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5-2018
Nanoscale Organization of the Small GTPase Rac1
Kelsey Maxwell
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Recommended Citation Maxwell, Kelsey, "Nanoscale Organization of the Small GTPase Rac1" (2018). The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences Dissertations and Theses (Open Access). 851. https://digitalcommons.library.tmc.edu/utgsbs_dissertations/851
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Approval Page
by Kelsey Maxwell, B.S.
APPROVED:
______John F. Hancock, M.A., M.B., B.Chir., Ph.D. Advisory Professor
______Jeffrey A. Frost, Ph.D.
______Alemayehu A. Gorfe, Ph.D.
______Kartik Venkatachalam, Ph.D.
______Darren F. Boehning, Ph.D.
APPROVED:
______Dean, The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences
NANOSCALE ORGANIZATION OF THE SMALL GTPASE RAC1
A
DISSERTATION
Presented to the Faculty of
The University of Texas
MD Anderson Cancer Center UTHealth
Graduate School of Biomedical Sciences
in Partial Fulfillment
of the Requirements
for the Degree of
DOCTOR OF PHILOSOPHY
by
Kelsey Nicole Maxwell, B.S.
Houston, TX
May 2018
Title Page
Dedication
To Matty, Mom, Dad, Amanda and William
iii Acknowledgements
I would like to thank my advisor, Dr. John Hancock, for his continued guidance and support through my training. There are many challenges on the road toward a PhD— failed experiments, defining positive and negative controls, and coming up with new ideas—to name a few. I am a better scientist because of his leadership through these challenges and more. I would also like to specifically acknowledge two others in our lab who have provided many hours of guidance. First, Dr. Yong Zhou, who has been instrumental to the development of this project. Dr. Zhou has given many hours to brainstorming sessions, answering endless questions, and editing over the last six years.
Second, Dr. Jin Cho, for not only helping me with technical aspects for possibly every experiment I ever laid hands on but, more importantly, for simply being an encouraging friend.
I would like to specifically thank the members of my advisory committee: Dr.
Alemayehu Gorfe, Dr. Jeff Frost, Dr. Kartik Venkatachalam, Dr. John Putkey, and Dr.
Darren Boehning: thank you for caring about me, this project, and for all of your creative ideas that made this work what it is today. Thank you also to the faculty and staff of the
Department of Integrative Biology and Pharmacology for their open doors, training, and support. To Xiaoping Ma, Wei Chen, and Hong Liang, thank you for helping me with so many experiments, always encouraging me, and for all the candies. The lab would fall apart without you.
To the University of Texas MD Anderson UTHealth Graduate School of
Biomedical Sciences faculty, staff, and Deans, Dr. Michael Blackburn and Dr. Michelle
Barton: thank you for all that you do for our school and students. I would also like to thank
iv the Rosalie B. Hite Graduate Fellowship in Cancer Research and the Hite Fellowship
Committee for funding me these past three years, and Mrs. Roquemore for her generous contributions to GSBS through the Fadine Jackson Roquemore Scholarship in Cancer
Research that has also provided me with generous financial support.
Without the friendship of my fellow students and colleagues my time in Houston would have been very lonely. To Dr. Randi Fitzgibbon, Dr. Dhananjay Thakur, Brittany
Jewell, Mimi Le, Tanya Baldwin, Ashabari Mukherjee, Janani Subramanian, Rachel Tay,
Sam Berkey, Courtney Olsen, Stephanie Bsaibes, and Stephanie Tabor: thank you for all of the laughter, venting sessions, solidarity, lunch dates, Ruben puppy-sitting, and motivation. I am lucky to have such driven and inspiring friends!
Most importantly, I would like to thank my family. To my niece, Evelyn Milam, who was born during the writing of this dissertation –you’re only six ten weeks old but you’re a great motivator! To my siblings, Amanda Milam and William Maxwell, thank you for being my best friends and the amazing people that you are. To my brother-in-law Jason
Milam, who actually read this entire thesis and provided edits: thank you for doing that!
To my aunts, uncles, granddaddy Ervin Englert, and grandma Marie Maxwell: thank you for always encouraging me to work hard and believing that I have the capacity –and stubbornness– to achieve my goals. To my grand-mama Gladys Englert and grandpa
Bill Maxwell, who unfortunately never got to see me enter graduate school but were always supportive of my education: we miss you. To my in-laws: for loving me and helping me along the way. To my parents: for their unconditional love and countless sacrifices for our education, happiness, and well-being, your efforts are so appreciated.
To my life partner, Matt Acosta: thank you for your patience and your endless well of encouragement. I love you!
v NANOSCALE ORGANIZATION OF THE SMALL GTPASE RAC1
Abstract
Kelsey Maxwell, B.S.
Advisory Professor: John F. Hancock, M.A., M.B., B.Chir., Ph.D.
Rac1 is a small, guanine-nucleotide binding protein that cycles between an inactive GDP-bound and active GTP-bound state to regulate actin-mediated motility, migration, and adhesion. Plasma membrane (PM) localization is essential for its biological activity. Rac1 PM targeting is directed by a C-terminal membrane anchor that encompasses a geranylgeranyl-cysteine-methyl-ester, palmitoyl, and a polybasic domain (PBD) of contiguous lysine and arginine residues. Using high-resolution imaging combined with spatial mapping analysis, I found that Rac1 forms nanoclusters on the
PM. Cycling between the GTP- and GDP-bound states, Rac1 forms nanoclusters that are non-overlapping, consequently undergoing guanine nucleotide-dependent spatial segregation. I further found that Rac1 selectively associates with phosphatidic acid (PA) and phosphoinositol (3,4,5)-trisphosphate (PIP3), resulting in nanoclusters enriched in these anionic lipids. I found these lipids to be structurally important as depleting the PM of PA or PIP3 impaired both Rac1 PM targeting and nanoclustering. Lipid binding specificity of Rac1 is encoded in the C-terminal PBD amino acid sequence in combination with the adjacent lipid anchors. Point mutations within the PBD, including highly conserved arginine to lysine substitutions or mutations exchanging the geranylgeranyl for farnesyl, profoundly altered Rac1 lipid binding specificity without changing electrostatics of the protein and resulted in impaired macropinocytosis and decreased cell spreading. In this thesis, I proposed that Rac1 nanoclusters act as lipid-based signaling platforms emulating the spatiotemporal organization of Ras proteins and further
vi showed that the Rac1 PBD-prenyl anchor has a biological function that extends beyond simple electrostatic engagement with the PM.
vii Table of Contents Approval Page ...... iii
Title Page ...... iv
Dedication ...... iii
Acknowledgements ...... iv
Abstract ...... vi
List of Illustrations ...... xii
List of Tables ...... xiv
Abbreviations ...... xv
Chapter 1 ...... 1
Introduction ...... 1
1.1 Membrane domains and plasma membrane complexity ...... 2
1.2 Signaling complexes ...... 7
1.2.1 Ras nanoclusters ...... 7
1.2.2 Receptors ...... 10
1.2.3 Arf GTPases ...... 11
1.3 Rac1 spatial regulation ...... 12
1.4 Rac1 biology ...... 13
1.4.1 Activity and effectors ...... 13
1.4.2 Trafficking to the plasma membrane ...... 15
1.4.3 Actin dynamics ...... 19
1.4.4 Endocytosis ...... 20
viii 1.4.5 Cell growth ...... 22
1.4.6 ROS ...... 23
1.5 Rac1 signaling in disease ...... 24
1.5.1 Cancer ...... 25
1.5.2 Cardiovascular disease ...... 26
1.5.3 Neurodegenerative disease ...... 27
1.5.4 Inflammation ...... 28
1.6 Drugs targeting Rac signaling pathways ...... 28
1.7 Summary and experimental approach ...... 29
Chapter 2 ...... 31
Materials and Methods ...... 31
2.1 Plasmids ...... 31
2.2 Cell culture and transfection ...... 31
2.3 Western blotting ...... 32
2.4 Electron Microscopy and spatial mapping analysis ...... 33
2.4.1 Univariate K-function ...... 33
2.4.2 Bivariate K-function ...... 34
2.5 Fluorescence lifetime imaging microscopy (FLIM) combined with fluorescence
resonance energy transfer (FRET) ...... 37
2.6 Macropinocytosis ...... 38
2.7 Cell spreading ...... 38
2.8 Statistical analysis and graphics ...... 38
2.9 Limitations ...... 39
Chapter 3 ...... 40
ix Rac1 nanoclustering ...... 40
3.1 Introduction ...... 40
3.2 Results ...... 41
3.2.1 Rac1 localizes to the PM and forms nanoclusters ...... 41
3.2.2 Rac1 nanoclusters are spatially segregated by nucleotide binding ...... 47
3.3 Discussion ...... 50
Chapter 4 ...... 52
Lipid-dependency of Rac1 nanoclusters ...... 52
4.1 Introduction ...... 52
4.2 Results ...... 53
4.2.1 Rac1 nanoclusters selectively associate with PA and PIP3 ...... 53
4.2.2 PA is a key structural component of Rac1 nanoclusters ...... 57
4.2.3 PIP3 is a key structural component of Rac1 nanoclusters ...... 60
4.2.4 Rac1 is insensitive to PtdSer-depletion ...... 62
4.3 Discussion ...... 63
Chapter 5 ...... 65
Rac1 nanoclustering and lipid specificity is determined by a prenyl-PBD code ...... 65
5.1 Introduction ...... 65
5.2 Results ...... 66
5.2.1 Rac1 clustering and PM localization is dependent on palmitoylation,
prenylation, and Arginine 185 ...... 66
5.2.2 Palmitoylation, geranylgeranylation, and Arg 185 determine PA/PIP3
association ...... 71
5.2.3 Loss of Rac1 clustering impairs macropinocytosis and cell spreading ...... 76
x 5.3 Discussion ...... 80
Chapter 6 ...... 83
Discussion and Future Directions ...... 83
6.1 The role of lipids ...... 84
6.2 Mechanistic insights ...... 87
6.3 Future directions and remaining questions ...... 89
Bibliography ...... 92
Vita ...... 121
xi List of Illustrations
Figure 1. Illustration of plasma membrane phase separation ...... 6
Figure 2. Important protein domains in Rac1 ...... 17
Figure 3. Lipidated membrane anchors of Ras and Rac1 ...... 18
Figure 4. Representative 2D plasma membrane sheet from BHK cells expressing GFP- tagged protein of interest ...... 36
Figure 5. Representative 2D plasma membrane sheet from BHK cells expressing GFP- and RFP-tagged constructs ...... 36
Figure 6. Rac1 localizes to the plasma membrane in BHK cells ...... 42
Figure 7. Rac1 forms nanoclusters as effectively as Ras ...... 44
Figure 8. Rac1.GTP and Rac1.GDP form nanoclusters on the plasma membrane ...... 45
Figure 9. Lmax is independent of gold particle number ...... 46
Figure 10. Rac1 proteins are distributed into monomers, ‘dimers’, and oligomers ...... 47
Figure 11. Rac1 forms spatially segregated nanoclusters ...... 49
Figure 12. Rac1 nanoclusters have a distinct lipid composition ...... 55
Figure 13. Rac1 plasma membrane localization and clustering is sensitive to PA- depletion ...... 58
Figure 14. PA depletion disrupts Rac1-PA association but not Rac1-PLD2 association
...... 59
Figure 15. Rac1 plasma membrane localization and clustering is sensitive to PIP3 depletion ...... 61
Figure 16. Rac1 nanoclusters are insensitive to PtdSer-depletion ...... 62
Figure 17. Model of Rac1 nanoclusters ...... 64
Figure 18. Sequences of Rac1 membrane anchor mutants ...... 68
xii Figure 19. Rac1 membrane anchor mutants are highly expressed in BHK cells ...... 69
Figure 20. Mutations in the membrane anchor of Rac1 disrupt clustering and plasma membrane localization ...... 70
Figure 21. Rac1 membrane anchor mutants lose association with PA and/or PIP3 ...... 72
Figure 22. Plasma membrane localization of Rac1.R185K and Rac1.R185K.R187K
PBD mutants are rescued by exogenous PA or PIP3, respectively ...... 74
Figure 23. Rac1.CVLS PM localization is sensitive to PtdSer depletion ...... 75
Figure 24. Expression of Rac1 membrane anchor mutants impairs dextran uptake in
BHK cells ...... 77
Figure 25. Expression of Rac1 membrane anchor mutants impairs macropinocytosis in
BHK cells ...... 78
Figure 26. Expression of Rac1 membrane anchor mutants impairs cell spreading in
BHK cells ...... 79
Figure 27. Hypothesis Model ...... 86
xiii List of Tables
Table 1. LBI values for co-clustering between lipid binding probes and Rac1/Ras ...... 56
Table 2. LBI values for co-clustering between lipid binding probes and Rac1 membrane anchor mutants ...... 73
xiv Abbreviations
A, alanine, e.g. Rac1.C178A mutation
A, aliphatic, e.g. C-A-A-X box motif
AP-1, activator protein-1
Arf, ADP ribosylation factor
Arp 2/3, actin-related protein 2/3
BCS, bovine calf serum
BHK, baby hamster kidney
C, Cys, cysteine
Cdc42, cell division control protein 42 homolog
C.I., confidence interval
DAG, diacylglycerol
DMEM, Dulbecco’s modified Eagle’s medium
DMSO, dimethyl sulfoxide
DRM, detergent-resistant membrane
DSM, detergent-soluble membrane
ECL, enhanced chemiluminescence
EGF, epidermal growth factor
EGFR, epidermal growth factor receptor
EM, electron microscopy
ER, endoplasmic reticulum
ERK, extracellular signal-regulated kinase
FLIM, fluorescence lifetime imaging microscopy
FRET, fluorescence resonance energy transfer
xv GDI, guanine-nucleotide dissociation inhibitor
GDP, guanosine diphosphate
GFP, green fluorescent protein
G, glycine
GM1, monosialotetrahexosylganglioside
GPCR, G-protein coupled receptor
GPI, glycophosphatidylinositol
GPMV, giant plasma membrane vesicle
GTP, guanosine triphosphate
GUV, giant unilamellar vesicle
HSD, honest significant difference
HVR, hypervariable region
Icmt, isoprenylcysteine-O-carboxyl methyltransferase
IP3, inositol 1,4,5-trisphosphate
JNK, c-Jun N-terminal kinase
K, Lys, lysine
LBI, Lbiv(r)–r integration
MAPK, mitogen-activated protein kinase
NADPH, nicotinamide adenine dinucleotide phosphate
NF-kB, nuclear factor kappa-light-chain-enhancer of activated B cells
Pak1, p21-activated protein kinase 1
PA, phosphatidic acid
PBD, polybasic domain
PBS, phosphate buffered saline
xvi PC, phosphatidylcholine
PCR, polymerase chain reaction
PE, phosphatidylethanolamine
PH, pleckstrin homology
PI, phosphoinositol/phosphatidylinositol
PI3K, phosphoinositol 3-kinase
PIP3, phosphoinositol (3,4,5)-trisphosphate
PIP2, phosphoinositol (4,5)-bisphosphate
PI4P, phosphoinositol 4-phophate
PI3P, phosphoinositol 3-phosphate
PLC, phospholipase C
PLD, phospholipase D
PKC, protein kinase C
PM, plasma membrane
P-Rex1, PIP3-dependent Rac1 exchanger 1
PtdSer, phosphatidylserine
PVDF, polyvinylidene difluoride
Q, glutamine
Rac1, Ras-related C3 botulinum toxin substrate 1
R, Arg, arginine
Rce1, Ras-converting enzyme 1
RFP, red fluorescent protein
Rho, Ras homolog gene family
RhoA, Rho family member A
RhoGDI, Rho guanine nucleotide dissociation inhibitor xvii ROS, reactive oxygen species
SEM, standard error of the mean
SPT, single-particle tracking
S, serine
TBS, Tris-buffered saline
Tiam1, T cell lymphoma invasion and metastasis 1
TMR, tetramethylrhodamine
TRP, transient receptor potential
V, valine
WT, wild-type
WASP, Wiskott-Aldrich syndrome protein
WAVE, WASP-family verprolin homolog
X, any amino acid, e.g. C-A-A-X box motif
xviii Chapter 1 Introduction
Ras-related C3 botulinum toxin substrate 1 (Rac1) is a ubiquitously expressed small GTPase that regulates a number of diverse cell signaling processes. Rac1 signaling is spatially organized and initiated on the plasma membrane (PM), where it interacts with effector proteins and signaling lipids. As a consequence of its localization,
Rac1 function is dependent on the complex behavior of the plasma membrane. Cell membranes are multifaceted and heterogeneous, containing a wide variety of domains and compartments that differ in their biophysical components and properties. The PM must also respond to the extracellular environment and selectively take in nutrients, while at the same time screen non-beneficial substances. In order to regulate signaling, recruit binding partners, and maximize signaling efficiency, many lipids and PM-localized molecules form complexes or clusters on the cell PM.
The ability of lipid-anchored and transmembrane proteins to form clusters has been a growing area of research. For example, Ras small GTPases form lipid-enriched oligomers that are essential for downstream signaling and in effect, act as signaling platforms for effector binding and recruitment on the PM. In this study, I discover the existence of nanometer scale clusters of active Rac1, distinct from Ras isoform-specific nanoclusters or inactive Rac1, which suggests a novel means of regulation for Rac1 signaling. Elucidation of these Rac1 clusters would shed light on the functional significance of Rac1 spatial organization and PM targeting. Therefore, the objective of
1 this dissertation is to identify the structural components and functional relevance of Rac1 nanocluster formation.
1.1 Membrane domains and plasma membrane complexity
The fluid mosaic model of cell membranes was first proposed almost fifty years ago in a ground-breaking article by S.J. Singer and G.L. Nicolson. In this model, the plasma membrane consists of a lipid bilayer with embedded proteins, giving rise to membrane fluidity and malleability, while also restricting lateral diffusion of membrane components (Singer & Nicolson, 1972). A major assumption of this model is that the fluid bilayer contains distinct domains defined by regions with divergent protein and lipid compositions. Thereafter, experimental evidence began to identify the existence of distinct membrane domains on micrometer scales, including those that may recruit glycolipid-anchored proteins (Ahmed et al., 1997, Brown & Rose, 1992, Varma & Mayor,
1998, Yu et al., 1973). For example, early experiments using non-ionic detergents establish that cell membranes can be divided into detergent-soluble membranes (DSMs) or detergent-resistant membranes (DRMs) with distinct compositions (Hanada et al.,
1995, Schroeder et al., 1994). The problem with this approach however is that the composition of DRMs and DSMs can vary widely depending on the types of detergents and experimental conditions used and do not necessarily reflect the native composition of membrane domains (Schuck et al., 2003). Increasing the complexity, membrane compositions are incredibly diverse between cell types and are not necessarily homogenous even within cell types (Levental & Veatch, 2016, Sezgin et al., 2017, van
Meer et al., 2008).
2 The discovery and use of biophysical tools such as computational modeling, giant unilamellar vesicles (GUVs), and giant plasma membrane vesicles (GPMVs) strongly demonstrate that lipids adopt distinct phases that are solid-like or fluid-like, characterized by their spatial arrangement and molecular freedom within the different phases (Levental
& Veatch, 2016, Sezgin et al., 2017, van Meer et al., 2008) (Fig. 1). The solid-like phases came to be known as lipid rafts and are enriched with sphingolipids and cholesterol. The lipid raft model theorizes that freely diffusing yet stable lipid rafts act as functional regulatory platforms, selectively coordinating protein-protein and protein-lipid interactions on the PM (Hancock, 2006, Levental et al., 2010). These rafts are between
10-200 nm in diameter but can be prompted to form larger domains (> 300 nm) by interactions with proteins and lipids (Pike, 2006, Sezgin et al., 2017). Due to their size and transient nature, direct observation of these rafts in vivo via classical microscopy methods has remained elusive. However, single-particle tracking (SPT) (Kusumi et al.,
2005, Suzuki, 2016), fluorescence resonance energy transfer (FRET) (Rao & Mayor,
2005), and the electron microscopy (EM) techniques used herein (Chapter 2, Materials and Methods), combined with advanced in vitro (Kahya et al., 2004) and in silico
(Ingolfsson et al., 2014, Niemela et al., 2007) modeling, support the existence of such domains and provide important clues as to their nature and functional relevance (Sharma et al., 2004, Simons & Ikonen, 1997).
The PM contains a plethora of different lipids, and lipid type greatly determines membrane domain assembly and function. Indeed, the disparity in head groups and aliphatic chains of lipids leads to the generation of thousands of different lipid species in a given eukaryotic cell (Sud et al., 2007). However, the synthesis and distribution of different lipid species is not homogenous, and geographical production of specific lipids contributes to the distinctive membrane compositions of organelles (van Meer et al., 3 2008). The most predominant classes of eukaryotic membrane lipids are the glycerophospholipids and sphingolipids (van Meer et al., 2008). Glycerophospholipids are the most abundant structural lipids of eukaryotic membranes and include phosphatidylcholine (PC), phosphatidylethanolamine (PE), phosphatidylserine (PtdSer), phosphatidic acid (PA) and phosphatidylinositols (PI) (Hishikawa et al., 2014, van Meer et al., 2008). Spatially changing the composition and packing density of these lipids promotes curvature stress on the PM and provides the structural basis for important cell signaling events such as membrane budding and fusion (Marsh, 2007). Sphingolipids contain a ceramide hydrophobic backbone and hydrophobic tails and therefore exhibit a tighter packing density than glycerophospholipids (Gault et al., 2010, van Meer et al.,
2008). These tightly packed phases of sphingolipids are integrated with cholesterol, creating the liquid-ordered (Lo) domains associated with lipid rafts (Hancock, 2006,
Levental & Veatch, 2016).
Phosphorylated PIs can act as second messengers. While the percentage of PIs on the PM is small (<1%), they are important for the recruitment of proteins to regions of localized signaling on the PM and endomembranes, as well as protein kinase activation
(Falkenburger et al., 2010). For example, phosphoinositol (4,5)-bisphosphate (PIP2) regulates transient receptor potential (TRP) channel activation (Thakur et al., 2016), potassium channels (Falkenburger et al., 2010, Rodriguez-Menchaca et al., 2012), and protein kinase C (PKC) signaling and intracellular calcium release through its cleavage products diacylglycerol (DAG) and inositol 1,4,5-trisphosphate (IP3), respectively
(Falkenburger et al., 2010). Another example, phosphoinositol (3,4,5)-trisphosphate
(PIP3), is synthesized from PIP2 by PI-3 kinase (PI3K) phosphorylation and recruits kinases such as Akt and Src to the PM, initiating growth factor signaling (Cantley, 2002,
4 Vanhaesebroeck et al., 2012); indeed, both Akt and Src are heavily implicated in cancer formation and tumorigenesis (Altomare & Testa, 2005, Yeatman, 2004).
Furthermore, asymmetric distribution of PM lipids between the inner and outer leaflets of the PM is functionally relevant. PtdSer and PE are normally enriched on the inner leaflet of the PM, but when exposed to the outer leaflet by the inactivation of flippases and activation of scramblases, PtdSer acts as an apoptotic and phagocytotic signal (Birge et al., 2016, Segawa & Nagata, 2015). PtdSer enrichment on the inner leaflet is also necessary for the transbilayer coupling that directs cholesterol-dependent
GPI-anchored nanocluster formation (Raghupathy et al., 2015). Lipid asymmetry across the PM bilayer also contributes to the membrane flexibility that promotes budding and vesicle formation (Pomorski & Menon, 2006).
Our current understanding of lipid-associated membrane domains can be logically divided into the following four categories: (1) exclusively-lipid clusters composed of saturated lipids, such as glycolipid clusters or ceramide domains (Harder & Simons,
1999, Zhang et al., 2009); (2) lipid-mediated protein clusters also composed of saturated lipids, which includes Ras nanoclusters and glycophosphatidylinositol (GPI)-anchored protein domains (Abankwa et al., 2007, Varma & Mayor, 1998); (3) separation of lipids into liquid-liquid phases (Hyman et al., 2014), whereby Lo domains composed of saturated lipids and an abundance of cholesterol are separate from liquid-disordered (Ld) domains composed of primarily unsaturated lipids (Kaiser et al., 2009, van Meer et al.,
2008, Veatch & Keller, 2003); (4) induced protein-mediated lipid clusters, such as cholera toxin binding to monosialotetrahexosylganglioside (GM1) or antibody binding to cell surface receptors (Shi et al., 2007). I found that Rac1, like other small GTPases, is organized into lipid-mediated protein clusters, discussed in detail in this thesis.
5
Liquid-ordered domain (L ) o
Liquid-disordered domain (Ld) Cholesterol
Unsaturated lipid
Saturated lipid
Figure 1. Illustration of plasma membrane phase separation
In model membranes and GPMVs, PM lipids segregate based on packing density, tail saturation, fluidity, and lipid orientation into “ordered” and “disordered” domains. These domains are facilitated by protein-protein and protein-lipid interactions, but in turn can modulate these interactions as well.
6 1.2 Signaling complexes
Depending on random macromolecular interactions to occur in the cellular environment is an inefficient way for signaling molecules to direct signal output. PM targeting is one way signaling molecules are able to locally interact with one another
(Cebecauer et al., 2010). Providing even more efficiency, and the focus of this dissertation, is the formation of transient clusters (defined as three or more interacting elements) that facilitate protein-protein and protein-lipid interactions. The reversibility of such complexes allows for direct regulation in turning on and off the signal as needed.
Interactions between anchored complexes on the PM and unanchored second messengers are hypothesized to be more effective than interactions between unanchored complexes or signaling molecules (Bray, 1998). Advances in imaging techniques have allowed for direct visualization of micro- and nanoscale signaling complexes, though what drives the formation of these complexes and the role they play in regulating signaling is not completely understood.
1.2.1 Ras nanoclusters
Perhaps some of the most studied signaling complexes are Ras nanoclusters.
Ras proteins are ubiquitously-expressed, PM-anchored small GTPases that cycle between an active, GTP-bound state and an inactive, GDP-bound state through the use of GTPase activating proteins (GAPs) and guanine-nucleotide exchange factors (GEFs)
(Bos et al., 2007, Willumsen et al., 1984). There are three isoforms encoded by different genes, K-Ras, H-Ras, and N-Ras, with two splice variants of K-Ras: K-Ras4B (commonly referred to as K-Ras) and K-Ras4A (Fernandez-Medarde & Santos, 2011, Tsai et al.,
2015). Though Ras proteins demonstrate isoform-selective signaling, they all play a role
7 in tumorigenesis and are frequently mutated in human cancer (Downward, 2003, Prior et al., 2012). Unsurprisingly, given the importance their role in cancer development, Ras proteins control a number of cell processes, including cell growth, proliferation, survival, and differentiation (Rajalingam et al., 2007). Finding clinically effective therapeutics targeting Ras has proved difficult (Cox et al., 2014, Papke & Der, 2017). Therefore, the biophysical and biochemical nature of Ras clustering on the PM is an important avenue of research for developing novel anti-Ras compounds, and their similarity to Rac1 provides insight to Rac1 spatial regulation and biology.
Ras isoforms contain 95% sequence identity and diverge from one another primarily in the hypervariable region (HVR) of the protein. Within the HVR is the C- terminus membrane anchoring region of the protein, which undergoes post-translational processing to target Ras to the PM (Abankwa et al., 2007). First, protein farnesyl transferase attaches a farnesyl chain to the C-terminal cysteine of the CAAX (C, cysteine;
A, aliphatic amino acid; X, any amino acid) motif (Reiss et al., 1990). Next, Ras moves to the endoplasmic reticulum (ER) where the CAAX motif is cleaved by Ras-converting enzyme 1 (Rce1), leaving only the farnesylated cysteine residue (Otto et al., 1999). The a-carboxyl group of the farnesylated cysteine is methylated by isoprenylcysteine-O- carboxyl methyltransferase (Icmt) (Gutierrez et al., 1989) and at this stage, K-Ras4A, H-
Ras, and N-Ras trafficking begins to diverge with K-Ras4B. K-Ras4A, H-Ras, and N-Ras are palmitoylated on cysteine residues in the HVR and traffic to the PM through the Golgi, while K-Ras4B traffics to the PM through cycles of solubilization with the recycling endosome (Apolloni et al., 2000, Choy et al., 1999, Schmick et al., 2014). Along with polybasic domains (PBD) in K-Ras4A and K-Ras4B, each of these modifications allows for maximal PM targeting. In fact, it is the precise but divergent mechanisms of PM
8 targeting between the different membrane anchors that determines nanoclustering of
Ras proteins and isoform-selective signaling from the nanocluster sites (Hancock, 2003,
Hancock & Parton, 2005, Prior & Hancock, 2012).
Ras nanoclusters are spatially non-overlapping, segregated by isoform-type and
GTP-binding (Abankwa et al., 2007). They are ~20 nm in diameter, contain approximately 6-8 proteins per cluster, and are the sole sites of Raf recruitment and extracellular signal-regulated kinase (ERK) activation on the PM (Cho & Hancock, 2013).
Raf/mitogen-activated protein kinase (MAPK)/ERK signaling is regulated by the relatively short lifetime of Ras clusters (<1s), as nanocluster disassembly ends signal output
(Harding & Hancock, 2008). Spatial segregation of GTP-bound and GDP-bound isoform- specific Ras nanoclusters is realized in part by membrane anchor orientation interacting with PM lipids, and conformational changes in the G-domain upon guanine-nucleotide binding (which in turn modifies hydrophobic and electrostatic contacts between the membrane anchor and PM) (Prakash & Gorfe, 2017).
Ras nanoclusters selectively sort phospholipids, which play a role in their structural integrity and effector binding and recruitment (Zhou et al., 2014). For example,
K-Ras nanoclusters are dependent on PtdSer for their formation and signaling, and depleting the availability of PtdSer with various compounds results in K-Ras mislocalization, declustering, and decreased signal output (Cho et al., 2012, Cho et al.,
2015, van der Hoeven et al., 2013). The minimal membrane anchors of H-Ras, K-Ras, and N-Ras attached to GFP are sufficient for PM trafficking and clustering, suggesting that it is phospholipid contacts with the membrane anchor that determine Ras nanocluster formation (Hancock & Parton, 2005). Indeed, phospholipid selectivity for K-
Ras is determined by key residues in the membrane anchor (Zhou et al., 2017). Ras clusters are also challenged by the dynamic PM environment, demonstrating sensitivity 9 to membrane depolarization and bile acids that perturb membrane domains (Zhou et al.,
2013, Zhou et al., 2015).
1.2.2 Receptors
The lateral segregation of transmembrane receptors is likewise important for signal transduction. T-cell receptors, in response to antigen presentation, stimulate actin polymerization, cytoskeletal remodeling, and promote the organization of second messengers at the T-cell-antigen presenting cell complex (Cebecauer et al., 2010). Rac1 plays a role in this process, activating the actin-related protein (Arp) 2/3 complex, which promotes actin filament formation, through the WASP (Wiskott-Aldrich syndrome protein) family verprolin-homologous protein (WAVE) complex (Huse, 2009). Activated T-cell complexes are able to accrue hundreds of signaling molecules at the immunological synapse and regulate the strength of the signal (Bethani et al., 2010). More recently, these receptors were shown to localize in cholesterol-rich nanodomains, which further aggregate into larger, more optically visible domains after antibody patching (Dinic et al.,
2015).
Likewise, some G-protein coupled receptors (GPCRs) laterally segregate on the
PM (Bethani et al., 2010). In one experiment, upon agonist or antagonist binding, a human delta opioid receptor incorporates into sphingomyelin-rich, ‘raft’-like domains in model membranes (Alves et al., 2005). In another example, b2-adrenergic receptors form clusters in the nanometer range in cardiomyocytes. These nanoclusters are sensitive to actin filament disruption, but not cholesterol manipulation, indicating that perhaps the b2- adrenergic receptor is not a raft-associated GPCR (Scarselli et al., 2012). At least for
GPCRs, clustering appears to be cell-type and receptor-type dependent.
10 The epidermal growth factor (EGF) receptor (EGFR) forms lipid-dependent nanoclusters. In serum-starved conditions, EGFR prefers cholesterol-dependent, actin- independent nanoclusters (Ariotti et al., 2010). Upon EGF-stimulation, EGFR clustering and MAPK signaling increases, a process that is dependent on phospholipase D2
(PLD2)-produced PA (Ariotti et al., 2010). This work suggests EGFR signaling is dependent on its nanocluster formation and PA-association. Similarly, TRPV4 channels form complexes that are dependent on membrane integrity, as shear stress, the force produced by blood flow, disrupted the clustering of TRPV4 channels (Baratchi et al.,
2017). While much is yet to be discovered about the spatial organization of transmembrane receptors, it is a promising area of study.
1.2.3 Arf GTPases
ADP ribosylation factor (Arf) small GTPases are localized to the PM and regulate membrane trafficking, vesicle formation, and lipid transport (Donaldson & Jackson,
2011). Arf GTPases recruit effectors to the PM and promote the formation of effector signaling complexes on the PM (Cherfils, 2014). Interestingly, a recent study using coarse-grained molecular dynamics simulations strongly suggests that efficient Arf activation by its GEF, Brag2, is facilitated by local PIP2 enrichment and a distinct orientation on the PM when in complex with one another (Karandur et al., 2017). While it is not known whether Arf GTPases form nanoclusters like Ras, the lipid-dependency of Arf activation suggests lipid domain assembly and phospholipid interactions play a functional role in Arf signaling.
11 1.3 Rac1 spatial regulation
Rac1’s role in regulating cytoskeletal changes leads to polarized signaling in different cell types. For example, one study found that in response to exogenous chemoattractant, neutrophils and fibroblasts will accumulate PIP3 in a polarized manner, due to a positive feedback loop between Rho GTPases and PIP3 synthesis by PI3K
(Weiner et al., 2002). Incidentally, in another study from that same year, a Rac1 GEF coined phosphatidylinositol (3,4,5)-trisphosphate dependent Rac exchange factor 1 (P-
Rex1) was discovered to be directly and synergistically activated by PIP3 and Gbg in vivo and in vitro (Weiner, 2002, Welch et al., 2002). These studies were some of the first to directly associate Rac1 activity and spatial organization with PIP3. Several years later,
PIP3 was found to be essential for efficient Rac1 localization to liposome membrane,
Rac1-Rho guanine-nucleotide dissociation inhibitor (GDI) dissociation, and guanine nucleotide exchange (Ugolev et al., 2008). RacB, a Rac-family member found in
Dictyostelium discoideum, demonstrates similar polarization and activation during chemotaxis (Park et al., 2004).
Cell fractionation experiments and immunogold-labeling in fibroblasts reveal that both Rac1 and RhoA exhibit polarized localization at the cell PM in actin-enriched raft- domains called caveolae (Michaely et al., 1999). Moreover, other studies conclude that
Rac1 targets cholesterol-rich lipid raft domains (del Pozo et al., 2004). More recent reports using FRET suggest Rac1 localizes initially to cholesterol-rich domain boundaries but prefers non-raft, cholesterol-poor regions (Moissoglu et al., 2014). While these studies provide valuable insight about Rac1 spatial regulation, many of the conclusions are drawn from experiments using detergents and cholesterol-depleting agents like methyl-b-cyclodextrin. The mechanism of action for cyclodextrins is not
12 completely known and their use can affect phospholipids and disrupt the boundaries of
PM domains (Zidovetzki & Levitan, 2007). Therefore, care must be taken when drawing conclusions from their use.
Advancements in imaging techniques have enabled researchers to make more accurate assessments about Rac1 spatial regulation (Maxwell et al., 2018). One study shows PIP3 to be enriched in active Rac1 clusters at the leading edge (Remorino et al.,
2017). This is consistent with a previous finding that Rac1 PM interactions are PIP3- dependent (Das et al., 2015). They also observe distinct mobile and immobile behaviors of Rac1 molecules that are PIP3-dependent (Das et al., 2015). Interestingly, cryo-electron microscopy experiments reveal activation of the WAVE regulatory complex by Rac1 requires two Rac1 proteins acting simultaneously, supporting a model by which effectors are able to sense the local density of active Rac1 in the cell (Chen et al., 2017). These studies reveal a higher order of Rac1 PM organization. In this study, I demonstrate the existence of Rac1 nanoclusters in cell plasma membrane and characterize them thoroughly and quantitatively in the following chapters.
1.4 Rac1 biology
1.4.1 Activity and effectors
Rac1 is a Rho family member of the Ras superfamily of small GTPases, all of which broadly influence cell morphology, cell growth, and gene transcription. Rho family members include RhoA, RhoB, RhoC, RhoE, RhoG, Cdc42, Rac2, and Rac3 (Haataja et al., 1997, Ridley, 1996, Zhang et al., 1998). Rac1, Rac2, and Rac3 are 92% homologous, sharing 58% amino acid sequence identity with Rho and 30% with Ras
(Didsbury et al., 1989). While Rac1 is highly expressed across all tested human tissue
13 types, Rac2 and Rac3 are less highly expressed, with Rac2 primarily found in bone marrow (hematopoietic cells), lymph nodes, spleen, and appendix, and Rac3 in testis, brain, and fat tissue (Fagerberg et al., 2014). All GTPases are molecular switches that cycle between an active and inactive state to signal to second messengers (Bourne et al., 1991). This process is controlled and aided by GAPs, which promote intrinsic GTP hydrolysis and inactivation, and GEFs, which activate the protein by accelerating nucleotide release, promoting GDP à GTP exchange (Bos et al., 2007). Rac1’s intrinsic ability to hydrolyze GTP is similar to other Rho family GTPases and is 10x faster than that of Ras (Zhang et al., 1998).
Guanine-nucleotide exchange of G proteins is facilitated by a conserved “G- domain,” specifically two regions called switch I and switch II that interact with the guanine nucleotide and demonstrate enhanced flexibility, adopting “closed” and “open” conformations that promote or discourage GEF/effector interaction (Vetter, 2014) (Fig.
2). The mechanism of conformational change from the GTP-bound form is thought to be universal for most GTPases. Oxygen from the g-phosphate forms hydrogen bonds with threonine in switch I and glycine in switch II (Vetter & Wittinghofer, 2001). Release of the g-phosphate after hydrolysis leads the switch regions to adopt the GDP-bound conformation, which is more divergent among GTPases than the GTP-conformation
(Vetter & Wittinghofer, 2001). Crystallization of Rac1 in complex with certain domains of the GEF T cell lymphoma invasion and metastasis factor 1 (Tiam1) reveal switch I and II are stabilized in conformations that disrupt Mg2+ binding and association with GDP, illustrating how GEFs like Tiam1 encourage GTP-binding (Worthylake et al., 2000).
Rac1 is pleiotropic and interacts with a multitude of effector proteins that control many cellular processes. Though not an exhaustive list, the following proteins are some
14 of Rac1’s most prevalent effectors (Bustelo et al., 2007, Soriano-Castell et al., 2017):
Cytoskeletal –Diaph1, Diaph2, Arfip2, Baiap2, IQGAP1, IQGAP2, Was, Nck1, Nckap1,
Cyfip2, Wasf1, Wasf2, Paks1-7, Cdc42bpgA, Cdc42bpgB, Fml1, Fhod1, Cyfip1, FlnA,
TubA1 (microtubule component); Ubiquitination –Smurf2; Cell polarity –Pard6A,
Pard6B, Fml1; Immune response -IL1Rap1; Signal transduction –Cdc42SE1,
Cdc42SE2, Hspc121, PlcB2, Map3K11, PrkCA; Transcription –Fhod1, Stat3, Hspc121;
Reactive Oxygen Species (ROS) –Nos2A, CybA, Ncf1, Ncf2; Endocytosis –SynJ2,
ROCK1. The ability of Rac1 to interact with so many different proteins highlights the evolutionary need for signaling molecules like Rac1 to form complexes and nanoclusters.
1.4.2 Trafficking to the plasma membrane
Translocation to the PM is absolutely essential for Rac1 activation and signaling.
While all Rac proteins localize to the PM, Rac2 is predominantly found in the Golgi, endoplasmic reticulum, and nuclear envelope, and Rac3 primarily localizes to endomembranes (Ridley, 2006, Roberts et al., 2008). Like Ras, Rac1 undergoes several post-translational modifications on the membrane anchor of the HVR to target to the PM
(Figs. 2-3). First, geranylgeranyltransferase type 1 covalently adds a geranylgeranyl isoprenoid chain to the cysteine residue of the CAAX motif, which is subsequently cleaved by Rce1 (Roberts et al., 2008). The now prenylated C-terminal cysteine residue is methylated by Icmt (Roberts et al., 2008). Like K-Ras4A and K-Ras4B, Rac1 contains a PBD of six contiguous basic residues (Fig. 3). While biotin-BMCC experiments suggest
Rac1 is not palmitoylated on an upstream cysteine residue (Drisdel & Green, 2004,
Roberts et al., 2008), Rac1 incorporates 3[H]-palmitic acid and is mislocalized by mutation of the cysteine residue to serine, suggesting that it likely is palmitoylated
15 (Navarro-Lerida et al., 2012). This carboxy terminal membrane anchor sequence is evolutionarily conserved in Rac1, Rac2, Rac3 and across multiple species, such as
Xenopus laevis, Canis familiaris, and Mus musculus, and all contain the putatively palmitoylated cysteine residue upstream of a polyproline sequence and the PBD
(Roberts et al., 2008).
Another class of regulators involved in controlling Rac1 PM localization and activation are Rho guanine-nucleotide dissociation inhibitors (GDI). As their name suggests, these proteins inhibit guanine nucleotide dissociation from Rho-family members and prevent their activation (Keep et al., 1997). RhoGDIs interact with the prenyl group (largely geranylgeranyl, few Rho proteins are farnesylated, such as RhoE
(Foster et al., 1996)) through hydrophobic contacts. This interaction not only prevents
PM binding, it inhibits contact with effectors by interfering with the switch I and II regions
(DerMardirossian & Bokoch, 2005). Because PM localization is necessary for Rac1 function, the shuttling of Rac1 between PM and cytosol by RhoGDI is a major regulator in controlling Rac1 signaling. Interestingly, there is evidence to suggest that PIP3 helps facilitate RhoGDI dissociation from Rac1, promoting Rac1 activation (Ugolev et al.,
2008).
16
1 25-40 166 189
Switch regions / other Effector binding domain Hypervariable region Linker Anchor
Rac1 L C P P P V K K K K K K C L L L
CAAX motif
Figure 2. Important protein domains in Rac1
Rac1 contains several regions required for effector binding, which are primarily located in the N-terminus approximately between amino acids 25-40 (Westwick et al., 1997), although other effector binding sites can be found toward the C-terminus (Diekmann et al., 1995). The evolutionarily conserved G-domain of the protein contains the switch regions, which bind to GTP/GDP and define small GTPases. Rac1 spatial organization is primarily driven by the hypervariable region, which contains the membrane anchoring domain. This region contains the PBD, palmitoylated residues, and CAAX motif, which is prenylated by a geranylgeranyl chain.
17
O O S S S S
LCPPPVKKRKRKC RLKKISKEEKTPGCVKIKKC Rac1 OMe K-Ras.4A OMe
O O O S S S S S S KKKKKKSKTKC GCMSCKC GCMGLPC K-Ras.4B OMe OMe OMe H-Ras N-Ras
Figure 3. Lipidated membrane anchors of Ras and Rac1
Rac1 and Ras target the PM in a similar manner, through a minimal membrane anchor in the C-terminus of the protein. Ras membrane anchors comprise a C-terminal farnesyl- cysteine-methyl-ester, plus dual palmitoyl lipid chains in H-Ras, a single palmitoyl in N- Ras and polybasic domains in K-Ras4A and K-Ras4B. Rac1 is likewise prenylated, with geranylgeranyl at the C-terminal cysteine residue of the CAAX motif and contains both polybasic sequence and a single palmitoyl.
18 1.4.3 Actin dynamics
Rac1’s role in regulating actin dynamics was realized not long after its initial discovery. In Swiss 3T3 cells, expression of a constitutively GTP-bound Rac1 mutant,
Rac1.G12V (amino acid 12 glycine à valine) induces the formation of membrane ruffles and actin stress fibers and rapidly increases the amount of polymerized actin in the ruffles
(Ridley et al., 1992). Furthermore, expression of dominant-negative mutant Rac1.T17N prevents membrane ruffling in growth-factor stimulated cells, suggesting membrane ruffling is a direct response to actin filament formation and Rac1 is a key regulator (Ridley et al., 1992).
Actin cytoskeletal reorganization is the driving force for cell migration, a cell process vital to healthy tissue, but also important in the metastasis of tumors (Nobes &
Hall, 1999). Three major types of filaments are produced in a migrating cell: lamellipodia, filopodia, and stress fibers. Lamellipoda are wide, flattened protrusions that consist of cross-linked actin filaments (Nobes & Hall, 1999, Sit & Manser, 2011). Filopodia are slender protrusions consisting of many, thin actin filaments (Nobes & Hall, 1999, Sit &
Manser, 2011). Both of these structures are at the leading edge of cells. Stress fibers form further back from the leading edge. They comprise actin-myosin filaments that insert into focal adhesion complexes and are thought to provide forward momentum for the lagging edge as the cell migrates (Nobes & Hall, 1999, Sit & Manser, 2011, Small et al.,
1996). A detailed illustration of these structures and the signaling molecules involved can be found in Sit & Manser, 2011. Genetic deletion of Rac1 in mice prevents lamellipodia formation, cell adhesion, cell spreading, and stress fiber formation in fibroblasts, supporting earlier evidence that Rac1 is a direct regulator of actin formation (Guo et al.,
19 2006). These studies provide a clear connection between Rac1 activity and actin polymerization.
Major downstream Rac1 effectors that control actin include p21-activated kinase
1 (Pak1), which phosphorylates LIM kinase (LIMK). LIMK directly phosphorylates and inactivates cofilin, a protein that severs actin filaments thus stabilizing actin filament formation (Spiering & Hodgson, 2011). Activation of cofilin is likewise an important mechanism by which cells can both create and destabilize dynamic structures for movement. Another effector is WAVE, which binds directly to Rac1 and activates the
Arp2/3 complex, which serves as a nucleation site for new actin growth and branched actin networks (Spiering & Hodgson, 2011).
Changing actin dynamics has implications for other important cell events. For example, Rac1-dependent actin reorganization is necessary for glucose-transporter-4 recruitment to the PM in skeletal muscle (Chiu et al., 2011). Dendritic spines, crucial neuronal structures for learning and memory, are formed by dynamic linear and branched filamentous actin networks that help maintain their plasticity (Dent et al., 2011). Irregular actin polymerization contributes to disease, which is discussed more fully in Chapter 1
Section 5, Rac1 signaling in disease. Finally, actin-driven membrane ruffling precludes macropinosome formation, a specialized type of endocytosis, which is discussed in the following section.
1.4.4 Endocytosis
Endocytosis is an important regulatory process in which cells actively uptake extracellular molecules against their concentration gradient. Clathrin-mediated endocytosis is facilitated by various receptors and clathrin-coated vesicles/pits
20 concentrated in the PM. Activated Rac1 inhibits transferrin-receptor mediated endocytosis and regulates the formation of clathrin-coated vesicles (Lamaze et al., 1996,
Malcez et al., 2000), though this process does not occur through Pak1 activation
(Dharmawardhane et al., 2000). Non clathrin-mediated endocytosis includes: caveolae, small pits in the PM enriched in cholesterol and caveolin; macropinocytosis, the nonselective uptake of fluid from the extracellular matrix arising from invaginations of the
PM; and phagocytosis, the internalization of extracellular matter involving larger particles.
While Rac1 signaling is important for both caveolin accumulation and phagocytosis (Castellano et al., 2000, Nethe et al., 2010), this study focuses on macropinocytosis. Rac1’s connection to macropinocytosis was first explored during early experiments connecting Rac1 signaling to membrane ruffling, as macropinocytosis is precluded by highly ruffled regions of the cell (Ridley et al., 1992). Some of these ruffles fold back into the PM, encapsulating extracellular fluid and forming 0.2 µm diameter (or larger) sized vesicles called macropinosomes that either recycle back to the PM, or more frequently, fuse with lysosomes (Fujii et al., 2013). Overexpression of Rac1.G12V induces macropinocytosis in fibroblasts (Ahram et al., 2000), while Rac1.T17N expression inhibits macropinocytosis in dendritic cells (West et al., 2000). Additionally, overexpression of constitutively-active Pak1 stimulates macropinocytosis while overexpression of a Pak1 autoinhibitory domain blocks macropinocytosis, even in the presence of growth-factor and constitutively-active Rac1 (Dharmawardhane et al., 2000).
These results provide a clear pathway between Rac1 and macropinocytosis through
Pak1 activation. Interestingly, experiments using photoactivatable-Rac1, which can be
21 both activated and deactivated, suggests Rac1 deactivation is just as important as activation in completing macropinosome closure and endocytosis (Fujii et al., 2013).
1.4.5 Cell growth
Rac1 affects cell growth in several ways. Rac1 regulates gene expression through the activator protein-1 (AP-1) transcription factor, which can be activated by multiple signaling events (Bosco et al., 2009). Rac1 activates nuclear factor kappa-light-chain- enhancer of activated B cells (NF-kB), c-Jun N-terminal kinase (JNK), and p38 MAPK, which in turn activate AP-1. This activation occurs through Pak activation or independently of Pak (Aznar & Lacal, 2001, Westwick et al., 1997). The Rac1 effector
POSH and Rac1-activated kinases MEKK4 and MLK3 have been implicated in JNK and p38 activation (Aznar & Lacal, 2001, Gerwins et al., 1997, Tibbles et al., 1996).
Additionally, Rac1 modulates the binding of nuclear factor of activated T cells (NFAT) to
AP-1 by NFAT dephosphorylation and nuclear transport, which is required for the proper expansion and growth of antigen-specific lymphocytes (Turner et al., 1998). Likewise, the Rac1 GEF Vav upregulates AP-1 to induce cell proliferation in Jurkat cells
(Kaminuma et al., 2002). AP-1 upregulates the expression of cyclin D1 and c-myc, which induce progression through G1/S transition in the cell cycle (Chiariello et al., 2001, Olson et al., 1995).
Overexpression of dominant-negative Rac1.T17N in rat fibroblasts abrogates cell growth and arrests cells in G2/Mitosis transition of the cell cycle, establishing that Rac1 is necessary for cell proliferation and demonstrating the importance of Rac1 signaling in multiple stages of cell division (Moore et al., 1997). Rac1 also induces cell transformation in NIH 3T3 cells, and downstream of K-Ras in fibroblasts and K-Ras-induced lung cancer
22 in mice (Kissil et al., 2007, Westwick et al., 1997). Rac1 is critical for cell growth in cancer cells, which is mediated in large part by NF-kB. In non-small cell lung carcinoma, siRNA- silencing and direct inhibition of Rac1 results in decreased cell proliferation and migration
(Gastonguay et al., 2012). Those cells exhibit dampened G1 progression in the cell cycle, decreased NF-kB transcription, and cell migration (Gastonguay et al., 2012).
1.4.6 ROS
Rac1-stimulated production of reactive oxygen species (ROS) through nicotinamide adenine dinucleotide phosphate (NADPH) is another mechanism by which
Rac1 signaling contributes to transcription factor activation, immune response, apoptosis, cell transformation, and cell proliferation (Bosco et al., 2009). Rac1-induced activation of NADPH oxidase produces toxic products essential for degrading bacteria in leukocytes (Bokoch, 1995). Additionally, Rac1 contains a novel effector region (amino acids 124-135) essential for superoxide production and mitogenesis in fibroblasts.
Removal of this effector region has no effect on membrane ruffling or JNK activation, implicating superoxide production as a potential independent pathway for Rac1- mediated cell growth (Joneson & Bar-Sagi, 1998).
NADPH oxidase activation requires the formation of a macromolecular complex at the PM, comprising integral membrane proteins gp91phox (also called Nox2) and p21phox and cytosolic proteins p67phox and p47phox (phox = phagocytic oxidase)
(Diekmann et al., 1994). Rac1.GTP binds directly to p67phox, the suspected effector in the complex (Diekmann et al., 1994, Nisimoto et al., 1997). However, Rac1.GTP also binds directly to Nox1, a homolog of gp91phox activated by Rac1 that produces ROS
(Cheng et al., 2006). Moreover, immunoprecipitation experiments show that Rac1 forms
23 complexes with Nox1 and the regulatory subunits Noxo1 and Noxa1, suggesting the
NADPH-Rac1 complex is more complicated than previously thought (Cheng et al., 2006).
A more recent study has found that Rac1-ROS signaling is spatially and temporally regulated in response to shear stress (Liu et al., 2013). Researchers discovered the existence of a novel, sensory complex that produces ROS in response to mechanical force on the PM. PECAM-1, an endothelial cell adhesion molecule, acts as a mechano-sensor and leads to Vav2 activation, a Rac1 GEF (Liu et al., 2013). Vascular endothelial-cadherin (VE-cadherin), p67phox, Tiam1, and Par3 complex with Rac1.GTP and polarize local Rac1 signaling to stimulate ROS (Liu et al., 2013). Depletion of VE- cadherin, Tiam1, or Par3 leads to reduced ROS production. These studies reinforce the hypothesis that Rac1 clustering is necessary and functionally relevant for directed and localized signaling.
1.5 Rac1 signaling in disease
Aberrant Rac1 signaling is involved in many different pathological conditions, including cancer, neurodegeneration, cardiovascular disease, inflammation, the immune response, and some kidney disorders (Marei & Malliri, 2017). Here I have detailed some of the most important mechanisms involved in Rac1-driven diseases. Given the complexity of Rac1 signaling and its association with both protective and malignant effects, successful therapeutics must have the capability to selectively inhibit Rac1 in malignant pathways, while simultaneously sparing its protective functions in other pathways. Thus, developing novel Rac1 inhibitors requires a comprehensive understanding of Rac1 functions within diverse cellular contexts. This study aims to further our present understanding.
24 1.5.1 Cancer
Rho family members are prominent regulators of angiogenesis, transcription, and cell growth; through these processes, Rac1 drives cancer initiation and progression
(Benitah et al., 2004, Mack et al., 2011, Merajver & Usmani, 2005). Furthermore, Rac1 is a key metastasis driver in tumor tissue through the activation of cell motility and invasion (Geiger & Peeper, 2009, Parri & Chiarugi, 2010, Yilmaz & Christofori, 2010).
Specifically, these processes occur when Rac1 stimulates the formation of actin-rich protrusions like lamellipodia, focal adhesions, and cell contraction, all of which contribute to cell migration (Miki et al., 2000, Nobes & Hall, 1995). Two major events in metastatic progression, mesenchymal-epithelial transition and epithelial-mesenchymal transition, are also regulated by Rac1 (Lv et al., 2013, Nakaya et al., 2004).
Rac1 protein levels are often overexpressed or overactivated in several types of cancer. For example, Rac1 is overexpressed in stomach, breast, and testicular cancer
(Kamai et al., 2004, Pan et al., 2004, Schnelzer et al., 2000). A splice variant of Rac1,
Rac1b, is likewise overexpressed in tumors, including colorectal, breast, and lung cancer
(Jordan et al., 1999, Schnelzer et al., 2000, Zhou et al., 2013). While less is known about
Rac1b compared to Rac1, this splice variant exhibits enhanced GTP/GDP exchange and is predominantly GTP-bound, allowing increased interaction with effectors (Schnelzer et al., 2000). In a transgenic mouse model of sebaceous adenoma, epidermal-specific activation of Rac1 leads to poorly-differentiated adenomas that resemble malignant sebaceous tumors, establishing a correlation between Rac1 activity and malignant skin tumors (Frances et al., 2015).
There are several oncogenic, activating mutations in the RAC1 gene. Rac1.P29S mutation substitutes serine for proline in the switch I region, and is the third most
25 prevalent mutation in sun-exposed melanomas (9.2%) identified by exome sequencing after BRAF and NRAS (Krauthammer et al., 2012). Rac1.P29S is also identified in head and neck squamous cell carcinoma and the breast cancer cell line MDA-MB-157
(Kawazu et al., 2013, Stransky et al., 2011). Rac1.P29S exhibits enhanced effector binding due to a favorable conformational change and rapid nucleotide exchange (Davis et al., 2012). Rac1.N92I (asparagine à isoleucine) and Rac1.C157Y (cysteine à tyrosine) are another pair of activating mutations identified in a fibrosarcoma cell line and a lung adenocarcinoma specimen from the COSMIC database of cancer genome mutations, respectively (Kawazu et al., 2013). However, comparatively these mutations are not prevalent.
Adding even greater complexity, there is evidence that Rac1 offers anti-cancer benefits, largely through mechanisms that promote cell adhesion and hinder cell invasion
(Marei & Malliri, 2017). For example, overexpression of Rac1.G12V or Tiam1 in Ras- transformed epithelial cells strengthens cadherin-mediated cell adhesion and actin polymerization at cell-cell contacts, affecting transformation and abrogating invasiveness
(Hordijk et al., 1997). In another example, RNAi silencing of Rac-specific D4-GDI in an invasive human breast cancer cell line paradoxically hinders tumor growth and lung metastasis in mice by stimulating anoikis, although the mechanism of how this occurs is not completely understood (Zhang et al., 2009).
1.5.2 Cardiovascular disease
Rac1 is involved in the development of cardiovascular disease. Transgenic mice expressing constitutively active Rac1 exhibit cardiomyopathy (Sussman et al., 2000), and cardiomyocyte-specific overexpression of Rac1 in mice leads to cardiac
26 hypertrophy, myocardial infarctions, and increased susceptibility to ischemic injury
(Talkuder et al., 2013). Rac1 is implicated in atherosclerosis through activation of
NADPH oxidases. ROS production contributes to atherosclerotic plaque formation in vivo
(Judkins et al., 2010) and suppression of ROS production in macrophages and vessel cells attenuates atherosclerosis. Increased endothelial permeability is one mechanism by which atherosclerotic plaque production is encouraged; this effect is likely downstream of Pak, as inhibition of Pak in vivo reduces endothelial permeability in regions with high atherosclerotic plaques (Orr et al., 2007).
1.5.3 Neurodegenerative disease
Rac1 signaling is essential for neuronal development and the maintenance of neuro-plasticity (Stankiewicz & Linseman, 2014). While Rac1 does exhibit some neuroprotective effects against Parkinson’s and neurodegeneration, it is implicated in a variety of neurological disorders (Marei & Malliri, 2017). Overproduction of ROS downstream of Rac1 contributes to motor neuron degeneration in an ALS (amyotrophic lateral sclerosis, commonly known as Lou Gherig’s disease) mouse model (Wu et al.,
2006). Rac1 is also implicated in Huntington’s disease through interactions with mutant huntingtin (Tourette et al., 2014) and in Alzheimer’s disease, through transcriptional regulation of an amyloid precursor protein (Wang et al., 2009). This protein generates amyloid-b, the polypeptide that accumulates in the brain tissue of Alzheimer’s patients
(Wang et al., 2009). Increased Rac1 activity in a mouse model of Fragile X syndrome
(FXS), a disorder characterized by intellectual disabilities, inhibits cofilin, an important regulator of dendritic spine structure. These mice also show dendritic spine abnormalities
27 in the somatosensory cortex, a hallmark of FXS, as a result of the increased Rac1 activity
(Pyronneau et al., 2017).
1.5.4 Inflammation
Rac1 mediates multiple inflammatory responses. For instance, Rac1 is overactivated in models of kidney disease and exacerbates ROS production and inflammatory cytokines (Sedeek et al., 2013). Rac1 deletion in macrophages and myeloid-specific Rac1 knockout in vivo protected against renal inflammation and injury by reducing inflammatory cytokines (Nagase et al., 2016). Additionally, a Rac1 inhibitor reduced arthritic symptoms in a mouse model of rheumatoid arthritis, implicating Rac1 in arthritis inflammation as well (Abreu et al., 2010). Finally, increased Rac1 expression is associated with inflammatory bowel disease and in fact, some classes of drugs used to treat inflammatory bowel disease target Rac1 signaling (Al Hadithy et al., 2005, Muise et al., 2011).
1.6 Drugs targeting Rac signaling pathways
Despite the difficulty in inhibiting Rac1 signaling, several compounds presently exist that target different mechanisms of Rac1 regulation. A small molecule screen identified NSC23766 as the first selective Rac1 inhibitor, disrupting Rac1-Tiam1 association without affecting Vav (another Rac1 GEF) function, RhoA or Cdc42 (Gao et al., 2004). This compound exhibits anti-tumorigenic effects and prevents osteoarthritis development in vivo, though ultimately NSC23766 is not efficacious enough for clinical application (Bid et al., 2013, Zhu et al., 2015). Optimization of NSC23766 led to the development of a more potent Rac1 inhibitor called EHop-016 (IC50 of 1.1 µM). This
28 compound blocks Rac1-GEF binding to Vav2 as opposed to Tiam1, and suppresses cell migration in a metastatic breast cancer cell line (Montalvo-Ortiz et al., 2012).
Other compounds interrupt nucleotide binding, such as EHT 1864, which blocks
GEF-mediated nucleotide exchange and effector binding with a fairly high potency (IC50
5 µM) (Shutes et al., 2007). Another group identified two compounds from a high- throughput screen that target the Rac1 nucleotide binding site and reduce cell proliferation and migration in pancreatic cancer cell lines (Arnst et al., 2017). While non- selective for Rac1, there are also some compounds that target Rac1 PM localization, such as geranylgeranyltransferase inhibitors, palmitoyl acyltransferase inhibitors, and methylation blockers (Marei & Malliri, 2017). There are also efforts underway to identify inhibitors of downstream targets, such as Pak or LIM kinase (Bid et al., 2013). For example, Phox-I1 is a compound that binds to p67phox and disrupts Rac1 interaction, leading to decreased superoxide production and inflammation (Bosco et al., 2012). 8- hydroxy-2-deoxyguanosine (8-OHdG) is another molecule that blocks superoxide production through Rac1 inhibition and provides atherosclerosis protection in vivo (Huh et al., 2012).
1.7 Summary and experimental approach
Rac1 is an essential signaling molecule. One conspicuous example of its importance is the result of germline deletion of RAC1, which causes embryonic death at an early stage (Duquette & Lamarche-Vane, 2014). It is also unsurprising that mutations or aberrations in Rac1 have disastrous consequences in cells and in the body. However, a greater understanding is required for the development of effective clinical therapies specifically targeting Rac1. The objective of this study is to investigate Rac1 regulatory
29 mechanisms on the PM, which is the connecting point for Rac1’s diverse signaling pathways and their attendant pathogenic manifestations. Particularly, this study aims to expand our comprehension of how Rac1 coordinates spatially and temporally regulates signal output in advance of other mechanisms.
Specifically, the first aim of my hypothesis is that Rac1 forms functionally-relevant clusters on a nanoscale level with Ras. Such clusters are approximately 20 nm sized complexes that must be visualized using high-resolution microscopy, and could form the basis for the locally-regulated Rac1 signaling conceptualized by several studies referenced in this chapter. The second aim of my hypothesis is that these clusters incorporate signaling phospholipids that assist in recruiting second messengers. Finally, the third aim of my hypothesis is that the mechanism(s) driving nanocluster formation is key interactions between PM lipids and the membrane anchor of the Rac1 protein.
This study employs EM combined with spatial mapping analysis (described in
Chapter 2, Materials and Methods). These methods are advantageous because the resolution of EM allows for the detection of nanoclusters between 10 and 100 nm, while
Ripley’s K function quantifies the point patterns of gold-labeled protein and lipid probes on the PM.
30 Chapter 2 Materials and Methods
2.1 Plasmids
Rac1 membrane anchor mutants were created from a green fluorescent protein conjugated (GFP) Rac1.G12V parent vector using the QuickChange XL site directed mutagenesis kit from Agilent and polymerase chain reaction (PCR) (Ridley et al., 1992).
Sergio Grinstein from The Hospital for Sick Children in Toronto, Canada gifted GFP-
LactC2. Guangwei Du from the University of Texas Health Science Center in Houston,
TX provided GFP-PASS (Zhang et al., 2014), GFP-pleckstrin homology domain (PH)- phospholipase C d (PLCd), and GFP-PH-Akt. Tamas Balla from the National Institute of
Child Health and Human Development in Bethesda, MD gifted GFP-FAPP1.
2.2 Cell culture and transfection
Wild-type baby hamster kidney (BHK) cells were grown in Dulbecco’s modified Eagle’s medium (DMEM) containing 10% bovine calf serum (BCS) or simply DMEM in serum- free conditions at 37° C and 5% CO2. Cells were transfected 24-hours after plating using
Lipofectamine (Invitrogen) reagent in Optimem media for 4-6 hours and replaced with regular DMEM after Lipofectamine incubation. Cells were fixed 24-hours post- transfection as applicable for the experiment. PIP3 levels were manipulated by treating cells for 1 hour with 15 μM PI3K inhibitor LY294002 (Sigma-Aldrich). PIP3 levels were rescued by co-treating cells with 15 μM LY294002 and 10 μM exogenous 18:0-20:4 PIP3
(Avanti Polar Lipids) or elevated with just supplemental PIP3. PA levels were manipulated
31 using 0.5 μM phospholipase D (PLD)-2 specific inhibitor ML298 (Sigma-Aldrich). PA levels were rescued by co-treating cells with ML298 and 10 μM egg PA (Avanti Polar
Lipids), or elevated with just supplemental PA. When the experiment called for PIP2 manipulation, PIP2 levels were elevated by co-treating cells with LY294002 and 10 μM brain L-a-PIP2 (Avanti Polar Lipids). Non water-soluble compounds were dissolved in dimethyl sulfoxide (DMSO), which was also used to treat control cells.
2.3 Western blotting
BHK cells expressing GFP-tagged Rac1 mutant constructs were grown to confluence and harvested. Cells were washed twice with ice cold phosphate buffered saline (PBS) followed by 5-minute incubation in 100 µL lysis buffer containing: 1% NP40, 50 mM Tris
(pH 7.5), 25 mM NaF, 75 mM NaCl, 5 mM MgCl2, 5 mM EGTA, 0.5 µg/mL aprotinin, 0.1 mM Na3VO4, 3 µg/mL leupeptin, and 1 mM DTT. Cell lysates were centrifuged at 1000
RPM at 4°C for 5 minutes. The concentration of total protein was measured and taken from the supernatant. 20 µg of total protein was mixed with sample buffer (10 mM Tris
(pH6.8), 20 mg/mL SDS, 10% glycerol, 0.5 mg/mL bromophenol blue dye and 15.4 mg/mL DTT), heated at 95°C for 5 minutes, and resolved by electrophoresis in 12% SDS- polyacrylamide gel. Proteins were electro-transferred to polyvinylidene difluoride (PVDF) membrane using pre-chilled transfer buffer (3.6 mg/mL glycine, 7.25 mg/mL Tris base,
0.46 mg/mL SDS, 20% methanol). PVDF membranes were rinsed 3x with TBST (10 mM
Tris (pH 7.5), 150 mM NaCl, 0.1% Tween 20) and blocked for 1 hour in 5% skim milk in
TBST. Primary antibody labeling was performed overnight by inoculating the PVDF membrane in blocking solution containing primary antibody: anti-GFP at 1:3000 dilution and anti-actin at 1:1000. PVDF membranes were washed 3x in TBST and incubated for
32 1 hour with secondary antibody in blocking solution (anti-rabbit at 1:2000, anti-mouse at
1:8000). PVDF membranes were washed 3x with TBST again and incubated with enhanced chemiluminescence (ECL) solution: 2 mL SuperSignal West Pico stable peroxide solution and 2 mL SuperSignal West Dura stable peroxide solution.
2.4 Electron Microscopy and spatial mapping analysis
2.4.1 Univariate K-function
This technique quantifies clustering of a single population on the inner leaflet of the cell plasma membrane (Prior et al., 2003) and has been described previously (Hancock &
Prior, 2005, Zhou et al., 2014, Zhou et al., 2017). BHK cells were seeded on glass coverslips at ~75% confluency on day one and transiently transfected with GFP-tagged protein or peptide on day two. On day three, after any experimental treatment, intact apical PM sheets were attached to EM grids, washed, fixed (4% paraformaldehyde/0.1% glutaraldehyde), labeled with 4.5 nm gold particles conjugated to anti-GFP antibody, and embedded in uranyl acetate. Pictures of the PM sheets were obtained using a JEOL
JEM-1400 transmission EM at x100,000 magnification (Fig. 4). From these images, 1
μm2 regions were selected and each gold particle on the region was assigned x and y coordinates using ImageJ. The gold particle distribution and extent of nanoclustering was calculated using Ripley’s K function, as shown in the following equations:
� � = A� � 1( � − � ≤ �) [1]
� � − � = − � [2]
33 whereby K(r) is the univariate K function for a pattern of n points in area A; r is the radius at which K(r) is calculated (length scale = 1 < r < 240 nm at 1-nm increments); ||xi – xj|| is Euclidean distance; 1((xi –xj|| ≤ r) is the indicator function with a value of 1 if ||xi – xj|| ≤
-1 r and a value of 0 otherwise; wij is the proportion of the circumference of the circle with center xi and radius ||xi – xj|| contained within A. L(r)–r is a linear transformation of K(r) and standardized on the 99% confidence interval (C.I.) estimated from Monte Carlo simulations. Under the null hypothesis of complete spatial randomness, L(r)–r has an expected value of 0 for all values of r. Positive deviations of the function from the confidence interval indicate significant clustering. For each condition in this study, 10-30
PM sheets were collected and analyzed. Bootstrap tests were used to determine statistical differences between replicates and significance was evaluated against 1,000
Bootstrap samples. A useful summary statistic for L(r)–r values is Lmax, the peak value from the K function curve. All statistical differences between Lmax values were determined by Bootstrap evaluation of their corresponding weighted mean L(r)–r values.
2.4.2 Bivariate K-function
This technique is prepared similarly to univariate K function analysis, but uses BHK cells co-expressing two different proteins or peptides tagged with GFP or red fluorescent protein (RFP) (Prior et al., 2003, Zhou et al., 2014, Zhou et al., 2017). After fixation, PM sheets were labeled with 2 nm gold conjugated to anti-RFP antibody and 6 nm gold conjugated to anti-GFP antibody and embedded in uranyl acetate (Fig. 5). The gold particle distributions were analyzed using a bivariate K function that calculates co- clustering or co-localization of the two different gold particle populations on a PM sheet:
34 � � = (� + � ) [� � � + � � � ] [3]
� � = � 1( � − � ≤ �) [4]
� � = � 1( � − � ≤ �) [5]
� � − � = − � [6]
whereby Kbiv(r) comprises two bivariate K functions. Kbs(r) characterizes large (6 nm) gold particle distribution with respect to each small gold particle and Ksb(r) characterizes small (2 nm) gold particle distribution with respect to each large gold particle. Area A contains nb, number of large gold particles, and ns, number of small gold particles.
Remaining notations are the same as equations [1-2]. Lbiv(r)–r is a linear transformation of Kbiv(r) and normalized against the 95% C.I. from Monte Carlo simulations. Under the null hypothesis of no spatial interaction between the two gold populations, Lbiv(r)–r has an expected value of 0 for all values of r. Positive deviations of Lbiv(r)–r from the C.I. indicate significant co-clustering between the two gold populations. A useful summary statistic to quantify the extent of co-clustering is the area under the Lbiv(r)–r curve over a fixed range (10 < r < 110 nm) which is termed Lbiv(r)–r integrated, or LBI, defined as:
��� = ��� � � − � . �� [7]
For each condition, 10-30 PM sheets were imaged, analyzed, and averaged. LBI values
> 100 (95% C.I.) indicate significant co-clustering. Statistical significance between bivariate point patterns is determined using Bootstrap tests as in 2.4.1 Univariate K- function. All statistical differences between LBI values were determined by Bootstrap evaluation of their corresponding weighted mean Lbiv(r)–r values. 35 A Supplemental Figure 1
D 4 A Supplemental Figure 1 3 D 4
Rac1.WT Rac1.G12V Rac1.T17N ax 2 3 m B C L 6 Rac1.G12V ax 1 Rac1.WT Rac1.G12V Rac1.T17N 2
m 99% C.I.
B C 4L 6
r 0 Rac1.G12V
- 1
99% C.I. V V V V 2 12 (r) 4 .G L
r 0
-
Rac1.G12 0 V V V V K-Ras H-Ras.G12N-Ras.G12 2 12
(r) .G
L 100 nm -2 0 Rac1.G12 Figure 4. Representative 2D plasma membrane sheet from BHK cellsK-Ras expressing0 H-Ras.G12 80N-Ras.G12160 240 GFP-tagged protein of interest r (nm) 100 nm E F G PM sheets from BHK-2 cells6 expressing GFP-Rac1.G12V were isolated, fixed, and 3 immunolabeled with 4.5 0nm anti-GFP8 0gold antibody160 and imaged240 by EM. Sample images G12V-G12V were processed in ImageJ and cropped to 1 µm2 regions. r (nm) G12V-T17N E F G r 95% C.I. 3 2 6 4 - G12V-G12V ax (r)
G12V-T17N m v
i L b
r 95% C.I. L 2 4 2 - 1 ax
(r) m v i L b 100 nm
2 0 L 1 0 200 400 600 0 80 160 240 2 r (nm) Gold partic100les /nmμm 0 0 200 400Figure60 5. Representative0 2D plasma membrane sheet from BHK cells expressing0 80 160 240 r (nm) Gold particGFPles-/ μandm RFP2 -tagged constructs PM sheets from BHK cells expressing GFP-Rac1.G12V and RFP-Rac1.T17N are isolated, fixed, and immunolabeled with 6 nm anti-GFP and 2 nm anti-RFP gold antibody and imaged by EM. Sample images were processed in ImageJ and cropped to 1 µm2 regions.
36 2.5 Fluorescence lifetime imaging microscopy (FLIM) combined with fluorescence resonance energy transfer
(FRET)
FRET occurs when an excited, donor fluorophore transfers energy to an acceptor fluorophore within range of a given distance, approximately 10 nm or less. FLIM measures the lifetime of the fluorophore signal as opposed to intensity, which offers the advantage of independence from excitation source and fluorophore concentration. When combined with FRET, this method provides information about the association between two molecular probes. A lower lifetime of the signal represents an increase in FRET, or association, between two fluorophores. FLIM-FRET assays were performed as described previously (Zhou et al., 2013, Zhou et al., 2017). BHK cells were grown to 70% confluency on glass coverslips and transiently transfected with both GFP-tagged and
RFP-tagged proteins. Cells were washed with 1X PBS and fixed in 4% paraformaldehyde. Lifetime measurements were measured with a FLIM unit from
Lambert Instruments (Roden, the Netherlands) mounted on a wide-field Nikon Eclipse microscope. GFP was excited using a sinusoidally stimulated 3-watt 497-nm LED at 40
MHz under epifluorescence coupled with a 60X Plan-Apo/1.4-NA oil immersion lens.
FRET efficiency, defined as [1 –(acceptor lifetime ÷ donor lifetime)], was calculated from the FRET lifetime values, which were averaged from 3 independent experiments and at least 120 different cells. FRET efficiency denotes the percentage of excitation photons that contribute to FRET. A higher efficiency value indicates an increase in FRET.
37 2.6 Macropinocytosis
The quantification of macropinocytosis was performed as described previously
(Commisso et al., 2014). In brief, BHK cells expressing GFP-tagged Rac1 mutant constructs or GFP-pC1 (empty vector) were serum-starved overnight, fixed, and treated with DMEM + 1 mg/mL 70 kDa tetramethylrhodamine (TMR)-conjugated dextran.
Approximately 10-20 fields per sample were taken on a Zeiss Axiovert inverted 200M wide-field microscope equipped with a 63X 1.4 NA phase objective lens and CCD camera. Using ImageJ, the total area of macropinosomes was divided by total cell area
(x100), giving the macropinocytic index as an indictaor of dextran uptake and macropinocytosis. Data averaged from 3 separate experiments and approximately 250 cells.
2.7 Cell spreading
BHK cells expressing Rac1 mutant constructs from the macropinocytosis assay were also utilized to calculate cell spreading. Cell area per field was measured using ImageJ and divided by the number of cells as an indicator of cell spreading. The results were averaged across all sample images (Wurtzel et al., 2012).
2.8 Statistical analysis and graphics
Data are expressed as means ± standard error (SEM) and statistical significance of differences were determined using Student’s unpaired t tests, one-way ANOVA followed by Tukey HSD (honest significant difference) post hoc test, or Bootstrap tests.
Differences were considered significant when p-values were below 0.05. Illustrations
38 were made in Microsoft PowerPoint. Graphs were prepared using Graphpad Prism,
Microsoft PowerPoint, Microsoft Excel, and Adobe Illustrator.
2.9 Limitations
A major limitation of the study is the use of overexpression. Overexpression of GFP- and
RFP-tagged constructs is required to visualize the constructs by the microscopy methods used herein, though the disadvantage is that expression levels of the proteins and probes are no longer physiological. At best, the constructs are a reflection of endogenous protein behavior but not definitively so. Furthermore, the lipid binding probes used to identify specific lipids bind to head groups, and cannot distinguish between different lipid species with the same head group. For example, the degree of saturation of hydrophobic chains can differ between two of the same lipid. The advantage of overexpression is that it ensures a robust signal, and care was taken with each experiment to include both positive and negative controls. Additionally, the monomeric form of GFP was used in all cases, preventing noncovalent GFP dimerization.
39 Chapter 3 Rac1 nanoclustering
3.1 Introduction
Similar to other Rho-family members, like RhoA and Cdc42, Rac1 is spatially and precisely coordinated on the PM to regulate cytoskeletal events (Kraynov et al., 2000,
Machacek et al., 2009, Ridley et al., 2003). Rac1 signaling is compartmentalized to the
PM, where it interacts with effector proteins, such as WAVE, and GEFs and GAPs, such as Tiam1, to coordinate actin dynamics and facilitate membrane ruffling (Chen et al.,
2017, Wertheimer et al., 2012). Rac1 signaling is further regulated by RhoGDI, which solubilizes cytosolic Rac1.GDP from the PM to abrogate the coordinated efforts of PM- mediated signal output (Bustelo et al., 2011).
Rac1 may be spatially organized in oligomers on the PM to coordinate signaling.
Recent findings show that Rac1 is distributed in a gradient from the leading edge of cells with phospholipids (Das et al., 2015). These gradients suggest a heterogeneous distribution of Rac1 on the PM, and provide greater insight to earlier studies showing
Rac1 and RhoA distributions are polarized (Michaely et al., 1999). Another study validates this finding, and further establishes that Rac1 forms oligomers of approximately
200 nm on the PM that precede WAVE and PIP3 PM association (Remorino et al., 2017).
Details of Rac1 oligomer structures are still not known. I hypothesize that Rac1 may form smaller nanoclusters, like Ras, which are approximately 16 nm in diameter rather than 200 nm, due to the similarities in the membrane anchor and PM binding between Rac1 and Ras isoforms. The minimal Ras membrane anchors comprise a C-
40 terminal farnesyl-cysteine-methyl-ester, plus dual palmitoyl lipid chains in H-Ras, a single palmitoyl in N-Ras and a polybasic domain in K-Ras. Rac1 is also prenylated, with geranylgeranyl at the C-terminal cysteine residue of the CAAX motif and contains both polybasic sequence and a single palmitoyl (Fig. 3).
In this chapter, I systematically examine the propensity for Rac1 to form nanoclusters using EM, and find that Rac1 forms nanoclusters that are approximately 20 nm in diameter and spatially segregate on guanine nucleotide binding. These findings suggest a higher level of regulation of Rac1 on the PM than previously realized.
3.2 Results
3.2.1 Rac1 localizes to the PM and forms nanoclusters
It is well established that Rac1 localizes to the PM for its function. To verify that the Rac1 constructs used in this study properly target to the PM in BHK cells, I ectopically expressed GFP-tagged Rac1.WT (wild-type), Rac1.G12V (constitutively active, GTP- bound), and Rac1.T17N (dominant-negative, GDP-bound) in BHK cells and imaged the cells using confocal microscopy. I found that regardless of GTP-binding, all the constructs localized to the PM and were thus suitable for EM imaging and analysis (Fig.
6).
41
Supplemental Figure 1 A
Rac1.WT Rac1.G12V Rac1.T17N B C Figure 6. Rac1 localizes6 to the plasma membrane in BHK cells D 4 Rac1.G12V BHK cells were plated onto glass coverslips and99% grown C.I .to 70% confluency. Cells were then transfected with GFP-tagged Rac1 constructs (WT, G12V, T17N) and 24-hr post transfection, fixed with4 4% paraformaldehyde and quenched with 50 mM3 NH4Cl. Cells
were imaged in a Nikonr A1R confocal microscope using a 60X oil immersion lens.
-
2 ax
(r) 2
m
L L
0 1
100 nm
-2 0 0 80 160 240 V V V V r (nm) 12
.G
Rac1.G12 K-Ras H-Ras.G12N-Ras.G12
E F G 3 6 G12V-G12V 42 G12V-T17N
r 95% C.I. 2 4 - ax (r) m v i L b 2 L 1
100 nm 0 0 200 400 600 0 80 160 240 r (nm) Gold particles/μm2 To examine Rac1 spatial organization on the PM, I used EM combined with spatial mapping analysis. Intact 2D PM sheets of BHK cells expressing GFP-Rac1.G12V
(sample image in Fig. 4) were attached to EM grids and immunolabeled with 4.5 nm anti-
GFP gold antibody. The spatial distribution of the gold particles was quantified using univariate K functions and plotted as L(r)–r (Fig. 7A). L(r)–r values above the 99% confidence interval (C.I.) indicate statistically significant clustering at length scale r. We used the peak L(r)–r value, called Lmax, to summarize the results. The Lmax value for
Rac1.G12V was similar to Lmax observed for Ras (Fig. 7B). Additionally, I measured the nanoclustering of GFP-tagged Rac1.WT, Rac1.G12V, and Rac1.T17N. The extent of gold labeling of each Rac1 protein was similar, indicating equal PM localization, concordant with the confocal images (Fig. 8A). Constitutively-GTP bound Rac1.G12V, constitutively-GDP bound Rac1.T17N, and Rac1.WT all showed significant Lmax values above the confidence interval, indicating that Rac1.GDP and Rac1.GTP both form nanoclusters on the PM (Fig. 8B). Rac1.G12V Lmax values from several different univariate EM experiments were plotted against corresponding gold particle number to demonstrate the quantitative independence of clustering and PM localization for Rac1 at the standard level of expression for all EM experiments (Fig. 9).
Ras proteins have a monomer to cluster ratio of approximately 20-30% (Plowman et al., 2005). To determine an estimate of the amount Rac1 proteins in the nanoclusters,
I calculated the average percent of gold particles that exist as monomers (1 particle),
‘dimers’ (2 particles), or oligomers (3+ particles) for every PM sheet. Rac1.WT and
Rac1.T17N exhibit a similar monomer to cluster ratio as K-Ras4B, while Rac1.G12V has a slightly higher population of oligomers (Fig. 10). It should be noted that the presence of gold particle ‘dimers’ in this instance is not indicative of a structural dimer interaction,
43 given that some non-gold-labeled GFP or endogenous Rac1 could interact in A Supplemental Figure 1 clusters/dimers with the gold-labeled Rac1 and would not be detected by EM. D 4
3 Supplemental Figure 1 A Rac1.WT Rac1.G12V Rac1.T17N ax 2 m
B AC B L A 6 BD Rac1.G12V 41 99% C.I. 4 3
r 0
-
V V V V 2 ax 12 Rac1.WT Rac1.G12V(r) Rac1.T17N 2 m .G L B C L 6 Rac1.G12 0 Rac1.G12V K-Ras1 H-Ras.G12N-Ras.G12 99% C.I. 100 nm 4 -2
r 0
- 0 80 160 240 V V V V 2 r (nm) 12 (r) .G E L F G 6 3 GRac1.G1212V-G12V 0 K-Ras H-Ras.G12N-Ras.G12 G12V-T17N 100 nm
r 95% C.I. 2 4 -2 - 0 80 160 240 ax (r)
m r (nm) v i L Figure 7. Rac1F forms nanoclusters as effectivelyb as Ras E G L 2 31 6 G12V-G12V A. PM sheets taken from BHK cells expressing GFP-tagged Rac1.G12V were fixed and G12V-T17N prepared as described for univariate100 EMnm and analyzed using univariate K functions. Plots
r 95% C.I. of the weighted mean standardized univariate 2K0 functions are shown. 99% C.I. is the 40 - 200 400 99%60 0 confidence interval for a random pattern. L(r)0 –r values80 derived160 from24 Ripley’s0 K ax (r)
function2 above the confidence interval denote significant clusteringr (nm )of a population. Data m v Gold particles/μm i L averaged from at least 20 different cells. B. PMb sheets taken from BHK cells expressing 2 GFP-tagged K-Ras.G12V (4B splice variant),L 1 H-Ras.G12V, N-Ras.G12V and Rac1.G12V were fixed and prepared as described for univariate EM and analyzed using univariate K functions to quantify100 the nm extent of clustering. The data were averaged and 0 summarized as Lmax ± SEM from at least 20 0different cells. Statistical significance of 200 400 differences600 between groups was determined 0 using Bootstrap80 16 tests0 . 24No0 significant r (nm) Gold particles/differencesμm2 were found between groups.
44
A B A B 500 4 2
m µ 400 3
300 2 200 Lmax
100 1
particles/ Gold 0 0
Rac1.WT Rac1.WT Rac1.G12VRac1.T17N Rac1.G12VRac1.T17N
Figure 8. Rac1.GTP and Rac1.GDP form nanoclusters on the plasma membrane
A. PM sheets taken from BHK cells expressing GFP-tagged Rac1.WT, Rac1.G12V, and Rac1.T17N were fixed and prepared as described for univariate EM. The number of gold particles per µm2 was counted and averaged as an indicator of PM localization for each mutant. Data averaged from at least 20 different cells. Statistical significance of differences between groups was determined using Student’s t tests and no significant differences were measured. B. The same PM sheets as described in (A) were analyzed using univariate K functions to quantify the extent of Rac1 nanoclustering. The data were averaged and summarized as Lmax ± SEM. Statistical significance of differences between groups was determined using Bootstrap tests. No significant differences were found between groups.
45 A Supplemental Figure 1
D 4
3
Rac1.WT Rac1.G12V Rac1.T17N ax 2 m
B C L 6 Rac1.G12V 1 99% C.I. 4
r 0
-
V V V V 2 12 (r) .G L
Rac1.G12 0 K-Ras H-Ras.G12N-Ras.G12 100 nm -2 0 80 160 240 r (nm) E F G 3 6 G12V-G12V
G12V-T17N
r 95% C.I. 2 4 -
ax (r)
m v i L b 2 L 1
100 nm 0 0 200 400 600 0 80 160 240 r (nm) Gold particles/μm2
Figure 9. Lmax is independent of gold particle number
Mean Lmax ± SEM values of Rac1.G12V from different univariate EM experiments were plotted against gold particle number per µm2 to demonstrate the independence of clustering with respect to gold particle labeling, within the typical experimental range of 100-700 gold particles per PM sheet. Data averaged from 15-20 different cells for each experiment.
46
60 Monomers Dimers Oligomers 50 40 30 20
% Total of Protein 10 0 K-Ras4B.G12V Rac1.WT Rac1.G12V Rac1.T17N
Figure 10. Rac1 proteins are distributed into monomers, ‘dimers’, and oligomers
PM sheets taken from BHK cells expressing GFP-tagged K-Ras4B.G12V, Rac1.WT, Rac1.G12V, and Rac1.T17N were fixed and prepared as described for univariate EM. The gold particle population for each protein was analyzed and summarized into the distribution of monomers, dimers (2 gold particles/cluster), and oligomers (3+ gold particles/cluster). Data averaged from at least 12 different cells.
3.2.2 Rac1 nanoclusters are spatially segregated by nucleotide binding
Ras proteins spatially segregate upon guanine nucleotide binding, such that
Ras.GDP and Ras.GTP nanoclusters are non-overlapping (Abankwa et al., 2007,
Plowman et al., 2008). In this way, effectors are more efficiently targeted to regions of activated Ras on the PM to propagate signaling, while GAPs can be more effectively localized as well to attenuate the signal. To determine whether Rac1 displays similar behavior, I analyzed the co-localization, or co-clustering, between Rac1.GTP and
Rac1.GDP proteins. Intact PM sheets of BHK cells co-expressing GFP-Rac1.G12V and
RFP-Rac1.T17N were serum-starved, attached to EM grids, and immunolabeled with 6
47 nm anti-GFP and 2 nm anti-RFP gold antibody (Fig. 5). Co-localization between the two populations of gold was quantified using bivariate K functions plotted as Lbiv(r)–r against radius size r (Fig. 11A). Lbiv(r)–r values above the 95% C.I. indicate significant co- localization. As a summary statistic, we used an integration of the bivariate K function curves termed Lbiv-integrated (LBI) (Zhou et al., 2014). LBI values above 100 indicate statistically significant co-localization and the greater the LBI value, the greater the extent of co-localization between the two populations. The LBI value for GFP-Rac1.G12V and
RFP-Rac1.G12V indicated significant co-clustering at 176.58 ± 20.18, consistent with significant Lmax values for univariate Rac1.G12V clustering (Fig. 11B). As another positive control, GFP-Rac1.WT and RFP-Rac1.T17N showed significant co-localization.
LBI values for GFP-Rac1.G12V and RFP-Rac1.T17N, and GFP-Rac1.G12V and RFP-
Rac1.WT were both below the C.I. of 100, indicating efficient spatial segregation between
GTP-bound Rac1 and GDP-bound Rac1 (Fig. 11B). These results indicate Rac1 forms spatially segregated nanoclusters in a guanine nucleotide dependent fashion.
To validate the EM results, I used FLIM-FRET to assess global co-localization between Rac1.GDP and Rac1.GTP in BHK cells. The data were summarized as FRET efficiency (%) between the GFP and RFP fluorophores as described in the methods.
Lower FRET efficiency indicates lower FRET, or association, between GFP and RFP.
FRET efficiency between GFP-Rac1.G12V and RFP-Rac1.G12V was similar to GFP-
Rac1.T17N and RFP-Rac1.WT, and significantly higher than FRET efficiency for GFP-
Rac1.G12V and RFP-Rac1.T17N and GFP-Rac1.G12V and RFP-Rac1.WT, corroborating the EM results (Fig. 11 C-D).
48 A Supplemental Figure 1
D 4
3
Rac1.WT Rac1.G12V Rac1.T17N ax 2 m
B C L 6 Rac1.G12V 1 99% C.I. 4
r 0
-
V V V V 2 12 (r) .G L
Rac1.G12 0 K-Ras H-Ras.G12N-Ras.G12 100 nm -2 0 80 160 240 r (nm)
2 E F AG B BB 6 m 500 3 μ G124V-G12V 250250
A / B C s 400 G12V-T17N * * e 3 200 l 200
r c
95% C.I. I ax i 300
t 2 4 - 150
B r 2
m 150 L ax 200 L (r) 100
LBI pa m v
i 1
L 100 100 b d
l 50 L 1 2 o 0 0 50 0 G T V N T V N V T T 100 nm W W 0 N 0 G120T17 G12T17 200 400 600 0 80 160 240 r (nm) Gold particles/μm2 G12V-WT17N-W G12V-G12G12V-T17G12V-WTT17N-WT C D G12V-G12VG12V-T17N C 8 2.4 D 2.4 72.36 * D 6 2.2 2.32 5 2.0 42.28 3 Lifetime(ns) 1.9 2.24 Lifetime (ns) Lifetime 2 G12V-G12V G12V-T17N 1 2.2 2.4 0 2.2 (%) Efficiency FRET 2.0
Lifetime(ns) 1.9 G12V-WT T17N-WT
Figure 11. Rac1 forms spatially segregated nanoclusters
A. PM sheets prepared from BHK cells co-expressing GFP-Rac1.G12V and RFP- Rac1.T17N were serum-starved, fixed, and prepared as described for bivariate EM and analyzed using bivariate K functions. Plots of the weighted mean standardized bivariate K functions are shown. 95% C.I. is the 95% confidence interval for a random pattern. Lbiv(r)–r values derived from Ripley’s K function above the confidence interval denote significant co-clustering of two populations. Data averaged from at least 20 different cells. B. PM sheets prepared from BHK cells co-expressing GFP- and RFP-Rac1 constructs were fixed and prepared as described for bivariate EM and analyzed using bivariate K functions to quantify the extent of co-clustering. The data were averaged and summarized as LBI ± SEM. Data averaged from at least 15-20 different cells. Statistical significance of differences betwen groups was determined using Bootstrap tests (*p < 0.001). C. BHK cells co-expressing GFP- and RFP-Rac1 constucts were serum starved, fixed, and imaged by FLIM-FRET. The data were averaged and summarized as FRET Efficiency (%) ± SEM from GFP lifetime values (ns). Data averaged from at least 120 different cells. Statistical significance of differences between averages of the original lifetime values was determined using one-way ANOVA followed by Tukey HSD post hoc test (*p < 0.01) D. Representative images from the FLIM experiment in (C). 49 3.3 Discussion
I found that Rac1 forms nanoclusters that are segregated depending on their activation state. In this regard, Rac1 shows similarity to Ras nanoclusters, which are isoform and guanine-nucleotide selective (Abankwa et al., 2007, Hancock & Parton,
2005). Ras nanoclusters are essential for downstream Ras signaling, and provide a mechanism by which Ras isoform-selective signaling output is generated, as Ras nanoclusters segregate by isoform-type as well as GTP-binding (Hancock, 2003). This isoform-specific selectivity is driven in part by the ability of Ras clusters to sort different
PM phospholipids and recruit effectors. Rac1 nanoclusters may regulate Rac1 signaling in the same way. In this regard, I hypothesize that Rac1.GTP clusters act as signaling sites for effector binding and recruitment. By selectively sorting specific PM lipids,
Rac1.GTP clusters may be able to attract GEFs, GAPs, and effector proteins with maximal efficiency. The ability of Rac1 nanoclusters to selectively sort phospholipids is explored in Chapter 4, Lipid-dependency of Rac1 nanoclusters.
Rac1 spatial organization has been studied before. As referenced previously in this chapter, SPT data shows that Rac1 is distributed in ~200 nm sized nanoclusters that are spatially distributed from the leading edge of cells in tandem with PIP3 (Remorino et al., 2017). One possibility that corroborates this finding with my results is that smaller
Rac1 nanoclusters are being recruited into larger oligomers when enclosed by the formation of ruffles and localized PIP3 in a ruffling cell or during macropinocytosis. This possibility is reinforced by work showing that PIP3 distributes in gradients that correlate with Rac1 distributions (Das et al., 2015). Additionally, Rac1 molecules diffusing laterally on the PM separate into two populations with fast and very slow diffusion rates (Das et al., 2015). I postulate this dynamic behavior reflects Rac1 proteins that are freely
50 diffusing as monomers and Rac1 proteins confined to nanoclusters. A similar integration of EM spatial mapping data with SPT data suggests that Ras proteins may be transiently confined in immobile nanoclusters or diffuse freely, supporting this idea (Hancock &
Parton, 2005, Murakoshi et al., 2004).
My discoveries using high-resolution EM demonstrate that Rac1 is spatially organized on a nanoscale level. This level of organization could promote more direct interactions with phospholipids that may be crucial for Rac1 signaling. The biological relevance of these clusters, what lipids they incorporate, and what protein domains drive the clustering are topics that are explored in the following chapters.
51 Chapter 4 Lipid-dependency of Rac1 nanoclusters
4.1 Introduction
Rac1 interactions with PM lipids are critical for Rac1 lateral diffusion, building on earlier work suggesting that Rac1 spatial organization and function are dependent on lipid lateral heterogeneity within the PM (Moissoglu et al., 2014). Specifically, PA is necessary for the recruitment of Tiam1 to the PM and downstream membrane ruffling
(Bohdanowicz et al., 2013). PIP3 is associated with Rac1 activation by correlative analyses (Das et al., 2015, Remorino et al., 2017) and through activation of the GEF P-
Rex1 (Welch et al., 2002). Why these particular lipids are important for Rac1 signaling is not understood. In context of the findings in Chapter 3, Rac1 nanoclustering, I hypothesize that Rac1.GTP nanoclusters selectively sort anionic phospholipids in a manner that is similar to Ras proteins, which form nanoclusters that comprise their own distinct lipid compositions, which in turn modulate effector recruitment and drive isoform- selective downstream signaling (Hancock, 2003, Zhou et al., 2014). For example, Ras nanoclusters selectively sort phospholipids and K-Ras clusters are structurally dependent on PtdSer (Cho et al., 2012, Cho et al., 2015, Zhou et al., 2014). Removing
PtdSer from the inner leaflet of the PM mislocalizes K-Ras from the PM, disrupts clustering, and stops MAPK signaling. As Ras nanoclusters are the sole sites for
Raf/MEK/ERK recruitment and activation, disrupting the nanoclusters has consequences for signal output (Tian et al., 2007).
52 I find that Rac1 nanoclusters similarly have a distinct lipid composition which potentially contributes to effector binding and downstream signaling. In this chapter, I use bivariate EM to determine the extent of co-clustering between Rac1 and different lipid probes and compare these results to previously quantified co-localization values between Ras isoforms and lipid probes (Zhou et al., 2014). Additionally, I explore the role phospholipids play in Rac1 clustering based on these data by manipulating the availability of specific lipids on the inner leaflet of the PM. These findings provide insight as to how PA and PIP3 contribute to Rac1 signaling.
4.2 Results
4.2.1 Rac1 nanoclusters selectively associate with PA and PIP3
To determine whether Rac1 associates with specific PM phospholipids and whether Rac1 nanoclusters have a distinct lipid composition, I performed a series of bivariate EM co-localization assays between Rac1 and different lipid probes. Intact PM sheets of BHK cells dually expressing RFP-tagged Rac1 constructs (Rac1.GTP or
Rac1.GDP) and GFP-tagged lipid probes were attached to EM grids. GFP and RFP were labeled with 6 nm and 2 nm gold, respectively. LBI values to quantify the extent of co- clustering were calculated and arrayed in a heat map (Fig. 12, Table 1), whereby red represents high LBI and blue represents low LBI values below the confidence interval.
As shown in the heat map, both Rac1.GTP and Rac1.GDP nanoclusters were enriched with PA and PIP3, demonstrated by high LBI values with the PA and PIP3 lipid probes.
The extent of co-clustering between K-Ras4B.GTP, H-Ras.GTP, and H-Ras.GDP and the lipid probes was consistent with previously performed co-clustering experiments (Fig.
12) (Zhou et al., 2014). Like K-Ras4B, the K-Ras4A.GTP splice variant showed high co-
53 localization with phosphatidylserine (PtdSer), consistent with previous work showing K-
Ras4A is sensitive to fendiline, a PtdSer depleting agent (Cho et al., 2015). The lipid composition of N-Ras nanoclusters had not been previously examined. I found that N-
Ras.GDP clusters prefer cholesterol while N-Ras.GTP nanoclusters prefer PIP3 (Fig.
12). It is not known whether PIP3 directly regulates N-Ras signaling, though PI3K is a major Ras-effector (Castellano & Downward, 2011), and previous work suggests that N-
Ras is sensitive to cholesterol depletion and localizes to raft-domains in the PM (Roy et al., 2005). Interestingly, microdomain targeting seems to be a membrane anchor/palmitoyl-driven event, as monopalmitoylation of H-Ras at C181, the same as N-
Ras, is sufficient to emulate N-Ras membrane behavior (Roy et al., 2005).
54
Figure 2
B
2 K-Ras4B K8-9Ras4B01+%: K8-9Ras4A01+%: 8H901-Ras+%/ ;H9-0Ras1+ N; -9Ras01+ e 600 PtdSerPSPtdSer!" l c i t r 400 PIPPI(4,5)P2PIP2 !#! $ 2 pa ** * d l 200 o ** PIPIP4P PI4P!#%! ** 4 G 0 Cholesterol CholesterolCholesterol&'()*+,*-() Rac1 PA N-Ras C 6 PIPPIP33 PIP3!#! . DMSO PA !PA/ 4 * x PA a m * PIPIPP PI3P ML298 33 !#. ! L 2 ML298 + PIP3 Low High ML298 + PA LBI = 0 LBI > 200 0 LBI = 100 Rac1 N-Ras 95% C.I. D E 1000 6 2 m * Figure 12. Rac1 nanoclustersμ 80 have0 a distinct lipid composition DMSO / s 4 PIP e x 3 PM sheets prepared from BHKl 60 cells0 co-expressing GFP-tagged lipid binding probes and * a c i * LY294 t r RFP-protein constructs (G12V or WT) were fixed and prepared as described form bivariate EM. The resulting LBI values40 for0 co-clustering between the* probe and proteinL were LY294 + PIP3 pa 2 LY294 + PA arrayed in a heatmap and binnedd by lipid-type. LBI values above the 95% confidence l 200 ** ** interval (100, white) demonstrateo significant co-localization between protein and lipid LY294 + PIP2 (red), while values below theG confidence0 interval show spatial segregation (blue).0 Data averaged from at least 15 cells. Lipid bindingRac1 probes: PS,PIP LactC2;H-Ra PI(4,5)Ps 2, PH-PLC�;Rac 1 H-Ras PI4P, FAPP1; cholesterol, D4; PIP3, PH-Akt; PA, PASS; PI3P,3 PH-2FYVE. 55 PtdSer Rac1.GDP Rac1.GTP K-Ras4B.GDP K-Ras4B.GTP K-Ras4A.GTP H-Ras.GDP H-Ras.GTP N-Ras.GDP N-Ras.GTP LBI 42.2 30.02 150.49 133.02 246.15 80.58 130.48 115.09 121.41 SEM 16.11 12.56 44.97 19.92 83.15 47.82 25.04 53.68 31.97 PIP2 Rac1.GDP Rac1.GTP K-Ras4B.GDP K-Ras4B.GTP K-Ras4A.GTP H-Ras.GDP H-Ras.GTP N-Ras.GDP N-Ras.GTP LBI 38.75 107.90 10.38 68.43 45.42 243.3 133.17 114.65 105.36 SEM 26.61 16.09 29.64 16.07 14.50 88.57 20.12 55.80 20.35 PI4P Rac1.GDP Rac1.GTP K-Ras4B.GDP K-Ras4B.GTP K-Ras4A.GTP H-Ras.GDP H-Ras.GTP N-Ras.GDP N-Ras.GTP LBI 55.79 70.42 95.81 113.31 64.66 100.77 104.44 103.88 100.72 SEM 28.18 10.60 29.36 29.09 18.82 26.02 17.84 33.83 20.24 Chol. Rac1.GDP Rac1.GTP K-Ras4B.GDP K-Ras4B.GTP K-Ras4A.GTP H-Ras.GDP H-Ras.GTP N-Ras.GDP N-Ras.GTP LBI 80.74 73.64 125.37 122.07 89.79 234.65 132.65 234.48 50.99 SEM 29.45 25.92 24.19 18.99 25.26 59.40 24.12 47.70 17.11 PIP3 Rac1.GDP Rac1.GTP K-Ras4B.GDP K-Ras4B.GTP K-Ras4A.GTP H-Ras.GDP H-Ras.GTP N-Ras.GDP N-Ras.GTP LBI 131.79 121.77 70.49 117.78 147.02 80.25 29.23 63.61 152.61 SEM 23.31 29.98 25.21 34.97 21.21 20.75 26.05 24.95 47.34 PA Rac1.GDP Rac1.GTP K-Ras4B.GDP K-Ras4B.GTP K-Ras4A.GTP H-Ras.GDP H-Ras.GTP N-Ras.GDP N-Ras.GTP LBI 182.31 136.10 139.52 98.95 90.60 62.40 137.31 52.76 72.80 SEM 28.94 23.89 35.40 17.97 37.79 23.43 24.78 10.41 18.15 PI3P Rac1.GDP Rac1.GTP K-Ras4B.GDP K-Ras4B.GTP K-Ras4A.GTP H-Ras.GDP H-Ras.GTP N-Ras.GDP N-Ras.GTP LBI 121.71 57.29 193.14 94.41 55.51 201.10 131.74 125.46 116.44 SEM 25.98 15.75 38.92 17.68 20.90 34.82 21.67 29.73 27.21 Table 1. LBI values for co-clustering between lipid binding probes and Rac1/Ras PM sheets prepared from BHK cells co-expressing GFP-tagged lipid binding probes and RFP-protein constructs (G12V or WT) were fixed and prepared as described for bivariate EM. Resulting LBI values from Fig. 12 are presented here. LBI values above 100 represent significant co-clustering between lipid and protein. Data averaged from at least 15 cells. Lipid binding probes: PS, LactC2; PIP2, PH-PLC�; PI4P, FAPP1; cholesterol (chol.), D4; PIP3, PH-Akt; PA, PASS; PI3P, PH-2FYVE. 56 4.2.2 PA is a key structural component of Rac1 nanoclusters To validate the finding that Rac1 nanoclusters are enriched with PA, I acutely depleted endogenous PA and measured changes in Rac1 clustering. BHK cells expressing the PA-probe (GFP-PASS) were treated for 1 hour with 0.5 µM ML298, a specific inhibitor of phospholipase D2 (PLD2), which catalyzes the conversion of PA from phosphatidylcholine (PC). Treatment with ML298 significantly decreased the amount of gold labeling for PA, indicating that PLD2 inhibition effectively depleted endogenous PA from the inner leaflet of the PM (Fig. 13A). ML298 treatment significantly mislocalized Rac1.G12V from the PM and disrupted the nanoclustering of Rac1.G12V remaining on the PM in BHK cells ectopically expressing GFP-Rac1.G12V (Fig. 13). To further validate PA involvement in Rac1 cluster formation, BHK cells expressing the PA-probe or Rac1.G12V were treated with ML298 and supplemented with 10 µM exogenous PA. PA was incorporated into the inner leaflet of the PM, because control experiments with the PA probe showed that supplemental PA restored PA levels to the inner leaflet of the PM in the presence of ML298 (Fig. 13A). N-Ras nanoclusters were not enriched with PA (Fig. 12, Table 1), and ML298 had no effect on N-Ras PM localization or clustering (Fig. 13). I hypothesized that Rac1’s sensitivity to ML298 arises from Rac1 co-clustering with PLD2 on the PM. Using bivariate EM to test the co-localization between PLD2 and Rac1.G12V in the presence of ML298, I found that ML298 treatment had no effect on the co-localization between Rac1.G12V and PLD2. However, the compound did disrupt the association between Rac1.G12V and the PA probe (Fig. 14). This result suggests that disrupted clustering induced by ML298 is a result of PA loss and not mislocalization of PLD2. 57 AA A B 6 2 * 800 m μ * / DMSO s 600 * e 4 l x PA c a i t r ML298 400 m * L pa ML298 + PIP * 2 3 d ** l o 200 ** ML298 + PA G ** 0 0 C Rac1 PA N-Ras D Rac1 N-Ras B 1000 6 2 A A BB6 6 m * 2 DMSO 2 800 800800 * * μ / m m s PIP3 μ μ 4 * * e / / l 600 DDMSMSOO x * s s c a LY294 600600 i * * t * e 4 e 4 l l r x PA x PA m c c a i a i L LY294 + PIP t t 400 * 3 pa r r ML298 m ML298 400 m * * 2 400 L d LY294 + PA L l pa pa ** ** ML298 + PIP o * 2 ML298 + PIP3 3 d ** * 2 200 d l ** LY294 + PIP2 l G o ML298 + PA o 200200 ** ML298 + PA G ** ** G ** 0 0 Rac1 PIP H-Ras Rac1 H-Ras 0 0 0 0 3 C C RacRac1 1 PAPA N-Ra N-Ras s D RacRac1 1 N-RaN-Ras s 1000 D 6 1000 6 2 2 Figure 13. Rac1 plasma membrane localization and clustering is sensitive to PA- m * DMSO m 800 * μ depletion DMSO 800/ μ / s PIP3 s 4 PIP e l 3 4x e 600 * l A. PM sheetsx taken from BHK cells expressing GFP-tagged Rac1.G12V, PA probe, or c a LY294 600i * t * c a LY294 i r N-Ras.G12V werem fixed and prepared as described previously for univariate EM. Cells t * r m 400 * were treated for 1L hour with 0.5 μM phospholipase D (PLD)LY294-2 specific + PIP inhibitor3 ML298, pa L LY294 + PIP 400 * 3 pa 2 d DMEM supplemented with 10 μM exogenous PA, ML298L Y+294 DMEM + PsupplementedA with l 2 d ** ** LY294 + PA o l 200 ** ** 10 μM exogenous PA, ML298 + DMEM supplemented with 10 μM PIP3, or DMSO. The o 2 LY294 + PIP2 200G number of gold particles per μm was counted and averagedLY294 + as P anIP 2 indictaor of PM G 0 localization for each0 mutant. Data averaged from 12-20 different cells. Statistical 0 significance of differences0 between treatment groups and DMSO for each construct was Rac1 PIP3 H-Ras Rac1 H-Ras Rac1 PIP3 H-Radetermineds using Student’sRac t 1tests (*pH-Ra < 0.05,s **p < 0.001). B. The same PM sheets described in (A) were analyzed using univariate K functions to quantify the extent of Rac1 and N-Ras clustering. The data were averaged and summarized as Lmax ± SEM. Statistical significance of differences between treatment groups and DMSO for each construct was determined using Bootstrap tests (*p < 0.05, **p < 0.001). 58 Supplemental Figure 2 300 DMSO ML298 * 200 * I B L 100 0 V V V PA-Rac1.G12 PLD2-Rac1.G12 Rac1.G12V-Rac1.G12 Figure 14. PA depletion disrupts Rac1-PA association but not Rac1-PLD2 association PM sheets prepared from BHK cells co-expressing GFP-PA-binding probe (PASS) and SupplementalRFP Figure-Rac1.G12V, 3 GFP-Rac1.G12V and RFP-Rac1.G12V, and GFP-PLD2 and RFP- Rac1.G12V were fixed and prepared as described for bivariate EM and analyzed using bivariate K functions to quantify the extent of co-clustering. Cells were treated for 1 hour with 0.5 μM ML298 or DMSO. The data were averaged and summarized as LBI ± SEM. Data averaged from at least 15 different cells. Statistical significance of differences betwen treatment groups was determined using Bootstrap tests (* p < 0.001). Rac1.G12VRac1.C178SRac1.K183QRac1.K184QRac1.R185QRac1.K186QRac1.R187QRac1.K188QRac1.R185K.R187KRac1.G12VRac1.SAAXRac1.R185KRac1.C178S.SAAXRac1.CVLS GFP 59 Actin 4.2.3 PIP3 is a key structural component of Rac1 nanoclusters Additionally, I hypothesized that Rac1 nanoclusters would show a similar dependency to PIP3 as PA. I acutely depleted endogenous PIP3 and measured changes in Rac1 clustering. BHK cells expressing the PIP3-probe (GFP-PH-Akt) were treated for 1 hour with 15 µM LY294002 (LY294), a specific inhibitor of phosphoinositide 3-kinase (PI3K), which catalyzes the conversion of PIP3 from phosphoinositol (4,5)-bisphosphate (PIP2). Treatment with LY294 significantly decreased the amount of gold labeling for PIP3, indicating that PI3K inhibition effectively depleted endogenous PIP3 from the inner leaflet of the PM (Fig. 15A). LY294 treatment significantly mislocalized Rac1.G12V from the PM and disrupted the nanoclustering of Rac1.G12V remaining on the PM in cells expressing GFP-Rac1.G12V (Fig. 15). H-Ras nanoclusters do not contain a significant amount of PIP3 (Fig. 12, Table 1), and LY294 had no effect on H-Ras PM localization and clustering (Fig. 15). In lipid addback experiments, supplementing LY294 treated cells with exogenous PIP3 and the PIP3-precursor PIP2 effectively rescued the PM localization and clustering of Rac1, suggesting that PIP2 add-back was able to overcome the inhibition of PI3K (Fig. 15). PIP3 added to the outer leaflet of the PM is delivered to the inner leaflet because control experiments showed adding supplemental PIP3 to BHK cells significantly enhanced gold-labeling of the PIP3 probe on the inner leaflet (Fig. 15A). To distinguish potential roles of PA vs. PIP3 in mediating Rac1 clustering, I performed cross supplementation experiments. As Fig.13A showed, adding back exogenous PIP3 in the presence of the PA-depleting ML298 did not rescue the nanoclustering of GFP-Rac1.G12V. Further, adding PA did not rescue the clustering of Rac1 with LY294002 treatment (Fig. 15B). These data suggest that PA is sufficient for Rac1 PM localization, but both PA and PIP3 are necessary for nanocluster formation. 60 A B 6 2 800 * m μ * / DMSO s 600 * e 4 l x PA c a i t r ML298 400 m * L pa * 2 ML298 + PIP3 d ** l o 200 ** ML298 + PA G ** 0 A 0 CA Rac1 PA N-Ras D Rac1 N-Ras 1000 6 2 A B 6 m * 2 A * B8060 DMSO 800 μ / 2 m 800 * s PIP μ 3 m * 4 e / DMSO l x μ * * s 600 / c DMSO a LY294 600 i * e 4 t * s l x PA 600 r * m e 4 c l a i x PA L LY294 + PIP t c * 3 a i 400 r pa ML298 t m * 400 r ML298 2 m L * d LY294 + PA l pa 400 L ** ** ML298 + PIP pa * o 3 2 ML298 + PIP d ** 200 l * 2 3 LY294 + PIP2 d ** G l o 200 ML298 + PA o 200 ** ML298 + PA G ** ** G ** 0 0 Rac1 PIP H-Ras Rac1 H-Ras 0 0 0 0 3 C C Rac1Rac1 PA PA N-Ra N-Ras DBsB D RacRac1 1 N-RaN-Ras s 10001000 6 6 2 2 m m * 800 800 * DMSDMSO O μ μ / / s s 4 4 PIPP3 IP3 e e l l x 600 600 x * * c a c a LY294 i LY294 i t * t * r m r m L LY294 + PIP 400 * L LY294 + PIP3 3 400 pa * pa 2 d 2 LY294 + PA l d LY294 + PA l ** ** o ** ** o 200 200 LY294 + PIP2 G LY294 + PIP2 G 0 0 0 0 Rac1 PIP3 H-Ras Rac1 H-Ras Rac1 PIP3 H-Ras Rac1 H-Ras Figure 15. Rac1 plasma membrane localization and clustering is sensitive to PIP3 depletion A. PM sheets taken from BHK cells expressing GFP-tagged Rac1.G12V, PIP3 probe, or H-Ras.G12V were fixed and prepared as described for univariate EM. Cells were treated for 1 hour with 15 μM phosphoinositide 3-kinase (PI3K) inhibitor LY294002 (LY294), DMEM supplemented with 10 μM exogenous PIP3, LY294 + DMEM supplemented with 10 μM exogenous PIP3, LY294 + DMEM supplemented with 10 μM exogenous PA, LY294 + DMEM supplemented with 10 μM exogenous PIP2, or DMSO. The number of gold particles per μm2 were counted and averaged as an indictaor of PM localization for each construct. Data averaged from 12-20 different cells. Statistical significance of differences between treatment groups and DMSO for each construct was determined using Student’s t tests (*p < 0.05, **p < 0.003). B. The same PM sheets described in (A) were analyzed using univariate K functions to quantify the extent of Rac1 and H-Ras clustering. The data were averaged and summarized as Lmax ± SEM. Statistical significance of differences between treatment groups and DMSO for each construct was determined using Bootstrap tests (*p < 0.02, **p < 0.004). 61 4.2.4 Rac1 is insensitive to PtdSer-depletion Unlike the K-Ras splice variants, Rac1 did not associate with PtdSer (Fig. 12, Table 1). To reinforce the notion that Rac1 association with PA and PIP3 is structurally relevant, I depleted PtdSer from the PM using 15 µM fendiline, an acid sphingomyelinase inhibitor that disrupts the synthesis of PtdSer and mislocalizes K-Ras from the PM (Cho et al., 2015, van der Hoeven et al., 2013). Fendiline treatment had no effect on the PM localization or nanoclustering of Rac1.G12V or Rac1.WT (Fig. 16). This result provided further support that the lipid composition of nanoclusters is specific and necessary for the nanoclustering of its protein constituents. AA BB 400 4004 2 2 DMSO DMSO m m µ µ Fendiline Fendiline 300 3003 200 2002 Lmax 100 1001 Gold particles/ Gold particles/ 0 00 Rac1.WT Rac1.WTRac1.WT Rac1.G12V Rac1.G12VRac1.G12V Figure 16. Rac1 nanoclusters are insensitive to PtdSer-depletion A. PM sheets taken from BHK cells expressing GFP-tagged Rac1.G12V and Rac1.WT were fixed and prepared as described for univarate EM. Cells were treated for 1 hour with 15 μM fendiline or DMSO. The number of gold particles per μm2 were counted and averaged as an indictaor of PM localization. Data averaged from 12-20 different cells. Statistical significance of differences between treatment groups for each construct was determined using Student’s t tests. No significant differences were seen between treatment groups. B. The same PM sheets described in (A) were analyzed using univariate K functions to quantify the extent of Rac1 clustering in the presence or absence of fendiline. Statistical significance of differences between treatment groups for each construct was determined using Bootstrap tests. No significant differences were found between treatment groups. 62 4.3 Discussion In this chapter, I systematically examine the lipid composition of Rac1 nanoclusters. Rac1 nanoclusters have a distinct lipid composition from previously characterized Ras, selectively associating with PA and PIP3 over PtdSer, PIP2, cholesterol, and other anionic phospholipids. This capacity to interact with PA and PIP3 is biologically relevant because depleting the PM of PA or PIP3 decreases Rac1 PM binding and reduces the nanoclustering of Rac1 remaining on the PM. In turn, Rac1 does not show selectivity for PtdSer and is insensitive to disruption of PtdSer synthesis. Rac1 nanoclusters are therefore structurally dependent on PA and PIP3, with lipid addback experiments further indicating that PA and PIP3 are both necessary but not individually sufficient for nanocluster formation. Interestingly, PIP2 rescues Rac1 nanoclustering and PM localization after PIP3-depletion, suggesting that sustained PIP3 synthesis from PIP2 is able to overcome the inhibition of PI3K. This result indicates that local generation of PIP3 at the nanocluster sites strengthens Rac1 clustering and PM localization. Though Rac1 shows significant co-clustering with PLD2, suggesting the PA found in Rac1 nanoclusters is locally generated, it is not known whether PC, the precursor for PLD2- generated PA, is able to rescue Rac1 clustering and PM localization in the absence of PA. As referenced previously, other studies link PM lipids to Rac1 function. For example, PA is required for membrane ruffling and macropinocytosis through recruitment of Tiam1 to the PM (Bohdanowicz et al., 2013). Similarly, PIP3 is required for the activation of P-Rex1 and is correlated with localized Rac1 activation in the leading edge of migrating cells (Das et al., 2015, Remorino et al., 2017, Weiner, 2002). The results presented here yield new mechanistic insight to this biology by demonstrating that the 63 Rac1 anchor encodes binding specificity for PA and PIP3, which results in the formation of nanoclusters enriched in these anionic lipids. Taken together, these studies along with my findings presented here suggest that Rac1 nanoclusters act as lipid based signaling platforms for efficient effector binding and recruitment, emulating the spatiotemporal organization of Ras proteins (Zhou & Hancock, 2017, Zhou & Hancock, 2015, Zhou et al., 2017). The basis for this anionic lipid specificity and its functional relevance is a topic that is explored in detail in Chapter 5, Rac1 nanoclustering and lipid specificity is determined by a prenyl-PBD code. Destabilization of Rac1.G12V nanoclusters Rac1.GDP Rac1.GTP PA PIP 3 Figure 17. Model of Rac1 nanoclusters Illustration depicting Rac1 nanocluster formation and sensitivity to phospholipid manipulation on the PM. Rac1.GDP nanoclusters are represented in blue and Rac1.GTP nanoclusters are represented in red. PM lipids incorporated in Rac1 nanoclusters (PA and PIP3) are colored green and turquoise. PM lipids not associated with Rac1 are not represented in the model. Depleting PA with ML298 (PLD2 inhibitor) or depleting PIP3 with LY294002 (PI3K inhibitor) destabilizes Rac1 nanocluster formation and results in decreased clustering of Rac1.GTP. 64 Chapter 5 Rac1 nanoclustering and lipid specificity is determined by a prenyl-PBD code 5.1 Introduction Co-localization analysis from Chapter 4, Lipid-dependency of Rac1 nanoclusters shows that Rac1 nanoclusters are enriched in PA and PIP3 and depend on these lipids specifically to properly maintain the oligomer structure. In this chapter, I show that the capacity for distinct lipid sorting is driven by complex interactions between the Rac1 C-terminal lipid-anchored PBD and PM lipids. In turn, Rac1 nanocluster formation and lipid sorting directly correlate with the ability of cells to undergo macropinocytosis and facilitate cell spreading. Macropinocytosis and cell spreading rely on coordinated and dynamic actin networks on the plasma membrane to occur. The membrane anchor of Rac1 comprises a geranylgeranylated C-terminal CAAX motif, an additional palmitoyl lipid, and a PBD (Fig. 3). In this regard, Rac1 resembles Ras proteins, which target the PM in a similar manner. Specifically, K-Ras4B and K- Ras4A both contain polybasic sequences similar to Rac1 that contribute to electrostatic interaction with anionic PM phospholipids, such as PtdSer. Rac1 PBD is necessary for membrane targeting, and effector interactions, and it is thought that the PBD interacts globally with the PM (Lam & Hordijk, 2013, van Hennik et al., 2003). Interestingly, recent findings show that the K-Ras4B PBD exhibits a specificity for PM lipids that cannot be explained by simple electrostatic interactions, but originates in the specific sequence of residues of the PBD (Zhou et al., 2017). I hypothesize that this may be similar to the Rac1 PBD and seek to determine the exact membrane anchor residues that drive lipid 65 interactions as well as Rac1 domain assembly. Furthermore, in order to test the hypothesis that Rac1 nanoclusters are important for signal output, I explore the effects of disrupted clustering on macropinocytosis and cell spreading, two cell processes that are driven by Rac1 signaling (Bohdanowicz et al., 2013, Masters et al., 2013, Wurtzel et al., 2012). 5.2 Results 5.2.1 Rac1 clustering and PM localization is dependent on palmitoylation, prenylation, and Arginine 185 To explore the role of the membrane anchor in facilitating nanoclustering, I generated a series of point mutations in the membrane anchoring domain of GFP- Rac1.G12V, represented in Figure 18. Each residue of the PBD, residues 183-188 (KKRKRK), was consecutively mutated to neutral glutamine (Q): GFP- Rac1.G12V.K183Q, GFP-Rac1.G12V.K184Q, GFP-Rac1.G12V.R185Q, GFP- Rac1.G12V.K186Q, GFP-Rac1.G12V.R187Q, GFP-Rac1.G12V.K188Q. To explore differences between arginine and lysine, both arginine residues were mutated to lysine to generate a hexa-lysine PBD identical to that of K-Ras4B: GFP- Rac1.G12V.R185K.R187K. Arginine (Arg) 185 was also changed to lysine: GFP- Rac1.G12V.R185K. To examine potential functions of the lipid anchors, I generated several mutants that are not lipidated properly: GFP-Rac1.G12V.C178S and GFP- Rac1.G12V.C178A can no longer be palmitoylated at cysteine (Cys) 178; GFP- Rac1.G12V.SAAX is no longer prenylated at the CAAX box; GFP- Rac1.G12V.C178S.SAAX removes all palmitoylation and prenylation; and GFP- 66 Rac1.G12V.CVLS contains an identical CAAX box to H-Ras that is farnesylated instead of geranylgeranylated. Hereafter, these mutants will be denoted by their membrane anchor mutation, i.e. GFP-Rac1.G12V.CVLS is Rac1.CVLS, or CVLS. The parent construct from which all the mutants were generated is used as a control and is denoted Rac1.G12V or simply G12V. Intact PM sheets from BHK cells ectopically expressing these mutants were fixed and analyzed by univariate EM. Western blots confirmed high expression of these mutants in BHK cells (Fig. 19). Rac1.C178S, which is no longer palmitoylated, was mislocalized from the PM (Fig. 20A). Rac1.C178A mirrored this result, suggesting the mislocalization of the palmitoyl mutant was not due to possible phosphorylation of the serine residue (Fig. 20A). Rac1.SAAX was likewise mislocalized, due to the loss of geranylgeranylation (Choy et al., 1999, Navarro-Lerida et al., 2012). Rac1.C178S.SAAX supported this result, which exhibited similar PM binding to Rac1.SAAX (Fig. 20A). Interestingly, Rac1.CVLS presented significantly enhanced PM binding, suggesting a potential difference between farnesylation and geranylgeranylation (Fig. 20A). All of the lipidation mutants showed significant decreases in clustering compared to Rac1.G12V (Fig. 20B). Of the sequential PBD mutants that contain a basic à neutral residue switch, only Rac1.R185Q showed impaired clustering (Fig. 20B). The hexa-lysine PBD mutant Rac1.R185K.R187K showed significantly decreased clustering compared to Rac1.G12V but displayed no change in PM localization (Fig. 20). Because of the phenotype presented by the R185Q mutant, I hypothesized the discrepancy in R185.R187K clustering was caused by Arg 185 rather than Arg 187. I tested the single Rac1.R185K mutant to see if it could phenocopy the hexa-lysine phenotype. I found that to be the case as Rac1.R185K exhibited similar PM binding and clustering compared to 67 Rac1.R185K.R187K (Fig. 20). These data show that the amino acid sequence of the Rac1 PBD drives Rac1 spatial distribution on the PM. It also highlights Arg 185 as a critical residue in the hypervariable region. A G12V LCPPPVKKRKRKCLLL B K183Q LCPPPVQKRKRKCLLL 2 800 * m μ K184Q LCPPPVKQRKRKCLLL / s 600 e R185Q LCPPPVKKQKRKCLLL l c i K186Q LCPPPVKKRQRKCLLL t r 400 R187Q LCPPPVKKRKQKCLLL pa d K188Q LCPPPVKKRKRQCLLL l * * o 200 G * R185K.R187K LCPPPVKKKKKKCLLL * * * R185K LCPPPVKKKKRKCLLL 0 C178S LSPPPVKKRKRKCLLL V S A X X S Q Q Q Q Q Q K K 12 C178A LAPPPVKKRKRKCLLL G SAA CVL SAAX LCPPPVKKRKRKSLLL C178C178 K183K184R185K186R187K188 R185 C178S.SAAX LSPPPVKKRKRKSLLL C178S.SAA R185K.R187 CVLS LCPPPVKKRKRKCVLS C D 5 Figure 18. Sequences of Rac1 membrane anchor mutants G12V C178SCVLS R185QR185KR185K.R187K 4 * Illustration depicting residue* sequences of different Rac1 membrane* anchor mutants.PA x 3 * * * Mutant Rac1 constructsa were generated from a Rac1.G12V parent vector and contain the G12V-activatingm mutation along with the introduced C-terminal mutations. PIP3 PBD mutants K183Q-L K188Q2 contain* individual* mutations in the PBD (KKRKRK) from basic arginine (R) or lysine (K) to neutral* glutamine (Q). PBD mutant R185K.R187K replacesPtdSer two arginines for lysine1 ; PBD mutant R185K contains a single arginine to lysine switch. Mutants C178S, C178A lose palmitoylation. SAAX and CVLS are CAAX box mutantsPIP2 that lose prenylation0 or replace geranylgeranyl for farnesyl, respectively. C178S.SAAX LBI > 140 mutation results in bothV prenylationS A X andX palmitoylationS Q Q Q loss.Q Q Q K K LBI = 0 12 G SAA CVL LBI = 100 C178C178 K183K184R185K186R187K188 R185 95% C.I. C178S.SAA R185K.R187 68 Supplemental Figure 2 300 DMSO ML298 * 200 * I B L 100 0 Supplemental Figure 2 V V V 300 DMSO ML298 * * PA-Rac1.G12 200 PLD2-Rac1.G12 I B L Rac1.G12V-Rac1.G12 100 Supplemental Figure 3 0 V V V PA-Rac1.G12 PLD2-Rac1.G12 Rac1.G12VRac1.C178SRac1.K183QRac1.K184QRac1.R185QRac1.K186QRac1.R187QRac1.K188QRac1.R185K.R187KRac1.G12VRac1.SAAXRac1.R185KRac1.C178S.SAAXRac1.CVLS Rac1.G12V-Rac1.G12 GFPGFP Supplemental Figure 3 ActinActin Rac1.G12VRac1.C178SRac1.K183QRac1.K184QRac1.R185Q Rac1.K186QRac1.R187QRac1.K188QRac1.R185K.R187KRac1.G12VRac1.SAAXRac1.R185KRac1.C178S.SAAXRac1.CVLS GFP GFP Actin Actin Figure 19. Rac1 membrane anchor mutants are highly expressed in BHK cells BHK cells ectopically expressing GFP-tagged Rac1 mutant constructs were harvested and blotted for anti-GFP and anti-actin antibodies as described in the methods. All of the cells showed high expression of the mutant constructs. 69 AA 2 800 * m µ 600 400 * 200 * * * Gold particles/ Gold * * * 0 G12V CVLS C178SC178ASAAX K183QK184QR185QK186QR187QK188Q R185K B C178S.SAAX B R185K.R187K 5 4 * 3 * * * * * Lmax 2 Lmax * * * 1 0 G12V SAAX CVLS C178SC178A K183QK184QR185QK186QR187QK188Q R185K C178S.SAAX R185K.R187K Figure 20. Mutations in the membrane anchor of Rac1 disrupt clustering and plasma membrane localization A. PM sheets taken from BHK cells expressing GFP-tagged Rac1 mutants (Fig. 18) were fixed and prepared for univariate EM. The number of gold particles per μm2 was counted and averaged as an indictaor of PM localization for each mutant. Data averaged from 15-20 cells. Statistical significance of differences between the mutants and G12V-control was determined using Student’s t tests (*p < 0.005). B. The same PM sheets from (A) were analyzed using univariate K functions to quantify the extent of clustering for the mutant Rac1 constructs. The data were averaged and summarized as Lmax ± SEM. Statistical significance of differences between the mutants and G12V-control was determined using Bootstrap tests (*p < 0.005). 70 5.2.2 Palmitoylation, geranylgeranylation, and Arg 185 determine PA/PIP3 association The difference in nanoclustering between the different Rac1 PBD mutants potentially arises from the distinct abilities of individual PBD residues to associate with PM lipids. To test this hypothesis, I performed another set of lipid mapping analysis similar to Figure 12 and arrayed the LBI values in a heat map. Average LBI values for each condition are detailed in Table 2. Rac1 membrane anchor mutants that demonstrated both impaired clustering and PM localization (C178S, R185Q) had impaired association with both PA and PIP3, and no attendant increase in PIP2 or PtdSer (Fig. 21). The CVLS mutant showed impaired co-localization with PA and PIP3 and enhanced association with PtdSer (Fig. 21). Interestingly, R185K showed impaired PA- association but maintained association with PIP3, while R185K.R187K demonstrated impaired PIP3 association and maintained association with PA (Fig. 21). These results imply that the Rac1 PBD sequence encodes lipid binding specificity for PA and PIP3, which is essential for spatial organization and PM binding. As a proof of concept, I added back exogenous PIP3 or PA to Rac1.R185K.R187K and Rac1.R185K, respectively, to see if the lipids were able to rescue the decreased clustering phenotype. I found that was indeed the case, as adding exogenous lipid was able to rescue the clustering of these mutants and in fact even enhanced their PM localization due to these mutants demonstrating no PM loss to begin with (Fig. 22). Additionally, to test whether Rac1.CVLS gained sensitivity to PtdSer, I treated BHK cells expressing Rac1.CVLS for 1 hour with 15 µM fendiline and found that while the Rac1.CVLS mutant did not show a decrease in clustering in the presence of fendiline, 71 Figure 3 B A 2 * Rac1.G12V L C P P P V K K R K R K C L L L m 800 μ / Rac1.K183Q L C P P P V Q K R K R K C L L L s e 600 l Rac1.K184Q L C P P P V K Q R K R K C L L L c i t Rac1.R185Q Figure L C3 P P P V K K Q K R K C L L L r 40B0 pa 2 Rac1.K186Q A L C P P P V K K R Q R K C L L L * d * m 800 Rac1.G12V L C P P P V K K R K R K C L L l L * Rac1.R187Q L C P P P V K K R K Q K C L L L μ * 200/ o Rac1.K183Q L C P P P V Q K R K R K C L L L s * G e 600 * Rac1.K188Q L C P P P V K K R K R Q C L L L l * * * Rac1.K184Q L C P P P V K Q R K R K C L L L c i 0t Rac1.R185K.R187K L C P P P V K K K K K K C L L L r Rac1.R185Q L C P P P V K K Qthe K Rmutant K C Llost L L its enhanced400 localization to the PM compared to Rac1.G12V, restoring pa V S A X S K K Rac1.R185K Rac1.K186Q L C P P P V LK C K P K P KP VR K K K CR LQ LR LK C L L L Q Q Q Q Q Q PM levels of the proteind back to Rac1.G12V* control levels (Fig. 23). Rac1.R187Q L C P P P V K K R K Q K C L L L l 200 * * Rac1.C178S L S P P P V K K R K R K C L L L G12o SAAX* CVL G C178C178 K183K184 K186 K188 R185 Rac1.C178A Rac1.K188Q L A P P P V LK C K P R P KP VR K K K CR LK RL QL C L L L * * * * R185 R187 Rac1.R185K.R187K L C P P P V K K K K K K C L L L 0 Rac1.SAAX L C P P P V K K R K R K S L L L Rac1.R185K L C P P P V K K K K R K C L L L C178S.SAAV S A X S Q Q Q Q Q Q K K R185K.R187 Rac1.C178S.SAAXRac1.C178S L S P P P V K L SK P R P KP VR K K K SR LK LR LK C L L L G12 SAAX CVL C178C178 K183K184R185K186R187K188 R185 Rac1.CVLS Rac1.C178A L C P P P V LK A K P R P KP VR K K K CR VK RL KS C L L L R185K. Rac1.SAAX L C P P P V K K R K R K S L L L G12V C178SC178S.SAACVLS R185QR185QR185KR185KR187KR185K.R187K C 5 Rac1.C178S.SAAX L S P P P V K K R K R KD S L L L G12V C178S CVLS R185K.R187 Rac1.CVLS L C P P P V K K R K R K C V L PASPA 4 G12V C178S CVLS R185Q R185K R185K.R187K C 5 * D * PA x * 3 * 4 * * PIP3PIP3 a * m * x * * * * PIP3 L 3 2 a * m * PtdSerPtdSer * L 2 * PtdSer 1 * * 1 PI(4,5)P2PIP2 0 PI(4,5)P2 0 V X Low High S A VS SQ A Q QX QS QQ QQQ KQ QK Q K K Low High LBI LBI= 0 = 0 LBILBI > 140> 140 G12 SAAX CVLG12 SAAX CVL C178C178 C178K183C178K184R185K186R187K183K184K188R185K186R185R187K188 R185 C178S.SAA LBI = 100 R185K.R187 LBI = 100 C178S.SAA 95% C.I. R185K.R187 95% C.I. Figure 21. Rac1 membrane anchor mutants lose association with PA and/or PIP3 PM sheets prepared from BHK cells co-expressing GFP-Rac1 mutant membrane anchor constructs and RFP-tagged lipid binding probes were fixed and prepared as described for bivariate EM. The resulting LBI values for co-clustering between the probe and protein were arrayed in a heatmap and binned by lipid-type. LBI values above the 95% confidence interval (100, white) demonstrate significant co-localization between protein and lipid (red), while values below the confidence interval show spatial segregation (blue). Data averaged from at least 15 different cells. Lipid binding probes: PS, LactC2; PIP2, PH-PLC�; PI4P, FAPP1; cholesterol, D4; PIP3, PH-Akt; PA, PASS; PI3P, PH- 2FYVE. 72 PA G12V C178S CVLS R185Q R185K R185K R187K LBI 138.37 56.26 95.73 31.22 24.46 122.28 SEM 32.28 46.03 15.48 14.41 26.04 46.89 PIP3 G12V C178S CVLS R185Q R185K R185K R187K LBI 119.34 48.84 30.87 -2.70 109.12 7.15 SEM 28.05 21.36 20.67 18.51 21.91 36.94 PtdSer G12V C178S CVLS R185Q R185K R185K R187K LBI 63.88 57.17 110.48 25.97 70.05 -0.01 SEM 18.29 22.22 14.61 37.78 19.88 33.42 PIP2 G12V C178S CVLS R185Q R185K R185K R187K LBI 106.00 71.28 46.91 32.08 76.31 98.11 SEM 40.51 20.87 23.09 19.02 30.27 26.66 Table 2. LBI values for co-clustering between lipid binding probes and Rac1 membrane anchor mutants PM sheets prepared from BHK cells co-expressing GFP-tagged Rac1 mutant membrane anchor constructs and RFP-tagged lipid binding probes were fixed and prepared as described for bivariate EM. Resulting LBI values from Fig. 21 are presented here. LBI values above 100 represent significant co-clustering between lipid and protein. Data averaged from at least 15 cells. Lipid binding probes: PS, LactC2; PIP2, PH-PLC�; PIP3, PH-Akt; PA, PASS. 73 A B 1000 6 2 * *** m 800 *** µ 4 600 * * 400 Lmax 2 200 Gold particles/Gold 0 0 ______PIP3 + PIP3 +3 ______PA G12V + PA + R185K G12VR185K G12V R185K R185K G12V R185K R185K R185K + PA R187K R185K + PA R187K R185K.R187K R185K.R187K R185K.R187K + PIP3 R185K.R187K + PIP Figure 22. Plasma membrane localization of Rac1.R185K and Rac1.R185K.R187K FigurePBD mutants 22. PM arelocalization rescued andby exogenous nanoclustering PA or of PIP3, Rac1.R185K respectively and A. PM sheets taken from BHK cells expressing GFP-tagged Rac1 PBD mutants (Fig. 18) were fixed and prepared for univariate EM. Cells were supplemented for 1 hour with DMEM ± 10 μM exogenous PA or PIP3 as indicated. The number of gold particles per μm2 was counted and averaged as an indictaor of PM localization for each mutant. Data averaged from 12-20 cells. Statistical significance of differences between treatment groups was determined using Student’s t tests (*p < 0.05). B. The same PM sheets from (A) were analyzed using univariate K functions to quantify the extent of clustering for the mutant Rac1 constructs. The data were averaged and summarized as Lmax ± SEM. Statistical significance of differences between treatment groups was determined using Bootstrap tests (*p < 0.001). 74 AA BB 800 800 2 2 * DMSO6 * * DMSO m m * * µ Fendiline µ Fendiline 600 600 4 400 400 Lmax 2002 200 Gold particles/ Gold particles/ 0 00 Rac1.G12VRac1.CVLS Rac1.G12VRac1.CVLSK-Ras.G12V Rac1.G12VRac1.CVLSK-Ras.G12VK-Ras.G12V Figure 23. Rac1.CVLS PM localization is sensitive to PtdSer depletion A. PM sheets taken from BHK cells expressing GFP-tagged Rac1.G12V, Rac1.CVLS, or K-Ras.G12V (4B-splice variant) were fixed and prepared for univariate EM. Cells were treated for 1 hour with 15 μM fendiline or DMSO. The number of gold particles per μm2 was counted and averaged as an indictaor of PM localization for each construct. Data averaged from 10-20 cells. Statistical significance of differences between treatment groups for each construct was determined using Student’s t tests (*p < 0.05). B. The same PM sheets from (A) were analyzed using univariate K functions to quantify the extent of clustering for the constructs. The data were averaged and summarized as Lmax ± SEM. Statistical significance of differences between treatment groups for each construct was determined using Bootstrap tests (*p < 0.001). 75 5.2.3 Loss of Rac1 clustering impairs macropinocytosis and cell spreading To explore the functional relevance of Rac1 nanoclusters, I measured the macropinocytic index of BHK cells expressing several of the Rac1 membrane anchor mutants. BHK cells expressing GFP-tagged pC1 (empty vector), Rac1.G12V, Rac1.C178S, Rac1.SAAX, Rac1.R185Q, Rac1.R185K, and Rac1.K186Q were serum- starved overnight and incubated with tetramethylrhodamine (TMR)-conjugated dextran before fixation (Fig. 24). The amount of dextran taken in by the cells through macropinocytosis was quantified and represented as the macropinocytic index, as described in Chapter 2, Materials and Methods. BHK expressing the pC1 empty vector had a macropinocytic index of 88.2 ± 10.1, while ectopic expression of Rac1.G12V significantly increased the macropinocytic index to 200.0 ± 17.1 (Fig. 25). Rac1 lipidation and PBD mutants, C178S, SAAX, R185Q, and R185K, all yielded significantly lower macropinocytic index values when expressed in BHK cells compared to cells expressing Rac1.G12V (Fig. 25). The K186Q mutant, which did not have an effect on PM localization and clustering, did not alter the macropinocytic index when expressed in BHK cells (Fig. 25). Rac1 additionally drives cell spreading (Wurtzel et al., 2012). To determine whether expression of these mutants influences cell spreading, I calculated the average surface area of BHK cells expressing the GFP-Rac1 membrane anchor mutants from the macropinocytosis assay in Figure 25. Rac1.G12V markedly enhanced the surface area of BHK cells (Fig. 26). Closely emulating the macropinocytosis results, each membrane anchor mutant that compromised nanoclustering also significantly affected the ability of cells to induce cell spreading (Fig. 26). In conclusion, each mutant with impaired 76 clustering exhibited a reduced ability to undergo macropinocytosis or drive cell spreading, suggesting that Rac1 nanoclustering is necessary for downstream Rac1 signaling. Figure 24. Expression of Rac1 membrane anchor mutants impairs dextran uptake in BHK cells BHK cells expressing GFP-tagged Rac1 mutant constructs or GFP-pC1 (empty vector) were serum-starved overnight, fixed, and treated with media + 1 mg/mL 70 kDa tetramethylrhodamine (TMR)-conjugated dextran. Representative sample images are shown in the GFP and rhodamine channels to visualize the protein or dextran-labeled macropinosomes, respectively. Images were processed in ImageJ and those featuring macropinosomes were brightened for visualization purposes. 77 ) 2 2.5 *** 2.0 * 1.5 * * 1.0 *** 0.5 0.0 Macropinocytic Index (10 Index Macropinocytic pC1 G12V SAAX C178S R185QR185KK186Q Figure 25. Expression of Rac1 membrane anchor mutants impairs macropinocytosis in BHK cells BHK cells expressing GFP-tagged Rac1 mutant constructs or GFP-pC1 (empty vector) were serum-starved overnight, fixed, and treated with media + 1 mg/mL 70 kDa tetramethylrhodamine (TMR)-conjugated dextran. The total area of macropinosomes was divided by cell area and multiplied x 102, giving the macropinocytic index as an indictaor of dextran uptake and macropinocytosis. Data averaged from 3 separate experiments and approximately 250 cells. Statistical significance of differences between Rac1 mutants and G12V-control was determined using Student’s t tests (*p < 0.02, ***p < 1x10-6). 78 2.0 *** 1.5 * 1.0 ** *** 0.5 0.0 Cell Area/No. of Cells (a.u.) pC1 G12V SAAX C178S R185QR185KK186Q Figure 26. Expression of Rac1 membrane anchor mutants impairs cell spreading in BHK cells Cell spreading was quantified by using data from the macropinocytosis assay performed previously (Fig. 25). Cell area was divided by the total number of cells, giving an average estimate for cell spreading per cell. Statistical significance of differences between Rac1 mutants and G12V-control was determined using Student’s t tests (*p < 0.001, **p < 1x10-4, ***p < 1x10-6). 79 5.3 Discussion In Chapter 4, Lipid-dependency of Rac1 nanoclusters, I explore the concept that Rac1 nanoclusters selectively sort PM phospholipids. Several lines of data indicate the basis for the anionic lipid specificity is encoded in the Rac1 C-terminal membrane anchor. Mutating arginine to lysine in the PBD, while maintaining the same net positive charge of the PBD, generates nanoclusters with different lipid compositions than Rac1 with a wild-type PBD (KKRKRK). For example, R185K shows reduced PA association while R185K.R187K loses PIP3 association. The R185Q mutant displays a reduction in both PA and PIP3 sorting, suggesting Arg185 plays a critical role in defining Rac1 PM lipid interactions. Switching the prenyl group from geranylgeranyl to farnesyl also changes Rac1 lipid composition, promoting selective association with PtdSer rather than PA or PIP3. This association promotes the Rac1.CVLS mutant to gain a sensitivity to PtdSer depletion, resulting in mislocalization upon fendiline treatment. Likewise, losing palmitoyl from the membrane anchor reduces selectivity for PA and PIP3, signifying that both the prenyl and palmitoyl chains are important for membrane affinity. These results suggest Rac1 interactions with membrane lipids are not simply governed by electrostatics. Rather, the membrane anchor of Rac1 comprises a prenyl-PBD code, in which the basic residues, arginine and lysine are non-equivalent and the length of the prenyl chain modifies the lipid binding specificity of the core PBD. This is similar to K- Ras4B, where specificity for PtdSer is encoded in a polylysine-farnesyl membrane anchor (Zhou et al., 2017), which is realized by distinct conformational structural dynamics of the anchor that favor interactions with PtdSer head groups (Zhou et al., 2017). Similarly, the Arf GEF Brag2 exhibits anionic lipid binding specificity for PIP2 through a polybasic domain that adopts defined conformational orientations (Karandur 80 et al., 2017, Zhou & Hancock, 2017). I speculate the Rac1 anchor also adopts a defined structure or structures on the PM that results in selective interactions of side chains on the specific sequence of lysine and arginine in the PBD with PA and PIP3 anionic lipid head groups. This hypothesis is supported by supplemental lipid addback experiments that show PA and PIP3 are able to restore the clustering of Rac1.R185K and Rac1.R185K.R187K, respectively. Each Rac1 membrane anchor mutant exhibits different extents of PM localization and nanoclustering and results in nanoclusters with different lipid compositions, likely reflecting distinct lipid binding specificities. The combined effect of these changes is reduced macropinocytosis and cell spreading compared to Rac1.G12V control, indicating a critical role for anchor lipid binding specificity in governing effector recruitment and Rac1 signal output. Dissecting out the relative importance of spatial organization and PM localization to Rac1 function is more difficult. Loss of palmitate or geranylgeranyl from the anchor or mutating R185àQ reduces both PM localization and nanoclustering, resulting in decreased macropinocytosis and cell spreading. By contrast, mutating R185àK has no effect on PM localization but diminishes clustering and impairs signal output. These results implicate nanocluster formation and lipid sorting as necessary steps for efficient downstream signaling. As mentioned previously, PA is required for membrane ruffling and macropinocytosis through Tiam1 (Bohdanowicz et al., 2013), and PIP3 is required for the activation of P-Rex1 and spatially-activated Rac1 (Das et al., 2015, Remorino et al., 2017, Weiner, 2002). The results presented in Chapter 5 yield new mechanistic insight to this biology by demonstrating that the Rac1 anchor encodes binding specificity for PA and PIP3, which results in the formation of nanoclusters enriched in these anionic lipids. Taken together, these studies suggest that Rac1 nanoclusters act as lipid based 81 signaling platforms for efficient effector binding and recruitment, emulating the spatiotemporal organization of Ras proteins (Zhou & Hancock, 2017, Zhou & Hancock, 2015, Zhou et al., 2017). More broadly, the results show that the PBD-prenyl anchors of small GTPases such as K-Ras4B and Rac1 are non-equivalent and have biological functions that extend beyond simple electrostatic engagement with the PM. 82 Chapter 6 Discussion and Future Directions In this study, I provide evidence that Rac1 forms lipid-dependent, spatially segregated nanoclusters on the PM that are biologically significant. I determine that specific residues in the Rac1 PBD, in combination with the adjacent lipid anchors, govern selective lipid sorting, which drives nanoclustering. All of these characteristics are biologically necessary; changing the PBD amino acid sequence, removing the adjacent lipid anchors, or depleting PM lipids they interact with blocks Rac1 clustering and has biological consequences. Most small GTPases are lipid-anchored and associate with bio-membranes for their biological functions. The most studied small GTPases in terms of membrane interactions are Ras proteins. Ras nanoclustering is mostly driven by the complex interactions between their C-terminal membrane-anchoring domains and the polar constituents in the PM (Abankwa et al., 2010, Prior et al., 2003). Like other PM-targeted small GTPases, Rac1 targets the PM through its membrane anchor, containing a palmitoyl chain conjugated to Cys178, a contiguous polybasic domain (KKRKRK, amino acids 183-188), and a C-terminal geranylgeranyl carboxyl ester conjugated to Cys189. This is very similar to Ras C-terminal membrane-anchoring domains, especially that of K-Ras4B (henceforth referred to as K-Ras in this chapter), which contains a hexa-lysine polybasic domain (Lys175-180) and an adjacent farnesyl carboxyl ester conjugated to Cys185. Thus, it makes sense that Rac1 undergoes similar nano-spatial segregation on the PM. Indeed, like Ras, Rac1 forms nanoclusters which spatially segregate in a GTP/GDP-specific manner. 83 Recent evidence shows that Rac1 forms large-scale nano-domains in the lamellipodia of migrating cells in conjunction with lipid gradients (Remorino et al., 2017). Previous studies build support for the hypothesis that domain assembly is a necessary precursor to efficient effector binding and recruitment (Bohdanowicz et al., 2013, Das et al., 2015, Knaus et al., 1998, Ugolev et al., 2008). This thesis provides strong evidence that Rac1 nanoclusters act as signaling platforms for effector binding and recruitment. By disrupting the ability of Rac1 to properly associate with PA or PIP3 through membrane anchor manipulation, we disrupt efficient nanocluster formation and see a subsequent impairment in macropinocytosis and cell spreading. We establish a connection between nanocluster formation and Rac1 biological function that has not been previously experimentally determined. This work provides insight into how Rac1 is regulated and organized on the plasma membrane at the molecular level. These findings have therapeutic implications. As highlighted in Chapter 1, Section 6, Drugs targeting Rac signaling pathways, finding direct and selective inhibitors of Rac1-mediated signaling has proved challenging, especially those that target particular signaling pathways while leaving others unaffected. Rac1 nanoclusters and associated phospholipids could now be used as a novel drug target for the development of new compounds. 6.1 The role of lipids PM lipids are important signaling components and have been well established as important structural components of Ras nanoclusters. K-Ras nanoclusters are specifically dependent on PtdSer. Disrupting the availability of this lipid, through fendiline, staurosporine, or other acid sphingomyelinase inhibitors, specifically abrogates K-Ras signaling and the proliferation of K-Ras mutant expressing tumor cell lines (Cho et al., 84 2015, van der Hoeven et al., 2017, van der Hoeven et al., 2013, Zhou et al., 2015). PM lipids also modulate actin dynamics, GEF recruitment, and regulate Rho-mediated signaling (Baumgart et al., 2011, Bohdanowicz et al., 2013, Ugolev et al., 2008). Six key findings lay the foundation for the discoveries and conclusions made over the course of this project. (1) PIP3 is first identified as an important regulator of Rac1 function though the discovery of a PIP3-activated GEF, P-Rex1 (Welch et al., 2002). (2) Highlighting the importance of phosphoinositide spatial organization in actin dynamics, PIP2 conversion to PIP3 in membrane ruffles is found to be critical for macropinosome formation and closure (Araki et al., 2007). (3) Several years later, in vitro studies using synthetic liposomes reveal that PIP3 is essential for Rac1 localization to liposome membrane, RhoGDI dissociation, and GDP to GTP exchange (Ugolev et al., 2008). (4) Another study shows that PA is necessary for membrane ruffling and macropinocytosis in macrophages and that both PIP3 and PA are necessary for PM recruitment of Tiam1, but make no mention of Rac1 PM localization (Bohdanowicz et al., 2013). (5) Later work establishes that PIP3 is distributed in a gradient that is highly correlated with Rac1 distribution (Das et al., 2015). (6) Finally, SPT-photoactivated localization microscopy experiments show that Rac1 is distributed in 100-200 nm sized clusters that are correlated with PIP3, Tiam1, and WAVE localization on the PM (Remorino et al., 2017). My findings show that Rac1 nanoclusters are much smaller and are dependent on PIP3 and PA, providing a structural mechanism for why these lipids are important for Rac1 assembly and signaling. Local pools of PIP3 and PA are necessary for Rac1 nanoscale organization. Similarly, specific residues in the membrane anchor of the protein drive Rac1 association with PA/PIP3 that in turn mediate clustering. Modifying residue sequences important for phospholipid interactions disrupts nanoclustering of active-Rac1 and, in consequence, macropinocytosis and cell spreading are interrupted, 85 two cellular functions dependent on Rac1 activity. A simplified model of Rac1 nanocluster formation based on these findings is depicted in Fig. 27. Targeting Rac1 nanoclusters via lipid modulation could be a method to selectively hinder Rac1 signaling without affecting RhoA or Cdc42. In another way, this method might allow for selective blocking of certain effectors and GEFs. Changing the lipid content on the PM could affect Rac1-phospholipid interactions and clustering in distinct ways, preventing some GEF and effector interactions while allowing others to occur. Plasma membrane inner leaflet plasma membrane inner leaflet PIP2 PIP PIP3 Rac1 3 Rac1 Rac1Rac1 PIP3 PIP Rac1PIP3 PIP2 Rac1 PIP2 PIP2 PIP3 PIP3 Rac1WAVE 3 PAGEFs PIP3 Rac1 Nanoclusters exhibit: Rac1 molecules freely PIP3 PA Rac1 Rac1 PIP3 PIP3 diffuse between mobile PIP3 Rac1 Tiam1 GAPs • PIP enrichment free diffusion between immobile GAPs Tiam1 PIP3 3 and immobile populations PA WAVE • Tiam1/WAVE recruitment nanoclusters and individual PIP3 molecules Rac1 forms nanoclusters enriched with PA and PIP3 Rac1 nanoclusters recruit effectors, GEFs, and GAPs PAK1 P DisruptingRac1 Rac1 Nanoclusters nanoclusters ormay: removing PM phospholipids results in impaired downstream signaling • Act as signaling platforms for effector binding and recruitment • Contribute to downstream signaling and phosphorylation of effectors • Promote actin filament formation Figure 27. Hypothesis Model Illustration depicting Rac1 nanocluster formation and effector binding and recruitment, based on current findings and previous work highlighted in Chapter 6. Figure adapted fromFigure (Maxwell 1. Active, et al., 2018) GTP -andbound used Rac1 with diffusespermission into by Elsenanoclustersvier. Maxwell, at theK. N., leading Zhou, edge of cell protrusions Y., and Hancock, J. F. (2018) Clustering of Rac1: selective lipid sorting drives signaling. Trends Biochem Sci 43, 75-77. doi.org/10.1016/j.tibs.2017.11.007 86 6.2 Mechanistic insights Traditionally, polybasic regions have mainly been thought to be a sensor for global electrostatics on the PM. In other words, association between a PBD and bio-membranes is dependent on the total number of basic residues in the PBD. However, current studies show that K-Ras PBD, which contains a farnesylated contiguous hexa-lysine PBD, samples various conformational orientations on the PM (Prakash et al., 2016). This conformational folding allows the K-Ras PBD to form a pseudo-helical structure on the PM. This means that different lysine residues have previously unrealized roles in interacting with PM lipids, and the amino acid sequence of the PBD determines selective lipid sorting. Indeed, each lysine in the K-Ras PBD demonstrates the distinct ability to select various lipids in the PM (Zhou et al., 2017). The Rac1 PBD shows similar behavior, thus possessing the capability to encompass a distinct lipid composition. My EM lipid mapping demonstrates that Rac1 selectively co-localizes with phospholipids. Further, single-point mutations of the Rac1 PBD have distinct clustering abilities. Specifically, only the R185Q mutant shows impaired clustering, while the other PBD single-point mutants cluster as efficiently as Rac1.G12V. This is also very similar to K-Ras PBD, where only mutants K177Q and K178Q show impaired clustering (Zhou et al., 2017). This is closely correlated with the ability of Lys177 and Lys178 to specifically sort PtdSer. K-Ras K177Q or K178Q mutants lose association with PtdSer but become enriched with PIP2 (Zhou et al., 2017). Because of the low levels of PIP2 in the inner leaflet of the PM, K-Ras K177Q or K178Q mutants are markedly mislocalized from the PM and show impaired clustering on the PM. Similarly, Rac1.R185Q loses the ability to specifically sort PA and PIP3, which leads to compromised clustering and PM binding. This view is further supported by the data that show RàK Rac1 mutants also cluster less 87 efficiently but are rescued by exogenous PA and PIP3, suggesting that specific conformation of the Rac1 PBD is key to Rac1 selective lipid sorting. Interestingly, changing the prenyl group of Rac1 from geranylgeranyl to farnesyl results in impaired clustering and increased PM binding. This mutation likewise disrupts the lipid composition of Rac1 nanoclusters and shows enhanced PtdSer association, suggesting the Rac1.CVLS mutant is no longer efficiently targeted to Rac1 clusters. This result indicates that farnesyl and geranylgeranyl are not equivalent in the membrane anchor. The increased PM binding suggests that Rac1 association with RhoGDI is potentially interrupted, as the geranylgeranyl moiety is important for RhoGDI binding (Keep et al., 1997). However, RhoE, RhoB, and RhoD are all farnesylated, and it is unclear how differing prenyl groups affect RhoGDI binding across Rho family members (Hoffman & Cerione, 2000-2013). Rac1 farnesylation has been explored before. In a previous study, farnesylated-Rac1 retains the ability to activate c-Jun, transform cells, and mediate membrane ruffling (Joyce & Cox, 2003). Farnesylated-Rac1 also rescues cell spreading and ruffling inhibited by geranylgeranyl-transferase inhibition, though it is not known whether Rac1 can be farnesylated in vivo in the absence of a functioning geranylgeranyltransferase (Joyce & Cox, 2003). While I did not test the functional consequences of Rac1.CVLS expression in this study, I found that Rac1.CVLS switches its lipid preference to PtdSer, resulting in PM mislocalization when exposed to fendiline. However, fendiline treatment does not affect clustering of Rac1.CVLS. This finding suggests that some Rac1.CVLS proteins are localizing to Ras-associated nanoclusters enriched in PtdSer, but some may retain association with Rac1 nanoclusters or exist as monomers. The loss of PtdSer causes mislocalization of PtdSer-associated Rac1.CVLS proteins, but does not affect the population of Rac1.CVLS on the PM that is not PtdSer- associated. Whether the Rac1.CVLS mutant has decreased signaling capacity or 88 effector binding compared to Rac1.G12V (or in the presence of fendiline) will be an interesting avenue of exploration going forward. Ultimately, the consequence of membrane anchor mutation and the attendant loss of clustering is likely the loss of second messenger signaling and recruitment via phospholipids. For example, the Rac1 PBD is important for binding to its effectors, such as protein kinase C-related kinase 1 (PRK1) and phosphatidylinositol 4,5-phosphate kinase (PIP5K) (Modha et al., 2008, van Hennik et al., 2003), and PA is sufficient to induce Pak1 autophosphorylation in vitro (Bokoch et al., 1998). Furthermore, Pak1 contains a small PBD of three adjacent lysines (K66-K67-K68) that are important for Rac1 binding and Pak activation. Mutating the lysines to neutral residues decreases Rac1’s ability to bind and activate Pak1, while mutating the lysines to arginines has no effect (Knaus et al., 1998). This Pak-PBD could potentially mediate Pak1 PM targeting and phospholipid interactions in Rac1-enriched areas. Based on my findings, I predict that the Rac1 membrane anchor drives recruitment of local pools of phospholipids, which in turn recruit second messengers to active-Rac1 cluster sites. This work provides a link between phospholipids and Rac1 signaling: the formation of small-scale nanoclusters that spatially organize second messengers to regulate signal output and mediate actin- dependent cellular processes such as macropinocytosis and cell spreading. 6.3 Future directions and remaining questions While this work quantitatively expands on the spatial organization of Rac1, many questions remain. First, how do nanoclusters recruit second messengers? One hypothesis is that different effectors, GEFs, and GAPs target different clusters, but how that could occur is likewise a mystery. Are there different pools of PA and PIP3 that help 89 recruit specific effectors, and how are those interactions distinguished by the membrane anchor? Additionally, is there competition between second messengers at Rac1 nanoclusters due to restrictions on cluster size or other biological factors? Likewise, does the Pak1 PBD encode any lipid binding specificity similar to PBDs in K-Ras and Rac1? Second, are there other residues in the membrane anchor that are important for Rac1 spatial organization and signaling? In addition to the polybasic sequence, Rac1 contains a conspicuous tri-proline sequence directly adjacent to palmitoylated Cys178. Sequences with contiguous prolines can form unique structures called ‘polyproline helices,’ and efforts are underway to try and determine the functional role(s) of these evolutionarily conserved sequences (Morgan & Rubenstein, 2013). One study found that removal of the polyproline region, or replacement with contiguous alanines, has no effect on PM localization (Das et al., 2015). However, as I demonstrate in this thesis, individual residues can have a great impact on phospholipid selectivity and clustering independent of PM localization. It would be interesting to see how changing the polyproline sequence affects clustering, phospholipid interactions, and effector binding/activation. Third, does the Rac1 PBD demonstrate second messenger or phospholipid selectivity outside the context of the PM? Interestingly, evidence suggests that the PBD encodes a nuclear localization sequence and mediates Rac1 proteasomal degradation (Lanning et al., 2004). Whether specific residues in the PBD mediate these processes and how those changes affect protein-protein interactions along the way is a fascinating area of exploration. Fourth, how do the Rac1 membrane anchor mutations explored in this study directly affect downstream signaling and effector binding? Are there conformational changes that occur when the protein is no longer associating with the correct phospholipids, and/or are there interactions between Rac1 proteins that are now 90 disrupted? An appropriate analogy is how K-Ras adopts distinct conformations depending on its PBD amino acid sequence (Zhou et al., 2017). Finally, are there other protein substrates present in Rac1 nanoclusters? I found that Rac1 co-clusters with PLD2, likely as a result of localized PA production. Additionally, Ras clusters are known to interact with different protein substrates that target isoform-specific nanoclusters. For example, K-Ras clusters are regulated by nucleolin and nucleophosmin while H-Ras clusters are dependent on caveolin and galectin-1 (Belanis et al., 2008, Inder et al., 2009, Roy et al., 1999). 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Membrane potential modulates plasma membrane phospholipid dynamics and K-Ras signaling. Science 349, 873-876 Zhu, S., Lu, P., Liu, H., Chen, P., Wu, Y., Wang, Y., Sun, H., Zhang, X., Xia, Q., Heng, B. C., Zhou, Y., and Ouyang, H. W. (2015) Inhibition of Rac1 activity by controlled release of NSC23766 from chitosan microspheres effectively ameliorates osteoarthritis development in vivo. Ann Rheum Dis 74, 285-293 Zidovetzki, R., and Levitan, I. (2007) Use of cyclodextrins to manipulate plasma membrane cholesterol content: evidence, misconceptions and control strategies. Biochim Biophys Acta 1768, 1311-1324 120 Vita Kelsey Nicole Maxwell was born to Frederick and Denise Maxwell on March 26, 1990 in Carrollton, Texas, USA. After completing high school, she enrolled at The University of Texas in Austin, Texas and completed a Bachelor’s of Science in Biology, Marine and Freshwater. After completing her studies at The University of Texas, she entered The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, Texas in August 2012. Permanent address: 3800 Holland Avenue, Unit 1 Dallas, TX 75219 121