galectin-9 inhibits b cell receptor (bcr) signaling by ... · b cells are part of the adaptive...

Galectin-9 inhibits B cell receptor (BCR) signaling by

regulating BCR organization and mobility

by

Anh Cao

A thesis submitted in conformity with the requirements

for the degree of Master of Science

Department of Immunology, Faculty of Medicine University of Toronto

© Copyright by Anh Cao 2017

ii

Galectin-9 inhibits B cell receptor signaling by regulating

BCR organization and mobility

Anh Cao

Master of Science

Department of Immunology

University of Toronto

2017

Abstract

B cells are part of the adaptive immune system and secrete antibodies targeting foreign

pathogen (referred to as antigen). B cells are activated by binding of antigen to the antigen-

specific B cell receptor (BCR), which initiates downstream signaling cascades necessary for the

production of antibodies. Recent data from our lab identified that the glycan-binding lectin

known as galectin-9 (Gal-9) binds to IgM-BCR and inhibits B cell activation. However, the

molecular mechanism for Gal-9 mediated inhibition B cell activation is still unknown. In this

study, we showed that exogenous Gal-9 reorganizes IgM-BCR into larger clusters within the

Gal-9 lattice, and concomitantly immobilizes IgM. We also demonstrated that Gal-9 increases

the density of inhibitory molecules including CD45 and CD22 together with IgM-BCR in the

Gal-9 lattice, consistent with the inhibitory effect of Gal-9 on B cell activation. These findings

elucidate a novel extracellular mechanism to regulate signal transduction through BCR.

iii

Acknowledgments

It has been a long journey for me from a Vietnamese high school student to a Vietnamese

Master's degree candidate at the University of Toronto. I am thankful to be here, to sit in this

office, to write these lines, to do what I love and especially to meet people, who keep bringing

positive changes into my life. When you read these lines, please accept my deepest thankfulness

for being a part of my life. Thank you!

To my supervisor, Dr. Bebhinn Treanor, thank you for your constant presence, support

and guidance. I still remember the first email I sent to Dr. Treanor to ask for a volunteering

position in my second year. She opened the door and let me in. She trusted me and let me pursuit

whatever came up into my mind. For a person like me, who has too little confidence in myself

and who is always in a constant self-doubt, that trust is indispensable. Even during the darkest

stage of my life, she was still there, supported and guided me through. It is impossible to express

my thankfulness for what she did. At this moment, I have a chronic regret. I wish I have had

done more. I wish I have had been less whiny and more productive. It is impossible for me to

return what I received. Hence, I will pay back by being there, supporting and guiding my future

students like the way Dr. Treanor did to me (under an assumption that I can accomplish my

Ph.D., my postdoctoral fellowship(s) and secure a position in academia).

To my thesis committee Dr. Cindy Guidos and Dr. Jean-Philippe Julien, thank you for

your encouragements and suggestions. Thank you for keep challenging me and for keep pushing

me on the path of finding answers for the role of Galectin-9. If I had a chance to start over, I

would have committee meetings once every four months.

iv

To my Mom, thank you for your non-stop emails for the last six years. It is hard to

imagine how I am here without my Mom. My Mom was there listening to all of my projects

since I was a second-year student. Her answer for all of my questions was: “You will make it.” It

is truly a blessing to know that I am her whole world. For me, she is also the biggest inspiration.

One day, she will not be with me anymore. However, her words will be with me to make this

world a better place (even just a tiny little bit better).

To my Dad, thank you for being there for Mom to support her. Thank you for raising and

loving me so much. I will try my best to be a son who you can be proud of.

To Karen, who adopted me as her grandson, who kept giving me food, thank you for your

generosity. During my six years in Canada, my biggest accomplishment is to find a place to call

home, to find a person treated me like her family member. I am proud of being Grandma's

grandson.

To my boyfriend, to Corgi, thank you so much for being with me. Although Corgi is the

last one I expect to read my thesis, I still want to save space for him. My life is no longer perfect

and straightforward as it used to be. However, thanks to Corgi my life is completed and

connected. I am thankful for his presence.

To my lab members, to Mithunah, to Tina, to Hifza, to Trisha, to Laabiah, to Nouf and

Zaki, thank you for being wonderful parts of my life in the last four years. We were like a cluster

of B cell receptors. There were ups and downs. However, we are still together, still noisy and

still “signaling” earnestly. To Mithunah, thank you for your magic hands, which can fix any

equipment by just touching and for being a role model. To Tina, thank you for your noodle, for

your eagerness and an unexplainable parallel between two of us. To Hifza, thank you for being a

perfect D98 buddy, for being a connection when I needed the most and for being a tough

v

competitor (I will never forget her name). To Trisha, thank you for listening to me, for lending

me your shoulder to cry on and for being such an amazing human being. To Laabiah, thank you

for your intellectually stimulating discussion, and please keep being confident in yourself and

enjoying sciences. To Nouf, thank you for being my sister and for forgiving my stubbornness. To

Zaki, thank you for being my brother and for sharing lunch with me. Thank you, everyone, for

completing my life, for making my life more meaningful and for sharing the best years of my

life.

I also want to acknowledge all of the staffs at the animal facility and the Centre of

Neurobiology of Stress (CNS) for their constant technical supports.

To more than 90 million Vietnamese people, who I will never know, who I will always

be in debt, thank you for trusting me and for being reasons for me to keep moving forward. The

quality I am proud the most about myself is being a Vietnamese. It is not because Vietnam is a

rich and powerful country. In contrast, millions of people are living in poverty, with incurable

diseases, and with inequalities. Even under those circumstances, I received hundred thousand

dollars for my education, which is unimaginable. Each Vietnamese person, who is living in

poverty, suffering an incurable disease or struggling for equality is a reminder why I need to

wake up every morning, to work and to move forward. I apologize for not working hard enough

in the last two years. In the future, I may be an unknown researcher in Vietnam, who barely

publishes in peer-reviewed journals. However, I will help to nurture the next generation of

Vietnamese scientists, who will help me pay back my debt and to make Vietnam and this world a

better place to live.

vi

Declaration:

The IgD colocalization data was acquired by Hifza Mohamad (Figure 10). All other work

presented in this thesis is my own.

This thesis, along with additional data, will be submitted for publication in a peer-reviewed

journal.

vii

One day, I added recombinant galectin-9 to B cells

They clumped and were shredded by the centrifugal force

Desperate, I was. Disappointed, I was

Now looking back, it was truly a blessing

Galectin-9 may regulate actin organization (please help me to prove it).

One day, I thought about dying

Jumping from a balcony, from the 11th floor

Desperate I was. Disappointed, I was

Now looking back, it was truly a blessing

I love the breath going through my nose, my chest right now.

One day, something may look so bad

Everything seems to end, myself seems to crumble

Desperate, I will be. Disappointed, I will be

Just breathe, it is truly a blessing

Every moment is a gift, I am alive.

I am doing what I love – finding answers.

Anh Cao

viii

Table of Contents

Contents

Acknowledgments ..................................................................................................................... iii

List of Figures ............................................................................................................................ x

Abbreviations ............................................................................................................................ xi

INTRODUCTION ................................................................................................................. 1

1.1 B Cells ............................................................................................................................ 1

1.2 B Cell Activation ............................................................................................................ 2

1.3 Regulation of BCR Signaling .......................................................................................... 5

1.3.1 CD19 .................................................................................................................. 6

1.3.2 CD45 .................................................................................................................. 8

1.3.3 CD22 .................................................................................................................11

1.4 The Actin Cortex Regulates BCR Mobility and Signaling ..............................................12

1.5 Surface Proteins Organize into Nanoclusters ..................................................................14

1.6 Galectin Lattice Regulates Protein Organization ............................................................19

1.7 Hypothesis and Aims .....................................................................................................25

MATERIALS AND METHODS ..........................................................................................26

2.1 Mice ..............................................................................................................................26

2.2 B Cell Purification .........................................................................................................26

2.3 Surface Staining and Confocal Microscopy ....................................................................26

2.4 B Cell Activation ...........................................................................................................28

2.5 Western Blot ..................................................................................................................28

2.6 Single Particle Tracking .................................................................................................29

2.6.1 Recombinant Galectin-9 and Fab Fragment Labeling .........................................29

2.6.2 Recombinant Galectin-9 Treatment ....................................................................30

ix

2.6.3 Glass Coverslip Coating .....................................................................................30

2.6.4 Cell Labeling for Single Particle Tracking ..........................................................30

2.6.5 Instrument ..........................................................................................................30

2.7 Co-Immunoprecipitation ................................................................................................31

2.8 Direct Stochastic Optical Reconstruction Microscopy (dSTORM) .................................32

2.8.1 Sample Preparation ............................................................................................32

2.8.2 dSTORM Acquisition and Image Reconstruction ...............................................33

2.8.3 Hopkins Index and Ripley’s H Function Analysis...............................................34

2.8.4 Bayesian Cluster Analysis ..................................................................................34

2.9 Statistical Analysis .........................................................................................................35

RESULTS ............................................................................................................................36

3.1 Recombinant Galectin-9 Reorganizes IgM into Larger Clusters .....................................36

3.2 Galectin-9 Reduces the Mobility of IgM-BCR ...............................................................41

3.3 IgM and CD45 Density is Increased in the Galectin-9 Lattice ........................................44

3.4 CD22 Density is Increased in the Galectin-9 Lattice ......................................................47

3.5 CD19 Phosphorylation is Enhanced in Galectin 9-KO Upon Activation .........................51

DISCUSSION ......................................................................................................................53

References .................................................................................................................................67

x

List of Figures

Figure 1. BCR-antigen microcluster and immunological synapse formation

Figure 2. CD19 amplifies BCR signaling

Figure 3. Schematic diagram of the structure of CD45 and localization during B cell activation

Figure 4. Schematic diagram of the structure of CD22 and inhibitory effect of CD22 on BCR

signaling

Figure 5. BCR and coreceptors are organized into distinct nanoclusters

Figure 6. Galectins and the galectin-glycoprotein lattice

Figure 7. rGal-9 alters IgM nanoclusters.

Figure 8. Gal-9 immobilizes IgM-BCR.

Figure 9. The Gal-9 lattice increases the molecular density of IgM and CD45.

Figure 10. The Gal-9 lattice does not affect the molecular density of IgD.

Figure 11. The Gal-9 lattice increases the molecular density of CD22.

Figure 12. Gal-9 does not increase the interaction between IgM and CD22

Figure 13. Phosphorylation of CD19 is increased in Gal-9-KO B cells.

Figure 14. Proposed model for Gal-9-mediated inhibition of BCR signaling

xi

Abbreviations

µg/ml

Microgram per millimeter

µM

Micromolar

ADCC

Antibody-dependent cell-mediated cytotoxicity

Ag

Antigen

APC

Antigen presenting cell

Asn

Arginine

B220

B cell isoform of 220kDa

BCR B cell Receptor

Blnk

B-cell linker

Btk

Bruton’s Tyrosine Kinase

Ca2+

Calcium ion

CD19

Cluster of Differentiation 19

CD22


CD45

Cluster of differentiation 45

CD81


CLP Common lymphoid progenitor

CRD Carbohydrate recognition domain

cSMAC

Central supramolecular activation cluster

DAG

Diacylglycerol

DC

Dendritic cell

EDTA

Ethylenediaminetetraacetic acid

EM Electron-multiplying

ER

Endoplasmic reticulum

ERK

Extracellular signal-regulated kinase

ERM

Ezrin-Radixin-Moesin proteins

FBS

Fetal Bovine Serum

FDC

Follicular dendritic cell

Gal-1

Galectin-1

Gal-3

Galectin-3

Gal-4

Galectin-4

Gal-9

Galectin-9

Gal-9-KO

Galectin-9 Knockout

GEM

Glycolipid-enriched membrane

HIV Human immunodeficiency virus

HRP

Horseradish peroxidase

ICAM-1 Intercellular adhesion molecules 1

IgD

Immunoglobulin D

xii

IgH

Immunoglobulin heavy chain

IgL

Immunoglobulin light chain

IgM

Immunoglobulin M

Igα Immunoglobulin alpha

Igβ

Immunoglobulin beta

IL-7

Interleukin 7

IP3

Inositol 1,4,5-triphosphate

ITAMs

Immunoreceptor tyrosine-based activation motifs

ITIMs Immunoreceptor tyrosine-based inhibitory motifs

LFA-1

lymphocyte function-associated antigen 1

MAPK

mitogen activated protein kinase

Mgat5

β 1,6-N-acetylglucoaminyl transferase V

MHC II Major Histocompatibility Complex Class II

mIg Transmembrane immunoglobulin

NaCl

Sodium chloride

NaN3

Sodium azide

NF-κB

Nuclear factor-κB

PBS

Phosphate buffered saline

pERK

Phosphorylated extracellular signal-regulated kinase

PFA

Paraformaldehyde

PI3K

Phosphatidylinositol-,4,5-bisphosphate 3-kinase

PIP2

Phosphatidylinositol-4, 5-bsiphosphate

PIP3

Phosphatidylinositol-3, 4, 5-trisphosphate

PKC

Protein kinase C

PLC-γ2

Phospholipase C gamma 2

pSMAC

Peripheral supramolecular activation cluster

PTPase

Protein phosphatase

PTPRC

Protein tyrosine phosphatase, receptor type, C

RAG-1

Recombination-activating genes-1

RAG-2

Recombination-activating genes-2

Ras GRP Ras gauanine nucleotide releasing protein

rGal-9 Recombinant galectin-9

RPMI

Roswell Memorial Institute Media

SEM

Standard Error of Means

Ser

Serine

SFK

Src family kinases

SH2 domain Src homolog 2 domain

SHP-1 Src homology domain containing phosphatase-1

SLE Systemic lupus erythematosus

Syk Spleen tyrosine kinase

TCR

T cell receptor

TH1

T helper 1

xiii

TH2

T helper 2

Thr

Threonine

Tim3 T cell Immunoreceptor mucin 3

TIRFM

Total internal Reflection Fluorescence Microscopy

Treg

Regulatory T cells

TSRI

The Scripps Research Institute

Tyr Tyrosine

v/v

Volume/volume

Vav

Protein encoded by VAV gene

VCAM-1 Vascular cell adhesion mediator-1

VLA-4

Very late antigen-4

VpreB

Immunoglobulin iota chain

WT Wild type

Λ5

Lambda 5

1

INTRODUCTION

1.1 B Cells

B cells are the centre of humoral immunity, which provides specific and long-lasting

protection from foreign molecules, referred as antigens. B cell receptors (BCRs) recognize

specific antigens and initiate B cell activation leading to proliferation and differentiation into

plasma cells, which secrete soluble antigen-specific antibodies, or into memory B cells, which

provide long-term protection (Pierce, 2002).

B cells are an important mediator of both the adaptive and the innate immune systems.

When BCR encounters specific antigens, ligand-receptor complexes are internalized through a

clathrin-dependent process (Stoddart et al., 2002). Antigens are degraded into peptides by

enzymes in the lysosome, and these peptides are presented on major histocompatibility complex

class II (MHC II) to CD4+ T-helper (TH) cells (Germain, 1994), which coordinate the adaptive

immune system by releasing various cytokines. Secreted antigen-specific antibodies also mediate

innate immune responses such as antibody-dependent cell-mediated cytotoxicity (ADCC) by

recruiting natural killer (NK) cells to lyse infected cells (Hashimoto et al., 1983). Antibodies can

also opsonize antigens and enhance the efficacy of phagocytosis by macrophages to clear

antibody-antigen complexes (Swanson and Hoppe, 2004).

Due to the wide range of B cell functions, the process of B cell activation and

differentiation needs to be tightly regulated to prevent immune disorders. The hyper-reactivity of

B cells may lead to autoimmune diseases such as systemic lupus erythematosus (SLE), in which

B cells secrete autoantibodies against nuclear complexes, which under normal conditions do not

2

activate B cells (Odendahl et al., 2000). On the other hand, in the context of chronic viral

infection such as human immunodeficiency virus (HIV) infection, B cells may become hypo-

reactive or exhausted, which impairs B cell activation and antibody secretion (Moir and Fauci,

2013). Hence, it is important to study the mechanisms that regulate B cell activation, which may

provide new targets for immunotherapy to treat B cell-related disorders.

1.2 B Cell Activation

B cells are activated by both soluble antigen and membrane-bound antigen presented by

antigen-presenting cells (APCs) such as follicular dendritic cells (FDCs), dendritic cells (DCs)

and macrophages (Defranco et al., 1982; Szakal et al., 1988; Batista et al., 2001, Wykes et al.,

1998; Harvey et al., 2007). B cells recognize specific antigen by BCR, which is a complex

comprised of transmembrane immunoglobulin (mIg) together with immunoglobulin α (Igα) and

immunoglobulin β (Igβ) in a 1:1 stoichiometry (Schamel and Reth, 2000). mIg is composed of

two heavy chains and two light chains connected by disulfide bonds. Each mIg has two antigen-

binding sites consisting of variable regions of both heavy and light chain. Because mIg has no

signaling motif, BCR signaling requires the intracellular domains of both Igα and Igβ containing

immunoreceptor tyrosine-based activation motifs (ITAMs) (Reth, 1989). Phosphorylated ITAMs

recruit other intracellular signaling molecules to transduce and amplify the signal from

extracellular BCR-antigen interaction into the cell to alter cellular activities, gene expression and

proliferation (Kurosaki, 2000). The engagement of BCR with membrane-bound antigen leads to

the formation of BCR microclusters. BCR microclusters, which are a group of 50 to 500 BCR

molecules, are observed in the early stage of B cell engagement with antigen-presenting

membrane (Fleire et al., 2006; Depoil et al., 2008). Although the mechanism for BCR

3

microcluster formation is still unknown, the process is dependent on at least two factors

including the rearrangement of the actin cortex (Fleire et al., 2006) and spontaneous clustering of

IgM Cμ4 domain (Tolar et al., 2009). BCR microclusters mediate the recruitment of multiple

intracellular molecules to the submembrane regions where BCRs engage antigens. The assembly

of signaling molecules at BCR microclusters defines a microsignalosome, which coordinates

intracellular signaling during B cell activation. Upon engagement of mIg with specific antigen,

BCR is brought closer to Lyn, a Src family kinase (SFK) highly expressed in hematopoietic

cells, which phosphorylates the ITAMs of Igα and Igβ (Saouaf et al., 1994). These

phosphorylated motifs provide binding sites and activate cytosolic spleen tyrosine kinase (Syk)

(Rowley et al., 1995). Syk phosphorylates the adaptor protein B-cell linker (Blnk), which

provides a platform to recruit Bruton's tyrosine kinase (Btk) and Phospholipase C Gamma 2

(PLC-γ2) (Oellerich et al., 2011). In parallel, BCR signaling activates phosphatidylinositol-,4,5-

bisphosphate 3-kinase (PI3 kinase), which converts phosphatidylinositol-4,5-bisphosphate

(PI(4,5)P2) into PI(3,4,5)P3. PIP3 further enhances the recruitment of Btk and PLC-γ2 to the

membrane. PLC-γ2 hydrolyzes PIP2 to generate inositol (1,4,5)-trisphosphate (IP3) and

diacylglycerol (DAG) (Hempel et al., 1992). IP3 binds to IP3-receptor to induce an elevation of

Ca2+ in the cytoplasm (Sugawara et al., 1997). DAG activates protein kinase C (PKC) as well as

Ras guanine nucleotide releasing protein (RasGRP) (Matthews et al., 2003). RasGRP activates

the mitogen-activated protein kinase (MAPK) pathway, which phosphorylates extracellular

signal-regulated kinase (ERK). Activated ERK regulates multiple cellular processes including

proliferation, migration, and cell survival (Stone, 2011). PKC also activates the nuclear factor-κB

(NF-κB) pathway (Su et al., 2002), which regulates gene expression to modulate cellular

activities and differentiation.

4

In parallel with BCR microcluster formation, B cells also spread their membranes across

the surface of APCs to maximize the contact area to increase the number of microsignalosomes.

The interface between B cells and the antigen-presenting membrane is defined as an

immunological synapse. The immunological synapse was first described in T cells and is defined

by a central cluster of T cell receptor (TCR) known as the central supramolecular activation

cluster (cSMAC), and an outer ring of adhesion molecules such as LFA-1 and VLA-4 known as

the peripheral SMAC (pSMAC) (Monks et al., 1998). The immunological synapse has also been

described in B cells with similar features including a central BCR cluster and peripheral ring of

adhesion molecules (Batista et al., 2001; Carrasco et al., 2004) (Figure 1). The spreading of the B

cell membrane to form an immunological synapse is coordinated through BCR signaling and

actin cortex rearrangement (Fleire et al., 2006). The formation of the cSMAC in T cells is

explained by a retrograde actin flow, in which actin polymerization at the edge of cells "pushes"

actin filaments inward. Simultaneously, actin depolymerizes at the centre of the immunological

synapse and TCR microclusters associated with the actin cytoskeleton move inward to form the

cSMAC (Kaizuka et al., 2007) (Figure 1). While peripheral microclusters of immunoreceptors

are actively signaling during the spreading phase, the aggregation of immunoreceptors at the

cSMAC is believed to mark the attenuation of signaling and the initiation of immunoreceptor

internalization (Yokosuka et al., 2005; Depoil et al., 2008; Weber et al., 2008, Nguyen et al.,

2008). During the internalization process, BCR-antigen complexes are brought to MHC-

containing endosomal/lysosomal vesicles, which degrade antigens into peptides and load these

peptides into MHC II for antigen presentation, which activates CD4+ T cells (Chesnut and Grey,

1981). Activated CD4+ T cells upregulate CD40 ligand (CD40L), which binds to the B cell

costimulatory receptor CD40, and secrete soluble cytokines such as IL-4, IL-5 and IL-6, which

5

regulate the proliferation and differentiation of B cells (Parker, 1993). Importantly, the degree of

B cell spreading is dependent on both the affinity and density of antigen in the membrane and

determines the amount of antigen which is gathered, internalized, processed and presented to T

cells and this directly impacts on the degree of T cell help the B cell receives (Fleire et al., 2006).

Figure 1. BCR-antigen microcluster and immunological synapse formation

After initial contact between the B cell and the antigen-presenting cell (APC), the B cell quickly

spreads on the surface of APC and reaches maximal spreading followed by the contraction

phase. The engagement between BCR and membrane-bound antigen leads to formation of BCR

microclusters (red circle). During the spreading phase, actin (green line) polymerization leads to

the outward movement of BCR microclusters. At maximal spreading, actin polymerization at the

edge of the cells "pushes" the actin filament inward. Simultaneously, actin depolymerizes at the

centre of the immunological synapse and BCR microclusters associated with the actin

cytoskeleton move inward. During the contraction phase, BCR microclusters gather at the centre

to form the central supramolecular activation cluster (cSMAC). Integrins like LFA-1 and VLA-4

form a ring structure (blue) surrounding the cSMAC, called the peripheral (p)-SMAC.

1.3 Regulation of BCR Signaling

Besides BCR signaling, B cells require further input from other receptors, which provide

additional information about the nature of the antigen and the context in which antigen is

presented. The additional input finely tunes B cell activation to initiate an effective immune

6

response while preventing overreaction, which may lead to autoimmune diseases. These

receptors are classified into activating receptors, which amplify BCR signaling, or inhibitory

receptors, which dampen BCR signaling.

1.3.1 CD19

CD19 is a component of a complex consisting of complement receptor 2 (CR2 or CD21),

the tetraspanin family protein CD81 and the interferon-induced transmembrane protein leu13

(CD225) (Matsumoto et al., 1991; Bradbury et al., 1992). CD19 contains two extracellular C2-

type Ig domains and nine intracellular domains. Upon B cell activation, the intracellular domains

of CD19 are phosphorylated and provide binding sites for other signaling molecules (Zhou et al.,

1991). Following BCR ligation, Lyn phosphorylates tyrosine residues in the intracellular domain

of CD19, which provide docking sites for Src-homology domain 2 (SH2) containing molecules

including Lyn, PI3K, and Vav, which synergistically enhance BCR signaling-mediated

recruitment of these proteins (Fujimoto et al., 2000) (Figure 2). Phosphorylation of CD19 creates

a positive feedback loop or ‘processive amplification’ by providing binding sites for Lyn, which

autophosphorylates and activates more Lyn to further amplify BCR signaling (Fujimoto et al.,

2000). In vitro, CD19 decreases the threshold of B cell activation by lowering the number of

antigens required to initiate BCR signaling; co-ligation of CD19 with IgM lowered the

concentration of anti-IgM required to initiate BCR activation by 100 times (Carter and Fearon,

1992). In addition, CD19 is crucial in promoting B cell spreading and microcluster formation in

response to membrane-bound ligand (Depoil et al., 2008). CD19-deficient B cells have lower

spreading area and consequently antigen accumulation is also decreased in comparison to WT B

cells in response to membrane-bound antigen (Depoil et al., 2008). In the presence of CD19, the

7

B cell has larger spreading area, which accumulates more antigens and leads to a higher degree

of T cell help. Supporting this model, in vivo, CD19-KO mice showed a severe impairment in

germinal centre formation and affinity maturation when immunized with antigens that require T

cell help (Rickert et al., 1995). CD19-KO mice also had IgM and IgG1 titers ten times lower than

WT control 8 days after being immunized with T cell dependent antigen (Engel et al., 1995).

Thus, through recruiting signaling molecules and facilitating B cell spreading, CD19 decreases

the threshold of B cell activation and amplifies BCR signaling.

Figure 2. CD19 amplifies BCR signaling

A) BCR stimulation induces the relocalization of BCR and CD19 into lipid rafts (indicated by a

red line) where Lyn phosphorylates CD19 at Tyr-513. B) Phosphorylated CD19-Y513 provides a

binding site for Lyn, which phosphorylates Tyr-482 and Tyr-391 through a process called

“processive phosphorylation.” C) Phosphorylated sites of CD19 are binding sites for Src-

homology domain 2 (SH2) containing molecules including PI3K and Vav, which synergistically

enhance BCR signaling (Fujimoto et al., 2000).

8

1.3.2 CD45

CD45, protein tyrosine phosphatase, receptor type C (PTPRC), is an abundant cell

surface glycoprotein expressed on all nucleated hematopoietic cells including B and T cells

(Hermiston et al., 2003). CD45 has multiple isoforms, generated through alternative splicing of

exons at the N-terminal, which are specific for cell type, developmental stage, and activation

state (Hermiston et al., 2003; Fujii et al., 1992; Johnson et al., 2002). B cells express the largest

isoform of CD45, B220, which has three alternative exons at the N terminus and a heavily

glycosylated extracellular domain (Okumura et al., 1996). The intracellular domain of CD45

consists of two protein phosphatase (PTPase) domains; however only the membrane-proximal

domain is phosphatase active and regulates immunoreceptor signaling (Desai et al., 1994)

(Figure 3A).

Although CD45 has been extensively studied over the last 30 years, the precise role of

CD45 in B cells is still ambiguous. The best-characterized substrate for the PTPase activity of

CD45 in B cells is Lyn (Katagiri et al., 1999, Shrivastava et al., 2004). Lyn has two tyrosine

phosphorylation sites: Y508, a negative regulatory site; and Y397, an autophosphorylation site.

Dephosphorylation of Y508 converts Lyn from a closed conformation to an active confirmation.

The open form of Lyn phosphorylates Y397 through intermolecular autophosphorylation to lock

the catalytic pocket into a fully active conformation (Ingley, 2012). Based on in vitro data, the

PTPase domains of CD45 dephosphorylates both positive (Y397) and negative (Y508) regulatory

tyrosine residues of Lyn (Katagiri et al., 1999). Currently, available evidence strongly indicates

that CD45 is a positive regulator of BCR signaling. In DT40, a chicken derived B cell line, CD45

deficient cells exhibited a lower level of total phosphorylation and delayed calcium signaling

9

(Yanagi et al., 1996). Supporting this finding, supra-physiological expression of CD45 in

primary murine B cells increases calcium signaling and phosphorylation of ERK (Zikherman et

al., 2012). However, these studies used soluble anti-BCR antibodies to activate cells, neglecting

the importance of the temporal and spatial regulation of CD45 (Shrivastava et al., 2004) in

modulating BCR signaling. In WEHI-231 cells, during the resting state, some CD45 molecules

constitutively associate with glycolipid-enriched microdomains (GEMs or lipid rafts), where

multiple signaling molecules participating in B cell activation localize, including Lyn. CD45

molecules residing inside lipid rafts inhibit Lyn by dephosphorylating both regulatory sites

(Figure 3B). Upon ligation of BCR in the early stage of B cell activation, CD45 is transiently

segregated from lipid rafts, which releases its inhibitory effect, enabling Lyn activation (Figure

3C). 15 minutes post BCR stimulation, CD45 re-associates with GEMs, which may play a role

in attenuating BCR signaling (Shrivastava et al., 2004). This finding elegantly emphasizes the

importance of CD45 membrane organization in regulating PTPase activity and controlling

activation of Lyn. Moreover, the context of antigen presentation is also critical when studying

the effect of CD45 on receptor signaling, which is not addressed when soluble cross-linking

antibodies are used. According to the kinetic-segregation model, the close apposition of the

plasma membrane of the T cell and the antigen-presenting membrane during formation of the

immunological synapse leads to exclusion of CD45 from the synapse due to its bulky

extracellular domain (Davis and van der Merwe, 2006). Importantly, a recent study demonstrated

that the segregation of CD45 out of the immunological synapse correlates with TCR signaling

(Chang et al., 2015). Using chimeric forms of the Src family kinase Lck fused to the

transmembrane and extracellular domain of CD45 of varying length, Chang and colleagues show

that the fraction of cells activated by anti-TCR coated glass is reversely correlated with the

10

length of the extracellular domain of the chimeric structures. The chimeric structure composed of

the extracellular domain of the B220 has the lowest activation efficiency. These findings

emphasize the importance of the temporal and spatial regulation of CD45 as well as the crucial

role of the extracellular domain of CD45 in regulating immunoreceptor signaling.

Figure 3. Schematic diagram of the structure of CD45 and localization during B cell

activation

A) CD45 in B cells is the largest isoform of protein tyrosine phosphatase, receptor type, C

(PRPRC or CD45). The intracellular domain consists of two phosphatase domains including one

membrane-proximal active domain. The putative wedge structure inhibits phosphatase activity of

an adjacent CD45 by inserting into the phosphatase domain active site. The extracellular domain

consists of three fibronectin type III repeats and one cysteine-rich region, which are similar

among CD45 isotypes. CD45 isoform expressed by B cells (B220) consists of all three

alternatively spliced exons at the N terminus. In addition, the extracellular domain is highly

glycosylated (red circles). B) In the resting state, some CD45 molecules localize inside lipid rafts

(indicated by red) and inactivate Lyn by dephosphorylating both phosphorylation sites. C) In the

first 15 minutes of BCR-antigen engagement, CD45 molecules are excluded from lipid rafts,

allowing the activation of Lyn.

11

1.3.3 CD22

CD22 is a well-characterized inhibitory coreceptor expressed on the surface of B lineage

cells. The extracellular domain of CD22 contains seven Ig-like domains including a sialic acid

binding domain, which specifically binds to α2,6-linked sialic acid (Engel et al., 1995) (Figure

4A). The sialic acid-binding domain was reported to be critical in regulating the homotypic

interaction between CD22 molecules as well as heterotypic interaction with other sialic acid-

containing surface proteins (Han et al., 2005; Ramya et al., 2010). The intracellular domain of

CD22 contains three immunoreceptor tyrosine-based inhibitory motifs (ITIMs), which are

rapidly phosphorylated upon BCR ligation (Leprince et al., 1993). These phosphorylated ITIMs

recruit other phosphatases such as Src homology region 2 domain-containing phosphatase-1

(SHP-1), which dephosphorylates Syk, PLC-γ2, and CD19 to dampen BCR signaling (Doody et

al.; 1995, Law et al.; 1996; Pani et al., 1997) (Figure 4B). The importance of CD22 is illustrated

by CD22 deficient primary B cells, which have a lower activation threshold and prolonged

calcium signaling upon IgM-BCR ligation compared to WT B cells. Surface expression of the

costimulatory receptor CD86 is also increased in CD22 deficient B cells and they proliferate

more upon stimulation (O'keefe et al., 1996). Moreover, CD22 deficient mice are more likely to

develop self-reactive antibodies such as anti-dsDNA and anti-cardiolipin after 12 to 18 months

compared to WT control mice (O'keefe et al., 1999). These findings clearly demonstrate the

importance of CD22 inhibitory signaling in regulating B cell activation.

12

Figure 4. Schematic diagram of the structure of CD22 and inhibitory effect of CD22 on

BCR signaling

A) The extracellular domain of CD22 contains seven Ig-like extracellular domains, which are

heavily glycosylated (red circle). The terminal Ig-like domain, called the sialic-binding domain,

binds to α2,6-linked sialic acid. The intracellular domain contains three immunoreceptor

tyrosine-based inhibitory motifs (ITIMs), which are phosphorylated in the early stage of BCR

activation. B) After BCR ligation, Lyn phosphorylates ITIMs of CD22, which recruits and

phosphorylates SHP-1, a phosphatase. SHP-1 dephosphorylates CD19, which attenuates the

amplification of Lyn. SHP-1 also dephosphorylates Syk.

1.4 The Actin Cortex Regulates BCR Mobility and Signaling

The actin cortex is the actin-based cytoskeletal structure immediately underneath the

plasma membrane. The primary function of the actin cortex is to physically support the

membrane and provide driving forces for various biological processes such as cellular mobility,

internalization, and division (Pollard and Cooper, 2009). The actin cortex is a dynamic structure,

in which actin filaments continuously reorganize through the balance between actin

polymerization and depolymerization. Actin reorganization is critical for B cell spreading on the

surface of APCs. Disrupting actin cortex by actin polymerization inhibitors such as Latrunculin

A (LatA) or Cytochalasin D (CytD) inhibits B cell spreading and antigen accumulation (Fleire et

al., 2006). Consistent with this finding, A20 cells expressing a constitutively inactive form of

13

cofilin, which is required for actin reorganization, failed to spread on immobilized anti-BCR

(Freeman et al., 2011). These findings confirm the importance of actin reorganization in

regulating BCR signaling upon engaging antigen. Moreover, the actin cytoskeleton also plays a

critical role in regulating the movement (or mobility) of the BCR on the cell surface and

consequently, BCR signaling (Treanor et al., 2011). Using dual-color total internal reflection

fluorescence microscopy (TIRF) and single particle tracking (SPT), Treanor et al. (2010)

reported that BCR mobility is decreased within actin-rich regions compared to actin-poor

regions. Treating B cells with actin disrupting reagents such as LatA or CytD increases the

mobility of IgM-BCR on the B cell membrane. Interestingly, the increase in IgM-BCR diffusion

rate correlates with an increase in calcium signaling even in the absence of antigen. LatA

treatment not only triggers calcium signaling but also phosphorylation of signaling molecules

such as CD19, Lyn, and ERK, similar to antigen induced B cell activation (Treanor et al., 2011;

Mattila et al., 2013; Gasparrini et al., 2015). The correlation between BCR mobility and BCR

signaling leads to the emergence of the collision coupling model in regulating BCR signaling

(Treanor, 2012). In this model, prior to cell activation the actin cortex confines the mobility of

BCR and separates BCR from activated kinases or co-receptor such as CD19. When the actin

cortex is disrupted, BCR mobility increases, which leads to higher collision with activated

kinases or co-receptors. This model is supported by the finding that when HEL-specific B cells

are activated by HEL-coated cover slips, IgM and CD19 molecules are brought close together

inside Syk clusters, where microsignalosomes are initiated (Mattila et al., 2013). Freeman et al.

(2015) reported that incubating B cells with a low concentration of LPS increases BCR mobility

by enhancing actin severing through cofilin activation. The increase in BCR mobility correlates

with a higher chance of collision between BCRs and higher tonic signaling, as evidenced by

14

increased ERK and Akt phosphorylation in the resting state. These findings confirm the

importance of the actin cortex in regulating BCR mobility, which is important in controlling

BCR signaling. Moreover, this study elucidated a mechanism in which Toll-like receptors

(TLRs) lower the threshold of B activation by regulating the actin cortex to increase BCR lateral

diffusion, and helps explain the efficiency of antigens associated with TLR ligands in triggering

strong immune responses in vivo (Mifsud et al., 2014).

1.5 Surface Proteins Organize into Nanoclusters

According to the fluid mosaic model, plasma membrane proteins were proposed to be

randomly and evenly distributed (Singer and Nicolson, 1972). However, many surface proteins

are non-randomly distributed and highly compartmentalized (Cambi and Lidke, 2011). The non-

random distribution of surface proteins was hypothesized to be based on a hierarchical

organization ranging from dimers, to oligomers, to nanoscale and micrometer-sized clusters. This

hierarchical order may regulate the function and organization of surface proteins at multiple

levels (Garcia-Parjo et al., 2014). Early evidence supporting the existence of constitutive protein

clusters came from flow-cytometry based Förster resonance energy transfer (FRET) experiments,

which can detect an intermolecular distance of less than 10 nm (Chakrabarti et al., 1992; Szöllósi

et al., 1996). Using this technique, MHC I molecules were found to be within the FRET distance,

which would be unlikely if MHC-I molecules were randomly distributed on the cell membrane

(Chakrabarti et al., 1992; Matko et al., 1994). Szöllósi et al. (1996) also described the hetero-

association between MHC II and proteins of the tetraspanin family on the plasma membrane of B

cells using flow-based FRET. The bimolecular fluorescence complementation (BiFC) assay,

which monitors the dimerization of proteins tagged with amino- and carboxy-terminal half

15

domains of a yellow fluorescent protein (YFP) through detection of a fluorescent signal upon

assembly of a complete YFP, has also been used to report on the organization of cell surface

proteins. Using this technique, Yang and Reth (2010) found that IgD-BCR molecules are homo-

associated, which the authors defined as "oligomers".

Although these findings confirmed that proteins on the cell membrane are not randomly

distributed, these studies did not describe how surface proteins are distributed on the cell

membrane, which can only be achieved by visualization of the proteins on the membrane.

However, visualization of single molecules on the cell membrane is restricted by the diffraction

limit of optical microscopy, which defines the minimum lateral distance that two points can be

differentiated and is equivalent to approximately half the illuminating wavelength. To overcome

this limitation, immuno-transmission electron microscopy (TEM), which utilizes short-

wavelength electron beam and metal-conjugated antibodies, was used to visualize the

distribution of proteins on the cell membrane (Damjanovich et al., 1995; Jenei et al., 1997).

Consistent with the FRET experiments, MHC-I was found to form clusters on the cell membrane

with an average of 25 molecules per cluster (Damjanovich et al., 1995; Jenei et al., 1997). To

visualize the distribution of all proteins on the cell membrane, Lillemeier et al. (2006) labelled

surface proteins on T cells with biotin and detected protein localization with gold-conjugated

streptavidin, which was visualized by TEM. The authors found that proteins on the membrane

organized into "protein islands", which ranged in size from 30 to 700 nm. However, these studies

have been criticized because TEM imaging requires extensive sample processing, which may

introduce shearing forces and alter epitopes, thus decreasing the binding efficacy of antibodies,

which is illustrated by a very sparse immunogold detection. These limitations may underestimate

the number of molecules residing in clusters and obscure information about the spatial

16

organization of surface proteins. To address these limitations, many super-resolution imaging

techniques using visible light with less extensive sample processing have been applied to study

the distribution of surface proteins (Hwang et al., 1998; Mattila et al., 2013; Oszmiana et a.,

2016). Direct stochastic optical reconstruction microscopy (dSTORM) is a super-resolution

imaging technique that can achieve a lateral resolution of 10 to 30 nm in fixed cells, a 10-fold

improvement over conventional microscopy (Heilemann et al., 2008). To achieve this

subdiffraction resolution, fluorophores are switched to a metastable dark state by high laser

intensity and subsequently reactivated with continuous low-intensity illumination to randomly

convert a small fraction of fluorophores to a fluorescent state, allowing the detection and

localization of single molecules by Gaussian functions. Using dSTORM, IgM and IgD molecules

were found to be organized in pre-existing nanoscale clusters on the surface of resting primary B

cells (Mattila et al., 2013). Approximately 70% of IgD molecules are present in these clusters

and are more densely packed (30-120 molecules per cluster) compared to IgM molecules, of

which approximately 40% reside in nanoscale clusters containing an estimated 20-50 molecules

per cluster (Mattila et al., 2013). Interestingly, the two isotypes of BCR form distinct

nanoclusters, which do not overlap with each other (Maity et al., 2015). CD19 was also found to

exist in nanoscale clusters with cluster size and density intermediate between IgM and IgD

(Mattila et al., 2013). Gasparrini et al. (2015) reported that CD22 molecules on resting primary B

cells also form nanoscale clusters with an average radius of 100 nm. The distribution of surface

proteins in nanoscale clusters was not only found on B cells but also many other cell types

including T cells, NK cells, and monocytes (Roh et al., 2015; Pageon et al., 2013; van Zanten et

al., 2015). Taken together, the biochemical data, combined with TEM and super-resolution

imaging firmly establish that many proteins on the cell membrane exist in preformed structures

17

ranging from oligomers to nanometer-scale to micrometer-scale clusters, and are a dominant

feature of plasma membrane organization (Garcia-Parajo et al., 2014). Although these studies

established that proteins are non-randomly distributed, there is no consensus nomenclature for

these structures, and they are variably referred to as "oligomer", "protein island", “protein patch”,

or "nanocluster", depending on the experimental perspective; nonetheless, it is likely that these

terms refer to similar structures. In this study, we used "nanoclusters" to describe groups of

molecules organized in clusters up to 200 nm in size, and on average contain fewer than 100

molecules. These structures are different from microclusters, which are structures formed upon

receptor activation, and are composed of hundreds of molecules and are larger in size compared

to nanoclusters (Figure 5).

Multiple mechanisms have been proposed to explain the formation of nanoclusters,

including vesicle trafficking, actin barriers and protein-protein interaction. Lavi et al. (2007)

reported that MHC I molecules are locally concentrated in the plasma membrane where vesicles

originating from the Golgi apparatus are delivered. The density of MHC I molecules inside these

patches decrease exponentially over time, as molecules laterally diffuse. Disruption of the actin

cortex by LatA treatment decreased the lifetime of MHC I patches, due to the increase in the

lateral diffusion of MHC I from patches (Lavi et al., 2012). Consistent with this model,

Lillemeier et al. (2006) reported that treating T cells with actin destabilizing reagents such as

LatA and CytD reduces protein density inside "protein islands", and consequently proteins

formed smaller clusters and were more dispersed from each other. Supporting the role of actin in

organizing surface proteins, the size of regions confined by actin filaments ranges from 50 to 200

nm, which is approximately the size reported for nanoclusters (Morone et al., 2006 and Brown et

al., 2012). Although actin is important in the organization of surface proteins, it is not the only

18

driving force as proteins are still organized into clusters upon actin disruption (Lillemeier et al.,

2006; Mattila et al., 2013), indicating that other factors mediate the organization of these

clusters. Protein-protein interactions are another possible factor organizing nanoclusters.

Evidence for protein-protein interactions in mediating the formation of nanoclusters comes from

the work of Reth and colleagues. Using BiFC and blue native polyacrylamide gel

electrophoresis, Reth and colleagues reported that BCR "oligomers" were dependent on

interactions between the transmembrane domain of mIgD and Igα /Igβ (Schamel and Reth, 2000;

Yang and Reth, 2010). Further support for the importance of protein-protein interaction in

organizing nanoclusters, Gasparrini and colleagues (2015) reported that homotypic interaction

between CD22 molecules is crucial in regulating the size of CD22 nanoclusters. A CD22 mutant

with a point mutation in the sialic binding domain, which mediates the homotypic interaction

between CD22 molecules, forms smaller nanoclusters. These findings suggest that protein-

protein interactions are important in the formation of protein nanoclusters.

Figure 5. BCR and coreceptors are organized into distinct nanoclusters

A) In the resting state, IgM-BCR, IgD-BCR and CD19 form distinct nanoclusters on the cell

membrane. B) Upon activation, BCR nanoclusters are brought closer to CD19 nanoclusters to

form BCR microclusters.

19

1.6 Galectin Lattice Regulates Protein Organization

Another potential mechanism regulating protein organization on the cell surface is the

interaction between glycosylated proteins and cell surface lectins. Glycosylation is a post-

translational modification in which chains of sugars are attached to the polypeptide backbone

catalyzed by different glycosyltransferases. Glycosylation regulates various biological functions

including protein folding, protein compartmentalization, and protein-protein interactions

(Cummings, 2009). The two most common mechanisms to attach glycans to proteins are amide

bonds to asparagine (Asn) side chain (N-glycosylation) and glycosidic bonds to the OH side

chain of serine (Ser), threonine (Thr) and tyrosine (Tyr) (O-glycosylation). N-glycosylation is

catalyzed by enzymes localized in the endoplasmic reticulum (ER) and Golgi complex. N-

glycosylation modifications are common in the extracellular domain of membrane-associated

proteins (Haltiwanger and Lowe, 2004). Glycosylation of extracellular domains is tightly

regulated, and is specific for different cell types as well as different stages of development or

activation (Wolfert and Boons, 2013). In the context of the immune response, the importance of

glycosylation is evidenced by aberrant phenotypes observed in mutant mouse strains deficient in

genes mediating glycan synthesis and glycan-protein interactions (Orr et al., 2012). For example,

Mgat5-/- mice, which lack the enzyme adding β1-6 N-acetylglucosamine to N-glycans, have a

higher risk of autoimmune diseases (Demetriou et al., 2001). Fut-2-/- mice, which lack

fucosyltransferase 2, exhibited abnormal hematopoiesis with lower red blood cell number and

extramedullary hematopoiesis in the spleen (Orr et al., 2012). These findings emphasize the

importance of glycosylation in regulating the development and the function of immune cells

(Demetriou et al., 2001).

20

Given that many surface proteins have glycosylated extracellular domains, these domains

provide potential binding sites for sugar binding proteins, which may regulate the organization of

surface proteins as well as mediate protein-protein interactions. Galectins are a family of β-

galactoside sugar binding proteins with conserved carbohydrate recognition domains (CRDs)

(Rabinovic and Toscano, 2009). Galectins are ubiquitously expressed in various types of tissue

and can be found from lower organisms such as nematodes and sponges to higher mammalian

species such as mouse and human. Galectins are classified into three subgroups including

prototype galectins (galectins-1, 2, 5, 7, 10, 11, 13, 14, 15) containing a single CRD with a short

N-terminal sequence; tandem-repeat-type galectins (galectins-4, 6, 8 ,9 and 12) containing two

non-identical CRDs connected by a short linker peptide; and chimera-type galectin, with

galectin-3 being the only member, containing one CRD with an extended proline-tyrosine-

glycine-rich N terminus, which can form pentamers (Rabinovich and Toscano, 2009) (Figure 6).

The CRDs of galectins have eight strongly conserved amino acids within the sugar-

binding site. These conserved amino acid residues form hydrogen bonds and van der Waals

interaction with the 4-OH, 6-OH of galactose (Gal) and the 3-OH of N-acetylglucose (GluNAc)

(Leffler and Barondes, 1986; Ahmed et al., 1996; Hirabayashi et al., 2002). Consistently, all galectins

have high affinity for N-acetyllactosamine (Galβ1-4GlcNAc) and its linkage isomer lactose-N-

biose (Galβ1-3GlcNAc), which share a similar configuration of the three essential OH groups.

Conversely, substitutions at these OH groups such as α2-6 sialylation greatly abolish affinity to

galectins (Hirabayashi et al., 2002). Within the galectin family, each galectin has a selectivity

toward certain types of saccharides, which depends on three factors including number of

repeated N-acetyllactosamine units, branching pattern and type of substitution (Hirabayashi et

al., 2002). This specificity may determine the ligands that galectins bind to and the cellular

21

pathways triggered by galectin-glycoprotein interactions. For example, in T cells, Galecin-1

(Gal-1) induces apoptosis through a CD7, CD43 and CD45 dependent pathway (Pace et al.,

2000; Hernandez, 2006; Nguyen et al., 2001). Cell lines that lack CD47, CD43 or CD45 are

resistant to apoptosis induced by Gal-1. Although Gal-9 also induces apoptosis in T cells, the

CD7, CD43 and CD45 deficient cell lines are still susceptible to apoptosis induced by Gal-9 (Bi

et al., 2008), indicating that Gal-9 induces apoptosis in T cells by a pathway distinct from Gal-1.

However, there is still a missing link between the specificity of galectins to different saccharides

and the specific saccharides decorated on the surface glycoproteins that bind to galectins. The

question of how proteins from a single cell have different glycosylation that allow specific

interactions with different galectins is still unanswered. Thus, it is critical to further study

glycosylation of surface proteins to understand the specificity of galectins in selectivity binding

to cell surface proteins to regulate cellular activities.

Galectins can form a lattice-like network through glycoprotein-galectin interactions on

the cell surface (Nabi et al., 2015). In T cells, galectins were reported to be crucial in organizing

CD45 and TCR on the cell membrane to regulate TCR signaling (Chen et al., 2007). In T cells,

the galectin lattice retains CD45 inside lipid rafts while preventing TCR partitioning into rafts,

which inhibits TCR signaling at the early immunological synapse (Chen et al., 2007). Gal-1 was

reported to organize surface proteins and induce apoptosis in human thymocytes (Pace et al.,

1999). Treating human thymocytes with recombinant Gal-1 (rGal-1) resulted in a redistribution

of glycoproteins on the cell surface; CD45 and CD3 were found on apoptotic blebs, while CD7

and CD43 were excluded from blebs. Taken together, these findings suggest that galectins play

an important role in organizing the distribution and regulating the function of cell surface

glycoproteins to mediate multiple biological processes.

22

Among the galectin family, Galectin-9 (Gal-9) appears to be an important regulator of the

immune response and is a potential target for immune therapies in human diseases (Wiersma et

al., 2013). Gal-9 is a tandem-repeat-type galectin, which has two non-identical CRDs. Gal-9 was

first isolated from mouse embryonic kidney cells and was found to be ubiquitously expressed in

rat and mouse tissues (Wada and Kanwar, 1997). The most well-known ligand of Gal-9 is T cell

immunoglobulin and mucin-domain-containing molecule 3 (TIM-3), which is expressed in

CD4+ TH1 cells (Zhu et al., 2005; Clayton et al., 2014) and also in a fraction of NK cells

(Gleason et al., 2012) and DCs (Nagahara et al., 2008) in humans. The function of Gal-9 appears

to be dependent on cell type and stage of activation or development. For example, Gal-9 binding

to TIM-3 induces intracellular calcium flux, aggregation, and death of TH1 CD4+ T cells,

selectively reduces the number of interferon-γ-producing cells, and suppresses TH1-mediated

autoimmunity in vivo (Zhu et al., 2005). In TH2 immunity, Gal-9 induces apoptosis of activated

alloreactive CD8+ cytotoxic T cells but not naive CD8+ cells (Wang et al., 2007). The function

of Gal-9 in humoral immunity is still largely unknown. Analysis of Gal-9-KO mice revealed an

enhancement in the humoral immune response with increased B cell proliferation, increased

germinal center size and higher antibody production (Orr et al., 2012). However, the cellular and

molecular mechanisms for these observations were not investigated. Recently, data from our lab

demonstrates that Gal-9 is bound to the surface of primary naïve B cells and is an inhibitory

regulator of B cell activation (Alluqmani, Cao & Treanor, unpublished data). Specifically, Gal-9-

KO primary B cells have higher antigen accumulation compared to WT control when spreading

on artificial lipid bilayers conjugated with model antigen. Consistent with this, BCR signaling

was also enhanced in Gal-9-KO B cells upon stimulation, as evidenced by higher levels of

phosphorylated ERK. Interestingly, treating Gal-9-KO primary B cells with recombinant Gal-9

23

(rGal-9) significantly diminished B cell activation. Although these findings suggest that Gal-9 is

an inhibitory regulator of B cell activation, the molecular mechanism for Gal-9 suppression on B

cell activation has not been investigated. However, we recently identified ligands of Gal-9 in

primary murine B cells (Cao & Treanor, unpublished). Using flag-tagged recombinant Gal-9

(rGal-9) we pulled down ligands of Gal-9 from primary B cell lysate, which were identified by

mass spectrometry. Six proteins were identified including CD45, IgM heavy chain, CD180, Igβ,

CD47, and M6PR. The identification of two main components of BCR including IgM heavy

chain and Igβ as ligands for Gal-9 suggests that BCR may reside within the Gal-9-glycoprotein

lattice, which may alter IgM-BCR organization and mobility on B cell surface, and consequently

B cell signaling and activation.

24

Figure 6. Galectins and the galectin-glycoprotein lattice

A) Schematic representation of the three subgroups of galectins: prototype, tandem-repeat type

and chimera type. Prototype galectins contain one carbohydrate recognition domain (CRD).

Tandem-repeat galectins contain two non-identical CRDs connected by a short peptide linker.

Chimera-type galectin has one CRD with an extended proline-tyrosine-rich N-terminus.

Galectins also form dimers and oligomers. B) Schematic representation of hypothetical galectin-

glycoprotein lattices. The structure and complexity of the lattice are dependent on the structure

and valency of both the galectin and the glycoprotein. (Pereira & Falcão, 2015)

25

1.7 Hypothesis and Aims

Given that Gal-9 is able to form Gal-9 lattices and binds to IgM-BCR as well as CD45, we

hypothesized that Gal-9 reorganizes IgM-BCR into larger clusters and brings inhibitory

glycoproteins closer to IgM-BCR complex within the Gal-9 lattice. We predicted that the

reorganization of IgM-BCR into larger clusters would restrict the lateral diffusion of IgM-BCR,

and thus attenuate BCR signaling. In addition, we also predicted that inhibitory molecules within

the Gal-9 lattice might directly inhibit BCR by regulating the phosphorylation of early signaling

molecules.

This study has three main aims:

Aim 1: Investigate how Gal-9 modulates the cell surface organization of IgM-BCR.

Aim 2: Investigate how Gal-9 modulates IgM-BCR mobility at the cell surface.

Aim 3: Investigate how Gal-9 regulates the localization of inhibitory molecules such as CD45

and CD22 relative to IgM-BCR on the cell surface.

26

MATERIALS AND METHODS

2.1 Mice

Galectin-9-/- (Gal9-KO) mice were provided by Stephen Beverly (Washington

University) on behalf of The Scripps Research Institute (TSRI). Galectin-9+/- (Gal9-Het) mice

were generated by breeding Gal9-KO with C57BL/6 (Wild-type; WT) mice obtained from

Charles River, USA. Littermate controls were generated by breeding Gal9-Het with each other.

Mice were used at 2-3 months of age for all functional and biochemical experiments. Mice were

housed in specific pathogen-free animal facility at the University of Toronto Scarborough,

Toronto, Canada. All experiments were approved by the Local Animal Care Committee (LACC)

at University of Toronto Scarborough.

2.2 B Cell Purification

Single cell suspensions of splenocytes were isolated from WT and Gal9-KO mice using a

70 µm cell strainer. Cells were centrifuged at 300g for 5 min at 4 oC. B cells were purified using

the negative isolation kit (EasySepTM, STEMCELL Technologies) according to the

manufacturer’s protocol.

2.3 Surface Staining and Confocal Microscopy

5 x 106 primary murine B cells from WT mice were treated with 1 µM recombinant

galectin-9 in 1% FBS in RPMI for 30 min at 37 oC (75 µL of reconstituted recombinant galectin-

9 in 125 µL of RPMI 1% FBS). Cells were washed once with 5 mL of PBS. Cells were

27

resuspended in PBS and allowed to spread on anti-MHC II coated Lab-Tek™ chambers for 15

min at 37oC. The supernatant was gently removed to eliminate unbound cells. Cells were fixed in

2% PFA at 37oC for 10 min. Cells were washed three times with PBS. Cells were incubated with

2 µg/ml purified rat anti-mouse CD16/32 (BDPharmingen) in 200 µL blocking buffer (PBS

containing 5 % BSA) for 1 h at 4 oC. Blocking buffer was gently removed and cells were

incubated with 1 µg/mL goat anti-mouse Gal-9 (R&D systems) for 1 h at 4 oC in 200 µL FACS

buffer (PBS, 1% BSA, 0.1% NaN3). Cells were washed three times with 400 µL PBS. Cells were

incubated with 1 µg/mL CyTM3-conjugated bovine anti-goat IgG (H+L) (Jackson

ImmunoResearch) in 200 µL blocking buffer for 1 h at 4oC. Cells were washed three times with

400 µL PBS. Cells were incubated with 1.5 µg/mL Alexa Fluor® 647 or 488 conjugated Fab

fragment goat anti-mouse IgM, µ chain specific (Jackson ImmunoResearch), 5 µg/mL Alexa

Fluor® 647 rat anti-mouse CD22 (clone OX-97, BioLegend) and 5 µg/mL Alexa Fluor® 488 anti-

mouse/human CD45R/B220 (Clone RA3-6B2, BioLegend) in 200 µL blocking buffer for 1 h at

4 oC. Cells were washed three times with 400 µL PBS. Cells were mounted in Fluoro Gel with

DABCO™ (Electron Microscopy).

Confocal images were acquired using a spinning disc confocal microscope (Quorum

Technologies) consisting of an inverted fluorescence microscope (DMI6000B; Leica) equipped

with an EM-CCD camera (Hamamatsu) and a 63x oil immersion objective, NA 1.4. Images were

acquired using the Metamorph software (Molecular Devices).

Images were analysed using Volocity software (Perkin Elmer). The fluorescence signal of

CD45, CD22 or IgD was combined to define a mask delineating the membrane region. Gal-9high

regions were determined by the fluorescence signal of Gal-9. Gal-9low regions were determined

28

by subtracting the galectin-9high regions from the membrane region. The mean flourescence

intensity of CD45, CD22, IgD, and IgM was calculated in Volocity.

2.4 B Cell Activation

5 x 106 primary murine B cells were suspended in 200 µL of RPMI and pre-warmed at 37

oC for 10 minutes. Cells were stimulated with 5 µg/mL F(ab’)2 goat anti-mouse IgM, µ chain

specific (Jackson ImmunoResearch) in a total volume of 400 µL. To stop the reaction, 1 mL of

ice-cold PBS was added at the indicated timepoint and tubes were quickly transferred onto ice.

Cells were pelleted at 15,000g for 30 s. The supernatant was removed before adding lysis buffer

(1 % NP40, 0.15 M NaCl, 20 mM Tris pH 8, 100 mM NaF, 10 mM Na3VO4, and Roche

cOmpleteTM protease inhibitor cocktail) at a concentration of 10 x 107 cells/mL. The cell lysate

was incubated on ice for 30 min with intermittent vortex. The cell lysate was centrifuged at

15,000g for 15 min to remove cell debris and the supernatant was transferred to a clean

microtube. 2X Laemmli buffer containing 0.1 M DTT was added to the cell lysate and boiled at

95oC for 5 min.

2.5 Western Blot

Proteins were resolved by sodium dodecyl sulfate polyacrylamide gel electrophoresis

(SDS-PAGE). For Western blot of phosphorylated proteins, proteins were transferred to PVDF

membranes followed by blocking for 1 h in TBST (20 mM Tris pH 7.5, 150 mM NaCl and 0.1 %

Tween 20) containing 5 % BSA at room temperature. Membranes were incubated with rabbit

anti-mouse phospho-CD19 (Tyr531) (Cell Signaling Technology), rabbit anti-mouse phospho-

SHP-1 (Tyr564) (Cell Signaling Technology) at 1:1000 v/v and mouse anti-phospho-CD22

29

(Tyr822, 12a/CD22) (BD Biosciences) at 1:200 v/v in TBST containing 1% BSA overnight at 4

oC. Membranes were washed three times in 50 mL TBST on a shaking rocker for 10 min.

Membranes were incubated with horseradish peroxidase (HRP)-conjugated Donkey anti-rabbit

IgG or Donkey anti-mouse IgG antibodies (Jackson ImmunoResearch) at 1:5000 v/v in TBST

containing 1% BSA for 1 hour at room temperature. Membranes were washed three times with

TBST as above. Membranes were incubated with Pierce® ECL Western Blotting Substrate and

imaged by ChemiDoc System (Bio-Rad). The intensity of each band was analyzed by ImageJ,

normalized to β-Tubulin and the fold changes were calculated using the signal of WT B cell at

time 0.

2.6 Single Particle Tracking

2.6.1 Recombinant Galectin-9 and Fab Fragment Labeling

Mouse recombinant galectin-9 (rGal-9, R&D Systems) was reconstituted at 0.2 mg/mL

(maximum concentration to reconstitute rGal-9) in 0.1 M NaHCO3 and incubated with 0.2

mg/mL Alexa Fluor® 555 NHS Ester for 1 h at room temperature with gentle mixing. Following

labeling, the mixture was dialyzed against 20 mM MOPS, 500 mM sodium chloride, 0.5 mM

EDTA, and 1 mM DTT using a 10,000 MWCO Slide-A-Lyzer ® Dialysis Cassette (Thermo

Scientific). After changing the buffer two times in 24 h, precipitation was observed in the

dialysis cassette. The membrane of the cassette was cut and protein was collected using a

micropipette. The mixture was mixed by pipetting to dissolve protein precipitation. Labelled

rGal9 was stored at -20oC.

Fab fragment labeling

30

20 µL of 1 M NaHCO3 was added to 200 µL of 1 mg/ml goat anti-mouse IgM, µ chain specific

(Jackson ImmunoResearch) and incubated with 40 µg/mL Attotec® 633 NHS Ester for 1 h at

room temperature with gentle mixing. Following labeling, the mixture was dialyzed against PBS

at 4oC. After changing the buffer two times in 24 h, labelled Fab fragment was collected and

stored at 4oC.

2.6.2 Recombinant Galectin-9 Treatment

5x106 primary murine B cells from Gal-9-KO mice were incubated with 500 µL complete

media containing 0.5 µM labeled rGal9 and 0.5 µM non-labeled rGal9 for 30 mins at 37 oC.

Cells were centrifuged at 300g, washed two times with ice-cold PBS, and resuspended in

chamber buffer (0.1 g/mL glucose, 0.5% heat inactivated FBS, 2 mM MgCl2, 0.5 mM CaCl2).

2.6.3 Glass Coverslip Coating

Glass coverslips were cleaned in chromic acid for 20 min followed by rinsing with water

and acetone. Coverslips were air-dried and then incubated with 1 µg/mL anti-MHC class II

(clone M5/114) for 2 h at room temperature and then washed with PBS.

2.6.4 Cell Labeling for Single Particle Tracking

Primary murine B cells from Gal-9-KO mice were labeled with 4 ng/mL Attotec® 633-

labeled goat anti-mouse IgM Fab fragment (Jackson ImmunoResearch) in 0.5% FBS in PBS for

15 min at 4oC followed by washing with PBS two times and resuspending in chamber buffer.

Labeled cells were stored on ice prior to imaging. Just before imaging, cells were incubated at

37oC for 5 min.

2.6.5 Instrument

Single-molecule fluorescence microscopy was performed with a total internal reflection

fluorescence (TIRF) microscope (Quorum Technologies) based on an inverted microscope

31

(DMI6000B; Leica), HCX PL APO 100x/1.47 oil immersion objective and Evolve Delta

EMCCD camera (Photometrics). Images were acquired continuously at 20 frame/s for 10

seconds with an electron-multiplying (EM) gain of 200 and the exposure time of 50 ms. SPT

analysis was performed as described previously (Treanor et al., 2010).

2.7 Co-Immunoprecipitation

Antibody conjugation was performed using a Pierce Co-Immunoprecipitation Kit

(Thermo Scientific). 30 µg of goat anti-mouse IgM µ-chain specific (Jackson ImmunoResearch)

or 10 µg of goat anti-mouse CD22 (R&D Systems) was conjugated to 50 µL AminoLink Plus

Coupling Resin according to the manufacturer’s protocol.

10 x 106 primary murine B cells from WT and Gal-9-KO were resuspended at 25 x 106

cells/mL in ice-cold PBS, containing 1 mM freshly prepared DTSSP. Cells were incubated for 30

min at 4 oC with gentle shaking. Cells were washed three times with 5 mL ice-cold PBS. The

cell pellet was lysed in 500 µL lysis buffer (1 % Triton X-100, 1 % NP40, 1 mM EDTA, Roche

cOmpleteTM Protease Inhibitor Cocktail in PBS,) for 30 min at 4 oC . Cell lysate was centrifuged

at 15,000g for 15 min at 4 oC. The supernatant was pre-cleared with agarose resin (Pierce®

Control Agarose Resin, Thermo Scientific) for 1 h at 4 oC with gentle shaking. Agarose resins

were removed using Pierce Spin Columns by centrifuging at 1000 g for 1 min.

400 µL of cell lysate was incubated with antibody-conjugated resins for 3 h at 4 oC with

gentle shaking. Resins were washed three times with ice-cold lysis buffer. Resins were incubated

with 70 µL 2X Laemmli buffer at room temperature for 5 mins and at 95 oC for 5 min. Eluent

was collected by centrifugation at 1,000g for 1 min through a Pierce Spin Column. 70 µL of lysis

32

buffer containing 50 mM DTT was added to the eluent. 30 µL of eluent was loaded into each

lane of a 7.5% polyacrylamide gel. Proteins were transferred to a PVDF membrane using transfer

buffer (0.05 % SDS, 10 % Methanol, 25 mM tris(hydroxymethyl)aminomethane (Tris base) and

190 mM glycine at 125 mA (70 V) for 3 h at 4 oC. Membranes were blocked with 5 % milk in

TBST (50 mM Tris, 150 mM NaCl and 0.1% Tween 20, pH 7.6) for 1 h at room temperature.

Membranes were incubated overnight with 1 µg/mL of anti-mouse IgM µ-chain specific

(Jackson ImmunoResearch), 0.1 µg/mL goat anti-mouse CD22 (R&D Systems) and 0.1 µg/mL

goat anti-mouse CD45 (R&D Systems) in TBST containing 1% BSA. Membranes were washed

three times in 50 mL TBST on a shaking rocker for 10 min. Membranes were incubated with

horseradish peroxidase (HRP)-conjugated donkey anti-goat antibodies (Jackson

ImmunoResearch) at 1:5000 v/v in 5% milk in TBST for 1 h at room temperature. Membranes

were washed three times with TBST as above. Membranes were incubated with Pierce® ECL

Western Blotting Substrate and imaged by ChemiDoc System (Bio-Rad). The intensity of each

band was analyzed by ImageJ (Schneider et al., 2012) and background signal subtracted. The

intensity of co-immunoprecipitated proteins were divided by the intensity of the

immunoprecipitated proteins. The ratio was then normalized to the lowest value, which was set

at 1.

2.8 Direct Stochastic Optical Reconstruction Microscopy

(dSTORM)

2.8.1 Sample Preparation

Primary B cells were stained with Alexa Fluor® 647-conjugated Fab fragment goat anti-

mouse IgM, µ-chain specific (Jackson ImmunoResearch) at 1.5 µg/mL in PBS containing 2 %

33

FBS for 15 min at 4 oC. Cells were centrifuged at 300g for 5 min and washed twice with 5 mL

PBS. Cells were resuspended in PBS and allowed to recover at 37 oC for 10 min. Cells were

allowed to spread on anti-MHC-class II coated coverslips (prepared as described above) for 10

min at 37 oC. Coverslips were washed gently with PBS to remove unbound cells. Cells were

fixed with fixation solution (4 % paraformaldehyde, 0.2 % glutaraldehyde in PBS) for 40 min at

room temperature. The coverslips were washed with PBS three times. Before imaging, samples

were incubated in PBS containing 0.1 M mercaptoethylamine (MEA), 0.5 mg/mL glucose

oxidase, 40 ug/mL catalase and 10 % glucose.

2.8.2 dSTORM Acquisition and Image Reconstruction

dSTORM images were acquired on a TIRF microscope as described above. For Alexa

Fluor® 647, photoconversion was achieved with a 633-nm laser (intensity ranged from 80 to 100

mW/cm2) illumination and conversion from the dark state with a 488-nm laser illumination

(intensity range from 5 to 20 mW/cm2). 10,000 images were acquired at a frame rate of 33

frames/s. Reconstructed images were acquired using the ThunderSTORM plugin for ImageJ

(Ovesný et al., 2014) with the camera setup as follows: pixel size (101.5 nm), photoelectron per

A/D count 3.6, base level [A/D count] 414 and an EM gain of 50. Wavelet filter (B-Spline) was

applied with a B-Spline order of 3 and a B-Spline scale of 2.0. Approximate localization of

molecules was detected by local maximum method with a peak intensity threshold of std

(Wave.F1) and a connectivity of 8-neighbourhood. Sub-pixel localization of molecules was

identified using integrated Gaussian with a fitting radius of 3 pixels and the weighted least

squares fitting method with multi-emitter fitting analysis enabled. The reconstructed images were

post-processed with drift correction using the built-in method in the ThunderSTORM plugin. To

reduce processing time, final images were selected from frames ranging from 4000 to 8000. In

34

addition, localizations with an uncertainty greater than 20 nm or intensity lower than 3000

photons were eliminated.

2.8.3 Hopkins Index and Ripley’s H Function Analysis

For each reconstructed image of WT and Gal-9-KO B cell, a 3 x 3 µm region was

randomly selected but excluded from the cell boundary. For each reconstructed image of rGal-9

treated Gal-9-KO B cell, a 3 x 3 µm region that colocalized with Gal-9, was selected for

analysis. The Hopkins index and Ripley’s H function analysis were performed by SuperCluster,

an analysis tool kindly provided by the University of New Mexico’s Spatio Temporal Modeling

Center via their website (http://stmc.unm.edu/).

2.8.4 Bayesian Cluster Analysis

Cluster analysis was performed by a Bayesian, a model-based approach (Griffié et al.,

2016). In brief, uncertainty, x and y coordinates of each localization in post-processed

reconstructed images were exported. For each reconstructed image of WT and Gal-9-KO B cells,

random but boundary-excluded 3 x 3 µm regions were selected for analysis. For each

reconstructed image of rGal-9-KO treated with rGal-9, a 3 x 3 µm region, which colocalized

with Gal-9, was selected for analysis. The selected regions were analyzed by the published

Matlab code of Bayesian cluster analysis (Griffié et al, 2016) with an alpha value of 20,

pbackground of 0.5, rseq of (5, 200, 10) and thseq of (0, 50, 5). The analyzed data were post-

processed to extract data about the percentage of molecules localizing in clusters, cluster radius,

number of clusters, and number of molecules per clusters.

http://stmc.unm.edu/

35

2.9 Statistical Analysis

Statistical analysis was performed using GraphPad Prism. The normal distribution of data

was tested using the D’Agostino-Pearson omnibus normality test. Comparisons between two

groups were performed using Student’s t test for data with normal distribution and Mann-

Whitney for data with non-normal distribution. Comparisons between multiple groups were

performed by ordinary one-way ANOVA for data with normal distribution and Kruskal-Wallis

test for data with a non-normal distribution. For post hoc analysis, Tukey’s multiple comparison

was used for normally distributed data and Dunn’s multiple comparisons test was used for non-

normally distributed data.

For Western Blot analysis, the Mann-Whitney test was used to test the statistical

significance of the difference between the phosphorylation level at different time points between

WT and Gal-9-KO B cells upon activation.

36

RESULTS

3.1 Recombinant Galectin-9 Reorganizes IgM into Larger

Clusters

Previously, we discovered that Gal-9 binds to IgM-BCR (Cao & Treanor, unpublished).

Galectins were hypothesized to form a galectin-glycoprotein lattice through the interaction

between CRDs and sugar side chains on cell surface glycoproteins (Nabi et al., 2015). Thus, we

hypothesized that Gal-9 regulates the organization of IgM-BCR on the surface of B cells, and

may be a molecular mechanism that organizes IgM-BCR into nanoclusters. To investigate if Gal-

9 is involved in the organization of endogenous IgM-BCR on the B cell membrane, we utilized

dSTORM, which permits the distinction of two points separated by a lateral distance of

approximately 10 to 30 nm in fixed cells, allowing the study of the organization of IgM-BCR at

the single molecule level (Heilemann et al., 2008; Mattila et al., 2013). Primary murine WT, Gal-

9-KO B cells and Gal-9-KO B cells treated with 1 µM rGal-9 were labelled with Alexa-Fluor

647 conjugated anti-IgM Fab fragment. Cells were allowed to settle on non-stimulatory anti-

MHC II coated coverslips for 10 min prior to fixation. Total internal reflection fluorescence

microscopy (TIRFM) was used to acquire images of IgM-BCR to selectively visualize the

organization of IgM-BCR at the contact surface. Using Thunderstorm software (Ovesný et al.,

2014), 4000 images were combined to generate reconstructed super-resolution images.

37

Consistent with previous findings, IgM molecules on the B cell membrane were

organized into nano-size clusters in WT primary B cells (Figure 7A) (Mattila et al., 2013). To

quantify the clustering tendency of IgM molecules on the surface of primary B cells, we used

two conventional methods including H functions derived from Ripley’s K function and the

Hopkins index (Zhang et al., 2006). First, we used the H function derived from Ripley’s K

function to quantify the extent of BCR clustering according to the number of proteins found

within a distance (r) for each molecule compared with that predicted for a random distribution.

We found that the radius of clusters of IgM molecules on the surface of WT primary B cells

estimated by the peak of Ripley’s H function was approximately 50-100 nm, which is consistent

with previously reported values (Mattila et al., 2013) (Figure 7B). Second, we used Hopkins

index to evaluate the clustering tendency of IgM molecules compared to the hypothetical random

distribution, whose value is 0.5. The Hopkins index of IgM-BCR on the surface of WT primary

B cells is 0.75, confirming that IgM are not randomly distributed on the B cell surface (Figure

7C).

To test the hypothesis that Gal-9 regulates the organization of IgM-BCR nanoclusters, we

studied the organization of IgM molecules in the absence of endogenous Gal-9 in the context of

Gal-9-KO primary B cells. We did not find a significant difference in Hopkins index nor

Ripley’s H function between WT and Gal-9-KO primary B cells (Figure 7B, 7C). This finding

suggests that Gal-9 does not mediate the formation of IgM nanoclusters. We previously found

that Gal-9 localizes in discrete puncta that are sparsely distributed on the B cell surface

(Alluqmani, Cao & Treanor, unpublished). Thus, the comparison between WT and Gal-9-KO B

cells based on randomly selected regions might not account for the effect of Gal-9 on IgM-BCR

organization inside the Gal-9 lattice. To focus on the effect of Gal-9 on IgM organization, we

38

wanted to examine the organization of IgM molecules inside the Gal-9 lattice. The conventional

method using fluorescently labelled anti-Gal-9 antibodies to label Gal-9 was not feasible due to

the low abundance of Gal-9 on the B cell surface (Alluqmani, Cao & Treanor, unpublished). In

addition, using antibodies against Gal-9 may induce crosslinking between Gal-9 molecules,

which possibly causes artificial effects on Gal-9 and IgM organization. To overcome this, we

treated Gal-9-KO primary B cells with fluorescently labelled recombinant Gal-9 (rGal-9) with

the concentration of 1 µM, which was reported to nearly abolish B cell activation (Alluqmani,

Cao & Treanor, unpublished). We then acquired dSTORM images and focused our analysis of

clustering on selected regions with high Gal-9 fluorescence intensity (Gal-9high). Interestingly,

Gal-9-KO primary B cells treated with 1 μM rGal-9 have fewer, but larger IgM clusters inside

Gal-9high regions (Figure 7A). Consistent with our observation from the reconstructed images, we

found that inside Gal9high regions, IgM-BCRs are significantly more clustered compared with

IgM on both WT and Gal-9-KO B cells (Figure 7B). The radius of clusters of IgM molecules

inside Gal-9high regions is approximately 150-250 nm, compared to 50-100 nm in WT cells.

Consistent with the Ripley’s H function analysis, inside Gal-9high regions, Hopkins index of IgM

is significantly higher compared to IgM on WT and Gal-9-KO B cells (0.83±0.02 compared to

0.75±0.02 and 0.75±0.02), indicating that IgM in these regions are significantly more clustered

(Figure 7C).

To further study the effect of rGal-9 on IgM organization on the surface of primary B

cells, we used a Bayesian cluster analysis (Griffié et al., 2016) to assign clusters and to identify

physical properties of these clusters. This method is more effective than the commonly used

Ripley's K function and Hopkins index for two reasons. First, this method accounts for a non-

negligible unclustered background, which can interfere with Ripley’s and Hopkins analysis.

39

Second, this method also takes account of the uncertainty associated with each localization

assigned by the Gaussian function. Using this method, we identified the number of clusters per

region, cluster radii, number of molecules per cluster, and percentage of localizations in clusters

from regions of interest (ROIs) 3000x3000 nm extracted from the reconstructed images. We

found a reduction in the number of clusters per ROI in rGal-9 treated cells (11.12±1.56)

compared to WT (30.92±2.11) and Gal-9-KO (34.12±1.78) cells (Figure 7D). We also observed

a significant increase in cluster radius (35.01 ± 1.48 nm compared to 20.27 ± 0.28 and 19.17 ±

0.23 nm for rGal-9, WT, and Gal-9-KO respectively) (Figure 7E) and number of molecules per

cluster (85.15 ± 9.29 compared to 27.23 ± 0.79 and 25.95 ± 0.62) (Figure 7F). Interestingly, we

did not find a significant difference in the percentage of localizations inside clusters between

WT, Gal-9-KO and Gal-9-KO cells treated with rGal-9 (Figure 7G). These findings indicate that

rGal-9 brings pre-existing IgM nanoclusters together to form larger clusters, which increases the

size of clusters and number of molecules per cluster and consequently reduces the overall

number of clusters, but without affecting the percentage of IgM molecules residing in clusters.

However, it is necessary to acknowledge a limitation of Bayesian cluster analysis is its

assumption that clusters are relatively homogenous in size, which compromises its ability to

detect clusters with large range in size.

40

Figure 7. rGal-9 alters IgM nanoclusters.

A)TIRFM image of surface IgM and fluorescently labelled rGal9 before bleaching for image

acquisition (two left panels respectively). dSTORM images reconstructed from single-molecule

localization processed by Thunderstorm software; the magnified region (3 x 3 µm) from ROI

(white box) is shown as 2D image (middle) and 3D surface plot (right) in the order of WT (top),

Gal-9-KO (middle) and Gal-9-KO + 1 µM rGal-9 (bottom). Scale bar represents 2 µm. B)

Quantification of the distribution of IgM by H function and C) Hopkins index of localizations

inside ROIs. Reconstructed images were analyzed by a model-based Bayesian approach to

identify nanoclusters and their physical properties. D) Number of clusters (one point per ROI).

E) Cluster radii (one point per cluster). F) Number of molecules (one point per cluster). G)

Percentage of localization in clusters (one point per ROI). Each category contains at least 15

ROIs from three independent experiments (at least 4 cells per experiment). Statistical analysis

was performed using Kruskal-Wallis test with Dunn's multiple comparison test (D, E, F) and

one-way ANOVA with Tukey`s multiple comparison test (C,G). Horizontal bars indicate mean

±S.E.M. *P < 0.05, **P < 0.01, *** P < 0.001, ****P < 0.0001

Distance (nm)

41

3.2 Galectin-9 Reduces the Mobility of IgM-BCR

IgM-BCR mobility is critical in regulating BCR signaling. Treanor et al. (2010) reported

that disruption of the actin cortex by LatA treatment increases IgM-BCR mobility and initiates

signaling cascades in the absence of specific antigen. Our dSTORM data demonstrates that Gal-9

drastically changes the organization of IgM molecules on the B cell surface. In addition, it was

reported that a larger cluster size might lead to a lower mobility of proteins on the cell surface

(Gasparrini et al., 2016). To investigate how the Gal-9-mediated change in organization of IgM

may affect IgM mobility on the B cell surface in the steady-state we labelled single particles of

IgM by staining primary B cells from WT and Gal-9-KO mice with a low concentration of Atto-

633 conjugated anti-IgM Fab fragments. Cells were allowed to settle on non-stimulatory anti-

MHC II coverslips and single molecules of IgM were visualized using TIRF microscopy. The

trajectory of each molecule was tracked and the diffusion coefficient of each track was calculated

as previously described (Treanor et al., 2010). Consistent with previous findings (Treanor et al.,

2010), the diffusion coefficient of IgM on the B cell surface is heterogeneous, ranging from

nearly immobilized to 0.25 μm2s-1 (Figure 8A, 8B). The median of diffusion coefficient of IgM-

BCR on primary WT B cells was 0.027 μm2s-1, consistent with previous findings (Treanor et al.,

2010). Interestingly, the median diffusion coefficient of IgM-BCR on primary Gal-9-KO B cells

was approximately 30% higher than primary WT B cells (0.037μm2s-1 compared to 0.027 μm2s-1)

(Figure 8A). Consistent with this, the relative frequency of the diffusion coefficients reveals that

the frequency of slow-moving IgM-BCR is decreased in Gal-9-KO B cells while the proportion

of fast-moving IgM-BCR is increased (Figure 8B). This finding indicates that Gal-9 decreases

the mobility of IgM-BCR on the surface of primary B cells.

42

To study the mobility of IgM-BCR inside the Gal-9 lattice on the surface of primary

naïve B cells, we treated Gal-9-KO B cells with fluorescently-labelled rGal-9 and allowed the

cells to settle onto non-stimulatory anti-MHC II coated coverslips and visualized and tracked

IgM-BCR as described above. To identify regions where rGal-9 bound to the cells, we created a

mask using the fluorescence intensity of Gal-9 to define regions of high and low Gal-9 and then

calculated the diffusion coefficient in these regions (Figure 8C). We observed that inside the

Gal-9 mask the median diffusion coefficient of IgM was greatly reduced compared to molecules

outside the Gal-9 mask (0.012 μm2/s-1compared to 0.028 μm2/s-1) (Figure 8D). The decrease in

the median diffusion coefficient is consistent with an increase in frequency of slow moving IgM

inside the Gal-9 mask compared to outside the mask (Figure 8E). About 65% of tracked IgM

molecules are largely immobilized inside the mask compared to 45% of tracked IgM molecules

outside of the mask. This finding provides direct evidence for the ability of Gal-9 to restrict the

mobility of IgM molecules on the B cell membrane.

43

Figure 8. Gal-9 immobilizes IgM-BCR.

Single-particle tracking of IgM in WT (black circle) or Gal-9-KO (blue triangle) primary B cells.

A) Diffusion coefficients with the median indicated in red. B) Frequency distribution histogram

for the indicated diffusion bins of single molecules of IgM. 500 representative diffusion

coefficients from a total of at least 1500 tracks from three independent experiments. **P < 0.01,

Mann-Whitney test. C) Representative image of fluorescently labelled rGal-9 on primary B cell

(left) and mask created to differentiate tracks inside Gal-9 regions (Gal-9high, black circle) and

tracks outside Gal-9 regions (Gal-9low, blue triangle). Scale bar represents 2 µm. D) Diffusion

coefficients with the median indicated in red and. E) Frequency distribution histogram of single

molecules of IgM in the indicated diffusion bins. 250 representative diffusion coefficients from a

total of at least 900 tracks from three independent experiments. Data ****p < 0.0001, Mann-

Whitney test.

44

3.3 IgM and CD45 Density is Increased in the Galectin-9 Lattice

Our data indicates that rGal-9 induces an increase in the size of IgM-BCR clusters and

immobilization of IgM, features which are consistent with activation of B cells (Tolar et al.,

2009). However, we have found that treatment with rGal-9 significantly diminishes BCR

induced downstream signaling (Alluqmani & Treanor, unpublished). Our data also identified that

Gal-9 binds to CD45, the most abundant phosphatase expressed on the B cell surface (Cao &

Treanor, unpublished). Hence, we predicted that Gal-9 not only alters the mobility and

organization of IgM molecules, but also modifies the interaction between IgM and CD45.

To study the effect of Gal-9 on the organization of CD45 relative to IgM-BCR, we

treated WT primary B cells with 1 μM rGal-9 and allowed the cells to spread on non-stimulatory

anti-MHC II coated coverslips. The cells were then fixed and stained for surface proteins using

anti-CD45, anti-IgM and anti-Gal-9 antibodies and z-section images were acquired by spinning

disk confocal microscopy. Interestingly, rGal-9 forms a cap at one side of the cells, consistent

with the ability of the galectin family of proteins to form a lattice-like structure through galectin-

glycoprotein interactions (Figure 9A). We also observed that within the Gal-9-cap, CD45 and

IgM molecules form clusters with higher fluorescence intensity (Figure 9B). To quantify this

observation, we developed a protocol to create masks defining Gal-9-high regions (Gal9high) and

Gal-9-low regions (Gal9low) (Figure 9C). Consistent with our observation, the mean fluorescence

intensity of both CD45 and IgM are significantly higher in Gal9high regions compared to Gal9low

regions (Figure 9D and 9E).

45

Figure 9. The Gal-9 lattice increases the molecular density of IgM and CD45.

A) Primary wild-type (WT) B cells were treated with 1 µM rGal-9 and settled on non-

stimulatory coverslips then fixed and stained for CD45 (green), IgM (red) and Gal-9 (magenta)..

B) Intensity profile of fluorescent signals of CD45, IgM and Gal-9 along the cell membrane

(indicated by the white segmented line) of a representative cell (shown in A). C) Representative

example of masking output of algorithm to detect Gal-9 high (Gal-9high; blue) and Gal-9 low

(Gal-9low; orange) regions. D) CD45 mean intensity and E) IgM mean intensity in Gal-9low (blue

circle) and Gal-9high (orange circle) regions. Data are representative of at least three independent

experiments. Each point represents one region (cell). Sample size ≥ 68 regions. Scale bar

represents 2 µm. The median is indicated in red. Mann-Whitney test, ***p < 0.001.

To examine if the reorganization of IgM and CD45 induced by Gal-9 was due to a global

change in membrane structure, which may affect all proteins on the surface, we studied the effect

of Gal-9 on the organization of IgD-BCR. IgD-BCR is an isotype of BCR, and is co-expressed

with IgM on the surface of mature naïve B cells. The expression of IgD is 10-fold higher than the

expression of IgM (Mattila et al., 2013), making IgD a perfect candidate to study the specificity

46

of Gal-9 in organizing surface proteins. Interestingly, we did not observe any increase in the

intensity of IgD within the Gal-9 lattice (Figure 10A, 10B). Indeed, quantification of the mean

intensity of IgD in Gal-9high regions was similar to that in Gal-9low regions (Figure 10C). This

finding confirms that the interaction between Gal-9 and CD45 and between Gal-9 and IgM is

specific, and that the increase molecule density in Gal-9high regions is a specific phenomenon that

does not affect all proteins on the B cell membrane.

Figure 10. The Gal-9 lattice does not affect the molecular density of IgD.

A) Primary wild-type (WT) B cells were treated with 1 µM rGal-9 and settled on non-

stimulatory coverslips then fixed and stained for IgD (green), IgM (red) and Gal-9 (magenta).

Scale bar represents 2 µm. B) Intensity profile of fluorescent signals along cell membrane of a

representative cell (shown in A). C) IgD mean intensity and D) IgM mean intensity in Gal-9low

(blue circle) and Gal-9high (orange circle) regions. Data are representative of at least three

independent experiments. Each point represents one region (cell) with at least 58 regions. The

median is indicated in red. Mann-Whitney test, **p < 0.01, ns not significant.

47

3.4 CD22 Density is Increased in the Galectin-9 Lattice

One of the best characterized inhibitory coreceptors expressed on B cells is CD22

(Nitschke et al., 1997). Interestingly, the extracellular domain of CD22 is also glycosylated and

contains a sialic-acid binding domain, which binds to α2,6-linked sialic acids and mediates

homotypic interactions between CD22 molecules and heterotypic interactions with other

glycosylated proteins on B cells including CD45 and IgM (Leprince et al., 1993; Cyster and

Goodnow, 1997; Zhang et al, 2004). Furthermore, the interaction between CD22 and CD45

regulates the interaction between CD22 and IgM in the resting state (Bakker et al., 2012 and

Gasparrini, 2016). Given that Gal-9 induces the reorganization of IgM and CD45 on the B cell

surface, we investigated if rGal-9 also changes CD22 organization on the plasma membrane and

its density inside Gal-9high regions. We observed clusters of CD22 with higher fluorescence

intensity inside Gal-9high regions compared to Gal-9low regions, which colocalized with IgM

clusters (Figure 11A and 11B). Using the same quantification method used for CD45 analysis,

we found that inside Gal-9high regions, the mean intensity of CD22 is also increased (Figure

11C). These findings suggest that rGal-9 increases CD22 density in Gal-9high regions, coincident

with the enrichment of IgM. This may enhance the interaction between IgM and CD22 and

provide a mechanistic basis for the inhibitory effect of Gal-9 on B cell activation.

48

Figure 11. The Gal-9 lattice increases the molecular density of CD22

(A) Primary wild-type (WT) B cells were treated with 1 µM rGal-9 and settled on non-

stimulatory coverslips then fixed and stained for CD22 (green), IgM (red) and Gal-9 (magenta).

Scale bar represents 2 µm. (B) Intensity profile of fluorescent signals along cell membrane of a

representative cell (shown in A). (C) CD22 mean intensity and (D) IgM mean intensity in Gal-

9low (blue circle) and Gal-9high (orange circle) regions. Data are representative of at least three

independent experiments. Each point represents one region (cell). Sample size ≥ 35 regions. The

median is indicated in red. Mann-Whitney test, *p < 0.05.

Zhang et al. (2004) reported that CD22 association with CD45 and IgM are sialic acid

independent. Thus, we hypothesized that Gal-9 may act as a mediator for the association between

CD22 with IgM and CD45. To examine if Gal-9 increases the interaction between CD22 and

IgM, as well as IgM and CD45, we immunoprecipitated IgM or CD22 and immunoblotted for

CD22, IgM and CD45. To preserve the interaction between these molecules, we treated cells

with 1 mM DTSSP, an ionic crosslinker, which crosslinks proteins within a distance of 8-atoms

(Bennett et al., 2000). We isolated primary B cells from WT and Gal-9-KO mice and also treated

WT B cells with 1 μM rGal-9. Cells were lysed and incubated with antibody-conjugated agarose

49

beads specific for CD22 or IgM. After incubation and washing steps, proteins were eluted from

agarose beads using Laemmli buffer. The elution was separated by SDS-PAGE and subjected to

immunoblot to confirm enrichment of immunoprecipitated proteins and to detect binding

partners. We calculated the ratio between CD22 detected and IgM immunoprecipitated and the

ratio between IgM detected and CD22 immunoprecipitated. The ratio was then normalized to the

lowest ratio, set to 1. We did not observe any difference in the normalized ratios between WT,

Gal-9-KO and WT B cells treated with rGal-9 (Figure 12). In addition, we did not detect CD45

co-immunoprecipitation with IgM or CD22, indicating a limitation in our protocol to detect

previously reported interactions between CD45 and both IgM and CD22. Despite this limitation,

our data suggests that Gal-9 does not increase the interaction between IgM and CD22. However,

it is still unknown if the inhibitory effect of CD22 on BCR signaling requires direct interaction

between IgM and CD22. This inhibitory effect may be mediated by CD22 recruitment of SHP-1.

Phosphorylated ITIMs on the intracellular domain of CD22 recruit and phosphorylate SHP-1,

which dephosphorylates activating coreceptors, such as CD19 (Pani et al., 1997). Thus, it is

possible that in Gal-9high regions, the density of IgM and CD22 both increase, which may

increase the number of "effective interactions" between SHP-1 and its substrates. Further

experiments are required to test this hypothesis.

50

Figure 12. Gal-9 does not increase the interaction between IgM and CD22

Wild-type (WT), Gal-9-KO and WT B cells treated 1 µM rGal-9 were crosslinked by DTSSP

and lysed for immunoprecipitation. A) Immunoprecipitation using anti-IgM, followed by

immunoblotting for IgM (top) CD22 (middle) and CD45 (bottom). B) Quantification of the ratio

between CD22 detected and IgM immunoprecipitated (mean ±S.E.M indicated by bar). The

ratios were normalized to the lowest ratio, which was set at 1. Each point represents one

experiment. Mann-Whitney test, not significant. C) Immunoprecipitation using anti-CD22,

followed by immunoblotting for CD22 (top) IgM (middle) and CD45 (bottom). D)

Quantification of the ratio between IgM detected and CD22 immunoprecipitated (mean ±S.E.M

indicated by the bar). The ratios were normalized based to the lowest ratio, which was set at 1.

Each point represents one experiment. Mann-Whitney test, not significant.

51

3.5 CD19 Phosphorylation is Enhanced in Galectin 9-KO Upon

Activation

Based on our finding that rGal-9 increases the molecular density of CD45, CD22 and

IgM-BCR inside the Gal-9 lattice, we predicted that Gal-9-KO B cells would have an abnormal

level of phosphorylation of signaling molecules in the early stage of B cell activation. Gal-9-KO

B cells were activated by soluble anti-IgM F(ab’)2 fragment and stopped at indicated time points

within the first 10 min of activation. Cells were lysed and separated by SDS-PAGE followed by

immunoblotting using antibodies specific for phosphorylated CD22, SHP-1 and CD19. We did

not observe any difference in the phosphorylation of CD22 (pY882) (Figure 13A, 13B) or SHP-1

(pY564) between WT and Gal-9-KO B cells upon IgM-BCR stimulation (Figure 13C, 13D).

Interestingly, however, we observed that Gal-9-KO B cells have significantly higher levels of

phosphorylated CD19 at 1 and 3 min post-stimulation compared to WT B cells (Figure 13E,

13F). Given the important role of CD19 in amplifying BCR signaling, the small, but significant

increase in the phosphorylation of CD19 may lead to more pronounced differences downstream.

This is consistent with previous findings form our lab, in which Gal-9-KO primary B cells have

significantly higher ERK phosphorylation, compared to WT B cells (Alluqmani & Treanor,

unpublished).

52

Figure 13. Phosphorylation of CD19 is increased in Gal-9-KO B cells.

Primary naïve B cells from WT and Gal-9-KO mice were activated with 5 μg/mL anti-IgM

F(ab’)2 fragments for the indicated time. Cells were lysed and separated by SDS-PAGE followed

by immunoblotting. (A, C, E) Immunoblot with A) anti-phospho CD22 (Y822), C) anti-phospho

SHP-1 (Y564), and E) anti-phospho CD19 (Y531) and anti-β-tubulin as loading control. Data

representative of four independent experiments. (B, D, F). Quantification of B) pCD22 (Y822),

D) pSHP-1(Y564), and F) pCD19(Y531) fold change across the time points, with the mean ±

S.E.M indicated by the bar. Mann-Whitney test, * p<0.05.

53

DISCUSSION

Recent findings from our lab identified Gal-9 as a negative regulator of B cell activation

(Alluqmani & Treanor, unpublished data). Specifically, BCR-antigen microclustering and

downstream signaling are increased in Gal-9-KO B cells upon stimulation with anti-IgM. In

addition, treating B cells with rGal-9 nearly abolished BCR signaling. Here, we investigated the

underlying molecular mechanism for the inhibitory effect of Gal-9 on B cell activation. We find

that Gal-9 plays a role in regulating the spatial organization and dynamics of IgM-BCR and its

localization with regulatory surface proteins. We propose that Gal-9 organizes IgM-BCR into

larger clusters to restrict the mobility of IgM-BCR and relocalizes inhibitory molecules including

CD45 and CD22 to directly inhibit BCR signaling (Figure 14).

54

Figure 14. Proposed model for Gal-9-mediated inhibition of BCR signaling Gal-9 lattice brings pre-existing IgM-BCR nanoclusters together to form larger clusters which

restrict the mobility of IgM-BCR. Gal-9 also relocalizes inhibitory molecules including CD45

and CD22 inside Gal-9 lattice together with IgM-BCR. These inhibitory molecules directly

inhibit BCR signaling.

55

The spatial organization of cell surface proteins in pre-existing nanoscale clusters is

emerging as a general principal of cell surface proteins (Garcia-Parajo et al., 2014). The

organization of proteins into nanoclusters was reported to play important roles in regulating

surface protein signaling and protein-protein interactions (Mattila et al., 2013; Gasparrini et al.,

2015; Maity et al., 2015; Pageon et al., 2016). However, the molecular mechanism regulating the

size, composition, and stability of these constitutive assemblies is an open question. In the case

of IgM-BCR, the formation and stability of these nanoscale clusters does not appear to be

dependent on the actin cortex, as treating B cells with the actin depolymerizing agent LatA does

not alter the size or density of IgM-BCR nanoclusters (Mattila et al, 2013). Thus, we

hypothesized that galectins, which specifically bind to β-galactoside side chains on glycoproteins

to form a network, the so-called galectin-glycoprotein lattice, may play a role in the nanoscale

organization of cell surface proteins. Indeed, galectins were reported to organize nanoclusters of

H-Ras and K-Ras on the inner leaflet of the cell membrane (Prior et al., 2003; Belanis et al.,

2008; Shalom-Feuerstein et al., 2009). As we previously identified IgM-BCR as a ligand of Gal-

9 (Cao & Treanor, unpublished), we investigated the effect of Gal-9 on the organization of IgM-

BCR on the surface of primary B cells using the super-resolution technique, dSTORM. This is

the first study to investigate the role of an extracellular component in the organization of IgM

molecules at the nanoscale level. We found no difference in the size or density of IgM-BCR

nanoclusters on the surface of Gal-9-KO B cells compared to WT B cells, suggesting that Gal-9

does not mediate the formation of IgM-BCR nanoclusters. This finding is consistent with our

observation of the sparse distribution of Gal-9 on the surface of primary naïve B cells

(Alluqmani, Cao, & Treanor, unpublished), compared to the relatively dense distribution of IgM-

BCR. Alternatively, it may be that the formation of BCR nanoclusters is largely dependent on

56

the direct interaction between BCR components. According to Reth and colleagues, class-

specific residues within mIg as well as the disulfide bond between Igα and Igβ linker regions are

important for the formation of ‘oligomers’ of BCR (Schamel and Reth, 2000; Yang and Reth

2010). In the case of IgD-BCR, mutation of the class specific hydrophilic and aromatic amino

acids in the transmembrane domain as well as the cysteine residue in the linker region of Igα

resulted in largely monomers of IgD as detected by blue native gel (Schamel and Reth, 2000)

and BiFC assays (Yang and Reth 2010). While these studies provide evidence for a role for the

class-specific amino acids in the transmembrane domain of mIg in the higher order structure of

IgD-BCR, they have not been investigated in the context of IgM-BCR, which may explain the

class-specific differences in the constitutive assemblies of IgM and IgD, with IgD having a much

higher clustering tendency and forming more compacted nanoclusters than IgM nanoclusters

(Mattila et al., 2013).

Although we detected no difference in the organization of IgM-BCR in Gal-9 deficient B

cells compared to WT cells, we found that treatment with rGal-9 significantly altered IgM-BCR

nanoclusters. Inside the Gal-9 lattice, the number of IgM-BCR nanoclusters decreased while the

radius of the clusters, and the number of molecules in each cluster increased. This observation

indicates that the Gal-9 lattice can indeed impact on the organization of IgM-BCR on the surface

of primary B cells. Thus, we propose that Gal-9 provides a second layer of organization to IgM-

BCR by merging pre-existing nanoclusters. This observation, together with our previous finding

that rGal-9 abolishes BCR signaling, are somewhat counter-intuitive given that BCR stimulation

induces the coalescence of BCR nanoclusters to form larger-scale signaling microclusters

(Mattila et al., 2013), and the cross-linking model of B cell activation proposes that clustering of

BCR induced by multivalent or membrane-bound antigens is required to initiate BCR signaling

57

(Bolen, 1995). So, how does the larger cluster of IgM induced by Gal-9 not activate B cells but

instead inhibit BCR signaling? We propose that Gal-9 mediated merging of pre-existing IgM

nanoclusters may regulate the lateral diffusion of IgM-BCR and the interaction between IgM

nanoclusters with other signaling molecules, which together suppress BCR signaling.

Using SPT, we found that Gal-9 immobilizes IgM-BCR. IgM-BCR inside the Gal-9

lattice has significantly lower mobility compared to IgM-BCR outside the Gal-9 lattice. The

decrease in IgM lateral diffusion induced by Gal-9 may be explained by the effect of Gal-9 in

gathering IgM nanoclusters to form bigger IgM clusters. Due to technical limitations of super-

resolution imaging and single particle tracking, it is not impossible to differentiate the mobility

of molecules inside and outside of nanoclusters. Hence, the reported diffusion coefficient is

composed of (at least) two components including the mobility of the whole cluster and the

mobility of individual BCR (or dimers, trimer, etc., which are not detectable by super-resolution

imaging). The mobility of IgM-BCR on the cell membrane may be described as Brownian

motion restricted by the underlying actin cytoskeleton (Freeman et al., 2015; Treanor et al.,

2010). According to the theory of Brownian motion, the larger the particle the slower the

movement of the particle (Bian et al., 2016). Hence, it would be predicted that the larger clusters

of IgM-BCR inside the Gal-9 lattice have a lower diffusion coefficient compared to the smaller

clusters outside the Gal-9 lattice, consistent with our data. Furthermore, the lateral diffusion of

molecules residing in clusters may be restricted by neighbouring IgM molecules, which act as a

fence to confine the diffusion of IgM-BCR inside clusters.

These interpretations are supported by the finding that there is a correlation between

cluster size of CD22 and CD22 lateral diffusion (Gasparrini et al., 2016). In CD45 deficient

58

primary B cells or B cells expressing a CD22 mutant lacking the sialic-acid binding domain, the

homotypic interaction between CD22 is enhanced, leading to the formation of larger CD22

clusters. Correlatively, CD22 molecules on these B cells also have a lower diffusion coefficient.

In addition, 70% of IgD-BCR localize inside nanoclusters, which are more densely packed with

30-120 molecules per cluster, compared to 40% of IgM-BCR (Mattila et al., 2013) localized

inside nanoclusters with around 20-50 molecules per cluster. This correlates with the

phenomenon that the diffusion coefficient of IgD-BCR is ten times lower compared to IgM-BCR

(Treanor et al., 2010). Thus, we propose that Gal-9 restricts IgM-BCR mobility by organizing

IgM-BCR into larger clusters. Importantly, the mobility of BCR on the surface is correlated with

BCR signaling; simply depolymerizing the actin cytoskeleton leads to increased BCR mobility

and spontaneous signaling (Treanor et al., 2011). Consistent with this, treating B cells with LPS

increases actin severing and BCR mobility, which lowers the threshold for B cell activation

(Freeman et al., 2015). The proposed mechanism for these observations is that the increased

diffusion of the BCR (or BCR nanoclusters) increases the probability that the BCR will

encounter the co-receptor CD19 (Treanor, 2012), and is supported by the finding that the

spontaneous BCR signaling induced by depolymerisation of actin requires CD19 (Mattila et al.,

2013). Thus, the decrease in IgM-BCR lateral diffusion induced by Gal-9 may decrease the

interaction between IgM-BCR nanoclusters and the activating coreceptor CD19 and

consequently inhibit BCR signaling. The lower number of interactions between IgM-BCR and

CD19 may suppress BCR signaling by increasing the threshold of B cell activation (Mattila et

al., 2013).

In addition, the decrease in IgM lateral diffusion may also reduce the recruitment of IgM

molecules to the immunological synapse, where antigens are presented. In T cells, it was

59

reported that the lower mobility of ligands presented by lipid bilayers prevents the formation and

maturation of the immunological synapse (Hsu et al., 2012). In the B cell context, during B cell

spreading on APCs, IgM molecules are recruited to the immunological synapse and involved in a

highly dynamic process including microcluster formation, centralization, and internalization. The

decrease in IgM lateral diffusion induced by Gal-9 may interfere with the formation and

maturation of the B cell immunological synapse and thus inhibit B cell activation. Indeed,

treating primary B cells with rGal-9 resulted in decreased microcluster formation and a smaller

spreading area upon activation with lipid bilayers containing anti-BCR as surrogate antigen

(Alluqmani & Treanor, unpublished). Conversely, increased BCR diffusion would be associated

with more microclusters and larger spreading area, consistent with our observation of higher

diffusion coefficient of IgM-BCR in Gal-9-KO B cells compared to WT control, and our

previous findings of enhanced B cell activation (Alluqmani & Treanor, unpublished).

We also propose that Gal-9 induced merging of BCR nanoclusters may regulate the

interaction between IgM and signaling molecules and consequently suppress BCR signaling. It

may be that the merging of nanoclusters and increased density of molecules within the

nanoclusters prior to activation effectively reduces the accessibility of kinases to phosphorylate

the ITAMs on Igα/Igβ. This interpretation is consistent with the model proposed by Reth and

colleagues, in which BCR forms auto-inhibited oligomers in the resting state; the tightly packed

BCR molecules hinders the association of signaling molecules such as Syk with the ITAMs on

Igα/Igβ preventing phosphorylation of these motifs and BCR signaling (Yang and Reth, 2010).

Alternatively, our data supports a model in which treatment with rGal-9 induces the localization

of inhibitory coreceptors, including CD45 and CD22, closer to IgM clusters and we believe this

contributes to the inhibitory effect of Gal-9 on B cell activation.

60

Our finding of CD45 enrichment in the Gal-9 lattice is consistent with our identification

of CD45 as a ligand for Gal-9 using a pull-down assay and mass spectrometry (Cao & Treanor,

unpublished). The N-terminal of CD45 has multiple N-glycosylation sites, which provide

potential binding sites for Gal-9, and therefore make it a likely candidate for interaction with

galectins. Indeed, in T cells, CD45 was reported to bind to Gal-1 and Gal-3 (Pace et al., 1999;

Symons et al., 2000; Stillman et al., 2006; Chen et al., 2007). Moreover, CD45 on diffuse large B

cell lymphoma has been shown to bind to Gal-3. Taken together, these findings suggest that

CD45 may be a promiscuous ligand of the galectin family, although the differential glycosylation

of CD45 in T cells compared to B cells, and perhaps also in B cell lymphomas, may provide

specificity for differential galectin binding. Nonetheless, the enrichment of CD45 inside the Gal-

9 lattice is consistent with the inhibitory effect of Gal-9 on BCR signaling. CD45 is important in

regulating the phosphorylation of Lyn, the earliest kinase involved in BCR signaling (Katagiri et

al., 1999; Shrivastava et al. 2004). The co-enrichment of CD45 and IgM-BCR inside the Gal-9

lattice may lead to the dephosphorylation and inactivation of Lyn, and consequently suppressed

B cell activation. This prediction is supported by previous findings regarding the role of galectins

in regulating the organization and function of CD45 in T cells (Chen et al., 2007). Lactose

treatment, which dissociates Gal-3 from CD45, decreased the localization of CD45 within lipid

rafts and increased the phosphorylation of the positive regulatory tyrosine of Lck (a homolog of

Lyn) in the resting state. Consistent with this finding, Mgat5-deficient (Mgat5-/-) T cells, which

lack the ligands required for galectin binding, has lower CD45 partitioning inside lipid rafts and

higher phosphorylation of the positive regulatory tyrosine of Lck. Furthermore, CD45

localization within the early immunological synapse is also decreased in Mgat5-/- T cells and

this correlates with increased phosphorylation of Lck at the positive regulatory tyrosine upon

61

activation with anti-CD3 conjugated beads. These findings suggest that the galectin lattice is

critical in regulating CD45 localization with respect to lipid rafts to regulate Lck phosphorylation

and thus, TCR signaling under both resting and activating conditions. To determine if our

observation of suppressed BCR signaling upon Gal-9 treatment is due to altered Lyn activity,

future studies should examine Lyn phosphorylation especially at the positive regulatory tyrosine.

Although there is currently no commercially available antibody specific for the positive

regulatory tyrosine in Lyn, antibodies specific for the positive regulatory tyrosine of Src are

available and may cross-react with Lyn.

We also observed the enrichment of the inhibitory molecule, CD22, inside the Gal-9

lattice. Although we did not detect CD22 in our pull-down assay using recombinant Gal-9 (Cao

& Treanor, unpublished), CD22 has been reported to bind to both IgM and CD45 (Zhang et al,

2004; Leprince et al., 1993). Moreover, CD45 is critical in regulating the spatial organization and

activity of CD22 in B cells (Gasparrini et al., 2015; Coughlin et al., 2015). Hence, the

enrichment of CD22 inside the Gal-9 lattice may be due to secondary effects of CD45 and IgM

enrichment. This explanation is consistent with our co-immunoprecipitation results, in which

Gal-9 did not increase the interaction between IgM and CD22. Although the mechanism for the

increase in CD22 density in the Gal-9 lattice is unknown, the higher number of CD22 molecules

colocalizing with IgM molecules inside Gal-9high regions may explain the inhibitory effect of

Gal-9 on BCR signaling. CD22 is a known negative regulator of BCR signaling, which recruits

the phosphatase SHP-1 (Doody et a., 1995). SHP-1 dephosphorylates CD19 and terminates the

amplification of BCR signaling induced by CD19 (Pani et al., 1997). In addition, SHP-1

dephosphorylates PLC-γ2 and attenuates Ca2+ and MAPK activation (Reviewed by Muller and

62

Nitscheke, 2014). Thus, the localization of CD22 together with IgM-BCR upon rGal-9 treatment

may contribute to the inhibitory effect of Gal-9 on B cell activation.

Although we did not detect any significant difference between WT and Gal-9-KO B cells

in the phosphorylation of CD22 and SHP-1, we observed a significant increase in the

phosphorylation of CD19, consistent with higher BCR signaling in Gal-9-KO B cells. CD19

recruits Vav and PI3-K, which are critical in the spreading of B cells on the antigen-presenting

surface (Weber et al., 2008). This is consistent with our previous finding that spreading and

antigen accumulation are enhanced in Gal-9-KO B cells compared to WT control upon activation

with lipid bilayers presenting anti-BCR. Furthermore, treating WT B cells with rGal-9 decreased

the spreading area and number of microclusters (Alluqmani & Treanor, unpublished). Hence,

Gal-9 may regulate the phosphorylation of CD19 by retaining CD45 and CD22 inside IgM

microclusters upon activation, thus decreasing B cell spreading and increasing the threshold for

B cell activation.

Many questions remain to be addressed regarding the mechanism of the inhibitory effect

of Gal-9 on BCR signaling. One important question is whether the Gal-9 lattice localizes with

lipid rafts, where BCR signaling is initiated (Cheng et al., 1999). We found that Gal-9 is

localized in discrete puncta on the surface of primary murine B cells in the steady-state

(Alluqmani, Cao & Treanor, unpublished). Interestingly, when primary B cells are treated with

rGal-9, the size of Gal-9 puncta increases and indeed, often forms a cap on one side of the cell.

Such capping of a protein is reminiscent of the polarization of lipid rafts upon lymphocyte

activation (Viola and Lanzavecchia, 1996; Round et al., 2005). These observations suggest that

Gal-9 may be distributed in lipid rafts in the B cell membrane. Galectin-4 (Gal-4), a member of

63

tandem-repeat type galectins, was reported to be enriched in detergent-resistant lipid rafts in the

microvillar membrane of intestinal brush borders (Braccia et al., 2003). Treating these cells with

lactose released Gal-4 and proteins from the lipid raft, which indicated that the carbohydrate

domains of Gal-4 are critical in retaining proteins inside lipid rafts. These findings are consistent

with the patchy distribution of Gal-9 and the polarization of rGal-9 on the B cell surface,

suggesting that Gal-9 may play an important role in organizing BCR and other coreceptors in

lipid rafts. The spatiotemporal positioning of CD45 with respect to lipid rafts is tightly regulated

during the early stage of B cell activation (Shrivastava et al., 2004). It was proposed that the

partitioning of CD45 inside lipid rafts negatively regulates Lyn to inhibit BCR signaling

(Shrivastava et al., 2004). In T cells, the galectin lattice was proposed to retain CD45 inside

lipid rafts to inhibit TCR signaling in both the resting state and upon activation (Chen et al.,

2007). Given our finding that CD45 is enriched inside the Gal-9 lattice, the localization of Gal-9

with respect to lipid rafts will provide further insight into the molecular mechanism for the

inhibitory effect of Gal-9 on BCR signaling. This could be visualized by treating cells with

cholera toxin B (CTB), which binds to ganglioside GM1, a marker of lipid rafts, following by

fluorescently-labeled CTB-specific antibodies. The localization of Gal-9 in lipid rafts, if

confirmed, will provide direct evidence for Gal-9-mediated localization of CD45 inside lipid

rafts and further contribute to our understanding of the molecular mechanism of the inhibitory

effects of Gal-9 on BCR signaling.

The effect of Gal-9 on the reorganization of IgM-BCR and CD45 raises the question of

whether Gal-9 has an effect on the underlying actin cortex. Since the actin cortex restricts the

lateral diffusion of IgM-BCR (Treanor et al., 2010 and 2011) the merging of IgM-BCR

nanoclusters observed by dSTORM may require the reorganization of actin cortex to allow the

64

movement of BCR nanoclusters. There is evidence to support the idea of coordination between

the galectin lattice and actin cortex in the organization of surface proteins. In T cells, galectins

were proposed to enhance the localization of CD45 inside lipid rafts while the actin cortex has

the opposite effect (Chen et al., 2007). Although the mechanism of actin-mediated exclusion of

CD45 from lipid rafts is still unknown, it was proposed to be dependent on the interaction

between the intracellular domain of CD45 and the ankyrin/spectrin/actin scaffold (Pradhan et al.,

2002). The coordination between the extracellular galectin lattice and intracellular actin cortex

provides counterbalancing mechanisms to regulate lateral movement and membrane partitioning

of CD45 on the cell membrane (Chen et al., 2007). Hence, it will be interesting to investigate the

coordination between Gal-9 and the actin cortex in regulating the organization of surface

proteins during both the resting state and immunological synapse formation.

An important question to address in the future is the in vivo function of Gal-9. Our

finding that Gal-9 regulates BCR signaling raises the question of whether Gal-9 has a role in

regulating pre-BCR signaling during B cell development and selection. Pre-BCR signaling

shares many common signaling molecules with BCR signaling cascades. Pre-BCR aggregation is

induced by an antigen-independent process controlled by charged and glycosylated residues on

pre-BCR molecules (Ohnishi and Melchers, 2003). In addition, galectin-1 has been implicated in

regulating pre-BCR signaling. Gal-1 was reported to bind to the surrogate light chain of pre-BCR

and mediate the formation of a synapse at the contact zone between pre-B cells and stromal cells

(Gauthier et al., 2002). While a role for Gal-9 in B cell development has not been reported,

according to the Immgen database for gene expression in mice (Heng et al., 2008), Lgals9, the

gene encoding Gal-9, is highly expressed in pro/pre-B cell populations in the bone marrow, at a

level four times higher compared to splenic follicular B cells. In addition, Lgals9 expression

65

increases approximately 2.5 times in the transition from pre-pro-B cells (Fraction A) to pre-B

cells (Fraction D), during which IgH locus rearrangement occurs. These findings suggest that

Gal-9 has important biological functions in B cell populations in the bone marrow. Given that

Gal-9 binds to the constant region of IgM heavy chain, and assuming that the glycosylation

pattern of IgM heavy chain is similar between pre-BCR and BCR, Gal-9 could bind to pre-BCR,

which is composed of IgM heavy chain and surrogate light chain. To study the role of Gal-9 in B

cell development, the percentage of different B cell compartments during development should be

compared between WT and Gal-9-KO mice. In addition, to investigate if Gal-9 is involved in B

cell selection, the BCR repertoire in the immature B cell population in the bone marrow could be

examined.

Finally, the interaction between IgM-BCR and Gal-9 needs to be further characterized.

First, how does Gal-9 bind to IgM-BCR? In our pull-down assay using rGal-9, we used lactose to

elute IgM-BCR from Gal-9, indicating that the Gal-9 and IgM-BCR interaction is carbohydrate

dependent. However, it is still unknown which N-glycosylation site is required for the interaction

between Gal-9 and IgM-BCR. To answer this question, site-directed mutagenesis of the N-

glycosylation sites on IgM heavy chain can be performed to find which mutation abrogates the

interaction between Gal-9 and IgM. Second, where does Gal-9 on the B cell surface come from?

Currently, it is not known if B cells secrete Gal-9, which binds to the cell surface or if other cells

secrete it and it binds to the B cell surface. Indeed, Gal-9 is expressed in multiple cell types

including B cells, T cells, NK cells and monocytes (Heng et al., 2008). Given the complexity and

abundance of glycoproteins on the cell membrane, which may restrict the diffusion of Gal-9, we

predict that Gal-9 on the surface of B cells is secreted by B cells themselves. Supporting this

prediction, Gal-9 is expressed on the cell surface of BALL-1, a human B cell line derived from B

66

cell leukemia (Hirashima et al, 2004). To test this prediction in primary B cells, B cells from

Gal-9-KO mice can be injected into B cell deficient mice, such as µMT and surface Gal-9

expression monitored by flow cytometry post-transplant.

In summary, our study sheds light on the molecular mechanism for the inhibitory effect

of Gal-9 on BCR signaling. Gal-9 binds to IgM-BCR, which organizes IgM-BCR into bigger

clusters and restricts the lateral diffusion of IgM-BCR molecules. In addition, Gal-9 also

increases the colocalization between IgM and inhibitory coreceptors including CD45 and CD22,

which modulates the phosphorylation of CD19. These findings elucidate a novel extracellular

mechanism to finely regulate BCR signaling, which may be important in the context of B cell

development, activation, and B cell pathologies. Thus, understanding this mechanism may

provide a potential therapeutic target to treat B cell related diseases.

67

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