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The in vivo characterization of spleen dendritic cell populations
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David Michael Duriancik

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5/08 K:IProi/Aoc&Pres/ClRC/Dateoue.indd

THE [N V1 V0 CHARACTERIZATION OF SPLEEN DENDRITIC CELL
POPULATIONS OF VITAMIN A-DEFICIENT C57BL/6J MICE

By

David Michael Duriancik

A DISSERTATION

Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of

DOCTOR OF PHILOSOPHY
Human Nutrition

2010

ABSTRACT

The in vivo characterization of spleen dendritic cell populations of vitamin A-deficient
C57BL/6J mice

By
David Michael Duriancik

Vitamin A deficiency affects 125 million preschool children each year. Vitamin
A-deficient populations have decreased T—dependent antibody responses and unaffected
or increased cell-mediated immune responses. The impaired T-dependent antibody
response is due to decreased stimulation by T helper (Th) 2 cells as well as cross-
regulation by increased Th1 cells. In the mouse, CD1 lb+ myeloid dendritic cells (DCs)
stimulate Th2 responses while CD80? lymphoid DCs stimulate Th1 responses. In
addition, vitamin A is important for myelopoiesis and differentiation of myeloid
progenitors into dendritic cells (DCs) and neutrophils. Therefore, we hypothesized that
vitamin A-deficient mice would have decreased numbers of myeloid DCs and increased
numbers of neutrophils compared to vitamin A-sufficient mice.

We developed a multicolor flow cytometry protocol for identifying DC
populations from individual C57BL/6J mouse spleen. The protocol is novel, lacking any
enrichment procedures or DC proliferating conditions. Previously, researchers have used
various enrichment procedures, pooling of tissues from multiple animals, or stimulating
DC proliferation to obtain sufficient numbers of cells to analyze. These procedures
potentially skew analysis of DCs to select DC populations or increase variability.
Particularly when studying vitamin A deficiency, it is important to analyze animals
individually. Each animal has variable vitamin A status, other health parameters, and

unique developmental age.

Using the protocol we developed, we show that vitamin A-deficient animals have
increased lymphoid DCs (R2 0.41, liver RAE B -0.63), lymphoid to myeloid DC ratio (R2
0.52, liver RAE [3 -0.68), neutrophils (R2 0.20, liver RAE B -0.44), memory CD8+ T
lymphocytes (R2 0.38, liver RAE is -0.62), and CD4+ T lymphocytes (R2 0.36, liver RAE
B 0.62) in their spleen. The increased lymphoid DC percentage was unexpected, but may
be inducing memory CD8+ T cells to proliferate and produce interferon- (IFN) 7. The
cytokine environment is therefore Th1 and memory CD8+ T cell biased and the increased
Th1 environment would be expected to down-regulate the Th2 antibody-mediated
immune response. Therefore, the slight decrease in myeloid DCs (R2 0.22, liver RAE B
0.32) combined with the increase in lymphoid DCs and memory CD8+ T cells provides
mechanistic evidence for the depressed antibody responses observed in vitamin A-
deficient populations.

The data we present here confirms reports of others, provides novel evidence of
the role of vitamin A on DC homeostasis, and provides a comprehensive overview of the
immune system in vitamin A-deficient mice. We confirm that vitamin A does not affect
splenic B lymphocyte (R2 0.08, liver RAE [3 -0.00) and CD8+ T lymphocyte numbers (R2
0.10, liver RAE B -0.07), and show that vitamin A deficiency also does not alter
precursor DC (R2 0.09, liver RAE [3 0. l 0) or plasmacytoid DC numbers (R2 0.02, liver
RAE B 0.08). We show that the immune response bias previously reported in vitamin A
deficiency correlates with an increased ratio of lymphoid to myeloid DCs in vitamin A-
deficient animals. We confirm neutrophils are increased in vitamin A-deficient

populations, but not to the dramatic extent previously reported.

ACKNOWLEDGMENTS

I would like to show my appreciation for the support and advice of my committee
members and the Department of Food Science and Human Nutrition. I am grateful for
the opportunity to have worked closely with my committee members and other faculty
members. This project could not have been completed without each of their individual
expertise. Dr. Zile provided expertise in vitamin A, Dr. Davis provided statistical
expertise, Dr. Gerlach provide expertise in immunology, and Dr. Bennink provided
expertise in diet composition and animal welfare. I would also like to acknowledge Dr.
Louis King, director of the Michigan State University flow cytometry core facility, for
his help in developing and analyzing the flow cytometry data.

I appreciate the assistance of other students in Dr. Hoag’s laboratory. Denise
Lackey has been a tremendous help through assisting with experiments, interpreting data,
and answering questions. Carmen Yu and Shanna Ashley assisted with experiments. All
of Dr. Hoag’s former students created a lively learning environment and my time spent in
the laboratory was an enjoyable experience.

I would also like to thank my family, friends, and colleagues for their support
throughout my graduate experience.

Most of all I would like to thank my advisor, Dr. Hoag. In every step of the
graduate education process, she has been a valuable mentor and friend. I learned so
much from working with Dr. Hoag. She took a chance by accepting me into her
laboratory and I hope to make her proud throughout my career.

Dr. Hoag, I sincerely thank you for all that you have done for me.

TABLE OF CONTENTS

List of tables ...................................................................................................................... vii
List of figures ................................................................................................................... viii
Introduction .......................................................................................................................... 1
Chapter 1: Literature review ................................................................................................ 3
Immune system overview .............................................................................................. 3
Dendritic cells ................................................................................................................ 5
Vitamin A metabolism ................................................................................................... 7
Vitamin A function ...................................................................................................... 10
Vitamin A deficiency ................................................................................................... 13
Vitamin A and innate immunity .................................................................................. 14
Vitamin A and adaptive immunity ............................................................................... 17
Vitamin A and DCs ...................................................................................................... 19
Experiment rationale .................................................................................................... 21

Chapter 2: The identification and enumeration of dendritic cell populations from

individual mouse spleen and Peyer’s patches using flow cytometric analysis .................. 24
Abstract ........................................................................................................................ 25
Introduction .................................................................................................................. 26
Methods ........................................................................................................................ 27

Animals .................................................................................................................. 27
Tissue isolation ...................................................................................................... 28
Antibody staining ................................................................................................... 29
F low cytometry acquisition & analysis .................................................................. 29
Flow cytometric analysis gating strategies ............................................................ 30
Population calculations and statistical analysis ..................................................... 32
Results .......................................................................................................................... 32
Animal characteristics ............................................................................................ 32
Average immune cell numbers obtained from healthy C57BL/6J mouse spleen

and Peyer’s patches ............................................................................................................ 33
Discussion .................................................................................................................... 35
Acknowledgements ...................................................................................................... 40

Chapter 3: Vitamin A deficiency alters splenic dendritic cell subsets and increases

CD8+Gr-1+ memory T lymphocytes in C57BL/6J mice. ................................................. 57
Abstract ........................................................................................................................ 58
Introduction .................................................................................................................. 59
Methods ........................................................................................................................ 61

Animals .................................................................................................................. 61
Vitamin A analysis ................................................................................................. 61
Tissue processing ................................................................................................... 62

Flow cytometry ...................................................................................................... 62

Statistical analysis .................................................................................................. 63
Results .......................................................................................................................... 63
Discussion .................................................................................................................... 66

Chapter 4: Conclusions & future directions ..................................................................... 101
Flow cytometry of DCs .............................................................................................. 101
Murine model of vitamin A deficiency ...................................................................... 101
Future directions ........................................................................................................ 103

Appendix I ....................................................................................................................... 108

Appendix II ...................................................................................................................... 1 18

Literature cited ................................................................................................................. 122

vi

LIST OF TABLES
Chapter 2

Table 2.1. Antibody-fluorochrome conjugate reagents employed in labeling cell
suspensions for flow cytometric analysis. ....................................................... 49

Table 2.2. Antigen expression of immune cell populations in mouse spleen and Peyer's
patches used in gating strategy. ....................................................................... 50

Table 2.3. Physiological characteristics of C57BL/6J mice used in the studies. .............. 52

Table 2.4. Percentage and total numbers of dendritic cell populations of C57BL/6J
spleen and Peyer's patches. .............................................................................. 53

Table 2.5. Percentage and total cell numbers of non-dendritic immune cell populations

enumerated in C57BL/6J spleen and Peyer's patches. ..................................... 55
Chapter 3'
Table 3.1. Liver weight of VAS and VAD male and female animals. ............................. 69
Table 3.2. Spleen weight of VAS and VAD male and female animals. ........................... 70
Appendix 1

Supplemental Table 1.1. BD® Biosciences LSR II flow cytometer laser parameters used
in flow cytometry data collection. ................................................................. l 15

Supplemental Table 1.2. BD® Biosciences LSR 11 general compensation matrix. ........ l 16

vii

LIST OF FIGURES
Some figures in this dissertation are presented in color.
Chapter 2

Figure 2.1. Gating strategy to identify dendritic cells and other immune cell populations
in mouse spleen. ............................................................................................. 41

Figure 2.2. Gating strategy to identify dendritic cells and other immune cell populations

in mouse Peyer's patches. ............................................................................... 45

Chapter 3
Figure 3.1. Body weights ofC57BL/6J mice consuming VAS and VAD diet. ............... 71
Figure 3.2. Vitamin A status of C57BL/6J mice. ............................................................. 73
Figure 3.3. Total spleen cells per animal of C57BL/6J mice ............................................ 77
Figure 3.4. Effects of depleted liver RAE on spleen DC populations. ............................. 79
Figure 3.5. Effects of depleted liver RAE on spleen lymphocyte populations. ................ 89
Figure 3.6. Effects of depleted liver RAE on spleen PMN ............................................... 99
Appendix I
Supplemental Figure 1.1. LSR II optics block and filter scheme of the fluorescent

channels utilized for data collection. ............................................................... 109
Supplemental Figure 1.2. Comparison of compensated and uncompensated data. ......... 111

Supplemental Figure 1.3. Flow diagram of Boolean logic used in sequential gating of
spleen cell populations. ................................................................................. l 13

Appendix 11

Supplemental Figure 2.1. Immune cell percentages of vitamin A-deficient mice ........... 1 18

viii

KEY TO SYMBOLS & ABBREVIATIONS
Allophycocyanin (APC)
American Institute of Nutrition (AIN)
Analysis of variance (ANOVA)
Antigen presenting cell (APC)
Cluster of differentiation (CD)
Cyan (Cy)
Cytotoxic T lymphocyte (CTL)
Dendritic cell (DC)
F luorescein isothiocyanate (F ITC )
Fluorescence activated cell sorting (FACS)
Forward scatter (FSC)
Granulocyte/macrophage-colony stimulating factor (GM—CSF)
Hank’s balanced salts solution (HBSS)
High performance liquid chromatography (HPLC)
Immunoglobulin (1g)
Interferon (IFN)
Interleukin (IL)
Major histocompatibility complex (MHC)
Matrix metalloproteinasc (MMP)
Michigan State University (MSU)
Monoclonal antibody (mAb)

Monocyte-derived dendritic cell (mDC)

Natural killer (N K)

Negative (NEG)

Not Applicable (N/A)

Peridinin chlorophyll protein (PerCP)
Peyer’s patch (PP)

Phycoerythrin (PE)
Polymorphonuclear neutrOphil (PMN)
Positive (POS)

Precursor DC (preDC)
Promyelocytic leukemia (PML)
Protein—energy malnutrition (PEM)
Retinoic acid receptor (RAR)
Retinoic acid response element (RARE)
Retinoid X receptor (RXR)

Retinol activity equivalents (RAE)
Retinol binding protein (RBP)

Side scatter (SSC)

T helper (Th)

Transthyretin (TTR)

T regulatory (Treg)

Tumor necrosis factor (TN F)
Vitamin A-deficient (VAD)

Vitamin A-sufficient (VAS)

INTRODUCTION

Each year approximately 125 million children suffer from vitamin A deficiency.
Vitamin A deficiency increases the risk of infection as well as the morbidity and
mortality of infection. The immune response of vitamin A-deficient populations is biased
to T helper 1 (Th1) cell-mediated responses with decreased Th2 antibody responses.
Previous research by others has focused on lymphocyte number and function of vitamin
A-deficient populations, but vitamin A has important roles in hematopoiesis, particularly
myelopoiesis. Acute promyelocytic leukemia results from a fusion of retinoic acid
receptor (RAR) or to the PML gene leading to an increase in immature neutrophil
numbers. In addition, previously our laboratory demonstrated that murine bone marrow
cells cultured in media containing granulocyte-macrophage colony stimulating factor
(GM-CSF) and retinoic acid develop into dendritic cells, while bone marrow cells
cultured with depleted retinoic acid or retinoic acid antagonists develop into neutrophils.
Furthermore, others have shown systemic myeloid cell, specifically neutrophil, expansion
in mice depleted of vitamin A.

Dendritic cells (DCs) are bone marrow derived antigen presenting cells
responsible for the stimulation of naive T cells in response to infection. In the mouse,
CD1 1b+ myeloid DCs stimulate Th2 immune responses, while CD80t+ lymphoid DCs
stimulate Th1 immune responses. Plasmacytoid DCs secrete large amounts of type 1
intereferons (IFN), IFN-or/B. Myeloid, lymphoid and plasmacytoid DCs are mature DCs
that arise from one common precursor cell circulating in blood and resident in secondary

lymphoid tissues. DCs are sentinels of the body, resident in sparse numbers in every

tissue, scanning the body for danger signals. Immature DCs are phagocytic cells,
sampling antigens for danger signals. Upon recognition of a danger signal, DCs undergo
maturation through the upregulation of antigen-presenting, T cell costimulatory, and
homing receptors to stimulate naive T cells in secondary lymphoid tissues.

The impaired Th2 immune responses of vitamin A-deficient populations may be
the result of skewed DC subsets. In addition, the requirement of retinoic acid for DC
development from bone marrow progenitors and the expansion of neutrophils of vitamin
A-deficient mice provide convincing evidence of skewed DC subsets of vitamin A-
deficient mice. Therefore, it is hypothesized that myeloid DCs would be decreased and
neutrophils would be increased in the spleens of vitamin A-deficient mice. Multicolor
flow cytometry was used to identify and compare the DC, neutrophil, and lymphocyte

populations of mice with varying levels of liver stores of vitamin A.

CHAPTER 1: LITERATURE REVIEW

Immune System Overview

The immune system is comprised of innate and adaptive arms that protect the
body from infection. The innate arm consists of non-specific, ubiquitous, and immediate
protection from pathogens. The adaptive arm consists of specific, localized, and delayed
protection against “danger” signals. “Danger” signals are foreign substances such as
pathogenic microorganisms and viruses, but also consist of altered “self” signals such as
tumor cells. The innate and adaptive arms of the immune system communicate through
various signals to provide the most effective protection from infection (1).

The innate immune system consists of epithelial barriers, non-specific phagocytic
and lytic cells, and other soluble and circulating biological factors. The epithelial barriers
include both skin and mucosal tissue. Phagocytic and lytic cells include macrophages,
neutrophils, and natural killer (N K) cells. Other biological factors include protease
enzymes, salts, pH differences, and other proteins such as complement. These biological
factors inhibit microbe proliferation or directly induce microbial cell death. Biological
factors work in concert with barriers and cells to provide optimal protection, such as the
antimicrobial products present in mucus. Bridging the innate and adaptive arms are the
phagocytic cells. The phagocytic cells, particularly dendritic cells, take up microbes,
digest them, and then present antigens to T lymphocytes. Presentation of antigen leads to
the initiation of an adaptive immune response through the production and secretion of
cytokines creating an environment for the most effective response to a given pathogen.
Other antigen—presenting cells (APCs) in addition to dendritic cells include macrophages

and B lymphocytes (1).

B and T lymphocytes are cells of the adaptive immune response that recirculate
through secondary lymphoid tissues, but are capable of homing to sites of infection
following stimulation by an APC. The processes of antigen uptake and presentation as
well as cell proliferation and differentiation that occur in an initial adaptive immune
response lead to a delay in the B and T cell responses. However, previously encountered
pathogens have more rapid subsequent responses due to the development of memory B
and T cells. B lymphocytes produce and secrete antibody to various antigens present on a
microbial cell or soluble microbial products such as a toxins. There are numerous subsets
of T lymphocytes distinguished by their T cell co-receptor expression and/or function.
Cluster of differentiation (CD) 4+ T cells recognize peptide antigens presented in major
histocompatibility complex (MHC) - 11 proteins on APCs and develop T helper (Th)
responses. There are 2 classes of Th effector cell responses; Th1 which participate in
cell-mediated immunity, and Th2 which aid B lymphocyte antibody immunity. T cells
expressing the CD8 T cell co-receptor, also called cytotoxic T lymphocytes, recognize
antigen in the context of MHC-I proteins and receive help from a CD4+ Th1 cell to
accomplish a cell-mediated response. T cells expressing the CD4 T cell co-receptor are
also responsible for inducing tolerance to non-dangerous or self antigens, and this subset
ofT lymphocytes is called T regulatory (Treg) cells. Thus, the adaptive immune system
is a highly complex and coordinated multi—cellular response designed to defend against
specific danger signals while at the same time being tolerant to self antigens (1).

Cooperation between the innate and adaptive arms of the immune system is
critical for optimal immune responses to danger signals as well as maintaining tolerance

to self signals. APCs are the critical link between the innate and adaptive immune

responses through non-specific phagocytosis of antigens, antigen processing and
presentation, and cytokine secretion directing the subsequent adaptive immune response.
Antigen presentation and cytokine secretion communicate the most effective T
lymphocyte response to a specific pathogen that has evaded the innate immune system.
Due to the cooperation and coordination of signals, any perturbation in the system can

lead to malfunctions of the other components of the immune system.

Dendritic Cells

Dendritic cells (DCs) are hematopoietic-derived APCs that efficiently stimulate
naive T lymphocytes (2-4). DCs migrate from the bone marrow and reside in peripheral
tissues in an immature state (5). Under stead-state conditions, a reservoir of precursor
and mature DCs are maintained in secondary lymphoid tissues, but DCs are sparse in
other tissues where they function as sentinels (5, 6). DCs comprise about 1-3% of the
cells of the spleen (7). Upon phagocytosis of pathogen molecules in tissues, the DCs
mature and migrate to secondary lymphoid tissues, such as the spleen and lymph nodes,
to stimulate T cells and induce an adaptive immune response. Maturation of DCs
involves upregulation of homing receptors and other surface proteins necessary to
efficiently stimulate T cells, including CD40, CD80, and CD86 (8, 9). I

Various DC subpopulations exist, and each has a bias to stimulate a particular
adaptive immune response. In the mouse, myeloid DCs preferentially stimulate
antibody-mediated (Th2) responses, while lymphoid DCs stimulate cell-mediated (Th1)
responses (10, 1 1). Plasmacytoid DCs produce large amounts of type 1 interferons (IFN),

IF N-a and —B, to augment antiviral immune responses (12-15). Myeloid DC 3 express

CD1 1c, MHC-II, and CD1 1b, but do not express CD80t. Lymphoid DCs express CD1 1c,

MHC-II, and CD80L, but do not express CD1 1b (10, l l). Plasmacytoid DCs express
CD11c, MHC-II, Gr-l , and B220 (CD45RB) (12, 16). Although controversial at one
point, it is now known that these DC subpopulations transcribe and translate the various
surface protein markers and do not acquire the surface proteins through cell-cell contact
interactions with other cell types, such as CD8 T lymphocytes (17, 18). The respective
DCs present antigens to Th cells and secrete cytokines to stimulate the appropriate
adaptive immune response.

DCs arise from both common myeloid or common lymphoid progenitors in the
bone marrow (4, 19). Precursor DCs (preDCs) migrate from the bone marrow to the
bloodstream and reside in peripheral tissues in an immature state (20-22). Upon
recognition of antigens through pattern recognition receptors, such as toll-like receptors,
the preDCs mature and home to secondary lymphoid tissues to stimulate naive T cells
(23). Myeloid and lymphoid DCs have half-lives of 1.5 to about 3 days, while
plasmacytoid DCs have a half-life of about 9 days (19, 24-26). During an infection,
cytokines stimulate DC differentiation of bone marrow progenitors, DC turnover
(proliferation and apoptosis), and migration from sites of infection through the lymphatic
system to the draining lymph node or through blood vasculature to the spleen during a
systemic infection.

Humans have a slightly different repertoire of DCs compared to mice, but mouse
models can be extrapolated to the human immune system (8). Human DCs may arise
from common myeloid progenitors, common lymphoid progenitors, or differentiate from
other cell types including blood monocytes (27). Human DCs are classified into mDC-l,

mDC-2, and plasmacytoid DCs. Human plasmacytoid DCs are similar to mouse

plasmacytoid DCs (15). Human mDC-l preferentially stimulate Th1 responses similar to
mouse lymphoid DCs, while human mDC-2 stimulate Th2 responses similar to mouse
myeloid DCs (28). Human DC research has been limited due to available tissues for
analysis, and most research has been extrapolated from in vitro differentiation of
immature DCs and monocytes obtained from the blood. In both humans and mice, DCs
possess a high level of marker expression and functional plasticity. The DC plasticity,
low percentages in a particular tissue, and lack of clear maturation processes make DC

research difficult and complex.

Vitamin A Metabolism

Vitamin A is a generic term that encompasses retinoid and carotenoid compounds
that supply humans with a biological need. The best characterized physiological forms of
vitamin A are all-trans-retinoic acid, the carboxylic acid form, retinal, the aldehyde form,
and retinol, the alcohol form, depending on specific physiological function and tissue
(29).

Mammals can not synthesize vitamin A de novo. Vitamin A is a fat-soluble
vitamin obtained by humans in two different dietary forms, i.e. retinyl esters or pro-
vitamin A compounds. The physiological form of vitamin A circulating in the
bloodstream, retinol, can he obtained in association with the fat of animal products as
retinyl esters, or retinol esterified to fatty acids. Pro-vitamin A compounds can be
consumed from yellow, orange, and some dark green plants. Pro-vitamin A compounds,
including or- and B-carotenes and B-cryptoxanthin, can be metabolized to retinoids
through enzymatic cleavage. The enzyme 15, 15’—monooxygenase is expressed in a

multitude of human tissues and converts pro-vitamin A compounds to biologically active

forms of vitamin A (30). The tissues expressing 15, 15’-monooxygenase include cells of
the gastrointestinal tract, reproductive organs, skin, liver, and skeletal muscle (30). The
highest expression of 15,15’-monooxygenase is in the gastrointestinal tract. Retinoids,
and a fraction of consumed carotenoids, are absorbed in the small intestine. Retinoid
absorption is described below, and carotenoids are absorbed intact via passive diffusion
and associate directly with chylomicrons (31).

Retinyl esters are hydrolyzed to retinol in the gastrointestinal tract by the
pancreatic and enterocyte enzymes retinyl ester hydrolases and non—specific lipases (32-
34). Retinol is absorbed by the enterocyte and esterified to a fatty acid, normally
palmitate or stearate, by the enterocyte enzymes lecithinzretinol acyltransferase and acyl-
CoAzretinol acyltransferase (35, 36). The retinyl ester is then complexed with
cholesterol, phospholipids, other fatty acids, and binding proteins and excreted from the
enterocyte into the lymphatic system within a chylomicron (37). The chylomicron
circulates through the lymphatic and vascular system delivering retinyl esters and other
chylomicron components to peripheral tissues (38). The depleted chylomicron, now a
chylomicron remnant, will deposit the remaining retinyl esters into liver stellate cells for
storage (39). Upon demand, liver reserves of retinyl esters are hydrolyzed to retinol
which binds to retinol binding protein (RBP). Retinol-RBP complex next binds to
transthyretin (TTR) in the liver prior to release into the bloodstream to maintain a
relatively constant level of circulating retinol (35). The circulating retinol-RBP-TTR
complex, in a l :1 :1 ratio, increases molecular mass and decreases loss through

glomerular filtration (40-42). In a healthy state, vitamin A is circulating in the blood at a

consistent level of retinol:RBP:TTR at approximately 1-2 micromolar (11M)
concentration, but during acute infections the concentration may decrease (43).

The membrane receptor Stra6 binds RBP and mediates the uptake of retinol into
the cell, but RBP remains extracellular (44). Extracellular RBP returns to circulation and
is filtered through the kidney glomerulus. A cell can store limited amounts of retinol
complexed with cellular retinol binding protein or will oxidize retinol to retinal, a
reversible reaction catalyzed by the enzyme retinol dehydrogenase. The retinal can then
be irreversibly oxidized to retinoic acid by retinal dehydrogenase (45). The cell can store
limited amounts of retinoic acid complexed with cellular retinoic acid binding protein
(46). In addition, a cell can also synthesize retinyl esters as well as metabolize retinoids
to polar derivatives to increase cellular reserves of retinoids (35).

Vitamin A is a hydrophobic molecule. Therefore, vitamin A is soluble in fat and
requires a protein carrier to be soluble in the bloodstream or cell cytosol. Liver is the
main storage site of vitamin A, but adipose and other tissues can store limited amounts of
vitamin A (47). Adipose and other tissues storing vitamin A can liberate the stores in a
mechanism similar to that in liver (48). Retinoic acid can be carried in the bloodstream
and delivered to peripheral tissues by albumin (3 8). However, adipose storage and
albumin delivery of retinoic acid are believed to be minor contributions to the overall
metabolism and transport of vitamin A.

Vitamin A is excreted in both the feces and urine (49, 50). Vitamin A can be
serially oxidized to increasingly polar metabolites; from retinyl-esters to retinyl-
glucoronides (51). All-trans-retinyl B-glucuronide and other polar metabolites are

secreted into bile and, if not reabsorbed, excreted in the feces (49). The glomerulus of

the kidney filters apo-RBP, but holo-RBP complexed with TTR is retained due to the size
of the complex, recycling retinol-protein complex back to circulation (52). However, the
kidney also expresses RBP and TTR binding proteins, specifically megalin, to aid in the
reabsorption of vitamin A (53). Vitamin A, in various forms, is removed from the body

through biliary excretion and urinary filtration.

Vitamin A Function

There are three general physiological functions of vitamin A requiring different
structural forms. Retinol and all-trans-retinoic acid are required for reproductive
processes in both males and females (54). Retinal, specifically 11-cis-retinal, is critical in
vision (55). All-trans-retinoic acid in complex with its nuclear receptor functions as a
steroid hormone family transcription factor (56, 57). All-trans-retinoic acid is a ligand
for a family transcription factors involved in the regulation of cell differentiation,
proliferation, maturation and apoptosis. The function of all-trans-retinoic acid as a ligand
for transcription factors has been extensively reviewed and will be explained in greater
detail below (58-60).

High intakes of vitamin A during pregnancy are teratogenic, but vitamin A,
specifically retinol and all-trans-retinoic acid, are critical in reproduction (61-63). The
role of all-trans-retinoic acid in reproduction is through gene regulation via nuclear
receptors. However, retinol may have unique functions in reproduction. All-trans-
retinoic acid alone can not support normal reproduction, supplemental retinyl esters or
all-trans-retinol are required to prevent fetal resorption (64, 65). The specific role of
retinol in late gestation is not clear, but is required for normal heart, brain, and eye

development. The temporal-spatial distribution of vitamin A metabolites is critical in

10

reproduction to allow for all stages of fetal development and prevention of fetal
resorption (54, 63).

Rhodopsin, a light sensing protein of the rod cells in the eye, requires ll-cis-
retinal as a cofactor (66). Ultraviolet light causes 1 1-cis-retinal to isomerize to all-trans-
retinal, leading to a conformational change in the rhodopsin protein (67). The
conformational change in rhodopsin signals light detection to the brain (68). All-trans-
retinal can be recycled and converted back to 1 l-cis-retinal in the eye (69). In the retina,
ll-cis-retinal functions in light adaptation. In the cone cells, ll-cis-retinal functions in
color vision (70). The function of 1 l-cis retinal is restricted to the eye.

All-trans-retinoic acid and 9-cis-retinoic acid are both ligands for the transcription
factor family retinoic acid receptors (RARs). However, only 9-cis-retinoic acid has been
characterized as the ligand for retinoid X receptors (RXRs). There are three isoforms of
RAR (or, B, y) and RXR (or, [3, y) (59). All-trans-retinoic acid translocates from the
cytosol to the nucleus and binds to RARs (59). Upon ligand binding, RARs
heterodimerize with RXRS and bind to a retinoic acid response element (RARE) of target
genes for regulatory function (59). The RAR/RXR heterodimer will recruit other gene
promoting or suppressing transcription factors to regulate the transcription of the
candidate gene (59). More than 532 genes have been described as responsive to retinoic
acid (71). However, only about 30% of these genes have promoters possessing classic
RAREs. Classical RAREs are six nucleotide base pairs of guanine and cytosine direct
repeats separated by one, two, or five nucleotides (59, 72, 73). However, non-classical
RAREs exist in which guanine and cytosine rich half-site regions are separated by any

number of base pairs (59). RAR/RXR transcriptional regulation is a complex and

11

multistep process of ligand binding, translocation to RARE sites, and association with
other transcription factors. Retinoid-mediated gene transcriptional control in the absence
of a classic RARE is commonly documented, but the molecular mechanism(s) involved is
largely unknown (59).

The RXR proteins may heterodimerize with other transcription factor partners or
may also homodimerize (59). In vitro, 9-cis-retinoic acid will bind to RXRs leading to
homodimerization and transcriptional regulation. However, 9-cis-retinoic acid has not
been measurable in tissue samples, and therefore the physiological relevance of 9-cis-
retinoic acid and RXR homodimer transcriptional regulation remains controversial.
RXRs also heterodimerize with other nuclear receptor superfamily transcription factor
partners, including vitamin D receptor, peroxisome proliferator activation receptors, liver
X receptors, and others (59). The alternate dimerization partners lead to binding to
different response elements and modulation of other genes regulated at the transcriptional
level.

In the immune system, the role of vitamin A is generally confined to a steroid
hormone family transcription factor through all-trans-retinoic acid-RAR-RXR gene
regulation. Acute promyelocytic leukemia (PML) is a hematopoietic cell cancer in which
the RARor gene on chromosome 15 reciprocally translocates and fuses to the PML gene
on chromosome 17 (74). The fusion of RARa to PML blocks the normal maturation of
granulocytes, including neutrophils. Prescription of high dose all-trans-retinoic acid has
been an effective treatment option through the induction of differentiation of the cancer

cells into mature granulocytes which then have a finite half-life (75). Therefore, at the

12

transcriptional level, all—trans-retinoic acid is well-established to be important in the

differentiation of myeloid progenitors in the bone marrow.

Vitamin A Deficiency

Vitamin A deficiency is a global nutrient concern affecting approximately 125
million children each year (76). In the US, the prevalence of vitamin A deficiency
estimated from National Health and Nutrition Examination Survey data may be as high as
25% depending on the cutoff of vitamin A deficiency and the subpopulation of interest
(77). Incidence of vitamin A deficiency is consistently higher in children, pregnant
women, and patients with fat malabsorption diseases. Vitamin A deficiency can result
from inadequate intakes of vitamin A rich foods or poor absorption of fat-soluble
molecules. Numerous strategies have been employed and studied to ameliorate vitamin
A deficiency including supplementation programs, crop distribution, and bioengineering
of crops to contain pro—vitamin A compounds, specifically B-carotene (78, 79). Vitamin
A supplementation efforts are a cost effective means to improve health of populations,
especially of young children. Supplementing vitamin A costs cents per dose and can
reduce childhood mortality on average by 30% (80, 81). Despite these efforts, to date
vitamin A deficiency remains a factor in childhood morbidity and mortality around the
world.

Detection of vitamin A deficiency is difficult. The high liver stores and constant
release of vitamin A maintains circulating serum retinol constant at about l-2uM until
chronic undemutrition depletes liver stores. Serum retinol can also be decreased during
the acute phase response of an infection (43). Therefore, a lower serum retinol (the most

readily available sample for analysis) may not be indicative of vitamin A status, and other

13

methods must be used to evaluate vitamin A status. There are other methods of assessing
vitamin A status including serum RBP concentrations, plasma RBP to TTR ratios, liver
vitamin A concentrations, relative dose response tests, stable isotope tracers, and early
symptoms of deficiency (82). Each method of analysis has advantages and
disadvantages. The role of vitamin A in vision leads to symptoms of deficiency
involving the eye, such as kertinization of the eye layers, Bitot spots and xerophthalmia.
Night blindness, or poor adaptation to levels of low light, is the first sign of vitamin A
deficiency but is commonly underreported and undiagnosed. As vitamin A deficiency
progresses, more severe eye symptoms develop that can cause permanent visual
impairment. Keratinization and Bitot spots are reversible by vitamin A supplementation,

but xerophthalmia is irreversible (82).

Vitamin A and Innate Immunity

Vitamin A helps to maintain an effective innate immune system. Vitamin A is
essential for maintenance of mucosal lining and skin integrity. Vitamin A is also
required for maintaining optimal innate immune cell numbers and/or lytic activity.
Through transcriptional regulation, vitamin A regulates immune cell differentiation,
maturation, apoptosis, and function as well as maintains effective barriers.

Vitamin A-deficient individuals have impaired mucosal barriers. Mucus is an
important coating for trapping and preventing pathogens from entering or damaging the
delicate mucosal tissues. Vitamin A is required for the production of mucus and mucus
glycoproteins by goblet cells and maintenance of goblet cell numbers (83, 84). The skin
of vitamin A-deficient populations is keratinized and increased in thickness (85). The

increased thickness of skin does not directly lead to increased susceptibility to infection.

14

However, increased kertinization makes the skin more fragile leading to decreased skin
integrity and increased susceptibility to infections caused by abrasions.

Natural killer (N K) cells are granular lymphocytes responsible for innate defenses
to viral and intracellular infections. Basal NK cell numbers and activity are reduced in
vitamin A-deficient animals, but are restored upon supplementation of vitamin A (86-89).
Basal NK cell lytic efficiency or the ability of each NK cell to lyse a target is not
impaired in vitamin A-deficient animals (90). In addition, upon stimulation with
polyinosinic:polycytidylic acid there is no difference in the IFN-y production, cell
proliferation, or lytic efficiency of NK cells from vitamin A-deficient and vitamin A-
sufficient animals (89). In summary, in vivo NK cells are depleted in vitamin A-deficient
animals, but the ability of NK cells to respond upon stimulation is unaffected by vitamin
A deficiency.

Macrophages are mononuclear phagocytes capable of ingesting and killing
microbes within phagolyosomes through the action of lytic peptides and degradative
enzymes. Macrophages also present antigens of the ingested microbes to T cells and
secrete cytokines and chemokines to initiate the adaptive immune response and increase
local inflammation (1). The effect of vitamin A on macrophage function is contradictory.
In pathogen-free mice, vitamin A deficiency increased macrophage numbers in secondary
lymphoid tissues but also impaired delayed-type hypersensitivity, or cell-mediated
immune responses (91 ). Vitamin A supplementation has been reported to increase
macrophage functions in vivo and in vitro (92-95). However, phagocytosis of antibody
opsonized cells was impaired due to decreased expression of receptors for antibody on

human macrophages cultured in vitamin A-deficient medium (96). Pro-inflammatory

15

cytokines produced by macrophages [interleukin (IL) -1 , IL-12, and tumor necrosis factor
(TNF) -or)] are increased and regulatory cytokines, including IL-10, are decreased in
vitamin A-deficient populations (97-101). Although the cytokines that are increased in
vitamin A deficiency are normally associated with induction of oxidative pathogen
killing, in the case of vitamin A deficiency, they are ineffective at stimulating the
pathogen killing mechanisms. Supplementation of vitamin A increases the response to
these cytokines and leads to effective pathogen killing (102—105). Therefore, vitamin A
deficiency increases macrophage—mediated inflammation, decreases oxidative burst
killing, but phagocytic function remains controversial. There are several pathways for
triggering macrophage phagocytosis, and the effect of vitamin A on each specific
pathway has not been elucidated.

Vitamin A deficiency increases granulocyte numbers (106, 107). In addition,
Kuwata et al. showed that vitamin A-deficient SENCAR mice had a marked significant
increase in neutrophil cell numbers in the spleen, peripheral blood, and bone marrow due
to impaired apoptosis (108). However, the function of granulocytes is decreased in
vitamin A deficiency (109). The chemotaxic, phagocytic, and oxidative burst functions
of neutrophils of vitamin A-deficient rats were significantly decreased compared to
neutrophils from vitamin A-sufficient rats (1 10). Therefore, despite increased numbers,
the ability of neutrophils to control early infections and induce a pro—inflammatory state
is depressed in vitamin A deficiency.

In summary, vitamin A deficiency impairs the innate immune system through
decreasing mucosal barriers and skin integrity. Interestingly, myeloid innate immune

cells (neutrophils and macrophages) are increased in vitamin A deficiency and lymphoid

16

innate immune cells (NK cells) are decreased. Vitamin A is required for normal
hematopoiesis, particularly myelopoiesis, through transcriptional regulation of genes for
differentiating bone marrow progenitor cells (1 1 1). Although myeloid innate immune
cells are increased in vitamin A-deficient populations, the function of innate immune
cells is impaired either at baseline or in response to stimuli resulting in increased
susceptibility to and severity of infections. The impaired innate immune system of
vitamin A-deficient populations results in uncontrolled infections and exacerbated

inflammation with potential downstream effects in the adaptive immune system.

Vitamin A and Adaptive Immunity

Vitamin A has important functions in the adaptive immune system (1 12). In vitro
assays have been used to determine direct effects of vitamin A on both B and T
lymphocytes. However, the in vivo effects of vitamin A on adaptive immune responses
are the combination of direct and indirect effects on lymphocyte populations. Vitamin A,
as a transcription factor ligand, affects the responses of B and T lymphocytes, but the
effects of vitamin A on APCs and other cells can alter the cytokines produced and
thereby alter the B and T cell responses as well. Vitamin A has effects on the
proliferation, differentiation, maturation, apoptosis, and other functions of adaptive
immune cells.

Vitamin A does not alter total T lymphocyte numbers of pathogen-free animals
(91). However, specific subsets of T cells are altered in vitamin A—deficient populations.
Cytotoxic T lymphocytes (CTLs) are antigen-specific cells that produce perforin and
granzyme to induce apoptosis of cells infected with intracellular pathogens (1). Delayed-

type hypersensitivity responses, CTL-mediated, are impaired in many, but not all vitamin

17

A—deficient populations (91, 113-115). IL-2 is an autocrine growth factor for CTLs and
vitamin A upregulates IL-2 receptor mRNA and protein (116-119). The direct effect of
vitamin A on CTL function has not been well established and relied primarily on
experiments with Th1 cell responses.

Vitamin A deficiency skews immune responses to a Th1 bias. The Th1 cytokines
IF N-y and IL-12 are constitutively synthesized in vitamin A-deficient mice and
supplementation with all—trans retinoic acid decreased IFN-y synthesis (97). In addition,
a severe influenza infection of vitamin A-deficient mice showed decreased influenza
specific IgA levels and increased influenza specific IgG levels compared to vitamin A-
sufficient control animals (120). In similar experiments, influenza infections of mice
supplemented with high doses of vitamin A had increased influenza specific IgA, IL-1 0,
decreased serum IgG, and IFN-y (120, 121). Production of IgG augments a Th1 response
through antibody-dependent cellular cytotoxicity. The depressed Th2 cytokines of
vitamin A-deficient populations are the combined result of decreased Th2 cell stimulation
and the cross—regulation of the increased Th1 cytokine IFN-y (122).

Vitamin A-deficient populations have impaired antibody responses to T-
dependent antigens (89, 91, 114, 120, 123-130). Antibody responses are produced
through the cooperation of APCs, B cells, and Th2 cells. Vitamin A deficiency does not
have direct effects on B cells; cell numbers, T-independent antibody responses, and
antibody responses stimulated with Th2 cells from a vitamin A-sufficient mouse were
unaltered in vitamin A-deficient populations (89, 1 14, 1 15, 131). Vitamin A
supplementation increases the mRNA of IL-4 and IL-5, the classical Th2 cytokines, and

decreases IFN—y, while vitamin A deficiency decreases IL-4 (97, 122, 132). Despite

18

unaffected antibody production per B cell, the clonal expansion of B cells is impaired in
vitamin A-deficiency (123). In addition, Th2 cells are decreased in vitamin A-deficient
mice and supplementing the mice with vitamin A restored Th2 cell numbers (131).
Therefore, impaired antibody responses in vitamin A-deficient populations are primarily
due to impaired Th2 cell help.

Vitamin A is important for antibody class switching. At mucosal sites, IgA is
important for innate protection, but vitamin A deficiency decreases mucosal IgA (120,
124, 126-130). In addition, B cell numbers are not altered in vitamin A-deficient
populations (1 15). Therefore, the effect of vitamin A on B lymphocytes is primarily a
result of impaired Th2 cell help and not directly on B cells. The depressed IgA synthesis
of vitamin A-deficient populations is due to the combination of altered cytokine
environment and impaired lymphocyte homing. The role of DCs in establishing the
cytokine environment and directing lymphocyte homing allows for the discussion of
depressed B cell function to continue in the vitamin A and DC section below.

Vitamin A deficiency increases Th1 responses with unclear CTL and macrophage
defects, while decreasing Th2 responses without altering intrinsic B cell function. The
altered cytokine environment and cell-cell communication skews the balance of T cells in
favor of Th1, leading to aberrant antibody and cell-mediated immune responses. Vitamin
A does have direct effects on T lymphocyte proliferation, but T cells also require

stimulation from APCs in which vitamin A may also have direct effects.

Vitamin A and DCs
Vitamin A, as a steroid hormone transcription factor, regulates hematopoiesis

(111). Hoag and Hengesbach showed that murine bone marrow cells cultured in

19

granulocyte/macrophage-colony stimulating factor (GM-CSF) differentiated into DCs in
the presence of vitamin A or RAR agonists (133). However, the bone marrow cells
cultured in medium containing GM-C SF and charcoal dextran filtered serum (depleted of
vitamin A) or RAR antagonists generated greater numbers of granulocytes and fewer

DC 5 (133). Human bone marrow cultures also increased differentiation of myeloid
progenitors from CD34+ stem cells in the presence of all-trans retinoic acid (134).
Therefore, in vitro vitamin A induces differentiation from CD34+ stem cells to myeloid
progenitors to myeloid DCs. Without vitamin A, hematopoietic stem cells differentiate
into myeloid progenitors at a slower rate and increase production of granulocytes instead
ofmyeloid DCs.

In vivo, SENCAR mice consuming a vitamin A-deficient diet had dramatically
increased myeloid cells in the spleen and bone marrow. The myeloid cells were
primarily granulocytes, specifically polymorphonuclear neutrophils. However, the
authors did not characterize the DCs of these mice (108). The effect of vitamin A on the
in vitro differentiation of bone marrow progenitors combined with the observed in vivo
increase in granulocytes in vitamin A deficiency leads to the hypothesis that in vivo
vitamin A-deficient animals would have decreased myeloid DCs and increased
neutrophils. Further, vitamin A-deficient populations have impaired Th2 antibody
responses due to decreased myeloid DCs.

Particularly important in intestinal tissue, DC 5 express enzymes for the
metabolism of vitamin A. Retinal dehydrogenase, the enzyme responsible for converting
retinal to retinol, is expressed in mucosal tissue DCs (135). Addition of vitamin A to

DCs in cultures leads to the increased expression of the vitamin A metabolic enzymes in

20

DCs and adoptive transfer of these DCs leads to homing of T and B lymphocytes to the
gut associated lymphoid tissue (135-138). The combined effects of impaired mucosal
homing of lymphocytes and decreased cytokines, specifically IL-10, in vitamin A
deficiency explain the depressed IgA levels of vitamin A-deficient populations.
Therefore the role of vitamin A in DC function, particularly in DCs of the gastrointestinal
tract and other mucosal sites, is critically important for intestinal immune homeostasis.
Vitamin A can regulate the transcription of cytokine genes. Generally, in the
presence of vitamin A, the Th1 cytokines are inhibited while the Th2 cytokines are
increased. However, without vitamin A, Th1 cytokines are increased while Th2
cytokines are decreased. Thus, one may speculate that vitamin A regulates the
differentiation of myeloid progenitors leading to skewed populations of APCs responsible

for stimulating adaptive T lymphocyte responses.

Experiment Rationale

Vitamin A deficiency affects about 125 million children worldwide each year
(76). Vitamin A deficiency is not as prevalent in the US. compared to other countries,
but is still a major nutrient concern, especially in preschool-age children and pregnant
women (77). Despite numerous supplementation programs targeting vitamin A-deficient
populations, vitamin A deficiency induced morbidity and mortality remains a global
concern.

Dietary depletion of vitamin A in mice leads to increased neutrophils in the
spleen, bone marrow, and peripheral blood, and Th1 biased immune responses (97, 108,
120, 122). Bone marrow cell cultures stimulated with GM—CSF, in medium depleted of

vitamin A or in the presence of RAR antagonist, produce increased numbers of

21

granulocytes with fewer myeloid DCs, while the bone marrow cultures stimulated with
GM-CSF in the presence of vitamin A produced primarily myeloid dendritic cells and
few granulocytes (133). Dendritic cell (DC) subsets of vitamin A-deficient animals have
yet to be characterized in vivo.

DCs are the primary cell type responsible for stimulating naive T cells for
adaptive immune responses. In the mouse, myeloid DCs stimulate primarily Th2
responses, while lymphoid DCs stimulate primarily Th1 responses (10, 11, 28).
Plasmacytoid DCs produce type 1 interferons which aid anti—viral responses by activating
NK cell killing (12, 14). All mature DC subsets have been shown to arise from a
common immature DC or preDC (22). In the mouse, all DC subsets express CD1 1c on
the cell surface, and MHC-II, CD8a, CD1 1b, Gr-l, and CD45RB (3220) can be used in
combination to distinguish the DC subsets.

We developed and employed multicolor flow cytometry to identify and quantify
the DC subsets of the spleen from vitamin A-deficient (VAD) and vitamin A-sufficient
(VAS) animals. We hypothesized that vitamin A deficiency would lead to significant
decreases in myeloid DCs and increases in neutrophil and preDC populations, while
the depletion of vitamin A would have no effect on lymphoid or plasmacytoid DC
subsets.

The role of vitamin A in DC phenotype and function has been thus far confined to
in vitro assays. The protocol we developed has established a reliable method for
analyzing DC populations of the spleen from individual C57BL/6ll mice using multicolor
flow cytometry. We will provide evidence addressing the knowledge gap in the

mechanism of depressed Th2 responses in vitamin A-deficient populations by quantifying

22

APCs responsible for stimulating naive T cell differentiation. Our research will provide a
direct in vivo role for vitamin A in maintenance of DC populations of the spleen. The
role of vitamin A in DC population homeostasis will contribute to our understanding of
the mechanism for the deficiency in Th2 and T cell-dependent antibody responses

observed in vitamin A-deficient populations.

23

CHAPTER 2: THE IDENTIFICATION AND ENUMERATION OF DENDRITIC

CELL POPULATIONS FROM INDIVIDUAL MOUSE SPLEEN AND PEYER’S

PATCHES USING FLOW CYTOMETRIC ANALYSIS

Duriancik DM and Hoag KA. 2009. Cytometry, part A. 75:951-9.

Supplemental Figures and Tables published online as supporting material for this

manuscript can be found in Appendix I.

24

ABSTRACT

Dendritic cell (DC) research currently involves pooling of tissues from multiple animals
followed by enrichment techniques to obtain sufficient numbers of DCs for analysis.
Enrichment techniques take advantage of DC adherence, buoyant density properties,
and/or positive or negative selection of cell populations using monoclonal antibodies.
However, enrichment techniques may significantly change the maturation and/or
activation status of DCs or selectively eliminate one or more subpopulations of DCs. To
overcome these drawbacks, we designed a multicolor flow cytometric technique for
simultaneous analysis of DC populations from tissues of individual mice. The spleens
and Peyer’s patches were mechanically and enzymatically digested, then incubated with a
panel of 6 monoclonal antibody-fluorochrome direct conjugate reagents. A BD®
Biosciences LSR II flow cytometer and FCS Express® software were used to identify 3
subtypes of mature DC 3 (myeloid, lymphoid, and plasmacytoid), precursor DCs,
polymorphonuclear neutrophils, B lymphocytes, and Gr-1+/CD80t+ memory T
lymphocytes in the spleen. Likewise, we also identified these DC subpopulations and B
lymphocytes in the Peyer’s patches. The three key parameters in analysis of the DC
populations were bi-exponential plotting in data analysis, collection of a minimum of
50,000 total events, and accurate color compensation. This procedure to analyze DCs
from individual mice can lead to further understanding of the role of DCs in many other
model systems as well as better understanding of how dietary or physiological factors

may affect in vivo DC homeostasis.

25

INTRODUCTION

Dendritic cells (DCs), first described by Steinman and Cohn in 1973, are antigen
presenting cells sparsely distributed throughout the body (2). The lineage development
of DCs remains vague due to the plasticity of DCs. In the mouse, myeloid DCs primarily
stimulate antibody-mediated T helper 2 responses and lymphoid DCs primarily stimulate
cell-mediated T helper 1 responses (11, 139). Plasmacytoid DCs secrete large amounts
of type 1 interferons to augment anti-viral responses (12-14). The mature DCs (myeloid,
lymphoid, and plasmacytoid) have been shown to share a common DC precursor (22).
However, others have reported two precursor populations, one for plasmacytoid DCs and
another for both myeloid and lymphoid DCs (140, 141). At mucosal sites, yet another
DC subpopulation expressing CD103 is responsible for stimulating T regulatory cell
maturation (142, 143).

The lifespan among the various subpopulations varies from about 1.5 days to 3.0
days for lymphoid DCs and myeloid DCs to about 9 days for plasmacytoid DCs (8, 19).
Therefore, hematopoietic precursors in the bone marrow produce new DCs to replace the
dying DCs. Typically, the spleen has been reported to possess less than 1% total DCs, or
1-3 x 105 DC per spleen, with a ratio of 3 to 1 of myeloid to lymphoid DCs (3, 8).
However, the authors’ used the DC properties of low buoyant density and adherence to
glass to isolate and enrich for DCs (3). A variety of factors may influence the number of
DC 3 in a tissue or animal at a given time, including infection, chronic disease states such
as diabetes, hormones such as estradiol, nutrients such as vitamins A and D, as well as
others (133, 144-148). However, previous methods used to analyze DCs have used

enrichment techniques from pooled samples, either multiple tissue or animal sources, to

26

obtain sufficient numbers of cells for analysis leading to potentially high variability and
inaccurate DC number assessments.

Despite numerous publications quantifying DCs in tissues and animals, accurate
analysis relies on in vivo expansion of DC prior to isolation, enrichment techniques, or
positive or negative selection procedures (145, 149-151). Also DC identification using
flow cytometry has been documented in Cytometry, Part A (152-155). However, these
publications have either identified only one of the multiple DC subpopulations or focused
on a specific tissue such as the lung, blood or bone marrow, not all identifiable DC
populations present in the spleen or Peyer’s patches. Due to the variability of immune
cell populations among animals and the high cell loss associated with various enrichment
techniques, we employed 6 monoclonal antibody- (mAb) fluorochrome direct conjugates
to identify all resident DC subpopulations of the spleen and Peyer’s patches of individual,
healthy mice. The use of multiple fluorochromes to detect low percent cell populations
requires digital compensation with fluorochrome-labeled compensation beads, bi-
exponential scaling on analysis plots, and the collection of minimally 50,000 flow
cytometric events. Here we describe the gating strategy used to detect DC populations,
as well as the percent and total cell numbers observed in individual, healthy, male and

female C57BL/6J mice.

METHODS

Animals
Male and female mice, C 57BL/6J, were obtained from Jackson Laboratories (Bar
Harbor, ME, USA) and used as breeder pairs. Pups were weaned at 3 weeks of age and

fed ad libitum commercial solid pellets (Harlan Teklad 22/5 Rodent Diet #8640). At 12

27

weeks of age, the mice were killed by C02 asphyxiation. All procedures were in
accordance with Michigan State University Laboratory Animal Resource guidelines, and
animals were housed in a facility accredited by the Association for Assessment and

Accreditation of Laboratory Animal Care.

Tissue Isolation

The spleen and Peyer’s patches were excised and placed in ice cold calcium and
magnesium free Hank’s balanced salts solution (HBSS, Gibco, Carlsbad, CA, USA). The
tissues were mechanically minced with scissors and enzymatically digested with Spleen
Dissociation Medium (Stem Cell Technologies, Vancouver, BC, Canada) following
manufacturer’s instructions. The tissues were filtered through 70pm mesh (Sefar
America Inc, Depew, NY, USA). Cell suspensions were centrifuged at 1200 RPM, 4°C,
10 minutes to pellet. All subsequent centrifugations were at 1200 RPM, 4°C, 10 minutes.
The spleen cells were resuspended and incubated for 2 minutes in PharmLyse (BD
Biosciences, San Jose, CA, USA) on ice. The spleen cells were diluted with HBSS and
centrifuged to pellet. The spleen and Peyer’s patch cells were resuspended in 5mL
fluorescence activated cell-sorting (F ACS) buffer [0.1% sodium azide (Fisher Scientific,
Pittsburgh, PA, USA), 1.0% fetal bovine serum (Hyclone, Logan, UT, USA) in
Dulbecco’s phosphate buffered saline, pH 7.4-7.6, sterile filtered]. The cells were
counted on a hemacytometer using trypan blue exclusion dye (Biowhittaker,
Walkersville, MD, USA). One million cells were dispensed into each tube for

subsequent antibody-fluorochrome staining and analysis.

28

Antibody Staining

The cells were centrifuged to pellet and incubated with 5ug of anti-FcRyII/III
antibody (2.4G2 hybridoma) on ice for 10 minutes. A master mix of all 6 mAb-
fluorochrome conjugates (Table 1; BD Biosciences, San Jose, CA, USA) was prepared
with lug of each mAb-fluorochrome conjugate per one million cells in SOuL total
volume. The cells were incubated with the master mix at 4°C for 30-60 minutes in the
dark. Cell suspensions were washed with lmL of F ACS buffer, centrifuged to pellet,
resuspended in 0.5mL FACS buffer, and transported to the MSU Flow Cytometry Core
Facility on ice and in the dark. The cells were run the same day and were only on ice for

1-2 hours prior to flow cytometer runs.

Flow Cytometry Acquisition & Analysis

An LSR 11 (BD Biosciences, San Jose, CA, USA) flow cytometer (Supplemental
Figure l) was used to collect at least 50,000 total events. The LSR II instrument
parameters and general compensation matrix are shown in Supplemental Tables 1 and 2,
respectively. Compensation was preformed prior to each data collection using BD
Bioscience anti-rat K CompBeads according to manufacturer’s recommendations. A
representative figure of the fluorochromes requiring the most compensation is shown in
supplemental figure 2. Anti-CDl4-PE was substituted for anti-CD1 lc-PE because the
CompBeads do not bind a hamster antibody. We assumed an equal fluorochrome to
protein ratio for both anti-CD14-PE and anti-CD1 lc-PE. The events were collected and
post-acquisition analysis was performed using FCS Express version 3 (De Novo

Software, Los Angeles, CA, USA).

29

Flow cytometric analysis gating strategies

Table 2 summarizes the cell surface antigens that were utilized to identify the
immune cell populations. The gating strategies for spleen and Peyer’s patch cells are
shown in Figs. 1 and 2, respectively. The forward scatter and side scatter plot is used to
distinguish the live cells from dead cells, debris, and doublet cells (Figs. 1A & 2A).

For spleen cell populations, the Gr-l-PeGC-Cy5.5 and CD8or-PE-Cy7 plot is
used to distinguish the memory Gr-l+/CD8(1+ T cells and granulocytes, since
granulocytes do not express CD80t (Fig. 18). Memory CD8+ T lymphocytes have been
shown to express Ly-6C, which along with Ly-6G is recognized by the anti-Gr-l
antibody (156, 157). Granulocytes (Gr-1+/CD80t' gate in Fig. 1B) are confirmed as
polymorphonuclear neutrophils (PMN) by the expression of CD1 1b in the histogram of
CD11b-APC-Cy7 (Fig. 1C). The histogram of CD1 lc-PE (Fig. 1D) is used to identify all
DC populations, and the histogram of MHC-II-FITC (Fig. IE) is used to distinguish
preDCs and mature DC 3, with preDCs lacking MHC-II expression and mature DCs
exhibiting MHC-II expression. In the mouse, all DC populations express CD1 1c (8, 11).
Mature mouse DC 5 (CD1 lcVMHC-IIJr gate) are divided into three distinct
subpopulations by the expression of Gr-l and B220 (plasmacytoid DCs; Fig. 1F), CD80.
(lymphoid DCs) and CD1 1b (myeloid DCs; Fig. 10) (8, 11). The preDCs
(CD1 1c+/MHC-II' gate) are confirmed by the expression of B220 and CD1 1b in the plot
of BZZO-APC and CD1 lb-APC—Cy7 (Fig. 1H; (22)). The histogram of Gr-l-PeGC-
Cy5.5 is used to identify potential B cells, which are negative for the expression of Gr-l
(Fig. 11). Any residual cells expressing Gr-l-PeGC-Cy5.5 not gated as PMN or

plasmacytoid DC will be eliminated (Fig 11). The B cells are further confirmed by the

30

expression of BZZO-APC and variable expression MHC-II-FITC (Fig. 1.1). The
expression of MHC-II on B cells is indicative of B cell antigen presentation. As cell
populations are identified, exclusion gates using “not” Boolean logic are used to
eliminate events from subsequent plots (Fig. 1K and supplemental figure 3). For
example, granulocytes and Gr-l + memory T cells are identified and excluded using the
minus granulocyte or Gr-l T gate with the Boolean logic not (granulocyte or Gr-l T).
The mAb-fluorochrome combination we utilized is limited to the identification of the cell
populations described above. Although the spleen contains other cell populations, our
gates do not identify all T lymphocytes, natural killer cells or macrophages, but they are
likely not contained in our final DC populations. A flow diagram of the gating strategy is
shown in supplemental figure 3 for clarity of Boolean logic employed.

The gating strategy of the Peyer’s patch cells is shown in Fig. 2. Since Peyer’s
patches do not have the blood filtering function of the spleen, PMNs should not be
present in appreciable numbers and thus are excluded from the gating strategy.
Therefore, the plot of Gr-l-PeGC-Cy5.5 and CD80t-PE-Cy7 used to identify
granulocytes and memory T cells in the spleen is not used in the Peyer’s patch cell
analysis. The histogram of CD1 lc-PE is used to identify potential DCs (CD1 1c+; Fig.
2B), and the histogram of MHC-II-FITC (Fig. 2C) is subsequently used to distinguish
preDCs (MHC-II') and mature DCs (MHC-IF). The mature DCs (CD11c+/MHC-II+) are
divided into the 3 distinct subpopulations by the expression of Gr-l and 8220
(plasmacytoid DCs; Fig. 2D), CD8ot (lymphoid DC) and CD1 1b (myeloid DCs; Fig. 2E).
A fourth mature DC population not expressing the markers employed in the analysis is

identified (“Other DC”; Fig. 2E). The preDCs (CD1 lcVMHC-II' gate) are confirmed by

31

the expression of 8220 and CD1 1b in the plot of BZZO-APC and CD1 lb-APC-Cy7 (Fig.
2F). The histogram of Gr-l-PeGC-Cy5.5 is used to identify potential B cells (Fig. 2G).
The B cells are further confirmed by the expression of B220 and variable expression of
MHC-Il (Fig. 2H). B cells do not express Gr-l, but do express B220. The expression of
MHC-II on B cells is indicative of B cell antigen presentation. As cell populations are
identified, exclusion gates using “not” Boolean logic are used to eliminate events from
subsequent plots (Fig. 21). There are other immune cell populations present in Peyer’s
patches (T lymphocytes), but our mAb-fluorochrome cocktail only allows for the

identification of the cell populations described above.

Population calculations and statistical analysis

The total cell numbers for each population per tissue were obtained by
multiplying the percentage of the respective gate from the flow cytometry analysis by the
total cells (for spleens) or total cells per Peyer’s patch counted on a hemacytometer.
Prism (GraphPad software , San Diego, CA) was used to perform a student’s t-test to
detect statistical significance (p< 0.05) between male and female animals for each cell

population percentage and total cell numbers.

RESUL TS

Animal characteristics

At 12 weeks of age, the male and female mice had an average body weight of 25g
and 19g, and spleens weights of 0.06g and 0.10g, respectively (Table 3). One female
mouse had an unusually large spleen weighing 0.24g. Eliminating this outlier caused the

average spleen weight for female mice to decrease to 0.07g. However, the female mouse

32

with an abnormal spleen weight contained similar number of nucleated cells, suggesting
the excessive weight may have been due to red blood cell retention in the spleen. The
nucleated cell number per spleen was 1.19 x 108 and 1.36 x 108 for males and females,
respectively (Table 3). The average number of Peyer’s patches excised was 7 for both
males and females, with a minimum of 5 and a maximum of 9. The number of cells per
Peyer’s patch was 8.40 x 105 and 8.19 x 105 for males and females, respectively (Table

3).

Average immune cell numbers obtained from healthy C57BL/6J mouse spleen and
Peyer’s patches

We used the techniques described above to determine the percent and total DC
subpopulation numbers (Table 4) and other non-DC immune cell populations (Table 5)
present in the spleens and Peyer’s patches of healthy male and female C57BL/6J mice.
Due to the varying numbers of Peyer’s patches excised per animal, the Peyer’s patch data
is shown as cells per Peyer’s patch and not total cell numbers.

The total mature and preDCs represent about 4% of the spleen and 3.75% of the
Peyer’s patches in both male and female mice. Myeloid DCs are the predominant DC
subpopulation present in spleen, representing about 2% of the total spleen cells in both
male and female mice (Table 4). In the Peyer’s patch, myeloid DCs compose only about
1% of the total cells in both males and females. The lymphoid DCs are about 0.7% of the
spleen cells in both male and female mice, and about 0.3% and 0.4% of male and female
Peyer’s patch cells, respectively. Female mice have a significantly higher percentage of
lymphoid DCs in the Peyer’s patches than male mice (p=0.035). Plasmacytoid DCs are

only about 0.2% of the total spleen cells in both males and females, but predominate in

33

the Peyer’s patches, representing about 1.3% of male and 1.1% of female total Peyer’s
patch cells. The preDCs represent approximately 1% of total cells in both spleen and
Peyer’s patches of both male and female mice. “Other” DCs are not present in the
spleen, but represent about 0.2% of the Peyer’s patch cells.

On average, there are 2.60 x 106 myeloid DCs, 8.02 x 105 lymphoid DCs, 2.38 x
105 plasmacytoid DC 3, and 1.51 x 106 preDCs in male mouse spleens (Table 4). Female
mouse spleens contained an average of 2.83 x 106 myeloid DCs, 8.89 x 105 lymphoid
DCs, 2.62 x 105 plasmacytoid DCs, and 1.47 x 106 preDCs. In each Peyer's patch of the
males, there are an average of 8.83 x 103 myeloid DCs, 2.25 x 103 lymphoid DCs, 9.98 x
103 plasmacytoid DCs, 8.07 x 103 preDCs, and 1.89 x 103 “other” DCs. In each female
mouse Peyer’s patch, there are an average of 9.50 x 103 myeloid DCs, 3.20 x 103
lymphoid DCs, 8.75 x 103 plasmacytoid DCs, 8.17 x 103 preDCs, and 1.82 x 103 “other”
DC 5.

B cells are the majority population in both the spleen (66% in males, 62% in
females) and Peyer’s patches (82% in males, 78% in females; Table 5). In the Peyer’s
patches, male mice have a significantly higher percentage of B cells than female mice
(p=0.008). The PMNs represent only about 2% of the spleen cells in both the male and
female mice, but were not quantified in the Peyer’s patches. Likewise, the Gr—1+/CD80t+
memory T cells were not quantified in the Peyer’s patches, but represent about 3% of the
cells in the spleen (Table 5). In the spleen, male mice have a significantly higher
percentage of Gr-1+/CD80t+ memory T cells than female mice (p=0.042).

The spleens of male and female mice contain averages of 2.76 x 106 and 3.00 x

10° PMNs, respectively (Table 5). Memory Gr-1+/CD80t+ T cells average 3.71 x 106 and

34

3.61 x 106 in the spleens of male and female mice, respectively. The male and female
mouse spleens contained 7.91 x 107 and 8.38 x 107 B lymphocytes, respectively, while
there are 6.87 x 105 and 6.43 x 105 B lymphocytes per Peyer’s patch in male and female

mice, respectively.

DISCUSSION

Using multiple mAb-fluorochrome conjugates in flow cytometry, we were able to
identify seven immune cell populations of the spleen and five immune cell populations of
the Peyer’s patches. Of note, all four mouse DC spleen subpopulations could be
quantified, and our analysis suggests a fifth relevant mature DC population is present in
the Peyer’s patches. The cell populations of the spleen included myeloid DCs, lymphoid
DCs, plasmacytoid DCs, preDCs, PMNs, B cells, and Gr-l+/CD80t+ memory T cells.
There have been reports of both one common or two separate precursor DC populations
present in the mouse (22, 140). Our staining and gating strategy can identify either of
these preDC populations, but here we chose to identify one common precursor DC
population as described by del Hoyo et al. (2002). In the Peyer’s patches, the preDCs,
myeloid DCs, lymphoid DCs, plasmacytoid DCs, and B cells were identified with the
mAb-fluorochrome conjugates and gating strategy used. The mature DCs present in the
Peyer’s patches that do not stain for CD80t or CD1 1b and which we classify as “other”
DC could be CD103+ DCs, but we have not verified this (Figure 2E; (158)). The
expression of CD80t and CD1 1b on CD103+ DCs is controversial, as lung and bronchial
lymph node CD103+ DCs have been reported to express low levels of CD1 lb (159, 160)

but not CD80L (159), while spleen marginal zone CD103+ DC have been reported to

express CD80t (161). Thus, further experimentation would be needed to identify the

35

“other” DC subpopulation composing approximately 6% of the Peyer’s patch DCs. We
suggest that DC subset analysis protocols designed for mucosal immune tissues should
contain antibodies to CD103 as well as CD1 1b and CD80L. This could be added to our
existing protocol since detection channels remained available on the LSR II flow
cytometer.

The protocol employed is novel in that single tissues from individual animals
were used for analysis instead of pooling multiple cell sources or use of any enrichment
techniques. In the past, enrichment techniques have led to the accidental depletion of DC
subsets, in particular plasmacytoid DCs. B220, or CD45RB, was used to negatively
select B cells leading to the simultaneous depletion of plasmacytoid DCs (16), yet this
negative selection technique is still marketed for spleen DC enrichment [BD Biosciences
IMagTM cell separation system technical document
(http://www.bdbiosciences.com/pdfs/brochures/04-7900030-2-Al .pdf; accessed
05/21/2009)]. Therefore, the complex expression pattern of DC subsets limits the value
of negative selection protocols based on a combination of surface proteins.

The low percent of DCs, particularly in the steady-state, has led to pooling of
multiple tissues or single tissues from multiple animals to obtain sufficient cell numbers
for enrichment and/or analysis. However, DCs from various tissues are heterogeneous
and variable depending on the tissues pooled. Lymph node analysis performed on lymph
nodes pooled from various areas of the body would likely be misleading. CD103+ DCs
are restricted to mucosal sites, thus pooling of mucosal lymph nodes with other lymph

nodes would be inadvisable. In addition, many factors can affect individual mouse

36

immune systems, including genetics, glucocorticoids (stress), diet, and others. Therefore,
inter-animal variability can be high despite numerous controls and use of inbred strains.

Many variables can affect DC percentages and total cell numbers in a tissue.
Infections cause DCs to mature, home to secondary lymphoid tissues, and proliferate.
However, the maturation, homing, and proliferation of DCs are site specific to the
secondary lymphoid organ of that area of the body. A respiratory infection will not cause
systemic changes in DCs, but the respiratory draining lymph nodes will have vastly
greater numbers of mature DCs. Due to the effect of estrogen on DCs, there may be
differences in DC percentages and total cell numbers between sexes of animals (162,
163), but our data show few gender differences. In addition, animal age has been shown
to modify DC populations (164, 165). Finally, the strain of the animal may also affect
the percent and total DC numbers (166). BALB/cJ and C57BL/6J animals appear to have
similar DC percentages and total cell numbers, but F VB/NJ animals of the same sex and
age have significantly fewer spleen DCs and reduced capacity of bone marrow to
generate DC in vitro (Duriancik, Lackey, and Hoag, unpublished data). Thus, we
conclude that it is extremely important to confine DC subpopulation analysis to single
tissues from individual animals, without inclusion of enrichment techniques prior to flow
cytometric analysis.

The strengths of the protocol presented include the analysis of DCs from single
tissues of individual animals, the lack of any cell enrichment techniques, the
identification and analysis of DC subpopulations using a single cocktail of mAb—
fluorochrome conjugates, and the use of direct mAb—fluorochrome conjugates. The use

of direct mAb-fluorochrome conjugates decreases the incubation time because there is

37

only one incubation step with the antibodies. The use of indirect conjugates may increase
the sensitivity of the fluorochromes, but the increased time for multiple incubations and
washing steps increases the chance for cell loss through death or DC activation due to
contamination. A single cocktail of mAb-fluorochrome conjugates to identify all the
DCs allows the identification of all DC subpopulations in a single tube decreasing
staining variability due to different cells per tube or mAb-fluorochrome conjugates per
tube. Also using a single tube of cells and elimination gates decreases the possibility of
counting cells as multiple populations due to expression of multiple antigens. The lack of
any cell enrichment procedure decreases cell loss, the potential of DC activation, and
unintentional selection or deletion of particular cell populations. The variability of DC
populations associated with different tissues including the spleen, Peyer’s patches and
other mucosal sites, various draining lymph nodes, and other DC sources is eliminated by
analyzing single tissues. Analysis of individual animals also increases statistical power
by allowing for analysis of inter-animal variability within treatment groups.

The protocol described also has minor limitations including potential cell loss
from multiple centrifugations, digital flow cytometric setup and analysis, sophisticated
post-acquisition gating analysis and utilization of exclusion gating, and the collection of
at least 50,000 events. Increased centrifugations may lead to increasing cell losses,
especially because of the low buoyant density of DCs. Digital electronics of the flow
cytometer are required to accurately establish the compensation matrix, and bi-
exponential plotting is required due to the compensation of the fluorochromes forcing
some events to fall below zero (167, 168). Bi-exponential plotting allows for better

separation of populations because data falling below zero is not condensed on either axis

38

(168). Better separation of populations leads to more accurate gating and identification
of populations. The complex compensation and low cell numbers expressing some of the
mAb—fluorochromes, such as CD1 1c, requires the use of compensation beads to establish
the compensation matrix. Without the compensation beads too many events would be
required to accurately detect the positive and negative peaks of some of the
fluorochromes leaving few cells left for analysis. Due to the complex expression profile
of DC subpopulations, a complex gating strategy must be employed to effectively
identify all of the individual DC populations. Not only must bi-exponential plotting be
used to create an appropriate cell population separation, but exclusion gates should also
be used to eliminate the potential of other cell populations from contaminating true
populations. If exclusion gates are not used, then the potential for other events
expressing a similar profile of markers could skew the population numbers. The
collection of at least 50,000 events is required because of the low percent of DCs in any
tissue. If the spleen contains less than 5% of cells as total DCs, collection of 50,000
events should show about 2500 total DCs, whereas collection of only 10,000 events
should show only 500 total DC 3. The separation of 2500 DCs compared to 500 DCs into
4 subpopulations allows for easier identification and analysis of DC subpopulations.
However, the collection of 50,000 events increases the amount of time the sample must
be run through the flow cytometer and requires a higher cell number to start.

In summary, our protocol for identification and enumeration of DC
subpopulations from the spleen and Peyer’s patches of individual uninfected C57BL/6J
mice is an efficient method for analyzing DC populations. Several advancements in

multicolor flow cytometry techniques were used in development of our protocol and

39

gating strategies (167-170). The lack of enrichment techniques decreases the associated
potential cell loss and variability. The accurate identification and enumeration of DCs
provides a technique that will allow for better analysis of DCs in steady-state and
inflammatory conditions. Finally, we present a protocol that could be consistently
adapted by multiple investigators, allowing for comparison of results from many studies

performed in different laboratories.

A CKNO WL EDGEMEN rs
We thank the MSU Flow Cytometry Research Technology Support Facility director, Dr.

Louis King for his assistance in developing the research protocol.

40

Figure 2.1. Gating strategy to identify dendritic cells and other immune cell populations
in mouse spleen. 1A, forward scatter (F SC) and side scatter (SSC) to identify live cells,
live cell gate, based on cell size and granularity. 1B, contour plot of CD80t-PE-Cy7 and
Gr—l-PeGC-Cy5.5 used to identify granulocytes, preGranulocyte gate, as Gr-1+/CD80t'
and memory T cells, Gr-l T cell gate, as Gr-1+/CD80t+. 1C, histogram of CD1 lb-APC-
Cy7 used to confirm preGranulocyte gated cells as PMN, Gr-l+/CD80t'/CD1 1b+. 1D,
histogram of CD1 lc-PE used to identify DCs, CD1 lc+. 1E, histogram of MHC-II-FITC
to separate DCs as preDCs, CD1 1c+/MHC-II', and mature DCs, CD11c+/MHC-II+. 1F,
contour plot of BZZO-APC andiGr-l-PeGC-Cy5.5 to identify plasmacytoid DCs,

CD1 lc+/MHC-II+/B220+/Gr-l +. 1G, contour plot of CD8a-PE-Cy7 and CD1 1b-APC-
Cy7 to identify non-plasmacytoid DCs as lymphoid DCs, CD1lc+/MHC-II+/B220'/Gr-l'
/CD8(1+/CDI lb', and myeloid DCs, CD11c+/MHC-II+/B220'/Gr-l’/CD8or'/CD11b+. 1H,
contour plot of CD11b-APC-Cy7 and B220-APC to confirm preDCs, CD11c+/MHC-II',
as CD1 lbva’iab'c/B220+. ll, histogram of Gr-l-PeGC-Cy5.5 to identify potential B cells
as Gr-l', eliminating cells expressing Gr-l-PeGC-Cy5.5 not gated as a PMN or
plasmacytoid DC. 1], dot plot of BZZO-APC and MHC-II-FITC to confirm potential B
cells, Gr—l', as B220+/MHC-Ilva"ab'e. 1K, Boolean logic used for hierarchical gating,
including exclusion gates such as minus granulocytes or Gr—l T. Numbers in parentheses
indicate cell population percentage that falls within the gate. Representative data from

one animal is shown.

41

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Figure 2.1 Continued. Gating strategy to identify dendritic cells and other immune cell
populations in mouse spleen. 1A, forward scatter (FSC) and side scatter (SSC) to
identify live cells, live cell gate, based on cell size and granularity. lB, contour plot of
CD80t-PE-Cy7 and Gr-l -PeGC-Cy5.5 used to identify granulocytes, preGranulocyte
gate, as Gr-l+/CD80t' and memory T cells, Gr-l T cell gate, as Gr-l+/CD80t+. 1C,
histogram of CD1 1b-APC-Cy7 used to confirm preGranulocyte gated cells as PMN, Gr-
1+/CD80t'/CD1 lbl. 1D, histogram of CD1 lc-PE used to identify DCs, CD1 lc+. 1E,
histogram of MHC-II-FITC to separate DCs as preDCs, CD1 lcVMHC-II', and mature
DCs, CD1 lc+/MHC-II+. 1F, contour plot of BZ20-APC and Gr-l-PeGC-Cy5.5 to
identify plasmacytoid DCs, CD11c+/MHC-II+/B220+/Gr-l+. 1G, contour plot of CD80L-
PE-Cy7 and CD1 lb-APC-Cy7 to identify non-plasmacytoid DCs as lymphoid DCs,
CD1lc+/MHC-ll+/8220'/Gr-1'/CD80t+/CD1lb', and myeloid DCs, CD1 lc+/MHC-
Il+/BZ20'/Gr-l'/CD80t'/CD1 1b+. 1H, contour plot of CD1 lb-APC-Cy7 and B220-AFC to
confirm preDCs, C D1 lcVMHC—II', as CD1 lbvarlab'e/8220+. ll, histogram of Gr-l-PeGC-
Cy5.5 to identify potential B cells as Gr-l', eliminating cells expressing Gr-l-PeGC-
Cy5.5 not gated as a PMN or plasmacytoid DC. 11, dot plot of B220-AFC and MHC-II-
FITC to confirm potential B cells, Gr-l', as B220+/MHC-Ilva"ab'“. 1K, Boolean logic used
for hierarchical gating, including exclusion gates such as minus granulocytes or Gr—l T.
Numbers in parentheses indicate cell population percentage that falls within the gate.

Representative data from one animal is shown.

43

CD8a PE—Cy7

(—

I

Lymphoid preDCZ

 

 

        

 

 

 

 

 

 

fi‘
N .
>
9 . >-

' e. j 0 E .l ‘

' , ~ a a
Myeloid / ‘13 0 y ,
CD11bAPC-Cy7—A 8 3220 AFC-A Gr1F’eGC-Cy55-A
Bcells K

g I Live Cells (73.14%)
I Gr-1 T cells (2.42%)
4, E] preGranulocytes
I Granulocytes (8.69%)

 

 

8220 APC-A

: I Minus granulocytes and Gr-1 T
MHC-Il FlTC-A : I CD110 pos (14.32%)
: I Mature DCs
I Plasmacytoid (0.68%)
E I Minus Plasmacytoid
I Myeloid DCs (5.59%)
I Lymphoid DCs (4.31%)
E I preDCs
. I preDCZ (1.66 %)
l—J I Minus all DCs
E E preB cells
I Bcells (55.55%)

Figure 2.2. Gating strategy to identify dendritic cells and other immune cell populations
in mouse Peyer’s patches. 2A, forward scatter (F SC) and side scatter (SSC) to identify
live cells, live cell gate, based on cell size and granularity. 28, histogram of CD1 lc-PE
used to identify DC 3, CD1 1c+. 2C, histogram of MHC-II-FITC to separate DCs as
preDCs, CD1 1c+/MHC-Il', and mature DCs, CD11c+/MHC-II+. 2D, contour plot of
BZ20-APC and Gr-l -PeGC-Cy5.5 to identify plasmacytoid DCs, CD11c+/MHC-
III/B220+/Gr-l+. 2E, contour plot of CD80r-PE-Cy7 and CD1 1b-APC-Cy7 to identify
non-plasmacytoid DCs as lymphoid DCs, CD1 1c+/MHC-II+/B220'/Gr-l'/CD80L+/CD1lb',
myeloid DCs, CD11c+/MHC-II+/BZ20'/Gr-l'/CD8or'/CD11b+, and “Other” DCs,
CD11c+fMHC-Il+/B220'/Gr-l"/CD80t'/CD1lb". 2F, contour plot of CD1 lb-APC-Cy7 and
BZZO-APC to confirm preDCs, CD11c+/MHC-II', as CD1 lb”“"“"'°/13220*. 20, histogram
of Gr-l-PeGC-Cy5.5 to identify potential B cells as Gr-l'. 2H, dot plot of BZZO-APC
and MHC-Il—FITC to confirm potential B cells, Gr-l', as B220+/1\/1HC-Ilva"ab"’. 21,
Boolean logic used for hierarchal gating, including exclusion gates. Numbers in
parentheses indicate cell population percentage that falls within the gate. Representative

data from one animal is shown.

45

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Figure 2.2 Continued. Gating strategy to identify dendritic cells and other immune cell
populations in mouse Peyer’s patches. 2A, forward scatter (FSC) and side scatter (SSC)
to identify live cells, live cell gate, based on cell size and granularity. ZB, histogram of
CD1 lc-PE used to identify DCs, CD1 1c+. 2C, histogram of MHC-II-FITC to separate
DCs as preDCs, CD11c+/MHC-II', and mature DCs, CD1 lc+/MHC-II+. 2D, contour plot
of B220-APC and Gr-l-PeGC~Cy5.5 to identify plasmacytoid DCs, CD11c+/MHC-
II+/8220+/Gr-l+. 2E, contour plot of CD8or-PE-Cy7 and CD1 lb-APC-Cy7 to identify
non-plasmacytoid DCs as lymphoid DC 5, CD11c+/MHC-II+/B220'/Gr-l'/CD8or+/CD1 lb',
myeloid DCs, CD1lc'i/MHC-Il’L/BZZOVGr-1'/CD8or'/CD11b+, and “Other” DCs,
CD1lCVMHC-IIVBZZOVGFI7CD8Q7CDI1b-. 2F, contour plot of CD1 1b-APC-Cy7 and
BZZO-APC to confirm preDCs, CD11c+/MHC—II', as CD1 le'a'iab'C/Bz20fi 20, histogram
of Gr-l-PeGC-Cy5.5 to identify potential B cells as Gr-l'. 2H, dot plot of B220-APC
and MHC-II-FITC to confirm potential B cells, Gr-l', as BZZO+/MHC-Ilva'lab'c. 21,
Boolean logic used for hierarchal gating, including exclusion gates. Numbers in
parentheses indicate cell population percentage that falls within the gate. Representative

data from one animal is shown.

47

Count

8220 APC-A

 

 

 

Gr1 PeGC-Cy5.5—A

B cells

 

 

MHC-Il FITC-A

t-‘
L .l

Live Cells (66.47%)

E} I CD11c pos (2.58%)
g I Mature DCs
% . I Plasmacytoid (0.52%)
I [3 I Minus Plasmacytoid

3 I Other DCs (0.68%)

_ I Lymphoid DCs (0.20%)

, : I Myeloid DCs (0.50%)
E] I preDCs
f I preDC2 (0.48%)
E] I Minus all DOS

[3. D preB cells

I Bcells (75.71%)

Table 2.1. Antibody-fluorochrome conjugate reagents employed in labeling cell

suspensions for flow cytometric analysis. The fluorochromes were selected for

maximum sensitivity (17] ).

 

 

 

 

 

 

 

Antigen Clone Isotype Fluorochrome
MHC-ll (l-A/l-E) 2G9 Rat Inga. K FITC
CD110 HL3 Arm. Hamster IgG]. 79 PE
CD80L 53-6.7 Rat Inga, K PE-Cy7
CD1 lb M1/70 Rat Ingb. K APC-Cy7
CD45RB (8220) RA3-6BZ Rat 1862a, K APC
Gr-l RB6-8C5 Rat Ingb. K PeGC-Cy5.5

 

 

 

 

 

 

49

Table 2.2. Antigen expression of immune cell populations in mouse spleen and Peyer’s

patches used in gating strategy.

 

Cell Population

 

Dendritic cells

 

 

 

 

 

 

 

 

Antigen Myeloid Lymphoid Plasmacytoid PreDC
CD1 lc posb P03 P03 P05
MHC-II P03 P05 P03 Negd
CD1 1b Pos Neg N/A Pos/Neg
CD80: Neg Pos N/A N/A
Gr-l Neg Neg Pos N/A
8220 Neg Neg P03 P03

 

 

 

 

 

50

 

 

Table 2.2

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F...——————

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\Cglnd

Table 2.2 Continued. Antigen expression of immune cell populations in mouse spleen

and Peyer’s patches used in gating strategy.

 

 

 

 

 

 

 

 

 

 

Cell Population
PMN“ B Cells Gr-VCDSa * T Cells

Antigen

CD1 1c N/p‘C N/A N/A
MHC-ll N/A Pos/Neg N/A
C D1 1b Pos N/A N/A
CD80L Neg N/A Pos
Gr-l Pos Neg Pos
8220 N/A Pos N/A

 

 

 

 

 

aPMN is polymorphonuclear neutrophil.
bPos indicates that the cell population is positive for the antigen.

cN/A indicates that the antigen is not used to distinguish the cell population in our gating

strategy.

dNeg indicates that the cell population is negative for the antigen.

51

Table 2.3. Physiological characteristics of C57BL/6J mice used in the studiesa.

 

 

 

 

 

 

 

 

 

Body Spleen Cells/Spleen Number of Cells/PPC
Weight Weight PPb
(g) (a)

Male 24.77 3: 0.06 : 119 x 108 i 6.9 i 1.4 340 x 105 i
(“27) 1'06 0'01 2.94 x 107 3.33 x 105
Female 19.03: 0.10: 1.36x108: 6.5:].1 319x105:
("26) 0'58 0'07 1.77 x 107 2.27 x 105

 

aAll data presented as mean i 1 SD.

bPP is Peyer’s patches.

CTotal cells counted for all Peyer’s patches from one animal divided by the total number

of Peyer‘s patches harvested from the animal.

52

 

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Table 2.5. Percentage and total cell numbers of non-dendritic immune cell populations

enumerated in C578L/6J spleen and Peyer’s patches.

 

 

 

 

Spleen
Mouse Neutropbils B lymphocytes CD8a+/Gr-l+
Sex T lymphocytes
Male 2.26 i 0.39a 66.22 i 1.24 3.10 i 039*
(2.76 x106: (7.91x107: (3.71x10":
1_14x106)b 2.10x107) 1.19x106)
Female 2.22 i 0.35 62.07 i 5.86 2.65 i 030*
6 7 6
(3.00 X 10 i (8.38 x10 : (3.61 x 10 j:
5 7 5
5.52x10 ) 1.02x10) 7.08x10)

 

 

 

 

 

 

55

Table 2.5 Continued. Percentage and total cell numbers of non-dendritic immune cell

populations enumerated in C578L/6J spleen and Peyer’s patches.

 

Peyer’s patches

 

 

Mouse Neutrophils B lymphocytes CD80L‘L/Gr-1+
Sex T lymphocytes
Male N/AC 81.58 2: 2.05"* N/A

(6.87 x 105 :t 2.75 x 105)6

 

Female N/A 78.18 i 1.64* N/A

(6.43 x105 3: 1.87 x105)

 

 

 

 

 

 

aAverage +/- standard deviation of spleen cell population percentages.
bAverage +/- standard deviation of total cells for each population in the spleen.
CN/A indicates cell populations not detected in Peyer’s patches.

dAverage +/- standard deviation of Peyer’s patch cell population percentages.

cAverage +/- standard deviation of total cells for each cell population in each Peyer’s

patch.

*Statistically significant difference between male and female mice.

56

CHAPTER 3: VITAMIN A DEFICIENCY ALTERS SPLENIC DENDRITIC
CELL SUBSETS AND INCREASES CD8+GR-l+ MEMORY T LYMPHOCYTES

IN C57BL/6J MICE.

Duriancik DM and Hoag KA. In preparation for submission to Journal of Nutrition.

57

Abstract

Vitamin A-deficient populations have impaired T cell-dependent antibody
responses. Dendritic cells (DCs) are the most proficient antigen presenting cells to naive
T cells. In the mouse, CD1 1b+ myeloid DCs stimulate T helper (Th) 2 antibody immune
responses, while CD80L+ lymphoid DCs stimulate Th1 cell-mediated immune responses.
Therefore, we hypothesized that vitamin A-deflcient animals would have decreased
numbers of myeloid DCs and unaffected numbers of lymphoid DCs. We performed
dietary depletion of vitamin A in C57BL/6J male and female mice and used multicolor
flow cytometry to quantify immune cell populations of the spleen, with particular focus
on DC subpopulations. We show that vitamin A-depleted animals have increased
polymorphonuclear neutrophils, lymphoid DCs, and memory CD8+ T cells and decreased
CD4+ T lymphocytes. Therefore, vitamin A deficiency alters splenic DC subpopulations,

which may contribute to skewed immune responses of vitamin A-deficient populations.

58

Introduction

Vitamin A has numerous roles in the immune system. Populations deficient in
vitamin A have impaired innate immunity, including loss of mucosal barrier integrity and
decreased numbers and/or function of innate immune phagocytic and natural killer cells
( 1 12). The adaptive immune response is also altered by vitamin A deficiency.
Lymphocyte homing, immunoglobulin A (IgA) responses in the mucosa, and T-
dependent antibody responses are decreased in vitamin A-deficient individuals (112, 121,
130, 135, 172, 173). Vitamin A is also required for the balance of T regulatory (Treg)
and T helper (Th) 17 cells in mucosal tissues (174). Therefore, vitamin A is required in
the maintenance of the immune system to adequately respond to danger signals and
maintain tolerance to self.

Dendritic cells (DCs) are the most proficient stimulators of na'1've T lymphocytes
and link the innate and adaptive arms of the immune system (20, 175). DCs derive from k
hematopoietic progenitors and reside in peripheral tissues and circulate in blood in an
immature state. DC half-lives range from 3 to 9 days (24). Multiple DC subsets have
been distinguished by differential surface protein expression, and these subsets
preferentially stimulate varying adaptive immune functions. In the mouse, CD1 lb+
myeloid DC 3 stimulate Th2 responses, while CD80t+ lymphoid DCs stimulate Th1
responses (10, l l). Plasmacytoid DCs are mature DCs that secrete type 1 interferons to
augment anti-viral immune responses (12-14). All mature mouse DC populations appear
to arise from one common precursor DC population (22).

Vitamin A plays a role in maintaining DCs, but the reports have been limited

primarily to in vitro studies. Hengesbach and Hoag (2004) have shown that bone marrow

59

precursors stimulated with GM-CSF differentiate into DCs in the presence of medium
containing vitamin A. However, if the medium was depleted of vitamin A or contained a
retinoic acid receptor antagonist, the precursors generated increased numbers of
neutrophils (133). In vivo, vitamin A-deficient SENCAR mice had increased neutrophils
and decreased lymphocyte populations compared to vitamin A-sufficient controls (108).
However, the authors did not characterize the DC populations of these animals. DCs are
hematopoietic-derived cells, but also reside and proliferate in secondary lymphoid tissues
(5). In addition, DCs are motile cells traveling between sites of infection and secondary
lymphoid tissues. The chemokine receptor for DCs to migrate to intestinal tissues is
depressed in vitamin A deficiency (147). The expression of matrix metalloproteinases
(MMPs) are also skewed in DCs of vitamin A deficient origin (176, 177). The
combination of depression of homing receptors and matrix degrading enzymes leads to
impaired trafficking of DCs to stimulate immune responses. Therefore, in specific-
pathogen free vitamin A-deficient animals, the DC populations may be skewed, even in
the absence of an infectious challenge.

Since vitamin A deficiency is known to impair Th2 responses and myeloid DCs
stimulate the Th2 response, we hypothesized that vitamin A-deficient (VAD) animals
would have decreased myeloid DCs compared to vitamin A-sufficient (VAS) control
animals. To address this hypothesis, we used multicolor flow cytometry to quantify the
DC populations in the spleen of C57BL/6J mice and compared these populations in
animals that were depleted of vitamin A or were vitamin A-sufficient. The combination

of surface markers used led to a comprehensive overview of the effect of vitamin A

60

depletion on many cells of the immune system over various degrees of vitamin A

deficiency in both male and female mice.

Methods
Animals

Male and female C 57BL/6J mice were purchased from Jackson Laboratories (Bar
Harbor, ME) and housed in a facility accredited by the Association for Assessment and
Accreditation of Laboratory Animal Care. Vitamin A-deficient mice were obtained
following an established protocol (91). Briefly, breeder pairs were allowed to mate and
upon indication of pregnancy the female was placed on a vitamin A-deficient AFN-93G
diet. The pups were weaned into one of four groups based on diet and gender. Weaned
mice were fed ad libitum pelletized AIN-93M diets free of vitamin A, or a control diet
containing 8 mg retinyl-palmitate/Kg diet. The control diet had added red dye to aid in
preventing accidental feeding of incorrect diet. All diets were purchased from Dyets, Inc.
(New Bethlehem, PA) and stored frozen in vacuum-sealed bags to maintain freshness.
Mice were weighed once per week starting at 3 weeks of age to monitor general animal
health. At 6 weeks of age, and every 2 weeks after, serum was obtained from saphenous
vein punctures and analyzed for serum retinol. Mice were sacrificed via carbon dioxide
asphyxiation at 8, 10, and 12 weeks of age and spleen, liver, and serum were collected.
All animal procedures were performed in accordance with protocols approved by the

Michigan State University Animal Care and Use Committee.

Vitamin A analysis
Vitamin A status was assessed using an adapted high performance liquid

chromatography (HPLC) protocol (178). A 75% acetonitrile, 25% water mobile phase

61

was used over a ten minute run time through a Prism RP column (Part #32103-153030)
purchased from Therrno Electron Corp (Waltham, MA). A luM retinyl-acetate (Sigma,
St. Louis, MO) internal standard was used to assess retinol concentrations. Retinol was
quantified in serum and following saponification of liver retinyl esters. The retinol was
extracted into hexane, dried under argon stream, and redissolved in acetonitrile. Liver
retinol is expressed as nanomole (nMole) retinol activity equivalents (RAE) per gram of

liver.

Tissue Processing

Spleens were processed for flow cytometric analysis as previously described .
Briefly, tissues were harvested, weighed and placed in ice cold calcium and magnesium
free Hank’s Balanced Salts Solution (Lonza, Basel, Switzerland). Tissues were digested
into single cell suspensions using enzymatic dissociation medium from Stem Cell
Technologies (Vancouver, BC, Canada) per manufacturer’s instructions. The digest was
filtered, red blood cells lysed, and cell suspensions resuspended in FACS Buffer [0.1%
sodium azide (Fisher Scientific, Pittsburgh, PA), 1% fetal bovine serum (Hyclone, Logan,

UT), in Dulbecco’s phosphate buffered saline, pH 7.4-7.6, sterile filtered].

Flow Cytometry

Spleen cell suspensions were blocked with 5 ug of anti-FcRyII/III antibody
(2.4G2 hybridoma) on ice for 10 minutes and then incubated with two separate cocktails
of monoclonal-antibody (mAb) fluorochrome conjugate reagents. A cocktail of 6
monoclonal antibodies (mAb) was used for DC analysis, as previously described by
Duriancik and Hoag (179), and a 3 mAb cocktail for T lymphocyte analysis [hamster

anti-mouse CD3e-PE (clone 500A2), rat anti-mouse CD4-AFC (clone RM4-5), and rat

62

anti-mouse CD8a-PE-Cy7 (clone 53-6.7)]. All mAb-fiuorochrome conjugates were
purchased from BD Biosciences (San Jose, CA). Flow cytometry data was collected on a
BD Biosciences LSR II flow cytometer and analyzed with F CS Express (version 3)

software to identify DC (179) and T lymphocyte cell populations.

Statistical Analysis

Statistical analysis was performed using SPSS statistics 17. All figures were
designed using Prism (GraphPad Software). Three factorial (age, gender, diet) analysis
of variance (ANOVA) and Bonferroni post-test was performed on spleen and liver
weights, liver RAE data, and total spleen cellularity. Body weight and serum retinol
were analyzed using repeated measures ANOVA at 12 weeks due to missing data at 8
and 10 week time points. Multiple linear regression analysis was performed to determine

the effects of vitamin A (nMole RAE/g liver) depletion on each spleen cell population.

Results

The VAD altered the growth kinetics of the mice (Figure 1). Male and female
mice gained weight through 1 1 weeks of age and maintained weight from 1 l to 12 weeks.
There was a significant effect of diet from 8 weeks through 11 weeks of age. Gender had
a significant effect on weight from 3 weeks through 9 weeks, with an exception from 7 to
8 weeks of age. The weight, diet, and gender interaction was significant starting at 9
weeks of age and continued through 12 weeks of age. Diet and gender both significantly
affected the weight of the animals.

As expected, livers of male mice weighed significantly more than livers of female
mice due to body size, but diet and age had no effect on liver weight of the mice (Table
1). Serum retinol significantly differed from previous measurement within a group only

63

from 6 to 8 weeks of age (Figure 2A). Diet, gender, and the diet-gender interaction
significantly affected serum retinol. Male animals on control vitamin A-sufficient diet
had higher serum retinol values than females, but lower liver RAE. Despite other factors,
diet, age, and gender each significantly affected liver RAE (Figure 2B). As expected, the
interaction of age and diet on liver RAE was significant, indicating that time consuming
the vitamin A-deficient diet affected liver stores. However, surprisingly, the interaction
of diet and gender was significant indicating that consuming a VAD diet affects male and
female mice differently.

Spleens of VAD animals weighed significantly more than spleens from VAS
animals, regardless of age or gender (Table 2). There was a diet-independent significant
interaction of age and gender for spleen weight, because as males aged the spleen weight
decreased, but as females aged the spleen weight increased. The total cellularity of the
spleen was unaffected by vitamin A depletion (Figure 3). The age and gender of the
animal significantly affected the total spleen cell counts. In addition, the interaction of
age and diet and age and gender significantly affected the cell counts.

Multiple linear regression analyses were performed on each cell population I
identified in the spleen (Figures 4, 5, and 6). The total spleen cells were not different
between VAD and VAS groups; therefore any difference in percent cell populations
would also be reflected in cell population total numbers. Therefore, only percent of each
cell type is shown. The liver RAE was log transformed and the logarithmic regression
line was interpolated. The table insets indicate the [3 coefficients and their p values as

well as the R2 value and the ANOVA significance of the model which was adjusted for

64

age and gender variables. Significant and strong associations were [3 coefficients and R2
values greater than 0.4 and p-values less than 0.05.

Myeloid DCs were only slightly decreased by VAD. Even though the model and
the [3 coefficient were significant, the [3 coefficient for liver RAE was small at 0.32
(Figure 4A). Lymphoid DC 3 increased as severity of VAD increased (Figure 4B). The B
coefficient for liver RAE and lymphoid DCs was -0.63. Since lymphoid and myeloid
DC 5 preferentially stimulate Th1 and Th2 responses, respectively, we also assessed the
lymphoid to myeloid DC ratio, as this would be assumed to alter the Th1/Th2 bias in
immune responses. The ratio of lymphoid DCs to myeloid DC significantly increased as
severity of VAD increased with a [3 coefficient of -O.68 (Figure 4C). Plasmacytoid and
preDCs were unaffected by VAD (Figures 4D and 4E).

Some, but not all, spleen lymphocyte populations were altered by VAD (Figure
5). B lymphocytes (B220+/MHC-Ilva’iab'°) , CD3+/CD4'/CD8' lymphocytes, and total
CDT/CD8+ T lymphocytes were unaffected by vitamin A deficiency (Figure 5A, B, and
D). However, CD3+/CD4+ T lymphocytes were decreased as severity of VAD increased
(Figure 5C). The 13 coefficient of liver RAE and CD4+ T cells was 0.62. Interestingly,
memory C D8+ T lymphocytes (Gr-1+/CD8+) were increased as severity of VAD
increased (Figure 5D). The [3 coefficient of liver RAE and memory CD8+ T cells Was -
0.62.

As hypothesized and previously reported by others, polymorphonuclear
neutrophils (PMN) percentage increased as severity of VAD increased (Figure 6). The B

coefficient of liver RAE and PMN was -0.44.

65

Discussion

The data presented here indicate the C57BL/6J animals depleted of vitamin A
have skewed splenic DC subpopulations that could contribute to the observed Th1 bias in
vitamin A-deficient populations. Vitamin A-deficient populations have impaired Th2-
dependent antibody immune responses, and enhanced Th1 cell-mediated immune
responses (114, 121, 122). In the mouse, CD1 1b+ myeloid DCs stimulate Th2 responses,
while CD80? lymphoid DCs stimulate Th1 responses (10, 11). Previously, in vitro work
has established that vitamin A is important for the differentiation of bone marrow
progenitor cells into myeloid DCs and blocking vitamin A signaling leads to greater
numbers of neutrophils (133). We hypothesized that myeloid DCs would be decreased in
the spleens of vitamin A-deficient mice. Interestingly, our data indicate that in vivo.
lymphoid DCs are increased in VAD mice and myeloid DCs are only modestly affected.
The skewed DC proportion was contradictory to our original hypothesis of decreased
myeloid DCs and unaffected lymphoid DCs. However, our data demonstrating an
alteration in lymphoid to myeloid DC ratio in VAD could directly contribute to
insufficient Th2 responses associated with VAD.

We used the dietary depletion protocol of Smith et a1 (1987) to obtain VAD
animals (91). We monitored feed consumption (data not shown) and body weights
(Figure 1) during the study to determine the point of inanition which would lead to
protein-energy malnutrition (PEM). Detection of inanition was established to be a loss of
greater than 10% of body weight. Interestingly, depletion of vitamin A in female mice
had slower kinetics than depletion of male mice (Figure 2). Few male mice reached the

12 week time point without losing 10% of their body weight, hence only four VAD males

66

at 12 weeks of age were analyzed. Therefore, the protocol produces marginally vitamin
A-deficient mice at 8-10 weeks of age and severe vitamin A deficiency in male animals
at 12 weeks.

Our data are in agreement with many of the spleen immune cell percentages of
vitamin A-deficient populations reported previously by others, including B and T
lymphocytes (91, 122, 131). However, the dramatic increase in neutrophils reported in
VAD SENC AR mice contradicts the mild increase observed in our mice [Figure 6,
(108)]. In conjunction with the large increase in neutrophils, VAD SENCAR mice also
had decreased lymphocyte populations (108). Therefore, the 14 week dietary depletion
of vitamin A may have resulted in PEM and/or correspondingly increased
glucocorticoids. Lymphocytes decrease and neutrophils increase in response to PEM and
increased levels of glucocorticoids (180-182). Chronic vitamin A deficiency leads to
PEM, which complicates study of vitamin A deficiency.

In agreement with previous reports, we show that B lymphocyte and CD8+ T
lymphocyte numbers were unaffected and CD4+ T lymphocyte numbers were decreased
in vitamin A-deficient mice. However, we can not distinguish CD4+ T cells as Thl , Th2,
or Treg using our multicolor flow cytometric analysis. Surprisingly, our gating strategy 1
identified a population ofCD8+ T lymphocytes that expressed Gr—lwere increased in
VAD. These CD80t+/Gr-1+ cells were previously characterized as memory CD8+ T
lymphocytes (156, 157). Vitamin A-deficient populations have an increased reliance on
memory cell immune responses and decreased reliance on na'1've cell immune responses
(131, 183, 184). Memory T cells, as well as natural killer (NK) and NKT cells, are

maintained by the growth factor interleukin- (IL) 15. Hepatic and pancreatic stellate

67

cells, as well as DCs, produce IL-lS (185-187). Hepatic stellate cells and DCs can also
store and metabolize vitamin A (135). Although vitamin A did not significantly alter IL-
15 cytokine production in vitro, the dual function of vitamin A metabolism and
production of IL-15 by DCs and liver stellate cells leads to a possible inter-relationship
that would be expected to selectively enhance memory T cell reliance during VAD (188).

Our data indicate, for the first time, that in vivo spleen DC populations are altered
by vitamin A deficiency. Vitamin A deficiency skews DC subset proportions in favor of
Th1 responses, leading to the down-regulation of Th2 responses. The altered DC
proportions provide mechanistic evidence for a role of DCs in altered immune responses
of vitamin A-deficient populations. Further work should expand the DC analysis of
vitamin A-deficient populations to other tissues and newly described DC subpopulations.
The DC populations of the mesenteric lymph nodes and Peyer’s patches should be
assessed during vitamin A deficiency. The effects of vitamin A deficiency on newly
identified DC populations such as CD103+ DCs should also be assessed due to the roles
of vitamin A and CD103+ DCs in the Treg and Thl7 immune cell balance in the

gastrointestinal tract (137, 143, 161, 174).

68

Table 3.1. Liver weight of VAS and VAD male and female animals. Week 8 (n=10
mice per group), week 10 (n=10 mice per group), week 12 VAS female and VAD female

(n=10 mice per group), week 12 VAS male (n=8) and week 12 VAD male (n=4 mice).

 

Week 8 Week 10 Week 12

 

 

 

VAS VAD VAS VAD VAS VAD
Male* 0.91 +/— 0.81 +/- 0.88 +/- 0.85 +/- 0.93 +/- 0.98 +/-

0_05a 0.05 0.04 0.04 0.02 0.03

Female 0.75 +/— 0.68 +/- 0.75 +/— 0.82 +/- 0.78 +/- 0.72 +/-

0.02 0.05 0.05 0.02 0.05 0.05

 

a Values are mean +/- standard error of the mean of weight in grams.

* Statistical significance (p<0.05). Male livers weighed significantly more than female

livers, regardless of age or diet.

69

Table 3.2. Spleen weight of VAS and VAD male and female animals. Week 8 (n=10
mice per group), week 10 (n=10 mice per grouP). week 12 VAS female and VAD female

(n=10 mice per group). week 12 VAS male (n=8) and week 12 VAD male (n=4 mice).

 

Week 8 Week 10 Week 12

 

 

 

VAS* VAD VAS* VAD VAS* VAD
Male 0.07 +/- 0.08 +/- 0.06 +/— 0.07 +/— 0.06 +/- 0.07 +/-

0,018 0.01 0.00 0.01 0.00 0.01

Female 0.06 +/- 0.07 +/— 0.07 +/- 0.08 +/- 0.07 +/- 0.09 +/-

0.00 0.01 0.01 0.01 0.01 0.01

 

a Values are mean +/- standard error of the mean of weight in grams.

* Statistical significance (p<0.05). Spleens of VAD animals weighed significantly more

than spleens of VAS animals, regardless of age or gender.

70

Figure 3.1. Body weights of C57BL/6J mice consuming VAS and VAD diet. VAS
animals are indicated by filled symbols and VAD animals are indicated by open symbols,
male mice are indicated by squares (I) while female mice are indicated by triangles (V).
*, Weight is significantly different from previous week (p<0.05). #, indicates a
significant time* gender interaction (p<0.05) at weeks 3-7 and 8 to 9. $, indicates a
significant time*diet interaction (p<0.05) at weeks 8-11. &, indicates a significant
time*diet* gender interaction at weeks 9-12 (p<0.05). The data represents 10 animals per

group. except VAD males (n=4).

71

Body Weight (g)

 

+VASM
+VADM
+VASF
——v-—VADF

 

 

l I I I 1 I
* * * It * * I I

3 4 5 6 7 a 9* 1o*11*12
Weekonge

72

Figure 3.2. Vitamin A status of C57BL/6J mice. VAS animals are indicated by filled
symbols and VAD animals are indicated by open symbols. Male mice are indicated by
squares (I) and female mice are indicated by triangles (V). A, serum retinol analyzed
with repeated measures ANOVA. *, serum retinol is significantly different from
previous week (p<0.05). Diet, gender, and the diet*gender interaction are significant
(p<0.05). The data represents 2-6 animals per group for serum retinol analysis. B, liver
retinol activity equivalents analyzed with 3-factorial ANOVA. Diet, gender, age*diet,
and diet* gender are significant (p<0.05). The data represents 10 animals per group,

except week 12 VAD males (n=4) for liver RAE analysis.

73

2000-

1000-

Serum Retinal (nM)

450‘

350-

250*

150-

501

 

 

+VA8M
-c3-VADM
+VASF
+VADF

 

6* {3* 10 1'2
Week of Age

74

 

 

 

~....,.——.-____-_._. “H‘— ._

Figure 3.

‘ 'I » LI
lIllJIbdlLd

animals

Figure 3.2 Continued. Vitamin A status of C57BL/6J mice. VAS animals are
indicated by filled symbols and VAD animals are indicated by open symbols. Male mice
are indicated by squares (I) and female mice are indicated by triangles (V). A, serum
retinol. *, serum retinol is significantly different from previous week (p<0.05). Diet,
gender, and the diet*gender interaction are significant (p<0.05). The data represents 2-6
animals per group for serum retinol analysis. B, liver retinol activity equivalents. Diet,
gender, age*diet, and diet* gender are significant (p<0.05). The data represents 10

animals per group, except week 12 VAD males (n=4) for liver RAE analysis.

75

 

I VASM
I: VADM
v VASF
V VADF

 

 

Tlvll. .lI TTIlllUVIllT. In
Till. TllfillTVll; um
II. T! TlTIlDIII woo
.............. ...r .. 6
0000000000 000
0000000054 21
98755432

:3: was. 2055
m5... 33..

Week of Age

76

Figure 3.3. Total spleen cells per animal of C57BL/6J mice. Filled bars are vitamin
A-sufficient animals, open bars are vitamin A-deficient animals. * indicates significant
differences (p<0.05). Week 8 is significantly different than week 10, but week 8 and 10
are not significantly different than week 12, regardless of gender and diet. Male and
female mice are significantly different regardless of time and diet. The diet*age and
age* gender interactions are significant as well. The data represents 10 animals per

group, except week 12 VAD males (n=4).

77

Cell Counts (x108)

1.0“

0.5a

 

0.0-

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

IL 1
a 1a
Male* Female Male* Female Male* Female
8* 10* 12
Week of Age

78

-VAS
[:3VAD

1 figure 3.4. Effc
DCS. 3.0111110“
preDCs. Tablci

mid.

Figure 3.4. Effects of depleted liver RAE on spleen DC populations. A, myeloid
DCs. B, lymphoid DCs. C, ratio of myeloid to lymphoid DCs. D, plasmacytoid DCs. E,

preDCs. Table insets indicate significant [3 coefficients, R2 values, and significance of

model.

 

79

Percent Myeloid DC ("/o)

 

 

 

 

 

 

 

 

 

 

 

Myeloid DC
4.5-
[3 p value

4 0 Liver RAE 0.32 0.00

' ‘ Age 0.02 0.80

Gender 0.40 0.00

3'5” R2 = 0.22 0.00
3.04
2.5«
2.0«
1.5-
1.0-
0.5«
0.0

 

 

l
Log Liver RAE

80

 

 

 

 

Figure 3.4 C out
myeloid 0C3. I}
30 E. preDCs

significance of n'

Figure 3.4 Continued. Effects of depleted liver RAE on spleen DC populations. A,
myeloid DCs. B, lymphoid DCs. C, ratio of myeloid to lymphoid DC 5. D, plasmacytoid

DC 5. E, preDCs. Table insets indicate significant [3 coefficients, R2 values, and

significance of model.

81

 

3°C 00 0.935).. .ceocea

 

 

 

Percent Lymphoid DC (%)

1 .6“
1.54
1 .4‘
1.3~
1.2.
1.1.
1.01
0.9.
0.84
0.7j
0.6~
0.5-

Lymphoid DC

 

 

0.44

B

p value

 

Liver RAE -0.63

0.00

 

0.34

Age

0.32

0.00

 

0.2-

 

Gender -0. 12

0.13

 

 

0.1-

R2=0.4l

 

 

0.00

 

 

0.0

 

31

.3
Log Liver RAE

82

Figure 3.4 Continued. Effects of depleted liver RAE on spleen DC populations. A,
myeloid DCs. B, lymphoid DCs. C, ratio of myeloid to lymphoid DCs. D, plasmacytoid
DCs. E, preDCs. Table insets indicate significant [3 coefficients, R2 values, and

significance of model.

 

83

 

 

 

 

 

 

 

 

 

 

 

 

 

C Lymphoid to Myeloid DC Ratio
1.2«
1.1‘
1.04
_9 0.9J
&
o 0.8‘
O
1:
E, 0.74
o
>~
2 0.6«
2
1:
«5 0.5
4:
a.
F. 0.4«
3 .
0 3 [3 p value
' Liver RAE —0.68 0.00
0.2, Age 0.24 0.00
Gender -0.39 0.00
0.1 R2 = 0.52 0.00
0.0 r

51

Gt

1
Log Liver RAE

84

Figure 3.4 Continued. Effects of depleted liver RAE on spleen DC populations. A,
myeloid DCs. B, lymphoid DCs. C, ratio of myeloid to lymphoid DCs. D, plasmacytoid
DC 3. E, preDCs. Table insets indicate significant 0 coefficients, R2 values, and

significance of model.

85

Percent Plasmacytoid DC (%)

Plasmacytoid DC

 

 

 

 

 

 

 

 

 

 

 

0-45‘ [5 p value

Liver RAE 0.08 0.40
0,40. Age -0.03 0.77

Gender 0.12 0.21

035. R2 = 0.02 0.57
0.30‘
0.25 _
0.20,
0.154 " _ "
0.10,
0.05-
0.00 .

 

 

'04
w.
h.

.2 -1 0 1
Log Liver RAE

 

 

86

Figure 3.4 Continued. Effects of depleted liver RAE on spleen DC populations. A,
myeloid DCs. B, lymphoid DCs. C, ratio of myeloid to lymphoid DCs. D, plasmacytoid
DCs. E, preDCs. Table insets indicate significant [3 coefficients, R2 values, and

significance of model.

 

 

87

Percent preDC (%)

 

 

 

 

 

 

 

 

 

 

 

 

 

preDC
2.251
B p value
2 00- Liver RAE 0.10 0.31
' Age -0.20 0.03
Gender 0.20 0.03
”5‘ R2 = 0.09 0.02
1.501
1.25‘
1.00-
0.751
0.50«
0.254
0.00 . .
-2 -1 0 1

Log leer RAE

88

Figure 3.5. Effects of depleted liver RAE on spleen lymphocyte p0pulations. A, B

cells. B, CD3+/CD4'/CD8' T cells. C, CD4 T cell. D, total CD8 T cells. E, Gr-lJ'/CD8+

memory T cells. Table insets indicate significant [3 coefficients, R2 values, and

significance of model.

 

89

 

 

 

 

 

 

 

 

 

 

 

 

 

A B Lymphocytes
77.5-
75.0
72.5«
70.0«
1‘: 67.5«
3
650
8
'5 62 54
e. ' —— -
_J
m 60.0"
§ 57.5
3’ 550
' I B p value
52.5- Liver RAE -0.00 0.98
Age 020 0.03
50'0' Gender 0.19 0.05
47.5« R2 = 0.08 0.03
45.0 I Y I
.2 -1 0 1
Log Liver RAE

90

Figure 3.5 Continued. Effects of depleted liver RAE on spleen lymphocyte

populations. A, B cells. B, CD3+/CD4'/CD8' T cells. C, CD4 T cell. D, total CD8 T

cells. E, Gr-1+/CD8+ memory T cells. Table insets indicate significant [3 coefficients, R2

values, and significance of model.

 

91

Percent C03+CD4-CDBo (%)

CD3+ICD4'ICDB' Lymphocytes

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

[3 p value
13- Liver RAE 0.04 0.70
12‘ Age -0.18 0.06
Gender -0.1 1 0.25
1“ R2=O.O4 0.18
10.
9- -"
8.
7'1
6~ w ,
5.
4.
3..
2.
1 .
0 .
-2 .1 1

Log Liver RAE

92

Figure 3.5 Continued. Effects of depleted liver RAE on spleen lymphocyte

populations. A, B cells. B, CD3+/CD4°/CD8' T cells. C, CD4 T cell. D, total CD8 T
cells. E, Gr-l+/CD8+ memory T cells. Table insets indicate significant [3 coefficients, R2

values, and significance of model.

93

Percent CD4 T cells (%)

CD3+ICD4" T Lymphocytes

 

-l
j“

[3 p value

Liver RAE 0.62 0.00
16+ Age 013 0.10

Gender 0.11 0.16
R2 = 0.36 0.00

 

.s
j

 

 

 

.s
S"

 

.s
'5‘

 

 

 

 

4.5.5.5
9'???

‘P

 

 

 

N4
0:

-2 31 0 i
Log Liver RAE

94

Figure 3.5 Continued. Effects of depleted liver RAE on spleen lymphocyte

populations. A, B cells. B, CD3+/CD4’/CD8' T cells. C, CD4 T cell. D, total CD8 T
cells. E, Gr-l+/CD8+ memory T cells. Table insets indicate significant [3 coefficients, R2

values, and significance of model.

95

Percent C08 T cells (%)

CDT/COB” T Lymphocytes

 

 

[3

p value

 

~ LiverRAE -0.07

0.44

 

Age 0.07

0.47

 

 

Gender -0.3 l

0.00

 

 

R2=0.10

 

 

0.01

 

 

 

31

I

1
Log Liver RAE

96

N4

#1

 

Figure 3.5 Continued. Effects of depleted liver RAE on spleen lymphocyte

populations. A, B cells. B, CD3+/CD4'/CD8' T cells. C, CD4 T cell. D, total CD8 T
cells. E, Gr-1+/CD8+ memory T cells. Table insets indicate significant [3 coefficients, R2

values. and significance of model.

97

CDB‘IGr-‘V Memory T Lymphocytes

7‘

S”

E"

‘P

 

w
1
J

 

Percent Memory CD8 T cells (%)

 

 

 

 

 

 

 

 

 

 

 

B p value
24 Liver RAE -0.62 0.00
Age 0.24 0.00
1. Gender -0.20 0.01
R2 = 0.38 0.00
O ' ' I ' I fl
-2 -1 0 1 2 3 4

Log Liver RAE

98

 

 

Figure 3.6. Effects of depleted liver RAE on spleen PMN. Table inset indicates

significant [3 coefficients, R2 values, and significance of model.

 

99

Neutrophils

 

 

 

 

 

 

 

 

 

 

Percent PMN (%)

 

 

 

97 B p value
Liver RAE —0.44 000
3‘ Age 0.14 0.10
Gender 0.06 0,51
71 R2 Z 0.20 0.00
6.
5.
4.1
3‘ '.
2‘
I I;
1.
0 . , . ‘ r _'
'2 ‘1 ° 1 2 3 4

Log Liver RAE

100

CHAPTER 4: CONCLUSIONS & FUTURE DIRECTIONS

Flow C ytometty of DCs

The multicolor flow cytometry protocol developed can identify relevant DC
populations of the spleen and Peyer’s patches of C57BL/6J mice (179). However,
research continues to identify more DC subpopulations, such as CD103+ DCs present in
the spleen and mucosal tissues capable of antigen cross-presentation, phagocytosis and
presentation of apoptotic cells, and induction of tolerance (143, 159, 161). The BD
Biosciences LSR II flow cytometer is capable of distinguishing and analyzing up to 18
separate fluorochromes. Therefore, open channels remain to add more monoclonal
antibody-fluorochrome conjugates to identify more antigens on the DCs and therefore
identify additional DC populations. However, careful antigen choices must be performed
to ensure the antigen is specific to only one DC population or can be used in combination
with other markers to distinguish one cell type. For example, in the murine spleen

CD103)r DCs are a subpopulation of CD80L+ lymphoid DCs (161).

Murine Model of Vitamin A Deficiency

Others have reported methods of inducing vitamin A deficiency in mice using
dietary restriction (91, 108, 120). The model employed in chapter 3 has characteristics of
human vitamin A deficiency including depressed Th2 responses. We did not measure
other parameters of deficiency, but the deficient animals likely suffer from impaired
mucosal integrity and eventual protein-energy malnutrition if the mice were continued on
the vitamin A-deficient diet. Weight, serum retinol, and liver retinol activity equivalent
data provide further insight into other immunological studies of vitamin A-deficient mice.
Dietary restriction of vitamin A leads to depleted serum retinol and liver retinyl-esters by

101

 

8 to 12 weeks of age (91). Continued restriction of vitamin A, over 8 to 12 weeks, leads
to complications such as protein-energy malnutrition (PEM) due to inanition (1 12). Any
animal that lost greater than or equal to 10% of total body weight was classified as
suffering PEM and was excluded from our analysis. Therefore, we believe our immune
cell population changes are attributable to VAD itself, and not related to PEM. The time
course of analysis of the animals also demonstrates the effects of various degrees of
vitamin A deficiency.
DC Populations of Vitamin A-Deficient Mice

Dietary depletion of vitamin A skews the proportion of DC populations in the
spleen of mice. In accordance with our hypothesis, mice with the lowest liver retinol
activity equivalents (RAE) had modestly decreased myeloid DCs and increased
neutrophils. Unexpectedly, the lymphoid DCs were increased with more severe vitamin
A deficiency. The increase of lymphoid DCs, responsible for stimulating Th1 responses
in mice, is in accordance with reports that vitamin A deficiency increases Th1 responses
at the expense of Th2 responses. The plasmacytoid and preDCs were unaffected by
_ depletion of vitamin A. In summary, the main effect was increased lymphoid DCs which
stimulate Th1 responses leading to cross-regulation of Th2 responses, but in combination
with decreased myeloid DCs that stimulate Th2 responses.
Other Immune Cell Populations of Vitamin A-Deficient Mice

Vitamin A-deficient mice have skewed T lymphocyte populations, in addition to
DC subpopulations. T helper CD4+ cells are decreased and memory CD8+ CTLs are
increased in vitamin A-deficient animals compared to vitamin A-sufficient animals. The

increased CTL population correlates with increased lymphoid DCs and Th1 responses of

102

vitamin A-deficient populations. B lymphocytes are unaffected by dietary depletion in
mice. Other reports have shown vitamin A deficiency does not alter B lymphocyte
numbers despite depressed Th2 antibody responses. In vivo, vitamin A deficiency has
only minor affects on PMNs. Kuwata et al reported a marked significant increase in
PMN populations in vitamin A-deficient SENCAR mice (108). However, the SENCAR
mice were depleted of vitamin A for 14 weeks and therefore severely depleted of vitamin
A and likely PEM. Glucocorticoid responses induced by PEM lead to increased PMN
and decreased lymphocytes, both of which were observed in SENCAR vitamin A-

deficient mice (181).

Future Directions

Vitamin A may have differential effects on other immune cell populations, DC
populations, and cell populations of various tissues. Others have demonstrated the effects
of vitamin A on various T cell populations. In vivo, we demonstrate the effects of
vitamin A depletion on total CD4+, CD8+, memory CD8+, and CD3+/CD4'/CD8' T cell
populations. However, there are other T lymphocyte populations dependent on vitamin
A. As DC research continues to identify new DC populations, the effects of vitamin A
deficiency on these populations can lead to more thorough understanding of the
cooperation and coordination of the immune response in vitamin A-deficient populations.
The effects of vitamin A are most dramatic in mucosal tissues, particularly the mucosal
tissues of the gastrointestinal tract. The skewed cell numbers of various immune cell
pOpulations is interesting, but the findings are limited in that we could not describe any
functional changes in these studies. Therefore, various infection models could be

employed in vitamin A—deficient animals to determine if the decreased cell populations

103

observed have the expected significant effects on the immune response to the various
pathogens. Also it is important to determine the effects of supplementing vitamin A-
deficient populations to determine if restoring adequate vitamin A status can return the
immune cell populations to similar levels of vitamin A-sufficient animals and the kinetics
of this restoration. Future directions therefore include expanding the research to include
other immune cell populations and other secondary lymphoid tissues, determining DC
function during vitamin A deficiency using infection models, and supplementing
deficient animals to determine if the period of deficiency permanently depresses immune
cell populations.

We characterized many immune cell populations in these studies. However, Treg
and Thl7 cells are also dependent on vitamin A (172, 174). In vitro T cells differentiate
into Th17 cells in media lacking vitamin A. However, in media containing vitamin A the
T cells differentiate into Treg cells (174). Thl7 cells are T lymphocytes that produce IL-
17 and have been shown to be responsible for development of autoimmune disease.
Therefore, not only is vitamin A responsible for stimulating adequate Th2 antibody
responses, but vitamin A may also be critical in maintaining tolerance to self antigens.
CD103+ DCs are present in mucosal tissues and the germinal center marginal zones of the
spleen and induce tolerance to self antigens (142, 143, 159, 161). Therefore, in
combination with the Treg and Thl7 balance, a role of vitamin A in CD103+ DCs may
provide insight into the observed imbalance of Treg and Thl7 cell populations in vitamin
A-deficiency. Comparison of Treg, Th17 and CD103+ DCs of vitamin A-deficient to
vitamin A-sufficient animals using similar in vivo methods would provide evidence of the

role of vitamin A in development of tolerance.

104

It is of particular interest that lymphoid DCs and memory CD8+ T lymphocytes
are both increased in vitamin A-deficient animals. Memory T cells, as well as natural
killer (N K) and NKT cells, are maintained by the growth factor IL-15. Hepatic and
pancreatic stellate cells, as well as DCs, produce IL-15 (185-187). Hepatic stellate cells
and DCs can also store and metabolize vitamin A (135). Although vitamin A did not
significantly alter IL-15 cytokine production by human T lymphocytes in vitro, the dual
function of vitamin A metabolism and production of IL-15 by DCs and liver stellate cells
leads to a possible inter-relationship that would be expected to selectively enhance
memory T cell reliance during VAD (188). Therefore, the source of IL-15, plasmacytoid
or lymphoid DCs, should be further investigated as well as any increase in lL-15 in VAD
animals.

Vitamin A has ubiquitous functions, but focused and major roles in the eye and
mucosal tissues. The role of vitamin A in the eye employs the ll-cis-retinal form of
vitamin A and not the all-trans-retinoic acid form functioning in the immune system.
Therefore, characterization of DC populations and other immune cells of mucosal tissues
in vitamin A-deficient animal may be significantly altered compared to vitamin A-
sufficient populations. Homing of gut B and T lymphocytes and development of mucosal
CD103+ DCs relies on the presence of vitamin A (135, 136, 143, 172, 189). Therefore,
the cell populations may be decreased in the whole animal or in specific tissues due to the
lack of homing responses. Since the spleen is a filtering organ for the entire body, our
findings are representative of the immune system as a whole, but can not be extrapolated
to specific body sites. The differences between vitamin A-sufficient and vitamin A-

deficient animals may be more pronounced in the intestinal mucosa due to the added

105

homing function of vitamin A. In addition, the intestine is the first encounter immune
cells have with dietary vitamin A and DCs possess the enzymes to convert vitamin A to
the biologically active form. So in combination, the homing of immune cells, the
enzymes to metabolize vitamin A, and the decreased immune cell populations observed
in the spleen indicate that the intestinal mucosa is an important site to characterize
immune cell populations of vitamin A-deficient animals. We show preliminary data for
the effects of VAD of immune cells of the Peyer’s patches is Appendix II, but the data
are unadjusted for age and gender and also lack statistical analysis.

Skewed immune cell numbers of vitamin A-deficient animals does not directly
indicate an increased susceptibility to infection or mortality from infection. Therefore,
the vitamin A-de'ficient animals and vitamin A-sufficient control animals should be
exposed to various pathogens eliciting various immune responses and self peptides
leading to tolerance to determine if the changes in immune cell numbers leads to changes
in disease susceptibility. A pathogen that leads to primarily a Th2 response is
hypothesized to lead to increased susceptibility and mortality in vitamin A-deficient
animals while a pathogen requiring primarily a Th1 response should survive the infection
better than vitamin A-sufficient controls. Exposing the animals to a “self” antigen that
leads to a Th17 autoimmune reaction is hypothesized to cause a more severe immune
reaction in vitamin A—deficient animals compared to control, vitamin A—sufficient,
animals. The models described could highlight the role of vitamin A, specifically the role
of vitamin A in DCs, in clearing infections or establishing an autoimmune prone
environment. Using the flow cytometry methodology described in chapter 2, a

comprehensive overview of the immune response to various pathogen challenges in

106

 

vitamin A-deficient animals could establish a mechanism of vitamin A in the immune
system during an immune challenge.

The changes in immune cell numbers during vitamin A deficiency may be
permanent changes. The deficient animals may not restore the immune cell numbers
comparable to the sufficient animals even after supplementation. Animals depleted of
vitamin A could be supplemented after various degrees of deficiency or after various
times after deficiency has been established to determine appropriate supplementation
strategies.

Analysis of immune cell populations using the multicolor flow cytometry
elucidated a role of vitamin A in maintaining DC subpopulations during homeostasis.
However, more research needs to be conducted to further describe the increased
susceptibility to infections in vitamin A-deficient populations. In addition,
supplementation strategies may be improved by focusing on improved immune cell

populations instead of circulating serum retinol levels.

107

 

APPENDIX I
Supplemental data from the identification and enumeration of dendritic cell
populations from individual mouse spleen and Peyer’s patches using flow cytometric

analysis (Chapter 2).

 

108

Supplemental Figure 1.1. LSR II optics block and filter scheme of the fluorescent

channels utilized for data collection.

 

 

 

109

Blue Laser
(488 nm)

PerCP-Cy5.5

 

pl Red Laser
(633 nm)
APC-Cy7 APC

 

 

 

 

 

Alexa Fluor 700

110

Supplemental Figure 1.2. Comparison of compensated and uncompensated data. Left
panels (A, C, E, G) are compensated contour plots. Right panels (B, D, F, H) are
uncompensated contour plots. Percentage of compensation employed, calculated from
CompBead fluorescence matrix from the automatic compensation feature of the DIVA

software, is represented above the compensated panel.

111

>

MHC ll-FlTC-A

Gr1 PerCP-Cy5.5-A

8220 APC-A

Gr1 PerCP-CyS.S-A

18.04%

 

 

 

 

 

 

CDllc PE-A

17.39%

 

 

C083 PE-Cy7-A

18.15%

 

 

 

 

 

 

c0110 APC-Cy7-A

13.02%

 

 

CDllc PE-A

MHC ll-FlTC-A

Gr1 PerCP-Cy5.5-A

8220 APC-A

Gr1 PerCP-Cy5.5-A

 

r I
.7639

 

 

 

CDllc PE-A

 

 

 

 

C083 PE-CV7-A

 

 

 

0}

 

CDl 1b APC-CV7-A

 

 

 

 

CDllc PE-A

112

Supplemental Figure 1.3. Flow diagram of Boolean logic used in sequential gating of

spleen cell populations. Final populations gated and enumerated are shown in red boxes.

113

 

      

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114

Supplemental Table 1.1. BD® Biosciences LSR II flow cytometer laser parameters

used in flow cytometry data collection.

 

 

 

 

 

 

 

 

 

 

Parameters (Channel) Voltage Scale

FSC 415 Linear

SSC 382 Linear
F ITC (FL 1 ) 484 Bi-exponential
PE (FL2) 415 Bi-exponential
PerCP-Cy5.5 (F L3) 725 Bi-exponential
PE-Cy7 (FL4) 525 ‘ Bi-exponential
APC (F L5) 516 Bi-exponential
APC-Cy7 (FL6) 535 Bi-exponential

 

 

 

 

115

Supplemental Table 1.2. BD® Biosciences LSR 11 general compensation matrix.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

F luorochromes Value (%)
PE - FITC 18.04
PeGC—Cy5.5 - FITC 1.91
PE-Cy7 — FITC 0.19
APC — FITC 0.28
APC-Cy7 — FITC 0.00
FITC — PE 0.46
PeGC-Cy5.5 — PE 13.02
PE-Cy7 — PE 1.26
APC — PE 0.10
APC-Cy7 — PE 0.00
FITC -— PeGC-Cy5.5 0.00
PE — PeGC-Cy5.5 0.00
PE-Cy7 — PeGC-Cy5.5 17.39
APC — PeGC-Cy5.5 1.94
APC-Cy7 — PeGC-Cy5.5 1.67
_ FITC — PE-Cy7 0.16
~ PE — PE-Cy7 3.27
K PeGC-Cy5.5 — Pe-Cy7 0.84

 

 

116

 

Supplemental Table 1.2 Continued. BD® Biosciences LSR II general compensation

 

 

 

 

 

 

 

 

 

 

 

 

matrix.

APC — PE—Cy7 0.14
APC-Cy7 — PE-Cy7 2.24
FITC —- APC 0.00

PE — APC 0.00
PerCP-Cy5.5 — APC 0.84
PE-Cy7 -— APC 0.14
APC-Cy7 — APC 3.09
FITC — APC-Cy7 0.00

PE — APC-Cy7 0.00
PeGC-Cy5.5 — APC-Cy7 0.11
PE-Cy7 —— APC-Cy7 4.37
APC — APC-Cy7 18.15

 

 

 

 

117

Preliminary data on the effects of vitamin A deficiency on immune cells of the Peyer ’s

patch es.

This data is not adjusted for gender or age effects, and has not been statistically analyzed.

APPENDIX 11

In addition this data has not been adjusted for total cell numbers per Peyer’s patch to

decrease variability of numbers of Peyer’s patches excised from each animal.

Supplemental Figure 2.1. Immune cell percentages of vitamin A-deficient mice.

A

Percent (%)

Percent (%)

Myeloi d y = 0.0234ln(x) + 0.7579

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

R2 = 0.0107
2.50
2.00 .0-
1 50 ’ ’
. 9 j .. . O: ..
O
1.00 ’ 43* ’ 41:...‘5—
—_ O O
0.50 .f '
t o o .0
Q 9 O O
0.00 1— I 1 4 1 A. 1 1
0.01 0.1 1 10 100 1000
Log Liver RAE
. y = 0.00281n(x) + 0.1545
Lympho'd R2 = 0.0083
0.5
0.4 ’
O
0.3
o 9 .
0-2 M
0’. Q 0 O
a L
0.11 ~f .fi .
O l l l l l
0.01 0.1 1 10 100 1000
Log Liver RAE

118

Percent (%)

Percent (%)

Plasmacytoid y = O°08()71r1(><) + 0.4428

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

R2 = 0.084
3.5
3 3—
2.5
2
1.5
O . O
1 ‘ . r
0-5 —— W
M Q
0 r , ,‘ ’ 0 o T .
0.01 0.1 1 10 100
Log Liver RAE
y = 0.037ln(x) + 0.0628
preDC R2 = 0.1001
1.2
1 ._
0
0.8 . . .
0.6 L,
. o
0.4 +
o /9.5
0.2 o O ‘ '
. W
0 . I
-0200] D 1 10 100 JflOO
Log Liver RAE

119

Percent (%)

'11

Percent (%)

y = -0.0111n(x) + 0.4129

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Other DCs R2 = 0.0062
1.2
1 O O
0.8 ‘ W
O
0.6
O . ‘
0.4 .
0.2 . ;+ 99‘ ’
”co 8
0 r r A; Q 1 1
0.01 0.1 1 10 100
Log Liver RAE
y = 0.58411n(x) + 17.825
75 T cells R2 - 00545
35
30
25 L #9 o o 0‘
O O
20 f
/M
15 . ' I ‘~ .
10 W + +
S
0 l I V I 1
0.01 0.1 l 10 100 1000
Log Liver RAE

120

1::

 

 

Percent (%)

Percent (%)

Or-‘NUJAU’I

CD4 T cells y = 0.2011110.) + 6-8741

 

 

 

 

 

 

 

 

R2 = 0.1138
10
o
8 0 o 3
NO . O . O.
6 . . .
4 9 9 0‘ g
’ o
2
O I I .
0.01 1 10 100 1000
Log Liver RAE
y = -0.059ln(x) + 25571
CD8 T cells R2 = 0.0306
0.01 10 100 1000
Log Liver RAE

121

10.

11.

12.

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