Investigating Intrinsic Mechanisms of Microtubule Dynamic Instability and Its

INVESTIGATING INTRINSIC MECHANISMS OF MICROTUBULE DYNAMIC INSTABILITY AND ITS

CONTRIBUTION TO CHROMOSOME SEGREGATION

COLBY PHILIP FEES

B.S., University of Colorado Denver, 2010

M.S., University of Colorado Denver, 2012

A thesis submitted to the

Faculty of the Graduate School of the

University of Colorado in partial fulfillment

of the requirements of the degree of

Doctor of Philosophy

Cell, Stem Cell, and Developmental Biology Program

2018

This thesis for the Doctor of Philosophy degree by

Colby Philip Fees

has been approved for the

Cell, Stem Cell, and Developmental Biology Program

Rytis Prekeris, Chair

Jeffrey Moore, Advisor

Michael McMurray

Lee Niswander

Chad Pearson

Date: 12/14/2018

Fees, Colby Philip (PhD., Cell Stem Cell, and Developmental Biology)

Investigating intrinsic mechanisms of microtubule dynamic instability and its contribution to

chromosome segregation

Thesis Directed by Assistant Professor Jeffrey K. Moore

ABSTRACT

The dynamic microtubule network is an essential cytoskeleton component that facilitates the organization of almost all eukaryotic cells, and is most notable for its role in chromosome segregation. Despite the conversed role of microtubules in cell division, there is limited understanding of how intrinsic molecular properties of the tubulin heterodimers contribute to the activity of the network. What modules of the tubulin heterodimer might cells target to change the dynamic behavior of their microtubule networks? And how might these modules contribute to the functional diversity of the microtubule network across cell types?

The negatively charged carboxy-terminal tail (CTT) domains of α- and β-tubulin are the

most molecularly diverse regions of the heterodimer. Recently, an unbiased whole-genome

genetic interaction screen identified a wide range of physiological roles that mapped

specifically to the CTT domains. Mutations in β-tubulin CTT (β-CTT) altered microtubule

dynamics and impaired the mitotic spindle in yeast. These findings led me to the central

hypothesis of this thesis work: β-CTT is a molecular module that cells can target to regulate the

dynamics and function of their microtubule network.

This thesis is organized into three focused questions that inform the central hypothesis:

1) what is the role of β-CTT in chromosome segregation, 2) how does β-CTT regulate

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microtubule dynamics and 3) how do microtubules rescue? I tested these questions using a combination of in vivo yeast cell biology, in vitro experiments with purified protein and computational modeling techniques. I identified a minimal region of β-CTT that is essential for proper chromosome segregation and mitotic spindle organization. I found evidence indicating that β-CTT acts to regulate the equilibrium between the free heterodimer and polymer states, using purified tubulin. Additionally, β-CTT sensitizes microtubules to divalent cation control of dynamics; likely through a mechanism involving changes to the structure of the dynamic plus- end of the microtubule. Finally, I show how this divalent cation-mediated mechanism affects the transition, from shrinking to growing, known as rescue. These novel findings provide insight into how the intrinsic properties of tubulin regulate the dynamics of the microtubules, allowing cells to differentially organize their networks for specific functions.

The form and content of this abstract are approved. I recommend its publication.

Approved: Jeffrey K. Moore

TABLE OF CONTENTS

CHAPTER

I. ROLES AND REGULATION OF THE DYNAMIC MICROTUBULE NETWORK ...... 1

Introduction ...... 1

Microtubule Structure and Composition ...... 1

Dynamic Instability ...... 3

Regulating the Microtubule Network During Cell Division ...... 9

Intrinsic Regulation of Microtubule Dynamics ...... 13

II. THE NEGATIVELY-CHARGED CARBOXY-TERMINAL TAIL OF β-TUBULIN

PROMOTES PROPER CHROMOSOME SEGREGATION ...... 17

Introduction ...... 17

Materials & Methods ...... 20

Yeast Strains and Manipulation ...... 20

Chromosome Loss Assay ...... 21

Liquid Growth Assay ...... 21

Budding Duration Analysis ...... 21

Time Course of Pds1 Levels ...... 22

Microscopy and Image Analysis ...... 22

Electron Tomography ...... 25

Benomyl Sensitivity ...... 26 v

Sequence Logo ...... 26

Results ...... 26

β-CTT Promotes Proper Chromosome Segregation ...... 26

β-CTT is Necessary For Timely Progression Through Mitosis ...... 27

β-CTT Promotes Kinetochore Positioning ...... 28

β-CTT is Necessary to Align Kinetochores with the Spindle Axis ...... 31

β-CTT Regulates Microtubule Organization During Spindle Assembly ... 33

Mapping the Region of β-CTT that is Important for Chromosome

Segregation ...... 34

Discussion ...... 36

III. REGULATION OF MICROTUBULE DYNAMIC INSTABILITY BY THE CARBOXY-

TERMINAL TAIL OF β-TUBULIN ...... 64

Introduction ...... 64

Materials & Methods ...... 67

In Vitro Microtubule Dynamics Assays ...... 67

Image Analysis ...... 69

Tubulin Washout Experiments ...... 69

Determining Tubulin Concentration ...... 71

Subtilisin Treatment ...... 71

Proteomics Analysis ...... 71 vi

Western Blotting ...... 72

Yeast Strains and Manipulation ...... 73

Growth assays measuring magnesium sensitivity ...... 74

Measuring Microtubule Length and Dynamics in Yeast ...... 74

Nocodazole Sensitivity Assay ...... 76

Results ...... 76

β-CTT Promotes Microtubule Dynamics ...... 77

β-CTT Alters the Structure of the Plus End ...... 78

β-CTT Promotes Plus-End Stability ...... 80

β-CTT Promotes Dissociation from Microtubules...... 81

Magnesium Cations Regulate Tubulin Equilibrium through the β-CTT .. 82

The Negative Charge of β-CTT Regulates Tubulin Equilibrium In Vivo. .. 86

Discussion ...... 89

IV. A UNIFIED MECHANISM FOR MICROTUBULE RESCUE ...... 116

Introduction ...... 116

Materials & Methods ...... 119

In Vitro Microtubule Dynamics Assays ...... 119

Image Analysis ...... 120

Tubulin Wash-In Experiments ...... 121

Determining Tubulin Concentration ...... 122 vii

Monte Carlo Simulations ...... 122

Free Calcium Estimation ...... 123

Results ...... 123

Microtubule Rescue Frequency is Independent of Tubulin Concentration

...... 123

Longer Microtubules are More Likely to Rescue ...... 125

Effect of Depolymerization Rate on Rescue ...... 126

Rescues Occur Repeatedly at Similar Sites Along the Microtubule ...... 128

Divalent Cations Suppress Rescues ...... 129

Rescue Activity is Determined During Depolymerization ...... 130

Discussion ...... 132

V. CONCLUSIONS AND FUTURE DIRECTIONS ...... 160

REFERENCES ...... 168

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LIST OF FIGURES

Figure

2.1.1 Schematic of chromosome loss assay ...... 41

2.1.2 Chromosome loss frequency ...... 41

2.2.1 Normalized doubling times for WT and CTT mutant cells ...... 42

2.2.2Duration of S/G2/M ...... 42

2.2.3 Time course of Pds1/securin levels in synchronized cells ...... 42

2.2.4 Pds1-13myc signal at each timepoint ...... 43

2.3.1 Maximum intensity projections of WT cells expressing Nuf2-GFP ...... 43

2.3.2 Maximum intensity projections of tub2-430∆ cells expressing Nuf2-GFP ...... 43

2.3.3 Maximum intensity projections of dam1-765 cells expressing Nuf2-GFP ...... 44

2.3.4 Volumetric distribution of Nuf2-GFP signal ...... 44

2.3.5 Distribution of spindle lengths in asynchronous populations of cells ...... 44

2.3.6 Proportion of cells exhibiting two peaks of Nuf2-GFP signal ...... 45

2.3.7 Distributions of Nuf2-GFP signal ...... 45

2.3.8 Kymograph of Nuf2-GFP in WT and tub2-430∆ cells ...... 46

2.4.1 Maximum intensity projections of cells expressing unseparated CENIV-GFP ...... 46

2.4.2 Maximum intensity projections of cells expressing separated CENIV-GFP ...... 47

2.4.3 Percent of preanaphase cells exhibiting separated CENIV ...... 47

2.4.4 Mean angle between separated CENIV foci and the SPB-SPB axis ...... 48

2.4.5 Mean distances between separated CENIV foci and the SPBs ...... 48

2.5.1 EM tomography of microtubule in wild type cell ...... 49

2.5.2 EM tomography of microtubule in tub2-430∆ cell ...... 49

2.5.3 Histogram of all microtubule lengths from tomograms ...... 50

2.5.4 EM tomography of microtubule in wild type cell in anaphase ...... 50

2.5.5 EM tomography of microtubule in tub2-430∆ cell in anaphase ...... 51

2.6.1 Amino acid sequence alignment of WT and mutant β-CTT ...... 51

2.6.2 Chromosome loss frequency per 1000 divisions ...... 52

2.6.3 Normalized doubling times ...... 52

2.6.4 Sequence logo for β-tubulin residues ...... 53

2.6.5 Genetic interactions with SAC mutants ...... 53

2.7.1 Model of spindle organization in wild-type cells and β-CTT mutants ...... 54

2.8.1 Distribution of Nuf2-GFP signal across half of wild type spindles ...... 54

2.8.2 Distribution of Nuf2-GFP signal across half of tub2-430∆ spindles ...... 55

2.8.3 Distribution of Nuf2-GFP signal across half of dam1-765 spindles ...... 55

2.8.4 Maximum intensity projections from 3D confocal images of Cse4-GPF ...... 55

2.8.5 Plot of the 2-dimensional distribution of Cse4-GFP signal ...... 56

2.9.1 Maximum intensity projections a wild-type cell expressing CENIV-GFP ...... 56

2.9.2 Alignment angle of a single CENIV sister with the SPB-SPB axis ...... 57

2.9.3 Distance between a single CENIV sister and proximal SPB ...... 57

2.9.4 Distance between CENIV sisters ...... 58

2.9.5 Distance between SPBs ...... 58

2.9.6 Maximum intensity projections a tub2-430∆ mcm21∆ double mutant cell expressing

CENIV-GFP ...... 59

2.10.1 Preanaphase wild type cell 1 tomogram ...... 59

2.10.2 Anaphase wild type cell 2 tomogram ...... 60

2.10.3 Anaphase wild type cell 4 tomogram ...... 60

2.10.4 Preanaphase tub2-430∆ cell 4 tomogram ...... 61

2.10.5 Preanaphase tub2-430∆ cell 1 tomogram ...... 61

2.10.6 Preanaphase tub2-430∆ cell 2 tomogram ...... 62

2.10.7 Preanaphase tub2-430∆ cell 5 tomogram ...... 62

2.10.8 Anaphase tub2-430∆ cell 3 tomogram ...... 63

2.10.9 Anaphase tub2-430∆ cell 8 tomogram ...... 63

3.1.1 Western blot of tubulin after a time course of subtilisin digestion ...... 95

3.1.2 Representative kymographs of tubulin polymerized from GMPCPP stabilized microtubule seeds ...... 96

3.1.3 Polymerization rate plotted as a function of tubulin concentration ...... 97

3.1.4 Depolymerization rate plotted as a function of tubulin concentration ...... 97

3.1.5 Microtubule length at catastrophe plotted as the cumulative fraction of the total population ...... 98

3.2.1 Representative images of a TIRF microtubules ...... 98

3.2.2 Intensity measurements plotted as a function of pixel position ...... 99

3.2.3 Mean tip standard deviation plotted as a function of polymerization rate ...... 99

3.3.1 Schematic of the washout experiment ...... 100

3.3.2 Representative kymographs of washout experiments ...... 100

3.3.3 Percentage of microtubules without a slow depolymerization rate ...... 101

3.3.4 Time to catastrophe ...... 101

3.3.5 Slow depolymerization rate ...... 102

3.3.6 Fast depolymerization rate ...... 102

3.4.1 Polymerization rate plotted as a function of tubulin concentration ...... 103

3.4.2 Depolymerization rate plotted as a function of tubulin concentration ...... 103

3.4.3 Slow depolymerization rate ...... 104

3.4.4 Fast depolymerization rate ...... 104

3.5.1 Representative growth curves of wild-type cells in different magnesium conditions 105

3.5.2 Median doubling times of cells in different magnesium conditions ...... 105

3.5.3 Representative images of cells in preanaphase expressing GFP-labeled microtubules

...... 106

3.5.4 Distribution of astral microtubule lengths measured in preanaphase ...... 106

3.5.5 Representative images of cells in G1 phase expressing GFP-labeled microtubules ... 107

3.5.6 Distribution of astral microtubule lengths measured in G1 ...... 107

3.6.1 Amino acid sequences of the β-CTTs of mutant yeast strains ...... 108

3.6.2 Representative images of preanaphase yeast strains expressing microtubules labeled with GFP-Tub1 ...... 108

3.6.3 Percent of asynchronous cultures exhibiting astral microtubules after drug exposure

xii

...... 109

3.6.4 Representative astral microtubule life plots ...... 109

3.6.5 Distributions of astral microtubule lengths in living preanaphase cells ...... 110

3.6.6 Distributions of astral microtubule polymerization rates of preanaphase cells ...... 110

3.6.7 Distributions of astral microtubule depolymerization rates of preanaphase cells ..... 111

3.6.8 Distributions of astral microtubule catastrophe frequency of preanaphase cells ...... 111

3.7.1 Coomassie stained SDS-PAGE gel of tubulin after a time course of subtilisin digestion

...... 112

3.7.2 Line scans of Coomassie stained SDS-PAGE gel after subtilisin digestion ...... 112

3.7.3 Densitometry of Coomassie stained SDS-PAGE gel after subtilisin digestion ...... 112

3.7.4 Western blot of SDS-PAGE gel after subtilisin probing for α-tubulin ...... 113

3.7.5 Line scans of western blot of SDS-PAGE gel after subtilisin ...... 113

3.7.6 Western blot of SDS-PAGE gel after subtilisin probing for β-tubulin ...... 113

3.7.7 Line scans of western blot of SDS-PAGE gel after subtilisin probing for β-tubulin ..... 114

3.7.8 Western blots of S-tubulin stocks ...... 114

3.8.1 Coomassie stained SDS-PAGE gel of tubulin after subtilisin digestion ...... 114

3.8.2 Amino acid sequence of residues 393-445 TUBB2B ...... 115

4.1.1 Purified tubulin sample separated by SDS-PAGE gel stained with Coomassie ...... 138

4.1.2 Representative kymograph with rescue ...... 138

4.1.3 Polymerization and depolymerization rates as a function of tubulin concentration .. 139

4.1.4 Catastrophe and rescue frequency as a function of tubulin concentration...... 140

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4.2.1 Experimental data relative distribution of microtubule lengths at catastrophe ...... 141

4.2.2 End-Driven Simulation Schematic ...... 141

4.2.3 Lattice-Driven Simulation Schematic ...... 142

4.2.4 Simulated rescue frequency as a function of incorporation frequency ...... 142

4.2.5 Experimental and Simulated distributions of microtubule lengths at catastrophe .... 143

4.2.6 End-driven simulated distribution of microtubule lengths at catastrophe ...... 143

4.2.7 Lattice-driven simulated distribution of microtubule lengths at catastrophe ...... 144

4.3.1 Distribution of depolymerization rates of experimental and simulated data ...... 144

4.3.2 Relative distributions of depolymerization rates leading to rescue ...... 145

4.3.3 Average depolymerization rates from experimental calcium data ...... 146

4.3.4 Average polymerization rate constants ...... 146

4.3.5 Average number of rescues per microtubule with calcium ...... 147

4.3.6 Rescue frequency per depolymerization time with calcium ...... 147

4.3.7 Length lost before rescue with calcium ...... 148

4.4.1 Representative kymograph showing repeated rescue events ...... 149

4.4.2 Percentage of the rescue population that rescues repeatedly ...... 150

4.4.3 Percent of repeated rescues calculated with increasing rescue frequencies ...... 150

4.5.1 Cumulative distribution of microtubule length lost before rescue ...... 151

4.5.2 Rescue position relative to catastrophe site ...... 151

4.5.3 Rescues occur repeatedly at similar sites along the microtubule ...... 152

4.5.4 Percent of rescues that occur repeatedly ...... 152

xiv

4.6.1 Representative kymograph of wash-in experiment ...... 153

4.6.2 Wash-in experiment polymerization and depolymerization rates ...... 154

4.6.3 Terminal location of first catastrophe ...... 155

4.6.4 Mean position of the first rescue after wash-in ...... 156

4.7.1 Unified model of microtubule rescue ...... 157

4.8.1 Rescue analysis for stabilized seeds ...... 158

4.9.1 Polymerization rates with calcium as a function of tubulin concentration ...... 158

4.10.1 Polymerization rate constant of reactions with different MgCl2 concentrations ..... 159

4.10.2 Depolymerization rate constant of reactions with different MgCl2 concentrations . 159

CHAPTER I

ROLES AND REGULATION OF THE DYNAMIC MICROTUBULE NETWORK

Introduction

The microtubule network is an essential component of the cellular cytoskeleton with a wide range of organizations and functions across all eukaryotic cell types. These networks are found in all dividing and differentiated cells. The structure of the networks can differ depending on the cell type and stage of the cell cycle. For example, terminally differentiated neurons utilize long tracks of stabilized microtubules for intracellular transport, in contrast, a rapidly dividing zygote requires a dynamic microtubule network for the multitude of cell divisions during development. In both examples, the networks are built from the same building blocks, heterodimers of α- and β-tubulin, but the behavior and function of these networks are

different. How do cells differentially regulate their microtubules in order to build diverse

organizations of microtubule networks with unique functions? The focus of this thesis work is

on understanding how the intrinsic molecular properties of the tubulin heterodimers contribute

to the regulation and activity of the dynamic microtubule network.

Microtubule Structure and Composition

Microtubule networks are composed of individual dynamic microtubules which in turn are noncovalent cylindrical polymers of repeating α- and β-tubulin heterodimers. Each tubulin protein within the heterodimer consists of a highly conserved globular domain and an unstructured-negatively charged C-terminal tail domain (CTT). During assembly, a heterodimer can bind up to four neighboring heterodimers; two longitudinal interactions form linear chains

known as protofilaments, and two lateral interactions bind adjacent protofilaments (Alushin et al., 2014). Purification studies have found each heterodimer binds two guanosine-triphosphate

(GTP) nucleotides, one at the Nonexchangeable-site of α-tubulin (N-site) and the other at the

Exchangeable-site on β-tubulin (E-site) (David-Pfeuty et al., 1977; MacNeal and Purich, 1978;

Spiegelman et al., 1977; Weisenberg et al., 1968). The heterodimers polymerize into a sheet known as the microtubule lattice, with curvature along the lateral axis that closes into a hollow cylinder of 11-15 protofilaments with cytosolic and luminal surfaces. The majority of microtubules observed in vivo are composed of 13 protofilaments, although there is mounting evidence that protofilament number is functionally important in specific cell types and organisms (Chaaban and Brouhard, 2017; Raff et al., 1997; Savage et al., 1989).

While microtubules are composed of heterodimer subunits, several different isoforms

(known as isotypes) of α- and β-tubulin have been identified in higher eukaryotes (Sullivan and

Cleveland, 1986; Villasante et al., 1986). These isotypes are highly conserved, with most of the sequence heterogeneity located in the negatively charged carboxy-terminal tail (CTT) domain

(Aiken et al., 2014). Previous work has shown that expression of specific isotypes is spatially and temporally regulated during development (Lewis et al., 1985). When purified, the isotypes exhibit unique polymerization dynamics including different protofilament counts (Panda et al.,

1994; Raff et al., 1997; Savage et al., 1989). Recent work has linked numerous cancer types and disease progression to an upregulation of the β-tubulin isotype TUBB3 (Lee et al., 2007; Levallet et al., 2012; Li et al., 2014). Recent purification advances have allowed for study of isotypically pure tubulin and illustrates that the behavior of an individual microtubule can be altered by the

isotype composition (Pamula et al., 2016; Vemu et al., 2017). Taken together, these findings

suggest isotype diversity may be a mechanism cells use to change the behavior of the

microtubule network.

The molecular diversity of the CTT domain is further compounded as it is the target of a

variety of post-translational modifications (PTMs). The diversity of PTMs and modification sites

on tubulin has led to the tubulin-code hypothesis – analogous to the histone code hypothesis

(Verhey and Gaertig, 2007). According to this model, ‘writers’ are proteins that apply

modifications to microtubules, which are ‘read’ by other factors that interact with the

microtubules. Some types of modifications are restricted to specific microtubule populations.

For example, glycylation is enriched in the axonemes and basal bodies and is associated with

more stable microtubules (Bré et al., 1996). The role these modifications play in regulating the

behavior of the microtubule network has been challenging to study in part because of the

difficulties associated with purification. Recent biochemical and purification advances have

enabled more direct study of PTMs in vitro; suggesting that cells may use glutamylation to

protect the microtubule network from restructuring enzymes like spastin (Sun et al., 2016). The

extensive molecular diversity associated with the CTTs hints at their functional importance,

which has led to the central hypothesis for this work: the tubulin CTT domains act as modules

that cells target to differentially regulate the dynamics of their microtubule networks.

Dynamic Instability

Microtubules exhibit a unique nonequilibrium polymerization behavior known as dynamic instability; characterized by prolonged phases of polymerization and depolymerization

with stochastic transitions between each phase (Mitchison and Kirschner, 1984). Previous work

with purified tubulin showed that both the α- and β-tubulin subunits bind the nucleotide

guanosine-triphosphate (GTP), however only β-tubulin hydrolyzes the nucleotide during

polymerization (David-Pfeuty et al., 1977; MacNeal and Purich, 1978). Nucleotide hydrolysis is

the driver of dynamic instability. During polymerization, GTP-rich subunits have a high affinity

for polymer (Lin and Hamel, 1987) and assemble in an orientation such that the α-tubulin subunit docks with the existing polymer, and is buried, while the β-tubulin subunit is cytosolically exposed until the next heterodimer binds. This end of the microtubule is known as the plus end and is primarily differentiated from the opposite side, the minus end, by faster polymerization rates (Mitchison and Kirschner, 1984; Walker et al., 1988).

The dynamic instability of the microtubule has been extensively studied and is characterized by four parameters; the rates of polymerization and depolymerization, as well as

the frequencies of transitions between growth and shrinking (catastrophe) and vice versa

(rescue) (Mitchison and Kirschner, 1984). During polymerization, GTP-rich tubulin

heterodimers are added to the plus end through longitudinal bonds with protofilaments then

stabilized through lateral contacts with neighboring heterodimers, yielding the term ‘GTP-Cap’

(Alushin et al., 2014; Mitchison and Kirschner, 1984). The naturally curved heterodimers are

straightened as the lattice closes into a cylindrical microtubule (McIntosh et al., 2018). Current

models propose that this heterodimer straightening introduces strain in the lattice as the

heterodimers have natural curve/kink along the protofilament axis (Buey et al., 2006; Nawrotek

et al., 2011; Rice et al., 2008). The strain is resisted by the strong lateral interactions between

protofilaments of GTP-tubulin (Driver et al., 2017; Manka and Moores, 2018; Rice et al., 2008).

The act of polymerizing also stimulates the hydrolysis activity of β-tubulin, which then weakens

the lateral contacts in the lattice (Alushin et al., 2014; Manka and Moores, 2018; Zhang et al.,

2018). During depolymerization, the straightened heterodimers spring back into their curved

conformation as first lateral bonds are lost followed by the longitudinal bonds. The highly

curved morphology is unique to depolymerizing protofilaments has been described as a ram’s

horn structure (Chrétien et al., 1995; Mandelkow et al., 1991).

During transitions from polymerization to depolymerization, known as catastrophes, the microtubule end likely exists in as a structural intermediate. Current models propose catastrophe is triggered by the loss of stability at the plus-end, either by hydrolysis overtaking polymerization (GTP-cap model) or asymmetric protofilament extensions that lack the

stabilizing lateral bonds (Aher and Akhmanova, 2018; Carlier and Pantaloni, 1981; Coombes et

al., 2013; Hyman et al., 1992; Seetapun et al., 2012). Both models necessitate that lateral

interactions maintain microtubule stability. Regulating the frequency of transitions may be a

primary target for cells to change the behavior of their microtubule network. Increasing the

catastrophe frequency in Xenopus egg extracts was sufficient to change a long-stable

microtubule network into a highly dynamic array of short microtubules (Belmont et al., 1990;

Desai and Mitchison, 1997; Verde et al., 1992).

In contrast to catastrophe, how microtubules escape the depolymerizing state and

resume polymerization (rescue), is poorly understood. As with catastrophe, the microtubule

end at the rescue transition must also be some structural intermediate of the assembling and

disassembling states. However, this proposed transition state would need to overcome the rapid heterodimer loss rate of depolymerization as well as the curvature of the protofilaments.

Early work demonstrated that rescues could not be explained kinetically, as changes in heterodimer on/off rates (Walker et al., 1988). Instead, rescue is likely the result of depolymerizing end being disrupted. Currently, two models have been proposed to explain rescue; first, the ‘GTP-island’ model suggests that regions of unhydrolyzed GTP-tubulin in the lattice (distal from the assembling end) halt disassembly by stopping the propagation of strain down the lattice through strong lateral interactions between heterodimers. This model is supported by the correlation between observed rescue in proximity to microtubule repair sites, presumably with GTP-tubulin, following either mechanical damage or activity of severing enzymes (Aumeier et al., 2016; Dimitrov et al., 2008; Vemu et al., 2018). Alternative non- nucleotide mechanisms for this model have also been proposed including changes in protofilament number or gaps in the lattice (Schaedel et al., 2018). However, many questions remain unanswered for this model. How large must the GTP-island or lattice gap be to promote rescue? What conformational changes do the GTP-tubulin heterodimers make to the surrounding GDP-heterodimers? What prevents hydrolysis from occurring in this region? The primary principle of this model is that the location of rescue must be pre-defined and embedded into the microtubule lattice prior to the depolymerizing end reaching that site. I speculate that a weakness of this model is that it could be challenging for cells to control the precise location of these pre-defined sites in the chaos of the cytoplasm.

The second model of microtubule rescue is that structure or activity of the

depolymerizing end informs the likelihood of a rescue occurring. In this model, the structure of

a depolymerizing end is constantly sampling different conformations or states that either favor

or disfavor rescue – analogous to the transition state of a substrate biochemical reaction.

Support for this model comes from models of how microtubule binding proteins interact with

the end of the microtubule and promote rescue including CLASP, CLIP-170, and Kip3/kinesin 8

(Al-Bassam et al., 2010; Arnal et al., 2004; Bratman and Chang, 2007; Dave et al., 2018;

Hiremathad et al., 2018; Komarova et al., 2002; Lawrence et al., 2018; Lindeboom et al., 2018).

The binding proteins may be promoting rescue by stabilizing the necessary transition end

structure/conformation similar to the activity of an enzyme in the analogy from above.

Additional non-protein factors, such as divalent cations, have also be shown to change the

structure of the depolymerizing end visualized by cryo-electron microscopy (further explained

below) (Tran et al., 1997). These findings suggest that controlling the structure/activity of the

depolymerizing end may be a key mechanism in how microtubule transitions are regulated. We

currently lack a mechanistic understanding of a fundamental property of dynamic instability -

how microtubules rescue.

The nonequilibrium behavior of dynamic instability is powered by the energy captured

from β-tubulin hydrolyzing GTP to GDP (Desai and Mitchison, 1997). The mechanism of

hydrolysis remains a contested area of microtubule biology. Early biochemical studies

demonstrated that nucleotide hydrolysis is slow in soluble tubulin but occurs very quickly for

subunits in polymer (Caplow and Shanks, 1990; David-Pfeuty et al., 1977). Recent work using

nucleotide analogs indicate that microtubules mature through potentially three distinct

structures relating to nucleotide state (Zhang et al., 2018) with potentially additional intermediate structures during hydrolysis (Manka and Moores, 2018). Tubulin is an abundant protein in almost all eukaryotic cell types, therefore it is reasonable to conclude that the maintenance of the microtubule network requires a significant amount of energy. While energetically expensive, directly coupling dynamic instability with nucleotide hydrolysis is beneficial to cells in two ways, the network can be reorganized rapidly, and it can perform mechanical work (Desai and Mitchison, 1997; Driver et al., 2017; Grishchuk et al., 2005;

McIntosh et al., 2010; Mitchison and Kirschner, 1984).

Understanding how microtubules convert chemical energy in the form of GTP into mechanical force has been of great interest and recent technological advances have pushed new insights into this area of microtubule biology. Microtubules are capable of generating both pushing and pulling forces (Coue et al., 1991; Dogterom and Yurke, 1997; Janson et al., 2003;

Koshland et al., 1988; Lombillo et al., 1995). One of the most recognized examples of the pulling force is during chromosome segregation, where the kinetochore complex is pulled along the depolymerizing end (see below; (Inoué et al., 1995; McIntosh et al., 2010)). While the magnitude of the pulling force has been measured (Akiyoshi et al., 2010; Volkov et al., 2013), the mechanism of how the force is generated is unclear. A recent study has linked the structure of the depolymerizing end of the microtubule with force production (Driver et al., 2017). Early structural studies using cryo-electron microscopy show that protofilaments lose lateral bonds and peel-away from the lattice in a shape described as a ram’s horn (Chrétien et al., 1995;

Mandelkow et al., 1991). This characteristic curling is due to strain induced by a conformation

change in the heterodimer following nucleotide hydrolysis (Buey et al., 2006; Nawrotek et al.,

2011; Rice et al., 2008). The current mechano-chemical model for tubulin is that during assembly, the energy from hydrolysis is stored in the microtubule as strain energy, which is released and harnessed during disassembly (Driver et al., 2017; Koshland et al., 1988). This work demonstrates that the specific structural conformations of the microtubule end enable it to generate forces and function correctly.

Regulating the Microtubule Network During Cell Division

The dynamic nature of the microtubule network has been studied for almost a century

with early cell biologists observing how the mitotic spindle changed over time (Wilson, 1928).

Advances in polarized light microscopy allowed the direct observation of polymerization

dynamics during mitosis (Inoue, 1967), which began the most well recognized role of the

microtubule network – cell division (Desai and Mitchison, 1997; Inoué et al., 1995). During cell division, the sister chromatids must be faithfully segregated to ensure that each daughter cell is genetically identical to the mother cell. One of the foundational questions in the field is how do cells ensure that each daughter cell receives a copy of each chromosome? The combined efforts of many researchers have shown that this complex process involves dozens of proteins that are both spatially and temporally choreographed, including the microtubule network.

While the role of many proteins and their associated complexes have been characterized, we lack a mechanistic understanding of how the microtubule network contributes to the complex process of cell division.

During mitosis, sister chromatids are segregated through a tightly controlled sequence

of events utilizing a bipolar organization of the microtubule network known as the mitotic

spindle (Fees et al., 2016). In order to establish the bipolar spindle, microtubules nucleated

from two opposite organizing centers (centrosomes in higher eukaryotes and spindle pole

bodies in S. cerevisiae) must sample space by repeatedly assembling and disassembling until

contact is made with protein complexes on the chromosome. This process is described as the

search-capture model (Kirschner and Mitchison, 1986). Capturing the sister chromatid

represents an interesting targeting problem. How does a 25 nm microtubule nucleating from

the centrosome find a specific point in 3-dimensional space on the order of microns cubed?

Cells leverage the dynamic instability of a microtubule to efficiently search for and capture

chromosomes during division. This capture is mediated by a complex of proteins known as the

kinetochore complex, which links the chromosome to the spindle microtubule. The

nonequilibrium polymerization dynamics of microtubules was modeled to be more efficient at

finding a specific point, the kinetochore, than equilibrium dynamics (Holy and Leibler, 1994).

Cells utilize additional mechanisms to increase the efficiency of this process, including signaling

mechanisms that direct microtubule regulatory proteins to stabilize microtubules growing

towards chromosomes while actively disassembling unattached microtubules (Dogterom et al.,

1996; Kalab and Heald, 2008; Karsenti et al., 1984; Zhang and Bruce Nicklas, 1995). These examples illustrate how cells utilize the intrinsic dynamics of the microtubule network to build a mitotic spindle.

Following capture, the sister chromatids must become bi-oriented, whereby the kinetochores are attached to microtubules from opposite centrosomes. The Spindle Assembly

Checkpoint is a signaling pathway that monitors bi-orientation and responds to aberrant attachments by preventing progression into anaphase (Etemad et al., 2015; Foley and Kapoor,

2013; Tauchman et al., 2015). Syntelic attachments, when both sister chromatids are attached to microtubules from the same centrosome, are corrected by Aurora B/Ipl1 kinase mediated microtubule release (Holland et al., 2009; Lampson and Cheeseman, 2011; Liu et al., 2009). This surveillance mechanism monitors tension across sister chromatids that results when correctly bi-oriented (Liu et al., 2009). The tension is the result of a balance of forces within the spindle microtubule network, with the strongest evidence found in the simple budding yeast spindle

(Winey and Bloom, 2012). Spindle microtubules generate outward forces through contributions from kinesin motor proteins, which is harnessed by the kinetochore complex to pull the sister chromatids and maintain tension (Koshland et al., 1988). The outward forces are opposed by inwardly-directed forces from cohesin holding the sister chromatids together, modeled as a chromosome spring (Bouck and Bloom, 2007; Chacón et al., 2014; Stephens et al., 2013). During anaphase, kinetochore microtubules undergo a synchronous depolymerization generating a pulling force that is harnessed by the cell to separate the sister chromatids. This model of how segregation occurs was originally proposed based on the observation that chromosomes would track with depolymerizing microtubule in vitro (Koshland et al., 1988). Recently, several groups have focused on quantifying the amount of force a depolymerizing microtubule can generate using sophisticated in vitro reconstitution assays with optical traps to directly measure forces

(Dogterom and Yurke, 1997; Driver et al., 2017; Grishchuk et al., 2005; McIntosh et al., 2010;

Volkov et al., 2013). Interestingly, these studies have found that microtubule depolymerization

generates a surprising amount of force and postulates that microtubules could be an

alternative to traditional motors. Recently, a group found approximately 25% of the energy

from GTP hydrolysis is stored as protofilament strain and utilized during depolymerization. This

measurement is similar to the energy utilized by kinesin motor proteins during ATP hydrolysis

(Driver et al., 2017). These studies demonstrate that the mechano-chemical force of the microtubule network can be harnessed by the cell in order to achieve specific functions. During cell division, these forces have to be carefully regulated to ensure proper chromosome segregation.

What is the role of the microtubule network in maintaining the balance of spindle forces? The most well characterized role is at the kinetochore-microtubule attachment

interface. The kinetochore complex is a super-complex of proteins that act together to link sister chromatids to spindle microtubules. The Ndc80 complex, a kinetochore complex, has two microtubule binding motifs, the calponin homology domains of Ndc80 and Nuf2 as well as the unstructured N-terminal tail domain of Ndc80 (Alushin et al., 2010; Sundin et al., 2011; Wang et al., 2008; Wei et al., 2007; Wigge and Kilmartin, 2001). The Ndc80 N-terminal tail enhances microtubule attachment through electrostatic interactions with the negatively charged CTT of the microtubule and this interaction is a target for Aurora B phospho-regulation (Alushin et al.,

2012; DeLuca et al., 2006; Guimaraes et al., 2008; Kemmler et al., 2009; Miller et al., 2008;

Sarangapani et al., 2013; Zaytsev et al., 2015). In budding yeast, the role of the Ndc80 tail overlaps with the Dam1 complex, which forms a ring around the microtubule and recruits the

Ndc80 complex to the microtubule ends (Lampert et al., 2010). Mutations in either the Ndc80

N-terminal tail or the Dam1 complex impair proper chromosome segregation, and the

combined mutations produce severe defects further underscoring their essential nature

(Demirel et al., 2012; Kalantzaki et al., 2015; Lampert et al., 2013). Importantly, the mechanism

by which these complexes bind to microtubules is the result of weak electrostatic interactions.

Removing the negatively charged CTT domains of microtubules disrupts the binding of Ndc80 and Dam1 in vitro (Ciferri et al., 2008; Westermann et al., 2005). Many questions remain regarding the interactions of kinetochores and microtubule. How can the kinetochore complex hold on to the dynamic end of a microtubule in order to maintain tension? How does kinetochore complex mechanistically regulate spindle microtubule dynamics? Recent structural work has found dynamic end of a microtubule to exhibit curls similar to the depolymerizing end

(McIntosh et al., 2018). These regions might be targeted by the kinetochore complex and used to track the microtubule ends. Work presented here suggests that microtubule dynamics may be regulated by changing the structure of the dynamic plus ends.

Intrinsic Regulation of Microtubule Dynamics

How do cells use the same tubulin heterodimer building blocks to make microtubule networks that are structurally, dynamically and functionally different? Controlling microtubule networks is essential for cells to function properly, and as such they have evolved numerous mechanisms of regulation. Work from many groups have characterized the mechanisms of numerous microtubule associated proteins (MAPs) and their role in either promoting or repressing microtubule dynamics. Much less is known about what the tubulin heterodimers themselves contribute to the stability and function of the microtubule network.

Dynamic instability is an intrinsic property of tubulin heterodimers; it is observed in

microtubules in living cells (Cassimeris et al., 1988) as well as in purified systems (Horio and

Hotani, 1986; Mitchison and Kirschner, 1984; Walker et al., 1988). The conventional model

equates microtubule stability, or lack thereof, with abundance of unhydrolyzed nucleotide at the plus-end of the lattice, the size of the GTP-cap. Extensive lateral bonding between protofilaments allows the GTP-cap to maintain microtubule stability, as this region resists the accumulated strain stored in the rest of the microtubule lattice (Alushin et al., 2014; Manka and

Moores, 2018; Zhang et al., 2018). Once in polymer, heterodimers rapidly hydrolyze the nucleotide, inducing a conformational change in the α-tubulin subunit that weakens lateral interactions and causes outward strain along the longitudinal axis of the microtubule (Buey et al., 2006; Driver et al., 2017; Grishchuk et al., 2005; Manka and Moores, 2018; Rice et al., 2008).

This model is supported by recent work which demonstrates that the stability of the microtubule is correlated with the rate of polymerization – such that faster polymerization generates a longer GTP-cap and a more stable microtubule (Duellberg et al., 2016a). While the simplicity of this model is appealing, many questions remain unanswered. An alternative model focuses on how the structure of the microtubule end may determine its stability. This model arose from the simple observation that longer-lived microtubules are less stable or more likely to catastrophe than young microtubules in vitro (Coombes et al., 2013; Gardner et al., 2011a;

Odde et al., 1995). These models both indicate that chemical and structural conformation of the microtubule end influence dynamics and may represent a regulatory target for the cell.

What unique molecular properties intrinsic to tubulin could cells leverage or utilize to fine-tune

the behavior/activity of their microtubule networks?

The microtubule network in all known higher eukaryotic cells is a mixture of several

different isotypes of α/β-tubulin heterodimers. Pioneering work demonstrated that isotype

specificity is essential for proper organization of microtubules in axonemes (Raff et al., 1997). In

vitro studies have shown that specific tubulin isotypes can change polymerization dynamics

(Pamula et al., 2016; Vemu et al., 2017). These findings support the multi-tubulin hypothesis,

that specific tubulin isotypes have specific cellular and developmental functions (Wilson and

Borisy, 1997). The sequences of many isotypes are nearly identical; however, it is clear that the

differences are functionally important and change the dynamic behavior of the microtubule network.

The CTT domains of tubulin have the greatest sequence diversity between isotypes and therefore represent a possible module that cells may target to differentially regulate their

microtubule networks. CTTs decorate the cytosolic surface of the microtubule (Nogales et al.,

1999). Early biochemical work found that enzymatically digesting/removing CTTs enhanced

polymerization. Additionally, the digested tubulin exhibited a propensity to assemble into non-

microtubule oligomers or aggregates that were resistant to depolymerization (Bhattacharyya et al., 1985; Sackett et al., 1985; Saoudi et al., 1995; Serrano et al., 1984). This suggests that the

CTTs are functional regulators of microtubule dynamics and structure. The CTTs have also been found to sensitize microtubules to divalent cation induced depolymerization. Work with purified tubulin has demonstrated that microtubule depolymerization is highly sensitive to divalent cations such as calcium and magnesium (Correia et al., 1987; Frigon and Timasheff,

1975; Huang et al., 1985; Lee and Timasheff, 1975; Lee et al., 1978; Rosenfeld et al., 1976). The

mechanism of this activity is unclear. Structural studies using cryo-electron microscopy show

that high magnesium changes the depolymerizing end structure, by increasing the

protofilament curvature, hinting at a possible mechanism (Tran et al., 1997). Interestingly, microtubules made of digested-tubulin are insensitive to calcium induced depolymerization

(Serrano et al., 1988). These results suggest the factors like divalent cations, or microtubule binding proteins, may be altering the structure of the microtubule end through interactions with the CTTs to regulate microtubule behavior. We lack mechanistic details of how cells could utilize the molecular diversity of the CTT domains to differentially organize their microtubule networks for specific functions.

These observations have led to the central hypothesis of this thesis work: the CTTs of tubulin are regulatory modules cells use to control microtubule dynamics. This work is organized into three chapters focused on the following questions that inform the central hypothesis: 1) How do the CTTs promote proper chromosome segregation, 2) How do the CTTs intrinsically regulate microtubule dynamics and 3) What intrinsic mechanisms govern microtubule rescue?

CHAPTER II

THE NEGATIVELY-CHARGED CARBOXY-TERMINAL TAIL OF β-TUBULIN PROMOTES PROPER

CHROMOSOME SEGREGATION 1

Introduction

During mitosis, sister chromatids are separated through a sequence of events orchestrated by a bipolar network of dynamic microtubules known as the mitotic spindle. The spindle assembles from two microtubule nucleation hubs, the spindle poles, which surround the duplicated genome. Microtubules growing out from the spindle poles sample space through cycles of assembly and disassembly until they form linkages that stabilize the spindle and attach to chromatids. The spindle is stabilized by interpolar microtubules (iMTs), a class of

microtubules from opposite poles that align in an antiparallel fashion, forming extensive lateral

contacts. Chromatids attach to kinetochore microtubules (kMTs), a class of microtubules that

bind to kinetochores (KTs), multi-protein complexes that assemble at centromeric regions of

DNA. These classes of spindle microtubules play unique and important roles that guide

chromatid separation.

Sister chromatids must first become bi-oriented, with the KTs of each sister attaching to

kMTs emanating from opposite spindle poles. The progress of bi-orientation is monitored by

signaling pathways that respond to aberrant attachment. Unattached KTs are detected by the

Spindle Assembly Checkpoint, or SAC, which blocks progression into anaphase (Etemad et al.,

1 Portions of this chapter are published with permission from our previously published article Fees CP, Aiken J, O’Toole E, Giddings T, Moore JK. The negatively charged carboxy-terminal tail of β-tubulin promotes proper chromosome segregation. Molecular Biology of the Cell. 2016. 27:1786-1796. 17

2015; Foley and Kapoor, 2013; Tauchman et al., 2015). Syntelic attachments, which arise when

both sister chromatids attach to microtubules emanating from the same spindle pole, fail to

generate tension and are disrupted by the aurora B/ Ipl1 kinase (Lampson and Cheeseman,

2011).

The positions of bi-oriented sister chromatids are controlled by a balance of forces within the spindle. The strongest evidence for this force balance model comes from studies in budding yeast, which features a relatively simple and well-defined spindle (Winey and Bloom,

2012). Spindle microtubules generate outwardly-directed forces that pull each sister toward opposite spindle poles. Major contributors to outward forces include the microtubule motors kinesin-14 and kinesin-5, which organize and generate forces on iMTs, respectively, to push the spindle poles apart (Hepperla et al., 2014; Saunders and Hoyt, 1992; Saunders et al., 1997).

Outward forces are opposed by inwardly-directed forces that hold sister chromatids together.

Major contributors to inward forces include loops of pericentric chromatin that are bound

together by cohesin (Bouck and Bloom, 2007; Chacón et al., 2014; Stephens et al., 2013). This

balance of forces sets the size of the spindle and prepares sister chromatids to separate and

move to the spindle poles at anaphase.

How are spindle microtubules regulated to support these various roles? The best

studied role for spindle microtubules is in KT attachment. KTs attach to microtubules through multiple binding modules. The Ndc80 complex binds microtubules through two modules --

calponin homology domains in the Ndc80 and Nuf2 subunits and an unstructured, positively-

charged N-terminal tail domain in Ndc80 (Alushin et al., 2010; Sundin et al., 2011; Wang et al.,

2008; Wei et al., 2007; Wigge and Kilmartin, 2001). The Ndc80 tail enhances binding through

electrostatic interaction with the microtubule surface and is critical for regulating attachment.

When sisters attach to microtubules from the same spindle pole, the microtubule-KT interface is disrupted by the aurora B/ Ipl1 and Msp1 kinases, which phosphorylate the Ndc80 tail, as well as other substrates (Alushin et al., 2012; DeLuca et al., 2006; Guimaraes et al., 2008; Kemmler et al., 2009; Miller et al., 2008; Sarangapani et al., 2013; Zaytsev et al., 2015). In budding yeast, the role of the Ndc80 tail appears to overlap with the Dam1 complex, which interacts with and recruits the Ndc80 complex to microtubule plus ends (Lampert et al., 2010). Like Ndc80, Dam1 binds to microtubules through electrostatic interactions, and is targeted for phosphorylation by aurora B/Ipl1 and Mps1 (Cheeseman et al., 2002; Shimogawa et al., 2006; Westermann et al.,

2005). Mutants impairing either Ndc80’s tail or Dam1 partially disrupt chromosome segregation, but combining these mutations produces severe defects (Demirel et al., 2012;

Kalantzaki et al., 2015; Lampert et al., 2013). The complexity of the microtubule-KT interactions underscores the importance of the regulatory mechanisms that promote proper attachment.

In contrast to the KT, we lack a mechanistic understanding of how microtubules contribute to this highly regulated process. Tubulin proteins exhibit molecular diversity in genetically encoded amino acid sequences, as well as posttranslational modifications. Nearly all of this diversity is found in the negatively charged carboxy-terminal tail (CTT) regions of α- and

β-tubulin, which decorate the surface of microtubules (Roll-Mecak, 2015). CTTs are known to support electrostatic interactions with KTs. For example, biochemically removing CTTs disrupts binding to Ndc80 and Dam1 in vitro (Ciferri et al., 2008; Westermann et al., 2005). However, the

roles of CTTs in a complex cellular environment are undefined. In this study, we use mutants

that alter or ablate CTTs of α- and β-tubulin in budding yeast to test the role of CTTs in vivo and gain new insight into chromosome segregation mechanisms. Our results demonstrate a specific role for the CTT of β-tubulin in promoting the fidelity of chromosome segregation and timely cell cycle progression. Mutant cells lacking β-CTT exhibit defects in KT positioning and disorganized spindle microtubules. We identify a short region of negatively-charged amino acid residues in β-CTT that are necessary and sufficient for high fidelity chromosome segregation and provide evidence that the role of β-CTT is conserved across species.

Materials & Methods

Yeast Strains and Manipulation

General yeast manipulation, media and transformation were performed by standard

methods (Lingbeek et al., 2002). Mutant alleles of TUB1, TUB2, and TUB3 were generated at the

native chromosomal loci, as described in (Aiken et al., 2014). ndc80-112∆, which lacks the N-

terminal tail encoded by the first 112 amino acids, was generated by removing codons 2-112

from the native NDC80 locus. dam1-1 was generated by creating a C111Y substitution mutation

at the native DAM1 locus, recreating the previously identified dam1-1 allele (Jones et al., 1999).

dam1-765 was generated by cloning the dam1-765 allele (a gift from Trisha Davis, University of

Washington), containing the S221F substitution, into a plasmid containing the S pombe his5

marker, creating plasmid pJM0283. The dam1-765::SpHis5 cassette was then amplified by PCR

and integrated into the native locus, replacing the native DAM1 allele. Strains expressing CENIV labeled with GFP were derived from those described in (Brito et al., 2010). All other fluorescent

fusions were integrated at the endogenous chromosomal loci.

Chromosome Loss Assay

Retention of a non-essential chromosome fragment carrying an ochre-suppressing allele

of the SUP11 tRNA, which suppresses premature termination of ade2-101 translation, was

measured as described in(Koshland and Hieter, 1987). Cultures were grown overnight at 30oC in

synthetic –URA dropout media. The saturated cultures were diluted 100-fold and cells were counted using a hemocytometer. 200 cells were plated on YPD and grown for 3-4 days at 30oC before moving to 4oC for 2 days for color development. The plates were scanned at 600dpi resolution in 24-bit color. Colonies were either counted by hand or using a custom-made

MATLAB program. The ½ red sectored colonies were divided by the total number of colonies to determine chromosome loss frequency. Each strain was tested in at least three separate experiments, with at least 1000 colonies scored per experiment.

Liquid Growth Assay

Cells were grown in 3ml YPD overnight at 30ºC and diluted 50-fold into fresh media.

The diluted cultures were then aliquoted into a 96 well plate, with eight replicates per

experiment, and incubated at 30ºC with single orbital shaking in a Cytation3 plate reader

(BioTek, Winooski, VT). The OD600 was measured every five minutes for 24 hours. Doubling

time was calculated by fitting the growth curves to a non-linear exponential growth curve

(GraphPad Prism v6.0, San Diego, CA).

Budding Duration Analysis

Budding duration was determined using time-lapse microscopy of α-factor synchronized

cultures grown to mid-log phase (WT: 2 hrs; tub2-430Δ 3 hrs). Cultures were released from

arrest by washing twice and resuspending cells in fresh media supplemented with protease,

and then mounted on slides and imaged every 15 minutes for 135min. Images were collected

on a Nikon Ti-E microscope equipped with a 1.49 NA 100× CFI 60 Apochromat objective, PEKA

light engine (Lumencore, Beaverton, OR) and sCMOS camera (ORCA-Flash 4.0 LT; Hamamatsu

Photonics; Shizuoka, Japan) using NIS Elements software (Nikon).

Time Course of Pds1 Levels

Cells were grown to early log phase at 30ºC in 100ml YPD and arrested with 2 pulses of

alpha factor for 90 and 60 min, respectively. The cells were pelleted, washed with water,

resuspended into 130mL fresh YPD, and returned to the 30ºC shaking incubator. 15mL culture

samples were collected at 15min intervals, washed, pelleted, and lysed by bead beating. The total protein concentration of the clarified lysate was determined by Bradford assay, and all

samples were normalized. Samples were run on SDS PAGE, transferred to nitrocellulose

membrane, and blocked overnight at 4ºC. Membranes were probed with mouse-α-myc

(Thermo Scientific MA1-980; @ 1:1000) and goat-α-mouse-800 (LI-COR 926-32210; @ 1:10000)

and imaged on an Odyssey Imager (LI-COR Biosciences, Superior, NE). Intensities of Pds1-myc

bands were quantified using ImageJ. Pixel intensities were measured across an equivalent area

for each western blot lane within a time course, and the intensities of these regions were

divided by the intensity of t=0 to obtain a ratio of each timepoint to the initial level.

Microscopy and Image Analysis

Images were collected on a Nikon Ti-E microscope equipped with a 1.45 NA 100× CFI

Plan Apo objective, piezo electric stage (Physik Instrumente, Auburn, MA), spinning disk

confocal scanner unit (CSU10; Yokogawa), 488-nm and 561-nm lasers (Agilent Technologies,

Santa Clara, CA), and an EMCCD camera (iXon Ultra 897; Andor Technology, Belfast, UK) using

NIS Elements software (Nikon). Cells were grown asynchronously to early log phase in

nonfluorescent media, mounted on a slab of 2% agarose, and sealed beneath a coverslip with

VALAP (Vaseline, lanolin and paraffin at 1:1:1). During acquisition, the temperature of the stage was 25°C. Z series consisted of 21 images separated by 300nm.

Preanaphase cells were identified in asynchronous populations based on spindle length,

which was determined by measuring the distance between SPBs labeled with Spc110-RFP. The

distribution of all spindle lengths from asynchronous cells shows a peak that reflects metaphase

spindle length, and the mean values for WT (1.8 µm) and tub2-430Δ (1.6 µm) cells are consistent with the average spindle length just prior to anaphase onset, which we previously defined using time-lapse imaging (Figure 2.3.5; (Aiken et al., 2014)). We therefore defined

‘preanaphase’ as bipolar spindles shorter than the mean of the total population.

The volume occupied by Nuf2-GFP was determined from single-timepoint confocal

stacks of cells expressing Nuf2-GFP and Spc110-DsRed from the native loci. Preanaphase cells

were identified and segmented from the field using a custom ImageJ macro. Following

segmentation, a low bandpass Butterfield filter and semi-automated Otsu’s intensity

thresholding application were used to identify Nuf2-GFP signal and generate a binary mask.

Masks were then converted to ROIs using connected components pixel labeling. The total

number of pixels in the ROI was then converted into µm3 using an empirically derived pixel

calibration in x and y (57 nm/pixel) and z-depth of the stack (300 nm). We validated this approach by comparing the sum of Nuf2-GFP pixel values measured in each cell and found that

the data exhibited a normal distribution that was not different between WT and β∆CTT mutant

cells.

The distribution of Nuf2-GFP along the spindle axis was measured by using a custom

MATLAB program to determine the centroid coordinates of the spindle poles (labeled with

Spc110-DsRed). Briefly, image stacks were compressed to a sum intensity projection, then

processed using a Laplacian filter and Otsu’s intensity thresholding to generate binary masks. 2-

dimensional ROIs were defined using connected components pixel labeling, and the x,y

coordinates of the centroids of these ROIs were calculated using MATLAB. Spindle lengths were

calculated between centroids in MATLAB using a distance formula. Each sum intensity

projection image was rotated using calculated angle which aligned the spindle along a

horizontal axis. Images were then cropped to 10 pixels (570nm) on either side of the SPBs and 7

pixels above and below the SPB-SPB axis (800nm total). Linescans of Nuf2-GFP intensity were

then generated by summing pixel values in each 14-pixel column along the SPB-SPB axis.

Centroids of CENIV-GFP and Spc110-RFP were determined by first segmenting individual

cells and then using a custom MATLAB program to determine the centroid coordinates. Briefly,

image stacks were processed using Laplacian filter and Otsu’s intensity thresholding to generate

binary masks. 3-dimensional ROIs were defined in z-stacks using connected components pixel

labeling, and the x,y,z coordinates of the centers of these ROIs were calculated using MATLAB.

kMT lengths were calculated in MATLAB using a 3-dimensional distance formula, based on the

distances from the centroid of a bi-oriented sister CENIV and the nearest SPB. kMT angles were

calculated using the inverse cosine of kMT length divided by the distance from CENIV to the

SPB-SPB axis.

Electron Tomography

Cells were prepared for electron microscopy as described in(Giddings et al., 2001).

Briefly, aliquots from log phase liquid cultures were collected onto 0.45 um Millipore filter by vacuum filtration. The cells were then frozen using a Wohlwend Compact 02 high pressure freezer, freeze substituted in 0.25% glutaraldehyde and 0.1 % uranyl acetate in acetone and embedded in Lowicryl HM20 resin. Serial sections (250nm) were collected onto formvar-coated slot grids and post stained with 2% uranyl acetate followed by lead citrate. Colloidal gold particles where affixed to the sections to serve as fiducial markers for alignment.

Tomography was performed essentially as described in(O’Toole et al., 2002). Dual axis tilt series were collected using a Tecnai F20 or F30 microscope using the SerialEM program for data acquisition (Mastronarde, 2005). Tilt series data were acquired from serial sections at a pixel size of 1.2 nm using a Gatan CCD camera. Tomographic volumes were computed using the

IMOD software package(Kremer et al., 1996; Mastronarde, 1997). Volumes from 3-4 serial sections were joined and the spindle microtubules were modeled using the 3dmod program of

the IMOD software package. Microtubule lengths were computed from the model contour

data.

Benomyl Sensitivity

Cells were grown in YPD to saturation at 30ºC and a 10-fold dilution series of each was spotted to either YPD or YPD supplemented with 10µg/mL benomyl. Plates were grown at 30ºC for 2 days.

Sequence Logo

Probability-weighted Kullback-Leibler sequence logo of β-CTT residues 426-445 was created using the Seq2Logo server (http://www.cbs.dtu.dk/biotools/Seq2Logo)(Thomsen and

Nielsen, 2012); based on 24 β-tubulin sequences from diverse species. Positive bit scores indicate amino acids that are enriched at each position and negative bit scores indicate amino acids that are depleted. Negatively charged residues are labeled red and positively charged residues are labeled yellow.

Results

β-CTT Promotes Proper Chromosome Segregation

We first tested whether CTTs of α- or β-tubulin are important for chromosome segregation by measuring the loss of a non-essential chromosome fragment (Figure 2.1.1;

(Spencer et al., 1990)). Mutants lacking β-CTT (tub2-430∆) exhibit a 5-fold increase in chromosome loss events, compared to WT (Figure 2.1.2). In contrast, mutants either lacking the major α-CTT (tub1-442∆) or all α-CTTs (tub1-442∆ tub3-442∆) are indistinguishable from WT in this assay. Mutants lacking both α-CTT and β-CTT exhibit a rate of chromosome loss that is similar to mutants lacking β-CTT alone (Figure 2.1.2). Therefore, β-CTT specifically promotes chromosome segregation.

To examine whether β-CTT might function in a common pathway with KT protein complexes, we used the chromosome loss assay to test for genetic interactions. Yeast mutants that disrupt the Ndc80 tail perturb chromosome segregation, and exhibit additive effects when combined with Dam1 mutants(Demirel et al., 2012; Kalantzaki et al., 2015; Kemmler et al.,

2009). We found that double mutants combining Ndc80 tail truncation (ndc80-112∆) with β-CTT truncation lost chromosomes at a rate similar to β-CTT single mutants, but greater than ndc80-

112∆ single mutants (Figure 2.1.2). In contrast, double mutants combining Dam1 impairment

(dam1-1, at semi-permissive temperature of 25ºC) with β-CTT truncation lose chromosomes at

a significantly higher rate than either single mutant (Figure 2.1.2). This additive effect is

reminiscent of the genetic interaction between ndc80-112∆ and dam1-1 and indicates that cells

depend on the function of both β-CTT and the N-terminal tail of Ndc80 when Dam1 is impaired.

β-CTT is Necessary For Timely Progression Through Mitosis

If chromosome loss in mutants lacking β-CTT arises from defects in spindle assembly, then these mutants might exhibit a Spindle Assembly Checkpoint (SAC)-dependent delay in cell cycle progression. We performed a series of experiments to test this prediction. First, we used liquid growth assays to show that mutants lacking β-CTT exhibit a 20% increase in doubling time compared to WT controls and mutants lacking all α-CTTs (tub1-442∆ tub3-442∆; Figure 2.2.1).

Second, we examined individual synchronized cells, measuring the time from bud emergence

(beginning of S-phase) to separation (end of mitosis) by time-lapse DIC microscopy. We found

that the duration of S/G2/M is longer and more variable in mutants lacking β-CTT, compared to

WT controls (Figure 2.2.2). Furthermore, 17% of β-CTT mutants remained as large budded cells

at the end of our 135-minute experiment, compared to less than 1% of WT cells. Finally, to

specifically measure anaphase, we monitored the kinetics of Pds1/securin degradation in

synchronized cells. In WT cells, Pds1/securin levels rise during S-phase (15-30min), decline at

anaphase onset, and rebound when cells enter the next S-phase (90min; Figures 2.2.3 & 2.2.4;

(Cohen-Fix et al., 1996)). In mutants lacking β-CTT, the decline in Pds1/securin levels is slower

and does not rebound during the course of our experiment (Figures 2.2.3 & 2.2.4). This suggests

that entry into anaphase and the subsequent S-phase may be delayed and/or variable in β-CTT

mutants. Together with our previous finding that mutants lacking β-CTT depend on SAC activity

for viability(Aiken et al., 2014), these results indicate that β-CTT is important for normal

progression through mitosis and are consistent with an important role for β-CTT during spindle

assembly.

β-CTT Promotes Kinetochore Positioning

We examined KT positioning to determine how β-CTT might contribute to sister chromatid separation. During spindle assembly in yeast, KTs resolve into two clusters as they

attach to microtubules emanating from the two spindle pole bodies (SPBs;(Goshima and

Yanagida, 2000; He et al., 2000; Pearson et al., 2001)). We monitored KT position by labeling all

KTs with a functional fusion of GFP to Nuf2. In addition to WT and β-CTT mutants, we also

included dam1-765 mutants in our analysis, as a positive control. dam1-765 is a point mutant in

the Dam1 complex that was previously shown to cause KTs to cluster near the spindle poles,

away from the spindle center(Shimogawa et al., 2006).

We first compared cells with spindle lengths indicative of metaphase (see Materials and

Methods). WT cells with metaphase-length spindles consistently exhibit two separated clusters

of Nuf2-GFP (Figures 2.3.1 & 2.8.1). In contrast, β-CTT mutants exhibit variable Nuf2-GFP localization, including single clusters, two WT-like clusters, and two clusters positioned very close to the SPBs (Figures 2.3.2 & 2.8.2). dam1-765 mutants consistently exhibit two clusters of

Nuf2-GFP very close to the SPBs, as expected (Figures 2.3.3 & 2.8.3). This initial result suggests that KT position may be more variable in β-CTT mutants.

We used several approaches to quantify differences in Nuf2-GFP localization across populations of pre-anaphase cells. First, we measured the volume within the cell that is occupied by Nuf2-GFP, based on single timepoint confocal Z-series imaging. This analysis shows that Nuf2-GFP samples a larger volume within β-CTT mutant cells than in WT controls (Figure

2.3.4). Importantly, total Nuf2-GFP signal per cell is similar in both WT and mutant strains;

therefore, changes in Nuf2-GFP localization in β-CTT mutants are attributable to changes in

position, rather than changes in protein levels or the number of KTs (Figure 2.3.4). In separate

experiments we found that the localization of an inner KT component, the centromeric histone-

variant Cse4/CENP-A, is similarly altered in mutants lacking β-CTT (Figures 2.8.4 & 2.8.5).

We next examined Nuf2-GFP localization across different spindle lengths. KTs only

resolve into two clusters once the spindle reaches a sufficient length(Marco et al., 2013). In the

course of our experiments, we observed that shorter spindles are more abundant in β-CTT

mutants (Figure 2.3.5), raising the possibility that differences in Nuf2-GFP localization could be

caused by differences in spindle length. To test this possibility, we compared the percentages of

cells exhibiting a bi-lobed distribution of Nuf2-GFP at different spindle lengths. This analysis

shows that a similar percentage of WT and β-CTT mutant cells exhibit bi-lobed Nuf2-GFP, regardless of spindle length (Figure 2.3.6). In contrast, more dam1-765 mutant cells exhibit bi- lobed Nuf2-GFP across all spindle lengths examined (Figure 2.3.6). In addition to comparing the separation of KT clusters, we also examined the positions of KT clusters along the spindle axis.

In WT spindles shorter than 1100nm, Nuf2-GFP localizes to a single lobe at the center of the spindle (Figure 2.3.7). In WT spindles longer than 1100nm, Nuf2-GFP signal shifts away from the spindle center and toward the poles, consistent with a bi-lobed distribution, and accumulates

further from the spindle center as spindle length increases (Figures 2.3.7 & 2.8.1). In dam1-765

mutants, Nuf2-GFP shifts away from the spindle center in spindles as short as 500nm and

becomes strongly concentrated near the poles as spindle length increases (Figures 2.3.7 &

2.8.3). Our results for WT and dam1-765 mutant cells are in excellent agreement with previous

findings(Marco et al., 2013; Shimogawa et al., 2006). β-CTT mutants do not consistently

accumulate Nuf2-GFP near the spindle poles. Although many β-CTT mutant cells exhibit two clusters of Nuf2-GFP, the positions of these clusters in the spindle are highly variable from cell

to cell. As a result, the cumulative distributions do not show clear peaks for β-CTT mutant cells,

even at longer spindle lengths that are indicative of metaphase and early anaphase (>1600nm;

Figure 2.3.7). These results suggest that β-CTT is required for maintaining the KT position during

spindle assembly and into anaphase.

To further examine the role of β-CTT in maintaining KT position, we used time-lapse

microscopy to image Nuf2-GFP in living cells as they transitioned into anaphase. In preanaphase

WT control cells, Nuf2-GFP signal gradually resolves into a bi-lobed distribution until the two

lobes rapidly move apart, indicating the onset of anaphase (Figure 2.3.8). In preanaphase β-CTT

mutant cells, Nuf2-GFP resolves into a bi-lobed distribution, but this state is interrupted by

prolonged periods of collapse into a single, unresolved lobe (Figure 2.3.8, arrow). At anaphase,

the rate of Nuf2-GFP separation in β-CTT mutants is slower than in WT cells; this is consistent

with our previous finding that the rate of spindle elongation is slower in β-CTT mutants(Aiken et

al., 2014). These results support our conclusion that β-CTT is necessary to maintain KT position

in the spindle.

β-CTT is Necessary to Align Kinetochores with the Spindle Axis

To identify the cause of KT positioning defects, we imaged a single pair of sister KTs by

labeling chromosome IV with GFP inserted 2 kilobases from the centromere (CENIV). Imaging

CENIV sisters clearly reveals the bi-oriented state – GFP-labeled CENIV resolves into two foci only when attached to kMTs from opposite spindle poles (Figures 2.4.1 & 2.4.2; 2.9.1 &

2.9.2;(Pearson et al., 2004)). Surprisingly, we found that a greater percentage of preanaphase

β-CTT mutant cells exhibit bi-oriented CENIV, compared to preanaphase WT cells (Figure 2.4.3).

It is important to note that our method did not resolve CENIV foci separated by less than

470nm; therefore, the ‘unseparated’ population may include sisters that are bi-oriented, but closely apposed. Nevertheless, β-CTT is clearly not necessary for bi-orientation.

We next used our CENIV imaging assay to compare the positions of bi-oriented sister centromeres in the three-dimensional space of the spindle. To determine whether centromeres are displaced further from the SPB-SPB axis, we calculated the alignment angles of each CENIV sister relative to the SPB-SPB axis (see Materials and Methods). These angles are significantly

increased in β-CTT mutants compared to WT controls (70 ± 2º (mean ± SEM) in tub2-430∆,

compared to 58 ± 3º for WT; Figures 2.4.4 & 2.9.2). kMT lengths, i.e. the distances between

each CENIV and the nearest SPB, are similar in β-CTT mutants and WT controls, as are the

distances between CENIV sisters (Figures 2.4.5, 2.9.3 & 2.9.4). Spindle lengths are slightly

shorter in β-CTT mutants (p=0.06; Figures 2.3.5, 2.4.5 & 2.9.5). These data suggest that β-CTT may normally act to confine bi-oriented sister centromeres near the SPB-SPB axis.

The positions of bi-oriented sister centromeres are determined by a balance of outward forces that pull them apart (tensile attachments to kMTs and forces from iMTs on the SPBs) and inward forces hold them together (cohesin, condensin)(Bouck and Bloom, 2007; Chacón et al.,

2014; Gardner et al., 2008; Stephens et al., 2013). To determine how β-CTT contributes to

forces in the spindle, we combined the β-CTT truncation mutation with cohesin mutations that

relax inward forces. Disrupting pericentric cohesin with an mcm21∆ null mutation increases the

percentage of preanaphase cells with bi-oriented CENIV, increases the distance between sister

centromeres, and increases spindle length (Figures 2.4.3 & 2.4.5; (Stephens et al., 2011)). We reasoned that if centromere positioning defects in β-CTT mutants are primarily caused by weaker outward forces, then simultaneously relaxing inward forces with mcm21∆ might rescue these defects and lead to intermediate phenotypes. We found that although double mutants combining tub2-430∆ with mcm21∆ do rescue CENIV alignment angles, spindle lengths are increased similar to mcm21∆ single mutants (Figures 2.4.4 & 2.4.5). In addition, double mutants exhibit significant increases in kMT length and distance between CENIV sisters, and a greater percentage of cells with separated CENIV than either single mutant alone (Figures 2.4.3 – 2.4.5).

Therefore, disrupting cohesin appears to exacerbate some spindle defects in β-CTT mutants.

β-CTT Regulates Microtubule Organization During Spindle Assembly

To examine how changes in microtubules might accompany changes in KT position, we

mapped the organization and lengths of spindle microtubules using electron tomography. We

first compared microtubules in preanaphase spindles, which we identified based on spindle

length. Preanaphase WT cells exhibit ~32 short microtubules (<600nm) and ~8 longer

microtubules (>800nm) (Figure 2.5.1). These correspond to kMTs that attach to each KT, and

iMTs that interdigitate to form antiparallel overlaps that stabilize the bipolar spindle,

respectively(Winey et al., 1995). Preanaphase spindles in β-CTT mutants differ from WT

controls in several respects. First, the distance between SPBs is decreased (Figures 2.10). This is consistent with our light microscopy results and our previous findings from time-lapse imaging

(Figure 2.3.5; (Aiken et al., 2014)).

Second, the two spindle halves are often misaligned and exhibit few interdigitating

microtubules (Figures 2.5.2 & 2.10). This is reminiscent of an early stage of spindle assembly,

before the bipolar state is stabilized(Winey and O’Toole, 2001). Three out of four tomograms of

preanaphase β-CTT mutant spindles exhibit a clear misalignment of spindle halves, therefore it

is unlikely that this represents a transient intermediate. Instead, β-CTT mutants may linger in

this state.

Third, β-CTT mutant spindles contain more microtubules and exhibit differences in

microtubule lengths. In general, β-CTT mutant spindles contain a greater frequency of shorter

(<200nm) microtubules, and fewer longer microtubules (Figure 2.5.3). Many of these

microtubules deviate far from the SPB-SPB axis (Figures 2.5.2 & 2.10). The changes in microtubule length and alignment in β-CTT mutants may explain changes in KT position that we observed by light microscopy, including aberrant Nuf2-GFP distribution and deviation of KTs from the SPB-SPB axis (Figures 2.3 & 2.4).

Fourth, β-CTT mutant spindles contain microtubule ‘fragments’ that are not connected to SPBs and exhibit flared morphology at both ends (Figures 2.5.2 & 2.10, colored yellow).

Fragments vary in length and number per cell, as do their location in the spindle. We did not find microtubule fragments in WT cells, and, to our knowledge, they have not been previously reported.

Although β-CTT mutants exhibit microtubule defects in preanaphase spindles, we did not observe defects in anaphase spindles. Anaphase cells for both WT and the β-CTT mutant exhibit ~16 very short (<100nm) microtubules from each SPB, along with a small number of much longer microtubules that overlap in the center of the spindle (Figures 2.5.4 – 2.5.5 &

2.10). These represent kMTs and iMTs, respectively. The lengths and number of these microtubules are not significantly different between mutant and WT. We conclude that β-CTT is important for regulating microtubule organization during spindle assembly.

Mapping the Region of β-CTT that is Important for Chromosome Segregation

Finally, we sought to gain insight into the mechanism of β-CTT function by mapping the amino acids that are important for its role in chromosome segregation. Yeast β-tubulin is

enriched for negatively charged amino acids at residues 431-438 (Figure 2.6.1). We term this

region the ‘acidic patch’. Restoring these 8 amino acids rescues the defects of tub2-430∆,

including chromosome segregation, growth rate, and viability in the absence of SAC activity

(Figures 2.6.2 & 2.6.3; data not shown). Restoring the acidic patch and the next 7 residues

(tub2-445∆) also rescues the defects of tub2-430∆ (Figures 2.6.2 & 2.6.3). These results indicate that the acidic patch is sufficient for β-CTT function in chromosome segregation.

Although the sequence of the acidic patch is not strictly conserved across β-tubulin isotypes, the enrichment of negatively charged residues is a common feature (Figure 2.6.4). To determine whether negative charge is necessary for function, we replaced glutamate and aspartate residues in β-CTT with glutamines. Neutralizing these residues in tub2-438∆ and tub2-

445∆ truncation mutants causes levels of chromosome loss that are equivalent to complete removal of β-CTT, and slightly increases doubling time (Figures 2.6.1 – 2.6.3). Both of these alleles are viable in combination with mutants that ablate the SAC (mad2∆); however, double mutants are sensitive to a low dose (10µg/mL) of the microtubule destabilizing drug, benomyl

(Figure 2.6.5). Neutralizing acidic patch residues in the context of full length β-CTT (tub2-polyQ) causes mild phenotypes -- slightly increasing chromosome loss with no effect on growth or synthetic effects with SAC mutants (Figures 2.6.1 – 2.6.4). The charge of the acidic patch is therefore necessary in the context of truncated β-CTT; however, remaining acidic residues in β-

CTT may provide some compensation in the context of full length β-CTT.

To test whether chromosome segregation is sensitive to sequence differences across β- tubulin isotypes, we replaced the native yeast β-CTT with the CTT from the human βIII isotype

TUBB3 (Figure 2.6.1). This tub2::CTTTUBB3 chimera fully rescues β-CTT function in chromosome segregation and growth rate, and is viable in the absence of SAC activity (Figures 2.6.2 – 2.6.4;

data not shown). Chromosome segregation is, therefore, highly sensitive to the negative charge

of β-CTT, but less sensitive to specific amino acid sequence.

Discussion

Our study provides the first in vivo evidence of tubulin’s contribution to the electrostatic interactions that regulate the mitotic spindle. We demonstrate that negatively-charged amino acid residues within the CTT region of β-tubulin promote proper chromosome segregation.

Ablating these residues or neutralizing the charges of their side chains increases chromosome segregation errors and makes cells reliant on the SAC for viability. We propose that the principal role of β-CTT is to organize spindle microtubules to stabilize the spindle and promote the ordered separation of sister chromatids.

The role of β-CTT in spindle assembly is supported by our findings as well as previous studies. Time course experiments with synchronized cells show that mutants lacking β-CTT delay the formation of bipolar spindles (Aiken et al., 2014). In the present study we find that preanaphase spindles are shorter in β-CTT mutants, based on distributions of spindle lengths in asynchronous populations of cells (Figure 2.3.5) and specifically in metaphase cells with bi-

oriented CENIV (Figure 2.4.4). These new results, combined with our previous findings, indicate

that β-CTT may contribute to both the early stages of spindle assembly and the subsequent

maintenance of spindle stability in metaphase. Our tomographic analyses provide insight into

the specific role of β-CTT in these contexts. In contrast to wild-type controls, β-CTT mutants

show misalignment of the two halves of the preanaphase spindle, and a concomitant lack of

interdigitating iMTs (Figures 2.5 & 2.10). We analyzed four β-CTT mutant cells with

preanaphase spindles (determined by the distance between SPBs and the lengths of kMTs), and

all four exhibit these defects. These characteristics are reminiscent of wild-type cells during the early stages of spindle assembly, and suggest that β-CTT may be necessary for either efficient transit through early stages of spindle assembly or the stabilization of the metaphase spindle

(Winey and O’Toole, 2001). Moreover, the kMTs in the mutant cells appear to be grossly

disorganized (Figure 2.5.2). The disorganization of kMTs is consistent with disorganization we

observed at the levels of kinetochores and sister centromeres (Figures 2.3 & 2.4). Importantly,

structural defects in β-CTT mutants are specific to preanaphase spindles; we did not observe

similar defects in tomograms of anaphase spindles (Figures 2.5 & 2.10). Determining the

molecular mechanism of β-CTT function during spindle assembly will require further study;

however, we speculate that this mechanism may involve electrostatic interactions with

positively-charged microtubule-binding proteins that promote the alignment and/or cross-

linking of interdigitating iMTs.

How could microtubule disorganization lead to defective KT positioning and, ultimately,

chromosome missegregation? Our results suggest that β-CTT is not necessary to bi-orient

individual sister KTs but is necessary to cluster KTs near the SPBs and maintain the alignment of

KTs along the SPB-SPB axis (Figures 2.3 & 2.4). Importantly, the defects we observe in β-CTT

mutants are quite different from those observed in mutants that alter KT-microtubule binding

(e.g. dam1-765; Figure 2.3.7; (Shimogawa et al., 2006)) or inhibit bi-orientation (e.g. ipl1 or stu2

mutants; (Marco et al., 2013)). We propose that β-CTT may contribute to both the generation

of outward forces on KTs and the maintenance of kMTs along the spindle axis (Figure 2.7). Our

analysis of tub2-430∆ mcm21∆ double mutants support this model. If the spindle defects in β-

CTT mutants were attributable to weakened outward forces alone, then simultaneously

weakening inward forces with the cohesin mutant would be expected to restore the balance of

forces in the spindle and double mutants should therefore exhibit intermediate phenotypes

relative to either single mutant alone. This relationship has been previously demonstrated for

mutants that disrupt kinesin-5 motors (Bouck and Bloom, 2007). We do find that tub2-430∆

mcm21∆ double mutants rescue KT alignment along the SPB-SPB axis, indicating that

imbalanced spindle forces contribute to KT positioning defects (Figure 2.4.4). However, tub2-

430∆ mcm21∆ double mutants also exhibit longer spindles that are similar to mcm21∆ single

mutants (Figure 2.4.5) and an even greater percentage of cells with separated CENIV than

either single mutant (Figure 2.4.3). Therefore, loss of the cohesin network (mcm21Δ) is

synergistic with loss of β-CTT mutants, indicating that β-CTT also has an important role in

clustering centromeres along the spindle axis. Taken together, these results show that while

outward forces are impacted in β-CTT mutants, the mechanism could also involve defective

kinetochore microtubule organization. Another question that arises is whether the distortions

of spindle structure in β-CTT mutants alter normal functions like the surveillance of KT

attachment by the SAC? This could reconcile the paradox of how β-CTT mutants experience cell

cycle delay and chromosome segregation errors, despite the apparent efficiency of bi- orientation.

An alternative, but not mutually exclusive model is that β-CTT could regulate electrostatic interactions at the KT-microtubule interface. We find no evidence of deficient bi-

orientation in β-CTT mutants, indicating that interactions with tubulin CTTs are not necessary for KTs to attach to microtubules in budding yeast. This result highlights molecular differences

between the yeast and mammalian systems, where electrostatic interactions play an important

role in regulating KT attachment(DeLuca et al., 2006; Guimaraes et al., 2008). One possible

explanation for these differences is that yeast possess other unique modes for binding KTs to microtubules, such as the Dam1 complex, which might act in parallel to electrostatic interactions with tubulin CTTs. Consistent with this, our epistasis experiments in Figure 2.1.2 reveal strongly additive chromosome segregation defects in double mutants that simultaneously ablate β-CTT and impair the Dam1 complex. Therefore, fully functional Dam1 appears to become necessary for chromosome segregation when β-CTT is missing, consistent

with the two acting in parallel. Previous studies of yeast mutants that lack the N-terminal tail of

Ndc80 found a similar relationship with the Dam1 complex (Demirel et al., 2012). Collectively,

these results support a model in which the Ndc80 tail and β-CTT act together to support a KT-

microtubule interface that acts in parallel to the Dam1 complex.

Our findings raise the question of whether molecular changes at the level of β-CTT,

either genetically encoded or posttranslational, may provide a mechanism for regulating mitotic

spindle function. CTT sequences are highly divergent across species and across tubulin isotypes

within a species. Furthermore, CTTs are major targets of posttranslational modifications that

dramatically alter CTT structure and charge (Roll-Mecak, 2015). Together, these differences

create a highly variable molecular landscape at the microtubule surface. Our results indicate

that sequence differences between β-tubulin isotypes do not alter spindle function, as long as

the CTT region contains a sufficient quorum of negatively charged amino acid residues (Figures

2.6). Whether post-translational modifications of β-CTT alter spindle function remains an open and important question.

Finally, chromosome segregation is a complex biological process that requires the corradiated interactions of many proteins with the microtubule network. Significant effort by many researchers has gone into understanding the specific role of these proteins in this complex process, however the contribution of the microtubules themselves is less clear. Our results demonstrate that β-CTT is required for the proper organization of microtubules within the mitotic spindle. How could the β-CTT organize spindle microtubules? Our results indicate that cells lacking β-CTT exhibit a variable disorganization of their kinetochores (Figure 2.3.2).

Importantly, this disorganization changes over time (Figure 2.3.8), hinting that the spindle microtubules themselves may sluggish or impaired dynamics. Consistent with this model, individual astral microtubules in mutants lacking β-CTT were longer and more stable than controls (Aiken et al., 2014). I posit that β-CTT may promote mitotic spindle organization as an intrinsic regulator of microtubule dynamics. The focus of the next section in this thesis is on understanding how β-CTT regulates microtubule dynamics.

Figure 2.1.1 Schematic of chromosome loss assay

Figure 2.1.2 Chromosome loss frequency Chromosome loss frequency per 1000 divisions. Values are based on the total number of half- sectored colonies divided by the total number of colonies analyzed, from at least three separate experiments. Error bars are standard error of proportion. Single asterisk denotes P = 0.01, double asterisks denotes P < 0.0001; determined by Chi-square with Yates’ correction. WT, n=14866; tub2-430Δ, n=16299; tub1-442Δ, n=8561; tub1-442Δ tub2-430Δ, n=5504; ndc80- 112Δ, n=35662; ndc80-112Δ tub2-430Δ, n=15479; dam1-1, n=12816; dam1-1 tub2-430Δ, n=7405.

Figure 2.2.1 Normalized doubling times for wild type and CTT mutant cells Values are the mean from at least four separate experiments. Error bars are SEM. Double asterisks denotes P < 0.0001; determined by t-test.

Figure 2.2.2 Duration of S/G2/M Duration of S/G2/M determined by measuring the time from bud emergence to separation in cells released from START. Dashed lines are the medians. Wild type, n=617; tub2- 430Δ, n=561.

Figure 2.2.3 Time course of Pds1/securin levels in synchronized cells Time course of Pds1/securin levels in synchronized cells released from START. Cells expressing Pds1-13myc were collected at 15-minute intervals, prepared for western blots and probed with myc antibodies.

Figure 2.2.4 Pds1-13myc signal at each timepoint Pds1-13myc signal at each timepoint normalized to t=0. Values are averages from three experiments. Error bars are SEM.

Figure 2.3.1 Maximum intensity projections of wild type cells expressing Nuf2-GFP Maximum intensity projections from 3D confocal images of WT cells expressing Nuf2- GFP and Spc110-DsRed. Scale bars = 1µm.

Figure 2.3.2 Maximum intensity projections of tub2-430∆ cells expressing Nuf2-GFP Maximum intensity projections from 3D confocal images of tub2-430∆ cells expressing Nuf2-GFP and Spc110-DsRed. Scale bars = 1µm.

Figure 2.3.3 Maximum intensity projections of dam1-765 cells expressing Nuf2-GFP Maximum intensity projections from 3D confocal images of dam1-765 cells expressing Nuf2-GFP and Spc110-DsRed. Scale bars = 1µm.

Figure 2.3.4 Volumetric distribution of Nuf2-GFP signal (i) Yellow bars denote the mean. P-value determined by t-test. Strains: wild type, n=101; tub2-430Δ, n=117. (ii) Sum of intensities of Nuf2-GFP in cells analyzed in 2.3.4i.

Figure 2.3.5 Distribution of spindle lengths in asynchronous populations of cells Distribution of spindle lengths in asynchronous populations of cells.

Figure 2.3.6 Proportion of cells exhibiting two peaks of Nuf2-GFP signal Proportion of cells exhibiting two peaks of Nuf2-GFP signal, as a function of spindle length. Error bars are standard error of proportion.

Figure 2.3.7 Distributions of Nuf2-GFP signal Distributions of Nuf2-GFP signal measured from the center of the spindle toward the spindle poles and sorted into bins according to spindle lengths. Intensity is internally normalized to the total GFP intensity of each cell.

Figure 2.3.8 Kymograph of Nuf2-GFP in wild type and tub2-430∆ cells Kymograph of Nuf2-GFP in wild type and tub2-430∆ cells. Horizontal lines represent time points at 2.5-minute intervals, with pixel values representing the sum of 15 pixels perpendicular to the spindle axis. Arrow shows Nuf2-GFP collapsing into a single lobe in the tub2-430∆ cell.

Figure 2.4.1 Maximum intensity projections of cells expressing unseparated CENIV-GFP

Maximum intensity projections from 3D confocal images of wild type and tub2-430∆ cells expressing CENIV-GFP and Spc110-tdTomato. Scale bars = 1µm. Preanaphase cells containing a single focus of CENIV-GFP were classified as unseparated.

Figure 2.4.2 Maximum intensity projections of cells expressing separated CENIV-GFP

Maximum intensity projections from 3D confocal images of wild type and tub2-430∆ cells expressing CENIV-GFP and Spc110-tdTomato. Scale bars = 1µm. Preanaphase cells containing two foci of CENIV-GFP were classified as separated.

Figure 2.4.3 Percent of preanaphase cells exhibiting separated CENIV Double asterisks denote P < 0.0001; determined by Fisher’s exact test. Wild type, n=1420; tub2-430Δ, n=857; mcm21Δ, n=685; mcm21Δ tub2-430Δ, n=276.

Figure 2.4.4 Mean angle between separated CENIV foci and the SPB-SPB axis Values are mean ± 95% ci. Double asterisks denote P < 0.0001; determined by t-test. Wild type, n=61 cells; tub2-430Δ, n=109; mcm21Δ, n=131; mcm21Δ tub2-430Δ, n=70.

Figure 2.4.5 Mean distances between separated CENIV foci and the Spindle Pole Bodies Mean distances between CENIV foci and the proximal SPBs, between separated CENIV foci, and between spindle pole bodies. Values are mean ±95% ci. Single asterisk denotes P < 0.01, double asterisks denotes P < 0.0001; determined by t-test.

Figure 2.5.1 EM tomography of microtubule in wild type cell (i) Model of spindle microtubules based on a tomographic volume of a preanaphase wild type cell. Green lines represent microtubules from one SPB, pink lines represent microtubules from the other SPB. All scale bars = 200 nm. (ii) Histogram of microtubule lengths in the wild type cell modeled in 2.5.1i.

Figure 2.5.2 EM tomography of microtubule in tub2-430∆ cell (i) Model of spindle microtubules based on a tomographic volume of a preanaphase tub2-430∆ cell. Yellow lines represent microtubule fragments. (ii) Histogram of microtubule lengths in the tub2-430∆ cell modeled in C.

Figure 2.5.3 Histogram of all microtubule lengths from tomograms Histograms of all microtubule lengths from 2 wild type preanaphase spindles (black, n=81 microtubules) and 4 tub2-430∆ preanaphase spindles (red, n=195 microtubules). Values are mean ± SD. P-value was determined by t-test.

Figure 2.5.4 EM tomography of microtubule in wild type cell in anaphase (i) Model of spindle microtubules based on a tomographic volume of an anaphase wild type cell. (ii) Histogram of microtubule lengths in the wild type cell modeled in 2.5.4i.

Figure 2.5.5 EM tomography of microtubule in tub2-430∆ cell in anaphase (i) Model of spindle microtubules based on a tomographic volume of an anaphase tub2- 430∆ cell. (ii) Histogram of microtubule lengths in the tub2-430∆ cell modeled in 2.5.5i.

Figure 2.6.1 Amino acid sequence alignment of wild type and mutant β-CTT The acidic patch is shaded gray.

Figure 2.6.2 Chromosome loss frequency per 1000 divisions Double asterisks denote P < 0.0001; determined by Chi-square with Yates’ correction. Wild type, n=14866; tub2-430Δ, n=16299; tub2-438Δ, n=20492; tub2-445Δ, n=23932; tub2- polyQ438Δ, n=17792; tub2-polyQ445Δ, n=15776; tub2-polyQ, n=6946; tub2::CTTTUBB3, n=15607.

Figure 2.6.3 Normalized doubling times Doubling times calculated by fitting growth curves to an exponential growth equation.

Figure 2.6.4 Sequence logo for β-tubulin residues Sequence logo for β-tubulin residues 426-445, created from amino acid sequences from 24 β-tubulins.

Figure 2.6.5 Genetic interactions with SAC mutants 10-fold dilution series of strains indicated at left were spotted to rich media or rich media supplemented 10 µg/mL benomyl and grown at 30oC.

Figure 2.7.1 Model of spindle organization in wild-type cells and β-CTT mutants Spindle microtubules (black lines) emanate from SPBs (gray) to form antiparallel overlaps in the spindle center and attach to KTs (green). KT position is determined by a balance of outward forces (black arrows) generated by iMTs and inward forces (green arrows) generated by chromatin (green dashed lines). β-CTT mutants are defective for spindle stability, and therefore predicted to generate weaker outward forces. This imbalance shortens the spindle and disrupts KT position.

Figure 2.8.1 Distribution of Nuf2-GFP signal across half of wild type spindles Distribution of Nuf2-GFP signal across half of the spindle that is between 1653 – 1881 nm long. Each line represents one half spindle from one representative wild-type cell. Intensity is normalized against the total Nuf2-GFP signal in each cell. The end of each line on the right indicates the position of the spindle pole.

Figure 2.8.2 Distribution of Nuf2-GFP signal across half of tub2-430∆ spindles Distribution of Nuf2-GFP signal across half of the spindle that is between 1653 – 1881 nm long, from four representative tub2-430∆ cells.

Figure 2.8.3 Distribution of Nuf2-GFP signal across half of dam1-765 spindles Distribution of Nuf2-GPF signal across half of the spindle that is between 1653 – 1881 nm long, from four representative dam1-765 cells.

Figure 2.8.4 Maximum intensity projections from 3D confocal images of Cse4-GFP

Maximum intensity projections from 3D confocal images of wild-type and tub2-430∆ cells expressing Cse4-GFP and Spc110-DsRed.

Figure 2.8.5 Plot of the 2-dimensional distribution of Cse4-GFP signal Dot plot of the 2-dimensional distribution of Cse4-GFP signal in preanaphase wild type and tub2-430∆ cells.

Figure 2.9.1 Maximum intensity projections a wild-type cell expressing CENIV-GFP Maximum intensity projections from 3D confocal images of a wild-type cell expressing CENIV-GFP and Spc110-tdTomato. Histogram of CENIV-GFP pixel intensity values from a line scan across the image in panel 2.9.1i.

Figure 2.9.2 Alignment angle of a single CENIV sister with the SPB-SPB axis Dot plot of the alignment angle of a single CENIV sister with the SPB-SPB axis, in preanaphase cells with separated CENIV.

Figure 2.9.3 Distance between a single CENIV sister and proximal SPB Dot plot of the distance between a single CENIV sister and proximal SPB, in preanaphase cells with separated CENIV.

Figure 2.9.4 Distance between CENIV sisters Dot plot of the distance between CENIV sisters in preanaphase cells with separated CENIV.

Figure 2.9.5 Distance between SPBs Dot plot of the distance between SPBs in preanaphase cells with separated CENIV.

Figure 2.9.6 Maximum intensity projections a tub2-430∆ mcm21∆ double mutant cell expressing CENIV-GFP

Maximum intensity projections from 3D confocal images of a tub2-430∆ mcm21∆ double mutant cell expressing CENIV-GFP and Spc110-tdTomato.

Figure 2.10.1 Preanaphase wild type cell 1 tomogram

Figure 2.10.2 Anaphase wild type cell 2 tomogram

Figure 2.10.3 Anaphase wild type cell 4 tomogram

Figure 2.10.4 Preanaphase tub2-430∆ cell 4 tomogram

Figure 2.10.5 Preanaphase tub2-430∆ cell 1 tomogram

Figure 2.10.6 Preanaphase tub2-430∆ cell 2 tomogram

Figure 2.10.7 Preanaphase tub2-430∆ cell 5 tomogram

Figure 2.10.8 Anaphase tub2-430∆ cell 3 tomogram

Figure 2.10.9 Anaphase tub2-430∆ cell 8 tomogram

CHAPTER III

REGULATION OF MICROTUBULE DYNAMIC INSTABILITY BY THE CARBOXY-TERMINAL TAIL OF

β-TUBULIN2

Introduction

Microtubules are polymers made of repeating subunits of αβ-tubulin heterodimers.

Each heterodimer binds four neighboring heterodimers; longitudinal interactions form linear

chains known as protofilaments, and lateral interactions bind adjacent protofilaments (Alushin

et al., 2014). In this way, heterodimers polymerize into a sheet known as the microtubule

lattice, with curvature along the lateral axis that closes the lattice into a cylinder of 11-15

protofilaments with cytosolic and luminal surfaces. Polymerization is an intrinsic property of

αβ-tubulin heterodimers, and the rate of polymerization depends on the concentration of available free tubulin. Microtubules also exhibit a unique non-equilibrium behavior of

stochastically switching between polymerization and rapid depolymerization; a behavior known

as dynamic instability. Like polymerization, dynamic instability is an intrinsic property of αβ-

tubulin heterodimers. It is observed for microtubules assembled in vitro from purified αβ-

heterodimers (Horio and Hotani, 1986; Mitchison and Kirschner, 1984; Walker et al., 1988), and

in living cells (Cassimeris et al., 1988). Cells use dynamic instability to organize networks of

microtubules and to do work, such as segregating chromosomes during mitosis (Inoué et al.,

1995).

2 Portions of this chapter are published with permission from our previously published article Fees CP, & Moore JK. Regulation of microtubule dynamic instability by the carboxy-terminal tail of β-tubulin. Life Science Alliance. 2018. v1:2 e201800054. 64

The conventional model for dynamic instability relies on allosteric coupling between αβ- tubulin heterodimers at the ends of microtubules, forming a stable cap. The stability of this cap is thought to depend on the nucleotide binding status of heterodimers near the microtubule end, with newly incorporated GTP-bound heterodimers promoting stability; hence the term

‘GTP-cap’ (Mitchison and Kirschner, 1984). When the number of GTP-bound heterodimers at the microtubule end drops below a threshold level, the microtubule undergoes catastrophe and switches to rapid depolymerization. This model is supported by recent work showing a direct correlation between polymerization rate and the stability of the microtubule end (Duellberg et al., 2016a). An alternative model emphasizes the structure of the microtubule end as a key determinant of stability. As a microtubule grows, the asymmetric growth of some protofilaments causes the microtubule to form a tapered plus end with extensions of incomplete lattices. Tapered plus ends were originally observed by cryo-electron microscopy of microtubules assembled from purified tubulin and more recently by TIRF microscopy of dynamic microtubules (Chrétien et al., 1995; Coombes et al., 2013). Tapered plus ends tend to form in an age-dependent manner and under faster polymerization conditions, and are predicted to be less stable due to fewer lateral bonds between protofilaments (Coombes et al.,

2013; Duellberg et al., 2016a; Gardner et al., 2011b). Despite our growing understanding of dynamic instability, we do not understand how these basic mechanisms are regulated in cells to guide the formation and function of microtubule networks.

Several lines of evidence suggest that carboxy-terminal tail domains (CTTs) of αβ-tubulin heterodimers might act as regulatory modules for controlling microtubule dynamics. CTTs are

unstructured domains that extend off of the cytosolic surface of the microtubule lattice

(Nogales et al., 1999). Early biochemical experiments showed removal of CTTs by the nonspecific protease subtilisin enhances tubulin polymerization. This subtilisin-digested tubulin

more readily assembled into large oligomers than undigested tubulin, as detected in bulk

assembly assays, and these oligomers were more resistant to destabilization by calcium ions

(Bhattacharyya et al., 1985; Sackett et al., 1985; Saoudi et al., 1995; Serrano et al., 1984).

However, the oligomers formed by subtilisin-digested tubulin often exhibited aberrant and/or

incomplete lattices(Sackett et al., 1985; Serrano et al., 1984; Serrano et al., 1988). This evidence

suggests that CTTs may negatively regulate tubulin assembly to guide the proper formation of

the microtubule lattice. In the in vivo context, CTTs in different cell types exhibit genetically-

encoded and posttranslational differences. Whereas the globular domains of α- and β-tubulin are highly conserved, CTTs exhibit diverse amino acid sequences when compared across species and between tubulin isotypes within a species (Fees et al., 2016). In addition, CTTs are targeted

for a variety of posttranslational modifications (Janke, 2014; Yu et al., 2015). This raises the

possibility that CTTs may act as regulatory handles for changing the functional properties of

microtubules; however, the roles of CTTs in regulating microtubule dynamics and interactions

remains poorly defined.

We previously characterized the roles of α- and β-CTT during mitosis in budding yeast,

and identified an important role for β-CTT in promoting proper spindle formation and

chromosome segregation (Fees et al., 2016). Live-cell imaging and electron tomography of

mutant yeast cells with genetically ablated β-CTT showed disorganized spindle microtubules.

Therefore, we hypothesized that the β-CTT may be required for cells to properly regulate

microtubule dynamics during spindle assembly.

Here we test this hypothesis using a combination of in vitro experiments with purified mammalian tubulin digested with subtilisin, and in vivo experiments in budding yeast with

mutants that alter or ablate β-CTT. We show that the β-CTT contributes to microtubule

dynamics by inhibiting polymerization and promoting depolymerization. Despite these

inhibitory effects on microtubule assembly, we find that microtubules with β-CTT are less prone to catastrophe and exhibit distinct plus end morphologies. Finally, we show that the ability of

magnesium ions to accelerate microtubule depolymerization requires β-CTT. Together our

findings define a role for β-CTT in regulating dynamic instability and suggest a mechanism for regulating microtubule function and organization through ionic control.

Materials & Methods

In Vitro Microtubule Dynamics Assays

Assays to measure microtubule dynamics by TIRF microscopy were based on previously

described methods (Gell et al., 2010). Double-Cycled microtubule seeds were assembled by incubating 20 µM rhodamine tubulin (Cytoskeleton, Inc; Denver, CO) in BRB80 buffer (80 mM

PIPES brought to pH6.9 with KOH, 1 mM ethylene glycol tetraacetic acid (EGTA), 1 mM MgCl2;

minor pH adjustments were made with NaOH) with 1 mM GMPCPP at 37oC for 30 minutes.

Sample was then centrifuged at 100,000 x g for 10 min at 30oC and the supernatant was

removed. Pellet was suspended in 0.8x starting volume of ice cold BRB80 buffer to

depolymerize labile microtubules. An additional 1 mM GMPCPP was added and microtubules

were polymerized at 37oC for 30 min and pelleted again. The pellet was suspended in 0.8x

starting volume warm BRB80. The reaction was then gently pipetted 8-10 times to shear the microtubules, aliquoted into 1 µL volumes, and either used immediately or snap frozen and stored at -80oC.

Imaging chambers were assembled using 22x22 mm and 18x18 mm coverslips. The coverslips were cleaned and silanized as previously described (Gell et al., 2010). The prepared glass coverslips were stored in desiccators at room temperature until used. The coverslips were mounted in a custom fabricated stage insert and sealed with melted strips of Parafilm.

GMPCPP-stabilized microtubule seeds were affixed to coverslips using anti-rhodamine antibodies (Fisher Scientific, Cat# A-6397; diluted 1:50 in BRB80). Chambers were flushed with

1% Pluronic-F127 in BRB80 to prevent other proteins from adhering to the glass and equilibrated with an oxygen scavenging buffer (40 mM glucose, 1 mM Trolox, 64 nM Catalase,

250 nM Glucose Oxidase, 10 mg/ml Casein) prior to free tubulin addition. The imaging buffer consisting of unpolymerized tubulin (15-20% Hylite-488 labeled tubulin (Cytoskeleton, Inc.) and

85-90% unlabeled porcine brain tubulin), 5 mM MgCl2, 1 mM GTP, the oxygen scavenging buffer, and BRB80 to 50 µL volume was then flowed into the imaging chambers. The chamber was sealed with VALAP (1:1:1 Vaseline, Lanolin, Paraffin wax) and warmed to 37oC using an

ASI400 Air Stream Stage Incubator (Nevtek; Williamsville, VA) for 5 minutes before imaging.

Temperature was verified using an infrared thermometer.

Images were collected on a Nikon Ti-E microscope equipped with a 1.49 NA 100× CFI160

Apochromat objective, TIRF illuminator, OBIS 488-nm and Sapphire 561-nm lasers (Coherent;

Santa Clara, CA), and an ORCA-Flash 4.0 LT sCMOS camera (Hammamatsu Photonics; Japan),

using NIS Elements software (Nikon; Minato, Tokyo, Japan). Images were acquired using two- channel, single-plane TIRF, at 3 second intervals.

Image Analysis

Images were analyzed using a custom-made MATLAB program. Seeds were identified by

thresholding image intensity, then the images were rotated and segmented along the axis of

the microtubule. Images were then automatically cropped to 4 pixels above and below the

microtubule axis, then max projected into a single line of pixels for each time point. The

timepoints were stacked to generate kymographs for analysis.

Polymerization and depolymerization rates were calculated by measuring the changes in microtubule length and time from the first and last points of the individual polymerization and

depolymerization events from the kymographs. Polymerization rate constants were estimated

by multiplying the slope of the polymerization rate linear model by the number of subunits in 1

µm and dividing by 60 to yield subunits per second (~1750 subunits in a 14 protofilament

microtubule nucleated by a GMPCPP seed). Depolymerization rate constants were determined

similarly but using the median depolymerization rate (µm*min-1) from all tubulin

concentrations pooled.

Tubulin Washout Experiments

For washout experiments, GMPCPP-seeded imaging chambers were similarly assembled, but not sealed with VALAP. Imaging chambers were warmed on the stage for 2-3

minutes, allowing the temperature to equilibrate to 37oC, then dynamic microtubules were

imaged for 30 seconds before free tubulin was removed from the imaging chamber with 4x

chamber volumes of warm reaction buffer, without tubulin. Images were acquired

continuously during the experiment at 1 second intervals.

Images were processed as described above, with the addition of post-acquisition image stabilization that was used to reduce minor XY drift during image acquisition using the Image

Stabilizer Plugin for ImageJ (Kang Li, 2008). Microtubule lengths were determined at each time

point using intensity thresholding. We calculated instantaneous polymerization velocities using

intensity thresholding to determine microtubule length at each time point. The microtubule

lengths were fit to a linear regression to determine average polymerization rate at 10-seconds

before washout. Washout initiation was defined computationally, as the timepoint when

background fluorescence was significantly lower than the previous three timepoints. We

defined washout termination similarly, by comparing the background fluorescence between

timepoints. Washout duration was defined as the time between initiation and termination. The

average washout duration for all washout experiments was ~5 seconds.

Time to catastrophe was determined using a custom MATLAB program designed to

iteratively fit groups of 5 microtubule lengths to a linear regression beginning from the

washout. A catastrophe event was defined empirically as a loss of more than 150 nm*s-1

(approximately 2 pixels). The slow depolymerization rate was determined by linear regression

of the microtubule lengths from washout to catastrophe. The fast depolymerization rate was

similarly determined, from the catastrophe time until the microtubule depolymerized back to

the GMPCPP seed.

Determining Tubulin Concentration

For each experiment, the tubulin concentration was determined by running a sample of

the imaging buffer containing unpolymerized tubulin on a 10% Bis-Tris SDS-PAGE gel, followed

by staining with Coomassie blue. The concentration was determined by averaging the

intensities of triplicates of each sample and comparing to standard curve made from BSA

standards run on the same gel.

Subtilisin Treatment

S-tubulin was prepared by treating 10 µM porcine brain tubulin with 1% (w/w) Subtilisin

(Cat#: P5380; Sigma Aldrich, Saint Louis, MO) at 30oC for 5 min. Proteolysis was inactivated with

1 mM PMSF and samples were moved to ice for 20 min to depolymerize microtubules. The S-

tubulin was concentrated in Amicon Ultra centrifugal concentrator columns (Cat#: UFC501096;

EMD Millipore), spun at 5,000 x g for 3 minutes at 4oC. Between spins the concentrated S-

tubulin was pipetted and visually checked for aggregation. At first signs of persistent

aggregation, the concentrator tubes were placed on ice for 20 minutes and checked for

aggregation again. When samples were concentrated approximately 5X and no aggregation

was visible, the S-tubulin was aliquoted, snap frozen and stored at -80oC. Stock concentration was determined as described above.

Proteomics Analysis

Proteomic analyses of subtilisin digested tubulin samples was performed using in-gel

digestion of bands cut from a SDS-PAGE gel (Dzieciatkowska et al., 2014). Bands were reduced,

alkylated, trypsin digested, and then analyzed by nanoUHPLC-MS/MS using a nanaEasy II

nanoLC and Thermo QExactive HF Orbitrap MS as described. Peptides were separated on a self-

made 20 cm C18 analytical column (100 µm) packed with 2.7 µm Phenomenex Cortecs C18

resin. After equilibration with 3 µL of 5% acetonitrile 0.1% formic acid, the peptides were

separated by a 70 min linear gradient from 4% to 30% acetonitrile with 0.1% formic acid at 350

nL/min. The mass spectrometer was operated in positive ion mode with data-dependent (top

15) acquisition. Full MS scans were obtained with a range of m/z 300 to 1800, 60,000

resolution, and ions were fragmented using HCD (28 NCE), and tandem mass spectra were

acquired at a mass resolution of 15,000. The dynamic exclusion time was 20 s. Raw files were

processed using Proteome Discoverer 2.2. and searched against the Sus scrofa uniprotKB and IPI

databases (release date 2018.02) using Mascot. Mass tolerances were +/- 10 ppm for MS

parent ions, and +/- 15 ppm for MS/MS fragment ions. Semi-Trypsin specificity was used

allowing for 1 missed cleavage. Variable modifications included Met oxidation, protein N-

terminal acetylation, peptide N-terminal pyroglutamic acid formation and Cys

carbamidomethylation. Secondary error tolerant searches were performed to identify

additional post-translational modifications. Area under the curve was calculated and used for

quantification at the peptide level.

Western Blotting

Samples were run on 10% Bis-Tris SDS-PAGE gels and transferred to PVDF membranes.

The membranes were blocked for 1 hour in Odyssey Blocking buffer (Cat#: 927-40000; LI-COR

Biosciences, Lincoln NE). All antibodies were diluted in Odyssey blocking buffer. The blots were probed for 60 minutes at room temperature using the following primary antibodies: pan α-

tubulin (4A1; (Piperno and Fuller, 1985)) at 1:100 and DM1A α-tubulin (T6199; Sigma Aldrich) at

1:1000. Pan β-tubulin antibody (9F3; Cell Signaling Technology Danvers, MA) at 1:1000 against

the N-terminus of β-tubulin. Primary antibodies against tyrosinated-α-tubulin (T9028; Sigma

Aldrich) used at 1:1000. The blots were washed with PBS-Tween (PBS components, 0.1%

Tween-20) before incubation with secondary antibodies, IRDye 800CW Goat Anti-Rabbit (P/N:

926-32211; Li-Cor) and IRDye 680RD Goat anti-Mouse (P/N 926-68070; Li-Cor) at 1:15000 in

blocking buffer, for 60 minutes at room temperature in the dark. The blots were washed three

times with PBS-T then again with PBS before being imaged on the Odyssey Imaging System

using the Image Studio software (LI-COR Biosciences, Lincoln NE). Band intensities were

measured using ImageJ after export from the Image Studio software.

Yeast Strains and Manipulation

General yeast manipulation, media and transformation were performed by standard

methods (Lingbeek et al., 2002). GFP-TUB1 fusion was integrated and expressed from the LEU2 chromosomal locus, so that the fusion protein was expressed in addition to the native TUB1

(Markus et al., 2015; Song and Lee, 2001). Mutations to the β-tubulin tail, were made at the

native chromosomal locus as previously described (Aiken et al., 2014; Fees et al., 2016). Human

β-tubulin tail chimeras were constructed by integrating the TRP1 auxotrophic marker 331

basepairs downstream of the TUB2 STOP codon. The genomic DNA from this strain was used as

a template for PCR with PAGE-purified chimeric forward primers to amplify the 3' end of the

TUB2 coding sequence through the integrated TRP1 marker and replace the native yeast tail

sequence after codon 427 with the human tail sequences. The PCR product was then

transformed into naïve competent yeast as previously described (Lingbeek et al., 2002).

Integration into transformed colonies was confirmed by sequencing the native TUB2 locus. The

mnr2∆ deletion mutant was generated by conventional methods (Petracek and Longtine, 2002).

Growth assays measuring magnesium sensitivity

Cells were grown to saturation in synthetic complete media containing 4mM MgS04

(Sunrise Science Products; San Diego, CA), then pelleted and washed twice with synthetic media

lacking MgSO4. After washing, cells were diluted 1:50 into 96-well plates with 200µL/well of

either synthetic complete media containing 4mM MgS04 or synthetic complete media lacking

MgS04 but supplemented with MgCl2. OD600 values measured at 5-minute intervals over 21

hours at 30ºC with orbital shaking using a Cytation 3 Plate Reader (Biotek; Winooski, VT).

Doubling time was calculated by determining the OD600 values to an exponential curve as

using a custom MATLAB code as described previously (Fees et al., 2016). Normalized doubling

times are based on data from at least 4 independent isolates for each genotype, measured in 5

separate experiments.

Measuring Microtubule Length and Dynamics in Yeast

Assays were performed as described previously (Fees et al., 2017). Cells were grown overnight in a shaking incubator at 30ºC in synthetic complete media, then diluted into fresh media and returned to the shaking incubator for ~4 hours before imaging. For experiments at

2+ low Mg , cells were diluted into synthetic complete media lacking MgSO4. Before imaging, cells were gently pelleted and mounted in parafilm slide chambers were made and coated with

Concanavalin A (2mg/mL; Cat# C5275) as described previously (Fees et al., 2017).

Images were collected on a Nikon Ti-E microscope equipped with a 1.45 NA 100× CFI

Plan Apo objective, piezo electric stage (Physik Instrumente; Auburn, MA), spinning disk confocal scanner unit (CSU10; Yokogawa), 488-nm and 561-nm lasers (Agilent Technologies;

Santa Clara, CA), and an EMCCD camera (iXon Ultra 897; Andor Technology; Belfast, UK) using

NIS Elements software (Nikon). During acquisition, the temperature of the stage was 25°C. For experiments in G1 cells, microtubule lengths were measured in maximum intensity projections of Z series consisting of 29 planes separated by 300 nm, collected at single time points.

Individual microtubules were distinguished from microtubule bundles based on the signal intensity of GFP-labeled α-tubulin. For experiments measuring dynamic microtubules in preanaphase cells, Z series consisting of 13 images separated by 500 nm were collected at 5- second intervals.

Polymerization and depolymerization events were defined as at least three contiguous data points that produced a length change ≥0.5µm with a coefficient of determination ≥0.80.

Polymerization and depolymerization rates of individual events were determined by dividing the change in microtubule length by the change in seconds and multiplied by 60 to convert seconds to minutes. Rates are reported in µm*min-1. Catastrophe frequencies were determined for individual astral microtubules by dividing the number of catastrophe events by the total lifetime of the microtubule, minus time spent in disassembly. Rescue frequencies were determined for individual astral microtubules by dividing the number of rescue events by the total lifetime, minus time spent in assembly. At least 34 astral microtubules were analyzed for each genotype. Dynamics measurements for individual microtubules were pooled for the

genotype, and then compared to pooled data for different genotypes using a Mann-Whitney U test to assess whether the values for different data sets are significantly different.

Nocodazole Sensitivity Assay

Cells were grown overnight in rich liquid media in a shaking incubator at 30oC, diluted

and grown to early log phase in fresh media the following day. Cultures were brought to 1.5%

DMSO and nocodazole was added to 1.5 µM. Cells were then returned to 30oC shaking

incubator for 60 minutes. Fixative (18.5% Formaldehyde, 0.5 M KPO4) was added to the cultures in a ratio of 1:3 and cultures were returned to shake at 30oC for 3 minutes. The cultures were pelleted at 1,500 x g for 2 minutes, the supernatant was removed, and the cells were suspended in a quencher solution (0.1% Triton-X, 0.1 M KPO4, 10 mM Ethanolamine). The cells were pelleted, supernatant decanted and washed twice in 0.1 M KPO4. The fixed cells were loaded into the coated slide chambers, washed with 0.1 M KPO4 and the chambers were sealed with VALAP (Vaseline, lanolin and paraffin at 1:1:1).

Images were collected on a Nikon Ti-E wide field microscope equipped with a 1.49 NA

100× CFI160 Apochromat objective, and an ORCA-Flash 4.0 LT sCMOS camera (Hammamatsu

Photonics, Japan) using NIS Elements software (Nikon, Minato, Tokyo, Japan). Images were acquired using 100 ms exposure, 11 z-planes with 300 nm separation.

Cells were segmented using a custom ImageJ macro previously described (Fees et al.,

2016). The data was blinded for analysis, and the segmented cells were visually scored for the presence of astral microtubules using a custom MATLAB code.

Results

β-CTT Promotes Microtubule Dynamics

To investigate how β-CTT impacts microtubule dynamics, we used a well-established protocol for proteolytically removing the tubulin CTTs using the non-specific protease subtilisin.

Previous work has shown that subtilisin removes β-CTT from purified tubulin before removing

the α-CTT (Bhattacharyya et al., 1985). Consistent with this, we find that limited proteolysis of

purified porcine brain tubulin with subtilisin preferentially removes β-CTT. In our experiment, 5-

minute digestion with subtilisin shifts the mobility of β-tubulin in SDS-PAGE, with approximately

70% of total β-tubulin protein running at a slightly lower molecular weight (Figure 3.1.1,

arrowhead; Figures 3.7). We used mass spectrometry to confirm that this sample contains β- tubulin species that are truncated at their carboxy-termini (Figures 3.8.1 & 3.8.2). In contrast,

removing α-CTT requires a longer subtilisin digest. Although our mass spectrometry analysis did identify some truncated species of α-tubulin after 5-minute digest, western blot analysis

indicates that only 20% of total α-tubulin exhibits a mobility shift and the amount of tyrosinated α-tubulin is minimally affected (Figure 3.1.1, 3.8.1 & 3.8.2). Thus, 5-minute

digestion with subtilisin produced a tubulin sample that predominantly lacks β-CTT. We refer to

this sample as S-Tubulin for subtilisin-digested tubulin.

We first compared the polymerization and depolymerization rates of S-tubulin to untreated tubulin using TIRF microscopy to image individual microtubules grown from

GMPCPP-stabilized seeds (Figure 3.1.2 & 3.1.3). By testing a range of concentrations, we find

that S-tubulin exhibits an apparent on-rate constant of 7.4 subunits*uM-1*s-1, which is nearly 2-

fold greater than the apparent on-rate constant of 3.96 subunits*µM-1*s-1 that we measured

for untreated porcine brain tubulin (Figure 3.1.4). Microtubules assembled from S-tubulin

depolymerize significantly slower (median = 250 subunits*s-1; 95% c.i.= 245-258) than those

assembled from untreated tubulin (median = 537 subunits*s-1; 95% c.i.= 516-560; Figure 3.1.5).

Together, these findings indicate that the β-CTT promotes dynamic microtubules by inhibiting polymerization and accelerating depolymerization.

Our TIRF experiments also permit analysis of catastrophe events, which are not accessible by bulk assays. We find that microtubules assembled from S-tubulin catastrophe at

shorter lengths than microtubules assembled from untreated tubulin (Figure 3.1.6). This

difference is most striking when comparing concentrations of S-tubulin and untreated that

polymerize at similar rates (Figure 3.1.4 and 3.1.6; compare 3.9 µM S-tubulin and 8.6 µM

untreated tubulin). This suggests that β-CTT prevents catastrophes.

In addition to the effects of β-CTT on the behavior of microtubule plus ends, we also

observed differences in the behavior of minus ends. Polymerization from both ends of the

GMPCPP seeds is more common in the presence of S-tubulin than untreated tubulin (Figure

3.1.2 and 3.1.3). However, there is still a clear asymmetry between the two microtubule ends;

polymerization at one end is always slower and reaches shorter terminal lengths. We presume

that the slower end is the minus end. Thus, β-CTT appears to impact microtubule assembly at

both plus and minus ends.

β-CTT Alters the Structure of the Plus End

Our finding that microtubules catastrophe more often in the absence of β-CTT, together with the previous finding that S-tubulin forms aberrant lattices in unseeded assembly assays,

prompted us to examine whether β-CTT alters the morphology of growing microtubule plus ends (Sackett et al., 1985; Serrano et al., 1988). Normally, plus-end morphology correlates with

polymerization rate; microtubules polymerizing at slower rates exhibit blunt ends, while

microtubules polymerizing at faster rates exhibit plus ends with long extensions of incomplete

lattices, also known as tapered ends (Coombes et al., 2013; Duellberg et al., 2016a; Gardner et

al., 2011b). We predicted that removing the β-CTT could promote faster polymerization and

more frequent catastrophes by either supporting longer plus-end tapers, or by maintaining

blunt ends at faster polymerization rates. We tested this using fluorescence analysis to measure

the steepness of the signal decay at the plus ends of growing microtubules in our TIRF

experiments (Figure 3.2.1). In our analysis, a blunt plus end has a steeper fluorescence decay, while a tapered end has a more gradual decay. The length of the tapered end can be estimated by fitting the fluorescence decay to a Gaussian survival curve and calculating the standard deviation (i.e. tip SD), as previously described (Coombes et al., 2013).

Consistent with previous studies, we find that control microtubules assembled from untreated tubulin exhibit plus-end tapers, and that these extensions are longer for microtubules polymerizing at faster rates (Figures 3.2.1 - 3.2.4; (Coombes et al., 2013)). In contrast, microtubules assembled from S-tubulin exhibit less tapered and more blunted plus ends, even at faster rates of polymerization (Figures 3.2.1 - 3.2.4). These results indicate that

the β-CTT determines how heterodimers assemble at the plus end and promotes the formation of plus-end tapers.

β-CTT Promotes Plus-End Stability

Having found that the β-CTT regulates plus-end morphology, we next examined effects on plus-end stability. We used a technique modified from Walker et al. (Walker et al., 1988) to measure plus-end behavior in the absence of polymerization. In our experiment, microtubules polymerize from GMPCPP-seeds affixed to the coverslip for approximately 30 seconds. Then, free tubulin is washed out to prevent further polymerization by pipetting 5x chamber volumes of assembly buffer through the imaging chamber while continuously acquiring images (Figure

3.3.1). Images are acquired at 1-second time intervals to detect rapid changes in microtubule

length. After washout, microtubules exhibit a period of slow depolymerization (Figure 3.3.2; labeled “S”) before undergoing catastrophe and switching to faster depolymerization (Figure

3.3.2; labeled “F”). We computationally determined the timepoint of catastrophe by iteratively fitting microtubule lengths across five consecutive timepoints to a linear regression.

Catastrophe was defined as the first timepoint when the slope of the regression was larger than

150 nm per second (~2 pixels*s-1; see Materials and Methods). We calculated time to

catastrophe as the number of seconds between washout initiation and catastrophe (Figure

3.3.2; labeled “D”). Under these conditions, the time between washout and catastrophe represents a read out of the stability of the plus end (Duellberg et al., 2016b; Walker et al.,

1988).

We compared the time to catastrophe of S-tubulin microtubules to undigested controls in the same polymerization rate range, using S-tubulin and untreated tubulin at concentrations that support a similar range of polymerization rates (2.3-4.6 µM and 3.1-11.6 µM; respectively).

We find that time to catastrophe is significantly shorter for S-tubulin microtubules, with most

(55%) exhibiting no detectable delay between washout and catastrophe (Figure 3.3.3). The other 45% of S-tubulin microtubules do exhibit a delay before catastrophe; however, this time is significantly reduced compared to controls matched for polymerization rates (Figure 3.3.4).

Based on these findings and our results in Figure 3.1, we conclude that the plus ends of microtubules assembled from S-tubulin are more prone to catastrophe.

β-CTT Promotes Dissociation from Microtubules

Our washout experiments also allowed us to estimate rates of tubulin dissociation from

microtubules in conditions that are not confounded by high concentrations of free tubulin. We

first measured the slow rate of depolymerization that occurs before catastrophe (Figure 3.3.2;

labeled “S”). This slow depolymerization is thought to represent the dissociation of GTP-bound

heterodimers from the plus end, and is different from the faster depolymerization that follows

catastrophe (Duellberg et al., 2016b). Importantly, this dissociation rate can be used to

determine the affinity of free tubulin for the polymer.

If β-CTT acts to repel heterodimers from the microtubule, we predicted that removing β-

CTT would decrease the dissociation rate. Consistent with our model, we observed slight but

significant reduction in dissociation rates for the S-tubulin microtubules that did not immediately catastrophe after washout (S-tubulin median = 28 0.14 subunits*s-1, 95% c.i.= 22-

40; untreated control median = 35 subunits*s-1; 95% c.i.= 32-43; Figure 3.3.5). This suggests that S-tubulin heterodimers have a higher affinity for microtubule polymer than undigested tubulin.

We also measured the fast rate of depolymerization after catastrophe, defined by the slope of a linear regression of the microtubule lengths after catastrophe (Figure 3.3.2; labeled

“F”). S-tubulin exhibits a significantly slower depolymerization rate than untreated control, consistent with measurements from our earlier dynamics experiments (Figure 3.1.5 and 3.3.6).

Therefore, microtubules assembled from S-tubulin depolymerize slower than undigested controls, and this difference is independent of free tubulin. Taken together, these results

support our conclusion that β-CTT regulates the equilibrium between microtubule polymer and

free heterodimer; removing β-CTT shifts this equilibrium and stabilizes the polymer state.

Magnesium Cations Regulate Tubulin Equilibrium through the β-CTT

How might β-CTT promote the dissociation of tubulin subunits from microtubules? We

hypothesized that the negatively charged β-CTT might facilitate the destabilizing effects of positively charged divalent cations, such as calcium and magnesium. For our experiments, we studied the effects of magnesium cations, which are important for GTP-binding and tubulin polymerization, but also promote depolymerization (Duellberg et al., 2016b; Walker et al.,

1988) We first tested how magnesium impacts microtubule dynamics by decreasing the concentration of MgCl2 in our assembly buffer from 5 mM to 1 mM. As expected, lower magnesium decreases the rate of polymerization for both S-tubulin and untreated tubulin

(Figure 3.4.1). However, we observed different effects on the rate of depolymerization. While the depolymerization rate of S-tubulin is unaffected by decreasing MgCl2 from 5 mM to 1 mM, the depolymerization rate of untreated tubulin is significantly slower at lower MgCl2 concentrations (Figure 3.4.2).

To further test our hypothesis and isolate the effect of divalent cations on microtubule

depolymerization, we modified our washout experiment. We first assembled microtubules from

GMPCPP-stabilized seeds in the presence of buffer with 5 mM MgCl2, using tubulin concentrations selected to achieve similar polymerization rates between S-tubulin and untreated tubulin (4.8-6.8 µM and 7.8-12.0 µM; respectively). We then washed out the free tubulin with buffer containing a 5-fold lower concentration of magnesium (1 mM). Therefore, the microtubules analyzed in this experiment were assembled in the presence of 5 mM MgCl2 but depolymerize in the presence of 1 mM MgCl2. We compared the rates of slow and fast depolymerization (i.e. before and after catastrophe, respectively) to our washout experiments in Figure 3.3, which have a constant concentration of 5 mM MgCl2. Consistent with previous results, we find that magnesium strongly affects the depolymerization of microtubules assembled from untreated tubulin. Both the rates of slow depolymerization and fast depolymerization are significantly decreased in the presence of lower magnesium concentration (Figures 3.4.3 & 3.4.4). In contrast, microtubules assembled from S-tubulin are less sensitive to the change in magnesium concentration. We find no difference in the rate of slow depolymerization and only a slight decrease in the rate of fast depolymerization in the

presence of low magnesium for S-tubulin (Figures 3.4.3 & 3.4.4). These findings indicate that β-

CTT is necessary for magnesium to promote tubulin dissociation and destabilize microtubules.

We next sought to test whether β-CTT mediates the effects of magnesium on

microtubule stability in living cells. We turned to budding yeast as a model system, which

affords two key advantages for our study. First, in contrast to many other eukaryotes, budding

yeast express a single β-tubulin gene TUB2. Second, TUB2 coding sequence can be readily altered by genetic mutations at the chromosomal locus, allowing precise manipulation of the

amino acid composition of β-CTT. We used this strategy to create a mutant yeast strain that

lacks the final 27 amino acids of β-tubulin, but retains helix 12 (tub2-430Δ) (Redeker et al.,

1992).

We predicted that β-CTT might regulate microtubule dynamics in response to shifts in

divalent cation concentrations in cells. Accordingly, β-CTT may become essential for processes that require dynamic microtubules when divalent cations are depleted. In support of this notion, our previous genome-wide genetic interaction screen identified a negative interaction between the tub2-430∆ mutant and alr2∆, a null mutation that disrupts one of the magnesium transporters at the yeast plasma membrane (Aiken et al., 2014; Wachek et al., 2006).

To further test whether loss of β-CTT sensitizes cells to low magnesium, we used growth assays to compare fitness during magnesium depletion. Wild-type cells exhibit robust growth in standard synthetic media, which contains 4 mM MgSO4, but are severely inhibited in media

without MgSO4 (Figure 3.5.1; see Materials and Methods). Adding back 10 µM MgCl2 restores

growth to intermediate levels, while adding back 50 µM MgCl2 fully restores growth (Figure

3.5.1). As a positive control for these experiments, we tested the mnr2∆ null mutant which

disrupts access to magnesium stores in the vacuole (Pisat et al., 2009). mnr2∆ mutant cells are

impaired at 10 µM MgCl2 but exhibit improved growth at 50 µM MgCl2 (Figure 3.5.2). The tub2-

430∆ mutant that lacks β-CTT is impaired at 10 µM MgCl2, and at 50 µM MgCl2 (Figure 3.5.2).

Therefore, β-CTT is important for cell proliferation under low-magnesium conditions.

We next asked whether β-CTT destabilizes microtubules in vivo, and whether this role depends on intracellular magnesium levels. We measured dynamic astral microtubules in preanaphase cells, which exhibit individual microtubules emanating from the two spindle poles

(Figure 3.5.3). We collected time-lapse image series at 4-5 second intervals, measured the

lengths of the astral microtubules at each time point for at least 5 minutes, and then compared

the distributions of astral microtubule lengths for different magnesium conditions (Fees et al.,

2017). We find that depleting magnesium by shifting mnr2∆ mutants to media without MgSO4

is sufficient to significantly increase microtubule length in cells expressing wild-type tubulin;

compared to controls with normal magnesium levels (p<0.001; Figures 3.5.3 & 3.5.4). To

determine if this magnesium sensitivity depends on β-CTT, we examined tub2-430∆ mutants

under normal and magnesium-depleted conditions. We find that microtubules are significantly

longer in tub2-430∆ mutants, whether in normal or magnesium-depleted conditions (Figures

3.5.3 & 3.5.4).

To confirm that the changes in astral microtubule length are not caused by indirect

effects of magnesium depletion on cell cycle progression, we measured the lengths of astral

microtubules during the G1 phase of the cell cycle (Figure 3.5.5). During G1, long astral

microtubules emanate from the single spindle pole while nuclear microtubules are extremely

short and keep kinetochores anchored proximal to the pole (O’Toole et al., 1999). The

combined lengths of the astral microtubules in a G1 cell provides an estimate for the amount of

microtubule polymer in that cell; however, the caveat of this experiment is that astral

microtubules often form bundles during G1 that can be difficult to distinguish by light

microscopy. Nevertheless, we observed trends that are similar to those in preanaphase cells.

tub2-430∆ mutants that lack the β-CTT exhibit significantly more microtubule polymer per cell

than wild-type controls, and double mutants combining mnr2Δ with tub2-430Δ that are shifted

to media without MgSO4 exhibit a similar amount of total microtubule polymer compared to

tub2-430∆ single mutants grown is the presence of MgSO4 (Figures 3.5.5 & 3.5.6). We conclude

that low intracellular magnesium stabilizes microtubules, high intracellular magnesium

destabilizes microtubules, and this relationship depends on β-CTT. However, removing β-CTT alone appears to have a stronger stabilizing effect than depleting magnesium.

The Negative Charge of β-CTT Regulates Tubulin Equilibrium In Vivo.

We reasoned that the role of the β-CTT in regulating the tubulin equilibrium might depend on its enrichment for negatively-charged amino acids. To test this hypothesis, we

created a series of mutant yeast strains that either remove amino acids from the β-CTT,

substitute negatively-charged side chains for neutral side chains, replace the native yeast tail

with sequences that mimic tails from human β-tubulins, or swap the tail sequences from α-and

β-tubulin (Figure 3.6.1).

We applied our panel of β-CTT mutants in two experiments to test the prediction that

negatively-charged amino acids in β-CTT inhibit the microtubule polymer state. First, we destabilized microtubules by treating log-phase cultures of cells with the microtubule poison

nocodazole, then fixed and imaged the cells to assess the degree of microtubule loss. Individual

cells were scored for presence or absence of astral microtubules (Figure 3.6.2). Under these

conditions, we find that approximately half of wild-type cells lack astral microtubules after one

hour in 1.5µM nocodazole (Figure 3.6.3). In contrast, mutants that lack β-CTT (tub2-430∆) are resistant to nocodazole and most cells maintain astral microtubules (Figures 3.6.1 – 3.6.3). This result is consistent with our in vitro work and suggests that removing β-CTT shifts the tubulin equilibrium towards the polymer state.

We next tested the relevance of negatively-charged amino acids in β-CTT. We find that adding back a minimal region that is enriched for negatively-charged amino acids restores sensitivity to nocodazole, resulting in microtubule loss that is similar to wild-type controls

(tub2-438Δ; Figures 3.6.1 & 3.6.3). We previously defined this domain as the ‘acidic patch’ because it contains the greatest enrichment of negatively-charged residues (Figure 3.6.1; (Fees et al., 2016)). To confirm the importance of the negatively-charged side chains in the acidic patch, we mutated glutamate and aspartate residues to glutamine, aiming to preserve the structure of the residues while neutralizing the charge. Mutating the acidic patch alone (tub2- polyQ) or combing the neutralizing mutations with a truncation of the rest of β-CTT (tub2- polyQ-438Δ) are sufficient to confer resistance to depolymerization by nocodazole (Figures

3.6.1 & 3.6.3).

As a complementary approach to better understand the role the acidic patch plays in regulating microtubule dynamics, we measured dynamic astral microtubules over time in living cells (Figure 3.6.4). We predicted that mutants lacking the acidic patch would exhibit microtubules that are more stable. Removing the β-CTT (tub2-430Δ) leads to significantly longer microtubules compared to wild-type controls (Figure 3.6.5). Polymerization and depolymerization rates of these mutant microtubules were similar to wild-type controls;

however, the catastrophe frequencies were significantly reduced (Figures 3.6.6 – 3.6.8). In

contrast, mutants that retain the acidic patch (tub2-438Δ) exhibit microtubules lengths and

dynamics that are similar to wild-type controls (Figures 3.6.5 – 3.6.8). Neutralizing negatively-

charged side chains in the acidic patch (tub2-polyQ) leads to longer microtubules with slower

polymerization and depolymerization rates (Figures 3.6.5 – 3.6.8). Together, these results

indicate that the negatively-charged side chains in the acidic patch regulate the equilibrium

between free heterodimers and microtubule polymer, and ablating this charged region shifts

the equilibrium towards polymer.

We next extended our analysis to ask whether sequence differences found in the tail

regions of human β-tubulin isotypes might elicit different effects on microtubule stability. We

tested this by generating chimeric β-tubulin composed of the yeast globular domain with the

tail region replaced with that of either human class I or class III β-tubulins (Figure 3.6.1). Both

chimeras displayed sensitivity to nocodazole, with approximately half of cells losing astral

microtubules, compared to untreated controls (Figure 3.6.3). Interestingly, we note that the

untreated controls for both chimeric mutants exhibit significantly lower percentages of cells

with visible astral microtubules, compared to cells expressing wild-type yeast β-tubulin (Figure

3.6.3). These results suggest that sequence differences between the yeast and human β-CTTs may lead to differential effects on microtubule dynamics.

Finally, we asked whether the role of β-CTT in regulating microtubule stability depended on its location on the tubulin heterodimer. We tested this by examining mutants that swapped

the α- and β-CTT sequences; replacing the native yeast α-CTT with β-CTT sequence and vice

versa (Figure 3.6.1; (Aiken et al., 2014)). We find that swapping the tails results in an intermediate nocodazole sensitivity phenotype; a greater percentage of cells exhibited astral microtubules than wild-type controls, but not as great as that seen in tub2-430∆ (Figure 3.6.3).

This suggests that the function of β-CTT may be partly rescued by replacing it with the shorter

α-CTT or by putting a longer tail on α-tubulin. Taken together, our findings indicate that the

negatively-charged β-CTT is a potent regulator of the tubulin equilibrium.

Discussion

CTTs are the most molecularly diverse regions of tubulin proteins and have therefore

generated great interest as potential sites for regulating microtubule function. In this study, we

identify an important role for β-CTT in regulating microtubule dynamics. Our findings build

upon previous studies that discovered that the removal of β-CTT by subtilisin enhances tubulin

assembly in bulk assays (Bhattacharyya et al., 1985; Sackett et al., 1985; Serrano et al., 1984).

Our study provides the first insight into how β-CTT impacts assembly and dynamics at the level of individual microtubules by measuring microtubules growing from GMPCPP-stabilized seeds

in vitro, or from microtubule organizing centers in living cells. We directly demonstrate that β-

CTT inhibits the microtubule polymer state by inhibiting tubulin assembly and promoting

disassembly. Our results also reveal an unexpected role for β-CTT in regulating the structure and stability of microtubule plus ends.

The effect of β-CTT on inhibiting the microtubule polymer state may be explained by an

electrostatic repulsion model, in which the negatively charged β-CTT of one αβ-tubulin

heterodimer repels the negatively charged CTTs of nearby heterodimers. The electrostatic

repulsion model predicts that the β-CTT could impact the microtubule polymer in two ways;

first by decreasing the affinity of heterodimers for the crowded and densely charged

environment of the growing microtubule end, and second by increasing the depolymerization

rate by promoting the outward splaying of protofilaments at the shrinking microtubule end. In

support of this model, we find that S-tubulin polymerizes faster and depolymerizes slower than

undigested tubulin in vitro, and exhibits a significantly higher affinity for microtubule polymer,

as measured by the off-rate in the absence of free tubulin (Figures 3.1 & 3.2.5). In addition, we

find that budding yeast mutants that remove β-CTT or neutralize negatively-charged amino acid

side chains within β-CTT result in longer microtubules that are resistant to nocodazole (Figures

3.6). Together, our findings are consistent with an electrostatic repulsion model where the

negative charge of β-CTT inhibits the microtubule polymer state and promotes the free heterodimer state.

In addition to regulating the equilibrium between free tubulin heterodimers and the

microtubule state, our results reveal a surprising effect of β-CTT on the structure and stability of

plus ends. Despite their faster polymerization, S-tubulin microtubules undergo catastrophe

sooner than control microtubules in our dynamics experiments (Figure 3.1F) and washout

experiments (Figures 3.3C-D). This suggests that β-CTT may normally stabilize the plus end, and

raises an apparent paradox -- how might β-CTT stabilize the plus end while lowering the affinity of tubulin for the microtubule? We propose that β-CTT might regulate plus ends in two ways,

which are relevant to the ‘GTP cap’ and ‘end structure’ models for dynamic instability. First, β-

CTT may promote the enrichment or retention of GTP-tubulin at the plus end, leading to a

larger ‘GTP cap’. We speculate that this could arise either through a yet undefined role for β-

CTT in regulating GTP hydrolysis once tubulin subunits assemble the microtubule lattice, or through preventing the direct incorporation of GDP-tubulin into the plus end. Interestingly, our experiments in yeast, which are known to retain more GTP-tubulin in the microtubule lattice

(Howes et al., 2017), do not show increased catastrophes when β-CTT is removed. Instead, we

see a slight but significant decrease in catastrophe frequency when β-CTT is removed (Figure

3.6.8). Whether this discrepancy can be attributed to differences between tubulin from

different species, the activity of microtubule associated proteins in vivo, or differences between

buffer conditions and the conditions of the cytoplasm is an important question.

Second, the effect of β-CTT on subunit-polymer affinity might guide the structure of the

growing plus end and/or prevent lattice defects. It is clear from previous studies that tubulin

with β-CTT favors assembly into microtubule lattices, while S-tubulin can spontaneously

nucleate non-microtubule aggregates or aberrant microtubule structures, including incomplete

or ‘hooked’ lattices (Serrano et al., 1984). We also observed that after long periods (>1 hour) in

polymerization conditions, S-tubulin formed non-microtubule aggregates that were distinct

from the microtubules extending from GMPCPP-stabilized seeds in our TIRF assays (data not

shown). This indicates β-CTT is important for inhibiting aberrant tubulin assembly. In addition, our TIRF assays reveal that microtubules assembled with S-tubulin tend to exhibit blunt plus ends, in contrast to the tapered plus ends seen in control microtubules (Figures 3.2). It is not obvious that these blunt ends represent a form of lattice defect; however, our current experiments do not permit the identification of incomplete lattices or changes in protofilament

number. Alternatively, blunt ends could represent complete or nearly complete cylindrical lattices at the plus end. Since the blunt ends of S-tubulin microtubules are less stable after free tubulin washout, this raises the possibility that plus ends with complete lattices may have different allosteric coupling than plus ends with tapered ends with incomplete lattices. Testing these models will be the focus of future studies.

Our results also indicate that the role of β-CTT in regulating the microtubule dynamics may involve interactions with multivalent cations. Divalent cations are known to have important and divergent effects on microtubule structure and dynamics. Zinc and manganese ions promote the assembly of tubulin sheets and rings, respectively, rather than microtubule cylinders (Larsson et al., 1976; Nicholson et al., 1999). Calcium and magnesium ions potently destabilize microtubules (Duellberg et al., 2016a; O’Brien et al., 1990; O’Brien et al., 1997). In contrast, oligocations such as spermine stabilize microtubules (Wolff, 1998). Interestingly, α- tubulin CTT peptides bind to calcium ions and spermine (Lefèvre et al., 2011). To our knowledge, β-CTT peptides have not been examined. Nevertheless, these results, together with our findings that magnesium ions require β-CTT to accelerate depolymerization, support a model in which multivalent cations could alter microtubule dynamics by binding and perhaps crosslinking negatively charged amino acid side chains in the CTTs. Such crosslinking could destabilize microtubules, as in the case of calcium and magnesium, by promoting the curling of protofilaments away from the lattice (Lobert and Correia, 1991; Vater et al., 1997; Weisenberg,

1972). Alternatively, crosslinking with oligocations could stabilize microtubules by strengthening the lattice, enhancing the local concentration of free tubulin subunits, or

bundling microtubules (Hamon et al., 2011; Mechulam et al., 2009; Wolff, 1998).

Given its potent effect on microtubule assembly and structure, β-CTT is likely to be a key point

for regulating microtubule dynamics in cells. It is worth noting that our studies in yeast show

that the effect of α-CTT on microtubule dynamics is minor, compared to that of β-CTT (Aiken et

al., 2014). The different effects of α-CTT versus β-CTT could be attributable to the shorter

length of the yeast α-CTT, which contains fewer negatively charged side chains than the yeast

β-CTT, or its position on the heterodimer (Figures 3.6.1 & 3.6.3). Most cells in higher eukaryotes contain a complex blend of heterodimer species with CTTs that differ in genetically-encoded β-

and α-CTT sequences and post-translational modifications. Changing the blend of heterodimer

species can profoundly alter microtubule dynamics in vivo (Honda et al., 2017), and this can be recapitulated in vitro using mixtures of recombinant tubulins (Pamula et al., 2016; Vemu et al.,

2017). Our findings indicate that differences in β-CTT composition could promote differences in assembly and plus-end stability across heterodimer species, providing a mechanism to tune microtubule dynamics in vivo.

An important and unexpected insight from this work indicates that β-CTT naturally inhibits microtubule rescue, transitions from depolymerization back to polymerization.

Historically, observing rescues in vitro using purified tubulin has been challenging because of low frequencies of occurrence and rapid disassembly rates. Surprisingly, we observed many S- tubulin microtubules that exhibited numerous rescues, clearly seen in kymographs (Figure

3.1.2). How might β-CTT inhibit rescues? Currently we lack a basic mechanistic understanding of microtubule rescues. The focus of the final section of this thesis work is on how microtubules

rescue and how the work described in this section contributes to a structural model of β-CTT as a mediator of the transitions of microtubule dynamics.

Figure 3.1.1 Western blot of tubulin after a time course of subtilisin digestion Porcine brain tubulin was treated with 1% subtilisin for indicated times at 30oC. Blots were probed with anti-β-tubulin (top), anti-α-tubulin (middle), and anti-α-tyrosinated tubulin (bottom). Arrowhead marks the faster-migrating species of β-tubulin produced after 5-minute digestion.

Figure 3.1.2 Representative kymographs of tubulin polymerized from GMPCPP stabilized microtubule seeds (i) Representative kymograph of tubulin (green) polymerized from GMPCPP stabilized microtubule seeds (red), collected at 3-second intervals. Tubulin concentration: 5.6 µM, vertical scale bar = 1 minute and horizontal scale bar = 1 µm. (ii) Representative kymograph of S-tubulin as in figure 3.1B. S-tubulin concentration: 4.9 µM, vertical scale bar = 1 minute and horizontal scale bar = 1 µm.

Figure 3.1.3 Polymerization rate plotted as a function of tubulin concentration Data points are mean ± 95% CI plotted for each concentration. Each data point represents at least 197 polymerization events from at least 36 microtubules, collected from 4 separate experiments. Linear regressions were fit to the data and plotted as solid lines. Black: tubulin, red: S-tubulin.

Figure 3.1.4 Depolymerization rate plotted as a function of tubulin concentration Each data point represents at least 62 depolymerization events from at least 28 microtubules, collected from 3 separate experiments. Data points are mean ± 95% CI plotted for each concentration.

Figure 3.1.5 Microtubule length at catastrophe plotted as the cumulative fraction of the total population Tubulin concentrations: 3.4 (black dotted line) and 8.6 µM (solid black line). N = 502 and 125 catastrophe events, respectively. S-tubulin concentrations: 1.6 (red dotted line) and 3.9 µM (solid red line). N = 74 and 112, respectively.

Figure 3.2.1 Representative images of a TIRF microtubules (i) Representative image of a microtubule assembled from the untreated tubulin (6.9 µM). Dashed white line is the coverage of the line scan across the microtubule with intensities plotted in Figure 3.2.2. Scale bar: 1 µm. (ii) Same as Figure 3.2.1i assembled with S-tubulin (4.0 µM).

Figure 3.2.2 Intensity measurements plotted as a function of pixel position Intensity measurements plotted as a function of pixel position of the microtubules in Figure 3.2.1i (black) and 3.2.1ii (red).

Figure 3.2.3 Mean tip standard deviation plotted as a function of polymerization rate Mean tip standard deviation plotted as a function of polymerization rate for tubulin (black) and S-tubulin (red). N = 355 time points per polymerization rate, from at least 5 different microtubules. Data points are mean ± 95% CI plotted for each concentration.

Figure 3.3.1 Schematic of the washout experiment

Figure 3.3.2 Representative kymographs of washout experiments (i) Representative kymograph of untreated tubulin (6.3 µM) and (ii) S-tubulin (4.3 µM) in the washout experiment. Labels denote aspects analyzed for the washout experiment, including: P-polymerization rate, D-delay time to catastrophe, S-slow depolymerization rate, F- fast depolymerization rate. S-tubulin kymograph does not exhibit the D and S states.

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Figure 3.3.3 Percentage of microtubules without a slow depolymerization rate Percentage of microtubules without a slow depolymerization rate (Figure 3.3.2i; labeled “S”) for all washout experiments (number of microtubules without S state/total number of microtubules * 100). N = 180 tubulin and n = 94 S-tubulin microtubules, pooled from at least 4 different experiments for each. Error bars are SEP. **p << 0.001, significance determined by Fishers Exact.

Figure 3.3.4 Time to catastrophe Time to catastrophe (Figure 3.3.2i, labeled “D”) in seconds for tubulin and S-tubulin microtubules after washout. N = 179 tubulin and n = 51 S-tubulin microtubules pooled from at least 4 different experiments. Lines represent the median. Microtubules without a detectable slow depolymerization phase were excluded from this analysis. * p = 0.04, significance determined by Mann-Whitney U test.

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Figure 3.3.5 Slow depolymerization rate Slow depolymerization rate (Figure 3.3B, labeled “S”) for tubulin and S-tubulin microtubules after washout. N = 162 tubulin and n = 41 S-tubulin microtubules pooled from at least 4 different experiments. Lines represent the median. Microtubules without a detectable slow depolymerization phase and/or a rate calculated to be zero were excluded from this analysis. * p = 0.05, significance determined by Mann-Whitney U test.

Figure 3.3.6 Fast depolymerization rate Fast depolymerization rate (Figure 3.3B, labeled “F”) for tubulin and S-tubulin microtubules after washout. N = 179 tubulin and n = 92 S-tubulin microtubules, pooled from at least 4 different experiments for each. ** p << 0.001, significance determined by Mann- Whitney U test.

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Figure 3.4.1 Polymerization rate plotted as a function of tubulin concentration

Crosses represent data from experiments conducted at 1 mM MgCl2, and dots are data points from Figure 3.1D from experiments conducted at 5 mM MgCl2. Black: tubulin, red: S- tubulin. Data points are mean ± 95% CI plotted for each concentration. Each data point represents at least 61 polymerization events from at least 24 microtubules, collected from at least 3 separate experiments. Linear regressions were fit to the data with at least 3 different concentrations and plotted as lines.

Figure 3.4.2 Depolymerization rate plotted as a function of tubulin concentration Depolymerization rate plotted as a function of tubulin concentration. Each data point represents at least 45 depolymerization events from at least 24 microtubules, collected from at least 3 separate experiments. Linear regressions were fit to the data with at least 3 different concentrations and plotted as lines.

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Figure 3.4.3 Slow depolymerization rate Slow depolymerization rate (Figure 3.3.2; labeled “S”) for tubulin and S-tubulin microtubules after washing out free tubulin. The Mg2+ concentration indicated is for the washout buffer. The 5 mM plots are data from figure 3.3E, provided for comparison. Data for the 1mM Mg2+ represent 45 microtubules for untreated tubulin (4.8-6.8 µM) and 158 microtubules for S-tubulin (7.8-12.0 µM), pooled from at least 4 different experiments for each. Microtubules without a detectable slow depolymerization rate and/or a rate calculated to be zero were excluded from this analysis. * p=0.05 and ** p<<0.001, significance determined by Mann-Whitney U test.

Figure 3.4.4 Fast depolymerization rate Fast depolymerization rate (Figure 3.3.2, labeled “F”) for tubulin and S-tubulin microtubules after washing out free tubulin. The 5mM plots are data from figure 3.3.6, provided for comparison. Data for the 1 mM Mg2+ represent 52 microtubules for untreated tubulin (4.8-6.8 µM) and 260 microtubules for S-tubulin (7.8-12.0 µM), pooled from at least 4 different experiments for each. N = 179 tubulin and n = 92 S-tubulin microtubules, pooled from at least 4 different experiments for each. * p=0.01 and ** p << 0.001, significance determined by Mann-Whitney U test.

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Figure 3.5.1 Representative growth curves of wild-type cells in different magnesium conditions

Control group was cultured in synthetic media with 4 mM MgSO4. Other groups were grown in synthetic media without MgSO4 but supplemented with MgCl2 at concentrations indicated. All cultures were grown at 30oC with agitation for 21 hours, and OD600 was measured every 5 minutes.

Figure 3.5.2 Median doubling times of cells in different magnesium conditions

Median doubling times of wild-type, mnr2Δ, and tub2-430Δ cells at indicated magnesium conditions, normalized to the doubling time of wild-type cells in 4 mM MgSO4. Each data point represents at least 11 replicates from 4 separate experiments. Error bars are ± 95% CI.

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Figure 3.5.3 Representative images of cells in preanaphase expressing GFP-labeled microtubules Representative images of cells in preanaphase expressing GFP-labeled microtubules, grown in synthetic media or without MgSO4. Scale bar = 1 µm.

Figure 3.5.4 Distribution of astral microtubule lengths measured in preanaphase Distribution of astral microtubule (aMT) lengths measured in preanaphase cells from an asynchronous culture. Lengths were measured every 4-5 seconds for 5 minutes. Cells were cultured in synthetic media with (+) or without (-) 4 mM MgSO4. Data pooled from 2 separate experiments, with at least 7 cells and 396 total measurements for each group. ** p << 0.001. Significance determined by Mann-Whitney U test. Lines denote median.

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Figure 3.5.5 Representative images of cells in G1 phase expressing GFP-labeled microtubules Representative images of cells in G1 phase expressing GFP-labeled microtubules, grown in synthetic media or without MgSO4. Scale bar = 1 µm.

Figure 3.5.6 Distribution of astral microtubule lengths measured in G1 Distribution of astral microtubule (aMT) lengths measured in G1 from an asynchronous culture. Cells were cultured in synthetic media with (+) or without (-) 4 mM MgSO4. Data pooled from 2 separate experiments, with at least 60 cells for each group. *p = 0.01, ** p << 0.001. Significance determined by Mann-Whitney U test. Lines denote median.

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Figure 3.6.1 Amino acid sequences of the β-CTTs of mutant yeast strains Amino acid sequences of the β-CTTs of mutant yeast strains characterized in Figure 3.6. The native (TUB2) allele provided for comparison.

Figure 3.6.2 Representative images of preanaphase yeast strains expressing microtubules labeled with GFP-Tub1 Representative images of preanaphase yeast strains expressing microtubules labeled with GFP-Tub1 either treated with 1.5 µM Nocodazole or DMSO as a control. Arrowheads indicate the plus-ends of astral microtubules.

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Figure 3.6.3 Percent of asynchronous cultures exhibiting astral microtubules after drug exposure Percent of asynchronous cultures exhibiting astral microtubules after exposure to 1.5 µM Nocodazole or DMSO for 1 hour. Bars represent the pooled mean ± SEP of 4 separate experiments pooled. Each group is composed of n ≥ 735 cells. ** p << 0.001. Significance determined by Fishers Exact test.

Figure 3.6.4 Representative astral microtubule life plots Representative astral microtubule life plots of wild-type and tub2-polyQ cells expressing GFP-labeled tubulin. Microtubule lengths were measured at 3-4 second time intervals.

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Figure 3.6.5 Distributions of astral microtubule lengths in living preanaphase cells Images were collected at 3-4 second intervals for 10 minutes. Results represent pooled data from at least 4 separate experiments, and at least 40 astral microtubules sampling n ≥ 3593 timepoints for each strain. Red bars indicate median values. **p << 0.001. Significance determined by Mann-Whitney U test.

Figure 3.6.6 Distributions of astral microtubule polymerization rates of preanaphase cells Distributions of astral microtubule polymerization rates, from cells analyzed in figure 3.3.5.

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Figure 3.6.7 Distributions of astral microtubule depolymerization rates of preanaphase cells Distributions of astral microtubule depolymerization rates, from cells analyzed in figure 3.3.5.

Figure 3.6.8 Distributions of astral microtubule catastrophe frequency of preanaphase cells Distributions of astral microtubule catastrophe frequency, from cells analyzed in figure 3.3.5.

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Figure 3.7.1 Coomassie stained SDS-PAGE gel of tubulin after a time course of subtilisin digestion

Figure 3.7.2 Line scans of Coomassie stained SDS-PAGE gel after subtilisin digestion Plot of line scans of lanes 2 (black), 4 (red), and 6 (blue) from figure 3.7.1.

Figure 3.7.3 Densitometry of Coomassie stained SDS-PAGE gel after subtilisin digestion Plot of sum of pixel intensity of respective bands in the Coomassie-stained gel in figure 3.7.1.

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Figure 3.7.4 Western blot of SDS-PAGE gel after subtilisin probing for α-tubulin Western blot of the same samples as in figure 3.7.1, probed with an antibody to α- tubulin (4A1; see Materials and Methods). This blot is the same as in Figure 3.1.1 middle.

Figure 3.7.5 Line scans of western blot of SDS-PAGE gel after subtilisin Plot of line scans of lanes 2 (black), 4 (red), and 6 (blue); from the blot in figure 3.7.4.

Figure 3.7.6 Western blot of SDS-PAGE gel after subtilisin probing for β-tubulin Western blot of the same samples as in 3.7.1 and 3.7.2, probed with an antibody to β- tubulin (9F3; see Materials and Methods). This blot is the same as in Figure 3.1.1 top.

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Figure 3.7.7 Line scans of western blot of SDS-PAGE gel after subtilisin probing for β-tubulin Plot of line scans of lanes 2 (black), 4 (red), and 6 (blue); from the blot in 3.7.6.

Figure 3.7.8 Western blots of S-tubulin stocks Western blots of the two independent S-tubulin samples made after subtilisin digestion for 5 minutes at 30ºC; probed for α-tubulin (Top; DM1A) and β-tubulin (Bottom; 9F3). These samples were snap frozen and stored at -80ºC in single use aliquots.

Figure 3.8.1 Coomassie stained SDS-PAGE gel of tubulin after subtilisin digestion Coomassie stained SDS-PAGE gel of tubulin after subtilisin digestion for 0 or 5 minutes. Boxes indicate the bands SF1 (black), SF4 (blue), SF5 (red) that were excised and analyzed by mass spectrometry.

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Figure 3.8.2 Amino acid sequence of residues 393-445 TUBB2B (i) Amino acid sequence of residues 393-445 of Sus scrofa TUBB2B shown for reference alongside peptides identified in the SF5 band. We were not able to identify peptides further towards the β-tubulin carboxy-termini in any of the bands analyzed, likely due to high level of heterogeneity introduced by posttranslational modifications. (ii) Amino acid sequence of residues 403-451 of Sus scrofa TUBA1B shown for reference alongside peptides identified in the SF1 (black), SF4 (blue) and SF5 (red) bands.

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CHAPTER IV:

A UNIFIED MECHANISM FOR MICROTUBULE RESCUE

Introduction

Transitions between microtubule assembly and disassembly involve major structural rearrangements at the microtubule end. During assembly, incoming tubulin subunits first form longitudinal interactions to generate curved protofilaments that extend from the microtubule end, and then form lateral interactions with neighboring protofilaments to straighten into the coherent microtubule lattice (McIntosh et al., 2018). During disassembly, protofilaments undergo the opposite transition; from the straight conformation of the lattice, to losing lateral interactions and sharply curling away from each other at the disassembling end (Chrétien et al.,

1995; Mandelkow et al., 1991; McIntosh et al., 2018). Under some conditions, protofilaments at depolymerizing microtubule ends adopt highly curled ”ram’s horn” morphologies that are not observed for polymerizing microtubules (Chrétien et al., 1995; Mandelkow et al., 1991). The outward curling of protofilaments during depolymerization has been estimated to generate at least 19pN*nm or energy per tubulin subunit, and is thought to propagate strain along the protofilament that drives the straight-to-curled transition for subunits further down the lattice

(Driver et al., 2017).

The transition from assembly to disassembly, known as catastrophe, must represent an intermediate state between the assembling and disassembling end structures. Current models posit that catastrophe may be caused by changes in the so-called ‘GTP cap’ at the assembling plus end, which would weaken subunit affinity for the lattice (Carlier and Pantaloni, 1981; Chen

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and Doxsey, 2012; Seetapun et al., 2012). Alternatively, catastrophe may be caused by the formation of asymmetric protofilament extensions during assembly that lack stabilizing lateral interactions and lead to a weakening of the lattice (Aher and Akhmanova, 2018; Coombes et al.,

2013). Both of these models imply that a quorum of stable lateral interactions is necessary to

maintain assembly, and catastrophe may represent a loss of quorum.

In contrast to catastrophe, the transition from disassembly to assembly, known as

rescue, is poorly understood. In principle, rescue must represent an intermediate state

between the disassembly and assembly end structures. This intermediate would need to

overcome both the fast rate of subunit loss and the energy driving the outward curling of protofilaments. Rescue is a property intrinsic to tubulin, as it can be observed in experiments using purified tubulin (Aumeier et al., 2016; Dimitrov et al., 2008; Tropini et al., 2012).

Importantly, this work showed that rescue frequency is largely independent of tubulin concentration, indicating that the mechanism is not simply kinetic (i.e. the subunit on rate does not overwhelm the subunit off rate during depolymerization (Gardner et al., 2013; Walker et al., 1988)). Instead, rescue may disrupt the depolymerizing end structure. Several conditions

have been shown to promote rescue, including mechanical stress on the microtubule (de

Forges et al., 2016), structural defects in the lattice (Aumeier et al., 2016), slowing GTP

hydrolysis (Tropini et al., 2012), and extrinsic regulation by microtubule-binding proteins (Aher

and Akhmanova, 2018; Al-Bassam et al., 2010; Arnal et al., 2004; Hiremathad et al., 2018;

Komarova et al., 2002; Lindeboom et al., 2018; Mimori-Kiyosue et al., 2005). Nevertheless, we

lack a basic understanding of the underlying mechanism.

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A fundamental question is whether rescues are a pre-determined property of the microtubule lattice or a state that may be sampled by the depolymerizing end. In the first model, ‘rescue sites’ are formed in the lattice of a growing microtubule. After a subsequent catastrophe, when the depolymerizing end returns to a rescue site in the lattice, further depolymerization is inhibited and the microtubule end returns to an assembling state. This model is supported by several observations, including rescues near ‘GTP islands’ which are detected in microtubule lattices, far from assembling ends (Dimitrov et al., 2008), and rescues near sites of mechanically induced damage, where subunits are thought to be lost from the lattice and new subunits may be incorporated (Aumeier et al., 2016). In the second model, changes in the structure and/or the activity of the depolymerizing microtubule end disrupt disassembly and promotes assembly. This model is primarily supported by previous studies of proteins that selectively bind to microtubule ends and promote rescue, including CLASP, CLIP-

170, and Kip3/kinesin 8 (Al-Bassam et al., 2010; Arnal et al., 2004; Bratman and Chang, 2007;

Dave et al., 2018; Hiremathad et al., 2018; Komarova et al., 2002; Lawrence et al., 2018;

Lindeboom et al., 2018). It is unclear whether this is also a property of tubulin alone.

Here we tested these two models through a combined approach; first we developed

Monte Carlo simulations to test simple predictions of each model. Then we validated the

simulations experimentally using purified tubulin and found support for a combination of the

two models. We find that regions of the microtubule lattice were capable of repeated rescues,

which strongly supports the lattice-driven model. In addition, we observed that divalent cations

are potent inhibitors of microtubule rescue, and used this to test whether conditions during

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lattice assembly or disassembly determine rescue activity. Our results support a combination of

the two models wherein rescues are regulated through coordination between defined rescue

sites in the lattice and changes at the depolymerizing end.

Materials & Methods

Chemicals and reagents were from Fisher Scientific (Pittsburgh, PA) and Sigma-Aldrich (Saint

Louis, MO), unless stated otherwise.

In Vitro Microtubule Dynamics Assays

Assays to measure microtubule dynamics by TIRF microscopy were based on previously

described methods (Fees and Moore, 2018; Gell et al., 2010). Double-Cycled microtubule seeds

were assembled by incubating 20 µM rhodamine tubulin (Cytoskeleton, Inc; Denver, CO) in

BRB80 buffer (80 mM PIPES brought to pH6.9 with KOH, 1 mM ethylene glycol tetraacetic acid

(EGTA), 1 mM MgCl2; minor pH adjustments were made with NaOH) with 1 mM GMPCPP at

37oC for 30 minutes. Sample was then centrifuged at 100,000 x g for 10 min at 30oC and the supernatant was removed. Pellet was suspended in 0.8x starting volume of ice cold BRB80 buffer to depolymerize labile microtubules. An additional 1 mM GMPCPP was added and microtubules were polymerized at 37oC for 30 min and pelleted again. The pellet was suspended in 0.8x starting volume warm BRB80. The reaction was then gently pipetted 8-10 times to shear the microtubules, aliquoted into 1 µL volumes, and either used immediately or snap frozen and stored at -80oC.

Imaging chambers were assembled using 22x22 mm and 18x18 mm coverslips. The coverslips were cleaned and silanized as previously described (Fees and Moore, 2018; Gell et

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al., 2010). The prepared glass coverslips were stored in desiccators at room temperature until used. The coverslips were mounted in a custom fabricated stage insert and sealed with melted strips of Parafilm. GMPCPP-stabilized microtubule seeds were affixed to coverslips using anti- rhodamine antibodies (Fisher Scientific, Cat# A-6397; diluted 1:50 in BRB80). Chambers were flushed with 1% Pluronic-F127 in BRB80 to prevent other proteins from adhering to the glass and equilibrated with an oxygen scavenging buffer (40 mM glucose, 1 mM Trolox, 64 nM

Catalase, 250 nM Glucose Oxidase, 10 mg/ml Casein) prior to free tubulin addition. The imaging buffer consisting of unpolymerized tubulin (15-20% Hylite-488 labeled tubulin (Cytoskeleton,

Inc.) and 80-85% unlabeled porcine brain tubulin), 5 mM MgCl2, 1 mM GTP, the oxygen scavenging buffer, and BRB80 to 50 µL volume was then flowed into the imaging chambers. The chamber was sealed with VALAP (1:1:1 Vaseline, Lanolin, Paraffin wax) and warmed to 37oC using an ASI400 Air Stream Stage Incubator (Nevtek; Williamsville, VA) for 5 minutes before imaging. Temperature was verified using an infrared thermometer.

Images were collected on a Nikon Ti-E microscope equipped with a 1.49 NA 100× CFI160

Apochromat objective, TIRF illuminator, OBIS 488-nm and Sapphire 561-nm lasers (Coherent;

Santa Clara, CA), and an ORCA-Flash 4.0 LT sCMOS camera (Hammamatsu Photonics; Japan), using NIS Elements software (Nikon; Minato, Tokyo, Japan). Images were acquired using two- channel, single-plane TIRF, at 1 second intervals.

Image Analysis

Images were analyzed using a custom-made MATLAB program as described

previously(Fees and Moore, 2018). Briefly, seeds were identified by thresholding image

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intensity, and used to segment the images along the axis of the microtubule. Images were then automatically cropped to 4 pixels above and below the microtubule axis, then the max intensity projection for each timepoint were stacked to generate kymographs for analysis.

Polymerization and depolymerization rates were calculated by measuring the changes in microtubule length and time from the first and last points of the individual polymerization and depolymerization events from the kymographs. Polymerization rate constants were estimated as the slope of the polymerization rate linear model. Depolymerization rate constants were determined similarly but using the median depolymerization rate (µm*min-1) from all tubulin concentrations pooled.

Rescue and catastrophe frequencies were calculated as the quotient of the number of transitions and depolymerization or polymerization duration respectively. Rescue abundance was determined by dividing the number of unique rescue events by the total length lost during depolymerization. Unique rescue events were defined as rescue sites separated by at least 200 nm from previous rescue sites on the microtubule lattice. Rescue position relative to the plus- end was calculated as the difference between microtubule length at catastrophe and length at rescue.

Tubulin Wash-In Experiments

For wash-in experiments, GMPCPP-seeded imaging chambers were similarly assembled, but not sealed with VALAP. Imaging chambers were warmed on the stage for 3-5 minutes, allowing the temperature to equilibrate to 37oC, then dynamic microtubules were imaged for

10 minutes before the imaging chamber with was flushed 4-5x chamber volumes of warm

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reaction buffer. Image acquisition was paused during the chamber flush and resumed immediately afterwards. The average wash-in duration for all wash-in experiments was ~60 seconds from time of acquisition pause to resumption.

Images were processed as described above, with the addition of post-acquisition image stabilization that was used to reduce minor XY drift during image acquisition using the Image

Stabilizer Plugin for ImageJ (Kang Li, 2008).

Determining Tubulin Concentration

For each experiment, the tubulin concentration was determined by running a sample of

the imaging buffer containing unpolymerized tubulin on a 10% Bis-Tris SDS-PAGE gel, followed

by staining with Coomassie blue. The concentration was determined by densitometry, directly compared to standard curve made from BSA standards run on the same gel.

Monte Carlo Simulations

Microtubule dynamics were simulated using a custom-made MATLAB program. The simulation modeled microtubule length changes over time based on empirically derived values for polymerization and depolymerization rates as well as transition frequencies. Rescues were simulated slightly differently between the two models; either stochastically based on the experimental rescue frequency for the free model. Rescues were simulated as sites incorporated into the growing microtubule at an empirically derived frequency. Following catastrophe, a rescue event was triggered as the depolymerizing end approached the incorporation site within 100 nm. For simplicity, rescue sites were lost after activation, resulting in only a single rescue per site. The simulated microtubules lengths were analyzed as

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experimental data.

Free Calcium Estimation

We used MaxChelator (maxchelator.stanford.edu) to estimate the concentration of free calcium in the reactions using theoretical values for ionic and chelator concentrations, pH and temperature. At standard conditions plus calcium (5 mM MgCl2, 10 µM CaCl2, 1 mM EGTA and

1 mM GTP at pH:6.9 and 37oC) it is estimated that more than 99.9% of the Ca2+ is bound to

EGTA.

Results

Microtubule Rescue Frequency is Independent of Tubulin Concentration

We sought to investigate the mechanism of microtubule rescue that is intrinsic to

tubulin. Using purified tubulin to study microtubule rescue in vitro has been historically

challenging, because rescues occur infrequently, compared to catastrophes. Therefore, we

developed an experimental protocol to address three limiting factors of studying rescue in vitro;

1) temporal resolution, 2) acquisition duration and 3) microtubule number. Microtubules were

assembled from purified porcine brain tubulin; determined to be >99% pure by Coomassie stain

following SDS-PAGE separation (Figure 4.1.1). This purified tubulin was mixed with 15-20%

Hylite-488 labeled -tubulin to visualize microtubule assembly from Rhodamine-labeled GMPCPP

seeds using TIRF microscopy and standard assembly conditions (5-10 µM tubulin in BRB80 with

5 mM MgCl2, 1 mM GTP and oxygen savaging buffer (Fees and Moore, 2018; Gell et al., 2010);

Figure 4.1.2). We imaged thousands of microtubules at a 1-second time resolution for 15

minutes using a cMOS camera and processed the images into kymographs to measure

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microtubule dynamics using a custom MATLAB program(Fees and Moore, 2018). Measured rates of polymerization and depolymerization were used to determine the apparent tubulin

-1 -1 association and dissociation constants, respectively (Kon: 3.97 subunits*µM *s Koff: 567.30 subunits*s-1; Figure 4.1.3i & 4.1.3ii). The catastrophe frequency (growing to shrinking) was calculated by dividing the number of transitions by the total polymerization time. We found no correlation between tubulin concentration and catastrophe frequency (Figure 4.1.4i). This experimental design allowed us to consistently detect rescues within the microtubule population and test models of the rescue mechanism.

We defined rescue as a transition from depolymerization to polymerization that occurred at least 150 nm from the GMPCPP-stabilized seed (Figure 4.1.2, arrowhead). This definition ensures the distinction of rescues that occur in the dynamic microtubule lattice from those that occur at the stabilized seed, within the temporal and spatial resolution of our system. We measured rescues across a range of tubulin concentrations and found an average rescue frequency of 0.01 events per second of depolymerization time, with no clear dependence on tubulin concentration (CI: 0.009 – 0.014 events*s-1; Figure 4.1.4ii). Overall,

18.7% of the 525 total seeds in our experiments nucleated microtubules that exhibited at least one rescue, and these rescued an average of 1.6 times per 15-minute acquisition (CI: 1.4-1.7;

Figures 4.8.1i & 4.8.1ii).

We then validated the results from our purified porcine brain tubulin by conducting parallel experiments with commercially available porcine brain tubulin under the same reaction conditions. The commercially available tubulin exhibits similar polymerization and

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depolymerization rates as our purified tubulin (Figures 4.1.3i & 4.1.3ii), and similar frequencies

of catastrophe and rescue (Figures 4.1.4i & 4.1.4ii). Importantly, neither set of experiments

revealed significant correlation between tubulin concentration, and therefore polymerization rate, and rescue frequency, consistent with previous findings (Gardner et al., 2013; Walker et al., 1988).

Longer Microtubules are More Likely to Rescue

We considered two general models for how rescues might be triggered. First, an ‘end-

driven’ model in which the depolymerizing end of the microtubule stochastically switches to a

stable state, resulting in rescue. Second, we considered a ‘lattice-driven’ model in which rescue-

sites were integrated into the lattice prior to depolymerization and produced a rescue when the

depolymerizing end reached that site in the lattice. Both models predict that longer

microtubules would have a greater likelihood of rescue. Consistent with our prediction, we

found that microtubules that rescue in our experiments were 51% longer at catastrophe than

the population on average (3.68 µm CI: 3.25 – 4.11 µm compared to 2.44 µm CI: 2.32 – 2.55

µm; Figure 4.2.1).

We then developed computational models of microtubule rescue using Monte Carlo simulations of experimentally derived parameters (polymerization and depolymerization rates as well as transition frequencies) to simulate a single filament with changes in length over time

as the output. For our ‘end-driven’ simulation, rescue events were stochastically triggered

during depolymerization, at a rescue frequency (Kr) that matched our experimental measurements (Figure 4.2.2). For our ‘lattice-driven’ simulation, rescue sites were incorporated

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during filament polymerization at a defined frequency (Ki). These sites would remain embedded in the lattice until, after catastrophe, the depolymerizing end reached the site and triggered a

rescue event. Initially we set these sites as fully active – if they were incorporated into a filament, then that site would always trigger a rescue, however this was modified later (see below; Figure 4.2.3). The Ki was computationally determined using our simulation to calculate the rescue frequency as a function of incorporation frequency (Figure 4.2.4). The simulated rescue frequency intercepted the experimental values at an incorporation frequency between

0.0001 – 0.0006 events*s-1 (Figure 4.2.4 inset).

We validated our end-driven and lattice-driven simulations first by comparing the lengths of microtubules at catastrophe to our experimental data. We found that both simulations accurately sample a similar range of microtubule lengths at catastrophe (Figure

4.2.5). Of note, the simulated data was more normally distributed for microtubules that

catastrophe above 6 µm in length. We attributed this to a limitation of our experimental setup,

specifically the inability to monitor longer microtubules that extend beyond the field of view.

Both simulations were consistent with our experimental results showing that rescuing

microtubules were significantly longer at catastrophe than the average distribution of lengths

(Figures 4.2.6 & 4.2.7). We then sought to use these simulations to determine the mechanism

of microtubule rescue by testing predictions from each model.

Effect of Depolymerization Rate on Rescue

We began by testing whether depolymerization rates influenced rescue frequency. If rescue occurs through stochastic transitions at the depolymerizing end, then increasing the

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time spent in the depolymerizing state might promote rescue. We used our simulations to test this prediction across a range experimentally derived depolymerization rates (Figure 4.3.1). The end-driven simulation showed that rescue events were preceded by significantly slower depolymerization rates (250.7 CI: 240.3-261.1 nm*s-1) than the rates exhibited by the total population (288.1 CI: 283.8-292.3 nm*s-1; Figure 4.3.2i). In contrast, the lattice-driven simulation showed no difference in depolymerization rates preceding a rescue event compared to the total population (Figure 4.3.2ii). Our experimental data showed no difference in the

depolymerization rates preceding a rescue event compared to the total population (274.5 CI:

243.73 – 305.3 nm*s-1 compared to 289.8 CI: 276.7-302.8 nm*s-1; Figure 4.3.2iii). In this case,

our experimental data is more consistent with the lattice-driven model.

The results from our end-driven simulation showed that the average difference in

depolymerization rates preceding rescue was approximately 30 nm*s-1, which is at the edge of

the detection threshold of our experiment. As an alternative approach, we tested the

prediction that decreasing the time spent in depolymerization, by increasing the rate of

depolymerization, would inhibit rescue. The divalent cation calcium (Ca) increases microtubule

depolymerization rates(Gal et al., 1988; Herzog and Weber, 1977; O’Brien et al., 1997;

Rosenfeld et al., 1976) without significantly altering polymerization rates(O’Brien et al., 1997).

Adding 10 µM CaCl2 to our experimental conditions significantly increased the depolymerization

-1 rate compared to control reactions (+CaCl2: 442.0 CI: 403.3 – 480.8 nm*s compared to no

-1 CaCl2: 324.4 CI: 314.2 – 334.9 nm*s ; Figure 4.3.3), without altering the apparent

polymerization rate constant (Figure 4.3.4 & 4.9.1). Under these conditions, we did not observe

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a significant change in the number of rescues per microtubule or the rescue frequency as a function of depolymerization time (Figures 4.3.5 & 4.3.6). However, we observed that more

lattice was lost during depolymerization in the presence of 10µM CaCl2 before rescue occurred

(Figure 4.3.7i). We calculated rescue position relative to catastrophe position as the difference

of microtubule length at catastrophe and length at rescue, which we will refer to as rescue

position. We found that adding CaCl2 significantly shifted the position of rescue sites, causing

more lattice loss before rescue (Figure 4.3.7ii). These results suggest that depolymerization

rate influences the position, but not the timing of rescue.

Rescues Occur Repeatedly at Similar Sites Along the Microtubule

How might the position of rescue sites be determined? We predicted that if rescues

were triggered by pre-defined sites integrated into the lattice, then the same sites might exhibit multiple rescues in our experiments. Consistent with this prediction, we observed 35% of rescues occur within the same region (± 200 nm) as a prior rescue (Figure 4.4.1 arrowheads).

We termed these events repeated rescues.

We used our simulation to test whether repeated rescues could be generated by an end-driven mechanism alone. Starting with our experimentally derived rescue frequency (0.01

events per second of depolymerization time), our end-driven simulation showed that <0.5% of

rescues occurred within 200nm of a previous rescue site (Figure 4.4.2 red bar). We then asked what rescue frequency would be required to generate the repeated rescue behavior observed

in our experiments? We ran multiple end-driven simulations and found that the overall rescue

frequency must be ~40x greater (i.e. 0.4 events per second of depolymerization time) to

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generate repeated rescue behavior that matches our experimental results (Figure 4.4.3). This suggests that an end-driven model alone is highly unlikely to generate repeated rescues. The

lattice is likely to play a determining role.

Divalent Cations Suppress Rescues

We next sought to identify factors that influence rescue sites. Recent work by Aumeier et al. reported microtubule rescues occurring closer to the site of catastrophe than we observed in our standard experimental conditions (Aumeier et al., 2016). An important difference between their experiments and our own is the concentration of magnesium – whereas Aumeier et al. used assembly buffer containing 1mM MgCl2, our assembly buffer contains 5mM MgCl2 (Aumeier et al., 2016). We confirmed the impact of magnesium on rescue by altering MgCl2 concentration in our experiments. Rescues occur closer to the site of catastrophe in 1mM MgCl2 compared to 5mM MgCl2 (1mM Mg: 1.4 CI: 1.4 – 1.7 µm; 5mM Mg:

3.0 CI: 2.7 – 3.3 µm; Figure 4.5.1 and 4.5.2). In other words, lower magnesium decreases the amount of the microtubule lattice that depolymerizes before rescue occurs. We further tested the impact of magnesium by titrating a range of MgCl2 concentrations in the reaction buffer.

Consistent with previous studies, we found that rates of polymerization and depolymerization increase in proportion with the concentration of MgCl2 in the reaction (Figures 4.10.1 & 4.10.2;

(Duellberg et al., 2016b; Fees and Moore, 2018)). Increasing MgCl2 concentration inhibits

rescue, as a function of both rescue position relative to the site of catastrophe and time in

depolymerization (Figures 4.5.2 & 4.5.3). Adding 10µM CaCl2 also causes rescues to occur

further from the site of catastrophe, even under low magnesium conditions (1mM MgCl2 +

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10µM CaCl2; Figure 4.5.1 and 4.5.2); however, adding 10µM CaCl2 did not increase the rescue frequency as a function of depolymerization time (Figure 4.5.3). This is likely attributable to differences in depolymerization rate, as 10µM CaCl2 increases depolymerization rate by > 150%

(Figure 4.3.4). These results suggest that divalent cations strongly influence the position of rescue sites.

We then asked whether divalent cations alter repeated rescue activity in the lattice. We observed that increasing concentrations of MgCl2 decreases the frequency of repeated rescues

(45% of rescues at 0mM MgCl2, compared to 25% of rescues at 3.5mM MgCl2 Figure 4.5.4).

Furthermore, adding 10 µM CaCl2 strongly decreases the frequency of repeated rescues (Figure

4.5.4). Together these results indicate that divalent cations suppress rescue.

Rescue Activity is Determined During Depolymerization

We reasoned that divalent cations could suppress rescue either by inhibiting the

integration of rescue sites into the lattice or inhibiting their activity. To distinguish between

these possibilities, we asked whether divalent cations influence rescues during the assembly of

the lattice or during depolymerization. We designed an experiment to isolate lattice assembly from depolymerization by assembling microtubules under one set of reaction conditions, then rapidly exchanging the reaction buffer and testing how the pre-formed lattice behaves under

different conditions (Figure 4.6.1). These experiments differ from previously described ‘washout’ experiments, in which the free tubulin was depleted from the reaction to halt further polymerization (Duellberg et al., 2016b; Fees and Moore, 2018; Walker et al., 1988). In contrast, our ‘wash-in’ experiments maintain the same concentrations of tubulin, GTP, and oxygen

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scavenging buffer throughout the experiment; only the concentration of MgCl2 is changed.

Accordingly, we observed that after buffer exchange, microtubules continued to polymerize from the existing lattice at rates consistent with the present MgCl2 concentration. Increasing or decreasing concentrations of MgCl2 was sufficient to increase or decrease the polymerization and depolymerization rates, respectively (Figures 4.6.2i, 4.10.1 & 4.10.2). Control experiments where reaction buffers with the same concentration of MgCl2 were washed in exhibited no difference in either polymerization or depolymerization rates (Figures 4.6.2i & 4.6.2ii).

Using this experiment, we tested the prediction that if rescue is determined during the assembly of the lattice, then microtubules grown under rescue-promoting conditions (1mM

MgCl2) would be insensitive to the introduction of rescue-suppressing conditions (5mM MgCl2), and vice-versa. We tested this prediction by examining the first catastrophe event after wash-in and asked whether and where along the lattice the microtubules rescue. Outcomes were classified into one of three groups; 1) No rescue, in which depolymerization reached the stabilized seed (Figure 4.6.3i); 2) Rescue in pre-existing lattice, in which the microtubule rescued at a region of the lattice that was assembled prior to wash-in (Figure 4.6.3ii); and 3)

Rescue in new lattice, in which the microtubule rescued at a region of the lattice that was assembled after the wash-in (Figure 4.6.3iii). Microtubules assembled in 1mM MgCl2 conditions

(rescue-promoting) and then introduced to 5mM MgCl2 conditions (rescue-inhibiting) exhibited fewer rescues than controls that maintained 1mM MgCl2 (Figure 4.6.3i). In contrast, microtubules assembled in 5mM MgCl2 and then introduced into 1mM MgCl2 were more likely to rescue than controls that maintained 5mM MgCl2 (Figure 4.6.3i). Importantly, the majority of

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rescues after the introduction of 1mM MgCl2 occurred in regions of the lattice that were assembled prior to wash-in, in the presence of 5mM MgCl2 (Figure 4.6.3ii & 4.6.3iii). We also

measured rescue position and found that introducing 1mM MgCl2 resulted in an average length

of lattice lost before rescue that was similar to what we observed for microtubules that

maintained 1mM MgCl2 throughout the experiment (Figure 4.6.4). Together these results

suggest that rescue activity is not exclusively determined during the assembly of the lattice.

Discussion

In this study, we sought to elucidate the mechanisms of microtubule rescue by testing

two models: 1) a lattice-driven model, in which rescue-sites were integrated into the lattice,

prior to depolymerization, and produced a rescue when the depolymerizing end returned to

that site in the lattice; and 2) an ‘end-driven’ model, in which the depolymerizing end of the

microtubule stochastically switches to a stable state, resulting in rescue. Our data are

consistent with rescue sites integrating into the lattice, supporting the lattice-driven model;

however, we also found that changing the depolymerization conditions after lattice assembly,

by altering the concentration of divalent cations in the reaction buffer, was sufficient to alter

rescue behavior. We therefore propose a unified model, in which the lattice contains defined

sites that can promote rescue, but the activity of these sites may be regulated by conditions

during disassembly.

Figure 4.7.1 depicts models for how features of the lattice and depolymerizing end

could stabilize an intermediate state of the microtubule end, between disassembly and

assembly. We speculate that the depolymerizing microtubule end is a heterogenous structure

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where individual protofilaments can visit different states of interactions with neighbors, curvature, and strain. In this model, states that enhance lateral interactions, reduce curvature, and/or reduce strain could lead to rescue. In addition, sites in the lattice could promote rescue by blocking the propagation of strain from the outwardly curled protofilament to the lattice. In this way, heterogenous regions of existing microtubule lattice disrupt depolymerization and promote the return to an assembling state.

The lattice-driven model is supported by several lines of evidence from previous studies, including the observation of apparent ‘GTP-islands’ -- regions of the lattice distal from the plus end that bind a GTP-tubulin specific antibody (Dimitrov et al., 2008). These islands were shown to correlate with sites of rescue, suggesting that regions of incomplete GTP hydrolysis may stabilize the microtubule lattice to promote rescue (Dimitrov et al., 2008). Along these lines,

Tropini et al. demonstrated that adding small amounts of a non-hydrolysable GTP analog

(GMPCPP) to microtubules assembling in GTP could increase the frequency of rescues(Tropini et al., 2012). Finally, recent work has shown that rescue events correlate with regions of microtubule repair, where new GTP-tubulin incorporates into sites along the lattice following mechanical damage (Aumeier et al., 2016). Our strongest evidence in support of the lattice- driven model comes from our observation of repeated rescue sites, which show successive rescues within the same region of lattice (Figures 4.4). Our simulations indicate that such behavior is not likely to arise from a stochastic rescue mechanism; therefore, the lattice at the repeated rescue site possesses a persistent, rescue-promoting activity. The exact nature of these repeated rescue sites in our experiments is uncertain, but clearly does not require the

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addition of non-hydrolysable nucleotide analogues or the induction of mechanical damage to microtubules. Those conditions may increase rescues, but they are not necessary for rescues to occur.

An important question that has not been addressed by previous studies is how long rescue sites remain active and what are the requirements for activity. In an effort to measure the lifetime of repeated rescue sites, we attempted to quantify the period of time over which repeated rescues can occur; however, we were only able to capture the complete lifetime of a few microtubules with repeated rescue sites. While sampling issues limit our ability to make general conclusions about the lifetime and activity of repeated rescue sites, two key points emerge from our observations. First, rescue sites do not always promote repeated rescues. Out of all rescue sites that we observed, only 35% exhibited a second rescue when a subsequent catastrophe returned the depolymerizing end to that same position (Figure 4.4.2). Furthermore, when we did observe two rescues at the same position, 43% of these microtubules went on to exhibit a third rescue while 57% were subsequently lost to depolymerization (data not shown).

Second, rescue sites can promote repeated rescues over a relatively long period of time. We observed rescues occurring at a similar lattice site as soon as 30 seconds after a previous rescue, or up to 399 seconds later (data not shown). This indicates that sites can maintain rescue-promoting activity over a timespan that is much longer than the expected lifespan of

GTP at the exchangeable site (i.e. ‘E-site’) of tubulin under our reaction conditions. In addition, we observed rescues occurring all along the length of the lattice, without bias toward the GTP- rich plus-end (Figures 4.5). These observations lead us to speculate that alternative lattice

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heterogeneities, independent of nucleotide state, could be required for rescue site activity. For example, previous work has shown that the microtubule lattice can transition between different numbers of protofilaments along its length, which could create a regions of lattice where protofilaments are ‘missing’ and the remaining protofilaments lack laterally bound neighbors(Chretien et al., 1992). We speculate that such an environment could disrupt the depolymerizing end by uncoupling the strain of outwardly curling protofilaments from subunits in the lattice (Figure 4.7.1).

Although our results are consistent with the presence of rescue sites in the lattice, we

also show that the formation of these sites is not sufficient to explain rescue activity. Our wash- in experiments provide the best evidence that depolymerization conditions determine rescue activity more strongly than lattice assembly conditions. By observing the same microtubules under alternating rescue-promoting or inhibiting conditions, we found that introducing rescue- promoting conditions during depolymerization is sufficient to recapitulate the rescue behavior of control microtubules that are constitutively exposed to rescue-promoting conditions (Figures

4.6). The key to these experiments was our finding that the divalent cations magnesium and calcium have potent effects on rescue activity. We speculate that high concentrations of divalent cations alter the structure of the depolymerizing end to inhibit rescue.

How could divalent cations impact the depolymerizing end to transition it to a rescue state? Divalent cations, like calcium and magnesium, are known to play important roles in microtubule dynamics. Magnesium promotes polymerization and depolymerization, and some of these roles are related to effects on GTP-binding and exchange (Correia et al., 1987; Huang

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et al., 1985; Lin and Hamel, 1987). However, magnesium also binds tubulin at additional sites outside of the GTP-binding pockets (Frigon and Timasheff, 1975; Lee and Timasheff, 1975; Lee et al., 1978; Na and Timasheff, 1986). Hence, magnesium may have additional roles in regulating tubulin activity. We have shown here that both magnesium and relatively small concentrations of calcium (estimated 1nM free calcium(Bers et al., 2010)) can drastically increase depolymerization rates, consistent with previous work (Duellberg et al., 2016a; Fees and Moore, 2018; O’Brien et al., 1997). We also show that both cations are potent inhibitors of

microtubule rescue (Figures 4.5). This suggests that the divalent cations may be suppressing rescue by altering how microtubules depolymerize. During depolymerization the protofilaments of a microtubule curl away from the lattice (Mandelkow et al., 1991), which would bring the negatively charged regions on the outer surface of tubulin subunits into close proximity (Figure 7.1.1). Divalent cations could stabilize the outwardly curled conformation of

the depolymerizing end by bridging the negatively charged side chains on the outer surface of

tubulin (Figure 7.1.1). Consistent with this notion, we have shown previously that magnesium

promotes depolymerization in a manner that requires the negatively charged c-terminal tail

(CTT) domain of β-tubulin(Fees and Moore, 2018). Interestingly, we observed that removing the

β-CTT slows depolymerization and also promotes microtubule rescue, mimicking the effect of low magnesium (Figure 3.1.2;(Fees and Moore, 2018)). Therefore, divalent cations may be inhibiting rescue by binding the negatively charged CTTs and promoting a conformation of the depolymerizing end that favors disassembly. Further studies are needed to determine how CTTs could change the activity or abundance of rescue sites in the lattice.

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Our findings on the intrinsic rescue behavior of tubulin proteins could be extended to cells, where extrinsic proteins may further regulate rescue by binding and selectively stabilizing structural intermediates at the depolymerizing end and/or rescue sites in the lattice. For

example, CLASP proteins promote rescue and suppress catastrophe, however, with only a

modest effect on assembly and disassembly rates (Lawrence et al., 2018). CLASP is known to

bind both at microtubule ends and along the lattice, raising the possibility that CLASP could

regulate rescue by either promoting a conformational state of the depolymerizing end that

favors rescue or by promoting the formation of rescue sites in the lattice (Aher and

Akhmanova, 2018; Al-Bassam et al., 2010; Lawrence et al., 2018). CLIP-170 proteins also

promote rescue, and localize to both microtubule ends and to ‘GTP-islands’ in the microtubule

lattice (Arnal et al., 2004; de Forges et al., 2016; Komarova et al., 2002). Interestingly, the N-

terminal microtubule-binding domain of CLIP-170 is sufficient to promote rescues in vivo

(Komarova et al., 2002). When combined with purified tubulin in vitro, the N-terminal domain

promotes rescue and induces the formation of curved tubulin oligomers at microtubule ends

and in solution, hinting that the rescue mechanism may be linked to the stabilization of specific

protofilament structures (Arnal et al., 2004). Further investigations of how these and other

regulators that promote rescue could yield important insights into the structure of a rescuing

microtubule end, the nature of rescue sites in the lattice, and how these may be related.

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Figure 4.1.1 Purified tubulin sample separated by SDS-PAGE gel stained with Coomassie Lane 1: protein ladder. Lanes 2-4: 10-fold serial dilutions of tubulin purified from Porcine brain. Protein loads are approximately 22.5 – 0.225 mg, respectively.

Figure 4.1.2 Representative kymograph with rescue Representative kymograph of purified tubulin (green) polymerized from GMPCPP stabilized microtubule seeds (red), rescue event denoted (arrowhead). Images were collected at 1-second intervals. Tubulin concentration: 7.2 µM, vertical scale bar = 0.5 minute and horizontal scale bar = 1 µm.

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Figure 4.1.3 Polymerization and depolymerization rates as a function of tubulin concentration (i) Polymerization rate plotted as a function of tubulin concentration. Protein purified from pig brain (black) compared to purchased protein purified commercially (red). Data points are mean ± 95% CI. (ii) Depolymerization rate plotted as a function of tubulin concentration.

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Figure 4.1.4 Catastrophe and rescue frequency as a function of tubulin concentration (i) Catastrophe frequency plotted as a function of tubulin concentration. Catastrophe frequency defined as the number of events per sum of polymerization time for each concentration. Data points represent the mean of catastrophe frequencies measured per microtubule, from all microtubules at a given tubulin concentration. Error bars represent 95% CI. (ii) Rescue frequency plotted as a function of tubulin concentration. Rescue frequency calculated as the number of rescues divided by total depolymerization time for each concentration. Data points represent the mean value of rescue frequencies measured per microtubule, from all microtubules at a given tubulin concentration. Error bars represent 95% CI. For figures 1C-F, at least 73 microtubules were analyzed for each tubulin sample, at each concentration.

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Figure 4.2.1 Experimental data relative distribution of microtubule lengths at catastrophe Relative distribution of microtubule lengths at catastrophe leading to rescue (dashed line) compared to the entire population (solid line) of the experimental data under standard conditions (5mM MgCl2). Data represents 1127 microtubule lengths from 525 stabilized seeds, pooled from 5 separate experiments. ** p << 0.001. Significance determined by Mann-Whitney U test.

Figure 4.2.2 End-Driven Simulation Schematic Cartoon schematic of our end-driven simulation. Rescue site is denoted by a red square. Rescue frequency is defined as the number rescue events per depolymerization time. Rescues were triggered stochastically during depolymerization at the experimentally derived rescue frequency (0.01 events*depolymerization s-1).

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Figure 4.2.3 Lattice-Driven Simulation Schematic Cartoon schematic of our lattice-driven simulation. Rescue sites (denoted by a red square) are incorporated during polymerization, and always trigger rescue when the depolymerizing end returns to that site.

Figure 4.2.4 Simulated rescue frequency as a function of incorporation frequency The simulated rescue frequency for each incorporation rate calculated from 1000 simulated microtubules.

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Figure 4.2.5 Experimental and Simulated distributions of microtubule lengths at catastrophe Cumulative distribution of microtubule lengths at catastrophe under standard conditions (black) compared to end-driven simulation (red) and the lattice-driven simulation (blue).

Figure 4.2.6 End-driven simulated distribution of microtubule lengths at catastrophe Relative distribution of microtubule lengths at catastrophe leading to rescue (dashed line) compared to the entire population (solid line) of the experimental data under standard conditions from end-driven simulation. Data represents 60,000 simulated microtubules from 6 separate simulations. ** p << 0.001. Significance determined by Mann-Whitney U test.

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Figure 4.2.7 Lattice-driven simulated distribution of microtubule lengths at catastrophe Relative distribution of microtubule lengths at catastrophe leading to rescue (dashed line) compared to the entire population (solid line) of the experimental data under standard conditions from end-driven simulation. Data represents 60,000 simulated microtubules from 6 separate simulations. The incorporation frequency set to 0.0001 events*s-1. ** p << 0.001, Significance determined by Mann-Whitney U test.

Figure 4.3.1 Distribution of depolymerization rates of experimental and simulated data Cumulative distribution of depolymerization rates of experimental data under standard conditions (5 mM MgCl2; black) compared to the end-driven simulation (red) and the lattice- driven simulation (blue).

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Figure 4.3.2 Relative distributions of depolymerization rates leading to rescue (i) Relative distribution of depolymerization rates leading to rescue (dashed line) compared to entire population (solid line) of the end-driven simulation. Plot represents 60,000 simulated microtubules. (ii) Relative distribution of depolymerization rates leading to rescue (dashed line) compared to entire population (solid line) of the lattice-driven model. (iii) Relative distribution of depolymerization rates leading to rescue (dashed line) compared to entire population (solid line) from experimental data under standard conditions (5 mM MgCl2). Rescue data represents 165 depolymerization events from 112 microtubules; solid line represents entire population data consisting of 1283 depolymerization events from 1164 microtubules from 5 separate experiments. ** p << 0.001, ns: not significant. Significance determined by Mann-Whitney U test.

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Figure 4.3.3 Average depolymerization rates from experimental calcium data Average depolymerization rates from experimental data of catastrophes terminating at the seed (No Rescue) compared to those terminating at a rescue site within the lattice (Rescue) with and without 10 µM CaCl2 (Black and grey respectively). Significance determined by comparing rescue to no rescue for each condition as well as rescue across conditions. Bars represent mean ± 95% CI, at least 230 depolymerization events from at least 3 experiments were examined for each condition.

Figure 4.3.4 Average polymerization rate constants Average polymerization rate constants from experimental observations of at least 275 microtubules for each condition. ns: not significant. Significance determined by Mann-Whitney U test.

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Figure 4.3.5 Average number of rescues per microtubule with calcium

Average number of rescues per microtubule with and without 10 µM CaCl2. Bars represent mean ± 95% CI from at least 275 microtubules over at least 3 separate experiments for each condition. ns: not significant. Significance determined by Mann-Whitney U test.

Figure 4.3.6 Rescue frequency per depolymerization time with calcium Average rescue frequency calculated as events per depolymerization time. Bars represent mean ± 95% CI from at least 275 microtubules over at least 3 experiments per condition. ns: not significant. Significance determined by Mann-Whitney U test.

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Figure 4.3.7 Length lost before rescue with calcium

(i) Cumulative distribution of the length lost before rescue with and without CaCl2. (ii) Mean ± 95% CI of the rescue position relative to the catastrophe site. Position defined as the difference of microtubule length at catastrophe and length at rescue. Bars represent at least 39 length measurements from 25 microtubules from at least 3 separate experiments. ** p << 0.001, ns: not significant. Significance determined by Mann-Whitney U test.

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Figure 4.4.1 Representative kymograph showing repeated rescue events Representative kymograph showing repeated rescue events (within 200 nm of previous rescue denoted with arrowheads. Dashed lines highlight the location of two separate rescue sites through time. Images were collected at 1-second intervals. Tubulin concentration: 5.6 µM, vertical scale bar: 30 s, horizontal scale bar: 1 µm.

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Figure 4.4.2 Percentage of the rescue population that rescues repeatedly Percentage of the rescue population that rescues repeatedly (within 200nm) under standard conditions (black) compared to simulated results of the end-driven simulation (red). Experimental data represented as mean ± SEP, pooled from 105 total rescues events from 5 separate experiments. Simulated data represents 60,000 microtubules from 10 separate simulations. ** p << 0.001. Significance determined by Fishers exact test.

Figure 4.4.3 Percent of repeated rescues calculated with increasing rescue frequencies Percent of repeated rescues calculated with increasing rescue frequencies using the end-driven simulation (solid red line). Experimental data represented as mean (solid black line) ± SEP (dashed black lines). Sets of simulations were preformed using 1,000 microtubules per rescue frequency tested. Rescue frequencies were increased by 0.005 intervals between 0.01- 0.99 events*s-1. Each simulation set was then repeated 5 separate times. Simulated data was pooled and plotted as mean ± SEP for each rescue frequency.

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Figure 4.5.1 Cumulative distribution of microtubule length lost before rescue

Solid lines are 5mM MgCl2 without (black) or with 10µM CaCl2 (blue). Dashed lines are 1mM MgCl2 without (black) or with 10µM CaCl2 (blue). Each line represents at least 39 lengths from at least 25 microtubules pooled from at least 3 separate experiments.

Figure 4.5.2 Rescue position relative to catastrophe site Bars represent mean ± 95% CI from at least 39 length measurements from 25 microtubules, pooled from at least 3 separate experiments. Significance determined by Mann- Whitney U test. *: p < 0.01, **: p << 0.001.

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Figure 4.5.3 Rescues occur repeatedly at similar sites along the microtubule Rescue frequency as a function of depolymerization time. Bars represent mean ± SEP for each condition. Data pooled from at least 87 microtubules from at least 3 separate experiments for each condition. Significance determined by Mann-Whitney U test. **: p << 0.001, ns: not significant

Figure 4.5.4 Percent of rescues that occur repeatedly Percent of rescues that occur repeatedly, as in figure 4.4.2. Bars represent mean ± SEP. Significance determined by Fishers exact test. *: p < 0.01, **: p << 0.001.

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Figure 4.6.1 Representative kymograph of wash-in experiment Dashed line denotes the wash-in timepoint. Dashed line denotes the pre-existing lattice prior to wash-in. The first termination site after wash-in (FT) denoted with arrow. Image acquisition was paused during wash-in and resumed immediately afterwards (see Materials and Methods). Images were collected at 1-second intervals. Tubulin concentration: 3.2 µM, Prewash-in = 5 mM MgCl2 & post wash-in = 5 mM MgCl2, vertical scale bar = 30s & horizontal scale bar = 1 µm.

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Figure 4.6.2 Wash-in experiment polymerization and depolymerization rates (i) Polymerization rates of microtubules before (black bars) and after (gray bars) wash-in for each condition. Bars represent mean ± 95% CI of pooled data from at least 43 polymerization rates from 30 microtubules and a minimum of 3 separate experiments. Significance denotes differences between rates before and after wash-in. (ii) Depolymerization rates of microtubules before (black bars) and after (gray bars) wash-in for each condition. **: p << 0.001, ns: not significant. Significance determined by Mann-Whitney U test.

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Figure 4.6.3 Terminal location of first catastrophe (i) Percent of first catastrophes after wash-in that depolymerize to the stabilized seed and did not rescue. (ii) Percent of first catastrophes after wash-in that rescue within the pre- existing lattice. (iii) Percent of first catastrophes after wash-in that rescue within lattice added after wash-in. Bars represent mean ± SEP.

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Figure 4.6.4 Mean position of the first rescue after wash-in Bars represent mean ± 95% CI of data from at least 9 rescues events immediately after wash-in pooled from 3 separate experiments per condition. **: p << 0.001, ns: not significant. Significance determined by Mann-Whitney U test.

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Figure 4.7.1 Unified model of microtubule rescue

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Figure 4.8.1 Rescue analysis for stabilized seeds (i) Percent of seeds with at least one observed rescue as a function of tubulin concentration. Protein purified from pig brain (black) compared to purchased protein purified commercially (red). Data points represent mean ± 95% CI of 870 separate GMPCPP seeds from 8 different experiments. (ii) Average number of rescues per seed as a function of tubulin concentration.

Figure 4.9.1 Polymerization rates with calcium as a function of tubulin concentration Polymerization rate as a function of tubulin concentration of reactions with (red) or without (black) 10 µM CaCl2. Points represent average microtubule polymerization rate at each concentration.

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Figure 4.10.1 Polymerization rate constant of reactions with different MgCl2 concentrations

Polymerization rate constant of each MgCl2 concentration determined as the slope of the regression of polymerization rate as a function of tubulin concentration. Bars represent at least 283 microtubules from at least 3 separate experiments for each condition.

Figure 4.10.2 Depolymerization rate constant of reactions with different MgCl2 concentrations Depolymerization rates of microtubules that rescue (solid bars) and microtubules that do not rescue (empty bars). Bars represent at least 283 microtubules from at least 3 separate experiments for each condition.

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CHAPTER V

CONCLUSIONS AND FUTURE DIRECTIONS

Microtubule networks are an essential cytoskeletal component to all eukaryotic cell

types. These networks can be assembled into different structures depending on the cell type,

from long linear arrays in neurons to dynamic mitotic spindles in dividing cells. The tubulin

building blocks of these networks are highly conserved across species hinting at the importance

of these proteins. There is also a wide range of molecular diversity in the form of multiple

isotypes of α/β-tubulin compounded by a several different types of post-translation

modifications. This suggests that the diverse and essential functions of the microtubule

network are highly regulated. The roles that the microtubule network itself contributes to this

behavior and ultimately its function remain unclear. Here I have focused on understanding the

intrinsic properties of tubulin modulate the dynamics of a microtubule network and how cells

might utilize these in order to promote proper chromosome segregation.

Cell division is an essential part of biology; during development a single cell must rapidly divide many times to produce a multi-celled organism. After development, cell division is required to replace old-dying cells and maintain organism homeostasis. It is essential that cells faithfully segregate the duplicated chromosomes, ensuring that each daughter cell receives the correct number of chromosomes. The actions of many different proteins are carefully regulated to ensure success, illustrating the complexity and importance of this process. The dynamic microtubule network serves as the backbone on which the process takes place. Dynamic spindle microtubules nucleated from opposite poles must search out and become attached by

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the sister-kinetochore complexes to establish bi-polarity. When correctly attached, the sister- kinetochores are separated by tension that results, in part, from the depolymerizing spindle microtubules. The Spindle Assembly Checkpoint surveys for aberrant attachments, seen as a lack of tension, which leads to an Aurora B/Ipl1 kinase mediated restructuring of the microtubule network. Microtubules contribute to the force required to separate the chromosomes during anaphase as the mechano-chemical conversion of energy from

nucleotide-hydrolysis is harnessed by the kinetochores during depolymerization. While the role

of microtubule network is clearly essential, its mechanistic contribution remains unclear. The

focus of the work described here is in identifying molecular modules intrinsic to tubulin

heterodimers that cells can use to control the behavior and function of their microtubule

networks.

The unstructured CTTs of the tubulin heterodimers are ideal candidates. Previous work

in the Moore lab used yeast strains with genetically ablated CTT domains of α/β-tubulin in an

unbiased genetic interaction screen. While not essential, each CTT domain had a wide range of

genetic interactions specific that subunit. β-CTT was shown to be an important regulator of

microtubule dynamics and the spindle assembly checkpoint (Aiken et al., 2014). The work

described here shows that the β-CTT is essential for proper chromosome segregation; strains

with the β-CTT genetically ablated missegregated chromosomes at five-fold greater rate than

controls. A combination of fluorescence and cryo-electron microscopy of yeast strains lacking

the β-CTT demonstrated defects in the organization of the mitotic spindle. This suggests that

the principle role of the β-CTT is in organizing the mitotic spindle. I worked on two mechanistic

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models of how the β-CTT promote mitotic spindle organization: 1) the extrinsic model - in

which other proteins interact with microtubules through the β-CTT and 2) the intrinsic model –

in which the β-CTT is a regulator microtubule of dynamics.

Previous work in support of the extrinsic model have shown that enzymatic digestion of

the CTT domains reduces the binding affinity of many microtubule associated proteins.

Furthermore, these interactions are electrostatic and are sensitive to ionic strength (Brouhard

et al., 2008; Ciferri et al., 2008; Gupta et al., 2010; Marya et al., 1994; Noujaim et al., 2014;

Okada and Hirokawa, 2000; Skiniotis et al., 2004; Thorn et al., 2000; Wang and Sheetz, 2000;

Westermann et al., 2005; Zanic et al., 2009). Consistent with this, I show here that point

mutations that neutralize the acid patch of the β-CTT phenocopy the missegregation phenotype

of the completely ablated tail strains. This is the first in vivo evidence that the negative-charge

in the β-CTT acidic patch is necessary for proper chromosome segregation. Interestingly, I found

that neutralizing the β-CTT did not have a strong effect on organization of the mitotic spindle.

The neutralized strains had a distribution of fluorescently labeled kinetochore proteins was

similar to wild-type controls. In contrast, strains lacking the β-CTT exhibited a variety of

kinetochore distribution patterns. Further time-lapse analysis indicated that individual cells

lacking the β-CTT were unable to maintain a constant kinetochore organization. Mutations in

kinetochore proteins that alter microtubule affinity were unable to phenocopy the dynamic

disorganization of the mitotic spindle observed in strains lacking the β-CTT. This led me to focus

on the second model, in which the β-CTT is an intrinsic regulator of microtubule dynamics.

Early biochemical work with purified tubulin showed that enzymatically removing the

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CTT domains enhanced polymerization dynamics (Bhattacharyya et al., 1985; Lobert and

Correia, 1992; Sackett et al., 1985; Sackett et al., 1986; Serrano et al., 1984). My work here builds on these findings by demonstrating the β-CTT promotes the dynamic behavior of individual microtubules. I propose a this is due to a simple electrostatic repulsion model, in which negatively charged CTTs repel other nearby heterodimers. The repulsive action would be in greatest effect when heterodimers are densely packed or crowded together. For example, heterodimers rapidly sample different binding sites on the growing microtubule plus end resulting in a high localized density. The β-CTT may decrease the affinity for this environment.

Consistent with this, I found that enzymatically removing the β-CTT enhances polymer formation by increasing assembly rates. Additionally, my findings suggest that digested heterodimers have a slightly greater affinity for polymer estimated using relative dissociation rates. Taken together, these data indicate the β-CTT may promote dynamic microtubules by dampening polymer formation. Consistent with this, I found that digested microtubules are less stable than controls when free tubulin is washed out of the reaction. As one model of microtubule stability is based on the size of the GTP-cap at the plus end, it is possible that the

β-CTT is promoting the enrichment or retention of GTP-tubulin at the plus end. Future work is necessary to resolve this paradoxical observation that the β-CTT seems to simultaneously dampen polymerization while enhancing microtubule stability. Studying the role of the β-CTT during depolymerization, and specifically the involvement of divalent cations has yielded additional insight.

Divalent cations have been shown to have divergent effects on microtubule structure

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and dynamics. Zinc and manganese ions drive alternative polymer structures, sheets or rings

(Larsson et al., 1976; Nicholson et al., 1999). Both calcium and magnesium are potent microtubule destabilizers (Duellberg et al., 2016a; O’Brien et al., 1990; O’Brien et al., 1997).

However, the mechanism by which calcium and magnesium promote depolymerization is unclear. Here I find that the β-CTT is necessary for magnesium enhanced depolymerization. This is consistent with previous findings that digested tubulin is resistant to calcium induced depolymerization (Bhattacharyya et al., 1985). This suggests that divalent cation induced depolymerization may be the result of direct interactions between calcium and/or magnesium ion with the β-CTT. During depolymerization, the outward curling protofilaments form a ram’s horn structure, which brings the negatively charged CTT domains into proximity with each other. Divalent cations may be crosslinking negatively charged amino acids of the CTTs, which would stabilize the depolymerizing end structure thereby increase depolymerization rate.

Consistent with this model, I found that enzymatically digested microtubules exhibit slower depolymerization rates that were insensitive to magnesium concentration. Previous structural studies have shown that both magnesium and calcium can increase the curvature of depolymerizing protofilaments. These data are consistent with a model where divalent cations promote depolymerization by crosslinking negatively charged amino acids in the CTT domains and stabilizing the end structure/conformation. The end-structure model has also yielded interesting insight in how microtubules rescue (transition from shrinking back to growing).

The mechanism of how microtubules rescue is a largely unknown section of microtubule biology. Previous work has focused on the GTP-island model for rescue, in which regions of

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GTP-tubulin distal from the plus end serve as rescue sites. Several groups have proposed mechanisms for the origins of these islands including incomplete hydrolysis, damage induce microtubule repair and recently microtubule network remodeling (Aumeier et al., 2016; Tropini et al., 2012; Vemu et al., 2018). Each group has demonstrated that microtubule rescue

locations correlate with the presence of these ‘GTP-islands’. In support of this model, I found

that approximately one third of rescue sites will promote a subsequent rescue at the same

location. Using computational modeling, I found that these repeated rescue sites are not the

result of chance, and instead are defined sites in the lattice. The end-structure model predicts

that rescue is the result of microtubule binding proteins changing the structure and/or activity

of the depolymerizing end to promote rescue (Al-Bassam et al., 2010; Arnal et al., 2004;

Bratman and Chang, 2007; Dave et al., 2018; Hiremathad et al., 2018; Komarova et al., 2002;

Lawrence et al., 2018; Lindeboom et al., 2018). My earlier work suggests divalent cations may

be acting in a similar way. Consistent with this model, I found there is an inverse relationship between magnesium concentration and rescue frequency. In order to isolate and test these two models of rescue, I developed a wash in experiment, where polymerization and depolymerization conditions were different. In this way any pre-defined rescue sites embedded during polymerization could be tested under different depolymerization conditions. My results suggest that rescue behavior is determined by the structure of the depolymerizing microtubule

end. Divalent cations are potent microtubule destabilizers and rescue inhibitors, likely by

stabilizing/enhancing the depolymerizing end structure to promote disassembly. I have also

demonstrated that divalent cation induced control of microtubules requires the β-CTT.

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Furthermore, digested microtubules exhibited more frequent rescue events than controls.

Taken together, I propose that β-CTT is the module through which the end structure of a microtubule can be altered which in turn changes the dynamics.

Divalent cation concentrations in cells are highly regulated. How might cells use ionic

strength to change their microtubule network? Magnesium is the second most abundant ion in

the cytoplasm (Cyert and Philpott, 2013). During division, cells require increased magnesium

concentrations which could serve to make the microtubule network more dynamic (Prescott et

al., 1988; Walker and Duffus, 1980). Consistent with this, injecting a modest amount of

magnesium into non-dividing cells was sufficient to depolymerize the entire microtubule network (Prescott et al., 1988). Calcium is used as a second massager in several signaling

cascades, and as such is activity kept at low concentrations in the cytoplasm (Cyert and

Philpott, 2013). Interestingly, microtubules are 10x more sensitive to calcium than to

magnesium in vitro (Gal et al., 1988), which suggests calcium could be regulating microtubules

at physiologically relevant concentrations. In development, fertilization triggers a wave of

calcium that travels across the zygote (Gilkey et al., 1978), I speculate this may also act to

stimulate the reorganization of the microtubule network and prime it for repeated divisions.

The interplay between the cytoskeleton and the ionome is largely unknown and future work

will yield interesting insights into cell and developmental biological processes.

The work here illustrates the interplay between the CTT and its environment has

dramatic effects on microtubule behavior. Small changes in ionic concentration were sufficient

to drastically reorganize networks of microtubules in cells (Prescott et al., 1988), from which I

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propose the CTT is an ‘antenna’ that cells may target to tune the behavior of their microtubule networks. The CTT is also the region of β-tubulin with the greatest amount of molecular diversity, with a variety of post-translational modifications of the β-CTT. We are only starting to

understand the effect these modifications have on microtubule dynamics and the activity of the

whole network. Could cells use modifications to make microtubules more or less sensitive to

changes in ionic concentration? The evolution of post-translational modifications on tubulin might to be linked to the evolution of cilia, a microtubule-based structure associated with cell signaling in multicellular organisms among other functions. The mechanistic details of these modifications and their effects on microtubule behavior is largely unknown. Future work will be required to understand how modifications on tubulin alter the dynamic behavior of individual microtubules and how these affects scale to the entire network. The cross-talk between ionic control and modifications is a largely unexplored section of microtubule field that will have broad impacts in cell and developmental biology.

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