism, collectively migrating cells display a wide variety of shapes that range from two-dimensional sheets to three- dimensional tubes, strands, chains or discrete clusters. Despite this seeming diversity, the mechanisms that underlie such collective cell movements are remarkably conserved
(Friedl and Gilmour, 2009). Primarily, cells that migrate collectively maintain tight cell- cell contacts. In addition, collectives are highly organized, with distinct leader and follower cells. This polarization allows cells at the front to produce protrusions that pull the entire group forward. Thus, collectives coordinate the activity of multiple individual cells to move as single, “super-cellular” units (Khalil and Friedl, 2010).
Migrating cells, whether individual or collective, achieve and maintain specific shapes while interacting with the local microenvironment (Friedl and Wolf, 2010;
Mogilner et al., 2009; Yin et al., 2014). Within tissues, cells encounter a heterogeneous arrangement of extracellular matrix, basement membrane and other cells. This poses a particular challenge for collectives, which have to withstand extracellular forces without falling apart or becoming disorganized. The distinctive arrangement and density of cells within the collective appears to impact migration speed and efficiency (Choi et al., 2013;
Leong et al., 2013; Vedula et al., 2012). Moreover, the shape of cells and their ability to migrate is influenced by the architecture of the three-dimensional tissue (Friedl and Wolf,
131 2010; Doyle et al., 2013; Wolf et al., 2013). Thus, the organization and shape of a collective could allow cells to withstand physical constraints from the surrounding environment. Because of the difficulty of studying these processes in vivo, the mechanisms that influence the formation and maintenance of organized collectives within the native tissue, and the precise impact this plays on migration efficiency, are still poorly characterized.
Drosophila border cells offer a genetically tractable system to determine how collectives organize and move through living tissues. Border cells undergo a highly regulated migration during ovarian development (Montell et al., 2012). The ovary consists of strings of progressively developed egg chambers, each of which contain the germline-derived oocyte and 15 nurse cells surrounded by a monolayer somatic follicle cell epithelium (Spradling, 1993) (Figure 4-1A). During mid-oogenesis, a pair of polar cells at the anterior end of the egg chamber recruits 4-8 epithelial-derived cells to form the migratory border cell cluster (Figure 4-1B). Subsequently, the cluster detaches from the follicular epithelium and migrates between the nurse cells to eventually reach the large oocyte at the posterior (Figure 4-1A). Like all collectives, border cells stay together in a cohesive group, and this cohesion is required for efficient movement in vivo (Llense and Martín-Blanco, 2008; Melani et al., 2008; Pinheiro and Montell, 2004). Border cells are also highly protrusive and undergo dynamic, intra-cluster rearrangements during their migration to the oocyte (Bianco et al., 2007; Poukkula et al., 2011; Prasad and Montell,
2007). Whether the border cell cluster as a whole retains—or needs—a specific overall shape while migrating the entire ~150 µm distance between nurse cells is not well understood.
132 Here we show that live border cells maintain a compact shape throughout their migration. We show that activation of Myo-II at the cluster periphery is necessary to produce the characteristic conformation of the group. Genetic assays with Myo-II phosphorylation mutants and live imaging of GFP-tagged Myo-II suggest that cluster shape requires dynamic Myo-II activity. Finally, we show that border cells increase the levels of activated Myo-II in response to constraints imposed by the nurse cells.
Together, our results suggest that border cells resist tissue-level forces to maintain an organized cluster shape and thus achieve normal, productive migration.
Results and Discussion
Myo-II is required for border cells to maintain cluster shape during migration
We performed time-lapse imaging of cultured egg chambers to address the extent to which live border cells display a specific shape during their migration. We used differential interference contrast (DIC) imaging to reveal the morphology of the unstained border cell cluster as well as the surrounding nurse cells (Figure 4-1A). Live border cell clusters were distinctly round. Moreover, clusters maintained this compact shape, with only minor deviations, for the entirety of their migration. Border cell clusters also visibly pushed against the surrounding nurse cell cortical membranes (Figure 4-1A)
(Stonko et al., 2015). These observations led us to ask how border cells maintain a defined shape despite having to navigate a confined pathway between the much-larger nurse cells.
Cluster shape could be achieved through border cells balancing oppositional compression forces from the adjacent nurse cells (Figure 4-1B). Myo-II establishes
133 cortical tension through its interaction with membrane-associated F-actin to promote rounding of cells during mitosis (Stewart et al., 2011; Ramanathan et al., 2015). Given this role of Myo-II, increased cortical actomyosin tension within the cluster itself could be a mechanism to establish and maintain cluster shape (Figure 4-1C). We therefore investigated the extent to which Myo-II is required for the shape of the border cell group during migration (Figure 4-1D-H). Myo-II is a hexameric protein complex that consists of three subunits: two heavy chains, two essential light chains, and two regulatory light chains (MRLC; Drosophila Spaghetti Squash [Sqh]). We knocked down Myo-II function using a GAL4-driven UAS-RNAi transgene that specifically reduced sqh levels in border cells (Figure 4-S1A-C and 4-S2). As expected from previous studies, loss of sqh disrupted migration and retraction of leading-edge protrusions (Edwards and Kiehart,
1996; Fulga and Rørth, 2002; Majumder et al., 2012) (Figure 4-S3A-E). Control border cell clusters were compact, measuring ~20-30 µm in length from front to back along the migration axis (Figure 4-1D and 4-F). In contrast, many sqh RNAi border cell clusters were visibly elongated along the migration pathway, with most clusters measuring between 30 µm and 80 µm (Figure 4-1E and 4-F). Moreover, sqh RNAi border cells remained in a cohesive cluster and did not break away from the group. We confirmed this phenotype by analyzing border cell clusters that contained cells homozygous mutant for a loss-of-function sqh allele (sqhAX3), generated by the mosaic clone method. Some clusters that contained several sqhAX3 mutant cells were more elongated compared to control
(Figure 4-1G and 4-H). Importantly, the stretched-out cluster shape caused by loss of
Myo-II is distinct from mutants that disrupt adhesion between individual cells and cause
134 the border cell cluster to partially or completely pull apart (Cai et al., 2014; Llense and
Martín-Blanco, 2008; Pinheiro and Montell, 2004).
Next, we asked whether Myo-II maintains cluster shape during live border cell migration. We analyzed fluorescently labeled control and sqh RNAi border cells using live time-lapse imaging (Figure 4-1I and 4-J). Once border cells began to move forward, sqh RNAi border cells visibly became more elongated than control. We quantified the variation in cluster length throughout their migration, primarily focusing on clusters that had detached, or were in the process of detaching, from the epithelium (Figure 4-1K).
Control border cells consistently retained a compact shape with minimal variation in the length of the cluster. In contrast, the length of sqh RNAi border cell clusters was highly variable and could stretch up to 80µm long during migration. Together, these results indicate that Myo-II maintains the compact morphology of the border cell cluster during their forward movement.
Activated Myo-II is localized to the cluster periphery and promotes cell and cluster shape
To further investigate the function for Myo-II in cluster shape, we next examined the localization of activated Myo-II in migrating border cells. Myo-II is activated through the regulatory light chain component, Sqh/MRLC, which undergoes dynamic cycles of phosphorylation by kinases and dephosphorylation by myosin phosphatase (Vicente-
Manzanares et al., 2009). Sqh/MRLC is phosphorylated at either Ser-21 (1P) or at both
Thr-20/Ser-21 (2P) (Jordan and Karess, 1997; Zhang and Ward, 2011). Border cells express high levels of polarized 1P-Sqh prior to detachment from the epithelium
(Majumder et al., 2012). Likewise, during mid-migration, we found that both 1P- and 2P-
Sqh accumulated in intense foci at the outer cortex of the cluster, with very little
135 localizing at membrane contacts between border cells (Figure 4-2A-A’’). Moreover, 1P- and 2P-Sqh generally overlapped (Figure 4-2A”). These data indicate that Myo-II is present in both phosphorylated forms at the periphery of migrating border cell clusters
(Figure 4-2A’’).
Serine-threonine kinases such as Rho-associated kinase (Rok) phosphorylate
Sqh/MRLC at the 1P and/or 2P sites (Amano et al., 1996; Ueda et al., 2002). Rok is required for border cell migration (Figure 4-S3A) (Majumder et al., 2012). We therefore analyzed the subcellular distribution of Rok using a functional Rok:GFP transgene
(Bardet et al., 2013). Rok:GFP localized to the cluster periphery in intense foci that largely, though not completely, overlapped with 1P- and 2P-Sqh (Figure 4-S4A-A’’’).
The presence of Rok at sites of activated Myo-II, and its requirement for 1P-Sqh levels in border cells (Majumder et al., 2012), led us to examine the role for Rok in cluster shape.
Border cell clusters that expressed a kinase-dead version of Rok, or that contained mosaic mutant clones of a strong loss-of-function Rok allele (Rok2), each resulted in elongated clusters (Figure 4-2B and 4-C). Thus, the Myo-II activating kinase Rok regulates border cell cluster morphology during migration.
We next determined whether the appropriate levels or localization of activated
Myo-II contribute to cell and/or cluster shape. The small GTPase RhoA (Drosophila
Rho1) activates Rok and thus influences Myo-II activation (Amano et al., 1996; Ishizaki et al., 1996; Matsui et al., 1996). To uniformly increase the levels of activated Myo-II, we therefore expressed a constitutively active form of RhoA (RhoAV14) in border cells.
Compared to control clusters, RhoAV14 individual border cells were much rounder
(Figure 4-2D-G), changing the overall appearance of the cluster. Consistent with a
136 previous report (Bastock and Strutt, 2007), 40% of RhoAV14 border cells did not complete their migration (Figure 4-S3A). The overall levels of 1P- and 2P-Sqh were elevated in RhoAV14 border cell clusters (Figure 4-2I). Moreover, activated Myo-II (1P- and 2P-Sqh) was expressed throughout RhoAV14 clusters and was particularly enriched between border cells (Figure 4-2E and 4-G). We confirmed that RhoA protein was elevated in these clusters (Figure 4-S4B-C’). Specifically, RhoAV14 clusters had high levels of RhoA protein around each border cell, compared to a more general distribution in control clusters. This altered localization supports the idea that RhoAV14 activated
Myo-II at individual border cell cortical membranes (Figure 4-2G). F-actin distribution within the cluster was also altered (Figure 4-2F’), consistent with the ability of RhoA to activate both F-actin and Myo-II (Narumiya et al., 2009).
To further address the importance of appropriate levels of active Myo-II, we next inactivated myosin phosphatase. Myosin phosphatase consists of a catalytic Protein
Phosphatase 1 subunit (PP1c) and a myosin-specificity subunit (Mbs) (Grassie et al.,
2011). Because myosin phosphatase dephosphorylates Sqh/MRLC, loss of the phosphatase results in increased levels of phosphorylated and activated Myo-II (Grassie et al., 2011; Kimura et al., 1996). Mbs is highly expressed in border cells and knockdown was previously found to elevate the levels of 1P-Sqh (Majumder et al., 2012). Therefore, we knocked down Mbs and assessed border cell shape. Mbs RNAi clusters contained rounder border cells than normal (Figure 4-2H-H’’). Taken together, our results indicate that both the cell and cluster morphology of migrating border cells require an appropriate level and restricted localization of activated Myo-II.
137 Other migrating collectives appear to have comparably organized Myo-II, with active Sqh/MRLC at the outer edge of the cell group (Hidalgo-Carcedo et al., 2011;
Reffay et al., 2014; Uchimura et al., 2002). Moreover, ectopic Myo-II activated via RhoA and Rok at cell-cell contacts disrupts the cohesion and migration of mammalian collectives (Hidalgo-Carcedo et al., 2011). Thus, restricted localization of activated Myo-
II specifically to the outer margins of cell groups may represent a conserved mechanism for collectives to stay together and move as coordinated units.
Maintenance of cluster shape requires dynamic Myo-II
Constriction of cells by actomyosin contraction facilitates large-scale tissue morphogenesis during development such as neural tube formation, gastrulation and salivary gland invagination (Fernandez-Gonzalez et al., 2009; Martin et al., 2009;
Nishimura et al., 2012; Röper, 2012). Moreover, highly dynamic cycles of Sqh phosphorylation and dephosphorylation are essential for tissue and cell rearrangements during Drosophila gastrulation (Kasza et al., 2014; Vasquez et al., 2014). We therefore next investigated the extent to which dynamic Myo-II activation maintains border cell cluster morphology during migration. We used phosphorylation-mutant variants that lock
Sqh/MRLC into either the unphosphorylated (Sqh-AA) or constitutively phosphorylated
(Sqh-EE) forms and are driven by the endogenous sqh promoter (Jordan and Karess,
1997; Winter et al., 2001). Because we wanted to manipulate the levels of activated Myo-
II, rather than Myo-II protein itself, we knocked down the Myo-II activator Rok in border cells. Expression of Rok RNAi strongly depleted Rok mRNA (Figure 4-S1D and E) and reduced 1P-Sqh levels in border cells (Majumder et al., 2012). Rok RNAi disrupted border cell migration (Figure 4-3A; Figure 4-S3A) (Majumder et al., 2012), produced
138 long protrusions that failed to retract (Figure 4-3B and 4-C), and caused border cell clusters to elongate (Figure 4-3F).
Introducing one copy of Sqh-EE, but not Sqh-AA, into the Rok RNAi background strongly suppressed the migration (Figure 4-3A) and protrusion defects (Figure 4-3B-E).
Next, the length of Rok RNAi clusters was measured from the front to back of the group, excluding visible cellular protrusions. Surprisingly, neither Sqh-EE nor Sqh-AA altered the length of Rok RNAi border cell clusters (Figure 4-3F). Despite the inability to repress
Rok RNAi cluster shape defects, Sqh-EE, visualized by a FLAG tag, localized to the cluster periphery in a pattern similar to 1P-/2P-Sqh staining (Figure 4-S4D). In biochemical assays, phosphomimetic Myo-II mutants do not have as high an activity as endogenously phosphorylated Myo-II (Kamisoyama et al., 1994). Nonetheless, the rescue of Rok-dependent migration and protrusion retraction defects by Sqh-EE suggests that these processes require a certain level of continuously activated Myo-II. Alternatively, these processes do not require dynamic cycling of Myo-II between active/inactive phosphorylation states. Conversely, the failure of Sqh-EE to rescue Rok RNAi cluster defects could indicate that normal cluster shape requires greater Myo-II cycling and/or higher levels of activated Myo-II.
To test this idea further, we next analyzed the localization of GFP-tagged Sqh
(Sqh:GFP) in live border cells. Our previous study showed that Sqh:GFP coalesces into discrete, highly dynamic foci (Figure 4-3G), that are lost upon inhibition of Myo-II phosphorylation (Majumder et al., 2012). Here we wanted to determine the extent to which Sqh:GFP foci at the cluster periphery correlated with specific changes in cell shape during migration. We therefore performed live time-lapse imaging of Sqh:GFP
139 fluorescence in conjunction with DIC imaging, which allowed us to explicitly visualize border cell membranes at the outer edge of the cluster (Figure 4-3H and 4-H’). Using this method, we analyzed Sqh:GFP and cluster membrane deflections over time (Figure 4-3I).
The appearance of enriched Sqh:GFP signal strongly correlated with cell membranes that pulled inward towards the body of the cluster, whereas disappearance of Sqh:GFP signal correlated with membranes that relaxed outward (Figure 4-3I). These cycles of Sqh:GFP signal were highly dynamic, with short timescales of about 60 sec (Figure 4-3I). Dynamic
Sqh:GFP enrichment thus coincides with alterations in border cell membranes at the cluster periphery during migration.
Our data support a model in which the maintenance of cluster shape requires dynamic cycles of, rather than sustained, Sqh phosphorylation. Sqh-EE mutants fail to coalesce properly to initiate contractile pulses during gastrulation (Vasquez et al., 2014), highlighting the importance of dynamic control of Sqh phosphorylation. Fibroblasts in suspension undergo oscillating cell shape changes that depend on actomyosin contractility (Paluch et al., 2005; Salbreux et al., 2007). Therefore, non-uniform and dynamic activation of Myo-II could help border cells quickly adjust the shape of the cell cortex during migration. Conversely, uniformly activated Myo-II is detrimental to border cell shape, such as when RhoA is activated or myosin phosphatase is impaired. We propose that cycles of activated Myo-II at the cluster periphery, coupled with F-actin, provide dynamic cortical tension to maintain the collective shape of migrating border cells (Figure 4-1B and 4-C).
140 Nurse cell confinement influences border cell cluster shape and migration
Despite the importance of nurse cells as a migratory substrate for the border cells
(Cai et al., 2014; Niewiadomska et al., 1999), their influence, if any, on cluster shape has yet to be addressed. Nurse cells have high levels of F-actin at cortical membranes
(Verheyen and Cooley, 1994; Groen et al., 2012; Spracklen et al,. 2014), suggesting that these cells have a certain level of tension or stiffness. Moreover, there is little apparent space between nurse cells (Figure 4-1A). Nonetheless, border cells navigate effectively between the densely packed nurse cells. If dynamic, activated Myo-II allows border cells to resist compression by the nurse cells (Figure 4-1B and 4-C), then increasing the contraction (or stiffness) of nurse cells is expected to overcome this resistance and result in altered cluster shape. To test this model, we specifically expressed RhoGEF2 in nurse cells. RhoGEF2 is a known activator of Drosophila RhoA GTPase, which in turn activates Rok and Myo-II to drive cell shape changes (Rogers et al., 2004; Häcker and
Perrimon, 1998). RhoGEF2-expressing nurse cells were rounder than control
(Figure 4-4A and B; Figure 4-S4E and 4-F). Moreover, RhoGEF2 led to higher levels of activated Myo-II (2P-Sqh staining) at nurse cell cortical membranes (Figure 4-4D-E’), consistent with elevated actomyosin contraction. We analyzed what happens to border cells in this more compressed environment. As expected, border cells in control egg chambers exhibited a normal cluster shape and completed their migration to the oocyte
(Figure 4-4A, A’ and C; Figure 4-S3A). Strikingly, in egg chambers in which nurse cells expressed high levels of RhoGEF2, a significant number of border cell clusters were elongated (Figure 4-4B, B’ and C) and had impaired migration (Figure 4-S3A). This
141 result indicates that increased nurse cell contraction is sufficient to constrict the border cell cluster.
Myo-II can be recruited to the cell cortex of physically constricted cells (Laevsky,
2003; Kim et al., 2015). It was hypothesized that this enriched cortical Myo-II was due to cells resisting constraints by the environment while attempting to move forward
(Laevsky, 2003). We therefore wanted to determine if border cells altered their actomyosin cytoskeleton in response to compression by nurse cells. We analyzed the levels and localization of activated Myo-II within border cell clusters from nurse cell>
RhoGEF2 egg chambers. Border cells in these egg chambers displayed elevated 2P-Sqh staining compared to control (Figure 4-4F-H). Moreover, the most intense 2P-Sqh staining was found near the cluster periphery (Figure 4-4H). Therefore, elevated pressure from nurse cells increased the levels of activated Myo-II in border cells, particularly in proximity to the outer edges of the cluster. A recent study applied mechanical force to
Drosophila S2 cells and found that Myo-II rapidly accumulated at the cell cortex, independent of Rok and RhoA (Elliott et al., 2015). Cells thus can react to acute mechanical tension by increasing cortical Myo-II. These data are consistent with cells responding directly to changes in their local environment, such as border cells moving between the tightly packed nurse cells.
We propose that the compact shape of wild type border cell clusters arises from actomyosin tension, which allows border cells to resist compression from nurse cells during their migration to the oocyte (Figure 4-4I). Cycles of Sqh/MRLC phosphorylation and dephosphorylation, which correspond to assembly- and disassembly-competent Myo-
II, respectively, influence the ability of Myo-II to form bipolar minifilaments and to bind
142 to F-actin (Vicente-Manzanares et al., 2009). Thus, higher levels of dynamic Myo-II activity at the border cell cluster periphery promote sufficient cortical tension to counteract the compressive forces from nurse cells, which arise in part from their own actomyosin cortical tension. A change from compact to elongated cluster shape occurs through an imbalance of forces and cortical tension, either through the loss of Myo-II activity in border cells or an overwhelming increase of Myo-II-driven cortical tension from the nurse cells. The phenomenon of balanced opposing forces, and actomyosin contraction, that determine the shape of cells has been difficult to model in vitro.
Therefore, our results highlight the importance of studying migrating cells, both single cells and collectives, in their native microenvironment.
Materials and Methods
Drosophila strains and genetics
Crosses were performed at 25˚C, except tub-GAL80ts (“ts-GAL80”) crosses that were set up at 18˚C. The following Drosophila stocks were obtained from the
Bloomington Drosophila Stock Center (unless otherwise indicated) and are described in
FlyBase (http://flybase.org/): hsp70-GAL4 (hs-GAL4), sqhAX3 FRT 19A, Rok2 FRT 19A,
FRT 19A, c306-GAL4, slbo-GAL4, tub-GAL80ts (“ts-GAL80”), matalpha4-GAL-VP16-
GAL4 (nurse cell GAL4), UAS-mCD8:GFP, UAS-Rho1.V14 (“RhoAV14”), UAS-
Venus:RokK116A (“Rok KD”; from J. Zallen), UASp-T7.RhoGEF2, UAS-sqh RNAi (line
7916; VDRC), UAS-sqh RNAi (TRiP HMS00830 ), UAS-Mbs RNAi (line 105762;
VDRC), UAS-Rok RNAi (line 9774R-2; NIG-Fly), UAS-Drak RNAi (line 107263,
VDRC), UAS-GFP dsRNA (“GFP RNAi”), sqh-sqhE20E21:FLAG (“Sqh-EE”) and sqhAX3;
143 sqh-Sqh:GFP (III) (“Sqh:GFP”, from R. Karess), sqh-sqhA20A21:FLAG (“Sqh-AA”, from
L. Luo), ubi-Rok:GFP (from V. Mirouse). The w1118 line was used as a wild-type control.
GAL4 lines were outcrossed to w1118 as controls. All UAS-RNAi lines were crossed to c306-GAL4 or to c306-GAL4, ts-GAL80. c306-GAL4 is expressed in anterior follicle cells and border cells earlier in oogenesis than slbo-GAL4 and generally achieves more efficient RNAi knockdown (Aranjuez et al., 2012; Laflamme et al., 2012; Murphy and Montell, 1996). slbo-GAL4 was used to overexpress proteins uniformly and at high levels in all border cells (Laflamme et al., 2012; Rørth et al., 1998) matalpha4-GAL-
VP16-GAL4 (“nurse cell GAL4”) was used to overexpress RhoGEF2 in nurse cells
(Hudson and Cooley, 2014; Spracklen et al., 2014). To induce optimal GAL4/UAS overexpression and RNAi knockdown, flies were incubated overnight (14-18 hours) at
29˚C prior to dissection. Expression of RhoGEF2 using the nurse cell GAL4 was achieved by incubating flies at 27˚C.
The ts-GAL80 line was used to suppress GAL4/UAS-RNAi during earlier stages of oogenesis in which an RNAi line could be cell lethal. UAS-sqh RNAi (VDRC 7196) phenotypes were stronger when using c306-GAL4 ts-GAL80 compared to c306-GAL4
(Majumder et al., 2012). This is presumably due to increased viability of cells with the greatest knockdown, since the pattern of c306-GAL4 expression with or without ts-
GAL80 was identical (see Figure 4-S2). Crosses with ts-GAL80 were kept at 18˚C to suppress GAL4-UAS (McGuire et al., 2003; 2004). Prior to dissection, flies were first heat shocked at 37˚C for 1 hour then shifted to 29˚C for 18-24 hours to completely turn off GAL80 and turn on GAL4/UAS.
144 Mosaic mutant clones of sqhAX3 FRT 19A, Rok2 FRT 19A, and control FRT 19A were produced by the FLP-FRT system (Xu and Rubin, 1993) using the ubi-mRFP.nls hsFLP FRT 19A stock. Homozygous mutant cell clones were marked by loss of nuclear mRFP. 3-5 day old flies of the correct genotype were selected and heat shocked for 1 hour at 37˚C, twice a day for 2 days, followed by recovery at 25˚C for 5 days. Clones for sqhAX3, FRT 19A and FRT 19A control were generated previously (Majumder et al.,
2012), but were re-analyzed independently to assess cluster shape (Figure 4-1G and 4-H).
Completely mutant sqhAX3 or Rok2 clusters were rarely observed.
Immunostaining and imaging
Ovaries were dissected and antibody stained as described (McDonald and
Montell, 2005). Fixation was generally in 4% methanol-free formaldehyde (Polysciences,
Inc.) in phosphate buffer (pH 7.4) for 10 min. For 1P-Sqh and 2P-Sqh staining, fixation was in fresh 4% paraformaldehyde (Polysciences, Inc.) in PBT (1X phosphate buffered saline, pH 7.2, 0.1% Triton X-100) for 20 min. Primary antibodies from the
Developmental Studies Hybridoma Bank (DSHB) were used at the following dilutions:
1:100 mouse anti-Arm (N27A1); 1:10 rat anti-E-cad (DCAD2); 1:10 mouse anti-GFP
(12A6); 1:10 mouse anti-Rho1 (p1D9); 1:50 mouse anti-Fascin (Sn7C). Other antibodies used: 1:100 mouse anti-FLAG (M2; Sigma-Aldrich); and 1:1000 guinea pig anti-1P-Sqh and 1:500 rat anti-2P-Sqh (from R. Ward). Secondary antibodies conjugated to Alexa
Fluor 488, 568, or 647 (Life Technologies) were used at 1:400. F-actin was visualized by
Alexa Fluor 568-phalloidin (1: 400; Life Technologies) and nuclei by DAPI (0.05µg/mL;
Sigma). Egg chambers were mounted in Aqua-Poly/Mount (Polysciences, Inc.) and
145 imaged on a Zeiss AxioImager Z1 microscope with the ApoTome system. Live imaging was performed as described (Majumder et al., 2012; Prasad et al., 2007).
RT-PCR
To measure RNAi efficiency (Figure 4-S1), UAS-RNAi transgenes were expressed in whole flies using hsp70-GAL4 to achieve ubiquitous knockdown. Trizol- extracted RNA was used for cDNA synthesis followed by RT-PCR, as described
(Aranjuez et al., 2012). RT-PCR was performed using Superscript III One-Step RT-PCR system (Life Technologies). PCR settings were as follows: 50˚C for 30 minute during the cDNA synthesis step; 55˚C for 30 seconds during the annealing step; 72˚C for 1 minute during the extension step. The number of PCR cycles was empirically determined for each reaction and primer set to avoid the plateau phase of PCR amplification; 29 cycles were used for both sqh and Rok. Band intensities of the RT-PCR products were measured using ImageJ (Schneider et al., 2012). GAPDH was used as the reference gene. The gene- specific primers used were: sqh fwd, TCACACTTGGCCTTCTCGTC; sqh rev,
CGAGATAGTCGTCCGTTGGG; Rok fwd, AGGAACGCGTCTCACTCAAG; Rok rev,
GTGAGGGAGAGCAGAGAGGA; GAPDH fwd, ACTCATCAACCCTCCCCCG;
GAPDH rev, GCGGACGGTAAGATCCACAA.
Image Analyses, Figures, Graphs and Statistics
Fiji (http://fiji.sc) (Schindelin et al., 2012), an ImageJ distribution, was used to generate kymographs and measure cluster length (excluding protrusions), protrusion length, and mean pixel intensity. Micrographs were acquired using the Zeiss ApoTome system and MRm CCD camera with a 20x Plan-Apochromat 0.75 numerical aperture
(NA) or a 40x Plan-Neofluar 1.3 NA oil-immersion objective. Z-stack images acquired
146 from live imaging of sqh:GFP were processed using the Axiovision deconvolution software module. Live imaging was performed as described (Prasad et al., 2007). For differential interference contrast (DIC) time-lapse imaging, Kohler illumination was optimized, followed by standard live imaging. Image brightness and/or contrast of images were adjusted in Fiji, Adobe Photoshop or Zeiss Axiovision 4.8 software.
Image analyses were performed with Zeiss AxioVision 4.8 or Fiji. The outlines of clusters and protrusions in mid-migration were visualized by Fascin immunofluorescence
(fixed samples) or mCD8:GFP expression (live samples). Cluster length was defined as the overall length of the cluster along the migration axis, excluding cellular protrusions.
Protrusion length (measured manually) was defined as the distance from the tip of the protrusion to its base, where it meets the main cluster body (Majumder et al., 2012). For measuring mean pixel intensity, control and RhoAV14 clusters were stained for 1P-Sqh and 2P-Sqh simultaneously and imaged using the same exposure settings. The average pixel intensity of 1P- and 2P-Sqh was measured using Fiji; the border cell cluster was defined by slbo-GAL4 > mCD8:GFP expression. For measuring the mean pixel intensity of 2P-Sqh staining in border cells (Figure 4-4F-H), the border cell cluster was defined by
Fascin staining. A new image was created to measure pixel intensity only within the area of the border cell cluster. The pseudocolored heat map images of 2P-Sqh stained border cell clusters were generated using the Fiji “Lookup Table” function. The Fiji “Reslice” function was used to generate kymographs from movies after a common line region of interest was defined in the GFP and DIC channels. RNAi knockdown efficiency was measured from RT-PCR gels using the Fiji “Gel Analyzer” function.
147 Figures and illustrations were created in Adobe Illustrator CS5. Graphpad Prism was used to generate graphs and statistical analyses (unpaired t-test and one-way
ANOVA).
148
Figure 4-1. Myo-II maintains the shape of the border cell cluster
149 Figure 4-1. Myo-II maintains the shape of the border cell cluster. (A) Frames from a
DIC movie showing border cells (false-colored yellow) migrating between the nurse cells
(nc; pink outlines) to reach the oocyte (n = 5 movies). The follicle epithelium surrounds the germline. Scale bar: 40 µm. (B) Model of border cell cluster shape, in which the cluster resists compression by nurse cells (red arrows) due to counteracting forces (blue arrows) from border cells (blue). Polar cells are in pink. (C) Illustration of proposed mechanism by which Myo-II (red) and cortical F-actin (blue) generate cortical tension to contract cluster membranes (arrows). (D-F) Knockdown of sqh disrupts cluster shape.
Dashed line, anterior oocyte border. (D, E) Fascin labels border cells of stage 9 control
(c306-GAL4, tsGAL80/+) and stage 10 sqh RNAi egg chambers (c306-GAL4, tsGAL80/+; UAS-sqh RNAi/+). (F) Quantification of cluster length along the migration axis (schematic), shown as the percentage of control (n = 30) and sqh RNAi (n = 75) mid-migration border cells. (G, H) Loss of sqh disrupts cluster shape. Examples of stage
AX3 9 FRT 19A control (G) and stage 10 sqh (H) mosaic mutant clusters (18%; n = 38), co-stained for E-cadherin (E-cad, green) to label cell membranes and DAPI (blue) to show nuclei. Loss of RFP (red) marks clones (arrowheads). Scale bars (E, H): 20 µm. (I,
J) Frames from control (I) and sqh RNAi (J) movies showing migrating border cells
(mCD8:GFP). (K) Quantification of cluster length from individual movies over time, shown as box and whisker plots. The whiskers represent the minimum and maximum, the
th th box extends from the 25 to the 75 percentiles, and the line indicates the median.
150
Figure 4-2. Activated Myo-II localizes to the cluster periphery and promotes cell and cluster shape
151 Figure 4-2. Activated Myo-II localizes to the cluster periphery and promotes cell and cluster shape. (A-A”) Example of a stage 9 wild type border cell cluster in which
1P-Sqh (green) and 2P-Sqh (red) are enriched in foci (arrowheads, dashed line); 1P- and
2P-Sqh co-localize (yellow in A”) at the cluster periphery (n = 17). Armadillo (Arm; white in A”) labels cell membranes. Scale bar: 5 µm. (B) Example of an elongated Rok
KD cluster (bracket) in a stage 10 egg chamber stained for Fascin (red), Venus:Rok KD
(green) and DAPI (blue nuclei). (C) Example of a cluster with stretched-out, trailing
2 Rok mutant border cells (loss of nuclear RFP; arrowheads) in a stage 9 egg chamber stained for Fascin (green) and DAPI (blue nuclei). Scale bar (B, C): 50 µm. (D-G)
Rounded border cells and disrupted cluster shape by activated RhoA (n = 36). Stage 9 control (slbo-GAL4, UAS-mCD8:GFP/+) and active RhoA (slbo-GAL4, UAS-
V14 mCD8:GFP/UAS- Rho1 ) border cells visualized by GFP (D, F) and F-actin (D’, F’).
(E, G) 1P- and 2P-Sqh localize to the periphery of control (E) but throughout the cluster of active RhoA (G; n = 19) border cells. Dashed lines, cluster boundary. (H-H'') Altered shape of Mbs RNAi (c306-GAL4/+; UAS-Mbs RNAi/UAS-mCD8:GFP) border cells (n
= 7), marked by GFP (H; green in H’’) and F-actin (H'; magenta in H’’). Scale bar (D-H):
5 µm. (I) Mean pixel intensity of 1P- and 2P-Sqh measured in control (n = 10) and active
RhoA (n = 6) border cell clusters. Error bars, standard error of the mean (SEM). ** P <
0.01; unpaired t-test.
152
Figure 4-3. Cluster shape correlates with dynamic Myo-II activity at the cluster periphery
153 Figure 4-3. Cluster shape correlates with dynamic Myo-II activity at the cluster periphery. (A) Sqh-EE strongly suppressed the Rok RNAi migration defects.
Quantification of complete (green), incomplete (yellow), and no (pink) migration in stage
10 Rok RNAi (c306-Gal4/+; UAS-Rok RNAi/+) egg chambers, with or without sqh mutant transgenes. N ≥ 50 egg chambers in each of 3 trials; *** P < 0.001; not significant
(n.s.) P ≥ 0.05; one-way ANOVA with Dunnett test compared to “complete migration”.
(B-E) Sqh-EE rescues the Rok RNAi border cell protrusion length defects. (B)
Quantification of mean protrusion length in the indicated genotypes. Dashed line shows the mean protrusion length of control (c306-GAL4/+). N ≥ 86 protrusions per genotype;
*** P < 0.001; n.s. P ≥ 0.05; one-way ANOVA with Dunnett test. Error bars in (A, B) represent SEM. (C-E) Stage 9 Rok RNAi egg chambers, with or without the indicated sqh transgene, stained for Fascin to label border cells and protrusions (brackets). Scale bar:
20 µm. (F) Sqh-EE and Sqh-AA did not alter the Rok RNAi cluster elongation defect.
Quantification of individual border cell cluster length measurements in the indicated genotypes. The line indicates the mean. N ≥ 46 clusters per genotype; ** P < 0.01; n.s. P
≥ 0.05; one-way ANOVA with Dunnett test. (G) Frames from a Sqh:GFP movie at indicated times. A focus of Sqh:GFP (arrowheads) was tracked at the cluster periphery until it disappeared (arrowhead outline). (H, H') Frame from a time-lapse Sqh:GFP movie. The cluster was imaged by fluorescence (H) and DIC (H’), to visualize cell membranes (n = 6). Scale bar (G, H): 5 µm. (I) Kymograph of the GFP (top) and DIC
(bottom) channels from boxed region in (H, H’) over time. Sqh:GFP (magenta, overlay) correlates with deflection of the cluster membrane (n = 9).
154
Figure 4-4. Border cells have elevated Myo-II activity upon nurse cell compression
155 Figure 4-4. Border cells have elevated Myo-II activity upon nurse cell compression.
(A-B') Elongation of wild-type border cell clusters upon increased contraction of nurse cells. Stage 9 control (nurse cell GAL4/+) and RhoGEF2 (nurse cell GAL4/UASp-
RhoGEF2) egg chambers stained for Phalloidin to label F-actin (A, B) and Fascin to label border cells (A’, B’). (A, A’) Control border cell cluster shape is normal. (B, B’)
Example of a border cell cluster that is elongated in a nurse cell > RhoGEF2 egg chamber. Scale bar: 20 µm. (C) Quantification of individual border cell cluster lengths from control (n = 20) and nurse cell > RhoGEF2 (n = 55) egg chambers. The line indicates the mean. * P < 0.05; unpaired t-test. (D-E’) Elevated 2P-Sqh staining
(arrowheads) on nurse cell membranes in a nurse cell > RhoGEF2 egg chamber (n = 24).
2P-Sqh staining of filaments within nurse cell cytoplasm (asterisks in D’, E’) may be non- specific. Scale bar: 20 µm. (F-H) Elevated 2P-Sqh levels in border cells when surrounding nurse cells express RhoGEF2. (F) Quantification of mean 2P-Sqh pixel intensity in border cells of control (n = 8) and nurse cell > RhoGEF2 (n = 11) egg chambers, represented as a box and whisker plot. The whiskers represent the minimum
th th and maximum, the box extends from the 25 to the 75 percentiles, and the line indicates the median. *** P < 0.001; unpaired t-test. (G, H) 2P-Sqh signal intensities in representative border cell clusters from control (n = 8) and nurse cell > RhoGEF2 (n =
11) egg chambers. Scale bar: 5 µm. (I) Proposed model for the maintenance of border cell cluster shape achieved through the balance of cortical tension via myosin activity and forces between border cells and nurse cells. See text for details.
156
Figure 4-S1. Specificity and efficiency of RNAi knockdown
157 Figure 4-S1. Specificity and efficiency of RNAi knockdown. Specificity and efficiency of RNAi knockdown was confirmed by RT-PCR. Target mRNA levels were measured from whole flies expressing sqh RNAi (A, B, D) or Rok RNAi (B, D, E). RNAi that targets GFP (A, B, D, E) or Drak (B, D), another Myo-II kinase, were used as controls.
(A, B, D, E) PCR products were run on gels with GAPDH as a reference gene and loading control. The band intensities were measured and the ratios of sqh (B), or Rok (D,
E), to GAPDH were calculated, along with the percent relative knockdown compared to control (GFP RNAi). (A) No sqh bands were detected in the sqh RNAi lanes. (B) RNAi to sqh, but not to Rok or controls, knocked down sqh mRNA levels. (C-C′′)
Representative example of a stage 9 sqh RNAi border cell cluster co-stained for 1P-Sqh,
2P-Sqh and Arm (to label cell membranes). 1P- and 2P-Sqh were severely reduced. Scale bar: 5µm. (D) RNAi to Rok, but not to sqh or controls, decreased Rok mRNA levels. (E)
Rok RNAi efficiently knocked down Rok mRNA levels.
158
Figure 4-S2. Expression patterns of c306-GAL4 and c306-GAL4, ts-GAL80
159 Figure 4-S2. Expression patterns of c306-GAL4 and c306-GAL4, ts-GAL80.
Expression patterns of c306-GAL4 (A, C) and c306-GAL4, ts-GAL80 (B, D) during oogenesis, revealed by UAS- mCD8:GFP (green in A′-D′). Egg chambers were co- stained for phalloidin (red in A′-D′) to label F-actin and DAPI (blue in A′-D′) to label nuclei. (A, C) c306-GAL4 drives expression in border cells just prior to migration (‘start of migration’, A) and continues through late stages of migration (‘end of migration’, C).
Border cells (arrowheads) begin (A-B’) or finish their migration (C’, D’) in the egg chambers found on the right-most side of each panel. (B, D) There are no major differences in the pattern of c306-GAL4, ts-GAL80 compared to c306-GAL4 in border cells (arrowheads), anterior follicle cells (brackets) or stalk cells (asterisks). Slight variability in c306-GAL4 expression can be found in the posterior follicle cell epithelium
(brackets), regardless of the presence of ts-GAL80 (A-D′). Scale bars: 50µm.
160
Figure 4-S3. Border cell migration and protrusion defects when Myo-II activity is altered
161 Figure 4-S3. Border cell migration and protrusion defects when Myo-II activity is altered. (A) Quantification of border cell migration in the indicated genotypes, shown as the percentage of stage 10 egg chambers with no (pink), incomplete (yellow) or complete
(green) migration to the oocyte. UAS-RNAi expression in border cells was driven by c306-GAL4, tsGAL80 or c306-GAL4. slbo-GAL4 (border cells) or matalpha4-GAL-
VP16-GAL4 (nurse cells) were used to drive overexpression of proteins. At least 3 trials were performed per genotype, n ≥ 50 egg chambers per trial. Total numbers (n) scored are indicated. *** P < 0.001, ** P < 0.01; unpaired t-test. Error bars, standard error of the mean. (B-E) Border cell protrusion defects were observed upon sqh RNAi knockdown.
Border cells were co-stained for Fascin (green), Phalloidin (red) to label F-actin, and
DAPI (blue) to label nuclei. (B) Example of a stage 9 control egg chamber (c306-GAL4, tsGAL80 / +) in which border cells (arrow), prior to migration, extended a normal protrusion. (C) Example of a stage 10 sqh RNAi egg chamber (c306-gal4, tsGAL80/+;
UAS sqh RNAi/+) in which the cluster (arrow) failed to initiate migration. (D) Close-up view of the same control border cell cluster shown in (B), showing a leading edge protrusion and its measured length (bracket). The average length is 7.4 µm (n = 61). (E)
Close-up view of the same sqh RNAi border cell cluster shown in (C), showing a long leading edge protrusion and its measured length (bracket). The average length is 17.9 µm
(n = 209). Fascin-positive cell fragments that lack nuclei, so-called “cytoplasts”
(Somogyi and Rørth, 2004), were also observed upon sqh RNAi knockdown (asterisk).
Scale bars: 20µm.
162
Figure 4-S4. Localization of Rok, Rho and Sqh-EE in migrating border cells
163 Figure 4-S4. Localization of Rok, Rho and Sqh-EE in migrating border cells. (A-
A’’’) Representative example of an ubi-Rok:GFP border cell cluster co-stained for 1P- and 2P-Sqh (n = 16). (A’’’) Rok:GFP (asterisks) was enriched in some outer cluster membrane regions that overlapped with 1P-Sqh (magenta) and 2P-Sqh (blue). (B-C’)
Localization of RhoA in control (slbo-GAL4, UAS-mCD8:GFP/+) and constitutively active RhoA (slbo-GAL4, UAS-mCD8:GFP/+; UAS-RhoAV14/+) border cells. Border cells were visualized by mCD8:GFP expression (B, C). RhoA protein was elevated and more uniform in border cells that expressed activated RhoA (C’; n = 9) compared to control (B’). (D-D’’’) Localization of phosphomimetic Sqh (Sqh-EE:FLAG) in border cells. Sqh-EE:FLAG was enriched in foci found at the cluster periphery (arrowheads).
Border cells were co-stained for E-cad (D, red in D’’’) to label cell membranes, anti-
FLAG (D’, green in D’’’) and DAPI to label nuclei (D’’, blue in D’’’). Scale bars: 5 µm.
(E and F) RhoGEF2 expression in nurse cells causes the cells to become rounder than normal (asterisks). Late stage 9 control (E; nurse cell GAL4/+) and stage 10 RhoGEF2
(F; nurse cell GAL4/UASp-RhoGEF2) egg chambers stained for Phalloidin to label F- actin. Scale bar: 20 µm.
164 Final Discussion
Continue to develop experimental models of collective migration
Part of the challenge of understanding collective migration is the diversity of examples of collective migration. Each mode of collective migration utilizes different cellular strategies to accomplish coordinated movement. One experimental model of collective migration is suitable only for that particular type. Therefore, it is important to develop and continually study different experimental models of collective migration to encompass all the different modes. In doing so, scientists can identify cellular mechanisms that are shared across collective migration types (such as cell-cell adhesions) as well as those that are unique to a particular example of collective migration (such as the stereotypic border cell protrusion behavior).
In vitro models of collective migration are amenable to high-resolution microscopy, assays, and experimental manipulation. Sheet migration lends itself well to in vitro modeling because the organization of the cells and the environment in vivo is closely matched by in vitro culture systems. We achieved a great understanding of sheet migration from studying wound healing using confluent monolayers of epithelial cells in culture (Vitorino et al., 2011).
Advances in developing 3D culture systems have allowed for study of collective cancer cell streaming from solid tumor explants (Yamada and Cukierman, 2007). Three- dimensional cultures now allow scientists to observe and study in detail how cancer cells modify the surrounding extracellular matrix to facilitate cell streaming from the main tumor body (Wolf et al., 2007). It also replicates, in vitro, the formation of a tube-shaped pathway for migrating cancer cells that is observed in vivo (Friedl and Wolf, 2008). Last,
165 a 3D culture system recreates migratory behavior that is more representative of what happens in vivo compared to culturing tumor explants on coated glass or plastic common to traditional tissue culture systems.
Recently, the group led by Erik Sahai successfully developed the A431 squamous cell carcinoma line as a model for collective migration of small clusters. A431 cells grown in 3D culture self-assembles into small compact cluster not unlike border cells
(Hidalgo-Carcedo et al., 2011). However, border cells are recruited and organized around a pair of polar cells that sit in the middle of the cluster. There is no central organizer in
A431 cells. Nonetheless, my work and work from Erik Sahai’s group has shown that, for small clusters, actomyosin activity at the periphery of the cluster is important for keeping the cluster together (Hidalgo-Carcedo et al., 2011). Furthermore, actomyosin activity is restricted to the periphery in both border cell and A431 clusters (Hidalgo-Carcedo et al.,
2011). Mislocalization of active Myo-II results in increased tension at cell-cell junctions of A431 cell clusters, loss of cell adhesions and subsequent dispersal of A431 cells
(Hidalgo-Carcedo et al., 2011). In the border cell cluster, mislocalization of Myo-II activity results does not result in the dispersal of border cells, most likely due to the highly stable adhesions between border cells and the central polar cell pair.
In vivo models push the limits of imaging and genetic manipulation strategies.
Studying collective migration within a living organism or intact tissue is ideal. In vivo models preserve all the mechanical and signaling inputs that collectives receive during migration. However, imaging collective migration in vivo is very challenging.
Visualization of the cells of interest typically requires transgenically-encoded fluorescent reporters. Furthermore, imaging of cells through thick samples results in a sacrifice of
166 imaging resolution, either spatially or temporally, in all but the most advanced imaging platforms. Intensive genetic approaches are often required to perform loss-of-function or structure-function studies of genes/proteins of interest, although the sharing of transgenic lines and constant improvement of the genetic tools are making this less of an issue. Not surprisingly, in vivo models of collective migration have emerged from strong genetic model organisms such as Xenopus (neural crest migration), Zebrafish (lateral line primordium), and Drosophila (gastrulation, sprouting/branching of tracheal tubes, border cell migration) (Affolter and Caussinus, 2008; Mayor and Theveneau, 2013; McMahon et al., 2008; Montell et al., 2012; Theveneau and Mayor, 2012). The small size of these organisms and the well-established platform for genetic manipulation have also ameliorated some of the imaging and experimental issues.
Computational modeling of collective migration allows for rapid hypothesis testing. An effective computational model is built on a large amount of data acquired from observing the actual collective migration event. Once built, investigators have the freedom to change individual parameters and simulate the effect on the whole collective.
The new hypothesis can then be tested in vivo or in vitro. New information gleaned from experiments is again used to update the in silico model to improve its accuracy.
Sheet migration of epithelial cells was effectively modeled in silico because of the homogenous composition of the sheet and two-dimensional freedom of movement
(Vitorino et al., 2011). Using computational modeling, the process of sheet migration during wound healing was predicted to depend on only four single-cell parameters
(Vitorino et al., 2011). The whole predicted model was confirmed experimentally in vitro
(Vitorino et al., 2011). Furthermore, the computational model predicted that, even in an
167 intact epithelium, epithelial cells move in small streams within the monolayer, as an emergent property of the individual cell movements (Vitorino et al., 2011). This was also confirmed experimentally in vitro (Vitorino et al., 2011). Computational models of border cell migration also exist (Stonko et al., 2015). This is a more challenging task because of the heterogeneous composition of the border cell cluster and its interaction with the surrounding nurse cell tissue. It would be interesting to see if the model can recreate aspects of border cell migration that have not been fully explained such as the border cell rearrangements, or tumbling, with the cluster, or the pauses in forward movement during the middle of migration. The beauty of computational modeling is that one can add or remove the effect of any cellular mechanism at will and see whether that recapitulates the observed behavior.
Strategies for identifying new components of collective migration mechanisms
Collective migration emerges from the action of cellular mechanisms that orchestrate individual cell motility to achieve coordinated movement (Rørth, 2012). It's been shown in different types of collective migration that disruption of these collective- level mechanisms does not block single cell motility.
Focusing on mechanisms that function above single cell motility. Early attempts to molecularly characterize collective migration focused on disrupting collective migration entirely (Montell et al., 2012; Vitorino and Meyer, 2008). Although the screens were successful in identifying genes that were required for collective migration in general, there was no attempt to distinguish between genes that were required for basic cell motility versus genes that operate at the collective level. In border cells alone, 31 genes that regulate actin cytoskeleton dynamics have been identified and characterized
168 (Montell et al., 2012) whose function in border cells is similar to its function in single cell migration. Though greatly improving our understanding of how border cells extend protrusions, it does not offer a lot of insights into how the cluster itself controls border cell protrusions.
Live imaging is indispensable in the study of collective migration. The advent of live time-lapse microscopy of collective migration brought to light the complexity and the dynamics of collective migration. Live imaging was instrumental in proving that disrupting mechanisms that are specific to promoting collective migration does not block single cell motility (Arboleda-Estudillo et al., 2010; Prasad and Montell, 2007; Vitorino et al., 2011). Using live imaging, scientists understood how leading edge cells can induce cells many rows behind them during wound healing (Vitorino et al., 2011), how neural crest cells utilize a balance of repulsion and attraction to maintain a loose aggregate
(Moore et al., 2013), and how border cell clusters only permit protrusion formation from the border cell at the front of the cluster (Prasad and Montell, 2007). Future studies will require the use of live imaging as a tool to observe the impact of different mechanisms on the dynamics of collective migration.
Quantitative analysis of collective migration can identify and analyze features of collective migration dynamics. Analysis of movies acquired from live imaging is a major bottleneck. Movies are data-dense, containing spatial and temporal information on collective migration. Prominent features such as protrusions direction, extension and retraction rates, as well as migration speed can still be analyzed 'by hand'. However, this approach is slow and cumbersome, which puts a limit on the number of movies that can be analyzed, and thus, the statistical power of the measurements. More complex
169 parameters such as cells shifting locations within the group, switching cell neighbors, or changes in cell shapes cannot be tracked efficiently by hand. Computational analysis of live imaging allows researchers to measure both simple and complex features simultaneously and determine their contribution towards collective migration. This approach was used successfully in epithelial sheet migration, Drosophila gastrulation and border cell migration (Cai et al., 2014; McMahon et al., 2008; Vitorino et al., 2011).
Studying the interplay between collective migration and the environment
Migrating cells can alter the environment through proteolytic degradation of the extracellular matrix or laying down of basement membrane to facilitate cell migration
(Friedl and Gilmour, 2009). Conversely, a migrating cell can be influenced by the environment via multiple pathways such as secreted cytokines, signalling via adhesions, and mechanotransduction (Lauffenburger and Horwitz, 1996). Scientists have a good understanding of the effect of cytokines in promoting cell motility and guiding migration direction. Scientists also understand that signalling occurs via cell-matrix adhesions such as integrins and neuronal guidance pathways such as the repulsive Slit-Robo pathway
(Dickson and Gilestro, 2006; Sheetz et al., 1998). We are only beginning to appreciate that the physical properties of the substrate also impact cell migration. Durotaxis involves the directed movement of migrating cells towards regions of increasing substrate stiffness
(Lo et al., 2000). Focal adhesions link the extracellular matrix with the internal cytoskeleton, which is under tension via the actomyosin machinery. Although the mechanism is not well understood, increasing substrate rigidity is telegraphed through the focal adhesions, leading to directed migration up the stiffness gradient (Lo et al., 2000).
170 In certain cell types, collective migration can arise solely from how permissive the environment is. Cells of mesenchymal origin such as fibroblasts or cancer cells undergoing EMT can switch between single cell migration and collective migration, depending on the microenvironment (Friedl et al., 1995; Theveneau and Mayor, 2013).
Wider ECM tracks promote collective migration of fibroblasts or cancer cells in vitro while narrower, more restrictive tracks favor single cell migration (Leong et al., 2013).
Cortical tension at the collective level
Active maintenance of cortical tension at the cluster periphery in A431 carcinoma cell clusters and Drosophila border cells cluster are examples of collectives taking single- cell attributes and ‘scaling it up’ to operate at the collective level (previous chapter)
(Hidalgo-Carcedo et al., 2011). Another example is the presence of front-rear polarity in the Zebrafish lateral line primordium and Drosophila border cell clusters, analogous to front-rear polarity in single cells (Dambly-Chaudière et al., 2007; Montell et al., 2012).
Freely-migrating collectives, in particular, are sometimes referred to as ‘super-cells’ because of such similarities to single cell migration.
I discovered that border cell clusters maintain cortical tension at the cluster periphery to resist compression from the surrounding nurse cell during migration
(previous chapter). Self-assembling A431 carcinoma cell lines also maintain Myo-II activity at the periphery of each aggregate to keep maintain cluster organization
(Hidalgo-Carcedo et al., 2011). It is unclear whether A431 cancer cells also require Myo-
II activity to resist mechanical compression from its surroundings in vitro. It is worth noting that border cell clusters and A431 cancer cell aggregates are small and typically composed of <10 cells. In physics, surface tension is inversely proportional to surface
171 area and proportional to degree of curvature. Small aggregates like A431 cancer cells and border cells have small surface area and more pronounced curvature. It is possible that actomyosin contractility at the periphery of the cluster increases the ‘surface tension’ of these small clusters to maintain the organization. Do larger models of freely-migrating collectives like the Zebrafish lateral line primordium rely at all on actomyosin tension at the periphery to maintain its organization? It is likely that forces are distributed to a sufficiently large number of cells that high actomyosin contractility at the periphery is not a major player in maintaining cluster organization.
Manipulating Myo-II activity to prevent collective cancer spread should be approached carefully. Based on my results from Drosophila border cells, increasing myosin activity can strongly disrupt collective migration while still keeping the border cells together (previous chapter). It is tempting to apply the same strategy as a therapeutic approach to prevent the migration and spread of collectively migrating cancer cells.
However, it has already been shown in A431 cancer cell aggregates that elevated myosin activity can cause the separation of the aggregates into individual cancer cells (Hidalgo-
Carcedo et al., 2011). Drosophila border cell clusters contain a pair of organizing polar cells that maintain strong E-cadherin-based adhesions with the border cells (Cai et al.,
2014; Niewiadomska et al., 1999) and likely prevents the dissociation of the cluster despite increased, mislocalized Myo-II activity. Dispersal of individual cancer cells from aggregates increases the risk of metastasis. However, it may be possible to finetune the manipulation of Myo-II activity to slow down collective migration of cancer cell aggregates without causing dispersal of individual cells.
172 Waves of contractility during collective migration
Dynamic Myo-II at the border cell cluster periphery results in waves of contraction followed by relaxation. Using live imaging, we have previously shown that
Myo-II activity in the border cell cluster is highly dynamic (Majumder et al., 2012). I further uncovered the existence of waves of Myo-II activity that are associated with contractility in collectively migrating border cells (previous chapter). Recent studies on apical constriction during Drosophila mesoderm invagination have shown that Myo-II dynamics manifest as contractile pulses that constrict the apical surface in a ratchet-like fashion (Mason and Martin, 2011; Vasquez et al., 2014). This shows that cycles of Myo-
II activity can result in distinct mechanical outputs (waves versus ratcheting). Possible explanations for this distinction include differences in Myo-II activity regulation and/or actin cytoskeleton architecture.
It is not known how the waves of Myo-II activity are: 1) initiated, and; 2) subsequently propagated in border cell clusters. Myo-II waves may occur stochastically, due to slight changes in the balance of kinase and phosphatase activites that regulate
Myo-II activity. My favored hypothesis is that Myo-II waves are the cluster’s response to local changes in tension along the periphery of the cluster due to compression from the surrounding nurse cells. We are in good a position to test this hypothesis using live imaging of Myo-II dynamics while genetically increasing nurse cell tension.
Waves of Myo-II activity suggest that the initial triggering event can be propagated. It is possible that local deformations on the cluster periphery alter the underlying actin cytoskeleton in a manner that can trigger active Myo-II recruitment. We already know that local deformations can recruit active Myo-II to the site of deformation
173 (2015b). Furthermore, it has been shown in vitro that the action of active Myo-II on F- actin itself can cause the addition of more Myo-II molecules (Soares e Silva et al., 2011).
Perhaps a similar mechanism is occurring in border cell cluster to propagate Myo-II waves during collective migration.
Do waves of Myo-II contractility along the border cell cluster periphery contribute to forward movement? Waves of Myo-II activity were only apparent when we imaged at short time intervals (every 10 seconds). During this window of imaging, the cluster is moving forward at a slow but constant rate. Furthermore, it was occurring in the absence of border cell protrusions. Are these waves of Myo-II-induced deformations moving the cluster forward, similar to peristalsis or the swimming motion of cuttlefish?
Myo-II waves were observed to propagate both forwards and rearwards. A more detailed analysis is required to determine whether a directional bias exists. The border cell cluster also performs a specific type of movement nearing the end of migration, wherein the cluster pivots to face the apical side of the border cells towards the oocyte (Montell et al.,
2012). This is important for the joining of the border cells and polar cells with the invading centripetal cells to restore a contiguous epithelium around the nearly mature egg. Preliminary data examining Myo-II waves at this point of migration reveals a burst of Myo-II waves on the future basal half of the border cell cluster that correlates with the pivoting of the cluster. It is possible that Myo-II waves are responsible for this pivoting motion.
In conclusion, collective migration is a highly versatile mode of cell movement that is involved in early embryogenesis, organogenesis, wound healing as well as cancer spread. Collective migration is facilitated by cellular mechanisms that shape and direct
174 the individual cell motilities to achieve efficient movement. Understanding collective migration involves the identification of these key cellular mechanisms and knowing that different types of collective migration utilize different strategies. Drosophila border cell migration is an excellent model to use to identify the key cellular mechanisms involves in the movement of small freely-migrating collectives. Through a screen, I identified dropout (dop) / MAST2 as a potential component of the pathway required for efficient border cell migration. Appropriately designed screens that specifically look for disruption of collective behavior will identify the key mechanisms that operate at the collective level. I also discovered that border cell clusters utilize actomyosin contraction at the periphery to counteract the compression of the surrounding tissue to maintain a compact cluster and efficient migration. This may prove to be a conserved mechanism common to small collectives to maintain their organization.
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