DETERMINING THE ROLE OF IRF6 IN
T CELL DEVELOPMENT AND FUNCTIONAL COMMITMENT
By
Tamer Ahmed Mansour

A DISSERTATION
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of
Genetics - Doctor of Philosophy
Quantitative Biology - Dual Major
2014

ABSTRACT
DETERMINING THE ROLE OF IRF6 IN T CELL DEVELOPMENT
AND FUNCTIONAL COMMITMENT
By
Tamer Ahmed Mansour
Interferon regulatory factor (IRF) is a protein family with nine members in
mammals known to orchestrate the homeostatic mechanisms of host defense. There
are functional and/or developmental defects of immune cells in the knockouts of eight
family members. Like other family members, IRF6 is involved in regulating the cell cycle
but in keratinocytes and mammary epithelial cell with mutations associated with
squamous cell carcinomas. However, Irf6 is the only IRF known to be involved in
morphogenesis. In humans, rare variants in IRF6 cause autosomal dominant orofacial
clefting disorders while common variants contribute risk to non-syndromic forms. IRF6 is
the only IRF family member with an as yet undetermined role in immunity.
Here, we used publically available microarray data to uncover a dynamic
expression pattern for Irf6 during hematopoietic development. We found that Irf6 is
expressed early in hematopoiesis in long term hematopoietic stem cells. Also we
identified Irf6 expression in T cell lineage, including developing and functionally
committed stages. Irf1, 2, 4, 8 are indispensable for a normal T cell development and
differentiation. Genetic variants in IRF5, IRF7 and IRF8 are associated to autoimmune
disorders of T cells. Furthermore, protein complexes between IRF6/IRF5 and IRF6/IRF8
were described. These data together with DNA conservation among the IRF members
and structural homology with IRF5 strongly suggests a role for Irf6 in the immune
system, specifically in T-cell development and functional commitment.

We utilized a mouse model to show that Irf6 was required for the regulation of
thymocyte development. We found that Irf6 was expressed in the subcapsular region
and medulla of the thymus. We further found that Irf6 regulated the distribution and
proliferation of developing thymocytes. In addition, loss of Irf6 led to an increase in
double negative cells with a concomitant increase in TCRγδ. Loss of Irf6 also led to a
reduction in double positive cells with no corresponding reduction in single positive cell
maturation. Also, we found that Irf6 dose is critical in development of both CD4+ and
CD8+ cells in an age-dependent manner. These data suggest a novel gene function for
Irf6 in thymocyte development and indicate further studies of IRF6 variants that might
increase the risk of autoimmune disease.
In the mouse, loss of Irf6 leads to perinatal lethality which hinders the ability to
test the necessity of Irf6 in the functionally committed T helper (Th) subsets. In silico
analysis suggested a model for Irf6 role in Th17/Treg balance. To test our hypothesis in
vivo and overcome perinatal lethality, we employed an adaptive transfer of Irf6 knockout
cells into lethally irradiated mice. Mice receiving Irf6 knockout cells had no deficit in
restoration of lymphocyte production. In addition, we used two in vitro models to assess
the necessity of Irf6 in the commitment of T helper cells. Using a stromal-free culture we
found that naive T cells lacking Irf6 could be differentiated efficiently into Th1, Th2, Th17
and Treg using a specific cytokine cocktail. In vitro differentiation of dendritic cells
showed significant increase of MHC-II expression after three days of culture. Irf6 might
be involved in post-translational regulation of MHC-II. These data indicate that intrinsic
Irf6 expression is not essential for T helper subset differentiation. However, a non-cell
autonomous role for Irf6 in T cell differentiation through dendritic cells remains plausible.

ACKNOWLEDGEMENTS

To begin, I would like to extend my deepest gratitude to my mentor, Dr. Brian
Schutte. His patience with me and confidence in my intellectual ability has been
tantamount to my success. I thank him for teaching my not only how to be a scientist
but also how to be a good, fair person. They have both been invaluable lessons. I
would also like to thank the members of my committee; Titus Brown, Sungjin Kim, and
Sainan Wei for the continued support and scientific contribution to this work.
I would like to thank the current and past members of the Schutte Lab: Bill,
Walid, Dan, Krysta, Raeuf, Larissa, Julie, Marissa, David, Nicole, Ari, Silas, Soumya,
Erica, and Bryana. I could not have asked for a more outstanding group of people to
share with both scientifically and socially. Lastly, I would like to thank Dr. Youssef
Kousa and Dr. Arriana smith for helping me to grow scientifically, emotionally, and
socially. Your guidance, support, and friendship have been invaluable.
I must thank the 5th floor community, members of the Friderici, Fyfe, and
Yuzbasiyan-Gurkan labs for their constant willingness to help and for making the 5th a
wonderful work environment. I would also like to that the Genetics Program staff and
my fellow colleagues. A special thank you goes to Jeannine Lee, Dr. Barbara Sears
and Dr. Cathy Ernst for their support and willingness to help in any and all
circumstances. I truly appreciate it.
I would also like to thank the many people who graciously contributed to this
work. To Jose Suarez-Martinez who pursed the in vitro differentiation studies of
dendritic cells. To Dr. Norbert Kaminski and all his lab members for their help, support

iv

and scientific contribution. I must also thank members of the Histopathology laboratory,
for sharing their expertise and warm attitudes, and members of ULAR, for there
diligence and help with all things mouse related.
Last, but certainly not least, I would thank my family and friends. I would to thank
my parents for a lifetime of love, guidance and support. Without them, none of this
would be possible. To my wife Dina and my kids Razan and Adam, thank you for being
sources of inspiration and laughter. I owe a great deal of gratitude to all of my friends
whom for fear of leaving anyone out, I will refrain from mentioning names. Undoubtedly,
you have all been my sanity. I sincerely thank you all for your encouragement, insight,
and friendship. This journey would not have been the same without you!

v

TABLE OF CONTENTS

LIST OF TABLES ………..……………………………………………………………..

viii

LIST OF FIGURES ...…………………………………………………………………...

ix

KEY TO SYMBOLS OR ABBREVIATIONS ...…………………………………...

xii

CHAPTER ONE .………………………………………………………………………....
Literature Review …...……………………………………………………………………
Significance …………………………………………………………………........
Thymus …………………………………………………………………………...
T-cells development …………..…………………………………………….......
Functional commitment of T helper subsets...………………………………...
Interferon regulatory factor family...…………………………………………….
Interferon regulatory factor 6……………………………………………………
Roles of IRFs in T-cell development and differentiation……………………..
REFERENCES ...………………………………………………………………..

1
1
2
2
4
6
11
15
16
21

CHAPTER TWO .…………………………………………………………………………
Meta-analysis of hematopoietic expression profiles reveals selective Irf6
expression in stem cells and in T-cells ……………………………………………......
Abstract …………………………………………………………………………...
Introduction ………………………………………………………………............
Materials and Methods…………………………………………………………..
Results ...……………………………………………………………………….....
Discussion…………………………………………………………………………
APPENDIX ...………………………………………………………...……………
REFERENCES …………………………………………………………………..

41
41
42
43
48
50
59
62
78

CHAPTER THREE ………………………………………………………………………
The role of Irf6 in T-cell development in the thymus ..………………………………..
Abstract ……………………………………………………………………………
Introduction .………………………………………………………………………
Materials and Methods…………………………………………………………..
Mice: ………………………………………………………………………………
Morphological assessment of Irf6 knockout thymi: …………………………..
Western blot analysis: …………………………………………………………..
Immunohistochemistry: ………………………………………………………….
Total cell count and Flow cytometric analysis: ……………………………….
Results……………………………………………………………………………..
Thymic expression of Irf6: ………………………………………………………
Characterization of thymic changes in Irf6 knock-out embryos: ……………
Effect of Irf6 heterozygosity in postnatal thymic proliferation: ………………

89
89
90
91
95
95
95
95
96
97
100
100
102
107

vi

Discussion…………………………………………………………………………
REFERENCES …………………………………………………………..............

110
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CHAPTER FOUR ...………………………………………………………………………
The role of Irf6 in functional commitment of T-cell subsets…………………………..
Abstract…………………………………………………………………………….
Introduction………………………………………………………………………..
Materials and Methods…………………………………………........................
Bioinformatic analysis: …………………………………………………………..
Mice and adoptive transfer: ……………………………………………………..
Th1, Th2, Th17 and Treg differentiation in vitro: ……………………………...
In vitro generation of bone marrow–derived DC: ……………………………..
Results……………………………………………………………………………..
Bioinformatic prediction of possible roles of Irf6 in T-helper commitment: ...
Efficiency of adoptive transfer: ………………………………………………….
Th1, Th2, Th17 and Treg differentiation in vitro: ……………………………...
Changes of bone marrow DCs in Irf6 adoptively transferred mice: ………...
Discussion…………………………………………………………………………
REFERENCES …………………………………………………………………..

118
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119
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122
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CHAPTER FIVE ….……………………………………………………………………..
Summary and Future Directions……………………………………………………….
Summary and Future Directions……………………………………………….
REFERENCES ………………………………………………………………….

147
147
148
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CHAPTER SIX …………………………………………………………………………..
Role of endogenous Avian Leukosis Virus in the pathophysiology
of spontaneous ALV-like tumors in chickens…………………………………………
Abstract……………………………………………………………………………
Introduction……………………………………………………………………….
Materials and Methods………………………………………...........................
Experimental treatment of chicken: ……………………………………………
Next generation sequencing of RNA: ………………………………………….
Bioinformatic analysis:
Results…………………………………………………………………………….
Tumor incidence in the studied groups: ……………………………………….
The performance of different preprocessing approaches: …………………..
Expression profile of Tumor samples is closer to bursa tissue than mature
splenic B cells: ……………………………………………………………………
Differential expression analysis: ………………………………………………..
Pathway enrichment analysis: ………………………………………………….
Discussion…………………………………………………………………………
APPENDIX ...……………………………………………...………………………
REFERENCES …………………………………………………….....................

165

vii

165
166
168
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170
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226

LIST OF TABLES

Table 2.1: Effects of IRFs intrinsic expression in hematopoietic cells..….…………

45

Table 2.2: Mouse expression profiles for hematopoietic cell types…………………

50

Table 2.3: Average rank expression for microarray platforms per gene …………...

58

Table A1: Average rank of IRFs expression for each cell type per experiment …..

74

Table 6.1: Yield and quality of NGS ...………………………………………………….

172

Table 6.2: Comparing the performance of different preprocessing approaches ….

179

Table 6.3: Pathway enrichment analysis……………………………………………….

184

Table A2: Genes significantly up regulated in malignant samples over both
bursa and spleen samples …………..……………………….….………..

191

Table A3: Genes significantly down regulated in malignant samples over both
bursa and spleen samples ………...……….................................………

208

viii

LIST OF FIGURES

Figure 1.1: Histological compartments of a thymic lobule with diagramatic
representation of T cell development …..…………………………………...

5

Figure 1.2: Differentiation of T-helper subsets …………………………....…………….

9

Figure 1.3: interferon regulatory factor (IRF) protein family in mammals……………..

14

Figure 1.4: Roles of IRFs in T-cell development and differentiation ………………….

19

Figure 2.1: Clustering analysis of average Irf gene rank expression …………………

54

Figure 2.2: Average Irf6 rank expression in early hematopoietic progenitor ...………

56

Figure 2.3: Average Irf6 rank expression in T cell stages ………………………...…… 57
Figure A1: Expression of Irf genes in immature hematopoietic progenitors at
3 months and 2 years of mouse age ………………..…...……………….… 63
Figure A2a: Expression of Irf genes in immature haematopoietic progenitors ………

64

Figure A2b: Expression of Irf genes in immature haematopoietic progenitors ………

64

Figure A2c: Expression of Irf genes in immature haematopoietic progenitors .……...

65

Figure A2d: Expression of Irf genes in immature haematopoietic progenitors …….... 65
Figure A3: Expression of Irf genes in early myeloid development .............................. 66
Figure A4: Expression of Irf genes during granulocytes development ……….....……

66

Figure A5a: Expression of Irf genes in thymocyte development ………………...……. 67
Figure A5b: Expression of Irf genes in thymocyte development…………………….…

67

Figure A6: Expression of Irf genes in LT-HSC versus terminally differentiated
haematopoietic cells ...……………………………………………….……….

68

Figure A7a: Expression of Irf genes in lymphocytes and DCs ………………………...

69

Figure A7b: Expression of Irf genes in lymphocytes and DCs ………………………...

69

Figure A8: Expression of Irf genes in resting and activated lymphocytes …...………. 70
Figure A9: Expression of Irf genes in mast cells and pre-mast cells ………..………..

ix

70

Figure A10a: Expression of Irf genes in T-helper subsets……………………………...

71

Figure A10b: Expression of Irf genes in T-helper subsets……………………………...

71

Figure A10c: Expression of Irf genes in T-helper subsets……………………………...

72

Figure A11: Expression of Irf genes in BM macrophage and whole thymus
suspension …………………………………………………………………….

73

Figure A12: Expression of Irf genes in non-lymphocyte thymic cells………………….

73

Figure 3.1: Stages of T cell development in the thymus………………………………..

94

Figure 3.2: Flowcytometric analysis of the thymocyte subpopulations………………..

98

Figure 3.3: Two-color western blot for Irf6 and Gapdh…………………………………. 100
Figure 3.4: Irf6 expression in the thymus………………………………………………… 101
Figure 3.5: Thymic-cardiac ratio………………………………………………………….. 103
Figure 3.6: BrdU incorporation in developing thymocytes……………………………… 104
Figure 3.7: Irf6 regulates proliferation and cell count of thymocytes…………………. 105
Figure 3.8: Loss of Irf6 leads to an abnormal distribution of proliferating cells………. 106
Figure 3.9: Frequency of TCRγδ…………………………………………………………. 106
Figure 3.10: Loss of Irf6 does not alter total cell death…………………………………. 107
Figure 3.11: Effect of Irf6 dosage on BrdU incorporation in thymocyte populations… 108
Figure 4.1: Gating scheme for identification of DC populations in BM……………….. 126
Figure 4.2: Efficiency of adoptive transfer………………………………………………. 128
Figure 4.3: Th1, Th2, Th17 and Treg differentiation in vitro……………………………. 129
Figure 4.4: Frequency of total DCs and their sub-populations in bone marrow……… 131
Figure 4.5: In vitro differentiation model of DCs………………………………………… 133
Figure 4.6: Expression of maturation markers on DCs in culture……………………… 136
Figure 4.7: Proposed model of Irf6 in Th17/Treg balance……………………………… 137
Figure 6.1: Snap shot from the FASTX toolkit analysis………………………………… 173
Figure 6.2: Snap shot of a genome browser (IGV) comparing the annotations
x

of different assemblies……………………………………………………...... 174
Figure 6.3: Diagrammatic representation of the TopHat/Cufflinks analysis…………. 175
Figure 6.4: Work flow for the pipeline of NGS analysis………………………………… 176
Figure 6.5a: Boxplot of expression profiles …..…………………………………………. 181
Figure 6.5b: Ward Hierarchical Clustering using Euclidean distance…………………. 181
Figure 6.6: Differential expression Analysis……………………………………………… 183
Figure 6.7: TUSC2 Molecular pathway…………………………………………………… 188

xi

KEY TO SYMBOLS OR ABBREVIATIONS

IRF - Interferon regulatory factor
Th – T helper
TEC – thymic epithelial cells
CK – cytokeratin
DN - double negative
TCR - T cell receptor
DP - double positive
TGFβ - Transforming Growth Factor β
SP - single positive
Treg - regulatory T-cells
T-bet - T-box expressed in T-cells
APC - Antigen Presenting Cell
GATA3 - GATA-binding protein 3
c-MAF - transcription factor Maf
RORγt - retinoic acid receptor–related orphan receptor γt
Klf4 - Kruppel-like factor 4
SLE - systemic lupus erythematosus
FOXP3 - Forkhead box P3
DBD - DNA-binding domain
IAD – Interferon Association Domain
ChIP - Chromatin immunoprecipitation

xii

HSC - Haematopoietic stem cells
RMA - Robust Multi-array Average algorithm
LT-HSC - long term hematopoietic stem cells
ST-HSC - short term hematopoietic stem cells
LMPP - lymphoid multipotent progenitors
CMP - common myeloid progenitors
CLP - common lymphoid progenitors
MEP - megakaryocyte-erythroid progenitor
GMP - granulocyte monocyte progenitors
BLP - B cell-biased lymphoid progenitor
NK - nature killer cells
iTreg - induced regulatory T cells
nTreg - natural regulatory T cell
DC - dendritic cells
pDC - plasmacytoid dendritic cells (pDC)
tDC - thymic dendritic cells
BM.MPh - bone marrow macrophages
tMPh - thymic macrophages
Nu RBCs - nucleated erythrocytes
cTEC - cortical thymic epithelial cells
mTEC - modularly thymic epithelial cells
BrdU - Bromodeoxyuridine
GM-CSF - granulocyte macrophage-colony stimulating factors

xiii

BM – Bone marrow
LC - Langerhans cells
LL - Lymphoid leukosis
ALV - Avian Leukosis Virus
TUSC2 - Tumor suppressor candidate 2
EIF4E - Eukaryotic translation initiation factor 4E
MDV2 - Marek's disease virus serotype 2

xiv

CHAPTER ONE
Literature Review

1

Significance
T-cells are the most abundant subset of blood lymphocytes and serve as the
core of the adaptive immune response. Changes in T-cell number or function can lead
to autoimmune diseases, immune deficiency, inflammatory disorders and cancer.
Evolution of lymphocytes with a cadre of highly diverse antigen-recognition receptors is
necessary for immune surveillance but also requires stringent screening for autoreactive clones. T-cells develop in the thymus, where a highly specialized
microenvironment educates the evolving T-cells (Janeway et al., 2001; Paul, 2008 ).
The Interferon regulatory factor (IRF) family of transcription factors are
indispensable for functional and developmental regulation of immune cells. While Irf6
shares DNA conservation and predicted structural homology with the IRF family
members, its role in immunity is unknown. However, we already know that IRF6 is
involved in protein-protein interactions with IRF5 and IRF8 (Li et al., 2011), both of which
regulate T-cell development and T helper differentiation. Preliminary bioinformatic
analysis shows expression of Irf6 during thymocyte development and functional
commitment of T helper subsets.
These preliminary findings support the investigation of Irf6 in T-cell development.
Furthermore, it provides a new diagnostic and therapeutic target in autoimmune
disorders such as psoriasis and systemic lupus erythematosus.

Thymus
Evolutionary studies show that thymus development began with jawed
vertebrates (Boehm & Bleul, 2007; Litman & Cooper, 2007). Mammals in general, and

2

humans in particular, have a single thymus located superior to the heart at the thoracic
inlet and is composed of bilateral lobes (Dooley et al., 2006; Rodewald, 2008;
Terszowski et al., 2006). Histologically, the thymus is composed of an inner medulla
and a peripheral cortex surrounded by an outer capsule. Thymus tissue is composed of
lymphoid cells (CD45+CD7+) and stromal cells with a ratio of 50 lymphoid cells for each
stromal cell (Rodewald, 2008; Singer et al., 1986). Non-hematopoietic stromal cells can
be further classified into thymic epithelial cells (TEC, Keratin+) and mesenchymal cells
(Keratin−) (Anderson et al., 1993). Dendritic cells and macrophages are CD45+ thymic
stromal cells, thus they constitute the hematopoietic component of the stromal mesh
(Rodewald, 2008) (Figure1.1).
In the mouse, thymus organogenesis starts around 10.5 days after fertilization
(E10.5) when endodermal epithelial cells of the third pharyngeal pouch initiate the
thymic primordium (Gordon et al., 2004; Hollander et al., 2006). Neural crest cells
migrate into the thymic capsule, interlobular septae, and stromal cell network to regulate
the early proliferation and differentiation of immature TECs (Ambrosiani et al., 1996; Itoi
et al., 2007; Jenkinson et al., 2003; Jenkinson et al., 2007; Johnston, 1966; Yamazaki et
al., 2005). Migration of lymphoid precursors (next section) to the thymus starts by E11.5
(Haynes & Heinly, 1995; Liu et al., 2006; Owen & Ritter, 1969). The interaction between
lymphoid cells and TECs is critical for normal development of both the lymphoid and
epithelial cell compartments (Anderson & Jenkinson, 2001). By E13.5, two thymic
epithelial populations can be appreciated by cytokeratin (CK) markers; cortical
epithelium (CK8+CK5−) and medullary epithelium (CK8−CK5+) (Klug et al., 2002).

3

However, additional expansion of medullary islands is observed as late as E18.5 and
corresponds with the emergence of mature T-cells (Irla et al., 2008).

T-cells development
T-cell precursors migrate from either the fetal liver or bone marrow to seed the
thymus. T-cell precursors are initially called double negative (DN) thymocytes because
they lack expression of both CD4 and CD8 (Godfrey et al., 1993; Pearse et al., 1989).
DN thymocytes undergo T cell receptor (TCR) rearrangement under guidance of cortical
TECs (Raulet et al., 1985; Shinkai et al., 1992; Takahama, 2006; Tourigny et al., 1997;
von Boehmer & Fehling, 1997; Xu et al., 1996). Cells with functional TCRs start to
express both CD4 and CD8 and are in turn called double positive (DP) thymocytes
(Irving et al., 1998; Michie & Zuniga-Pflucker, 2002). In the subcapsular region,
transforming growth factor β (TGFβ) signaling suppresses the proliferation of pre-DP
thymocytes to regulate the production of DP cells (Benz et al., 2004). DP cells migrate
back through the cortex where positive and negative selections occur. In total, only 35% of cells survive and reach the thymic medulla. DP cells lose either CD4 or CD8 to
reach the single positive (SP) stage. SP thymocytes, either CD4+ (T helper) cells or
CD8+ (T cytotoxic) cells, continue their maturation and central tolerance in the medulla
before being shuttled out of the thymus (Blackburn & Manley, 2004; Germain, 2002;
Hoffmann et al., 2003; Lind et al., 2001; Plotkin et al., 2003; Prockop & Petrie, 2000)
(Figure1.1).

4

Figure 1.1: Histological compartments of a thymic lobule with diagramatic
representation of T cell development. Haematopoietic precursors seed the
thymus at medullary cortical junction. Recent thymic immigrants lack both CD4
and CD8 and thus called double negative (DN) thymocytes. CD44 and CD25 are
two surface markers which mark 4 major developmental sub-populations of DN
thymocytes (DN1, CD44+CD25-; DN2, CD44+CD25+; DN3, CD44-CD25+; and
DN4, CD44-CD25-). Transition from DN4 (also called pre-DP) to DP cells occurs
in the subcapsular region. DP cells migrate back through the cortex and reach
the thymic medulla. Meanwhile, DP cells lose either CD4 or CD8 to reach the
single positive (SP) stage. (Figure is modified from Blackburn and Manley, 2004)

5

Functional commitment of T helper subsets
T-cells execute their designated functions by either secreting soluble cytokines or
through direct cell-cell interaction. T helper (Th) lymphocytes are widely understood to
function as the conductors of the adaptive immune orchestra. Upon antigen exposure, T
helper cells differentiate into specialized subsets. Each T helper subset differentiates
under a unique signaling pathway and lineage-specific transcription factors to produce a
characteristic cytokine milieu (Fietta & Delsante, 2009; Hirahara et al., 2011). T helper
subsets include Th1, Th2, Th17, regulatory T-cells (Treg), T follicular helper cells, Th9
and Th22 cells (Bluestone et al., 2009; Shevach, 2010). The balance between different
T helper cells is most typically defined by mutually exclusive expression of lineagespecific transcription factors.
Relative to all T helper subsets, Th1 and Th2 development and function has
been most clearly elucidated. Differentiation of Th1 initially requires expression of
a transcription factor called T-box expressed in T-cells (T-bet). Subsequent exposure to
IL12 and IL18 among other cytokines produced by Antigen Presenting Cells (APCs)
induces completion of the differentiation process. Th1 cells regulate cellular immunity
and are essential for the eradication of intracellular pathogens (Matsuoka et al., 2004;
Rautajoki et al., 2008). Alternatively, Th2 differentiation necessitates IL4 mediated
signaling and expression of the lineage specific transcription factors GATA-binding
protein 3 (GATA3) and transcription factor Maf (c-MAF). Th2 cells are responsible for
regulating humoral immunity and are implicated in the pathophysiology of allergy
(O'Garra & Arai, 2000; Rautajoki et al., 2008).

6

Th17, a T helper subset producing IL17, has pro-inflammatory effects and
protects against bacterial infections in the intestine and the airways (Miossec et al.,
2009; Mitsdoerffer et al., 2010). TGFβ in the presence of IL6 can initiate Th17
commitment (Bettelli et al., 2006; Dong, 2006; McGeachy et al., 2009). The Th17
lineage specific transcription factor is retinoic acid receptor–related orphan receptor γt
(RORγt) (Ivanov et al., 2006). Kruppel-like factor 4 (Klf4) is another important
transcription factor required for full commitment of Th17. T-cell-specific Klf4-knockout
mice show 24% reduction of IL-17+ CD4+ T-cells (Botti et al., 2011). Defects in Th17
development can lead to several autoimmune diseases, including rheumatoid arthritis,
asthma and systemic lupus erythematosus (SLE) (Maddur et al., 2013; Oukka, 2008;
Tesmer et al., 2008). Additional roles for Th17 in graft rejection and inflammatory bowel
disease have been described (Agorogiannis et al., 2012; Dong, 2008; Kolls & Linden,
2004).
Treg is a suppressor T helper cell subset that controls the amplitude of the
immune response and prevents the development of autoimmune diseases. Impairment
of the reciprocal differentiation between Th17 and Treg has been implicated in several
autoimmune disorders such as experimental autoimmune encephalomyelitis; a mouse
model of multiple sclerosis, and type I diabetes mellitus (Pan et al., 2011). Moreover,
Th17/Treg imbalance is associated with tumors (Hu et al., 2011) and graft-versus host
rejection(Dander et al., 2009). While there are different subpopulations of Tregs,
Forkhead box P3 (FOXP3) is a common marker in this lineage (Green et al., 1983). Two
subsets of Tregs are determined by their developmental origin, whereas nTregs arise
naturally in the thymus, iTregs are induced peripherally. Treg subtypes might have

7

similar or overlapping functions but are not identical. For example, Foxp3 deficiency
causes fatal systemic autoimmune disease due to preferential Th1 and Th17 induction
(Bennett et al., 2001; Brunkow et al., 2001). However selective deficiency of iTreg is
associated with Th2 allergic response at mucosal sites when systemic autoimmunity of
Th1 or Th17 has not been implicated (Josefowicz et al., 2012). Importantly, prior work
showed that TGFβ signaling inhibits both Th1 and Th2 differentiation. More recent work
shows that TGFβ signaling is essential for induction of Foxp3 expression and
commitment of Treg, either nTreg or iTreg (Chen & Wahl, 2002). The ability of TGFβ
signaling to induce FOXP3 while concomitantly suppressing the Th17 cell lineage is
mediated by the protein Inhibitor of DNA binding 3 (ID3) (Chen & Wahl, 2002;
Maruyama et al., 2011; Pan et al., 2011) (Figure 1.2).

8

Figure 1.2: Differentiation of T-helper subsets. Naïve T cells undergo
functional differentiation under cytokine direction form antigen presenting cells
(APC).

9

Figure 1.2 (cont’d): Th1 requires expression of the transcription factor T-box (Tbet). IL12 and IL18 induce STAT4 signaling to induce IFN-γ production to
complete the differentiation and expression of IL2, IFN-γ and TNF. Th1 cells
invoke cell mediated immunity and induce destruction of intracellular pathogens.
IL4 and IL6 induce STAT6 signaling and expression of GATA-binding protein3
(GATA3) and transcription factor Maf (c-MAF) to allow Th2 differentiation. Th2
cells secrete IL4, IL5, IL6, IL10 and IL13 to initiate humoral immunity. Th2
response is required for control of helminthes infection. TGFβ and IL6 initiate
STAT3 signaling and expression of retinoic acid receptor–related orphan
receptor γt (RORγt) and Kruppel-like factor 4 (Klf4) to induce Th17 commitment.
Th17 cells produce IL21, IL17, IL22 and TNF. Th17 cytokines have proinflammatory effect and protect against bacterial infections. TGFβ can induce
STAT5 signaling and mediate expression of FOXP3 causing Treg commitment.
Tregs produce more TGFβ and IL10 to suppress the response. Figure is modified
from Fietta and Delsante, 2009.

10

Interferon regulatory factor family
The interferon regulatory factor (IRF) family of transcription factors has nine
members in mammals (Huang et al., 2010; Nehyba et al., 2009). All family members
share a highly conserved N-terminal DNA-binding domain (DBD) that possesses five
conserved tryptophan residues, each separated by 10–18 amino acids (Kondo et al.,
2002; Lohoff & Mak, 2005; Tamura et al., 2008). There is also a shared, but less
conserved protein-binding domain at the C-terminus (Kondo et al., 2002; Lohoff & Mak,
2005) (Figure 1.3). The IRF family is known to orchestrate homeostasis of host defense
(Tamura et al., 2008; Taniguchi et al., 2001). The functions of different IRFs can be
categorized into three main targets. First category is transcriptional regulation of type I
interferon responses which is an indispensible downstream target of IRFs (Barnes et al.,
2001; Fujita et al., 1989; Honda et al., 2005a; Honda et al., 2005b; Honda et al., 2006;
Honda & Taniguchi, 2006; Hoshino et al., 2006; Izaguirre et al., 2003; Marie et al., 1998;
Matsui et al., 2006; Matsuyama et al., 1993; Moynagh, 2005; Negishi et al., 2005; Sato
et al., 1998; Sato et al., 2000; Tailor et al., 2007; Takaoka et al., 2005; Taniguchi et al.,
2001; Taniguchi & Takaoka, 2002; Tsujimura et al., 2004; Yoneyama et al., 1998; Zhao
et al., 2006). Second broad category of IRF family functions is their necessary roles in
development and function of a cadre of immune cell types, innate immune cells such as
phagocytes (Hida et al., 2005; Holtschke et al., 1996; Kamijo et al., 1994; Salkowski et
al., 1999; Scheller et al., 1999; Tamura et al., 2000; Tamura et al., 2005b; Testa et al.,
2004; Tsujimura et al., 2002) and natural killers (Duncan et al., 1996; Lohoff et al., 2000;
Ogasawara et al., 1998; Taki et al., 2005) or adaptive immune cells for example
dendritic cells (Gabriele et al., 2006; Honda et al., 2004; Ichikawa et al., 2004; Schiavoni

11

et al., 2002; Schiavoni et al., 2004; Suzuki et al., 2004; Tamura et al., 2005a; Tsujimura
et al., 2003) and lymphocytes (Brien et al., 2011; Brustle et al., 2007; Fragale et al.,
2008; Klein et al., 2006; Lee et al., 2006; Lohoff et al., 1997; Lohoff et al., 2002; Lu et
al., 2003; Ma et al., 2011; Ma et al., 2006; Mittrucker et al., 1997; Ouyang et al., 2011;
Penninger et al., 1997; Penninger & Mak, 1998; Scharton-Kersten et al., 1997;
Sciammas et al., 2006; Taki et al., 1997; Tian et al., 2012; Tominaga et al., 2003; White
et al., 1996; Zhang et al., 2011; Zheng et al., 2009). Lastly, IRF family members are
also involved in the regulation of cell cycle control and oncogenic pathogenesis. IRF1
(Bouker et al., 2005; Connett et al., 2005; Giatromanolaki et al., 2004; Harada et al.,
1993; Kano et al., 1999; Passioura et al., 2005a; Romeo et al., 2002; Tanaka et al.,
1994; Tanaka et al., 1996; Yim et al., 2003), IRF3 (Duguay et al., 2002; Heylbroeck et
al., 2000; Kim et al., 1999; Kim et al., 2003), IRF5 (Barnes et al., 2003; Hu et al., 2005;
Mori et al., 2002; Yanai et al., 2007) and IRF8 (Deng & Daley, 2001; Hao & Ren, 2000;
Liu & Abrams, 2003; Yang et al., 2007a; Yang et al., 2007b) are negative regulators of
cell proliferation with known pro-apoptotic and tumor suppressor activities. On the other
hand, IRF2 antagonizes the tumor suppressor effect of IRF1 (Connett et al., 2005;
Passioura et al., 2005a; Passioura et al., 2005b; Yim et al., 2003). The role of IRF4 in
oncogenesis is context dependent. For example, IRF4 is oncogenic in late
developmental stages of lymphoid lineage (Hrdlickova et al., 2001; Iida et al., 1997; Ito
et al., 2002; Shaffer et al., 2008; Tsuboi et al., 2000) and a tumor suppressor in myeloid
leukemia (Jo & Ren, 2011; Ortmann et al., 2005; Schmidt et al., 2000) and B cell
malignancies of early developmental stages (Acquaviva et al., 2008; Pathak et al.,

12

2011). Combined deficiency of IRF4 and IRF8 produces both myeloid and lymphoid
tumors (Jo et al., 2010).

13

Figure 1.3: interferon regulatory factor (IRF) protein family in mammals
(Modified from Lohoff & Mak. Nat.Immunol. 2005). IRF family has nine members
in mammals. IRFs are characterized by a highly conserved DNA binding domain
(DBD) and a much less conserved protein association domain (regulatory
domain; IAD – Interferon Association Domain).

14

Interferon regulatory factor 6
IRF6 has the canonical family DBD and its protein-binding domain is most closely
related to IRF5 (Huang et al., 2010; Nehyba et al., 2009). Like other family members,
IRF6 is involved in regulating the cell cycle with an anti-proliferative function in
keratinocytes and mammary epithelial cell (Bailey et al., 2008; Ingraham et al., 2006;
Richardson et al., 2006). Consistently, mutations in IRF6 have also been found in
patients with squamous cell carcinomas, re-emphasizing tumor suppressor function
(Bailey et al., 2009; Botti et al., 2011; Stransky et al., 2011). However, Irf6 is the only
IRF known to be involved morphogenesis (Ingraham et al., 2006; Richardson et al.,
2006; Richardson et al., 2009; Thomason et al., 2010). In humans, haploinsufficiency of
IRF6 causes syndromic orofacial clefting (Kondo et al., 2002). Furthermore, a common
DNA variant at the IRF6 locus accounts for 12% of isolated orofacial clefting risk worldwide (Rahimov et al., 2008; Zucchero et al., 2004). Despite sequence conservation and
structural similarity, Irf6 is the only IRF family member with an as yet undetermined role
in immunity. In 2005, Lohoff and Mak wondered if IRF6 is even expressed by
haematopoietic cells (Lohoff & Mak, 2005). Furthermore, functional and genetic studies
of Irf6 in the immune system are hindered because the knockout mouse model is
perinatal lethal (Ingraham et al., 2006). Thus refined technical strategies are required to
test the role of IRF6 in the immune system.
IRF6 is a putative transcription factor. The DBD of IRF6 binds a sequence highly
analogous to the IRF family consensus (Botti et al., 2011; Little et al., 2009). We know
that the DBD is critical for IRF6 function because mutations in this region can lead to
more severe forms of orofacial clefting, e.g. popliteal pterygium syndrome (Kondo et al.,

15

2002). Evidence for the transcriptional activity of IRF6 is also shown in Sabel et al.
2009. Expression of the IRF DBD in xenopus embryos results in failure of gastrulation.
Co-expression of IRF6 rescues the phenotype suggesting specific competition at the
DNA (Sabel et al., 2009). Chromatin immunoprecipitation (ChIP) sequencing for IRF6
binding sites confirmed the direct DNA binding of Irf6 to many genes (Botti et al., 2011).
As a result of these studies, we know that IRF6 binds to and regulates an important
transcriptional network

Roles of IRFs in T-cell development and differentiation
IRF family members regulate T-cell biology either by intrinsic transcriptional
activities in T-cells or through extrinsic roles in non-T-cells like thymic stromal cells and
other immune cells. While IRFs have an important role in immunity, their role in T helper
cell development and differentiation is of particular interest for both biological and
clinical applications (Figure 1.4).
Although several Irfs are expressed in thymocytes (Colantonio et al., 2011;
Hrdlickova et al., 2001; Nordang et al., 2011; Simon et al., 1997), Irf1 is the only family
member with functional studies supporting a role in T-cell development (Lee et al.,
1999; White et al., 1996). Non-cell autonomous Irf1 reduces expression of the major
histocompatibility complex related genes in the thymic microenvironment (Lee et al.,
1999; White et al., 1996). However it is the Irf1 intrinsic activity in T-cells that is required
for development and thymic selection of naïve CD8 T-cells (Matsuyama et al., 1993;
Penninger et al., 1997; Penninger & Mak, 1998). In Irf4 knockout mice, while cell count
changes during thymocyte development appear to be unaffected, the proliferative

16

capacity, antiviral cytotoxicity, allogenic graft rejection, tumor surveillance and cytokine
production are markedly impaired (Mittrucker et al., 1997).
Likewise, Irf1, Irf2 and Irf8 are critical for mounting a Th1 response (Lohoff et al.,
1997; Lohoff et al., 2000; Scharton-Kersten et al., 1997; Taki et al., 1997) mainly
through transcriptional activation of Il12 (Coccia et al., 1999; Galon et al., 1999; Giese
et al., 1997; Liu et al., 2003; Maruyama et al., 2003; Salkowski et al., 1999); a
macrophage derived cytokine mandatory for Th1 differentiation (Murphy & Reiner,
2002). Th1 is further supported by IRF-mediated suppression of IL4 production in Th2
and basophils (Elser et al., 2002; Lohoff et al., 1997; Taki et al., 1997) and activation of
APCs (Fantuzzi et al., 2001; Lohoff et al., 2000; Niedbala et al., 2002; Ogasawara et al.,
1998; Oppmann et al., 2000). Similarly, Irf4 is indispensable for a Th2 response (Lohoff
et al., 2002; Tominaga et al., 2003). Irf4 induces intrinsic transcription of Il4 (Hu et al.,
2002; Rengarajan et al., 2002) and enhances key transcriptional regulators of Th2 (e.g.
GATA3 (Lohoff et al., 2002) and GFI1 (Tominaga et al., 2003)). Irf4 knockout mice also
have abnormal dendritic cell development, potentiating the T-cell defect (Suzuki et al.,
2004; Tamura et al., 2005a).
The Irf family also has a prominent role in Th17 commitment. For example, Irf4 is
essential for Th17 differentiation (Brustle et al., 2007; Zhang et al., 2011) by direct
transcriptional induction of Il17A and Il21 (Chen et al., 2008; Fanzo et al., 2006). In
contrast, Irf8 is capable of suppressing Th17 differentiation by direct repression of
RORγt; the lineage-specific transcription factor of Th17 (Ouyang et al., 2011; Tian et al.,
2012). Similar patterns of balanced regulation can be seen in Treg cells. Irf1 suppresses
the production of both nTreg in the thymus and iTreg in the periphery by direct

17

transcriptional repression of the Foxp3 promoter through a highly conserved Irf binding
site (Fragale et al., 2008; Ma et al., 2011). On the contrary, Irf4 is a direct downstream
target of Foxp3 and mediates the immune suppressive effect of Tregs (Zheng et al.,
2009). TGF- β1 is known to control the differentiation of both Th17 and Treg (Bettelli et
al., 2006; Chen & Wahl, 2002; McGeachy et al., 2009). Previous studies in the palate
show Irf6 as a downstream target to Tgf-β signaling (Le et al., 2012; Xu et al., 2006).
However, this interaction has not been tested in T helper subsets.

18

Figure 1.4: Roles of IRFs in T-cell development and differentiation. Irfs are
required for intra thymic development and functional commitment in peripheral
lymphoid organs.

19

Finally, autoimmune disease association studies have further implicated the IRF
family in orchestrating T-cell development and function. Genetic variants in IRF5, IRF7
and IRF8 are associated to psoriasis, multiple sclerosis and SLE susceptibility (De
Jager et al., 2009; Demirci et al., 2007; Gateva et al., 2009; Graham et al., 2006; Harley
et al., 2008; Leppa et al., 2011; Patel, 2011; Sanchez et al., 2008). Shitao Li at al 2011
performed a proteomic study to define the candidate protein complexes involved in
regulating interferon type I. Interestingly, affinity purification identified protein complex
formation between IRF6/IRF5 and IRF6/IRF8, the latter was further confirmed by
coimmunoprecipitation in HEK293 cells (Li et al., 2011). Protein-protein interactions
between IRF6 and more canonically described immune IRF family members, DNA
conservation of the DBD and structural homology with IRF5 strongly suggests a role for
Irf6 in the immune system, specifically in T-cell development and functional
commitment.

20

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21

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39

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40

CHAPTER TWO
Meta-analysis of hematopoietic expression profiles reveals selective Irf6
expression in stem cells and in T-cells

41

Abstract
The IRF family of transcription factors regulates critical immune functions,
including development and differentiation of progenitors and commitment of effecter
cells. IRF6 regulates cutaneous, craniofacial and limb development. Here, we used
publically available microarray data to uncover a dynamic expression pattern for Irf6
during hematopoietic development and functional commitment. We found that Irf6 is
expressed early in hematopoiesis, especially in long term hematopoietic stem cells
making Irf6 a target for further investigation in long term engraftment studies. Consistent
with a role in differentiation, we found abrupt attenuation of Irf6 expression in
hematopoietic lineage committed progenitors. Moreover, we observed an age
dependent increase of expression in common myeloid progenitor and myeloid erythroid
progenitor. Also we identified Irf6 expression in T cell lineage, including developing and
functionally committed stages. Thymic expression of Irf6 in double positive and single
positive thymocytes suggests a possible role in T cell development. We found high Irf6
expression in naïve T cells with persistence in functionally committed T cell subsets
except Th1. Considering co-expression and dynamic changes of Irf6 with other Irf family
members, this work suggests a complex, interwoven network that regulates T cell
lineage. This data further highlights the importance of developing an animal model to
study Irf6 function in hematopoiesis.

42

Introduction
Haematopoiesis is the process of production of all blood cells from a common
pluripotent stem cell. Haematopoietic stem cells (HSC) differentiate into both myeloid
and lymphoid lineages. While the myeloid lineages gives rise to erythrocytes, platelets,
granulocytes, macrophages, and dendritic cells, the lymphoid lineages gives rise to Tcells, B-cells, and NK-cells. T-cells are the most abundant subset of blood lymphocytes
and serve as the core of the adaptive immune response. Changes in T-cell number or
function can lead to autoimmune diseases, immune deficiency, inflammatory disorders
and cancer (Janeway et al., 2001; Paul, 2008 ).
The interferon regulatory factor (IRF) family of transcription factors has nine
members in mammals (Huang et al., 2010; Nehyba et al., 2009). All family members
share a highly conserved N-terminal DNA-binding domain (DBD) and a less conserved
C-terminal protein-binding domain (Kondo et al., 2002; Lohoff & Mak, 2005; Tamura et
al., 2008). The functions of IRFs in the hematopoietic and immune system are
indispensable. IRFs perform their functions by either intrinsic expression in their target
cells (Table 2.1) or indirectly by influencing the environment of these cells (Tamura et
al., 2008). Intrinsic expression of IRFs are usually constitutive in haematopoietic cells
however it can be further induced or activated by external signals (Tamura et al., 2008).
Common and rare IRF6 variants cause and contribute risk toward craniofacial
defects. While rare variants lead to Van der Woude and Popliteal Pterygium Syndrome
(Kondo et al., 2002), common IRF6 variants contribute 12% of orofacial clefting risk
(Zucherro et al., 2004). The Irf6 knockout mouse revealed its role in craniofacial and
limb development. Regulation of keratinocyte proliferation and differentiation contributes

43

to the cutaneous defects. However perinatal mortality hinders our ability to study the
role of Irf6 in the haematopoietic system (Ingraham et al., 2006; Richardson et al., 2006;
Richardson et al., 2009; Thomason et al., 2010). In 2005, Lohoff and Mak wondered if
IRF6 is even expressed by haematopoietic cells (Lohoff & Mak, 2005).
Shitao Li at al 2011 performed a proteomic study to define the candidate protein
complexes involved in regulating interferon type I. Interestingly, affinity purification
identified protein complex formation between IRF6/IRF5 and IRF6/IRF8, the latter was
further confirmed by coimmunoprecipitation in HEK293 cells (Li et al., 2011). Proteinprotein interactions between IRF6 and more canonically described immune IRF family
members, DNA conservation of the DBD and structural homology with IRF5 strongly
suggests a role for Irf6 in the haematopoietic system.
Haematopoietic system is characterized by having different cell lineages each
one of them goes through several developmental stages. Many of the developmental
stages have rare frequencies and require several markers or cumbersome procedures
for isolation. Bench work required for assessment of expression for a given gene
throughout the whole haematopoietic system is devastating. In the current era with the
explosion of the publically available whole transcriptomic data for almost every cell type,
bioinformatic analysis is a very promising approach to replace the classical tedious
techniques. In this study, we are trying to profile the expression of Irf6 in different
haematopoietic cells to predict the stages likely to be affected by its deficiency. Also we
are identifying the pattern of expression of all other family members in the same cell
types to identify the possible genetic interactions which are commonly seen between
different members of Irf family.

44

IRF

Intrinsic function in hematopoietic cells

IRF1

* Required for CD8α+ (Penninger et al., 1997) and suppress pDCs (Gabriele et
al., 2006)
* Development of myeloid lineage (Testa et al., 2004)
* Required for macrophage functions (Blanco et al., 2000; Brien et al., 2011)
* Induction of NK cell-mediated cytotoxicity (Duncan et al., 1996)
* Regulation of thymocyte development (Simon et al., 1997)
* Induce Th1 differentiation (Kano et al., 2008; Liu et al., 2003; Lohoff et al.,
1997; Taki et al., 1997).
* Suppress IL4 production by Th2 (Elser et al., 2002)
* Suppress Treg cells (Fragale et al., 2008)
* Differentiation of CD8+ T cells (Brien et al., 2011)

IRF2

* Self-renewal HSCs (Sato et al., 2009)
* Development of epidermal and CD4+ DCs (Ichikawa et al., 2004)
* Suppresses basophil expansion (Hida et al., 2005)
* Megakaryocytic differentiation (Stellacci et al., 2004)
* Regulates macrophage function (Cuesta et al., 2007; Salkowski et al., 1999)
* Development of NK cells (Lohoff et al., 2000; Taki et al., 2005)
* Regulation of thymocyte development (Simon et al., 1997)
* Suppress IL4 production by Th2 (Elser et al., 2002; Zheng et al., 2009)
* Possible oncogenic effect in leukemic cells (Passioura et al., 2005)
* Suppression of CD8+ CTL activity (Hida et al., 2000)

Table 2.1: Effects of IRFs intrinsic expression in hematopoietic cells

45

Table 2.1 (cont’d)
IRF3

* Required for apoptosis of macrophages (Hsu et al., 2004)
* Induction of IFN-β in stimulated DCs (Kim et al., 2014; Sakaguchi et al.,
2003; Sato et al., 2000)
* Prostaglandin E2 production in LPS-primed monocyte (Endo et al., 2014)

IRF4

* Development of CD11bhi CD8α- DCs (Suzuki et al., 2004) and CD4+ DCs
(Tamura et al., 2005a)
* Induce IL4 production by Th2 (Hu et al., 2002; Lohoff et al., 2002;
Rengarajan et al., 2002)
* Th17 differentiation (Brustle et al., 2007; Chen et al., 2008; Zhang et al.,
2011)
* B cell development (Lu et al., 2003; Ma et al., 2006)
* Plasma cell differentiation (Klein et al., 2006; Sciammas et al., 2006)
* Oncogenic effect in multiple myeloma (Iida et al., 1997; Shaffer et al., 2008)
& CLL (Ito et al., 2002; Tsuboi et al., 2000)
* Tumor suppressor in early B-cell development (Acquaviva et al., 2008;
Pathak et al., 2011) & myeloid transformation (Jo & Ren, 2011)

IRF5

* Production of type I IFNs and IL6 in macrophages after viral infection (Yanai
et al., 2007) and pDCs after CpG-A stimulation (Yasuda et al., 2007).
* Required in apoptosis in DCs (Couzinet et al., 2008)
* Monocytes and B-cells from Irf5-/- mice have an intrinsic defect in their
response to pristane-induced lupus (Savitsky et al., 2010; Stone et al., 2012;
Yang et al., 2012)

46

Table 2.1 (cont’d)
IRF6

No intrinsic functions identified in hematopoietic cells

IRF7

* Induction of type I IFN in stimulated DCs and macrophage (Honda et al.,
2005; Hsieh et al., 2014; Kim et al., 2013; Tamura et al., 2008)
* Control monocyte differentiation to macrophage (Lu & Pitha, 2001)
* Induction of IL33 in monocytes and macrophages (Sun et al., 2014)
* Contradictory effect on antiviral response of CD8 T cells (Gracias et al.,
2013; Li et al., 2013; Zhou et al., 2013)

IRF8

* Development of CD8α+ DCs (Schiavoni et al., 2002) and pDCs (Becker et
al., 2012; Tamura et al., 2005a)
* Development and trafficking of Langerhans cells and dermal DCs (Schiavoni
et al., 2004)
* Macrophage development (Tamura et al., 2005b)
* Suppression of neutrophil production (Becker et al., 2012)
* B cell development (Lee et al., 2006; Lu et al., 2003; Ma et al., 2006)
* Knock out is associated with CML-like disease (Holtschke et al., 1996)
* Suppress Th17 differentiation (Ouyang et al., 2011; Tian et al., 2012).

IRF9

* Regulation of B cell activity and isotype switch in response to self antigen
(Thibault et al., 2008)
* Modulation of IFN-I and IFN-II responsiveness in macrophages (Farlik et al.,
2012; Weiden et al., 2000)

47

Materials and methods
Gene Expression Omnibus repository (www.ncbi.nlm.nih.gov/geo/), Gene
Expression Atlas (www.ebi.ac.uk/gxa/), and the murine haematopoietic data base Blood
Express (Miranda-Saavedra et al., 2009) are publically available repositories of
microarray experiments. I searched for experiments covering one or more mouse
developmental hematopoietic lineages. Raw data of each experiment was analyzed
independently using R computing environment (http://www.r-project.org/). The probe
intensities were subjected to background correction and quantile normalization using
Robust Multi-array Average algorithm (RMA) (Irizarry et al., 2003). Probe numbers for Irf
genes were identified in the tested microarray platforms using the BioMart ID
conversion tool (Kasprzyk, 2011). Relative expression of Irf genes was plotted for each
experiment. To ensure the reliability of comparison, average expression ranks of Irf
genes were calculated for every cell type per experiment. To study the co-expression
pattern, expression ranks of Irf genes in all experiments were pooled and clustered
using “gplots” package in R computing environment.
Four Affymatrix (Santa Clara, California, USA) microarray platforms were used in
the studied experiments; Murine Genome U74Av2 [MG_U74Av2], Mouse Genome 430
2.0 [Mouse430_2], Mouse Expression 430A [MOE430A], and Mouse Gene 1.0 ST Array
[MoGene1]. Studied experiments covered the expression profiles of hematopoietic stem
cells (HSC) and its two main subpopulations; long term hematopoietic stem cells (LTHSC) and short term hematopoietic stem cells (ST-HSC). Several lineage-committed
precursors were also compiled, including lymphoid multipotent progenitors (LMPP),
common myeloid progenitors (CMP), common lymphoid progenitors (CLP),

48

megakaryocyte-erythroid progenitor (MEP) and granulocyte monocyte progenitors
(GMP). Lymphocyte lineages were heavily covered during early development and after
maturation in resting and activated conditions. Early developmental stages included
CD4+ CD8+ double negative thymocytes (DN), CD4- CD8- double positive thymocytes
(DP), B cell-biased lymphoid progenitor (BLP), and pre-ProB cells. Also mature splenic
T cells and B cells and nature killer cells (NK) were tested. Resting T cells were
identified by being negative for B220 marker and resting B cells were indentified by
being negative for CD43 marker. Also naïve and activated T cells with its main
subpopulations; T helper and T cytotoxic were analyzed. Functionally committed T
helper subpopulations were covered including T-helper 1 (Th1), T-helper 2 (Th2), Thelper 17 (Th17), induced regulatory T cells (iTreg) and natural regulatory T cell
(nTreg). BM precursors of myeloid linage were also covered like promyelocytes and
myelocytes. Other tested terminally differentiated cells included dendritic cells (DCs),
plasmacytoid dendritic cells (pDC), thymic dendritic cells (tDC), monocytes, bone
marrow macrophages (BM.MPh), thymic macrophages (tMPh), granulocytes, nucleated
erythrocytes (Nu RBCs), and precursor and mature mast cells. Whole thymocytes as
well as cortical and modularly thymic epithelial cells (cTEC,mTEC) were included.

49

Results
Several members of the IRF family of transcription factors are essential for
hematopoeisis. However, there are no data on IRF6 because mice that lack Irf6 die
shortly after birth from abnormal morphogenesis. As a first step in identifying a potential
function for IRF6 in hematopoeisis, we performed a meta analysis on gene expression
profiles in murine hematopoietic tissues. We identified 20 such studies (Table 2.2) that
included 241 microarrays from 50 unique hematopoietic cell types.

Publication

lineages

Affymetrix Chip

LT-HSC, NK, Naïve & activated
CD4 & CD8 T-cells, B-cells,
(Chambers et al., 2007)

Mouse Genome 430-2
Monocytes, Granulocytes, Nu.
Erythrocytes
Resting and activated T cells and

(Holwerda et al., 2013)

Mouse Gene 1.0 ST
B cell

(Derbinski et al., 2005)

Thymic stromal cells

Murine Genome U74Av2

DC, B cells, CD4 T cells. CD8 T
(Dudziak et al., 2007)

Mouse Genome 430-2
cells

(Lin et al., 2014)

Th1,Th2, Th17

Mouse Gene 1.0 ST

(Ficara et al., 2008)

LT-HSC, ST-HSC

Mouse Genome 430-2

mast cell precursors and mature
(Haddon et al., 2009)

Mouse Expression 430A
mast cells

Table 2.2: Mouse expression profiles for hematopoietic cell types.

50

Table 2.2 (Cont’d)
(Mansson et al., 2007)

HSC

Mouse Genome 430-2

(Robbins et al., 2008)

DCs, NK, B cells, CD8 T cells

Mouse Genome 430-2

(Rodriguez et al., 2007)

Th1 vs Th2

Mouse Genome 430-2

(Tothova et al., 2007)

HSC, MLP

Mouse Genome 430-2

LT-HSC, ST-HSC, MPP

Mouse Genome 430-2

(Vigano et al., 2013)

HSC, pro Tcells, DP T cells

Mouse Gene 1.0 ST

(Wang et al., 2010)

HSC vs CMP and GMP

Mouse Genome 430-2

(Venkatraman et al.,
2013)

Naïve T-helper, Th1, Th2, Th17,
(Wei et al., 2009)

Mouse Genome 430-2
iTreg, nTreg

(Weischenfeldt et al.,
macrophage vs T-cells

Mouse Genome 430-2

2008)
LT-HSC,ST-HSC, LMPP, CMP,
(Beerman et al., 2014)

Mouse Genome 430-2
CLP, GMP, MEP, BLP, pre-ProB
HSC, Promyelocytes, Myelocytes,

(Wong et al., 2014)

Mouse Gene 1.0 ST
Granulocytes

(Egawa & Littman, 2011) DN, DP, CD4SP, CD8SP

Mouse Genome 430-2

(Kawazu et al., 2007)

Mouse Genome 430-2

DN cells

51

Plotting of non-logarithmic normalized data for each study allowed us to compare
the level of expression for all 9 Irf genes in 50 mouse hematopoietic cells types
(Supplementary Figures A1 - A12). However, testing the reproducibility across
experiments, following the expression changes along the haematopoietic tree, and
prediction of possible family member interactions required a more integrative approach.
To facilitate inter-experimental comparison of expression of Irf genes, we calculated and
ranked the relative expression for each Irf gene in each cell type per experiment
(Supplementary table A1). We then performed cluster analysis for the average rank
expression of Irf genes (figure 2.1). We observed two main patterns of differential
expression for family members across the studied cell lineages. First, Irf1, Irf3 and Irf9
have consistently high expression levels in most studied cell lineages with average rank
expressions of 93, 83 and 84, respectively. All other family members showed more
variable expression among different cell types. Irf6 was expressed mainly in two stages
of hematopoietic development. Early in development, Irf6 was expressed in HSC and
their immediate downstream progeny LMPP. Irf6 expression was maintained in CLP
with apparent down regulation in all myeloid capable progenitors (CMP, MEP, and
GMP). However, suppression of Irf6 expression in CMP and MEP was lost in
progenitors obtained from 2 year-old mice (figure 2.2). Regulation of Irf6 expression was
also observed in development of T cells. In two out of three studies, Irf6 expression was
barely detectable in early developing DN thymocytes. We found an increase of Irf6
expression in DP thymocytes and even more in single positive CD4 and CD8 progeny.
Expression of Irf6 peaked to exceed 80% in naïve T-helper and cytotoxic populations.
No changes in Irf6 expression were seen in activated T-helper cells, however the rank

52

expression profile lost about 10 percentiles in activated T-cytotoxic cells. Terminally
differentiated T-helper sub-populations maintained a 63-71% expression rank except
TH-1, where Irf6 rank of expression went down to the 30th percentile for 2 out of 3
ranked studies. Finally, the thymic-derived natural Treg came at the 79th percentile on
the rank of expression (Figure 2.3).

53

L M PP,M a n s s o n 2 0 0 7
L M PP,Be e rm a n 2 0 1 4
L M PP.Ol d _ m i c e ,Be e rm a n 2 0 1 4
L M PP,Ve n k a tra m a n 2 0 1 3
ST-HSC,Fi c a ra 2 0 0 8
ST-HSC,Ve n k a tra m a n 2 0 1 3
CL P,Be e rm a n 2 0 1 4
CL P.Ol d _ m i c e ,Be e rm a n 2 01 4
CM P.Ol d _ m i c e ,Be e rm a n 2 0 1 4
HSC,Vi g a n o 2 0 1 3
HSC,Wo n g 2 0 1 4
Pro m y e l o c y te s ,Wo n g 2 0 1 4
Gra n u l o c y te s ,Wo n g 2 0 1 4
M y e l o c y te s ,Wo n g 2 0 1 4
M o n o c y te ,Ch a m b e rs 2 0 0 7
CM P,To th o v a 2 0 0 7
BL P.Ol d _ m i c e ,Be e rm a n 2 01 4
BL P,Be e rm a n 2 0 1 4
tM Ph ,De rb i n s k i 2 0 0 5
p re -Pro B.Ol d _ m i c e ,Be e rm an 2 0 1 4
p re -Pro B,Be e rm a n 2 0 1 4
Th 1 ,We i 2 0 0 9
NK,Ro b b i n s 2 0 0 8
NK,Ch a m b e rs 2 0 0 7
CD1 1 c +CD8 +DCs ,Du d z i a k 2 0 0 7
BM .M Ph ,We i s c h e n fe l d t 2 00 8
CD8 +DCs ,Ro b b i n s 2 0 0 8
p DCs ,Ro b b i n s 2 0 0 8
CD1 1 b +DCs ,Ro b b i n s 2 0 0 8
tDC,De rb i n s k i 2 0 0 5
B-Ce l l ,Ro b b i n s 2 0 0 8
B-Ce l l ,Du d z i a k 2 0 0 7
Re s ti n g B-Ce l l ,Ho l we rd a 2 01 3
Ac t. B-Ce l l ,Ho l we rd a 2 0 1 3
B-Ce l l ,Ch a m b e rs 2 0 0 7
Th 1 ,Ro d ri g u e z 2 0 0 7
CD1 1 c +CD8 -DCs ,Du d z i a k 20 0 7
Re s ti n g T-Ce l l ,Ho l we rd a 2 01 3
DN th y m o c y te s ,Vi g a n o 2 0 1 3
DP th y m o c y te s ,Vi g a n o 2 0 1 3
Gra n u l o c y te s ,Ch a m b e rs 2 0 0 7
Sp l e n i c T-c y to ,Ro b b i n s 2 00 8
Nu .RBCs ,Ch a m b e rs 2 0 0 7
L T-HSC,Ve n k a tra m a n 2 0 1 3
Wh o l e .Th y m u s ,We i s c h e n fe l d t 2 0 0 8
L T-HSC.Fe ta l _ L i v e r,Be e rm a n 2 0 1 4
n Tre g ,We i 2 0 0 9
Na ïv e T-h e l p e r,We i 2 0 0 9
Na ïv e T-h e l p e r,Ch a m b e rs 2 0 0 7
Na ïv e T-c y to ,Ch a m b e rs 2 00 7
L T-HSC,Ch a m b e rs 2 0 0 7
HSC,To th o v a 2 0 0 7
L T-HSC,M a n s s o n 2 0 0 7
L T-HSC,Be e rm a n 2 0 1 4
L T-HSC.Ol d _ m i c e ,Be e rm a n 2 0 1 4
ST-HSC,M a n s s o n 2 0 0 7
ST-HSC,Be e rm a n 2 0 1 4
ST-HSC.Ol d _ m i c e ,Be e rm a n 2 0 1 4
CD4 SP th y m o c y te s ,Eg a wa 2 0 1 1
CD8 SP th y m o c y te s ,Eg a wa 2 0 1 1
Sp l e n i c T-h e l p e r,Du d z i a k 2 0 0 7
Sp l e n i c T-c y to ,Du d z i a k 2 00 7
i Tre g ,We i 2 0 0 9
L T-HSC,Fi c a ra 2 0 0 8
Th 2 ,Ro d ri g u e z 2 0 0 7
Ac t. T-h e l p e r,Ch a m b e rs 2 00 7
Ac t. T-c y to ,Ch a m b e rs 2 0 0 7
Th 1 7 ,We i 2 0 0 9
Th 2 ,We i 2 0 0 9
Ac t. T-Ce l l ,Ho l we rd a 2 0 1 3
Th 2 ,L i n 2 0 1 4
Th 1 7 ,L i n 2 0 1 4
Th 1 ,L i n 2 0 1 4
M EP.Ol d _ m i c e ,Be e rm a n 2 0 1 4
HSC,Wa n g 2 0 1 0
DP th y m o c y te s ,Eg a wa 2 0 11
DN th y m o c y te s ,Ka wa z u 2 00 7
DN th y m o c y te s ,Eg a wa 2 0 11
M EP,Be e rm a n 2 0 1 4
CM P,Be e rm a n 2 0 1 4
GM P,Be e rm a n 2 0 1 4
GM P.Ol d _ m i c e ,Be e rm a n 2 0 1 4
CM P,Wa n g 2 0 1 0
GM P,Wa n g 2 0 1 0
p re -m a s t.c e l l s ,Ha d d o n 2 0 0 9
m a s t.c e l l s ,Ha d d o n 2 0 0 9

Color Key

Irf9

Irf3

Irf1

Irf8

Irf7

100

Irf5

80

Irf2

60

Irf4

40

Irf6

20

Figure 2.1: Clustering analysis of average Irf gene rank expression.

54

Figure 2.1 (Cont’d): Irf1, Irf3, and Irf9 show non-selective high rank of expression in most
haematopoietic cell types. The other family members have high variability between cell
types, especially Irf6 and Irf4. Irf6 expression is almost exclusively in early haematopoietic
progenitors and T cell developmental and functional cells.

55

100

80

60

40

0

20

CLP.Old_mice,Beerman 2014
CLP,Beerman 2014
GMP.Old_mice,Beerman 2014
GMP,Beerman 2014
GMP,Wang 2010
MEP.Old_mice,Beerman 2014
MEP,Beerman 2014
CMP.Old_mice,Beerman 2014
CMP,Beerman 2014
CMP,Wang 2010
CMP,Tothova 2007
LMPP.Old_mice,Beerman 2014
LMPP,Beerman 2014
LMPP,Venkatraman 2013
LMPP,Mansson 2007
ST-HSC.Old_mice,Beerman 2014
ST-HSC,Beerman 2014
ST-HSC,Venkatraman 2013
ST-HSC,Ficara 2008
ST-HSC,Mansson 2007
LT-HSC.Old_mice,Beerman 2014
LT-HSC.Fetal_Liver,Beerman 2014
LT-HSC,Beerman 2014
LT-HSC,Venkatraman 2013
LT-HSC,Ficara 2008
LT-HSC,Mansson 2007
LT-HSC,Chambers 2007
HSC,Wong 2014
HSC,Vigano 2013
HSC,Wang 2010
HSC,Tothova 2007

Figure 2.2: Average Irf6 rank expression in early hematopoietic progenitors. Every
bar represents the Irf6 rank of expression in a given microarray. The horizontal axis
represents the percentile rank while the vertical axis shows the names of the studied
cell type appended to the names of first author and year of publication. Cells are
arranged from the most immature (at the origin of the figure) to the mature stages.

56

100

80

60

40

20

0

nTreg,Wei 2009
iTreg,Wei 2009
Th17,Lin 2014
Th17,Wei 2009
Th2,Lin 2014
Th2,Wei 2009
Th2,Rodriguez 2007
Th1,Lin 2014
Th1,Wei 2009
Th1,Rodriguez 2007
Act. T-cyto,Chambers 2007
Act. T-helper,Chambers 2007
Naïve T-cyto,Chambers 2007
Naïve T-helper,Wei 2009
Naïve T-helper,Chambers 2007
Splenic T-cyto,Robbins 2008
Splenic T-cyto,Dudziak 2007
Splenic T-helper,Dudziak 2007
Act. T-Cell,Holwerda 2013
Resting T-Cell,Holwerda 2013
CD8SP thymocytes,Egawa 2011
CD4SP thymocytes,Egawa 2011
DP thymocytes,Vigano 2013
DP thymocytes,Egawa 2011
DN thymocytes,Vigano 2013
DN thymocytes,Egawa 2011
DN thymocytes,Kawazu 2007

Figure 2.3: Average Irf6 rank expression in T cell stages. Every bar represents the
Irf6 rank of expression in a given microarray. The horizontal axis represents the
percentile rank while the vertical axis shows the names of the studied cell type
appended to the names of first author and year of publication. Cells are arranged from
the most immature (at the origin of the figure) to the mature stages.

57

To assess a possible bias of probes across arrays, biological replicates done
using different microarray platforms were identified. Average rank expression was
calculated for each platform per gene. Paired t-test analysis showed significant bias for
Irf1 and Irf2 but insignificant changes for the other Irf genes (Table 2.3).
Irf1

Mouse
Mo430-2 Gene1
HSC
92
86
DN
95
91
DP
87
85
Th1
95
68
Th2
93
74
Th17
93
66
p-value = 0.030*

Irf2 Mouse Mo430-2 Gene1
HSC
76
90
DN
57
93
DP
58
93
Th1
79
88
Th2
76
88
Th17
79
78
p-value = 0.034*

Mouse
Mo430-2 Gene1
HSC
73
77
DN
81
78
DP
84
83
Th1
80
79
Th2
76
82
Th17
83
82
p-value = 0.661

Irf4

MoMouse
430-2 Gene1
HSC
52
35
DN
39
57
DP
35
53
Th1
86
96
Th2
90
95
Th17
86
97
p-value = 0.217

Irf5 Mouse Mo430-2 Gene1
HSC
59
72
DN
56
75
DP
46
55
Th1
73
66
Th2
72
65
Th17
64
66
p-value = 0.319

MoMouse
430-2 Gene1
HSC
61
53
DN
19
59
DP
51
64
Th1
29
63
Th2
70
62
Th17
71
63
p-value = 0.298

Irf7

Irf8 Mouse Mo430-2 Gene1
HSC
70
86
DN
70
78
DP
44
76
Th1
86
90
Th2
79
77
Th17
79
76
p-value = 0.149

Mouse
Mo430-2 Gene1
HSC
56
72
DN
71
74
DP
63
76
Th1
72
37
Th2
62
37
Th17
72
37
p-value = 0.330

Irf3

Irf6

Irf9

Mouse
Mo430-2 Gene1
HSC
85
83
DN
76
85
DP
69
88
Th1
85
61
Th2
80
60
Th17
86
60
p-value = 0.384

Table 2.3: Average rank expression for microarray platforms per gene

58

Discussion
In this study, we uncovered a novel expression pattern for Irf6 in murine
hematopoietic cell lineages. Our results showed that Irf6 expression is selectively
expressed in hematopoietic cell progenitors and terminally differentiated cells. We found
that hematopoietic stem cells and T cells are the two main developmental windows
where Irf6 expression was evident.
Highest expression of Irf6 is seen in LT-HSC which requires further investigation
for a possible role of IRF6 in long term BM engraftment. Interferons directly stimulate
HSC proliferation and differentiation (Schuettpelz & Link, 2013). Intrinsic expression of
Irf2 was shown to preserve the self-renewal and multilineage differentiation capacity of
HSCs by suppression of INF-I signaling (Sato et al., 2009). Our meta-analysis showed
that expression of both Irf2 and Irf6 were very comparable in HSCs and Irf6 was even
higher in the long term compartment. Loss of Irf6 expression in more differentiated
myeloid progenitors with persistent expression in CLP suggested an important role in
the lineage commitment of these critical progenitor cells (Figure 2.1). Regulation of early
lineage commitment is one of the documented functions in the family. For example, Irf8
promotes DC lineage commitment over neutrophil production in myeloid progenitors
(Becker et al., 2012) while Irf4 favors macrophage commitment (Yamamoto et al.,
2011). Recently, age dependent increase of activated Irf3 was shown to promote the
inflammatory response in aging kidney cells (Xi et al., 2014). The relative increase of
Irf6 in CMP and MEP obtained from old mice might have a similar effect on these
progenitors.

59

In T cells, the expression was evident during thymic development, T-cell
activation and terminal functional commitment (Figure 2.2). A possible role of Irf6 in T
cell development can be predicted from intrinsic expression in evolving thymocytes as
well as expression in cortical and medullary thymic epithelium. Persistent Irf6
expression in activated and functionally committed T cell subsets except Th1 suggests
a potential role for Irf6 in functional commitment and terminal differentiation of T cells.
Variability of expression in the same cell type among different experiments was
sometimes striking. Two obvious examples were Irf6 expression in DN thymocytes and
Th1 cells, where 2 out of 3 experiments show very low rank of expression (< 30%) while
3rd experiment is uniquely high (> 60%). One possible explanation is a difference
between probe sensitivities across the platforms. However comparing all replicates
using the two commonly used platforms (Mouse430_2 & MoGene1), we failed to
observe a consistent bias. Another possible explanation is that DN cells are a rare
population and can be easily contaminated by thymocytes with low expression of CD4
and CD8 (Lucas & Germain, 1996). Finally, induction of Th1 cell in vitro is difficult
without contamination from other functional subsets. These biological obstacles may
explain the high variability we see in our analysis. Non-linear noise signature of
microarray experiments is another important factor that always should be considered in
these meta-analysis studies (Chen et al., 2011; Leek et al., 2010).
Publically available expression profile data sets are an underutilized tool that can
help researchers to understand the expression pattern of their target genes as well as
possible interactions with other co-expressed genes. Transcriptional expression of Irf
members is constitutive and IFN-inducible in most targets tissues (Tamura et al., 2008).

60

IRFs are considered master regulators of hematopoietic development and
differentiation. Until now, the expression pattern of Irf6 in haematopoiesis has not been
studied. Also, perinatal lethality of Irf6 knockout mouse hindered efforts to study its
function in haematopoiesis. We found that Irf6 expression is selectively expressed early
HSC and in various T-cell lineages, including Th1, Th2 and Th17. This work strongly
supports a regulatory function of Irf6 in haematopoiesis.

61

APPENDIX

62

LT-HSC.Fetal_Liver
Irf1
Irf2
Irf3
Irf4
Irf5
Irf6
Irf7
Irf8
Irf9

LT-HSC
LT-HSC.Old_mice
ST-HSC
ST-HSC.Old_mice
LMPP
LMPP.Old_mice
CMP
CMP.Old_mice
MEP
MEP.Old_mice
GMP
GMP.Old_mice
CLP
CLP.Old_mice
BLP
BLP.Old_mice
pre-ProB

8000

6000

4000

2000

0

pre-ProB.Old_mice

Figure A1: Expression of Irf genes in immature hematopoietic progenitors at 3
months and 2 years of mouse age. Expression data obtained from Beerman et al
(2014).

63

Irf1
Irf2
Irf3
Irf4
Irf5
Irf6
Irf7
Irf8
Irf9

HSC

Irf1
Irf2
Irf3
Irf4
Irf5
Irf6
Irf7
Irf8
Irf9

LT-HSC

1500

1000

500

0

1400

1200

1000

800

600

400

200

ST-HSC

0

CMP

Figure A2a: Expression of Irf genes in

Figure A2b: Expression of Irf genes in

immature haematopoietic progenitors.

immature haematopoietic progenitors.

Expression data obtained from Tothova et

Expression data obtained from Ficara et al

al 2007.

2008.

64

Irf1
Irf2
Irf3
Irf4
Irf5
Irf6
Irf7
Irf8
Irf9

LT-HSC

Irf1
Irf2
Irf3
Irf4
Irf5
Irf6
Irf7
Irf8
Irf9

LT-HSC

ST-HSC

ST-HSC

8000

6000

4000

0

600

400

200

0

2000

LMPP

LMPP

Figure A2c: Expression of Irf genes in

Figure A2d: Expression of Irf genes in

immature haematopoietic progenitors.

immature haematopoietic progenitors.

Expression data obtained from Mansson et

Expression data obtained from

al 2007.

Venkatraman et al 2013.

65

Irf1
Irf2
Irf3
Irf4
Irf5
Irf6
Irf7
Irf8
Irf9

HSC

Irf1
Irf2
Irf3
Irf4
Irf5
Irf6
Irf7
Irf8
Irf9

HSC

Promyelocytes

CMP

Myelocytes

GMP

800

600

400

200

0

1400

1200

1000

800

600

400

200

0

Granulocytes

Figure A3: Expression of Irf genes in

Figure A4: Expression of Irf genes

early myeloid development. Expression

during granulocytes development.

data obtained from Wang et al 2010.

Expression data obtained from Wong et al
2014.

66

Irf1
Irf2
Irf3
Irf4
Irf5
Irf6
Irf7
Irf8
Irf9

HSC

Irf1
Irf2
Irf3
Irf4
Irf5
Irf6
Irf7
Irf8
Irf9

DN thymocytes

DP thymocytes

DN Thymocytes
CD4SP thymocytes

DP Thymocytes

1500

1000

500

0

2000

1500

1000

500

0

CD8SP thymocytes

Figure A5a: Expression of Irf genes in

Figure A5b: Expression of Irf genes in

thymocyte development. Expression data

thymocyte development. Expression data

obtained from Vigano et al 2013.

obtained from Egawa et al 2011.

67

Irf1
Irf2
Irf3
Irf4
Irf5
Irf6
Irf7
Irf8
Irf9

LT-HSC
NK
Naïve T-helper
Naïve T-cyto
Act.T-helper
Act.T-cyto
B-Cell
Monocyte
Granulocytes

10000

8000

6000

4000

2000

0

Nu.RBCs

Figure A6: Expression of Irf genes in LT-HSC versus terminally
differentiated haematopoietic cells. Expression data obtained from Chambers
et al 2007.

68

Irf1
Irf2
Irf3
Irf4
Irf5
Irf6
Irf7
Irf8
Irf9

CD11c+CD8-DCs

CD11c+CD8+DCs

Irf1
Irf2
Irf3
Irf4
Irf5
Irf6
Irf7
Irf8
Irf9

CD8+DCs

CD11b+DCs

pDCs

B-cells
NK

Splenic T-helper
B-Cell

Splenic T-cyto

8000

6000

4000

2000

0

7000

6000

5000

4000

3000

2000

1000

0

Splenic T-cyto

Figure A7a: Expression of Irf genes in

Figure A7b: Expression of Irf genes in

lymphocytes and DCs. Expression data

lymphocytes and DCs. Expression data

obtained from Dudziak et al 2007.

obtained from Robbins et al 2008.

69

Irf1
Irf2
Irf3
Irf4
Irf5
Irf6
Irf7
Irf8
Irf9

Resting B-Cell

Act.B-Cell

Irf1
Irf2
Irf3
Irf4
Irf5
Irf6
Irf7
Irf8
Irf9

pre-mast.cells

Resting T-Cell

mast.cells

120

100

80

60

40

20

0

1000

800

600

400

200

0

Act.T-Cell

Figure A8: Expression of Irf genes in

Figure A9: Expression of Irf genes in

resting and activated lymphocytes.

mast cells and pre-mast cells.

Expression data obtained from Holwerda

Expression data obtained from Haddon et

et al 2013.

al 2009.

70

Irf1
Irf2
Irf3
Irf4
Irf5
Irf6
Irf7
Irf8
Irf9

Th1

Irf1
Irf2
Irf3
Irf4
Irf5
Irf6
Irf7
Irf8
Irf9

TH1

TH2

Th2

1000

800

600

400

200

0

1400

1200

1000

800

600

400

200

0

TH17

Figure A10a: Expression of Irf genes

Figure A10b: Expression of Irf genes in T-

in T-helper subsets. Expression data

helper subsets. Expression data obtained

obtained from Rodriguez et al 2007

from Lin et al 2014.

71

Irf1
Irf2
Irf3
Irf4
Irf5
Irf6
Irf7
Irf8
Irf9

Naïve T-helper

Th1

Th2

Th17

iTreg

4000

3000

2000

1000

0

nTreg

Figure A10c: Expression of Irf genes in T-helper subsets. Expression data
obtained from Wei et al 2009.

72

Irf1
Irf2
Irf3
Irf4
Irf5
Irf6
Irf7
Irf8
Irf9

BM.MPh

Irf1
Irf2
Irf3
Irf4
Irf5
Irf6
Irf7
Irf8
Irf9

cTEC

mTEC

tDC

Whole.Thymus

20000

15000

10000

5000

3000

2500

2000

1500

1000

500

0

0

tMPh

Figure A11: Expression of Irf genes in

Figure A12: Expression of Irf genes in

BM macrophage and whole thymus

non-lymphocyte thymic cells.

suspension. Expression data obtained

Expression data obtained from Derbinski

from Weischenfeldt et al 2008.

et al 2005 (No probes for Irf2).

73

Author
Chip type
Cell
Irf1 Irf2 Irf3 Irf4 Irf5 Irf6 Irf7 Irf8
and year
lineage
Tothova et al Mouse430_2
HSC
95 98 73 76 65 81 53 58
2007
Wang et al
Mouse430_2
HSC
89 55 74 28 54 41 60 83
2010
Vigano et al
MoGene1
HSC
81 91 78 36 73 53 70 89
2013
Wong et al
MoGene1
HSC
91 90 76 34 71 54 75 83
2014
Chambers et Mouse430_2
LT-HSC
96 83 87 65 64 98 64 65
al 2007
Mansson et al Mouse430_2
LT-HSC
98 70 86 26 74 90 75 62
2007
Ficara et al
Mouse430_2
LT-HSC
98 70 86 98 83 66 76 84
2008
Venkatraman Mouse430_2
LT-HSC
94 60 86 59 60 64 69 70
et al 2013
Beerman et al Mouse430_2
LT-HSC
96 78 85 32 70 86 68 62
2014
Beerman et al Mouse430_2
LT-HSC
94 66 81 47 68 77 64 73
2014
(Fetal Liver)
Beerman et al Mouse430_2
LT-HSC
98 65 86 29 66 85 75 53
2014
(Old mice)
Mansson et al Mouse430_2
ST-HSC
96 73 86 34 71 71 71 70
2007
Ficara et al
Mouse430_2
ST-HSC
94 73 84 49 65 60 68 79
2008
Venkatraman Mouse430_2
ST-HSC
96 63 86 39 63 56 65 85
et al 2013
Beerman et al Mouse430_2
ST-HSC
95 73 84 28 71 70 66 67
2014
Beerman et al Mouse430_2
ST-HSC
96 73 90 26 70 71 73 58
2014
(Old mice)
Mansson et al Mouse430_2
LMPP
97 72 85 34 72 64 74 94
2007
Venkatraman Mouse430_2
LMPP
97 71 87 45 65 55 74 90
et al 2013
Beerman et al Mouse430_2
LMPP
95 72 86 32 77 68 73 86
2014
Beerman et al Mouse430_2
LMPP
96 71 87 38 63 69 70 88
2014
(Old mice)
Tothova et al Mouse430_2
CMP
89 94 75 48 61 30 46 74
2007
Table A1: Average rank of IRFs expression for each cell type per experiment
74

Irf9
87
84
85
81
76
85
78
75
86
78
90
80
82
71
83
89
85
78
85
88
82

Table A1 (Cont’d)
Wang et al
2010
Beerman et al
2014
Beerman et al
2014
Beerman et al
2014
Beerman et al
2014
Wang et al
2010
Beerman et al
2014
Beerman et al
2014
Beerman et al
2014
Beerman et al
2014
Beerman et al
2014
Beerman et al
2014
Beerman et al
2014
Beerman et al
2014
Chambers et
al 2007
Dudziak et al
2007
Robbins et al
2008
Holwerda et
al 2013
Holwerda et
al 2013
Weischenfeldt
et al 2008
Derbinski et
al 2005
Derbinski et
al 2005

Mouse430_2

CMP

85

44

73 28 50 25 50

92

78

Mouse430_2

CMP

95

60

83 29 72 26 69

98

82

Mouse430_2

CMP
(Old mice)
MEP

93

69

86 27 60 47 65

94

87

83

47

81 35 60

72

52

79

88

71

87 31 52 44 71

74

87

Mouse430_2

MEP
(Old mice)
GMP

83

34

71 28 53 23 49

92

72

Mouse430_2

GMP

93

47

81 33 77 13 64

97

75

Mouse430_2

GMP
(Old mice)
CLP

93

64

86 24 61 14 57

95

80

95

71

86 42 75 47 73

99

86

CLP
(Old mice)
BLP

96

80

85 45 66 45 71

96

89

95

70

87 57 77 35 72

99

85

BLP
(Old mice)
pre-ProB

95

80

86 59 72 39 72

96

90

96

74

88 69 67 22 75

99

88

95

78

86 67 74 24 71

95

89

Mouse430_2

pre-ProB
(Old mice)
B-Cell

98

90

89 92 85 50 82

96

87

Mouse430_2

B-Cell

95

81

90 89 96

86

98

91

Mouse430_2

B-Cell

97

86

91 95 92 10 86

98

89

MoGene1

91

95

83 92 92 46 71

96

81

92

90

75 97 85 48 78

97

84

89

74

81 50 56 54 73

70

79

MG_U74Av2

Resting
B-Cell
Activated
B-Cell
Whole
Thymus
cTEC

98

NA 70 68 67 76 47

91

92

MG_U74Av2

mTEC

97

NA 64 97 80 95 65

93

94

Mouse430_2
Mouse430_2

Mouse430_2
Mouse430_2
Mouse430_2
Mouse430_2
Mouse430_2
Mouse430_2

MoGene1
Mouse430_2

75

9

9

Table A1 (Cont’d)
Kawazu et al
2007
Egawa et al
2011
Vigano et al
2013
Egawa et al
2011
Vigano et al
2013
Egawa et al
2011
Egawa et al
2011
Holwerda et
al 2013
Holwerda et
al 2013
Dudziak et al
2007
Dudziak et al
2007
Robbins et al
2008
Chambers et
al 2007
Wei et al
2009
Chambers et
al 2007
Chambers et
al 2007
Chambers et
al 2007
Rodriguez et
al 2007
Wei et al
2009
Lin et al 2014
Rodriguez et
al 2007
Wei et al
2009
Lin et al 2014

Mouse430_2

DN
thymocytes
DN
thymocytes
DN
thymocytes
DP
thymocytes
DP
thymocytes
CD4SP
thymocytes
CD8SP
thymocytes
Resting
T-Cell
Activated
T-Cell
Splenic
T-helper
Splenic
T-cytotoxic
Splenic
T-cytotxic
Naïve
T-helper
Naïve
T-helper
Naïve
T-cytotoxic
Activated
T-helper
Activated
T-cytotoxic
Th1

98

64

82 38 54 16 76

70

76

91

51

81 40 58 23 67

70

76

91

93

78 57 75 59 74

78

85

87

58

84 35 46 51 63

44

69

85

93

83 53 55 64 76

76

88

97

69

85 70 45 73 94

49

95

97

72

85 49 49 71 94

49

93

95

92

84 65 65 57 81

81

79

93

87

76 95 58 62 59

82

76

97

68

90 61 58 54 91

61

92

98

65

90 52 58 38 93

59

91

98

84

86 51 63 56 87

71

89

99

79

85 64 56 93 85

87

88

98

82

91 69 56 70 90

70

93

99

81

86 60 61 83 88

87

88

98

72

83 97 62 82 69

94

84

98

64

84 97 61 72 73

95

81

92

78

80 96 73 31 64

89

80

Mouse430_2

Th1

97

80

80 77 72 26 81

82

90

MoGene1
Mouse430_2

Th1
Th2

68
93

88
79

79 96 66 63 37
74 96 82 69 58

90
86

61
80

Mouse430_2

Th2

92

72

78 83 63 71 66

73

80

MoGene1

Th2

74

88

82 95 65 62 37

77

60

Mouse430_2
Mouse430_2
MoGene1
Mouse430_2
MoGene1
Mouse430_2
Mouse430_2
MoGene1
MoGene1
Mouse430_2
Mouse430_2
Mouse430_2
Mouse430_2
Mouse430_2
Mouse430_2
Mouse430_2
Mouse430_2

76

Table A1 (Cont’d)
Wei et al
2009
Lin et al 2014
Wei et al
2009
Wei et al
2009
Chambers et
al 2007
Robbins et al
2008
Dudziak et al
2007
Dudziak et al
2007
Robbins et al
2008
Robbins et al
2008
Robbins et al
2008
Derbinski et
al 2005
Wong et al
2014
Wong et al
2014
Weischenfeldt
et al 2008
Derbinski et
al 2005
Chambers et
al 2007
Chambers et
al 2007
Wong et al
2014
Chambers et
al 2007
Haddon et al
2009
Haddon et al
2009

Mouse430_2

Th17

93

79

83 86 64 71 72

79

86

MoGene1
Mouse430_2

Th17
iTreg

66
95

78
81

82 97 66 63 37
85 97 83 65 71

76
99

60
84

Mouse430_2

nTreg

98

87

89 83 72 79 86

77

94

Mouse430_2

NK

99

83

84 53 55 45 84

96

84

Mouse430_2

NK

99

83

89 47 70 21 93

99

83

CD11c+
96
CD8-DCs
Mouse430_2
CD11c+
95
CD8+DCs
Mouse430_2 CD11b+DCs 98

74

87 86 96 14 92

60

92

73

85 51 98 23 93 100 91

82

88 87 95 31 94

Mouse430_2

CD8+DCs

96

83

87 57 96 53 94 100 91

Mouse430_2

pDCs

99

78

88 85 92 34 98 100 91

MG_U74Av2

tDC

96

NA 71 92 96 39 90

99

96

MoGene1

84

77

76 29 68 44 49

96

72

MoGene1

Promyelocytes
Myelocytes

89

91

74 38 66 45 82

71

84

Mouse430_2

BM.MPh

98

81

74 50 93 21 95

95

86

MG_U74Av2

tMPh

91

NA 77 57 83 42 67

97

92

Mouse430_2

Monocyte

100

91

86 50 79 46 93

75

91

Mouse430_2 Granulocytes 98

83

81 61 65 48 87

82

89

95

77 28 80 45 92

62

91

Mouse430_2

MoGene1

Granulocytes 94

92

91

Mouse430_2

Nu.RBCs

88

79

79 61 59 49 91

69

91

MOE430A

mast.cells

82

5

74 42 49 23 49

30

70

MOE430A

premast.cells

82

5

73 46 51 25 47

43

71

77

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regulatory factor 4 expression and Th17 cell differentiation in CD4+ T cells derived from
patients with multiple sclerosis. J Immunol 187, 3431-3437.
Zheng, Y., Chaudhry, A., Kas, A. & other authors (2009). Regulatory T-cell
suppressor program co-opts transcription factor IRF4 to control T(H)2 responses.
Nature 458, 351-356.
Zhou, S., Cerny, A. M., Fitzgerald, K. A., Kurt-Jones, E. A. & Finberg, R. W. (2013).
Role of interferon regulatory factor 7 in T cell responses during acute lymphocytic
choriomeningitis virus infection. J Virol 86, 11254-11265.

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CHAPTER THREE
The role of Irf6 in T-cell development in the thymus

89

Abstract
Rare variants in IRF6 cause Van der Woude and Popliteal Pterygium
Syndromes, two autosomal dominant orofacial clefting disorders. Common IRF6
variants contribute risk toward non-syndromic orofacial clefting. In addition, rare somatic
mutations in IRF6 are associated with squamous cell carcinoma. However, unlike the
other eight members of the IRF family, a role for IRF6 in haematopoietic development
has not been described. Previously, we used publically available data to discover
dynamic IRF6 expression in developing thymocytes. Here, we utilized a mouse model to
show that Irf6 was required for the regulation of thymocyte development. We found that
Irf6 was expressed in the subcapsular region and medulla of the thymus. We further
found that Irf6 regulated the distribution and proliferation of developing thymocytes. In
addition, loss of Irf6 led to an increase in double negative cells with a concomitant
increase in TCRγδ. Loss of Irf6 also led to a reduction in double positive cells with no
corresponding reduction in single positive cell maturation. Finally, we found that Irf6
dose is critical in development of both CD4+ and CD8+ cells in an age-dependent
manner. While perinatal lethality has limited investigation of Irf6 in hematopoiesis, we
report here a novel gene function for Irf6 in thymocyte development. These data
suggest that IRF6 variants may increase risk toward autoimmune disease and that
individuals with VWS and PPS may require more rigorous immunological screening.
With this work, all Irf family members have an important role in immunity.

90

Introduction
The thymus gland is a specialized organ necessary for T-cell development. It is
composed of an inner medulla and a peripheral cortex surrounded by an outer capsule
(Rodewald, 2008; Singer et al., 1986). Thymus tissue is composed of lymphoid cells
(CD45+CD7+) and stromal cells with a ratio of 50 lymphoid cells for each stromal cell
(Rodewald, 2008; Singer et al., 1986). Non-hematopoietic stromal cells can be further
classified into thymic epithelial cells (TEC, Keratin+) and mesenchymal cells (Keratin−)
(Anderson et al., 1993). Dendritic cells and macrophages are CD45+ thymic stromal
cells, thus they constitute the hematopoietic component of the stromal mesh (Rodewald,
2008). T-cell precursors seed the thymus at the medullary cortical junction. Recent
thymic immigrants are called double negative (DN) thymocytes because they lack the
expression of both CD4 and CD8 (Godfrey et al., 1993; Pearse et al., 1989). CD44 and
CD25 are two surface markers which mark 4 major developmental sub-populations of
DN thymocytes (DN1, CD44+CD25-; DN2, CD44+CD25+; DN3, CD44-CD25+; and DN4,
CD44-CD25-) (Godfrey et al., 1993). DN3 is an obligatory check point where expression
of pre-TCR or γδTCR identifies transition from DN3a to DN3b (Michie & ZunigaPflucker, 2002). Pre-TCR signaling drives expression of CD4 and CD8 producing
double-positive (DP) thymocytes (Hoffman et al., 1996). Developing thymocytes have to
migrate through the cortex toward the capsule then back to the medullary space
(Takahama, 2006). Transition from DN4 (also called pre-DP) to DP cells occurs in the
subcapsular region. Transforming growth factor β (TGFβ) signaling suppresses the
proliferation of pre-DP thymocytes to regulate the production of DP cells (Benz et al.,
2004). DP cells migrate back through the cortex where positive and negative selections

91

occur allowing only 3-5% of cells to survive and reach the thymic medulla. DP cells lose
either CD4 or CD8 to reach the single positive (SP) stage. SP thymocytes, either CD4+
(T helper) cells or CD8+ (T cytotoxic) cells, continue their maturation and central
tolerance in the medulla before being shuttled out of the thymus (Blackburn & Manley,
2004; Germain, 2002; Hoffmann et al., 2003; Lind et al., 2001; Plotkin et al., 2003;
Prockop & Petrie, 2000).
Regulation of peripheral CD4:CD8 cell ratios originate in the thymus. Species
and strain differences contributed to TCR selection-dependent mechanisms or thymic
lineage commitment signaling (Damoiseaux et al., 1999; Rocha et al., 1989; Sim et al.,
1998; van Meerwijk et al., 1998). Genetic variations of TCRα loci and MHC haplotypes
represent the most important TCR selection-dependent factors (Damoiseaux et al.,
1999; Sim et al., 1998). Intrinsic activity of Notch is an example of other factors that
influence the thymic lineage commitment and affect the CD4:CD8 T cell ratio (Fowlkes
& Robey, 2002; Huang et al., 2003; Robey et al., 1996). Also the interaction of Notch
ligand Jagged1 on thymic stroma and DP thymocytes controls the CD4:CD8 ratio
(Jimenez et al., 2001). Thymic involution was shown to be induced by Jagged1
expression in thymocytes (Beverly et al., 2006) and is associated with increased
CD4:CD8 ratio (Kozlowska et al., 2007).
Although several Irfs are expressed in thymocytes (Colantonio et al., 2011;
Hrdlickova et al., 2001; Nordang et al., 2011; Simon et al., 1997), Irf1 is the only family
member with functional studies supporting a role in T-cell development. Irf1 reduces
expression of the major histocompatibility complex related genes in the thymic
microenvironment (Lee et al., 1999; White et al., 1996). However it is the Irf1 intrinsic

92

activity in T-cells that is required for development and thymic selection of naïve CD8 Tcells (Matsuyama et al., 1993; Penninger et al., 1997; Penninger & Mak, 1998). In Irf4
knockout mice, while cell count changes appear to be unaffected, the proliferative
capacity, antiviral cytotoxicity, allogenic graft rejection, tumor surveillance and cytokine
production of CD8 cells are markedly impaired (Mittrucker et al., 1997).
Recently, I performed a meta-analysis of microarray experiments on
hematopoietic lineages and found that the steady-state level of Irf6 is induced in the DP
and SP thymocytes as well as medullary thymic epithelium. Previous studies in the
palatal and skin development show Irf6 as a downstream target to Tgf-β and notch
signaling (Le et al., 2012; Restivo et al., 2011; Xu et al., 2006). However, these
interactions have not been tested in T cells.
In this study, we used a mouse model to test for the necessity of Irf6 in
thymocyte development. We found that Irf6 regulated the distribution and proliferation
of developing thymocytes. Also, we found that Irf6 dose is critical in development of
both CD4+ and CD8+ cells in an age-dependent manner. These data suggest that Irf6
like all other family members is involved in regulation of immune system.

93

Figure 3.1: Stages of T cell development in the thymus. Figure modified from Ciofani and Zuniga-Pflucker,
Nat Rev Immunol 2010.

94

Materials and methods
Mice: C57BL/6 mice were obtained from The Jackson Laboratory (Bar Harbor,
ME) or raised in house. Mice with Irf6 gene trap allele (Irf6gt/+) were described
before (Ingraham et al., 2006). Four week-old wild type C57BL/6 mice were used
for initial assessment of Irf6 expression in the thymus. To compare wild-type and
Irf6-knockout thymi, Irf6gt/+ mice were mated to produce wild-type and Irf6knockout embryos. Pregnant females were sacrificed, embryos were dissected
and thymi were collected at E17.5. To study the effect of Irf6 heterozygosity on
postnatal thymic proliferation, Irf6gt/+ and wildtype mice were allowed to mate and
heterozygous mice were compared to their wild type littermates at 6-7, 12-13, or
18-20 weeks of age.
Morphological assessment of Irf6 knockout thymi: Relative size of thymi was
determined as previously described (Candi et al., 2007). Briefly, thymic gland
and heart were dissected using a dissecting microscope. Thymus/heart ratio was
defined using the longest dimension of each.
Western blot analysis: Whole cell extracts were prepared by mechanical
disruption of thymi in RIPA buffer (150 mM NaCl, 1% Triton X-100, 0.5% sodium
deoxycholate, 0.1% SDS, 50 mM Tris, pH 8.0) supplemented by protease
inhibitors (Complete Protease Inhibitor Cocktail Tablets, Roche). Samples were
denatured by incubating at 95°C for 10 minutes. Protein extracts were separated
by 10% SDS-PAGE Electrophoresis System and transferred onto PVDF
membranes with 1X Tris-Glycine Transfer Buffer. Membranes were blocked with
Odyssey blocking solution and probed with polyclonal rabbit antibody against Irf6

95

(1:250) (Ingraham et al., 2006) and mouse antibody against GAPDH (sc-365062,
1:15000, Santa Cruz Biotechnology, Santa Cruz, CA). Proteins of interest were
detected with Infrared IRDye-labeled secondary Donkey anti-Rabbit and goat
anti-mouse IgG (H + L) antibodies (1:15000, Thermo Scientific, Rockford, IL)
imaged with the Odyssey Infrared Imaging System using the 700 and 800
channels (Thermo Scientific, Rockford, IL), according to the provided protocol.
Immunohistochemistry: Thymi were fixed in 10% formaldehyde, embedded in
paraffin and cut into 3 µm-thick transverse sections. To deparaffinize and
rehydrate sections, slides were passed through three changes of Xylene,
followed by reducing concentrations of ethanol. Antigen retrieval was conducted
by boiling in 10mM Sodium Citrate pH6.0 for 10 minutes. Sections were
permeabilized in 0.5% Triton X-100 and blocked for one hour in blocking solution
(10% normal goat serum, 0.1% Bovine Serum Albumin in 1X Phosphate Buffered
Saline) at room temperature. Sections were then incubated in primary antibodies
diluted in blocking solution overnight at 4°C. Following incubation, slides were
washed three times in 1X PBS and incubated in fluorescent labeled secondary
antibodies for 45 min at room temperature. To detect nuclei, slides were
incubated in DAPI (Invitrogen, D3571) diluted 1:10,000 in distilled water for 10
minutes. Slides were imaged using the Nikon i90 upright fluorescent microscope.
Primary antibodies included rabbit polyclonal anti-Irf6 antibody (Ingraham et al.,
2006) and rat monoclonal anti-BrdU (Abcam, Ab6326).. Secondary antibodies
included Oregon Green 488 goat anti-rabbit (Molecular Probes, O-6381) and
Alexa Fluor 555 Goat Anti-Rat (Molecular Probes, A-21434).

96

Total cell count and Flow cytometric analysis: Single cell suspension was
prepared from dissociated thymi by filtering through a 48-µm nylon mesh. Total
thymocyte count was calculated using hemocytometer. Cell count was adjusted
to 1x106 followed by FcR blocking with anti-FcR mAb 2.4G2. Subsequent
flowcytometric analysis included one of the following:
a) Analysis of the thymocyte subpopulations: To cover most of the

developing T-cell populations, cells were co-stained for 6 surface markers;
against CD4 (APC, Catalog# 100516, Biolegend), CD8a (FITC, Catalog #
140404, Biolegend), TCRβ (PE-cy7, Catalog# 109222, Biolegend), TCRγδ
(PE-cy5.5, Catalog# 118118, Biolegend), CD25 (PE, Catalog# 100610,
Biolegend), CD44 (APC-cy7, Catalog# 103028, Biolegend) (figure 3.2).
Natural Treg was assessed separately by co-surface staining of CD4
(APC, Catalog# 100516, Biolegend) and CD8a (FITC, Catalog # 140404,
Biolegend) with intracellular staining for Foxp3 (PE, Catalog# 12-5773-82,
eBioscience) according to the recommended protocol of the company.

97

Figure 3.2: Flowcytometric analysis of the thymocyte subpopulations. Left scatter
diagram plots CD4 and CD8 expression to classify the developing thymocytes into four
major groups (DN; black, DP; blue, CD4-SP; red, and CD8-SP; green). Middle scatter
diagram plots CD25 and CD44 expression to tease apart the sub-populations of DN
thymocytes. Right scatter plots TCRβ and TCRγδ expression.

98

b) Bromodeoxyuridine (BrdU) incorporation analysis: For embryonic studies,

pregnant dams were injected intraperitoneally with BrdU (100ug/gm body
weight) one hour before euthanasia. For postnatal studies, each animal
was injected intraperitoneally with BrdU (100ug/gm body weight) two
hours before euthanasia. After blocking of non-specific binding of Fc
receptors, surface staining was done using monoclonal antibodies against
CD4 (Alexafluor 700, Catalog# 100536, Biolegend) and CD8a (FITC,
Catalog # 140404, Biolegend). Cells were fixed and permeabilized with
BD Cytofix/Cytoperm Buffer (Catalog# 554722, BD) for 20 min on ice. For
nuclear permeabilization, cells were kept overnight at -80oC in a freezing
medium. On the next day, cells were thawed and washed with FACS
buffer prior to refixation in BD Cytofix/Cytoperm Buffer for 5min on ice.
DNA was partially digested by incubating the cells with 30 µg of
DNase/106 cells (Catalog# D-4513, Sigma) for 45min at 37oC. Cells were
stained with Anti-BrdU (PerCP-Cy 5.5, Catalog# 560809, BD).
c) Detection of apoptosis was performed using combinations of Annexin V

Apoptosis Detection Kit PerCP-eFluor® 710 and Fixable Viability Dye
eFluor-780 (eBioscience)

99

Results
Thymic expression of Irf6: Whole-cell protein extracts were prepared from
thymi of 4 week-old mice (n=3). Western blot analysis showed a protein band at
the expected size (~53kD) (figure 3.3). To determine in situ localization of Irf6
expression, immunofluorescent staining was done on paraffin embedded tissue
(n=3). Irf6 expression was mainly confined to the thymic medulla and subcapsular compartments (Fig.3.4). The thymic structure did allow delineating the
exact cell type expressing Irf6 without co-staining for additional markers. Unlike
expected for transcriptions factors, Irf6 expression was confined to the
cytoplasmic spaces. However this pattern of Irf6 expression is consistent with
previous studies of in other tissues (Bailey et al., 2005; Ingraham et al., 2006).

Irf6
Gapdh

Figure 3.3: Two-color western blot for Irf6 and Gapdh. Irf6 is Red
(~53kD) and Gapdh is green (~37kD). Lanes from left to right
represent protein standard, TNT (IRF6 protein synthesized by invitro transcription and translation system) and whole-cell protein
extract from thymus from a 4 week old mouse (n=3).

100

A

D

B

E

C

Figure 3.4: Irf6 expression in the thymus. (A) Schematic representation of the
thymus. (B-E) Immunofluorescent staining of thymic sections obtained from 4
week-old mice. Nuclear DAPI staining (Blue), Irf6 staining (green). The primary
Irf6 antibody was pre-incubated with (B) or without (C) blocking peptide and
images were taken under 4x objective magnification. 40x magnification is shown
for the sub-capsular region (D) and thymic medulla (E) (n=3).

101

Characterization of thymic changes in Irf6 knock-out embryos: Thymi of
E17.5 littermates were compared. No obvious changes of the position, shape or
size were detected (figure 3.5). Total thymocyte count showed insignificant
changes from wild type littermates by paired t-test analysis. However further
analysis of developing thymocyte sub-populations showed significant increase of
DN population (p = 0.017) and significant decrease of DP population (p < 0.001)
with insignificant change of single positive populations. We used BrdU staining
as a marker of DNA synthesis and cellular proliferation. While the total uptake of
BrdU incorporation was not different, we found that DN cells were more
proliferative (p = 0.007) while DP cells were less proliferative (p = 0.018) (figure
3.6 and 3.7). Immunostaining for BrdU in paraffin embedded thymi showed a
different distribution of proliferating thymocytes through the cortical space
compared to the typical localization in the subcapsular space (Figure 3.8).
Surface staining for TCR showed significant increase of TCRγδ in
knockout thymi by a paired t-test analysis between littermates (p = 0.01). We did
not find a significant difference in TCRβ (Figure 3.9). Testing nuclear expression
to mark nTreg showed that embryonic thymi did not start producing Foxp3 +ve T
cells at E17.5. We also analyzed Annexin V as a marker of cellular apoptosis and
the fixable viability dye to quantify dead cells. Co-staining for both markers
showed no change in the total rate of thymocyte apoptosis or cell death in Irf6
knockout thymocytes (Figure 3.10).

102

A. Wild type
T

H

B. Irf6gt/gt
T

H

Figure 3.5: Thymic-cardiac ratio in A) wild type and B) Irf6gt/gt
mice. (T) refers to the thymus and (H) refers to the heart. There
was no obvious change in the thymic-cardiac ratio (n=3).

103

Wild type
Irf6gt/gt
Figure 3.6: BrdU incorporation in developing thymocytes. BrdU incorporation
in thymocyte populations in wild type (upper panel) and Irf6gt/gt (lower panel)
using flowcytometry. Left column scatter plots show the size scatter on x-axes
and forward scatter on y-axes. Right side scatter plots show the surface
expression of CD4 on the x-axes and CD8 on the y-axes. Red color identifies the
thymocytes from black colored debris. Blue color overlay the red color to identify
the cells incorporating BrdU. There is no significant difference in total BrdU
incorporation. However, we observed a shift of BrdU incorporation from the DP
compartment to the DN compartment which compensated the overall proliferation
in Irf6 knockout mice.

104

Figure 3.7: Irf6 regulates proliferation and cell count of thymocytes.
Statistical analysis of BrdU incorporation and cell counts of total thymocytes and
the developing sub-population shows underline the importance of Irf6. In spite of
insignificant changes in total proliferation and cell count, there is a significant
increase in the count and proliferation of DN cells with a corresponding reduction
in DP cells.

105

Irf6gt/gt

Wild Type

Figure 3.8: Loss of Irf6 leads to an abnormal distribution of proliferating
cells. Immunofluorescent staining of thymic sections from E17.5 embryos.
Nuclear DAPI staining (Blue), BrdU staining (red). Compared to wildtype embryos,
Irf6 knockout embryos have more cellular proliferation in the thymic medulla (n=3).

%

3

2

2.8

1

2.6
2

2.4
2.2
2

2
4
2

1.8
1.6

3
2

1.4
Irf6-WT

Irf6-KO

Figure 3.9: Frequency of TCRγδ. The figure shows the average frequency of
TCRγδ for each genotype per littermates. Each point is labeled by the numbers of
embryos. Paired t-test is significant with p = 0.01

106

Figure 3.10: Loss of Irf6 does not alter total cell death. Analysis of apoptosis
and cell death. Left scatter diagram shows the plotting of the fixable viability dye
on the x-axes versus Annexin V on the y-axes where (Q1) square the apoptotic
cells and (Q2) square represents the dead cells. Par plot on the right shows
insignificant changes in the total count of apoptotic or dead cells in Irf6 knock-outs
(n=11 wildtype/heterozygous and 7 knockouts from 4 litters).

Effect of Irf6 heterozygosity in postnatal thymic proliferation: To study the
effect of Irf6 dosage on thymic proliferation over time, we tested mice at postnatal week 6-7, 12-13, or 18-20. At each time point, three heterozygous mice
were compared to their matching wild type littermates. BrdU incorporation study
showed an insignificant change of total BrdU uptake but the ratio CD4-SP subpopulation positive for BrdU shows significant increase (p = 0.04) versus a
significant reduction in the CD8-SP compartment (p = 0.03) at 18-20 weeks of
age (figure 3.11).

107

A

B

Figure 3.11: Effect of Irf6 dosage on BrdU incorporation in thymocyte
populations.

108

Figure 3.11 (Cont’d) (A) Flowcytometric analysis of thymi from Irf6 wild type
(Upper panel) and Irf6gt/+ (lower panel). Scatter plots shows the surface
expression of CD4 on x-axes and CD8 on y-axes at 6-7, 12-13, or 18-20 weeks
of age (from left to right). Red color identifies the thymocytes incorporating BrdU.
(B) Bar diagram represents the changes of total thymocytes proliferation and the
four major sub-populations (mean and SD) at 6, 12 and 20 weeks of age in Irf6+/+
and Irf6gt/+ thymi (n=3 for each time point and genotype). There is a significant
increase of CD4 population with a significant reduction of CD8 thymocytes after
20 weeks of age.

109

Discussion
The IRF family of transcription factors has been widely studied and
regarded in hematopoiesis as master regulators. While IRF6 is a paralog, sharing
a highly conserved DNA and protein binding domains, a role in immunity has not
been reported. Instead, previous and ongoing IRF6 studies have focused on
other important roles in cutaneous, limb and craniofacial development. In
humans, common variants in IRF6 lead to common, complex diseases, including
cleft lip and palate and squamous cell carcinoma. Furthermore, rare variants in
IRF6 lead to Van der Woude (VWS) and Popliteal Pterygium Syndromes (PPS).
Most proximally, these data suggest that individuals with common and rare IRF6
variants are at increased risk for immunological diseases.
Like cutaneous development, we found that Irf6 regulates proliferation in
thymocytes. In skin and thymus, loss of Irf6 leads to an expansion of the
progenitor cells; germinative and double negative cells respectively. Also a
concomitant reduction in downstream daughter cells (keratinizing and double
positive cells). However, in contrast to skin which loses the cornified cells, DP
cells are not lost. Also we did not see changes in the terminally differentiated
single positive thymocytes. Therefore, while some parallels exist, a more
complex relationship in the thymus plausible.
Importantly, we found that counts of single positive cells were not altered
despite a significant reduction in double positive cells. Our analysis did not show
a change in the total number of apoptotic or dead cells. However, further
analysis for the thymic sub-population is mandatory. These data can be

110

explained by either enhanced survival of single positive cells or thymic retention
of naïve lymphocytes. The enhancement of survival of single positive cells may
occur at the expense of negative selection and predisposition for autoimmune
responses. We further observed that Irf6 was expressed in the subcapsular
cortex and medulla. However, we have not delineated if this expression was in
the thymic epithelium or thymocytes or both. Considering that negative selection
is a product of the interaction between the thymic epithelium and double positive
thymocytes, enhanced survival could be a cell-autonomous mechanism or results
from a milieu of factors. Retention of thymic naïve lymphocytes can be presented
as reduced peripheral lymphocyte count. Neonatal lymphopenia itself was shown
to predispose for autoimmune disease (Gleeson et al., 1996; Sakaguchi &
Sakaguchi, 1989). While an association between VWS/PPS and autoimmune
diseases or neonatal lymphopenia has not been described in the literature,
multiple factors are likely involved. For example, VWS/PPS are rare congenital
anomalies and subclinical manifestations of immunological diseases can be
missed. Also considering the role of the Irf family members in T cell development,
redundant function is possible. Interactions between Irf6 and other Irf family
members seem plausible, if not likely. Pursuing this gene regulatory network may
provide novel gene functions for Irf6 in T cell subsets.
Thymic involution is a very controlled process which starts early in life and
considered as a central driver of T cell aging (Goronzy & Weyand, 2013)
(Aspinall et al., 2010). Aging of thymus is associated with altered distribution of
thymocyte populations. Whereas the DN cells start to accumulate, the DP cell

111

counts decrease with a significant increase of CD4:CD8 ratio (Kozlowska et al.,
2007). Embryonic absence of Irf6 is associated with similar changes of DN and
DP populations but without significant changes of CD4:CD8 ratio. However, by
the age of 6 month, animals with reduced dose of Irf6 start to show significant
increase of CD4:CD8 ratio. . Two transgenic lines with constitutively active form
of Notch in DP cells showed a decrease in CD4 SP thymocytes and a
corresponding increase in CD8 SP thymocytes (Fowlkes & Robey, 2002). IRF6 is
a known mediator of Notch in keratinocytes (Restivo et al., 2011). Irf6
heterozygosity in thymocytes might be associated with decreased downstream
signals of Notch pathway causing increased CD4:CD8 ratio. Also Expression of
the Notch ligand Jagged1 in thymocytes results in thymic involution by inducing
apoptosis of thymic stromal epithelial cells (Beverly et al., 2006). This might
explain the altered CD4:CD8 observed in mice heterozygous for Irf6 as a sign of
early thymic involution.
In summery, we describe a novel gene function for Irf6 in hematopoietic
development. To our knowledge, this is the first study to ascribe immunological
function to Irf6, completing the family so that all nine paralogs now have a role in
haematopoiesis.

112

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CHAPTER FOUR
The role of Irf6 in functional commitment of T-cell subsets

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Abstract
The IRF family of transcription factors is essential in the differentiation of T helper
subsets. In contrast, IRF6 is canonically known for critical roles in craniofacial, limb and
cutaneous development. In the mouse, loss of Irf6 leads to perinatal lethality. Recently,
we showed that Irf6 is also involved in the regulation of thymic proliferation. However,
little is known about the necessity of Irf6 in the development of functionally committed T
helper subsets. Here, we used in silico, in vivo and in vitro assays to determine the role
of Irf6 in T cell differentiation. Using in silico analysis, we found and propose a model for
Irf6 function in Th17/Treg balance. To test our hypothesis in vivo and overcome
perinatal lethality, we employed an adaptive transfer of Irf6 knockout cells into lethally
irradiated mice. We observed a 100% survival of chimeric mice receiving Irf6 knockout
fetal liver, and mice receiving Irf6 knockout cells had no deficit in restoration of
lymphocyte production. In addition, we used two in vitro models to assess the necessity
of Irf6 in the commitment of T helper cells. Using a stromal-free culture we found that
naive T cells lacking Irf6 could be differentiated into Th1, Th2, Th17 and Treg using a
specific cytokine cocktail. We found no differences in cell frequency and mean
fluorescence intensity of intracellular cytokines between wild type and Irf6 knockout
cells. In vitro differentiation of dendritic cells showed significant increase of MHC-II
expression after three days of culture. Irf6 might be involved in post-translational
regulation of MHC-II. In conclusion, we found that intrinsic Irf6 expression was not
essential for T helper subset differentiation. However, a non-cell autonomous role for
Irf6 in T cell differentiation through dendritic cells remains plausible.

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Introduction
T helper (Th) lymphocytes function as the conductors of the adaptive immune
orchestra. Upon antigen exposure, T helper cells differentiate into specialized subsets.
Each T helper subset differentiates under a unique signaling pathway and lineagespecific transcription factors to produce a characteristic cytokine milieu (Fietta &
Delsante, 2009; Hirahara et al., 2011). T helper subsets include Th1, Th2, Th17,
regulatory T-cells (Treg), T follicular helper cells, Th9 and Th22 cells (Bluestone et al.,
2009; Shevach, 2010). The balance between different T helper cells is most typically
defined by mutually exclusive expression of lineage-specific transcription factors. In vitro
activation of T cells can be done using specific antibodies against the T-cell receptors.
The combination of anti-CD3 and anti-CD28 is used to mimic antigenic stimulation
(Bjorndahl et al., 1989; Verwilghen et al., 1991). Cytokine cocktails can be formulated to
simulate the physiological signaling pathways and drive the differentiation of naïve CD4
T cells into one of several lineages of T helper cells (Constant & Bottomly, 1997; Zhu et
al., 2010). Differentiation of naïve cells can be analyzed by measuring their secreted
cytokine profile. As such, stromal free models of in-vitro differentiation enable
identification of cell autonomous mutant T cell effects.
Normal differentiation of T cells requires antigen presentation and cytokine
guidance, two essential functions of Dendritic cells (DCs). DCs are a heterogeneous
population of hematopoietic cells known as professional antigen presenting cells (APCs)
that display different anatomical localizations, cell surface phenotypes and functions.
They all come from CD34 bone marrow stem cells and express CD11c (Merad et al.,
2013; Miller et al., 2012). In their immature state they have the ability to respond to

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danger signals, engulf antigen, mature and migrate to lymphoid organs. Once the
antigen, foreign or self, is internalized they are degraded and presented in the surface
of the DCs in the context of major histocompatibility complex class I (MHC-I) and class
II (MHC-II). DC maturation involves an increase in surface expression of MHC-II and costimulatory molecules like CD86 (Banchereau et al., 2000). There are four major
categories of DCs: Conventional DC, Langerhan cells, plasmacytoid DCs and
monocyte-derived DCs. All except for the Langerhan cells are derived from bone
marrow cells. Conventional DCs are specialized for antigen presentation and have two
further subdivisions, CD11b+ and CD103+ DCs (Belz & Nutt, 2012). DCs can
differentiate in vitro from bone marrow and blood using a combination of growth factors
like granulocyte macrophage-colony stimulating factors (GM-CSF), IL-4, Flt3 ligand and
tumor necrosis factor-α (Lipscomb & Masten, 2002).
We have shown abnormal thymic development in Irf6 deficient mice (Chapter 3).
With abnormal thymic development, we can predict abnormal counts of mature T cell
subsets in the peripheral blood and/or biased T cell immune response (Tanigaki et al.,
2004). Irf1, 2, 4, 8 are indispensable for normal T helper differentiation (Kano et al.,
2008; Lohoff et al., 2000; Lohoff & Mak, 2005; Tamura et al., 2008; Tominaga et al.,
2003). Also, variants in IRF5, IRF7 and IRF8 are associated with psoriasis, multiple
sclerosis and systemic lupus erythematosus (SLE) (De Jager et al., 2009; Demirci et al.,
2007; Gateva et al., 2009; Graham et al., 2006; Harley et al., 2008; Leppa et al., 2011;
Patel, 2011; Sanchez et al., 2008). Conservation of IRF family members (Lohoff & Mak,
2005) and documented protein-protein interactions between IRF6 and both IRF5 and
IRF8 (Li et al., 2011) suggest a role of IRF6 in T helper cell commitment. My meta-

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analysis of microarray studies in T helper subsets reveals a significant reduction of Irf6
expression in Th1 compared to Th2, Th17 and Treg suggesting an intrinsic role for Irf6
in development of T helper subsets (Chapter 1).

Materials and methods
Bioinformatic analysis: Publically available microarray studies were meta-analyzed
using R computing environment (http://www.r-project.org/) to predict the possible roles
of Irf6 in T-helper commitment. We examined two microarray studies altering Irf6
expression; 1) Irf6 knockout mouse skin (Ingraham et al., 2006) and 2) IRF6 knockdown
in human keratinocytes (Botti et al., 2011). We also analyzed two microarray studies
that altered Foxp3 expression; 1) knockout (Williams & Rudensky, 2007) and 2) overexpression (Fontenot et al., 2005) of Foxp3 in CD4+CD25+ cells. Data were integrated
with Chip-seq analysis of IRF6 binding sites in human keratinocytes (Botti et al., 2011).
Mice and adoptive transfer: Mice heterozygous for the Irf6 gene trap allele (Irf6gt/+)
were mated to produce wild-type (Irf6+/+) and Irf6-knockout (Irf6gt/gt) embryos. Fetal livers
were harvested from E12.5 embryos and suspended in Iscove’s modified Dulbecco’s
medium supplemented by 2% fetal calf serum. A single-cell suspension was prepared
by passage through a 26-gauge needle. Crude DNA extraction was done and rapid
PCR-based genotyping was performed. Recipient mice were congenic strain of
C57BL/6 mice that carry the differential B cell antigen designated CD45.1 (NCI,
Washington, DC. NY). Ten-week old recipient mice were lethally irradiated using XRAD320 Irradiation System (PXi, North Branford, CT). Recipient mice received two
doses of irradiation (5.5 Gy each) with a three hour interval. Irradiated mice were

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injected retro-orbitally with the liver cell suspensions after 6 hours from the first dose of
irradiation. Host mice were maintained on autoclaved water containing trimethoprimsulfamethoxazole (0.65-1.6 mg/ml). All mice were maintained at the Michigan State
University pathogen-free facility.
Th1, Th2, Th17 and Treg differentiation in vitro: Single cell suspensions of the
spleens were prepared under sterile conditions. Naïve T-cells were purified by negative
selection using magnetic beads from EasyStep Mouse Naïve CD4+ T Cell Isolation Kit
(STEMCELL Technologies Inc, Vancouver, BC, Canada). Unwanted cells were targeted
for removal with biotinylated antibodies directed against non-naïve CD4+ T cells (CD8,
CD11b, CD11c, CD19, CD24, CD25, CD44, CD45R, CD49b, TCRγ/δ, TER119) then
captured by streptavidin-coated magnetic particles. Naïve T-cells were cultured in Xvivo medium supplemented by 1 mM sodium pyruvate, nonessential amino acids, and
L-glutamine. Culture plates were coated with anti-CD3ε antibody (clone 145-2C11, BD,
Cat# 553058) 5µg/mL. Soluble anti-CD28 antibody (clone 37.51, BD, Cat# 553295)
2µg/ml, 2-mercaptoethanol 50 µM and 1X Penicillin-Streptomycin were added to the
culture medium. Medium was enriched by the differentiation cocktail of Th1 [IL2
(20ng/ml, R&D, cat# 202-IL-010) + IL12 (20ng/ml, R&D, cat# 419-ML-010) + anti- IL4
(10µg/ml, R&D, cat# AB-404-NA)], Th2 [IL2 (20ng/ml, R&D, cat# 202-IL-010) + IL4
(100ng/ml, R&D, cat# 404-ML-010) + anti-IFN-γ (10µg/ml, BD, cat# 554408) + anti-IL12
(10µg/ml, Biolegend, cat# 505304)], Th17 [TGFβ1 (1ng/ml, R&D, cat# 7666-MB-005) +
IL6 (100ng/ml, R&D, cat# 406-ML-005) + anti- IL4 (10µg/ml, R&D, cat# AB-404-NA) +
anti-IFN-γ (10µg/ml, BD, cat# 554408) + FICZ (300 nM, Santa cruz, cat# sc-300019A)],
or iTreg [IL2 (20ng/ml, R&D, cat# 202-IL-010) + TGFβ1 (5ng/ml, R&D, cat# 7666-MB-

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005) ] or under non-skewing conditions [only IL2 (20ng/ml, R&D, cat# 202-IL-010)].
Cells were cultured for four days, washed, and re-suspended in new wells at 106
cells/ml in the presence of 10ng/ml PMA and 1µg/ml ionomycin (extra supplementation
with 2-mercaptoethanol and IL2 or IL6 enhance the cells’ viability). After 1 hour, 1x
Monensin was added for another four hours to block the cytokine secretion. Ice-cold
EDTA was added to a final concentration 2 mM and incubated for 15 min at room
temperature to decrease the cellular clumping. All cells were collected, washed and
stained for viability with the fixable viability dye eFluor 780 as recommended
(eBioscience, cat# 65-0865). Each cell type was intracellularly stained for CD4 and the
appropriate differentiation marker (INF-γ for Th1, IL4 for Th2, IL17 for Th17 and Foxp3
for iTreg) (Bettelli et al., 2006; Ghoreschi et al., 2010; Huh et al., 2011; Maruyama et al.,
2011; McKenzie et al., 1999; Nurieva et al., 2009; Rodriguez et al., 2007; Zhou et al.,
2003).
In vitro generation of bone marrow–derived DC: Femurs and tibias of adoptively
transferred mice were flushed for bone marrow, and 5 x 106 cells were grown in a 6-well
plate (BD Falcon Franklin Lakes, NJ). We used 4 ml of RPMI media supplemented with
10% serum containing 100 units/ml penicillin and 100 µg/ml streptomycin (Gibco,
Invitrogen). This media was applied for 9 days in the presence of granulocytemacrophage colony stimulating factor (20 ng/ml; Peprotech, Rocky Hill, NJ). In total,
50% of the media was replaced on days 3, 6, and 8. After 9 days, bone marrow–derived
DC (BM-DC) were left unstimulated (naive) or stimulated with LPS (1 µg/ml) in 2% fetal
bovine serum RPMI. After 24 h, BM-DC were stained for CD8 (53–6.7), CD103 (2E7),
CD11b (M1/70), CD11c (N418), CD86 (GL-1), Gr-1 (RB6-8C5), MHC-I (H-2Kb AF6-

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88.5), and MHC-II (I-A/I-E, M5/114.15.2) and analyzed by flow cytometry for analysis of
DC populations. Total CD11c+ were counted to represent the total DCs. We attempted
to identify three subsets of DCs, including CD8 DCs, CD103 DCs and CD11b DCs (for
gating scheme, see figure 4.1)

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Figure 4.1: Gating scheme for identification of DC populations in BM. Living
cells were either gated for CD8+CD11c DCs (A) or gated to identify CD103+ and
CD11b+ cell populations (B). CD103+ cell population were further gated to identify
CD103+CD11c+CD8- DCs (C). CD11b+ cell population were further gated to identify
CD1b+CD11c+CD8- DCs (D)

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Results
Bioinformatic prediction of possible roles of Irf6 in T-helper commitment:
Analysis of microarray data from Irf6-knockout skin (Ingraham et al., 2006) showed a
13-fold reduction of Il18. However, no significant change of IL18 was found when IRF6
was knocked down in keratinocytes (Botti et al., 20xx). Microarray analysis of the
Foxp3 knockout cells showed a 2-fold reduction of Irf6, Foxp3 overexpression increases
Irf6 transcription (citation). Therefore, Foxp3 regulates Irf6 expression in CD4+ CD25+ Tcells. Integration of gene expression profiling and Chip-seq analysis of IRF6 in
keratinocytes showed significant effects on several T cell transcription factors. There
was significant up-regulation of Id3 (p=0.000584) and down-regulation of GATA3
(p=0.015595) and Klf4 (p=0.006814). ChIP sequencing for IRF6 binding sites showed
six candidate binding sites in a gene desert downstream to GATA3. In addition, IRF6
bound to and was required for the regulation of Klf4 (Botti et al., 20xx). Thus,
bioinformatic analyses of publicly-available data suggest that Irf6 is involved in a gene
regulatory network during T-helper commitment.
Efficiency of adoptive transfer:
As expected, 100% mortality was observed in untreated control animals 11-14 days
after irradiation. Post-mortem examination showed severe pallor of the internal organs
and gastrointestinal bleeding. On the other hand, all irradiated mice treated with
adoptive transfer survived beyond this time point. To further confirm the efficiency of
adoptive transfer, peripheral blood lymphocytes of recipient mice were stained for
CD45.1 (the native marker of the recipient mice) and CD45.2 (marker of donor mice).

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Flowcytometric analysis of living lymphocytes showed a predominance of the donor
marker in recipient mice (Figure 4.2).

Figure 4.2: Efficiency of adoptive transfer. The histograms show the level of
CD45.1 expression (marker of the recipient mice) and CD45.2 expression
(marker of donor mice) in peripheral blood lymphocytes of adoptively transferred
mice.

Th1, Th2, Th17 and Treg differentiation in vitro:
We cultured naïve CD4 cells isolated from the spleens of recipient mice after adoptive
transfer from either wild type or Irf6 knockout fetal livers. Cultured CD4 cells originating
from wild type or Irf6 knockout cells were not different in both non-skewing (Il2 only) or
differentiation conditions specific for Th1, Th2, Th17 or Treg (Figure 4.3).

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A

Figure 4.3: Th1, Th2, Th17 and Treg differentiation in vitro.

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Figure 4.3 (Cont’d)

% of activated cells

B

90
80
70
60
50
40
30
20
10
0

WT/Het (n=7)
Irf6-ko (n=6)

INFg

IL4

IL17

Foxp3

Naïve T-cells were cultured with plate-bound anti-CD3 and soluble anti-CD28 in
X-vivo medium enriched by differentiation cocktails for Th1, Th2, Th17, Treg or
under non-skewing conditions for 4 days. Cultured cells were reactivated with
PMA and ionomycin for 5 hours and cytokine expression was blocked by
Monensin. Living cells were determined by the viability dye assay. Differentiated
cells were identified by co-expression of intracellular CD4 and either INF-γ, IL4,
IL17, or Foxp3 as markers of Th1, Th2, Th17 and iTreg populations. Thresholds
were identified by comparison to matching populations grown under non-skewing
conditions (Panel A from top to bottom respectively shows a representative
sample). Mean and standard error of Irf6 expression in non-mutants and Irf6
knockouts were blotted in the bar graph (B).

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Changes of bone marrow DCs in Irf6 adoptively transferred mice:
Bioinformatic analysis suggested a possible role for Irf6 in regulating the functions of
DCs. Flowcytometric analysis of living cells in bone marrow (BM) from adoptively
transferred animals showed ~ 3.5 % CD11c+ cells in both wild type and Irf6 knockout
samples. CD8+CD11c+ cells were barely detected (< 0.2%). The two main DCs subpopulations identified were CD103+CD8- and CD11b+CD8- cells. Subpopulation size did
not allow for a statistical analysis (Figure 4.4).

Figure 4.4: Frequency of total DCs and their sub-populations in bone marrow.
About 3.5 % are positive for CD11c+. CD8+CD11c+ cells were barely detected (< 0.2%).
The two main DCs sub-populations identified were CD103+CD8- and CD11b+CD8- cells
(n= 3 for each genotype).

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In GM-CSF supplemented culture, the cells soon became adherent to the plates. Cells
were sampled for flowcytometric analysis at days 3, 6, and 9 of culture. The cultures
were sampled after another 24 hours under normal culturing conditions or under
stimulation with LPS. The frequency of CD11c+ cells increased gradually with selective
differentiation into CD11b+CD8- sub-population. We also found a CD11bhi population
between days 3 to 6 day that was lost by the ninth day. We saw a relatively faster
increase in the frequency of CD11c+ and CD11b+ at day 9 of culture in Irf6 knockout
mice but this was not statistically significant (Figure 4.5).

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A

C

B

Total DCs cells (CD11c+)

CD11b+CD11c+CD8- DCs

D

Figure 4.5: In vitro differentiation model of DCs.

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Figure 4.5 (Cont’d): Cells were cultured with GM-CSF and sampled at culture days 0,
3, 6 and 9. Cells were than cultured for another 24 hours either unstimulated or under
stimulation with LPS. (A) A representative sample showing the expression of CD11b
and CD103 on living cells. The cells gradually progressed towards CD11b positive
phenotypes. There is a CD11bhi population seen between days 3 to 6 that was than lost
by the end of day 9. (B) The expression of CD11c and CD8 on CD11b+ cells from panel
A. There is gradual increase in CD11c+ cells through the days of cultures until reaching
almost 100%. (C,D) Bar diagrams showing the changes in the frequency of total DCs
(All CD11c+ cells) (C) and changes in the CD11b+CD11c+CD8- DCs sub-population (D)
in wild type and Irf6 knockout cells. GM-CSF skewed the differentiation of the bone
marrow cells into CD11c+ CD11b+DC. By day 3, almost all cells that are CD11c+ are
CD11b+ as well. There is no significant difference between the cell frequencies of wild
type and Irf6 knockout mice (n= 3 for each genotype)..

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MHC-II and the co-stimulatory molecule CD86 are surface markers of DCs associated
with maturity and functional commitment (Banchereau et al., 2000). We followed up the
expression of maturation markers at the time of BM sampling and throughout the in vitro
differentiation course. We saw an initial increase of both MHC-II and CD86 in knockout
Irf6 DCs at the time of BM collection, however it was not statistically significant. Cultured
cells showed a marked increase of maturation markers at day 3 followed by gradual
decrease until day 9. Interestingly, the level of MHC-II expression was significantly
higher in knockout Irf6 DCs at day 3 (p value = 0.016). As expected, there was a
marked increase in the expression of MHC-II and CD86 after 24 hours of LPS
stimulation but there was no significant difference between wild type and knockout Irf6
cells (Figure 4.6).

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A

B

Figure 4.6: Expression of maturation markers on DCs in culture. Mean
fluorescence intensity of MHC-II (A) and CD86 (B) expression from DCs following in
vitro differentiation cultures of wild type and Irf6 knockout mice. Cells were sampled at
days 0, 3, 6 and 9. Then further sampled 24 hours after culturing either unstimulated or
under stimulation with LPS. A significant increase in MHC-II expression was observed
at day 3 between Irf6 wild type and knockout mice (n= 3 for each genotype).

136

Discussion
Irf family members are either indispensable for normal T helper differentiation or
have known variants associated with autoimmune disorders of T cells (Lohoff & Mak,
2005; Patel, 2011). Foxp3, Id3, Gata3 and Klf4 are a group of transcription factors
responsible for developmental regulation and balance between Th17 and Treg.
Microarray analysis of Foxp3 altered expression in CD4+ CD25+ suggested that Irf6 is a
downstream target of Foxp3. Botti et al 2011 performed gene expression profiling in
primary human keratinocytes after siRNA-mediated IRF6 depletion. Expression analysis
showed a significant increase of ID3 and significant reduction of GATA3 and KLF4. In
the same study, genome-wide analysis of IRF6 binding sites suggested direct
transcriptional regulation with both GATA3 and KLF4. These data favor an intrinsic role
of Irf6 in regulating the balance of Th17 and Treg with less Th17 differentiation relative
to Tregs in the Irf6 knockout (Figure 4.7)

Figure 4.7: Proposed model of Irf6 in Th17/Treg balance. Bold
black lines represent the well-known pathway of Th17/Treg. The
red lines represent the predicted genetic interactions with Irf6

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Adoptive transfer is a successful tool to overcome the problem of perinatal
lethality. One major advantage compared to the tissue specific knockout approaches is
the ability to study the effect of gene deficiency in the whole haematopoietic system with
the known extensive cell-cell interactions. However, adoptive transfer experiments
generate animal chimeras which reduce but do not eliminate the effect of the target
gene. Using adoptive transfer, we found that Irf6 knockout fetal livers were sufficient to
replenish the bone marrow of lethally irradiated mice. Lymphocytes in the peripheral
blood of chimeric mice were shown to be mostly derived from donor stem cells
indicating functional competency of Irf6-deficient stem cells.
We successfully completed in vitro culture to yield different T helper subsets
using conditioned medium. The rate and efficiency of differentiated T helper subsets
seen here was highly comparable to prior work. We compared the frequency and mean
fluorescence intensity of cytokine production with and without Irf6. Remarkably, in vitro
differentiation of Irf6 knockout cells successfully produced all T helper subsets targeted
in this work. In fact, the production of these T helper subsets was not different from wild
type cells. Therefore, we conclude that intrinsic Irf6 expression is not essential for the
differentiation of Th1, Th2, Th17 or Treg.
Bioinformatic analysis showed a 13-fold reduction of Il18 in Irf6-deficient skin but
no change after knocking down IRF6 in human keratinocytes. Because keratinocytes
and APCs are the predominate source of Il18 in skin (Huising et al., 2004), these
findings favor APCs as the primary driver for changes in Il18 expression in the Irf6deficient skin (Nicklin et al., 1994; Suttles et al., 1990). Il18 is a powerful Th1-polarizing

138

cytokine (Fietta & Delsante, 2009) so that systemic deficiency of APC-derived Il18 in Irf6
knockout would be expected to prevent the appropriate polarization of Th1. To test this
hypothesis we studied the Irf6-deficient bone marrow DCs from adoptively transferred
mice. We tested for any changes in counts and adopted an in vitro approach to test their
development and functional commitment Direct analysis of bone marrow from
adoptively transferred animals showed that 3.5% of bone marrow cells stained positive
for the DC marker CD11c independent of the genotype. Most of CD11c+ cells were
almost equally divided between CD103 and CD11b sub-populations. The paucity of this
subpopulation did not allow for a valid statistical analysis. For in vitro differentiation with
GM-CSF, we saw a gradual increase in CD11c+ cells until reaching almost 100%. GMCSF skewed the differentiation of the bone marrow cells into CD11c+ CD11b+ DC. By
day 3, CD103 DCs were lost and almost all cells that are CD11c+ became CD11b+ as
well.
It is interesting to note the appearance of the CD11b-high population between
days 3 to 6. However, the magnitude of CD11b expression decreased by day 9. CD11b,
also known as Integrin alpha M and macrophage-1 antigen (Mac-1), is a member of the
β2-integrin family of adhesion molecules. It plays a role in cell adhesion, phagocytosis
and extravasation (Springer, 1990). Initially, in our in vitro model, the seeded bone
marrow cells that were non-adherent become adherent upon persistent GM-CSF
addition. This observation might explain the requirement of such high levels of integrin
expression. High confluence in the plates at the later time points of the culture could
trigger a reduction in expression.

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As markers of maturation, we assessed the expression of MHC-II antigens and
the co-stimulatory molecule CD86 throughout the days of culture. Significantly, we
found a trend toward increased MHC-II expression in knockout CD11b+ DCs that
became statistically significant at culture day 3. MHC-II expression in APCs is tightly
regulated (Pai et al., 2002). In mouse immature dendritic cells, MHC-II beta-chain
cytoplasmic tail is ubiquitinated which is partly required for the sequestration of MHC-II
(Shin et al., 2006; Tze et al., 2011). Interestingly, Irf6 is known to negatively regulate
transcriptional factor P63 (Tp63) by targeting it for proteasome-mediated degradation
(Thomason et al., 2010). Proteasomal degradation of Tp63 is also ubiquitin-mediated (Li
et al., 2008; Westfall et al., 2005). Irf6 might be required at least partially for ubiquitinmediated regulation of MHC-II in immature DCs. This finding supports our bioinformatic
suggestion for a non-cell autonomous role of Irf6 in T cell differentiation through
dendritic cells. Normal counts and overall developmental pattern of Irf6-deficient bone
marrow DCs suggests functional redundancy of Irf6. However, our bioinformatic
proposal is mainly pointing to Langerhans cells, a DC population that predominate the
skin. Considering that Irf6 function has most clearly been delineated in skin, specific
analysis of Langerhans cells might be a valuable determination.

140

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141

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CHAPTER FIVE
Summary and Future Directions

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Summary and Future Directions
IRF protein family has nine members in mammals (Huang et al., 2010; Nehyba et
al., 2009). All family members share a highly conserved N-terminus DBD and a less
conserved protein-binding domain at the C-terminus (Kondo et al., 2002). The IRF
family is known to orchestrate homeostasis of host defense. They are mandatory for
regulation of type I interferon responses, development and function of a cadre of
immune cell types, and cell cycle control and oncogenic pathogenesis (Tamura et al.,
2008; Taniguchi et al., 2001). Irf6 is the only IRF known to be involved in skin and
craniofacial development. Haploinsufficiency of human IRF6 cause two Mendelian
clefting disorders (Kondo et al., 2002). Furthermore, a common DNA variant at the IRF6
locus contributes risk for isolated cleft lip and palate (Rahimov et al., 2008). Mice
deficient for Irf6 display severe skin, limb, and craniofacial defects while heterozygosity
for Irf6 null allele is associated with oral adhesions (Ingraham et al., 2006; Richardson
et al., 2006). However, IRF6 shares some native functions of its family. IRF6 is involved
in regulating the cell cycle with an anti-proliferative function in keratinocytes and
mammary epithelial cell (Bailey et al., 2008; Ingraham et al., 2006; Richardson et al.,
2006). Also, mutations in IRF6 have also been associated with oncogenesis (Bailey et
al., 2009; Botti et al., 2011; Stransky et al., 2011). Regulation of the immune system, a

major function of all family member, is yet undetermined for IRF6. Proteomic studies
showed IRF6/IRF5 and IRF6/IRF8 as candidate protein complexes involved in
regulating interferon type I. Despite DNA conservation of the DBD, and structural
homology and interactions between IRF6 and more canonically described immune IRF
family members, expression data of IRF6 in haematopoietic system is completely
lacking. Cost and labor required for assessment of expression for a given gene through
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out the whole haematopoietic system is devastating. We decided to use a bioinformatic
approach to examine the expression of Irf6 thought out the developing stages of
haematopoietic cells using the publically available whole transcriptomic data for almost
every cell type.
We found that Irf6 is expressed early in haematopoiesis specially in long term
hematopoietic stem cells with abrupt attenuation of Irf6 expression in hematopoietic
lineage committed progenitors. Common myeloid progenitor and myeloid erythroid
progenitor isolated from old mice showed relative increase of Irf6. Also we identified Irf6
expression in T cell lineage, including developing and functionally committed stages.
Future studies should confirm the expression of Irf6 in these predicted populations.
Flowcytometric sorting of targeted populations from bone marrow, thymus and splenic
suspensions followed by RT-PCR and western plot for Irf6 would be ideal.
In our bioinformatic analysis, Variability of expression of in the same cell type
among different experiments was sometimes striking. Unavoidable biological variability
and using different probes in different microarray chips are valid reasons for variability.
However non-linear noise signature of microarray experiments (also called batch effect)
is another important factor that always hinders similar meta-analysis studies. Batch
effect can be defined as the systematic error introduced when samples are processed in
multiple batches. Several approaches have been developed for removing batch effects
from microarray data (Scherer, 2009). However, having different cell types running in
separate batches is the worst possible experimental design. Chen et al said “No way to
correct for poor experimental design. If cases and controls are run in separate batches,
genuine biological variation can be entirely confounded by batch effects. No method
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was able to reduce the batch effects sufficiently without also removing the variation
caused by case-control differences” (Chen et al., 2011). I am proposing a new approach
to deal with similar conditions. If we can collect enough number of microarray profiles
done in multiple batches for a given cell type, we can assume that only true biological
data is shared among all profiles. Simple data decomposition approaches can
discriminate between batch effects and biological variance. Once we identify the batch
effect for every experiment, we can correct the profiles of other cell types tested in these
experiments. Working on development of such technique that might allow us to correct
for batch effects in cases of poor experimental design would open a new horizon for
utilizing the tremendous resources of publically available data.
Selective Expression of Irf6 in HSC and T cell lineage suggested its functional
requirement. However perinatal mortality of Irf6 knockout mouse hindered our ability to
study the role of Irf6 in the haematopoietic system (Ingraham et al., 2006). To test if
expression of Irf6 expression is essentially required for development of these cells, Irf6
knockout fetal livers were adoptively transferred to lethally irradiated adult mice.
Chimeric mice were able to recover and lymphocytes in their peripheral blood were
mostly derived from donor stem cells. This indicates the ability of Irf6 knockout stem
cells to replenish the bone marrow and develop into mature lymphocytes.
Chimeric mice are particularly useful because they enable us to test the effect of
Irf6 deficiency in adult mice. Furthermore, it enables the study of the gene effect in the
whole haematopoietic system with the known extensive cell-cell interactions. Future
studies should employ the Chimeric mice to explore the function of Irf6 in HSC. LTHSC should be able to repopulate the bone marrow for life while ST-HSC has a
150

repopulation potential not exceeding 8-12 weeks (Passegue et al., 2005). High level of
Irf6 expression in LT-HSC suggests a possible role in long term engraftment studies.
Chimeric mice should be followed up for more than 3 moths to detect later complete or
partial failure of engraftment. One alternative approach to test for this is serial
transplantation, in which donor HSCs are engrafted into a primary host then
subsequently isolated and engrafted into secondary hosts (Weissman, 2000). Serial
transplantation of HSC can replenish the recipient bone marrow in successive but
limited transplants. The number of transplants correlates with the efficiency of HSC.
Furthermore, the stem cell exhaustion in serial transplantations mimics an accelerated
aging process, thus we can use this to test if deprivation of exhausted progenitors of Irf6
would accelerate their aging process (Ramkumar et al., 2013). Competitive repopulation
is another assay to detect the minimal defects by mixing Irf6 deficient HSC with wild
type HSC. Blood cells produced by each genotype should be proportional to the mixing
ratio unless the Irf6 deficient progenitor is sub-optimally efficient for one or more lineage
commitment (Harrison, 1980).
To test for the effect of Irf6 expression in developing lymphocytes, we
characterized the thymi in Irf6 knockout embryos at E17.5 in comparison to their wild
type littermates. We found that Irf6 regulates proliferation in thymocytes where loss of
Irf6 leads to an expansion of DN thymocytes and reduction of DP thymocytes. This
effect is parallel to some extend to what Irf6 does in the skin. In contrast to the proapoptotic effect of Irf6 in the skin, we did not see change in the total no of apoptotic or
dead cells. Further analysis for the no of apoptotic cells in the thymic sub-popualtion is
mandatory to identify any hidden balanced disturbances. Our rate of apoptosis of
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embryonic thymocytes is about 2% which comparable to those observed by others in
adult mice (Ismael et al., 1998; Jung et al., 2004). However this rate of spontaneous
apoptosis might not be able to detect saddle effect. Apoptosis induction experiments
(e.g. Anti-CD3 inducted apoptosis) should be done to verify the effect of Irf6 deficiency
on thymic apoptosis (Chrest et al., 1995). In vitro apoptosis assays can be used as well
to test for activation-induced cell death by culturing thymocytes with anti-Fas, platebound anti-CD3, or dexamethasone for 24 h to induce apoptosis (Shui et al., 2007).
We observed sustained counts of single positive thymocytes in spite of the
reduction of their immediate precursors. This was not an effect of increased proliferation
as shown by normal level of BrdU incorporation. We still have 2 possible explanations;
enhanced survival of single positive cells and thymic retention of naïve lymphocytes.
We need to test for the efficiency of negative selection to see how if it is reduced to
compensate for lower rate of proliferation in DP cells. We can breed The Irf6
heterozygous mice onto a background expressing alloreactive TCR (e.g. BM3.6
transgenic mice). Efficient negative selection would eliminate all the DP thymocytes
(Sponaas et al., 1994). To test the hypothesis of thymic retention, we can mate the
Irf6gt/+ with Rag2p-GFP mice. Boursalian et al created these mice to identify recent
emigrants of the thymus. GFP is expressed at a high level in DP thymocytes under the
Rag2 promoter. Because GFP is a relatively stable protein, cells recently emigrating
from the thymus can be identified by a “shoulder” of GFP expression (Boursalian et al.,
2004). McCaughtry et al calculated the half-life of GFP protein in RAG2p-GFP
transgenic mice to calculate the thymic retention time (McCaughtry et al., 2007).

152

We showed that Irf6 is expressed in the subcapsular cortex and medulla.
However, we did not delineate if this expression is in the thymic epithelium or
thymocytes or both. One of the important future directions is to determine the exact site
of expression underlying the reported altered proliferation. One of the best genetic
approaches would be the conditional knockouts. Floxed Irf6 strain can be crossed with
different tissue specific Cre recombinase strains to induce Irf6 excision only in these
target tissues. Lck-Cre and CD2-Cre transgenic mice are typically used to generate Tcell-specific conditional knockout mice. Regulatory sequences of the two stains drive
the Cre recombinase enzyme expression very early in DN thymocytes, however CD2Cre target the B cells as well (Garvin et al., 1988; Wildin et al., 1991; Zhumabekov et
al., 1995). CD4-Cre mouse is another transgenic strain which expresses the Cre
recombinase under the control of a CD4 minigene. The CD4 minigene is composed of
the proximal enhancer, the promoter and the silencer of CD4 gene. These regulatory
sequences start the Cre expression in DN3 (CD44-CD25+) stage (Wolfer et al., 2001).
To target developing thymocytes starting from the DP stage, Rorc-Cre strain would
drive the expression of Cre recombinase in DP thymocytes and their single positive
progeny whereas DN precursors would be untouched (Eberl & Littman, 2004).
By the age of 6 month, we saw significant increase of CD4:CD8 ratio in Irf6
heterozygous mice. A more prolonged study is required to test the effect of Irf6
heterozygocity in older mice. We proposed impaired notch signaling in developing
thymocytes mediating the altered CD4:CD8 ratio. Notch suppression can be assessed
by measuring its canonical target gene HES1 transcription.

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Adoptively transferred mice is another approach to discriminate between
haematopoietic form non-haematopoietic mechanisms. In chimeric mice, the
haematopoietic elements of the thymus should be Irf6 deficient while thymic epithelium
would be wild type. Careful analysis should be considered because of the possible left
over recipient Irf6 wild type cells. Also we should consider the possible differences
between embryonic developmental events we are trying to replicate and the adult
thymic microenvironment we have in chimeric mice. Whereas the adoptively transferred
mice is a very suitable model to study the Irf6 dosage effect on the postnatal thymic
changes of CD4:CD8 ratio.
Future studies should be designed to determine the underlying molecular
changes in Irf6 knockout thymocytes. Previous transcriptomic studies tried to figure out
transcriptional networks regulated by Irf6 but non of them was completely successful.
There are 3 microarray studies on mammalian tissues aimed to profile the
transcriptional signature of IRF6. The earliest study examined murine knockout skin. An
important caveat is that skin includes both the epidermis and dermis. Considering that
Irf6 is only expressed in the epidermis, the dermal tissue added unavoidable noise to
the expression profile. The other studies were done by knocking down IRF6 in primary
human keratinocytes and erythroid progenitors (Botti et al., 2011; Xu et al., 2012).
Silencing efficiency limits the generalizability of these results. Microarray experiment
utilizing a homogenous cell population and a knockout model would be the most
sensitive assays at detecting transcriptional regulation by IRF6. We need to identify
beyond doubt the primary cell type causing the altered proliferation of developing

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thymocytes. The ease of sorting the thymic population would provide an almost
homogenous population where transcriptional analysis would be very valuable.
Common variants in IRF6 contribute risk toward orofacial clefting. Rare variants
in IRF6 lead to Van der Woude and Popliteal Pterygium Syndromes. This data suggests
that individuals with common and rare IRF6 variants are at increased risk for
immunological diseases. Future studies should be designed to screen for the neonatal
lymophocyte counts in those patients. Furthermore, old patients with Irf6 variants should
be tested for early thymic involution.
Irf family members are either indispensable for normal T helper differentiation or
have known variants associated with autoimmune disorders of T cells (Lohoff & Mak,
2005; Patel, 2011). We tried to utilize the publically available microarray studies to
suspect the possible roles of Irf6 in T-helper commitment. We predicted a possible role
of intrinsic Irf6 expression in induction of Th17 differentiation on the expense of Treg
differentiation. Also we expected that Irf6 regulates the DC functions to mediate a noncell autonomous polarization of Th1.
To test our hypothesis about the necessity of intrinsic Irf6 expression for
regulation of T helper differentiation, we adopted an in vitro differentiation model. Naive
T cells lacking Irf6 could be differentiated into Th1, Th2, Th17 and Treg using a specific
cytokine cocktail. This experiment proves for the first time that intrinsic Irf6 expression is
not essential for T helper subset differentiation. There is no difference in cell frequency
and mean fluorescence intensity between wild type and Irf6 knockout cells. However,
the sensitivity of the in vitro model to detect saddle changes is questionable. The

155

concentrations of cytokines used in culture to induce differentiation are too high to
saturate the signaling pathways. Also the signaling pathways essential for in vitro
differentiation are not always the same those invoked in vivo. For example, IL-4 is
essential for in vitro Th2 differentiation and naive T cells with mutant genes in the IL4/STAT6 signaling pathway can not produce Th2 cells. By contrast, in vivo Th2
differentiation can occur in mice that have deletions in IL-4, IL-4Rα or STAT6 (van
Panhuys et al., 2008). In the future, in vivo differentiation analysis of Irf6 deficient T cells
should complement our current work. Adoptively transferred mice can be used in these
experiments; however it will not be able to differentiate between the autonomous T cell
defects and non-autonomous defects mediated by other Irf6 deficient haematopoietic
cells. The lineage specific conditional knockout would be the best model for such
experiments. Irf6 floxed strain needs to be crossed with Cre transgenic mice expressing
the recombinase enzyme under the regulatory sequences of lineage specific promoters.
Tbet-Cre, Gata3-Cre, RORγt-Cre, and Foxp3-Cre have been published before (Eberl &
Littman, 2004; Francius et al., 2013; Haddad et al., 2013; Rubtsov et al., 2008).
For the analysis of the possible role of Irf6 in DC functions, we tested the bone
marrow of adoptively transferred mice for DC populations. Irf6 deficiency has no effect
total DC count in BM. In vitro differentiation of BM in GM-CSF enriched medium,
showed the same differentiation pattern of DCs of both wild type and Irf6 knock chimeric
mice. There is a trend toward increased MHC-II expression in knockout CD11b+ DCs
that became statistically significant at culture day 3. This finding supports our
bioinformatic suggestion for a non-cell autonomous role of Irf6 in T cell differentiation
through dendritic cells. Future studies should cover detailed analysis of DC functions
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under Irf6 deficiency. In vitro differentiated DCs should be tested for cytokine production
including IL6, IL12p40 and IL10. Flowcytometric analysis if intracellular cytokines is on
approach and direct measurement of cytokines in culture supernatants is another one
(Said et al., 2014). Migration assay is another important functional determination. The
assay is usually done in transwell plates where activated DCs are cultured in the upper
wells and allowed to migrate to the lower wells under the guidance of chemotactic
agents e.g. CCL5 and CCL21 (Gibbs et al., 2013). T cell co-culture is one of the most
canonical experiments to assess the antigen presenting capacity of DCs. To
standardize the response, MHC restricted T cells with antigen specific TCRs are used
for co-culture. For example, MHC class I-restricted, ovalbumin-specific, CD8+ T cells
can be co-cultured with the target DCs after being pulsed with OVA peptide. The Coculture should be harvested after 4 days and CD8 T cell proliferation and IFN gamma
secretion would be measured (Clarke et al., 2000).
Langerhans cells (LC) are distinguishable from other DC populations. LCs are
characterized by lower MHC-II levels, and very high levels of the C-type lectin langerin
(Merad et al., 2008). Irf8 controls the trafficking of LCs to the regional lymph nodes
(Schiavoni et al., 2004) while Irf2 is required to maintain normal counts of LCs (Ichikawa
et al., 2004). Our bioinformatic analysis suggests DCs as the main source of IL8
deficiency in Irf6 knockout skin. If this holds true, Irf6 would have an important non-cell
autonomous role in T cell differentiation. Considering that Irf6 function has most clearly
been delineated in skin, specific analysis LCs should be considered in future studies.
Most LCs develop from fetal liver monocytes and self-renew throughout life
independently from the BM (Hoeffel et al., 2012; Merad et al., 2002). Importantly, with
157

severe LC depletion, LCs are repopulated by blood-borne monocytes (Ginhoux et al.,
2006). These data indicate that we can study Irf6 deficient LCs in our adoptively transfer
mice. One technical problem that usually faces the analysis of LCs is the contamination
of cell preparations with significant numbers of conventional dermal dendritic cells. Initial
digestion of the skin with dispase enzyme allows mechanical separation of epidermis
without any attached dermal component. Trypsinization of epidermal cells allows the
formation of single cell suspension for subsequent sorting of LCs (Pena-Cruz et al.,
2001). Culturing and functional studies can be done the same way as described above
with conventional DCs.
In conclusion, this research tried for the first time to connect Irf6 to the canonical
roles of the other family members in the immune system. We made use of publically
available microarray data to predict the possible roles of Irf6 throughout the
haematopoietic system. Successfully, we were able to confirm a novel role of Irf6 in the
proliferation of developing thymocytes. Unlike other family members, intrinsic Irf6
expression is not essential for T helper subset differentiation. However, a non-cell
autonomous role for Irf6 in T cell differentiation through dendritic cells remains plausible.

158

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164

CHAPTER SIX
Role of endogenous Avian Leukosis Virus in the pathophysiology
of spontaneous ALV-like tumors in chickens

165

Abstract
Lymphoid leukosis (LL) is a B cell tumor of the chicken bursa with a huge
economic impact on the poultry industry. The tumors are induced by infection with Avian
Leukosis Virus (ALV). However, endogenous ALVs is known to have little or no
oncogenic potential. Marek’s disease (MD) is another viral neoplastic disorder with hogh
mortality rate in chicken. Serotype 2 MD vaccine (MDV2) is an attenuated virus and
naturally non-oncogenic but has been shown to enhance the development of both
exogenous ALV-induced and spontaneous lymphoid leukosis. AF-227 is a new field
strain of subgroup E endogenous ALV (ALV-E). AF-227 has been isolated from
commercial chicken experiencing spontaneous ALV-like LL. Although ALV-E viruses are
known to be non-oncogenic, the influence of ALV-E and its possible interaction with
MDV2 on the enhancement of spontaneous ALV-like LL are still unclear. In this study
we used RNA-Seq to generate an expression profile from spontaneous ALV-like LL
obtaine from chickens inoculated with strain AF-227 of ALV-E in conjunction serotype 2
MD vaccine to uncover potential molecular oncogenic events.. We identified the
absence of Tumor suppressor candidate 2 (TUSC2 ) in all tumor samples. TUSC2 is a
tumor suppressor gene which is considered as a molecular link between inflammatory
response and mitochondrial homeostasis (Hood et al., 2013; Uzhachenko et al., 2012).
TUSC2 can influence and complement the PI3K/AKT and p53 pathways. Our pathway
enrichment analysis showed significant dysregulation of both pathways. We also
identified overexpression of another important proto-oncogene called Eukaryotic
translation initiation factor 4E (EIF4E). While EIF4E overexpression could be a
downstream effect of TUSC2 loss and abnormal Akt signaling, another possible

166

scenario is the convergence of the both genetic defects to induce the B cell
transformation. In this work we identified candidate molecular targets of LL in chickens
infected by a new endogenous ALV in conjunction with MDV2.

167

Introduction
Lymphoid leukosis (LL) is a B-cell lymphoma of chickens. LL usually appears in
chickens of about 4 months of age and older. Tumors typically involve the liver, spleen,
and bursa of Fabricius (Fadly & Nair, 2008). Tumors are usually composed of
aggregates of lymphoblasts of B-cell origin and characterized by monoclonal production
of IgM (Payne & Rennie, 1975). The primary lesion usually presents as a well defined
mass at the site of the bursa, but at a time the normal bursa tissue is usually gone
(Fadly & Nair, 2008).
LL can be induced by transmissible strains of retroviruses called Avian Leukosis
Virus (ALV). These strains are defined as exogenous for being transmitted as infectious
virus particles. Exogenous ALVs multiply in most tissues and organs of the body but the
infection persists longer in bursal lymphocytes, the target cells of neoplastic
transformation (Baba & Humphries, 1985). Exogenous non-defective ALVs do not
harbor any oncogene; they have been shown to induce lymphoid leukosis by activation
of cellular myc oncogene. Only Defective exogenous ALV harbor an oncogene such vmyc, v-src, v-myb, etc. and have been shown to induce acute tumors in susceptible host
(Fadly & Nair, 2008). On the other hand, spontaneous ALV-like LL has been shown to
develop in certain lines of chickens at one year of age or older. These spontaneous
ALV-like LL tumors are detected in certain genetic lines of chickens in absence of
infection with any of the subgroups of exogenous ALV, and hence the name
spontaneous is used to describe such tumors (Crittenden et al., 1979)

168

Based on envelope glycoproteins, ALVs that occur in chickens are classified into
six groups, A, B, C, D, E and J (Payne et al., 1991; Vogt, 1997; Weiss et al., 1982).
Unlike exogenous ALVs, subgroup E viruses are avian retrovirus-like elements that are
transmitted genetically in a Mendelian fashion and are termed endogenous viruses
(Fadly & Nair, 2008). Domestic chicken genome carries at least 16 endogenous ALV
proviral loci (ev-1 through ev-16) (Rovigatti & Astrin, 1983). Many endogenous viruses
are genetically defective and incapable of giving rise to infectious virions (Crittenden &
Astrin, 1981), whereas others are not and may be expressed in an infectious form
(Crittenden et al., 1983). In this form, they then are transmitted similarly to exogenous
viruses, although most chickens are genetically resistant to such exogenous infection
(Fadly & Nair, 2008). Rous-associated virus type-0 (RAV-0), a subgroup E endogenous
virus had little or no oncogenic potential (Motta et al., 1975). However RAV-60,
subgroup E recombinants of endogenous and exogenous viruses enhanced the
development of lymphoid leukosis (Crittenden et al., 1980; Robinson et al., 1980).
Endogenous ALVs also influence the response of the bird to infection by exogenous
ALV (Crittenden et al., 1982; Smith & Fadly, 1988). Recently, a field strain of
endogenous ALV-E, termed AF-227 was isolated from blood from commercial chickens.
The ability of serotype 2 Marek's disease virus serotype 2 (MDV2) to enhance
the development of LL after ALV exposure was reported (Bacon et al., 1989; Fadly &
Witter, 1993). Bursa cells co-infected with ALV and MDV2 are more likely to be
transformed (Fynan et al., 1992). MDV2 was shown to increase ALV gene expression
and virus production (Pulaski et al., 1992). This interaction presents a huge danger
because it holds true with the marek’s vaccine attenuated viruses (Marsh et al., 1995).
169

Furthermore, vaccination of exogenous ALV-free chicken against Marek's disease
increased the chance of developing LL (Crittenden et al., 1979). The interaction of MDV
with endogenous ALVs in these birds is not well understood. In this study, in order to
uncover the molecular oncogenic events of spontaneous ALV-like LL, we used RNASeq to generate an expression profile from tumor tissues collected from chickens
embryonically inoculated with strain AF-227 of ALV-E and vaccinated with MDV2 at
hatch.

Materials and methods
Experimental treatment of chicken: RFS is a strain of chicken that is free of
endogenous virus but is susceptible to ALV infection (Zhang et al., 2005). Studied
chicken were categorized into 4 groups; control group of 24 chickens injected with

phosphate buffered saline (PBS), 36 chickens injected with AF227 alone, 24
chickens treated with MDV2 vaccine alone, and 36 chickens treated with AF227
plus MDV2 vaccination. The PBS and AF227 were given via yolk-sac inoculation at
7 days of embryonation. The MDV2 vaccination was given at hatch intraabdominally. Chickens were followed up closely for the appearance of the tumors at 46 month of age. After euthanasia, tumor samples were collected as well circumscribed
masses from the anatomical site of the bursa.
Next generation sequencing of RNA: Six tumor samples from the group treated
simultaneously with AF-227 inoculation and MDV2 vaccination were used for the

170

analysis. As controls, we collected 3 whole bursa samples from 3-week old chickens,
and we collected 3 B cell samples from spleens of age matched chicken. We extracted
mRNA from tissue homogenates as recommended (Qiagen, RNeasy Kit, Valencia,

CA). RNA-seq libraries were prepared using the Illumina TruSeq Stranded mRNA kit
following the manufacturer's instructions (Illumina, San Diego, CA). Sequencing was
performed on an Illumina HiSeq 2500, run in High Output mode in a paired end
2x100bp format using Illumina TruSeq PE Cluster Kit (v3) and TruSeq SBS Kit (v3).
Samples were divided on 3 lanes (4 samples per lane). Raw reads were filtered to
remove adaptors, ambiguous reads and low-quality reads. Approximately 661 million
clean paired reads (132 Gbp) were obtained (Table 6.1).

171

Lane

R1
PF

% per

clusters

lane

Sample

R2 R1 Ave R2 Ave Insert
Yield

%≥

%≥

Q-

Q-

Size
(Gbp)

Q30

Q30 Score Score

(bp)

1

Tumor 1

49,249,019

21.6

91.1

68.1

35.7

27.9

484

9.85

1

Tumor 2

37,972,904

16.6

91.0

69.3

35.6

28.4

521

7.59

1

Tumor 3

93,542,998

41.0

91.0

67.9

35.6

27.9

506

18.71

1

Tumor 4

43,807,926

19.2

91.0

68.8

35.6

28.2

530

8.76

2

Tumor 5

38,709,590

17.2

88.4

62.2

34.9

26.0

547

7.74

2

Tumor 6

90,399,233

40.1

87.9

62.1

34.7

25.9

503

18.08

2

B cells 1

52,096,499

23.1

87.7

62.3

34.7

26.0

495

10.42

2

B cells 2

40,697,155

18.1

86.2

58.1

34.2

24.5

540

8.14

3

B cells 3

72,903,292

33.2

86.5

62.2

34.3

26.1

542

14.58

3

Bursa 1

76,772,884

35.0

86.1

61.3

34.2

25.7

541

15.35

3

Bursa 2

34,194,285

15.6

87.0

64.0

34.5

26.7

524

6.84

3

Bursa 3

30,856,288

14.0

86.4

63.0

34.3

26.3

532

6.17

Total

661,202,073

132.2

Table 6.1: Yield and quality of NGS. PF clusters: Paired end clusters passing
filter. R1: first strand read (the antisense or 3’ read). R2: The sense or the 5’
read. Q-scores: quality score (A property that is logarithmically related to the
base call error probability P where Q=-10 Log10 P). Q30: quality score of 30

172

Bioinformatic analysis: Initial exploration of the data quality was done using the
FASTX toolkit (FASTX Toolkit by Hannon lab). Quality checking showed abnormally
rapid deterioration of the quality scores of the reverse reads (Figure 6.1a). Also there
was abnormal distribution of the nucleotide content of the initial 13 bases of either

Percentage

Quality score

forward or revere reads (Figure 6.1b).

Position in read (bp)

Position in read (bp)

Figure 6.1: Snap shot from the FASTX toolkit analysis. (a) Quality score of the
reverse reads samples. (b) Sequence content across all bases

Trimmomatic software was used with its palindromic approach to remove readthrough adaptor sequences and isolate orphans from true paired reads (Bolger et al.,
2014). Simultaneous head cropping of the first 13 bases was done. Three approaches
of data processing were compared to identify the optimal gene model with minimal loss
of data. The first two approaches used the Trimmomatic built-in functions. 1) The
SlidingWindow method scans from the 5’ end and clips the read once the average
quality within the window falls below a threshold. 2) The MaxInfo method is an adaptive
173

quality trimmer that balances read length and error rate to maximize the value of each
read. Short reads (less than 36 bases) were excluded after both trimming approaches.
3) We used the pre-processing method (FASTX toolkit) which trims the last 20 bases
then reads were filtered out if they fell below a threshold.
TopHat v2.0.10 Software was used to map the reads back to the chicken
genome (Kim et al., 2013). The most updated versions of the chicken genome assembly
and annotation (Galgal4, release 74) were obtained from the ensemble Genome
Browser (ftp.ensembl.org/pub/release-74). Ensemble annotation was preferred over the
reference annotation of the Genebank because it covered more genes in our
transcriptome (Figure 6.2).

Figure 6.2: Snap shot of a genome browser (IGV) comparing the annotations of
different assemblies. A representative stretch of the Genebank reference annotation
(1st lane), ensemble annotation (2nd lane), and our assembly (3rd lane). The ensemble
annotation covered our transcriptome more completely.

174

One of the known issues about the current chicken assembly is the possibility of
replication of some genes between chromosomal and non-chromosomal sequences. To
ensure appropriate alignment of the reads to their corresponding genes, we performed
two rounds of mapping. For the first round, we mapped all reads against the
chromosomal sequences only and limited the initial transcriptome indexing to the
chromosomal annotation. The unmapped reads were used for another round of
assembly using the non-chromosomal sequences and annotation.
Transcriptome assembly and subsequent differential expression analysis was
done using the Cufflinks package v2.1.1 as shown before (Figure 6.3) (Trapnell et al.,
2012)

Figure 6.3: Diagrammatic representation of the TopHat/Cufflinks analysis
(Trapnell et al., 2012)

175

Downstream clustering analysis was done using R software. For pathway
analysis, DAVID annotation tool was used (Figure 6.4 summarizes the whole pipeline).

Figure 6.4: Work flow for the pipeline of NGS analysis

176

Results
Tumor incidence in the studied groups: In the experiment, there are four

experimental groups; PBS control group, a group injected with AF227 alone, a
group treated with MDV2 vaccine alone, and a group treated with AF227 plus
MDV2 vaccination. The incidence of LL-like tumors was 8% in the PBS control
group, 14% with chicken injected with AF227 alone, 17% in the group of MDV2
vaccination alone, and 42% in the group treated with both AF227 injection and
MDV2 vaccination.
The performance of different preprocessing approaches: The MaxInfo approach
caused the minimal loss of data during pre-processing. This allowed the preservation of
the highest number of paired ended clusters. However most of these data were lost
during the mapping with TopHat. The SlidingWindow and Tail trimming approaches
were almost equal when we compared the no of dropped reads after quality filtering.
However, SlidingWindow was more successful in keeping longer reads giving the
highest average read length after pre-processing. Furthermore, SlidingWindow showed
a slightly better sensitivity compared to the other two techniques. Interestingly, the
SlidingWindow approach was the least no to map reads outside the annotated genome
giving the smallest number of isoforms of new genes. This allowed the SlidingWindow
approach to achieve the highest locus specificity (Table 6.2). Considering the quality of
the assembly and the accompanying annotation, we recognize that this comparison is
not fair and we do not have the appropriate control to judge the actual specificity of

177

these techniques. In this analysis, our original plan did not include identification of new
genes so adding more non-annotated transcripts to our assembly would do nothing but
decreasing the statistical power. Thus we selected the SlidingWindow technique to be
our preprocessing approach. Future study to compare pre-processing approaches
should consider using of a simulated data.

178

MaxInfo

SlidingWindow Tail trimming

Statistics after sample pre-processing
paired end

75,377,436

59,487,520

57,131,448

singletons

280,950

7,676,613

8,536,476

75,658,386

67,164,133

65,667,924

5,583,750,709

5,493,544,077

4,596,335,379

74

82

70

Total reads
# of bases
Average read length

no of alignments and unmapped reads after TopHat analysis
no of alignments

44,900,716

42,669,372

41,734,076

unmapped-qc passed

31,102,767

25,340,970

25,375,868

31,536

5,115

11,067

76,035,019

68,015,457

67,121,011

properly paired

22,512,228

20,867,419

18,671,936

improperly paired

16,213,828

13,747,268

13,719,707

6,174,660

8,054,685

9,342,433

unmapped-qc failed
Total
Quality of mapping

singletons

Comparison to reference annotation (Cuffcompare stat)
All assembled isoforms

48,353

48,609

51,395

Ref isoform: Complete match

12,393

12,465

12,438

7,174

5,852

7,539

Locus Sensitivity

78.7

79

78.8

Locus Specificity

51

57.6

52.2

Isoforms of new genes

Table 6.2: Comparing the performance of different preprocessing approaches.
Results shown represent the smallest sample in size (Tumor sample 2).

179

Expression profile of Tumor samples is closer to bursa tissue than mature
splenic B cells: The normalized read counts mapped to cufflink genes were used for
analysis. Testing the rate of gene expression across all the studied samples showed
unequal depth of sequencing (Figure 6.5a). To overcome the sequencing depth effect,
genes that showed up uniquely due to high sequencing depth were excluded. Ward
Hierarchical Clustering was done using Euclidean distance to generate the distance
matrix. The new clustering analysis showed low biological variability between samples
of the same biological origin and also showed a clear distinction of tumor and bursa
samples from splenic B cells (Figure 6.5b)

180

Figure 6.5a: Boxplot of expression profiles

Figure 6.5b: Ward Hierarchical Clustering using Euclidean distance

181

Differential expression analysis: We performed pairwise comparisons for each of the
three groups. Significant differential expression was considered with p value < 0.05 after
correction for multiple analyses (Figure 6.6). To identify genes relevant to
tumorigenesis, we chose genes that showed significant change in tumor tissues when
compared to both bursa and splenic B cells (385 went up and 395 went down;
supplementary table A2 and A3). Among the differentially expressed genes TUSC2 is a
tumor suppressor gene that was completely lost in all tumor samples but was the
highest expressed gene in both bursa and splenic B cells (Kondo et al., 2001; Meng et
al., 2013). On the other hand, the oncogene EIF4E ranked 2nd among the up regulated
genes in malignant samples compared to being undetected in the other tissues (Carroll
& Borden, 2013; Culjkovic-Kraljacic et al., 2012; Mamane et al., 2004)

182

Figure 6.6: Differential expression Analysis: The differentially
expressed genes between every two studied tissues are counted in a
circle. (a) MvsB.sign: Significant differentially expressed genes
between malignant and bursa tissue. (b) MvsS.sign: Significant
differentially expressed genes between malignant and splenic B cells.
(c) SvsB.sign: Significant differentially expressed genes between
splenic B cells and bursa tissue. Intersections count the genes that
appeared significant in more than one comparison

183

Pathway enrichment analysis: For pathway analysis, significant hits were further
enriched by moving the p value threshold to 0.01 and excluding genes with low level of
expression (20 FPKM was used as an arbitrary cut off). With these criteria we had 228
differentially expressed genes (144 went up and 84 went down). To overcome the poor
annotation of chicken genome and low resources of their genetic pathways, we
identified 218 human orthologs. KEGG pathway analysis showed significant enrichment
of several cancer pathways (Table 6.3). PANTHER Pathway analysis ensured the
involvement of p53 signaling pathway.

KEGG Pathways

PValue Genes

Prostate cancer

0.013

E2F3, PIK3CD, PIK3R5, RB1,TCF7L2, CTNNB1

p53 signaling pathway

0.023

BID, CD82, FAS, CCNG1, THBS1

Chronic myeloid leukemia

0.031

E2F3, HDAC1, PIK3CD, PIK3R5, RB1

Small cell lung cancer

0.044

E2F3, ITGA6, PIK3CD, PIK3R5, RB1

Alzheimer's disease

0.045

BID, APP, PSEN1, SDHD, IL1B, FAS, ATP5A1

Apoptosis

0.049

BID, PIK3CD, IL1B, PIK3R5, FAS

Endometrial cancer

0.049

PIK3CD, PIK3R5, TCF7L2, CTNNB1

Non-small cell lung cancer

0.054

E2F3, PIK3CD, PIK3R5, RB1

Cytosolic DNA-sensing

0.057

POLR1D, IL18, IL1B, IRF3

Cell adhesion molecules

0.058

ALCAM, SDC1, PTPRF, ITGA6, PVRL3, SDC4

Pathways in cancer

0.075

BID, E2F3, HDAC1, ITGA6, PIK3CD, PIK3R5,
RB1, FAS, TCF7L2, CTNNB1
Table 6.3: Pathway enrichment analysis (DAVID Functional annotation tool 6.7)

184

Table 6.3 (Cont’d):
Toll-like receptor signaling

0.077

MAP2K3, PIK3CD, IL1B, PIK3R5, IRF3

Glioma

0.078

E2F3, PIK3CD, PIK3R5, RB1

PANTHER Pathways

PValue

Genes
E2F3, PIK3CD, PIK3R5, RB1, CCNG1,

p53 feedback loops 2

0.000
TPTE, CTNNB1
E2F3, HDAC1, CD82, PIK3CD, PIK3R5, FAS,

p53 pathway

0.000
CCNG1, THBS1, TPTE

185

Discussion
Avian tumor viruses of economic importance include Marek’s disease virus and
avian retroviruses, namely avian leukosis virus (ALV) and reticuloendotheliosis virus
(Witter, 1997). Avian retroviruses are associated with neoplastic diseases that represent
a serious burden in poultry industry. Lympohoid leukousis is a B cell tumor that usually
arises from the bursa tissue and is known to be enhanced by ALV (Baba & Humphries,
1985). ALV mortality and morbidity has a huge economic burden estimated to be in
millions of U.S. dollars each year (Fadly & Nair, 2008). Marek’s vaccine is routinely
used in poultry industry; however the attenuated vaccine strains were shown to
enhance the development of LL after ALV exposure (Fynan et al., 1992; Marsh et al.,
1995).
Understanding the molecular events of oncogenesis mediated by interactions of
ALV and MDV2 is critical for development of better strategies of prevention and control.
The development of the RFS, a strain of chicken which is endogenous virus free but still
susceptible to endogenous AVL infection, represents a proper negative control to the
virally infected chicken. However, the late onset of the disease was an obstacle for the
study design. By the time the tumors develop, there was no visible bursa tissue in the
non-infected controls. To have the appropriate control, we compared the tumor samples
to bursa tissues from 3 month old chicken as well as B cells sorted from the spleens of
the age matched chicken.
Bursa of Fabricius is known as the primary site of LL (Fadly & Nair, 2008). In our
analysis, the phylogenic convergence of tumor samples with bursa samples confirms
the bursa as a tissue of origin for these tumors. The homogeneity of tumor samples
186

seen in the clustering analysis suggests a common underlying molecular pathogenesis
in all samples. Exogenous ALV was shown to enhance the B cell transformation by
activation of the c-myc oncogene by adjacent integration of ALV provirus (Kung & Liu,
1997). In our experiment, we did not find a change of Myc expression in any tumor
samples. Instead, TUSC2 (FUS1) is a tumor suppressor gene that was completely lost
in all tumor samples. Among the differentially expressed genes, TUSC2 was the highest
expressed gene in both normal bursa and splenic B cells. The TUSC2 gene resides in
the 3p21.3 human chromosomal region. Chromosomal abnormalities in the 3p21.3
region are observed in lung, breast, cervical, and other cancers (Lerman & Minna, 2000;
Senchenko et al., 2003; Zabarovsky et al., 2002). The impaired expression of TUSC2 is
a pathognomonic feature in most types of lung cancers (Ivanova et al., 2009; Prudkin et
al., 2008) and an effective therapeutic target also in these tumors (Meng et al., 2013).
Mice lacking one or both copies of the Tusc2 gene develop a chronic inflammatory
autoimmune disorder and produce tumors at the sites of chronic inflammation (Ivanova et
al., 2007).

TUSC2 is considered as a molecular link between inflammatory response and

mitochondrial homeostasis (Hood et al., 2013; Uzhachenko et al., 2012). TUSC2 was
shown to influence and complement the PI3K/AKT and p53 pathways (Figure 6.7) (Ji &
Roth, 2008; Meng et al., 2013). Our pathway enrichment analysis showed significant
dysregulation of both pathways.

187

Figure 6.7: TUSC2 Molecular pathway (Ji & Roth, 2008)

We also observed increased expression of EIF4E, a the proto-oncogene
associated with many cancers including colon (Zimmer et al., 2000), head and neck
(Franklin et al., 1999; Nathan et al., 1997; Nathan et al., 1999), and breast cancers
(Larsson et al., 2007; Soni et al., 2008). Transgenic mice overexpressing Elf4e by the
ubiquitous β-actin promoter show high incidence of tumors including B-cell lymphomas,
angiosarcomas, lung adenocarcinomas and hepatocellular adenomas (Ruggero et al.,
2004). Importantly, a cooperation between Elf4e and c-Myc in B-cell lymphomagenesis
was shown. In this co-operation, Elf4e suppresses the c-Myc-induced apoptosis while
c-Myc antagonized Elf4e-induced growth arrest (Ruggero et al., 2004). The Elf4e

188

expression is usually secondary to apparent phosphoinositide-3 kinase and Akt
signaling (Culjkovic et al., 2008; Ruggero et al., 2004; Zimmer et al., 2000). EIF4E
overexpression seen in our tumor sample could be a downstream effect of TUSC2 loss
and abnormal Akt signaling. Another possible scenario is the convergence of the both
genetic defects to induce the B cell transformation
Future studies should confirm the transcriptional changes of TUSC2 and EIF4E
in the tumor samples. We also need to confirm this association in more independent
ALV-like tumors. Transgenic mice overexpressing EIF4E by the ubiquitous β-actin
promoter have been published before (Ruggero et al., 2004). This mouse is good
source to test the interaction between EIF4E and TUSC2. We can knock down TUSC2
in non-transformed bursa cells obtained from these mice and observe for
transformation. Both TUSC2 and EIF4E are known therapeutic targets in cancer (Meng
et al., 2013; Soni et al., 2008). Meng et al transfected the lung cancer cells with vectors
expressing TUSC2 to increase the susceptibility of the cells to apoptotic agents.
Similarly, Soni et al. knocked-down EIF4E by small interfering RNA to inhibit growth in
different breast cancer cell subtypes. We should implement similar experiments on
malignant lymphoblasts obtained from LL tumors to prove the necessity of these genes
in the oncogenic changes of LL.

189

APPENDIX

190

Normalized expression

q value

Gene

Malig vs
Bursa

Spleen vs
Bursa

Malig

Bursa

2.47E+09

3.63E+08

0

0.006024

0.000508

0.000508

EIF4E

490456

0

0

0.009843

0.009843

1

FAM117A

235448

0

0

0.0281

0.0281

1

ENSGALG00000022685

82305.1

19629.8

7658.55

0.000859

0.000859

0.101893

RPS15

7755.37

5058.15

3563.76

0.028294

0.000508

0.137016

RPS25

5165.21

3229.19

2909.53

0.013846

0.003083

0.692084

RPL17L

5141.41

1722.61

993.123

0.015006

0.000859

0.434835

RPL7A

4936.85

3163.14

1930.87

0.042044

0.000508

0.040248

RPS4

3878.47

2553.37

2268.59

0.046946

0.010375

0.662805

ENSGALG00000027261

3439.99

600.394

1154.29

0.000859

0.035828

0.393214

ATP5A1W

2978.79

674.247

415.53

0.000859

0.000859

0.507745

GNG4

2542.9

769.284

15.0153

0.000508

0.002755

0.007117

RPS12

2093.8

1344.21

1231.73

0.026069

0.008392

0.758037

FAM13B

1786.25

292.282

48.545

0.000508

0.000508

0.000508

EEF1D

1724.06

1151.83

961.716

0.049562

0.002755

0.50061

CRIP2

1716.01

836.72

672.093

0.000508

0.000508

0.398886

BTF3

1572.08

252.772

293.264

0.000859

0.001511

0.857955

1525.8

1027.51

918.353

0.040248

0.007615

0.671937

POLN

1447.48

247.656

98.4941

0.000508

0.000508

0.018625

SLC39A14

1327.44

202.748

135.206

0.000859

0.000859

0.394509

SLC25A37

RPL35A

Spleen

Malig vs
Spleen

Table A2: Genes significantly up regulated in malignant samples over both bursa
and spleen samples

191

Table A2 (Cont’d)
HSPD1

874.058

551.08

215.93

0.021405

0.000508

0.000508

MYBL1

736.129

271.897

7.88936

0.002755

0.000508

0.000508

EIF2S3

692.205

441.462

270.228

0.032432

0.000508

0.038893

656.52

42.5198

233.911

0.000859

0.03681

0.005736

DMTN

649.783

97.4209

79.6317

0.000859

0.000859

0.764011

ENSGALG00000014584

614.533

122.83

128.524

0.003299

0.005347

0.958976

IL18

605.775

140.243

171.42

0.000508

0.000508

0.578448

LDHA

594.428

330.738

172.09

0.003402

0.000508

0.004024

ENSGALG00000000140

572.084

81.5445

127.872

0.000859

0.009905

0.610247

ENSGALG00000013312

569.394

51.6126

233.934

0.000859

0.03933

0.001511

SC4MOL

552.336

288.086

225.925

0.000939

0.000508

0.39884

ACTR3B

539.3

311.391

67.1201

0.004604

0.000508

0.000508

PIWIL2

509.391

9.13099

22.159

0.000859

0.000859

1

VMP1

483.036

231.505

80.8812

0.000508

0.000508

0.000508

PDIA6

477.388

275.772

153.555

0.004889

0.000508

0.004314

TPI1

418.364

280.348

217.931

0.036676

0.000508

0.317232

FKBP4

406.854

229.886

188.823

0.003707

0.000508

0.454642

WDR66

395.587

156.601

4.3107

0.000508

0.000508

0.000508

RSU1

392.048

245.256

99.3984

0.018835

0.000508

0.000508

CSDE1

367.353

233.261

234.746

0.019233

0.020418

0.980924

FAM60A

354.441

201.146

164.332

0.003707

0.000508

0.468064

TM9SF2

350.555

217.29

132.598

0.01606

0.000508

0.027709

341.85

198.31

179.967

0.006845

0.000508

0.733828

ENSGALG00000028307

SEPT6

192

Table A2 (Cont’d)
ENSGALG00000020895

338.071

193.897

124.225

0.003707

0.000508

0.091393

BLNK

333.235

224.644

169.254

0.049562

0.000508

0.249674

SPINW

332.045

42.6419

101.209

0.000859

0.010258

0.177605

DHDH

319.533

145.858

35.3754

0.042556

0.000859

0.031897

ENSGALG00000019602

315.064

74.458

143.549

0.000508

0.002413

0.014947

SNORA16

312.335

171.836

142.492

0.024147

0.004024

0.639895

IPO7

308.907

199.58

94.4668

0.033302

0.000508

0.000508

LGALS1

306.282

92.8542

38.9417

0.000508

0.000508

0.05016

BID

298.275

162.666

46.1011

0.000508

0.000508

0.000508

ENSGALG00000022335

293.607

36.9159

29.3242

0.009485

0.005736

0.838977

gga-mir-762

291.055

148.323

85.3646

0.000508

0.000508

0.009893

GPI

284.194

190.053

81.8

0.039549

0.000508

0.000508

UBE2E1

283.805

159.514

116.691

0.002061

0.000508

0.217859

HSPA4L

278.009

169.702

97.8684

0.023951

0.000508

0.032086

BZW2

269.658

181.166

97.6183

0.041552

0.000508

0.006024

PNN

268.402

171.811

150.208

0.020792

0.003402

0.618302

CCNC

266.684

136.492

58.9468

0.000508

0.000508

0.000508

EIF3B

264.604

171.512

109.77

0.021596

0.000508

0.042792

GMPR

263.33

107.439

18.6393

0.000508

0.000508

0.000508

SDHD

260.804

110.21

64.9439

0.000508

0.000508

0.021

DUSP22

257.983

115.374

78.0294

0.010857

0.000508

0.326545

PRKCD

248.53

138.264

53.4495

0.004314

0.000508

0.000508

CCND3

247.497

166.089

121.128

0.042792

0.000508

0.203596

193

Table A2 (Cont’d)
ODC1

239.001

114.771

63.6004

0.000508

0.000508

0.016719

PURH

229.909

150.336

111.574

0.032991

0.001337

0.258521

RBM38

229.787

151.976

7.25138

0.032086

0.000508

0.000508

FAM103A1

227.108

136.682

64.5182

0.006301

0.000508

0.002755

RAB9A

219.558

107.797

41.7997

0.000508

0.000508

0.000508

PKNOX1

214.933

29.8019

37.4982

0.000508

0.000508

0.501767

NUCB2

203.125

91.7936

63.5541

0.000508

0.000508

0.255855

C18orf25

199.863

23.698

23.4113

0.000859

0.001511

0.991141

PRKD3

198.314

98.7271

65.1583

0.001337

0.000508

0.162981

TPD52L2

195.664

99.5018

129.402

0.000508

0.035035

0.303313

CXCL13L2

191.823

109.278

18.7992

0.012919

0.000508

0.000508

NFI1

188.678

52.1044

72.4373

0.000508

0.000508

0.344425

HBS1L

188.626

117.347

78.164

0.046054

0.000508

0.180928

LRMP

187.84

49.8108

42.2991

0.000508

0.000508

0.56758

DNAJB6

182.228

101.845

96.1518

0.000939

0.000939

0.842609

CENPW

177.263

102.599

9.06266

0.007117

0.000508

0.000508

RCC2

176.647

85.701

114.035

0.000508

0.023544

0.2502

RCSD1

176.273

83.8259

81.6743

0.000508

0.000508

0.92688

SRD5A3

173.525

78.4872

49.0575

0.000508

0.000508

0.08419

STOM

172.502

55.8967

36.2095

0.000508

0.000508

0.089407

RASGRP3

171.546

73.8425

97.1626

0.000508

0.003707

0.30215

PAFAH1B2

171.149

102.004

64.456

0.033141

0.000508

0.165353

CCNG1

166.591

86.5334

42.4034

0.000508

0.000508

0.003707

194

Table A2 (Cont’d)
HSPH1

165.808

58.8905

62.9261

0.000508

0.000508

0.839923

NMT2

160.332

87.4131

92.5816

0.001707

0.004604

0.850306

HDAC1

159.336

78.9645

80.4277

0.000508

0.000939

0.948897

WEE1

156.792

104.255

13.4923

0.042377

0.000508

0.000508

MND1

156.462

90.6355

70.8982

0.034519

0.002755

0.500816

ANXA5

152.907

90.9782

96.2058

0.019233

0.038529

0.859449

VGLL4

152.286

69.2409

59.312

0.000508

0.000508

0.566259

IMPAD1

148.816

80.0987

28.648

0.031558

0.000508

0.010375

HSPA4

147.318

81.99

84.4473

0.001337

0.003083

0.919397

USP10

146.244

96.6553

68.995

0.032259

0.000508

0.169499

POLR1D

145.753

67.6363

57.0256

0.003402

0.000508

0.665455

NCOA7

141.926

86.2869

61.679

0.013626

0.000508

0.172718

TEC

140.088

70.6503

9.15593

0.000508

0.000508

0.000508

SASS6

131.796

77.3956

38.1112

0.014731

0.000508

0.009636

C19ORF12

131.184

60.3329

72.1503

0.000508

0.008392

0.562318

GGA.31975

129.111

84.9572

40.5779

0.03415

0.000508

0.000939

LYAR

127.994

83.0236

30.6556

0.025695

0.000508

0.000508

R3HDM1

126.274

54.4965

38.6039

0.000508

0.000508

0.163572

METTL14

125.206

75.238

27.3061

0.009143

0.000508

0.000508

LRRC4

124.358

8.82336

33.1291

0.001511

0.007037

0.133254

SRPK1

123.492

74.8857

62.7456

0.016284

0.000508

0.540002

MED7

122.227

67.8779

41.24

0.008904

0.000508

0.093999

121.27

56.1096

59.1198

0.000508

0.000508

0.854879

MOSPD2

195

Table A2 (Cont’d)
SEPT11

120.431

57.4969

11.085

0.004314

0.000508

0.000939

URI1

118.785

78.1492

52.5295

0.037726

0.000508

0.114861

PPIF

118.176

73.6934

41.4491

0.019233

0.000508

0.015839

ZNF644

118.023

53.6857

26.3529

0.029363

0.000508

0.023367

CD1A1

118.021

19.2832

61.4972

0.000508

0.004889

0.000939

FAM129A

117.796

55.668

46.4483

0.000508

0.000508

0.487016

USP15

117.323

67.2571

56.5604

0.000508

0.000508

0.49222

WAPAL

116.197

78.5842

75.9481

0.048049

0.027354

0.907135

SDR16C5

113.738

44.0725

52.0214

0.000508

0.000508

0.546921

MTR

109.581

61.3958

52.282

0.027354

0.001707

0.66036

ENSGALG00000006723

108.964

67.3431

41.6608

0.029707

0.000508

0.081169

P4HA2

108.706

42.9481

27.6161

0.000508

0.000508

0.117276

EXOC6

107.899

67.9605

61.3801

0.024758

0.004604

0.728121

TMEM248

107.301

68.3728

59.4079

0.035516

0.005742

0.638185

RNF139

106.856

57.1786

40.3825

0.003083

0.000508

0.222124

MAP2K3

106.519

48.9303

52.8649

0.001707

0.001707

0.828237

TMEM65

102.539

48.417

17.1679

0.001707

0.000508

0.003083

FAS

100.487

40.2208

15.6239

0.006024

0.000508

0.021791

STAMBPL1

98.5653

56.5554

14.698

0.010626

0.000508

0.000508

97.395

56.7945

32.6014

0.030653

0.000508

0.054967

ZEB1

92.5795

55.8862

60.1504

0.029363

0.045323

0.831729

DTNBP1

91.8084

43.069

36.0961

0.020792

0.003402

0.676834

EHBP1

90.6004

57.3104

20.4566

0.046498

0.000508

0.000508

MRPL3

196

Table A2 (Cont’d)
TRIP12

87.9382

52.68

59.2868

0.004889

0.040878

0.65692

BEND7

87.3775

54.7902

44.5491

0.045615

0.004604

0.511324

MTSS1

86.1399

33.4998

49.5651

0.000508

0.049102

0.252313

IL20RA

85.2251

13.2447

13.7549

0.000508

0.000508

0.933827

HDAC11

85.0971

33.0563

25.9839

0.000508

0.000508

0.433117

BLOC1S4

85.0875

47.8756

38.9432

0.046792

0.004604

0.619534

SLC20A2

83.7792

36.4321

14.754

0.000508

0.000508

0.000508

INPP5K

83.4341

45.9041

34.41

0.004024

0.000508

0.293996

CHORDC1

82.6696

50.9036

36.955

0.008392

0.000508

0.180993

DDRGK1

82.5846

38.1112

48.2685

0.000508

0.018195

0.420128

SCN4B

82.3832

17.9699

4.2067

0.000508

0.000508

0.019045

PLS3

81.0464

39.4989

6.53773

0.000508

0.000508

0.000508

SCIN

80.5355

52.5698

24.6277

0.035705

0.000508

0.003083

LPCAT3

80.169

47.095

33.5928

0.013846

0.000508

0.264015

FABP4

78.3716

2.08767

1.12654

0.001337

0.003402

0.59548

NFYA

78.2558

50.3369

33.5311

0.024561

0.000508

0.096047

GGA.46369

76.9525

32.6288

19.9279

0.018409

0.000508

0.292991

DNAJC10

74.7917

46.3469

28.5099

0.035705

0.000508

0.081406

CA13

73.5144

15.4671

7.89494

0.015156

0.003707

0.508024

LIN7C

73.11

45.1682

34.8197

0.013626

0.000508

0.316899

SLC7A10

72.2439

4.85704 0.506395

0.000508

0.000508

0.014287

VLDLR

70.5922

19.8233

11.9741

0.000508

0.000508

0.171605

70.535

32.3002

32.5506

0.000508

0.000508

0.980794

C12ORF35

197

Table A2 (Cont’d)
RIF1

70.3058

45.3766

14.7656

0.035035

0.000508

0.000508

SERHL2

68.804

22.1829

39.1919

0.000508

0.033141

0.115818

NUDCD1

68.3359

39.6472

18.4469

0.046498

0.000508

0.021596

RRN3

67.9998

35.1339

36.232

0.000939

0.001707

0.920274

PGAM5

65.5674

42.8371

39.9614

0.045615

0.021178

0.827901

HIVEP2

65.0191

33.9471

33.0092

0.000508

0.001337

0.924921

PSEN1

64.5392

16.969

21.6597

0.000508

0.000508

0.529285

ZCCHC10

62.9052

37.6818

23.9699

0.016936

0.000508

0.106616

SLC25A12

62.8992

38.8356

29.5155

0.029707

0.000508

0.364586

RECQL5

61.5121

14.4256

5.69456

0.000939

0.000508

0.068378

TPK1

61.4578

23.2101

16.919

0.010133

0.001337

0.555679

ENSGALG00000027836

61.2314

11.8349

12.4052

0.019632

0.004833

1

LYRM1

60.4049

25.6746

30.0922

0.010375

0.040248

0.758453

PIT54

59.1434

4.81754

2.2398

0.006625

0.043255

1

MAP2K2

58.011

32.9457

33.5946

0.030281

0.046645

0.960897

OSBPL3

56.6664

17.8905

13.8551

0.000508

0.000508

0.397079

KCNK17

56.5455

15.8311

20.4354

0.000508

0.000508

0.501887

GGA.31495

56.3451

19.7133

14.4842

0.000508

0.000508

0.357609

56.03

18.7015

21.4638

0.007615

0.010857

0.816414

FAM125B

55.8808

31.6062

24.6963

0.003083

0.000508

0.367547

NOC3L

55.1614

34.3804

22.0094

0.023367

0.000508

0.103494

COL6A1

54.2246

34.0331 0.340008

0.034691

0.000508

0.000508

GMPS

54.0928

32.0966

0.010133

0.000508

0.139349

CCDC34

22.1008

198

Table A2 (Cont’d)
ACSL5

53.9811

33.1821

26.4646

0.023181

0.000508

0.454946

MOXD1

53.6435

14.8233 0.253233

0.000508

0.005742

0.012254

TOMM34

53.4712

24.6449

18.191

0.002755

0.000508

0.429937

MB21D1

53.3292

25.9723

12.8736

0.009387

0.000508

0.073075

KIAA1430

52.8945

27.9942

23.5556

0.005455

0.001337

0.616729

PGPEP1

52.5872

20.3683

18.9546

0.004314

0.002413

0.892303

E2F3

52.2599

24.161

6.26593

0.003083

0.000508

0.004889

FKBP7

52.1913

26.6998

5.84127

0.026647

0.000508

0.002061

TPTE

51.8425

13.1802

2.25835

0.000508

0.000508

0.000508

SLCO4A1

51.6363

23.5946

1.47743

0.000508

0.000508

0.000508

SNX10

51.4705

28.2791

31.1594

0.004604

0.020593

0.764273

ABCF2

50.9524

32.9192

24.0782

0.029178

0.000508

0.240839

ACSL3

50.9399

26.1913

11.2004

0.000508

0.000508

0.002755

ARID1B

50.6929

33.42

28.4761

0.032786

0.003083

0.549158

50.607

22.2764

1.78904

0.01756

0.000508

0.004604

GZMA

50.4736

7.8734

11.696

0.000508

0.000508

0.40661

MAMDC2

50.1389

12.7447

2.81786

0.000508

0.000508

0.001337

HSF2

49.8087

29.6242

18.9939

0.019233

0.000508

0.123308

RB1

49.5251

26.7261

22.8285

0.002755

0.000939

0.625532

ZNF598

49.3741

31.3705

32.6548

0.023737

0.04055

0.894454

ANKRD27

48.2475

28.6898

17.8925

0.012919

0.000508

0.076479

ENSGALG00000027080

47.4958

10.2974

10.7812

0.009485

0.028262

0.964009

LCA5L

47.4075

9.8274

2.95464

0.000508

0.000508

0.012919

AK1

199

Table A2 (Cont’d)
RAG2

47.3762

26.5306 0.219988

0.025695

0.038204

0.065085

RFLB

46.9476

2.0684

3.19626

0.002061

0.000508

0.653075

LSS

46.0031

21.4339

24.1398

0.002755

0.029556

0.78243

NAA25

45.7087

30.3296

25.4397

0.036199

0.001337

0.517014

ENSGALG00000021862

45.6503

14.2771

6.47071

0.037877

0.005455

0.367625

LRRCC1

45.4312

25.1816

8.99862

0.005742

0.000508

0.000508

PITRM1

44.9262

22.7592

18.0313

0.001707

0.000508

0.440357

TNFRSF1B

44.0166

13.1469

27.0104

0.000508

0.04055

0.008648

FGF12

43.8994

18.8334

4.47353

0.015617

0.000508

0.007875

ZFP92

43.794

24.4881

7.88216

0.024147

0.000508

0.001337

DDX47

43.5566

24.7253

23.9979

0.010133

0.006301

0.930252

JMJD6

43.3195

20.7141

27.1286

0.000508

0.023181

0.335209

C3H2ORF43

43.2789

21.2175

19.7676

0.000508

0.000508

0.822123

DHX40

43.0061

20.6415

19.8732

0.006571

0.002755

0.927894

TADA1L

42.8508

24.6315

22.821

0.048785

0.022381

0.852897

G0S2

40.9724

1.22642

4.25318

0.002061

0.000939

0.199004

CD200

40.9574

20.6454

21.9917

0.001337

0.027037

0.869328

ANKRD60

40.7367

25.1179

23.0967

0.032596

0.014519

0.807934

TRABD

40.5668

25.6072

15.4532

0.029178

0.000508

0.044542

MTHFD1

40.2411

23.3022

10.1842

0.004889

0.000508

0.001707

PGS1

39.4513

17.8118

13.3978

0.000939

0.000508

0.475205

GLT1D1

39.3647

13.3234

7.87353

0.000508

0.000508

0.116698

BTBD11

39.3412

18.2841

7.07037

0.000508

0.000508

0.002061

200

Table A2 (Cont’d)
NCAPD3

39.2565

24.5125

4.12427

0.023544

0.000508

0.000508

DENND3

38.9517

3.77331

10.8735

0.000508

0.000508

0.005162

PTPRF

38.931

18.485

1.1627

0.000508

0.000508

0.000508

FBXO38

38.848

22.2507

13.0786

0.008648

0.000508

0.046792

GFER

38.4681

20.0902

14.8312

0.012919

0.000939

0.432814

TNFSF4

38.2446

3.50097

1.72807

0.001337

0.006024

0.552373

WDR4

38.2083

22.1554

12.5673

0.024758

0.000508

0.07635

COL6A2

37.8591

24.4129 0.406533

0.034691

0.000508

0.000508

GUCY1B3

36.5347

16.6291

10.5002

0.003083

0.000508

0.228148

FAM160B1

36.1975

20.6031

18.1704

0.004889

0.000508

0.663588

FAF1

35.1842

21.1093

20.0421

0.019843

0.009893

0.877255

IFIH1

34.6964

12.5105

19.0742

0.000508

0.007357

0.129485

NEK4

33.7175

18.1395

5.38546

0.022381

0.000508

0.000508

WDHD1

33.4796

21.5096

3.32527

0.041371

0.000508

0.000508

TTPAL

33.0189

14.7241

8.14469

0.012919

0.028453

0.364262

MAPK11

32.9836

16.3064

1.62906

0.032432

0.000508

0.000508

RNF111

32.9516

19.5606

17.5657

0.032786

0.006571

0.758753

C4ORF21

32.9371

21.9377

0.87406

0.017772

0.000508

0.000508

8.46255 0.265238

0.000508

0.000508

0.000508

PVRL3

32.775

PCM1

32.7401

18.9889

18.9329

0.003707

0.004024

0.992666

ENSGALG00000006092

32.3991

13.9438

12.3499

0.000508

0.000508

0.748264

ARFGAP1

31.7584

19.4565

20.5426

0.021178

0.043084

0.865256

SYT1

31.0921

1.16967 0.238788

0.000508

0.000508

0.051824

201

Table A2 (Cont’d)
PUS1

30.584

17.5488

14.9951

0.018195

0.003707

0.649421

GPHN

30.0662

9.25647

6.73085

0.000508

0.000508

0.40091

LRRK2

29.1594

11.5437

15.7136

0.000508

0.003707

0.285162

PAX3

28.1347

13.0398

0.32904

0.000508

0.000508

0.000508

FADS1

28.0537

7.39644

6.88725

0.000508

0.000508

0.884774

SH3GL3

27.9784

3.8012

2.63069

0.000508

0.000508

0.562974

SELO

27.9732

8.76824

8.94975

0.000508

0.000508

0.956393

MAD1L1

27.9508

15.3401

15.0505

0.012707

0.012482

0.960504

THBS1

27.4234

8.26698

0.58576

0.000508

0.000508

0.000508

ENSGALG00000006966

27.2309

15.2322

13.0741

0.007875

0.000939

0.620797

DNAJB9

26.9246

16.7054

12.0064

0.028632

0.000508

0.252139

TCF7L2

26.7849

8.60307

9.09137

0.000508

0.000508

0.894998

STK32C

26.3767

3.2101 0.453204

0.000508

0.000508

0.040086

MED13

25.6803

15.2209

15.6017

0.010133

0.015839

0.936589

CALB1

25.3831

3.99522 0.226361

0.000508

0.000508

0.000508

ENSGALG00000014126

25.2239

11.2387 0.440083

0.048785

0.000508

0.005455

24.762

13.2319

13.2114

0.009143

0.008392

0.996596

FNIP2

24.7337

5.55651

5.60829

0.000508

0.000508

0.983789

DSCAM

24.5632

1.38692 0.239229

0.000508

0.000508

0.007875

TUBE1

23.8774

12.0403

13.3382

0.016936

0.041552

0.812206

RNF157

23.849

12.4548 0.539129

0.043558

0.000508

0.000508

MYOM1

22.8251

1.02126 0.262186

0.000508

0.000508

0.046366

CIP1

22.6267

4.85745

0.000508

0.032432

0.244025

FAM114A1

9.41451

202

Table A2 (Cont’d)
ORAI2

21.508

11.0767

9.72237

0.011088

0.003707

0.745265

CEP192

21.288

12.3918

2.61824

0.025695

0.000508

0.000508

ENSGALG00000021106

21.1327

2.37149

1.10897

0.026458

0.040406

0.580822

FAM217B

21.0359

9.85268

9.38811

0.000508

0.000508

0.889108

SNAP47

20.9185

12.3021

12.7205

0.027709

0.034867

0.925882

NLE1

20.1023

10.0715

6.48369

0.016284

0.001337

0.286941

GPSM1

19.9191

11.8862

14.4637

0.003402

0.043422

0.582877

19.697

11.2927

9.11708

0.012254

0.002413

0.519092

BAG3

19.5765

9.61523

8.35796

0.004889

0.006301

0.729573

DHX35

18.8492

9.35007

8.2523

0.010375

0.005162

0.765197

METTL4

18.735

11.2722

10.0986

0.048939

0.016508

0.776927

TVB

18.4069

7.27926

2.97432

0.004889

0.000508

0.103013

MOCOS

17.9925 0.933332

3.0796

0.000508

0.000508

0.035868

MYCN

17.5292

1.05757

1.79772

0.000508

0.000508

0.498167

ASPG

17.3655

9.10193

5.39033

0.018195

0.000508

0.139711

SLC25A33

17.1409

8.93389

3.0606

0.045917

0.000508

0.02791

gga-mir-1815

16.7175

1.26115 0.873254

0.003083

0.002413

0.732404

DPYSL4

16.5991

4.54763 0.247559

0.001337

0.001707

0.012021

NEK3

16.4941

6.51483

1.55727

0.018625

0.000508

0.017772

THNSL1

16.4157

7.04515

7.33576

0.004889

0.006024

0.933537

RGS6

16.3934

3.0426 0.126573

0.000508

0.005742

0.022785

RHOBTB1

16.1637

8.78056

1.72698

0.029556

0.000508

0.000508

ENSGALG00000006123

15.9964

6.82803

4.8052

0.000508

0.000508

0.265098

FN1

203

Table A2 (Cont’d)
EPHA7

15.9705 0.870556 0.216215

0.000508

0.000508

0.073203

SULT4A1

15.9019

6.81603

0.036028

0.006301

0.611525

PRSS12

15.7126 0.706388 0.368459

0.000508

0.002413

0.583741

PGF

15.6582

0.011573

0.004604

0.033141

CLASP1

15.6231

9.1885

8.76924

0.025882

0.010857

0.89444

LATS2

15.5634

8.09807

9.69152

0.004314

0.046792

0.574291

ENSGALG00000014441

15.5108

6.24225

5.81998

0.01756

0.014947

0.911365

STAM

15.3471

8.41972

8.63155

0.006845

0.008392

0.94356

ICOS

15.1298

5.19889

3.15919

0.000508

0.000508

0.264839

RGS2

15.0883

3.47913

2.14216

0.000508

0.000939

0.454777

RTTN

14.9542

8.57434

5.56366

0.008648

0.000508

0.141326

SYBU

14.6854

3.02304

1.381

0.000508

0.000508

0.13819

FIG4

13.5863

6.75678

7.9271

0.009893

0.041024

0.674637

COL6A3

13.5388

2.04746 0.210838

0.000508

0.000508

0.000508

PARM1

13.1774

1.89581

1.66977

0.009387

0.028816

0.914599

SLC35F2

13.1249

6.45205

1.14313

0.046366

0.000508

0.006845

ERI2

12.941

7.84044

1.76924

0.043084

0.000508

0.000508

AEN

12.9359

4.91742

4.76232

0.01315

0.009893

0.960825

ZNF407

12.9052

6.47408

7.63422

0.002061

0.021

0.610378

ASIC2

12.6336 0.186888

3.14368

0.002413

0.003083

0.011573

6.61767 0.803416

0.025324

0.000508

0.000508

6.25508

6.39541

0.008648

0.012482

0.953462

3.19749 0.216423

0.000508

0.000508

0.000508

DIAPH3
KIAA1009
PLCH2

11.932
11.68
11.3226

8.53686

4.49558 0.409219

204

Table A2 (Cont’d)
ALPL

11.3097

2.07703 0.229626

0.003707

0.025512

0.093999

BBS9

11.1582

5.31855 0.349019

0.017363

0.000508

0.001707

BDKRB1

11.1482

1.13409 0.085925

0.000939

0.036028

0.072279

SLMO1

11.0455

5.55538

4.23482

0.037726

0.012254

0.594906

DOC2B

10.7208

2.40364 0.237793

0.007117

0.000508

0.018625

SCNN1B

10.7128

1.79812 0.123963

0.000508

0.001337

0.005742

FAM171A1

10.2727

5.25634 0.515851

0.049562

0.000508

0.000508

FAM184B

10.202

2.98471 0.811926

0.001337

0.000508

0.017363

0.29021

0.035035

0.001707

1

CHRDL2

10.0199 0.110684

AFAP1

9.8054

4.43864 0.276595

0.001707

0.000508

0.000508

OLFM3

9.73988

3.42697 0.377979

0.005162

0.000508

0.003083

TPCN3

9.40088

3.21536

2.99359

0.010375

0.008139

0.91364

ANXA10

9.27805 0.772904

0.43548

0.009143

0.006571

0.656324

ANKRD50

9.2712

3.15949 0.512016

0.001707

0.000508

0.000508

RGS9BP

9.1278

4.34957

1.58088

0.009143

0.000508

0.018409

MMS22L

8.14548

4.89063

2.08418

0.032596

0.000508

0.009893

OSBPL6

7.84098

2.15542 0.124638

0.000508

0.000508

0.002755

CRYM

7.57204

2.50579

3.35413

0.008139

0.042516

0.624648

PTCHD1

7.39944

1.74242 0.429777

0.000508

0.000508

0.029707

GPR37

7.30112

3.28242 0.027359

0.003707

0.015384

0.029707

C10orf71

7.04963

1.12007 0.658535

0.000508

0.000508

0.368281

CNKSR2

6.83599 0.616383

2.98556

0.000508

0.045323

0.023367

TTC8

6.80802

3.78663

0.000939

0.034519

0.261578

2.47346

205

Table A2 (Cont’d)
MAGI2

6.70258 0.465019 0.151336

0.000508

0.000508

0.141028

LAG3

6.39963

2.01941

0.006845

0.042921

0.423992

SDR42E2

6.22889 0.913805 0.726087

0.000508

0.000508

0.778251

C7

6.11809

2.60805

0.15911

0.020418

0.000508

0.000508

FHL5

6.11332

1.37099 0.033903

0.001707

0.03838

0.062552

PLCH1

6.01695 0.293417

1.57899

0.000508

0.000508

0.002061

ITGA8

5.80644

2.53176 0.205959

0.035868

0.000508

0.000508

RFWD3

5.57267

2.90438

1.54593

0.019233

0.000508

0.117474

MYO3A

5.37341 0.158732 0.030312

0.000508

0.013846

1

5.1624 0.067117 0.774846

0.000508

0.000508

0.011088

0.004024

0.000508

0.019233

0.6655

0.001337

0.001707

0.843259

ENSGALG00000005470

1.0365

ENSGALG00000002318

4.78461

1.7871 0.403331

NXPH2

4.69216 0.556137

TMEM136

4.56306

1.41621

0.008648

0.017363

0.836256

HTR4

4.21403 0.589149 0.814369

0.006571

0.009143

0.74288

SLC1A6

3.77089

1.27315 0.919667

0.041194

0.046199

0.716556

RUNX2

3.72993

1.42735

1.71663

0.009636

0.036839

0.732349

KIAA1107

3.62648

1.07242 0.332517

0.000939

0.000508

0.033623

GLRB

3.61473 0.140573 0.022559

0.000508

0.002755

1

ENSGALG00000019276

3.43791

1.00268 0.224975

0.007117

0.001707

0.065488

ATRNL1

3.25328

1.23281 0.271235

0.01315

0.000508

0.018625

RALY

3.07879 0.545744 0.463026

0.000939

0.000508

0.835098

ANGPTL4

2.94338 0.400201 0.085569

0.003707

0.019638

1

ENO2

2.91482 0.392882 0.734461

0.005162

0.014287

0.451768

1.21256

206

Table A2 (Cont’d)
TLE1

2.8866

1.03826 0.928909

0.010133

0.004024

0.856261

COL28A1

2.67343 0.239752 0.018897

0.000508

0.003707

1

LRRC2

2.48313 0.123282

0.09928

0.000939

0.001337

1

CORIN

2.32654 0.089815 0.126155

0.000508

0.000508

1

CDKL1

1.93031 0.830798 0.713016

0.041024

0.027354

0.81552

SCML4

1.84969 0.089224 0.039091

0.002413

0.002061

1

SYCP2

1.84611

0.64382 0.366739

0.015617

0.000939

0.374654

BICD1

1.70598

0.23625 0.109174

0.000508

0.000508

1

PLXNA2

1.53473 0.471503 0.381628

0.006845

0.001337

0.741867

NPAS3

1.46558

0.28029 0.123925

0.004314

0.000939

1

CD109

1.44851 0.413856 0.051065

0.020225

0.001707

0.033141

THBS2

1.36734 0.451123 0.023163

0.037211

0.045761

0.086943

ATP7B

1.29929 0.059637 0.218104

0.006845

0.005162

1

RBP2

1.27597

0

0

0.000508

0.000508

1

NPFFR2

1.17549

0

0

0.000508

0.000508

1

CMYA5

1.13088 0.140566 0.205191

0.000508

0.002061

1

SYPL1

1.09834 0.248076 0.153447

0.010626

0.017989

1

0

0.020593

0.020593

1

GGA.46638

0.768885

0

GPR123

0.662402

0.16424 0.037028

0.031014

0.024758

1

GPR179

0.574987 0.060694 0.033828

0.006571

0.010133

1

207

Normalized expression
Gene
Malig

Bursa

Spleen

q value
Malig vs
Bursa

Malig vs
Spleen

Spleen vs
Bursa

TUSC2

0 99564600 28623200

0.000508

0.009143 0.366455

TMEM194B

0

343911

359023

0.005742

0.013846 0.976893

ENSGALG00000027490

0

11363

4.38036

0.000508

0.000508 0.000508

GGA.13329

0

59.6019

14.739

0.000508

0.000939 0.140236

GRP

0

1.37458

37.4668

0.000508

0.000508 0.007117

PTPRO

0.031932

0.636413

2.64164

0.037019

0.033808 0.007875

CPAMD8

0.067003

0.883522

0.898642

0.004604

0.004604 0.979558

TSPAN7

0.070848

3.17931

4.88668

0.03838

ENSGALG00000000584 0.091068

1.59459

0.829181

0.000508

0.000508 0.178746

CASZ1

0.091848

1.06893

0.708934

0.006845

0.017158 0.500045

PCSK5

0.096798

0.895686

0.387093

0.004314

0.045917 0.145658

0.10418

2.00911

2.55017

0.015156

0.012021 0.751984

NCAM1

0.108176

1.04421

2.52137

0.008904

0.000939

PAMR1

0.110381

0.878291

0.905252

0.032432

0.030281 0.971404

LAPTM4B

0.110602

5.93472

3.35264

0.001707

0.002061 0.185641

DTNA

0.113291

1.79288

0.771049

0.003083

0.022785 0.217859

GPR64

0.121855

2.28681

33.2029

0.001337

0.000508 0.000508

MB21D2

0.133389

0.838088

3.24251

0.022182

0.001337 0.012254

ZBTB42

0.145226

3.81675

4.88122

0.034324

0.033302 0.717333

SLCO6A1

0.146579

2.65498

1.46056

0.000939

0.001707 0.182957

RASGEF1C

0.038204

0.47944

0.05016

Table A3: Genes significantly down regulated in malignant samples over both
bursa and spleen samples

208

Table A3 (Cont’d)
SVEP1

0.156212

1.81515

0.501278

0.000508

0.036367 0.004604

SEMA5B

0.162812

0.880199

2.64042

0.016508

0.000508 0.041371

APCDD1

0.165404

5.56992

1.03581

0.002755

0.041876 0.028632

PCDH1

0.184728

1.03515

1.64086

0.005742

0.000508 0.363706

SLC16A7

0.187369

1.00218

7.13907

0.006845

0.000508 0.000508

NT5E

0.201322

15.8623

1.99502

0.011088

0.044686 0.000508

ILDR1

0.20429

2.74872

1.84065

0.000508

0.002755 0.357978

PLCB4

0.205287

1.58669

1.65557

0.001337

0.001337 0.939069

ZNF618

0.230157

1.53125

1.97447

0.000939

0.000508

0.66241

MISP

0.243856

1.48565

5.44347

0.041024

0.002755

0.10673

K60

0.244816

4.90364

12.8634

0.011573

0.006301 0.045323

AMOTL1

0.247764

2.55799

1.26129

0.003083

0.027183 0.241526

ZNF385B

0.260418

1.03713

2.33625

0.041552

0.004604 0.163572

CDS1

0.267813

3.5723

2.94819

0.000939

0.001707 0.685291

ENSGALG00000014946 0.275816

2.24047

2.70998

0.000939

0.000508 0.800608

SLC2A9

0.277643

13.3941

3.54365

0.000508

0.000508 0.000508

RAB11FIP1

0.284042

5.70401

1.7216

0.000508

0.007875 0.022381

DOCK5

0.288369

0.902696

4.29257

0.036367

0.000508 0.001337

ENSGALG00000020561 0.300321

3.05807

4.94227

0.000508

0.000508 0.216986

0.302022

0.968187

5.41723

0.03397

0.000508 0.000508

0.30577

7.29672

8.87752

0.014062

0.012707 0.758476

NFATC2

0.306909

1.13348

9.42593

0.040248

0.000508 0.000508

EPHB2

0.314025

2.64524

3.18688

0.002061

0.000939 0.734497

ADAM12
P2RY2

209

Table A3 (Cont’d)
HUNK

0.32346

1.38466

1.51163

0.009387

0.007117 0.888702

TLR5

0.326069

1.93421

1.49421

0.009636

0.023544 0.677327

PTGS1

0.329113

2.77142

8.7778

0.019436

0.002061 0.072681

PKD1

0.33125

1.50511

2.80855

0.000939

0.000508 0.082469

PPL

0.333797

10.0143

1.4017

0.000508

0.015839 0.000508

ME3

0.336629

2.92409

2.07601

0.012021

0.02626 0.607226

KCNK5

0.353873

5.85075

6.0595

0.000508

0.000508 0.932756

SH3TC2

0.365139

3.61872

6.31777

0.002061

0.000508 0.283832

PKIB

0.365324

2.44848

2.53344

0.002755

0.002061 0.953462

ENC1

0.372791

1.8575

3.83025

0.037019

0.007615 0.214132

0.38573

5.91293

1.66328

0.000508

0.005455 0.001337

BEND5

0.391377

1.57968

2.04879

0.01315

0.005162 0.624454

CNFI-A4

0.392051

3.09856

1.57607

0.003083

0.031908 0.257037

ARHGEF17

0.407019

1.93121

1.26498

0.006024

0.045323 0.472769

ACOX2

0.413346

3.82857

2.90633

0.002755

0.006301 0.628671

DAB1

0.427216

1.43655

6.36677

0.032596

0.000508 0.000508

GATA3

0.43529

3.54972

3.10249

0.000508

0.000939 0.776628

ABI3BP

0.440246

2.00226

5.98381

0.006845

0.000508 0.012482

PCTP

0.446443

1.68886

2.06064

0.040878

0.021178 0.775497

KANK1

0.447502

1.33973

9.90361

0.009636

0.000508 0.000508

MAT1A

0.44817

2.66387

2.14173

0.006024

0.013626 0.708843

INADL

0.453643

3.67865

1.58889

0.000508

0.013386 0.035381

OSGIN1

0.456752

4.06742

4.14297

0.003402

0.003083 0.975903

SEMA3C

210

Table A3 (Cont’d)
KLF4

0.457453

4.11843

4.44655

0.000508

0.000508 0.899002

GIPC2

0.466792

16.878

9.15438

0.001707

0.003402 0.201631

NIPAL1

0.470516

2.93558

5.2957

0.000939

0.000508 0.145118

SHROOM2

0.488595

9.49892

3.3803

0.000508

0.000508 0.013846

ENSGALG00000011717 0.527863

2.22384

1.913

0.008904

0.017989 0.807146

SLC6A8

0.545285

4.02394

4.25168

0.000939

0.000508 0.928389

ENSGALG00000006325 0.548728

3.3864

6.34173

0.005742

0.000939 0.214199

PTPN13

0.555312

3.00328

6.98901

0.000508

0.000508 0.008392

MAML3

0.559631

1.98999

4.44928

0.048939

0.003707 0.154647

EDN3

0.577767

3.03137

4.30061

0.002755

0.000508

ER81

0.583483

4.149

1.78656

0.000508

0.030653 0.041717

GOLPH3L

0.621384

39.7059

6.37631

0.000508

0.007117 0.001337

CHTL1A

0.625205

5.27514

3.06649

0.004024

0.027709 0.343902

OTUD7A

0.632015

4.35121

17.2045

0.003083

0.000508 0.000508

MAGI1

0.665257

2.73304

4.46245

0.007357

0.000508 0.335038

0.70358

3.98005

4.65347

0.000508

0.000508 0.709315

0.738464

3.02799

11.5467

0.017363

0.000508 0.007357

ENSGALG00000011930 0.740705

216.926

4.34613

0.000508

0.009893 0.000508

ENPP2

0.74349

2.80555

3.83038

0.043558

0.012482 0.665226

KALRN

0.745573

6.52853

10.1921

0.001337

0.000508 0.331392

IRK1

0.754172

5.58922

2.86682

0.001337

0.021791 0.184982

HIC2

0.767319

3.08165

6.69065

0.009387

0.000508 0.104579

ODZ3

0.800241

4.48102

18.6009

0.000508

0.000508 0.000508

ARHGAP6
HELIOS

211

0.42898

Table A3 (Cont’d)
NFATC1

0.801019

2.40936

21.9287

0.042202

0.000508 0.000508

PLD1

0.820285

4.26886

4.49194

0.000508

0.000508 0.898116

0.8324

2.48963

2.21298

0.009893

0.020418 0.803342

TLR3

0.832801

7.75684

2.17355

0.000508

0.043902 0.000939

MAPK13

0.847589

5.66231

6.29015

0.009387

0.006571 0.871348

PLCD1

0.861296

9.24695

3.76277

0.000508

0.000939 0.004024

IL8

0.878176

6.55452

7.2487

0.000508

0.000508 0.831875

PPM1H

0.890929

12.4084

8.43123

0.003083

0.011088 0.654779

HOMER2

0.896948

3.86327

3.72386

0.038204

0.043558 0.960148

ARHGEF16

0.919259

4.193

4.40153

0.011088

0.008904 0.943239

STON2

0.923557

3.55556

8.60936

0.006301

0.000508 0.040248

SOCS2

0.928653

5.7905

11.0307

0.024561

0.004889 0.306597

0.94884

2.71065

4.45746

0.035705

0.000939

MLKL

0.953769

7.04383

3.69207

0.003083

0.023544 0.221607

EML1

0.965554

5.16482

3.64939

0.000508

0.003083 0.422559

SSBP2

0.969702

5.67496

4.77462

0.007357

0.014062 0.798599

MAPKAPK3

0.971674

12.6696

3.57895

0.000939

0.047893 0.006301

0.97844

3.04846

15.3345

0.043558

0.000508 0.000508

0.9896

4.01014

5.68699

0.000508

0.000508 0.300943

LRRC16A

0.996079

4.62987

5.59665

0.000508

0.000508 0.638122

TMEM116

1.02607

8.94275

3.22859

0.000939

0.046054 0.048181

RBM47

1.03047

9.2107

26.7843

0.000508

0.000508 0.004314

IL17REL

1.0513

27.0148

9.85616

0.000508

0.000508 0.007875

SEMA6D

CABLES1

RXRA
FAM135A

212

0.3128

Table A3 (Cont’d)
ENSGALG00000019861

1.05626

10.9267

10.7654

0.000508

0.000508 0.973018

PLEKHA5

1.06136

3.77473

4.65813

0.001337

0.000508 0.569319

BACE2

1.06673

16.3356

9.0057

0.000508

0.000508 0.047893

BAAT

1.10777

5.504

5.16292

0.013846

0.019233 0.929253

EPHA3

1.11099

3.42788

4.95961

0.005162

0.000508 0.352751

MEIS1

1.11402

27.4519

5.38298

0.000508

0.010133 0.004889

SMAD6

1.12881

4.20661

19.2271

0.025134

0.000508 0.011803

UGT8

1.15209

31.7668

15.0632

0.000508

0.000508 0.012919

DOCK1

1.15404

4.09638

4.78731

0.000508

0.000508 0.730117

IQSEC1

1.15571

4.40534

6.71355

0.002413

0.000508 0.304986

BEAN1

1.17527

7.51839

7.33904

0.007615

0.007615 0.969601

HS1BP3

1.17598

3.32188

4.11694

0.030281

0.009387 0.673603

ARHGAP20

1.20295

3.18119

5.13516

0.020024

0.000939 0.250639

RGN

1.22335

6.0976

5.13665

0.029178

0.049102 0.791728

CSRP2

1.22801

21.5894

4.16787

0.000508

0.003707 0.000508

CAMKK1

1.24793

3.95076

5.2606

0.019045

0.004889 0.581677

FAM134B

1.26981

6.78977

5.36433

0.000508

0.000939 0.514154

IRF6

1.29576

20.0255

8.48929

0.000508

0.004314 0.024352

GNAL

1.29741

10.7448

7.62893

0.001337

0.007875 0.509265

CCBP2

1.30546

6.55851

4.69991

0.007357

0.024561 0.558356

1.3269

3.7982

6.80223

0.047893

0.001707 0.242418

ENSGALG00000002326

1.33972

6.4645

4.7818

0.000508

0.000508 0.395182

AASS

1.34746

13.0536

3.31588

0.000508

0.023367 0.000508

KIAA1598

213

Table A3 (Cont’d)
EPHX2

1.35934

4.31111

8.24317

0.028294

0.000939 0.203465

ABCB1

1.3627

6.29988

19.4842

0.000508

0.000508 0.000508

MBOAT2

1.36666

4.80178

24.4073

0.012021

0.000508 0.000508

SRC

1.38908

5.73228

24.9429

0.011328

0.000508 0.002061

CDC42EP1

1.40803

7.30381

11.7118

0.020792

0.006301 0.511948

GAB1

1.42744

4.08284

5.57697

0.004024

0.000508 0.413481

SH3RF3

1.43413

9.60105

5.17429

0.000508

0.001337 0.052287

SLC41A3

1.44033

4.4492

5.19179

0.021596

0.009636 0.785341

ENSGALG00000012808

1.44155

3.29659

5.45497

0.028453

0.000939 0.160928

SRD5A2

1.44869

15.7273

7.11396

0.000508

0.006024 0.046366

TFEC

1.45138

5.14745

17.7414

0.000508

0.000508 0.000508

SATB1

1.47683

5.74643

7.94949

0.001707

0.000508 0.380986

1.4802

3.66993

12.5907

0.019233

0.000508 0.000508

CCR7

1.50697

3.83858

32.2082

0.023181

0.000508 0.000508

MICALL2

1.55982

6.74082

4.66559

0.001707

0.01606 0.452392

KLF9

1.56472

6.55789

7.65751

0.009387

0.005455 0.780665

PDE5A

1.59226

4.86121

4.56863

0.010857

0.014947 0.902645

DDAH1

1.60018

8.89949

12.4112

0.043084

0.020593 0.736545

FARP1

1.61098

5.96495

6.94414

0.000508

0.000508 0.686913

DYRK2

1.65168

7.3765

19.2492

0.001337

0.000508 0.003402

IRF2BPL

1.66892

5.8896

6.80751

0.043246

0.023181 0.846017

PCGF5

1.71267

7.82354

27.8981

0.014287

0.000508 0.004314

CPNE2

1.71884

9.4985

4.79694

0.000939

0.03738 0.151019

FAM117B

214

Table A3 (Cont’d)
AFAP1L2

1.72013

6.95002

8.24132

0.000508

0.000508

KLF3

1.72173

7.14715

13.457

0.000508

0.000508 0.022588

CASS4

1.72638

6.18142

109.568

0.007615

0.000508 0.000508

TNRC18

1.74806

6.55669

6.06989

0.000508

0.000508 0.822839

ARRDC1

1.74908

5.0054

4.48179

0.014287

0.032786 0.832429

MGLL

1.75663

8.69318

27.0603

0.000508

0.000508 0.000508

LAMP5

1.76243

22.534

38.597

0.000508

0.000508 0.285486

PARD6G

1.78558

4.67688

24.6255

0.02791

0.000508 0.000508

WWTR1

1.84592

5.44693

6.83097

0.042792

0.017772 0.697029

1.847

6.50435

6.08477

0.032259

0.043422 0.924091

ARHGEF10L

1.87624

7.25919

16.5192

0.000939

0.000508 0.019233

SPIC

1.87633

24.1935

105.371

0.000508

0.000508 0.000508

LRP5

1.87986

5.38799

13.969

0.000508

0.000508 0.001337

TSPAN15

1.90083

24.7261

6.37061

0.000508

0.009893 0.001707

IGF2

1.91937

6.52536

49.4515

0.010375

0.000508 0.000508

PLK2

1.94176

5.44003

6.83741

0.004889

0.000508 0.551246

LAMA3

1.96362

9.81279

23.9708

0.000508

0.000508 0.000508

TIAM1

1.96809

5.03917

23.7046

0.030477

0.000508 0.000508

NOX1

1.98415

9.28745

9.17865

0.008648

0.009143 0.982039

KIAA1462

2.03437

4.99135

44.7799

0.007357

0.000508 0.000508

ABCA3

2.04775

4.21727

16.9987

0.036676

0.000508 0.000508

ANKRD6

2.07171

6.69452

14.0766

0.026069

0.000508

EMB

2.08889

6.70126

11.1727

0.009893

0.000508 0.098245

TMEM37

215

0.66036

0.12758

Table A3 (Cont’d)
SULF2

2.08961

9.0529

46.7473

0.000508

0.000508 0.000508

TRIO

2.1088

5.19878

13.6031

0.014731

0.000508 0.006024

ENSGALG00000011008

2.1592

5.33329

5.90774

0.028816

0.016719 0.829355

HAAO

2.21228

9.76345

16.4075

0.010857

0.001337 0.363627

CRIM1

2.22432

6.98092

5.98069

0.004024

0.013626 0.730172

TMEM51

2.25335

9.94368

6.94165

0.003707

0.0281 0.498167

WDFY3

2.30125

5.42357

11.8498

0.000508

0.000508 0.000508

RNF144A

2.31835

4.87521

26.0959

0.019233

0.000508 0.000508

TSPAN9

2.32859

7.75289

7.96248

0.004314

0.003402 0.957816

TREM-B2V2

2.35699

8.34008

24.2048

0.023367

0.000508 0.023367

IGF2BP2

2.3577

8.21414

22.5546

0.002061

0.000508 0.000939

GNA11

2.37698

7.18044

17.4372

0.035516

0.000508 0.049562

SYT8

2.38611

13.1117

15.7214

0.000508

0.000508 0.664939

CHST9

2.40234

12.2818

17.7513

0.007615

0.001707 0.461785

TRAM2

2.40576

6.17187

25.4709

0.032086

0.000508 0.000508

FAM65B

2.45146

5.424

133.542

0.034691

0.000508 0.000508

CD36

2.56963

9.67458

117.539

0.004604

0.000508 0.000508

SERPINB1

2.57356

6.29345

6.67028

0.020225

0.012707 0.892507

C3H8ORF80

2.69404

20.8806

16.3182

0.000508

0.000508 0.446232

ALCAM

2.75823

127.507

20.4103

0.000508

0.000508 0.000508

TNFAIP2

2.75853

7.71761

102.071

0.011803

0.000508 0.000508

TAAR1

2.77091

26.0546

9.51468

0.000508

0.020792 0.019045

SLCO2B1

2.77915

14.05

26.7004

0.000508

0.000508 0.113746

216

Table A3 (Cont’d)
C1orf198

2.78438

8.417

7.74482

0.002061

0.005742 0.840492

IL13RA2

2.79102

7.89643

59.5155

0.025695

0.000508 0.000508

ENSGALG00000026622

2.84781

6.31692

7.62765

0.020024

0.003707 0.651155

DCLK3

2.87625

8.71786

67.4573

0.004314

0.000508 0.000508

FNDC3B

2.90721

5.53627

13.5541

0.021

0.000508 0.001337

IDUA

2.94622

5.73965

10.1385

0.037877

0.000508 0.063686

TOM1

2.96555

6.65738

11.872

0.041717

0.000939 0.137252

TCN2

2.99622

7.7554

17.166

0.043558

0.000508 0.087339

GPR126

3.00488

14.0896

12.9421

0.000508

0.000508 0.793093

GSTA3

3.00787

61.8514

34.6994

0.000508

0.000508 0.086553

CMPK2

3.01488

10.1194

6.68449

0.000508

0.016284 0.209266

XDH

3.01609

14.616

10.2905

0.000508

0.000508 0.215215

ADIPOQ

3.02201

7.79763

31.0603

0.044819

0.000508 0.001707

gga-mir-1723

3.04244

8.47628

11.7999

0.005455

0.000508 0.368638

TMEM55A

3.08481

5.79507

15.8879

0.048472

0.000508 0.000939

F10

3.16978

16.7194

23.6763

0.000508

0.000508 0.297434

SMAD7A

3.17377

23.2823

7.90604

0.000508

0.046498 0.014287

TRIM25

3.18202

9.43226

22.0807

0.006571

0.000508 0.012707

ARHGAP21

3.25739

6.65078

6.65756

0.014062

0.014287 0.997074

KIAA0284

3.30032

6.37151

6.19441

0.032596

0.046498 0.945797

3.3069

15.4853

12.9723

0.000508

0.000508 0.658316

PMP22

3.35261

11.3209

21.0397

0.004604

0.000508 0.119606

MBNL2

3.48825

8.89566

8.91137

0.004314

0.004314 0.995875

JUP

217

Table A3 (Cont’d)
ARRDC4

3.51399

14.5759

11.5788

0.000508

0.000508 0.453215

LCAT

3.52257

24.0232

15.2853

0.000508

0.000508 0.133525

KLF4

3.63126

11.7424

418.52

0.001707

0.000508 0.000508

SLC46A2

3.65269

13.9485

23.8908

0.023367

0.001337 0.372919

K123

3.74981

38.4522

45.4698

0.000508

0.000508

OLFML2A

3.76175

10.4987

22.559

0.007875

0.000508 0.028453

IL34

3.77065

33.7574

25.9901

0.000508

0.003083 0.646678

CRIP2

3.842

14.1945

18.8438

0.024939

0.008648 0.651942

GAS6

3.86588

9.10347

9.46873

0.017158

0.014731 0.929763

IGSF1

3.88626

8.15236

19.9483

0.042516

0.000508 0.008139

PLEKHG3

3.92112

9.48711

9.37216

0.032596

0.030101 0.981859

TNFRSF11A

3.93804

9.61676

20.5085

0.003707

0.000508 0.007875

EFNB2

3.99331

18.9242

26.2589

0.000508

0.000508 0.245044

CHST2

4.04376

19.8605

57.621

0.000508

0.000508 0.000508

CYP46A1

4.13707

8.11952

16.4152

0.049259

0.000508 0.029707

DENND4C

4.23717

7.63147

17.4612

0.036199

0.000508 0.003402

CX3CR1

4.27272

47.8291

100.933

0.000508

0.000508 0.003083

MAOA

4.36015

17.5283

136.06

0.001337

0.000508 0.000508

LHFP

4.46778

11.7735

38.4283

0.0281

0.000508 0.003707

ATP11A

4.51971

11.3786

34.7579

0.015384

0.000508 0.003707

LAMB1

4.54205

16.7902

22.5519

0.000508

0.000508 0.307152

ENSGALG00000026188

4.58184

11.7172

14.2484

0.001337

0.000508 0.565392

REV-ERBB

4.72928

10.7032

19.8868

0.028294

0.000508 0.063937

218

0.54621

Table A3 (Cont’d)
TGM4

4.75004

68.7104

294.359

0.000508

0.000508 0.000508

MERTK

4.91421

11.5692

62.3949

0.002755

0.000508 0.000508

TESC

5.06102

20.2355

31.7507

0.005455

0.000939 0.400379

ST14

5.18044

19.941

15.431

0.000508

0.000508

5.4587

13.0119

40.6049

0.002413

0.000508 0.000508

AHR

5.51759

27.2075

14.5191

0.000508

0.000939 0.007615

VNN1

5.84053

77.7306

15.2045

0.000508

0.003083 0.000508

MAP4K4

5.88045

40.107

24.2251

0.000508

0.000508 0.164038

RUNX3

6.01675

13.6743

39.3157

0.002061

0.000508 0.000508

CBFA2T3

6.02418

20.1362

16.4519

0.000508

0.005455 0.624648

SOWAHC

6.07657

16.9537

170.749

0.007615

0.000508 0.000508

ENSGALG00000008755

6.08812

15.2984

22.1987

0.028453

0.003707 0.360152

ENSGALG00000010336

6.10817

76.9616

778.603

0.000508

0.000508 0.000508

ETS2

6.22195

34.9152

74.1951

0.000508

0.000508 0.002755

PLA2G4A

6.42729

14.5428

32.1337

0.002061

0.000508 0.001337

ATP2B4

6.6352

23.8871

77.9922

0.009893

0.000508 0.011803

AAED1

6.64458

20.0226

29.5981

0.040248

0.006571 0.446072

CPD

6.71025

16.3524

14.6723

0.000508

0.001707 0.732428

CLSTN1

6.7327

13.8602

26.6486

0.007615

0.000508 0.020792

ITGA6

6.77825

21.7023

37.8627

0.000508

0.000508 0.027709

HPGD

6.89161

23.3354

225.344

0.02626

0.000508 0.000508

SVIL

6.96261

12.8806

42.8868

0.0281

0.000508 0.000508

ENSGALG00000028304

6.98649

12.2243

37.6404

0.01606

0.000508 0.000508

SASH1

219

0.44242

Table A3 (Cont’d)
EDNRA

7.16296

13.8902

13.9197

0.030101

0.029363 0.995352

GNG10

7.25267

66.5739

102.052

0.000508

0.000508 0.143124

BACH2

7.31154

14.2218

18.7513

0.031558

0.002413

MARK1

7.33468

16.2673

16.3064

0.003083

0.003083 0.994128

SESTD1

7.39224

16.9444

43.6512

0.004314

0.000508 0.000508

INPP5A

7.39908

13.5694

21.2495

0.036676

0.000939 0.128116

ADAM9

7.53558

14.0712

21.5508

0.035868

0.000508 0.177888

DNASE1L3

7.63404

17.6706

84.9297

0.044542

0.000508 0.000508

HES4

7.76319

29.602

61.8236

0.001337

0.000508 0.037211

ENSGALG00000008518

7.94889

59.4623

46.1109

0.000508

0.000508 0.507657

FOXI1

7.94947

18.1078

26.1027

0.007117

0.000508 0.259917

ABCC3

8.06548

19.8229

35.7362

0.046946

0.000508 0.227132

PIK3IP1

8.23974

34.201

101.858

0.000508

0.000508 0.000508

HIC1

8.28923

18.492

30.8397

0.008648

0.000508 0.128215

MCF2L

8.41862

16.1075

58.257

0.033141

0.000508 0.000508

SMPD1

8.49132

28.4941

74.3114

0.000508

0.000508 0.000508

CHDZ

8.77642

14.099

35.7552

0.047277

0.000508 0.000508

ANTXR2

9.04841

17.9383

42.7212

0.047112

0.000508 0.004024

MADH2

9.1565

21.433

18.3724

0.000939

0.007875 0.627012

ST3GAL2

9.1924

21.7255

53.9813

0.038701

0.000508 0.023951

AGPAT2

9.41875

23.4749

27.0785

0.031197

0.011573 0.771527

SULT1B1

9.4671

35.8784

37.6757

0.000508

0.000508 0.909989

9.54049

32.4843

37.8953

0.002755

0.000508 0.734885

BATF3

220

0.43691

Table A3 (Cont’d)
KAT2B

9.59892

18.7419

50.6204

0.004889

0.000508 0.000508

MCCC2

9.90912

24.542

22.6469

0.001707

0.007357 0.834804

UGCG

9.96857

18.9801

40.8877

0.008392

0.000508 0.001337

SCARB2

10.3897

22.9079

39.6122

0.002755

0.000508 0.045154

TRIM3

10.4111

20.1098

31.4078

0.044542

0.000508 0.204185

SDC1

10.4561

34.821

33.3641

0.000508

0.000508 0.897981

FYB

10.4835

22.8407

129.24

0.000508

0.000508 0.000508

GNPTAB

10.8809

27.0677

52.5856

0.000508

0.000508 0.003402

ST3GAL6

11.0098

34.544

21.161

0.000508

0.035207 0.112432

TPRN

11.3196

24.8641

64.7043

0.003402

0.000508 0.000508

TNFSF11

11.9016

26.3119

34.0054

0.002755

0.000508 0.367069

S100A9

12.32

37.9278

50.3173

0.024352

0.005455 0.574868

SMPD1

12.4333

27.9909

84.0121

0.019233

0.000508 0.001707

CLCN5

12.5258

21.1835

53.5637

0.046199

0.000508 0.000508

ELF3

12.7239

58.9684

44.3292

0.000508

0.000508 0.440149

FABP7

13.3715

49.0606

440.517

0.000939

0.000508 0.000508

IRF-3

13.6281

33.7315

51.8144

0.007615

0.000508 0.254839

NFKBIE

13.8619

27.5447

69.07

0.017158

0.000508 0.000939

BDH2

13.8849

25.3178

42.8935

0.022785

0.000508 0.049562

PPDPF

14.0051

69.0516

143.646

0.000508

0.000508 0.025324

TMBIM1

14.1906

32.8311

56.4239

0.009387

0.000508 0.088765

PLOD2

14.2848

28.5609

28.1654

0.003402

0.005162 0.966534

SNTB1

14.7221

32.5636

51.9525

0.000508

0.000508 0.077064

221

Table A3 (Cont’d)
PLAU

14.8344

34.9595

149.627

0.000939

0.000508 0.000508

RASGEF1A

15.1064

30.2156

56.8098

0.004889

0.000508 0.007357

TBC1D1

15.2898

29.97

28.1608

0.003707

0.010375 0.848791

B3GNT2

15.4667

47.576

52.3391

0.000508

0.000508 0.751878

TSPAN12

15.6636

32.2947

34.4499

0.007357

0.002413 0.858718

MPP1

16.2099

33.2076

53.3024

0.003083

0.000508 0.076905

SYDE1

16.6019

51.4967

535.765

0.029356

0.000859 0.000859

VDAC1

16.9436

46.7329

93.2086

0.003083

0.000508 0.027183

ZDHHC9

17.7021

32.6889

30.5284

0.009893

0.022182 0.842465

BRT-1

18.3421

63.9373

87.8242

0.000508

0.000508 0.430863

DUSP7

18.583

62.6784

79.7746

0.000508

0.000508 0.363473

ENSGALG00000026611

19.0873

109.709

93.5736

0.000859

0.000859 0.773962

TCP11L2

19.7596

32.3972

69.5213

0.03738

0.000508 0.001337

NFE2L2

19.9299

44.8117

56.0434

0.000939

0.000508 0.416203

S100A16

19.9739

72.0528

203.875

0.022182

0.000508 0.025324

SUB1

20.7937

35.206

56.0618

0.015156

0.000508 0.052287

FAM3C

20.9968

42.1351

71.7236

0.005162

0.000508 0.038204

PDLIM5

21.2027

38.6722

45.6607

0.004314

0.000508 0.545845

SGK3

21.5071

38.5786

59.721

0.004604

0.000508 0.066133

PLEKHB2

22.1496

61.6623

37.139

0.000508

0.014947 0.029006

TUBB6

22.2051

43.1896

63.5819

0.008904

0.000508

0.17283

CTNS

22.6631

41.255

68.6362

0.012919

0.000508

0.04743

LTB4R

23.4651

84.2222

160.847

0.000508

0.000508 0.015156

222

Table A3 (Cont’d)
SAMHD1

23.6977

61.057

42.2576

0.000508

0.004889 0.129821

TGIF1

23.7211

46.0039

52.9727

0.021596

0.001707 0.687532

ADD3

24.7541

93.2358

110.55

0.000508

0.000508 0.541107

TAGAP

25.3725

42.189

73.8918

0.017989

0.000508 0.015384

CFD

25.3762

871.565

88.8297

0.000508

0.000508 0.000508

ASS1

26.0674

97.1358

226.195

0.000508

0.000508 0.000508

LTF

27.4783

1381.72

175.752

0.000508

0.000508 0.000508

CTNNB1

27.6302

100.267

62.8923

0.000508

0.000508 0.036839

PLSCR4

28.525

92.5301

120.117

0.000508

0.000508 0.393532

DRAM1

29.3442

53.5489

75.6942

0.009143

0.000508 0.184697

EPSTI1

31.2086

55.3298

70.2693

0.030653

0.001337 0.442132

ENSGALG00000011190

31.3789

164.558

1878.44

0.000939

0.000508 0.000508

HEXB

31.9978

68.2679

213.893

0.001707

0.000508 0.000508

LITAF

32.5128

69.7379

331.117

0.017772

0.000508 0.000508

C4BPA

32.5148

116.301

284.629

0.028816

0.000508

LYZ

33.8154

5986.69

452.803

0.000508

0.000508 0.000508

CD82

34.6493

76.1211

163.19

0.000508

0.000508 0.000508

gga-mir-147

36.0413

93.5739

66.9866

0.000508

0.007615 0.183878

GNE

36.1216

58.6151

73.1789

0.014062

0.000508

VCAM1

37.2466

58.9571

289.64

0.039913

0.000508 0.000508

PIK3CD

37.3971

75.4274

81.9943

0.009143

0.001707

GPR137B

38.7829

66.8007

177.188

0.043246

0.000508 0.000508

FAM49A

41.6386

90.4122

364.548

0.000508

0.000508 0.000508

223

0.06968

0.3883

0.80464

Table A3 (Cont’d)
S100A11

42.144

178.771

109.105

0.000508

0.011803 0.161481

TAX1BP3

50.3501

88.4422

149.038

0.008392

0.000508 0.026069

APP

50.3658

243.817

161.855

0.000508

0.000508 0.075505

SMAP2

55.0645

90.871

313.284

0.037538

0.000508 0.000508

ISG12(2)

58.855

156.207

472.858

0.006301

0.000508 0.000508

HADH

61.5091

107.228

125.483

0.012919

0.001707 0.584976

SLC40A1

69.1872

291.578

1925.16

0.000508

0.000508 0.000508

gga-mir-3526

73.1726

145.732

119.473

0.000508

0.016284

SDC4

77.3579

167.166

224.543

0.006301

0.000508 0.361273

TIMD4

77.5062

562.83

393.011

0.000508

0.000508 0.162875

ITM2B

77.9683

187.133

273.665

0.000508

0.000508 0.142615

ATP1B1

78.6643

150.195

138.533

0.005455

0.016936 0.797835

NADK

81.5953

155.52

141.061

0.001337

0.003402 0.720354

CREG1

82.046

135.421

687.759

0.011088

0.000508 0.000508

ACVRL1

99.2499

244.363

2377.99

0.015801

0.000859 0.000859

ANXA11

99.3072

168.218

247.286

0.014519

0.000508 0.144181

ANKRD13D

115.107

362.664

518.865

0.002181

0.000859 0.480855

TSPAN3

127.301

215.206

257.599

0.011088

0.000939 0.538153

MYH9

164.506

244.164

488.105

0.043558

0.000508 0.007357

179.44

574.432

645.614

0.000508

0.000508 0.673321

184.523

345.639

581.711

0.000939

0.000508

192.66

710.049

650.191

0.000859

0.000859 0.873133

220.355

395.444

366.86

0.001707

0.009387 0.781003

ENSGALG00000026970
PNRC1
IL-1BETA
SH3BGRL3

224

0.43925

0.01756

Table A3 (Cont’d)
AKR1B10

287.807

476.292

689.201

0.046199

0.000508 0.234754

C3D

294.952

1090.11

1118.3

0.000859

0.000859 0.954525

IGJ

305.666

646.79

688.118

0.000508

0.001337 0.858204

LAPTM4A

327.313

533.039

836.049

0.008904

0.000508 0.044394

382.61

976.528

1588.47

0.001511

0.000859 0.203152

ENSGALG00000015398

530.181

1095.66

1125.56

0.008139

0.006301 0.927452

SP1

777.066

1503.76

2167.92

0.028769

0.000859 0.350366

PIK3R5

225

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226

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